Large-scale climatic variability in the North Atlantic region modulates seasonal rainfall and river flow across the British Isles. We show how the North Atlantic Oscillation (NAO) dramatically increases orographic enhancement of upland precipitation. NAO variations cause large differences in seasonal precipitation totals compared to NAO-neutral conditions, an effect amplified with altitude – what we term “double orographic enhancement”. For NAO conditions since 1825, this gives a maximum range of 150% in precipitation totals at the wettest (upland) location compared to NAO-neutral conditions. In autumn, winter and spring, there is a strong positive relationship between upland precipitation and NAO; this is not seen at low altitude except on northwest coasts. In summer, significant negative relationships are evident in the English lowlands. These precipitation patterns directly translate to seasonal runoff. Our findings show that the hydroclimatology of rainfall and river flow in upland areas is closely coupled to the strength of atmospheric circulation, an effect which strengthens with increasing altitude. Identified effects are large enough to cause very high river flow during periods of highly positive NAO, but may also lead to severe drought when NAO is highly negative.

Travel times are fundamental catchment descriptors that blend key information about storage, geochemistry, flow pathways and sources of water into a coherent mathematical framework. Here, we analyze travel time distributions (and related attributes) estimated on the basis of the extensive hydro-chemical information available for the Hupsel Brook lowland catchment in the Netherlands. The relevance of the work is perceived to lie in the general importance of characterizing non-stationary travel time distributions to capture catchment transport properties, here chloride flux concentrations at the basin outlet. The relative roles of evapotranspiration, water storage dynamics, hydrologic pathways and mass sources/sinks are discussed. Different hydro-chemical models are tested and ranked, providing compelling examples of the improved process understanding achieved through coupled calibration of flow and transport processes. The ability of the model to reproduce measured flux concentrations is shown to lie mostly in the description of nonstationarities of travel time distributions at multiple timescales, including short-term fluctuations induced by soil moisture dynamics in the root zone and long-term seasonal dynamics. Our results prove reliable and suggest, for instance, that drastically reducing fertilization loads for one or more years would not result in significant permanent decreases in average solute concentrations in the Hupsel runoff because of the long memory shown by the system. Through comparison of field and theoretical evidence, our results highlight, unambiguously, the basic transport mechanisms operating in the catchment at hand, with a view to general applications.

In many industrialized regions of the world, atmospherically deposited sulfur derived from industrial, non-point air pollution sources reduces stream water quality and results in acidic conditions that threaten aquatic resources. Accurate maps of predicted stream water acidity are an essential aid to managers who must identify acid-sensitive streams, potentially affected biota, and create resource protection strategies. In this study, we developed correlative models to predict the acid neutralizing capacity (ANC) of streams across the southern Appalachian Mountain region, USA. Models were developed using stream water chemistry data from 933 sampled locations and continuous maps of pertinent environmental and climatic predictors. Environmental predictors were averaged across the upslope contributing area for each sampled stream location and submitted to both statistical and machine learning regression models. Predictor variables represented key aspects of the contributing geology, soils, climate, topography, and acidic deposition. To reduce model error rates, we employed hurdle modeling to screen out well-buffered sites and predict continuous ANC for the remainder of the stream network. Models predicted acid-sensitive streams in forested watersheds with small contributing areas, siliceous lithologies, cool and moist environments, low clay content soils, and moderate or higher dry sulfur deposition. Our results confirmed findings from other studies, and further identified several influential climatic variables and variable interactions. Model predictions indicated that one-quarter of the total stream network was sensitive to additional sulfur inputs (i.e., ANC <100 µeq L^-1), while <10% displayed much lower ANC (<50 µeq L^-1). These methods may be readily adapted in other regions to assess stream water quality and potential biotic sensitivity to acidic inputs.

Headwater catchment hydrology and biogeochemistry are influenced by climate, including linear trends (non-stationary signals) and climate oscillations (stationary signals). We used an analytical framework to detect non-stationary and stationary signals in yearly time series of nutrient export [dissolved organic carbon (DOC), dissolved organic nitrogen (DON), nitrate (NO₃^--N), and total dissolved phosphorus (TDP)] in forested headwater catchments with differential water loading and water storage potential at the Turkey Lakes Watershed in Ontario, Canada. We tested the hypotheses that (1) climate has non-stationary and stationary effects on nutrient export, the combination of which explains most of the variation in nutrient export; (2) more metabolically active nutrients (e.g., DON, NO₃^--N and TDP) are more sensitive to these signals; and (3) catchments with relatively low water loading and water storage capacity are more sensitive to these signals. Both non-stationary and stationary signals were identified, and the combination of both explained the majority of the variation in nutrient export data. More variation was explained in more labile nutrients (DON, NO₃^--N, TDP), which were also more sensitive to climate signals. The catchment with low water storage potential and low water loading was most sensitive to non-stationary and stationary climatic oscillations, suggesting that these hydrologic features are characteristic of the most effective sentinels of climate change. The observed complex links between climate change, climatic oscillations, and water nutrient fluxes in headwater catchments suggest that climate may have considerable influence on the productivity and biodiversity of surface waters, in addition to other drivers such as atmospheric pollution.

The paper describes a numerical model for simulating sediment transport with eddy-resolving 3D models. This sediment model consists of four submodels: pick-up, transport over the bed, transport in the water column and deposition, all based on a turbulent flow model using large-eddy simulation. The sediment is considered as uniform rigid spherical particles. This is usually a valid assumption for sand-bed rivers where underwater dune formation is most prominent. Under certain shear stress conditions, these particles are picked up from the bed, due to an imbalance of gravity and flow forces. They either roll and slide on the bed in a sheet of sediment, or separate from the bed and get suspended in the flow. Sooner or later, the suspended particles settle on the bed again. Each of these steps is modelled separately, yielding a physics-based process model for sediment transport, suitable for the simulation of bed morphodynamics. The sediment model is validated with theoretical findings such as the Rouse profile, as well as with empirical relations of sediment bedload and suspended load transport. The current model shows good agreement with these theoretical and empirical relations. Moreover, the saltation mechanism is simulated and the average saltation length, height and velocity are found to be in good agreement with experimental results.

Synoptic weather events are known to strongly influence the isotope composition of precipitation in continental locations. In this study we present hourly values of water vapor isotopologues (HDO and H₂¹⁸O) measured over a 30-day period in locally extreme weather conditions, including Santa Ana winds and winter rainstorms, in San Diego, California, U.S.A. We investigate how atmospheric and hydrological processes influence HDO and H₂¹⁸O using an isotope enabled GCM model (IsoGSM). Combining measurements and IsoGSM simulation, we demonstrate that convective mixing of marine and continental air masses are responsible for the isotopic variation of near-surface water vapor in this coastal location. The isotopic variability is most pronounced during Santa Ana winds. The Santa Ana winds represent a unique boundary layer condition in which atmospheric mixing becomes the process that dominantly controls the changes in the isotopic composition relative to air humidity. We demonstrate that a two-source mixing approach (Keeling plot) can reliably be used to estimate the isotopic composition of the source moisture, and from that, to infer the location of the moisture origin that contributes to the atmospheric moisture content in southern California. The present study is unique because it combines large-scale isotope GCM modeling with a robust and high resolution isotope dataset to disentangle the control of atmospheric and hydrologic processes on the atmospheric humidity in an extratropical climate. Our results demonstrate the utility of using single-point, ground-based isotope observations as a complementary resource to existing satellite isotope measurements for constraining isotope-enabled GCMs in future investigation of atmospheric water cycle.

Biochemical reactions that occur in the hyporheic zone are highly dependent on the time solutes are in contact with sediments of the riverbed. In this investigation, we developed a two-dimensional longitudinal flow and solute transport model to estimate the spatial distribution of mean residence time in the hyporheic zone. The flow model was calibrated using observations of temperature and pressure and the mean residence times were simulated using the age-mass approach for steady state flow conditions. The approach used in this investigation includes the mixing of different ages and flow paths of water through advection and dispersion. Uncertainty of flow and transport parameters was evaluated using standard Monte-Carlo and the generalized likelihood uncertainty estimation method. Results of parameter estimation support the presence of a low-permeable zone in the riffle area that induced horizontal flow at depth within the riffle area. This establishes shallow and localized flow paths and limits deep vertical exchange. For the optimal model, mean residence times were found to be relatively long (10 - 32 days). The uncertainty of hydraulic conductivity resulted in a mean inter-quartile range of 13 days across all piezometers and was reduced by 24% with the inclusion of temperature and pressure observations. To a lesser extent, uncertainty in streambed porosity and dispersivity resulted in a mean inter-quartile range of 2.2- and 4.7 days, respectively. Alternative conceptual models demonstrate the importance of accounting for the spatial distribution of hydraulic conductivity in simulating mean residence times in a riffle-pool sequence.

In this paper, new methodologies for interpolating rainfall data in individual time intervals [ranging from a day to a year] using Gaussian copulas and unsymmetrical v-copulas, with a variety of treatments of altitude as an exogenous variable, are discribed. For shorter time aggregations, zeros were treated as censored variables. For each selected time step the marginal distributions of precipitation amounts were modeled using non-parametric density estimators, while the spatial dependence structures were estimated using a maximum likelihood methodology. The methodology was compared to other common geostatistical interpolators such as Ordinary Kriging and External Drift Kriging. Several measures of bias and error structure have been used to assess the efficacy of the methods in a range of comparative split-sampling studies. The data-set chosen for the study comprises daily precipitation time series over 41 years in three regions in Germany measured at more than 1200 locations over a large area (126 144 km²). Among the many findings in the paper, the ones that stand out are:

1. correlation between precipitation and topography increases with the length of time interval and is significantly improved by directional smoothing of topography;

2. the copula methods are superior to Kriging methods in terms of quality of interpolation (bias and uncertainty estimation);

3. the treatment of zeros as censored variables improves interpolation quality for daily and pentad values (monthly and annual data in this region present no dry periods);

4. the copula methods yield full conditional distributions of estimates at a target point, in an interval, improving substantially on the simple uncertainty estimates derived from Kriging;

5. the Gauss copula in particular performs best overall in terms of computational efficiency, combined with useful error profiles in the interpolations, and of all methods is the most realistic in its error estimates

Developing a better understanding of single-/multi-phase flow through reservoir rocks largely relies on characterizing and modelling the pore system. For simple homogeneous rock materials, a complete description of the real pore structure can be obtained from the pore network extracted from a rock image at a single resolution, and then an accurate prediction of fluid flow properties can be achieved by using network model. However, for complex rocks (e.g., carbonates, heterogeneous sandstones, deformed rocks), a comprehensive description of the real pore structure may involve several decades of length-scales (e.g., from sub-micron to cm), which cannot be captured by a single-resolution image due to the restriction of image size and resolution. Hence, the reconstruction of a single 3D multiple-scale model of a porous medium is an important step in quantitatively characterising such heterogeneous rocks and predicting their multi-phase flow properties.

In this paper, we present a novel methodology for the numerical construction of the multi-scale pore structure of a complex rock from a number of CT images/models of a carbonate sample at several length scales. The success of this reconstruction relies heavily on image segmentation, pore network extraction and stochastic network generation, which are provided by our existing software system, referred to as Pore Analysis Tools (PAT). Specifically, the statistical description of pore-networks of 3D rock images at multiple resolutions makes it possible for us to: (a) construct an arbitrary sized network which is equivalent in a specified domain, and (b) integrate multiple networks of different sizes into a single network incorporating all scales. Using multi-scale networks of carbonate rocks generated in this manner, two-phase network modeling results are presented to show how the resulting flow properties are dependent on inclusion of information from multiple scales. These outcomes reinforce the importance of capturing both geometry and topology in the hierarchical pore structure for such complex pore systems. The example presented reveals that isolated large-scale (e.g. macro-) pores are mainly connected by small-scale (e.g. micro-) pores, which in turn determines the combined effective petrophysical properties (capillary pressure, absolute and relative permeability). It is also demonstrated that multi(three)-scale networks reveal the effects of the interacting multi-scale pore systems (e.g. micro-pores, macro-pores and vugs) on bulk flow properties in terms of two-phase flow properties.

Observations of 408 monsoon low pressure systems (MLPS) including 196 monsoon depressions (MDs) that formed in the Bay of Bengal during the 1951-2007 period, and the gridded analysis of daily rainfall fields for the same period, were used to identify the association of antecedent rainfall (1-week average rainfall prior to the genesis of MLPS) with the genesis of MLPS and length of inland penetration by monsoon depressions. Pre-storm rainfall is treated as a surrogate to pre-storm ground wetness conditions due to unavailability of historical soil-moisture data over the monsoon region. These observations were analyzed using Self Organizing Maps (SOM) to group nine different pre-storm monsoon rainfall patterns into different transition states like active, active-to-break, break-to-active, break, etc. The analysis indicates that MLPS are 4 times more likely to form on a day during active monsoon state compared to break state. Analysis of MLPSs linked to each monsoon state represented by SOM nodes shows that MDs with higher inland penetration were associated with higher antecedent rainfall. On the other hand there was no significant difference in low level atmospheric circulation for MDs with shortest and longest inland penetration.

The aim of this paper is to investigate the effect of uncertainty originating from model inputs, parameters and initial conditions on 10 day ensemble low flow forecasts. Two hydrological models, namely GR4J and HBV, are applied to the Moselle River and performance in the calibration, validation and forecast periods, and the effect of different uncertainty sources on the quality of low flow forecasts are compared. The forecasts are generated by using ECMWF meteorological ensemble forecasts as input to the GR4J and HBV models. The ensembles, each consisting of 51 members, provided the uncertainty range for the model inputs. The Generalised Likelihood Uncertainty Estimation (GLUE) approach is used to estimate parameter uncertainty. Part of the GLUE behavioural parameter set, containing parameters directly linked to storage estimates, and the observed discharge on the forecast issue day are used to update the model storages and reflect the uncertainty from the initial conditions. The quality of the probabilistic low flow forecasts has been assessed by the relative confidence interval, reliability and hit/false alarm rates. The daily observed low flows are captured by the 90% confidence interval for both models most of the time. However, the GR4J model usually overestimates low flows whereas HBV is prone to underestimate low flows. This is particularly the case if the parameter uncertainty is included in the forecasts. The total uncertainty in the GR4J model outputs is higher than in the HBV model outputs. The forecasts issued by the HBV model incorporating input uncertainty resulted in the most reliable forecast distribution. The number of hits is about equal for both models if only the input uncertainty is considered. The parameter uncertainty was the main reason reducing the number of hits. The number of false alarms in GR4J model is twice the number of false alarms in HBV when considering all uncertainty sources. The results of this study, in general, showed the parameter uncertainty has the largest effect whereas the input uncertainty had the smallest effect on the medium range low flow forecasts.

Considering that past climate changes have significantly impacted groundwater resources, quantitative predictions of climate change effects on groundwater recharge may be valuable for effective management of future water resources. This study used 16 global climate models (GCMs) and three global warming scenarios to investigate changes in groundwater recharge rates for a 2050 climate relative to a 1990 climate in the US High Plains region. Groundwater recharge was modeled using the Soil-Vegetation-Atmosphere-Transfer model WAVES for a variety of soil and vegetation types representative of the High Plains. The median projection under a 2050 climate includes increased recharge in the Northern High Plains (+8%), a slight decrease in the Central High Plains (-3%) and a larger decrease in the Southern High Plains (-10%), amplifying the current spatial trend in recharge from north to south. There is considerable uncertainty in both the magnitude and direction of these changes in recharge projections. Predicted changes in recharge between dry and wet future climate scenarios encompass both an increase and decrease in recharge rates, with the magnitude of this range greater than 50% of current recharge. On a proportional basis, sensitivity of recharge to changes in rainfall indicates that areas with high current recharge rates are least sensitive to change in rainfall and vice versa. Sensitivity analyses indicate an amplification of change in recharge compared to change in rainfall, this amplification is in the range of 1 to 6 with an average of 2.5 to 3.5 depending upon the global warming scenario.

The objective of this paper is to analyze the improvement in the performance of the particle filter by including a resample-move step or by using a modified Gaussian particle filter. Specifically, the standard particle filter structure is altered by the inclusion of the Markov chain Monte Carlo move step. The second choice adopted in this study uses the moments of an ensemble Kalman filter analysis to define the importance density function within the Gaussian particle filter structure. Both variants of the standard particle filter are used in the assimilation of densely sampled discharge records into a conceptual rainfall-runoff model.

The results indicate that the inclusion of the resample-move step in the standard particle filter and the use of an optimal importance density function in the Gaussian particle filter improves the effectiveness of particle filters. Moreover, an optimization of the forecast ensemble used in this study, allowed for a better performance of the modified Gaussian particle filter compared to the particle filter with resample-move step.

The operation of large-scale water resources systems often involves several conflicting and non-commensurable objectives. The full characterization of tradeoffs among them is a necessary step to inform and support decisions in the absence of a unique optimal solution. In this context, the common approach is to consider many singleobjective problems, resulting from different combinations of the original problem objectives, each one solved using standard optimization methods based on mathematical programming. This scalarization process is computationally very demanding as it requires one optimization run for each trade-off and often results in very sparse and poorly informative representations of the Pareto frontier. More recently, bio-inspired methods have been applied to compute an approximation of the Pareto frontier in one single run. These methods allow to acceptably cover the full extent of the Pareto frontier with a reasonable computational effort. Yet, the quality of the policy obtained might be strongly dependent on the algorithm tuning and preconditioning. In this paper we propose a novel multiobjective Reinforcement Learning algorithm that combines the advantages of the above two approaches and alleviates some of their drawbacks. The proposed algorithm is an extension of fitted Q-iteration (FQI) that enables to learn the operating policies for all the linear combinations of preferences (weights) assigned to the objectives in a single training process. The key idea of multiobjective FQI (MOFQI) is to enlarge the continuous approximation of the value function, that is performed by singleobjective FQI over the state-decision space, also to the weight space. The approach is demonstrated on a real-world case study concerning the optimal operation of the HoaBinh reservoir on the Da river, Vietnam. MOFQI is compared with the reiterated use of FQI and a multiobjective parameterization-simulation-optimization (MOPSO) approach. Results show that MOFQI provides a continuous approximation of the Pareto front with comparable accuracy as the reiterated use of FQI. MOFQI outperforms MOPSO when no a priori knowledge on the operating policy shape is available, while produces slightly less accurate solutions when MOPSO can exploit such knowledge.

Accurate estimates of water losses by evaporation from shallow water tables are important for hydrological, agricultural, and climatic purposes. An experiment was conducted in a weighing lysimeter to characterize the diurnal dynamics of evaporation under natural conditions. Sampling revealed a completely dry surface sand layer after five days of evaporation. Its thickness was < 1 cm early in the morning, increasing to reach 4-5 cm in the evening. This evidence points out fundamental limitations of the approaches that assume hydraulic connectivity from the water table up to the surface, as well as those that suppose monotonic drying when unsteady conditions prevail. The computed vapor phase diffusion rates from the apparent drying front based on Fick's law failed to reproduce the measured cumulative evaporation during the sampling day. We propose that two processes rule natural evaporation resulting from daily fluctuations of climatic variables: (i) evaporation of water, stored during nighttime due to redistribution and vapor condensation, directly into the atmosphere from the soil surface during the early morning hours, that could be simulated using a mass transfer approach, and (ii) subsurface evaporation limited by Fickian diffusion, afterwards. For the conditions prevailing during the sampling day, the amount of water stored at the vicinity of the soil surface was 0.3 mm and was depleted before 11.00 AM. Combining evaporation from the surface before 11.00 AM and subsurface evaporation limited by Fickian diffusion after that time, the agreement between the estimated and measured cumulative evaporation was significantly improved.

Identifying the sources of continental precipitation has received increasing attention in recent years. With the use of various numerical methods, sources of precipitation have been identified from local to global scales. In this paper we identify the oceanic sources based on an atmospheric backtracking analysis of continental precipitation. We find that the strongest source areas are located close to the continents. In general, we define an oceanic area as a significant source when on average more than 20% of the total evaporation, and at least 250 mm/year of evaporation ends up as continental precipitation. We grouped these identified source areas into 15 regions and performed a forward tracking analysis of oceanic evaporation. We identified the areas on the adjacent continents that receive this oceanic moisture and whether this is nearby or remote. Moreover, we showed how the oceanic sources vary over the year in time and space. Furthermore, we correlated sea surface temperatures in the 15 source regions and the Niño 3.4 region with precipitation on all continents. For South America, we found that the El Niño Southern Oscillation (altering wind patterns) has a larger effect on precipitation than local sea surface temperatures. For West Africa, however, we show that sea surface temperature in the source regions is strongly correlated with precipitation in the rainy season. In Australia, both local sea surface temperature as well as the Niño 3.4 region appears to have a big influence on precipitation. As such this research provides new insight in the ocean-atmosphere-land coupling, which can be useful for studying seasonal weather predictions as well as climate change impact.

This paper presents a combinatorial approach for improving spatial predictions. First, copulas are used to interpolate a spatially distributed point rainfall field to a uniform spatial grid. It is observed that results vary substantially depending on the parameters chosen for interpolation leading to the hypothesis that it may be advantageous to estimate copula parameters locally or to combine local and global copula predictions. It is found that by modifying the method of forecast combinations [Bates and Granger, 1969], prediction errors in the spatial interpolation of rainfall can be reduced. Although this method of combining predictions is applied in the context of rainfall interpolation using local and global copula predictions, it can be used on other spatial variables and interpolation methods.

Effective sampling of hydrogeological systems is essential in guiding groundwater management practices. Optimal sampling of groundwater systems has previously been formulated based on the assumption that heterogeneous subsurface properties can be modeled using a geostatistical approach. Therefore, the monitoring schemes have been developed to concurrently minimize the uncertainty in the spatial distribution of systems states and parameters, such as the hydraulic conductivity K and the hydraulic head H, and the uncertainty in the geostatistical model of system parameter using a single objective function that aggregates all objectives. However, it has been shown that the aggregation of possibly conflicting objective functions is sensitive to the adopted aggregation scheme, and may lead to distorted results. In addition, the uncertainties in geostatistical parameters affect the uncertainty in the spatial prediction of K and H according to a complex nonlinear relationship, which has often been ineffectively evaluated using a first order approximation. In this study, we propose a multi-objective optimization framework to assist the design of monitoring networks of K and H with the goal of optimizing their spatial predictions and estimating the geostatistical parameters of the K field. The framework stems from the combination of a data assimilation (DA) algorithm and a multi-objective evolutionary algorithm (MOEA). The DA algorithm is based on the Ensemble Kalman Filter (EnKF), a Monte Carlo-based Bayesian update scheme for nonlinear systems, which is employed to approximate the posterior uncertainty in K, H, and the geostatistical parameters of K obtained by collecting new measurements. Multiple MOEA experiments are used to investigate the tradeoff among design objectives and identify the corresponding monitoring schemes. The methodology is applied to design a sampling network for a shallow unconfined groundwater system located in Rocky Ford, Colorado. Results indicate that the effect of uncertainties associated with the geostatistical parameters on the spatial prediction might be significantly alleviated (by up to 80% of the prior uncertainty in K and by 90% of the prior uncertainty in H) by sampling evenly distributed measurements with a spatial measurement density of more than 1 observation per 60 m × 60 m grid-block. In addition, exploration of the interaction of objective functions indicates that the ability of head measurements to reduce the uncertainty associated with the correlation scale is comparable to the effect of hydraulic conductivity measurements.

Buoyancy driven hydrodynamic instabilities of a miscible reactive interface in a homogeneous porous medium is examined. A bimolecular chemical reaction (A+B → C) is triggered at the interface between two reactant solutions A and B resulting in a chemical product solution C with different density and the viscosity from those of the reactants. The effects of the chemical reaction and a transverse flow parallel to the initial interface between the reactants are numerically analyzed. It was found that as a result of the transverse flow, fingers with sharp concentration gradients tend to develop and advance fast downward leading to higher rates of chemical production. Furthermore, a detailed analysis of the finger growth and the effects of buoyancy, transverse flow and chemical reaction allowed to reach a physical interpretation of the trends observed. Finally, a special tuning of the transverse velocity is proposed to ensure maximum or minimum chemical production applicable to subsurface flows.

In lowland karst areas of Ireland topographic depressions which get intermittently flooded on an annual cycle via groundwater sources are termed turloughs. These are sites of high ecological interest as they have communities and substrate characteristic of wetlands. The flooding in many turlough basins is due to insufficient capacity of the underground karst system to take increased flows following excessive precipitation events, causing the conduit-type network to surcharge. Continuous water level measurements have been taken in five linked turloughs in the lowland karst area of south Galway over a three year period. These water level fluctuations, in conjunction with river inputs and precipitation, were then used to elucidate the hydrogeological controls forming the hydraulic system beneath the ground. A model of the karst network has been developed using a pipe network model with the turloughs represented as ponds. The contribution to the karst network from diffuse flow through the epikarst via the matrix and fracture flow has also been modelled using a combination of an infiltration module and network of permeable pipes. The final model was calibrated against two separate hydrological years and in general provided a good simulation for all of the turloughs water levels particularly for the year with one main filling event. The model also accurately picked up the tidal response observed in these turloughs at shallow depths. The model has been used to predict the groundwater discharge to the coast via the main spring which had not heretofore been possible to measure, being below the sea level.

We study dispersion in heterogeneous porous media for solutes evolving from point-like and extended source distributions in d = 2 and d = 3 spatial dimensions. The impact of heterogeneity on the dispersion behavior is captured by a stochastic modeling approach that represents the spatially fluctuating flow velocity as a spatial random field. We focus here on the sample to sample fluctuations of the dispersion coefficients about their ensemble mean. For finite source sizes, the definition of dispersion coefficients in single realizations is not unique. We consider dispersion measures that describe the extension of the solute distribution, as well as dispersion coefficients that quantify the solute spreading relative to injection points of the partial plumes that constitute the solute distribution. While the ensemble averages of these dispersion quantities may be identical, their fluctuation behavior is found to be different. Using a perturbation approach in the fluctuations of the random flow field, we derive explicit expressions for the temporal evolution of the variances of the dispersion coefficients between realizations. Their evolution is governed by the typical dispersion time over the characteristic heterogeneity scale and the dimensions of the source distribution. We find that the dispersion variance decreases towards zero with time in d = 3 spatial dimensions, while in d = 2 it converges towards a finite long time value that is independent of the source dimensions.

Residual errors of hydrological models are usually both heteroscedastic and autocorrelated. However, only a few studies have attempted to explicitly include these two statistical properties into the residual error model and jointly infer them with the hydrological model parameters. This technical note shows that applying autoregressive error models to raw heteroscedastic residuals, as done in some recent studies, can lead to unstable error models with poor predictive performance. This instability can be avoided by applying the autoregressive process to standardized residuals. The theoretical analysis is supported by empirical findings in three hydrologically distinct catchments. The case studies also highlight strong interactions between the parameters of autoregressive residual error models and the water balance parameters of the hydrological model.

We investigated the effect of the Three Gorges Project and other dams on the load of phosphorus (P) to the middle and lower Yangtze River (MLY) and discussed the alteration of P on the ecosystem of the MLY. We collected data for continuous flow and sediment over the past 60 years and observed the concentrations of total P (TP) and particulate P (PP) in the pool reaches of the Three Gorges Reservoir (TGR), both before and after the impoundment in 2003. As a result, we obtained highly positive correlations between P and sediment and revealed two changes that were caused by the impoundments: 1) the sediment load to the MLY decreases by 91% and the river becomes almost clear; and 2) the loads of TP and PP to the MLY are sequestered by 77% and 83.5% annually and 75% and 92% in dry seasons, respectively. Because P was the limiting nutrient for bioactivity in the MLY before 2003, such significant reductions, along with the many other consequences of the dams, will not only further reduce the bio-availability of P but also increase the existing high ratio of nitrogen (N) to P. Therefore, it is quite possible to alter the nutrient regime and reduce the aquatic primary productivity of the MLY. Given that many large dams with huge reservoirs are under construction or planned upstream and elsewhere, studies focused on the long term effects of sediment and P reduction deserve a high priority for the protection of lowland rivers and aquatic ecosystems.

The ability to monitor the carbon dioxide (CO₂) that has been injected underground is important to large-scale implementation of Carbon Capture and Sequestration. The focus of this study is to understand how flow processes during CO₂ injection impact the pressure observed at a nearby monitoring well. In particular, we are interested in how the reservoir structure (layering and anisotropy) and CO₂ plume migration influence the pressure transients at different depths. For a multilayered geologic model, four basic combinations of homogeneity/heterogeneity and isotropy/anisotropy conditions are examined. Numerical simulations using TOUGH2 show different CO₂ plume migration and large pressure buildups in the storage reservoir and the seal for each scenario. Pressure buildups normalized to the pressure buildup at the depth of injection are diagnostic of the approximate height of the CO₂ plume and provide information on the reservoir structure. Vertical pressure gradients normalized to the initial hydrostatic pressure gradient are diagnostic of reservoir structure soon after the start of injection. Over time, they provide information on the height of the CO₂ plume. The diagnostic features in the pressure response are evident long before the CO₂ arrives at the monitoring well and can be attributed to buoyancy induced and gravity segregated aqueous flows caused by the advancing CO₂ plume. The identified diagnostics will aid in the ultimate goal, which is to develop a monitoring technique based on multilevel pressure measurements.

Surface microtopography affects overland flow, infiltration, soil erosion, pollutant transport, and other fundamental hydrologic and environmental processes across scales. Under the influence of surface depressions, overland flow essentially features a series of progressive puddle-to-puddle (P2P) filling, spilling, merging, and splitting processes. The objectives of this study are to characterize puddles and their hierarchical relationships and model the microtopography-controlled P2P processes. We proposed a new modeling framework for simulating the P2P overland flow dynamics through cell-to-cell (C2C) and P2P routing for a set of puddle-based units (PBUs) in a well-delineated, cascaded P2P drainage system. Testing of the P2P model demonstrated its potential to improve overland flow modeling and hydrologic connectivity analysis by explicitly incorporating the hydrologic roles of depressions and quantifying the real microtopography-controlled P2P dynamics.

Many large–scale water resources systems, especially in trans–boundary contexts, are characterized by the presence of several and conflicting interests and managed by multiple, institutionally independent decision–makers. These systems are often studied adopting a centralized approach based on the assumption of full cooperation and information exchange among the involved parties. Such a perspective is conceptually interesting to quantify the best achievable performance, but might have little practical impact given the real political and institutional setting. In this work, we propose a novel decision-analytic framework based on Multi–Agent Systems to model and analyze different levels of cooperation and information exchange among multiple decision–makers. The Zambezi River basin is used as a case study. According to the proposed agent–based optimization approach, each agent represents a decision–maker, whose decisions are defined by an explicit optimization problem considering only the agent's local interests. The economic value of information exchange is estimated comparing a non–cooperative setting, where agents act independently, with the first basic level of cooperation, i.e. coordination, characterized by full information exchange. The economic value of cooperation is also estimated by comparison with the ideal, fully cooperative management of the system. Results show that coordination, obtained with complete information exchange, allows the downstream agents to better adapt to the upstream behaviors. The impact of information exchange depends on the objective considered, and we show coordination to be particularly beneficial to environmental interests.

Fecal bacteria are frequently found at much greater distances than would be predicted by laboratory studies, indicating that improved models that incorporate more complexity are might be needed to explain the widespread contamination of many shallow aquifers. In this study, laboratory measurements of breakthrough and retained bacteria in columns of intact and repacked sediment cores from Bangladesh were fit using a two-population model with separate reversible and irreversible attachment sites that also incorporated bacterial decay rates. Separate microcosms indicated an average first order decay rate of 0.03 log₁₀ / day for free bacteria in both the liquid phase and bacteria attached to the solid phase. Although two-thirds of the column results could be well fit with a dual deposition site, single population model, fitting of one third of the results required a two-population model with a high irreversible attachment rate (between 5 and 60 hr^-1) for one population of bacteria and a much lower rate (from 5 hr^-1 to essentially zero) for the second. Inferred attachment rates for the reversible sites varied inversely with grain size (varying from 1 - 20 hr^-1 for grain sizes between 0.1 and 0.3 mm) while reversible detachment rates were found to be nearly constant (approximately 0.5 hr^-1). Field simulations based on the fitted two-population model parameters predict only a two-fold reduction in fecal source concentration over a distance of 10 m, determined primarily by the decay rate of the bacteria. The existence of a secondary population of bacteria with a low attachment rate might help explain the observed widespread contamination of tubewell water with E. coli at the field site where the cores were collected, as well as other similar sites.

Tree canopy snow interception is a significant hydrological process, capable of removing up to 60% of snow from the ground snowpack. Our understanding of canopy interception has been limited by our ability to measure whole canopy water storage in an undisturbed forest setting. This study presents a relatively inexpensive technique for directly measuring snow canopy water storage using an interceptometer, adapted from Friesen et al. [2008]. The interceptometer is composed of four linear motion position sensors distributed evenly around the tree trunk. We incorporate a trunk laser-mapping installation method for precise sensor placement to reduce signal error due to sensor misalignment. Through calibration techniques, the amount of canopy snow required to produce the measured displacements can be calculated. We demonstrate instrument performance on a western hemlock (Tsuga heterophylla) for a snow interception event in November, 2011. We find a snow capture efficiency of 83 ±15% of accumulated ground snowfall with a maximum storage capacity of 50 ±8 mm snow water equivalent (SWE). The observed interception event is compared to simulated interception, represented by the Variable Infiltration Capacity (VIC) hydrologic model. The model generally underreported interception magnitude by 33% using an LAI of 5 and 16% using an LAI of 10. The interceptometer captured intrastorm accumulation and melt rates up to 3 mm SWE hr^-1 and 0.75 mm SWE hr^-1, respectively, which the model failed to represent. While further implementation and validation is necessary, our preliminary results indicate that forest interception magnitude may be underestimated in maritime areas.

The spatial distribution of solute concentration in heterogeneous aquifers is extremely complex and variable over scales ranging from a few millimeters to kilometers. Obtaining a detailed spatial distribution of the concentration field is an elusive goal because of intrinsic technical limitations and budget constraints for site characterization. Therefore, local concentration predictions are highly uncertain and alternative measures of transport must be sought. In this paper, we propose to describe the spatial distribution of the concentrations of a nonreactive tracer plume by means of suitable spatial statistical transport measures, as an alternative to approaches relying only on the ensemble mean concentration. By assuming that the solute concentration is statistically distributed according to the Beta distribution model, we compare several models of concentration moments against numerical simulations and Cape Cod concentration data. These measures provide useful information which are: (i) representative of the overall transport process, (ii) less affected by uncertainty than the local probability density function and (iii) only marginally influenced by local features. The flexibility of the approach is shown by considering three different integral expressions for both the spatial mean and variance of concentration based on previous works. Aiming at a full statistical characterization, we illustrate how the Beta relative cumulative frequency distribution (obtained as a function of the spatial concentration) compares with the numerical cumulative frequencies. Our approach allows to estimate the probability of exceeding a given concentration threshold within the computational or observational domain, which could be used for sampling data campaigns, preliminary risk assessment and model refinement. Finally, our results highlight the importance of goal-oriented model development

Prediction of flow through variably saturated porous media requires accurate knowledge of the soil hydraulic properties, namely the water retention function (WRF) and the hydraulic conductivity function (HCF). Unfortunately, direct measurement of the HCF is time-consuming and expensive. In this study we derive a simple closed-form equation that predicts the saturated hydraulic conductivity, K_s from the WRF parameters of Brooks and Corey (1964). This physically based analytical expression uses an empirical tortuosity parameter (τ) and exploits the information embedded in the measured pore-size distribution. Our proposed model is compared against the current state of the art using more than 250 soil samples from the GRIZZLY and HYPRES databases. Results demonstrate that the proposed model provides better predictions of the saturated hydraulic conductivity values with reduced size of the 90 % confidence intervals of about three orders of magnitude.

In this paper we use empirical modeling to predict and understand phytoplankton dynamics in a reservoir affected by water transfers. Prediction of phytoplankton biovolume is central to the management of water resources, particularly given the significant impacts on quality of the water-quantity oriented management of transfers between reservoirs. A novel tree-based Iterative Input variable Selection algorithm is applied for the first time in an ecological context, and identifies a maximum of eight driving factors out of 77 candidates to explain the biovolume of chlorophytes, cyanobacteria and diatoms. The stepwise forward-selection to iteratively identify the most important inputs leads to a physically interpretable model able to infer the physical processes controlling phytoplankton biovolume. Reservoir inflows and outflows are found to exert a strong control over diatom and chlorophytes dynamics while water temperature, nitrate and phosphorus determine the biovolume of cyanobacteria. Following the selection of the most relevant inputs, the one-week ahead predictions of four different data-driven model classes, i.e., neural networks, extra-trees, model trees and linear regressions, are compared based on performance indices and statistical tests. Extra-trees are found to outperform the other models by providing accurate predictions of cyanobacteria, chlorophyte and diatom biovolume by explaining 66.6%, 66.9% and 80.5% of the variance, respectively. The methodology is applicable to different environmental studies and combines the strength of empirical modeling, i.e., compact models and accurate predictions, with a good understanding of the physical processes involved.

Microbes have been identified as a major contaminant of water resources. Escherichia coli (E. coli) is a commonly used indicator organism. It is well recognized that the fate of E. coli in surface water systems is governed by multiple physical, chemical, and biological factors. The aim of this work is to provide insight into the physical, chemical, and biological factors along with their interactions that are critical in the estimation of E. coli loads in surface streams. There are various models to predict E. coli loads in streams, but they tend to be system or site specific or overly complex without enhancing our understanding of these factors. Hence, based on available data, a Bayesian Neural Network (BNN) is presented for estimating E. coli loads based on physical, chemical, and biological factors in streams. The BNN has the dual advantage of overcoming the absence of quality data (with regards to consistency in data) and determination of mechanistic model parameters by employing a probabilistic framework. This study evaluates whether the BNN model can be an effective alternative tool to mechanistic models for E. coli loads estimation in streams. For this purpose, a comparison with a traditional model (LOADEST, USGS) is conducted. The models are compared for estimated E. coli loads based on available water quality data in Plum Creek, Texas. All the model efficiency measures suggest that overall E. coli loads estimations by the BNN model are better than the E. coli loads estimations by the LOADEST model on all the three occasions (three-fold cross validation). Thirteen factors were used for estimating E. coli loads with the exhaustive feature selection technique, which indicated that six of thirteen factors are important for estimating E. coli loads. Physical factors included temperature and dissolved oxygen; chemical factors include phosphate and ammonia; biological factors include suspended solids and chlorophyll. The results highlight that the LOADEST model estimates E. coli loads better in the smaller ranges, whereas the BNN model estimates E. coli loads better in the higher ranges. Hence, the BNN model can be used to design targeted monitoring programs and implement regulatory standards through TMDL programs.

Advective transport in the porous matrix of sediments surrounding burrows formed by fauna such as Chironomus plumosus has been generally neglected. A positron emission tomography study recently revealed that the pumping activity of the midge larvae can indeed induce fluid flow in the sediment. We present a numerical model study which explores the conditions at which advective transport in the sediment becomes relevant. A 0.15 m deep U-shaped burrow with a diameter of 0.002 m within the sediment was represented in a 3-D domain. Fluid flow in the burrow was calculated using the Navier-Stokes equation for incompressible laminar flow in the burrow, and flow in the sediment was described by Darcy's law. Non-reactive and reactive transport scenarios were simulated considering diffusion and advection. The pumping activity of the model larva results in considerable advective flow in the sediment at reasonable high permeabilities with flow velocities of up to 7.0×10^-6 m s^-1 close to the larva for a permeability of 3×10^-12 m². At permeabilities below 7×10^-13 m² advection is negligible compared to diffusion. Reactive transport simulations using first order kinetics for oxygen revealed that advective flux into the sediment downstream of the pumping larva enhances sedimentary uptake, while the advective flux into the burrow upstream of the larvae inhibits diffusive sedimentary uptake. Despite the fact that both effects cancel each other with respect to total solute uptake, the advection induced asymmetry in concentration distribution can lead to a heterogeneous solute and redox distribution in the sediment relevant to complex reaction networks.

This study presents an explicit stochastic optimization-simulation model of short-term deficit irrigation management for large scale irrigation districts. The model which is a nonlinear non-convex program with an economic objective function is built on an agro-hydrological simulation component. The simulation component integrates 1) an explicit stochastic model of soil moisture dynamics of the crop-root zone considering interaction of stochastic rainfall and irrigation with shallow water table effects, 2) a conceptual root zone salt balance model, and 3) the FAO crop yield model. Particle Swarm Optimization (PSO) algorithm, linked to the simulation component, solves the resulting non-convex program with a significantly better computational performance compared to a Monte Carlo-based implicit stochastic optimization model. The model has been tested first by applying it in single-crop irrigation problems through which the effects of the severity of water deficit on the objective function (net benefit), root-zone water balance, and irrigation water needs have been assessed. Then the model has been applied in Dasht-e-Abbas and Ein-khosh Fakkeh Irrigation Districts (DAID and EFID) of the Karkheh Basin in southwest of Iran. While the maximum net benefit has been obtained for a stress-avoidance (SA) irrigation policy, the highest water profitability has been resulted when only about 60% of the water used in the SA policy is applied. The DAID with respectively 33% of total cultivated area and 37% of total applied water has produced only 14% of the total net benefit due to low-valued crops and adverse soil and shallow water table conditions.

We investigated scaling of conservative solute transport using temporal moment analysis of 98 tracer experiments (384 breakthrough curves) conducted in 44 streams located on 5 continents. The experiments span 7 orders of magnitude in discharge (10^-3 – 10³ m³/s), span 5 orders of magnitude in longitudinal scale (10¹ – 10⁵ m), and sample different lotic environments – forested headwater streams, hyporheic zones, desert streams, major rivers, and an urban manmade channel. Our meta-analysis of these data reveals that the coefficient of skewness is constant over time (CSK = 1.18±0.08, R² > 0.98). In contrast, the CSK of all commonly used solute transport models decreases over time. This shows that current theory is inconsistent with experimental data and suggests that a revised theory of solute transport is needed. Our meta-analysis also shows that the variance (second normalized central moment) is correlated with the mean travel time (R² > 0.86), and the third normalized central moment and the product of the first two are very strongly correlated (R² > 0.96). These correlations were applied in four different streams to predict transport based on the transient storage and the aggregated dead zone models, and two probability distributions (Gumbel and log-normal).

We examine the use of numeric flow solutions of the Navier-Stokes (N-S) equations to improve flow modeling in fractured karst aquifers. The N-S equations were discretized with both the meshed finite difference method (FDM) and the meshless smoothed particle hydrodynamics (SPH) method. The results using the FDM model were successfully compared with those taken from the literature. The N-S equations were solved numerically for two practical problems in karst aquifers: a) the horizontal displacement of the saltwater/freshwater sharp interface in fissures due to groundwater overexploitation, and b) the pressure and velocity profiles in fissures in the vicinity of an injection well. In the first problem, the numeric N-S solution suggests an exponential time advancement of the freshwater/saltwater interface in fissures. In the second problem, the unsteady water velocity and pressure profiles were determined in fissures having variable apertures in the vicinity of an injection well. The N-S simulation results agreed well with the data collected during the test, thereby removing any uncertainty in the estimation of the aquifer transmissivity.

Peat cores from a recently burned peatland and one over 75 years since fire in Alberta, Canada were analyzed for physical properties and water retention. Wildfire exposed denser peat at the peat surface, more so in hollow than hummock microforms. Water retention in peat has implications for post-fire Sphagnum regeneration, as this more dense peat requires smaller volumes of water loss before a critical growth-inhibiting pore-water pressure of -100 mb is reached. Simulations of water retention after fire showed that hollow microforms are at a higher risk of losing low-density surface peat, which moderates water table declines via high specific yield. Exposure of dense peat to the surface after fire increases surface moisture under a constant water table. The net effect of decreasing specific yield and increasing water retention at the surface has implications on hydrologic stability and resilience of boreal peatlands to future wildfire risk under a changing climate. Earth system models incorporating wildfire disturbance in boreal peatlands would benefit from the inclusion of these hydrological feedbacks in this globally-significant carbon reservoir.

Model applicability to core-scale solute transport is evaluated using breakthrough data from column experiments conducted with conservative tracers tritium (³H) and sodium-22 (²²Na), and the retarding solute uranium-232 (²³²U). The three models considered are single-porosity, double-porosity with single-rate mobile-immobile mass-exchange, and the multirate model, which is a deterministic model that admits the statistics of a random mobile-immobile mass-exchange rate coefficient. The experiments were conducted on intact Culebra Dolomite core samples. Previously, data were analyzed using single- and double-porosity models although the Culebra Dolomite is known to possess multiple types and scales of porosity, and to exhibit multirate mobile-immobile-domain mass transfer characteristics at field scale. The data are reanalyzed here and null-space Monte Carlo analysis is used to facilitate objective model selection. Prediction (or residual) bias is adopted as a measure of the model structural error. The analysis clearly shows single- and double-porosity models are structurally deficient, yielding late-time residual bias that grows with time. On the other hand, the multirate model yields unbiased predictions consistent with the late-time -5/2 slope diagnostic of multirate mass transfer. The analysis indicates the multirate model is better suited to describing core-scale solute breakthrough in the Culebra Dolomite than the other two models.

In this study, we analyse satellite-based daily rainfall observations to compare and contrast the wet and dry spell characteristics of tropical rainfall. Defining a wet (dry) spell as the number of consecutive rainy (non-rainy) days, we find that the distributions of wet spells appear to exhibit universality in the following sense. While both ocean and land regions with high seasonal rainfall accumulation (humid regions; e.g., India, Amazon, Pacific Ocean) show a predominance of 2-4 day wet spells, those regions with low seasonal rainfall accumulation (arid regions; e.g., South Atlantic, South Australia) exhibit a wet spell duration distribution that is essentially exponential in nature, with a peak at one day. The behaviour that we observed for wet spells is reversed for the dry spell characteristics. In other words, the main contribution to the dry part of the season, in terms of the number of non-rainy days, appears to come from 3-4 day dry spells in the arid regions, as opposed to 1-day dry spells in the humid regions. The total rainfall accumulated in each wet spell has also been analysed, and we find that the major contribution to seasonal rainfall for arid regions comes from 1-5 day wet spells; however, for humid regions, this contribution comes from wet spells of duration as long as 30 days. We also explore the role of chance as well as the influence of organised convection in determining some of the observed features.

Knowledge about the strength and travel-times of hyporheic exchange is vital to predict reactive transport and biogeochemical cycling in streams. In this study, we outline how to perform and analyze stream-tracer tests using pulse injections of fluorescein as conservative and resazurin as reactive tracer, which is selectively transformed to resorufin when exposed to metabolically active zones, presumably located in the hyporheic zone. We present steps of preliminary data analysis and apply a conceptually simple mathematical model of the tracer tests to separate effects of in-stream transport from hyporheic exchange processes. To overcome the dependence of common parameter estimation schemes on the initial guess, we derive posterior parameter probability density functions using an adaptive Markov chain Monte Carlo scheme. By this, we can identify maximum-likelihood parameter values of in-stream transport, strength of hyporheic exchange, distribution of hyporheic travel times as well as sorption and reactivity coefficients of the hyporheic zone. We demonstrate the approach by a tracer experiment at River Goldersbach in southern Germany (60 L/s discharge). In-stream breakthrough curves were recorded with on-line fluorometers and jointly fitted to simulations of a one-dimensional reactive transport model assuming an exponential hyporheic travel-time distribution. The findings show that the additional analysis of resazurin not only improved the physical basis of the modeling, but was crucial to differentiate between surface transport and hyporheic transient storage of stream solutes. Parameter uncertainties were usually small and could not explain parameter variability between adjacent monitoring stations. The latter as well as a systematic underestimation of the tailing are due to structural errors of the model, particularly the exponential hyporheic travel-time distribution. Mean travel times were in the range of 12 min, suggesting that small streambed structures dominate hyporheic exchange at the study site.

Capillary pressure and relative permeability drainage curves are simultaneously measured on a single Berea Sandstone core by using three different fluid pairs, namely gCO₂/water, gN₂/water and scCO₂/brine. This novel technique possesses many of the characteristics of a conventional steady-state relative permeability experiment and consists of injecting the nonwetting fluid at increasingly higher flow rates in a core that is initially saturated with the wetting phase, while observing fluid saturations with a medical x-ray CT scanner. Injection flow rates (0.5-75 ml/min) are varied so as to generate a large range of capillary pressures (up to 18 kPa), whereas fluid-pairs and experimental conditions are selected in order to move across a range interfacial tension values (γ₁₂ = 40-65 mN/m), while maintaining a constant viscosity ratio (μ_w/μ_nw ≈ 30). Moreover, these experiments, carried out at moderate pressures (P = 2.4 MPa and T = 50°C), can be compared directly with results for gas/liquid pairs reported in the literature and they set the benchmark for the experiment at a higher pressure (P = 9 MPa and T = 50°C), where CO₂ is in the supercritical state. Contrary to some prior investigations, from these experiments we find no evidence that the scCO₂/brine system behaves differently than any of these other fluid pairs. At the same time, capillary pressure data show a significant (but consistent) effect of the different values for the interfacial tension. The fact that the three different fluid pairs yield the same drainage relative permeability curve is consistent with observations in the petroleum literature. Additionally, the observed end-point values for the relative permeability to the nonwetting phase (k_r,nw ≈ 0.9) and the corresponding irreducible water saturations (S_w,irr ≈ 0.35) suggest that water-wet conditions are maintained in each experiment. The reliability of the measured relative permeability curves is supported by the very good agreement with data from the literature obtained on Berea Sandstone cores and with various gas/liquid pairs. The Brooks-Corey model is used to describe the capillary pressure data and the parameters derived from these matches provide a good prediction of the relative permeability curves. It is shown that the apparent low end-point relative permeabilities to the nonwetting phase reported in previous experimental studies are caused by the low viscosity of CO₂ relative to water, rather than by the rock heterogeneity. In fact, the former controls the level of capillary pressure that can be achieved experimentally, thus restricting the applicability of some of the conventional methods to measure relative permeability curves for gas/liquid systems.

Performing stream-tracer experiments is an accepted technique to assess transport characteristics of streams undergoing hyporheic exchange. Recently, combining conservative and reactive tracers, in which the latter presumably undergoes degradation exclusively within the hyporheic zone, has been suggested to study in-stream transport, hyporheic exchange, and the metabolic activity of the hyporheic zone. The combined quantitative analysis to adequately describe such tests, however, has been missing. In this paper, we present mathematical methods to jointly analyze breakthrough curves of a conservative tracer (fluorescein), a linearly degrading tracer (resazurin), and its daughter compound (resorufin), which are synchronously introduced into the stream as pulses. In-stream transport is described by the one-dimensional advection-dispersion equation, amended with a convolution term to account for transient storage within the hyporheic zone over a distribution of travel times, transformation of the reactive tracer in the hyporheic zone, and two-site sorption of the parent and daughter compounds therein. We use a shape-free approach of describing the hyporheic travel-time distribution, overcoming the difficulty of identifying the best functional parameterization for transient storage. We discuss how this model can be fitted to the breakthrough curves of all three compounds and demonstrate the method by an application to a tracer test in the third-order stream Goldersbach in Southern Germany. The entire river water passes once through the hyporheic zone over a travel distance of about 200 m with mean hyporheic residence times ranging between 16 and 23 minutes. We also observed a secondary peak in the transfer functions at about 1 hour indicating a second hyporheic flow path. We could jointly fit the breakthrough curves of all compounds in three monitoring stations, and evaluated the parameter uncertainty of the individual and joint fits by a method based on conditional realizations of the hyporheic travel-time distribution. The approach gives insight into in-stream transport, hyporheic exchange, metabolic activity, and river-bed sorption of the stream under investigation.

Accurate estimates of mass-exchange parameters in transient storage zones are needed to better understand and quantify solute transport and dispersion in riverine systems. Currently, the predictive mean residence time relies on an empirical entrainment coefficient with a range in variance due to the absence of hydraulic and geomorphic quantities driving mass exchange. Two empirically derived relationships are presented for the mean residence time of lateral cavities—a prevalent and widely recognized type of transient storage—in gravel-bed rivers and streams that incorporates hydraulic and geomorphic parameters. The relationships are applicable for gravel-bed rivers and streams with a range of cavity width to length (W/L) aspect ratios (0.2 to 0.75), shape, and Reynolds numbers (Re, ranging from 1.0 × 10⁴ to 1.0 × 10⁷). The relationships equate normalized mean residence time to nondimensional quantities: Froude number, Re, W/L, depth ratio (ratio of cavity to shear layer depth), roughness factor (ratio of shear to channel velocity), and shape factor (representing degree of cavity equidimensionality). One relationship excludes bed roughness (13) and the other includes bed roughness (14). The empirically derived relationships have been verified for conservative tracers (R² of 0.83) within a range of flow and geometry conditions. Topics warranting future research are testing the empirical relationship that includes the roughness factor using parameters measured in the vicinity of the cavity to reduce the variance in the correlation, and further development of the relationship for non-conservative transport.

We address the problem of extracting surface velocities from a pair of multispectral remote sensing images over rivers using a new nonlinear multiple-tracer form of the Global Optimal Solution (GOS). The derived velocity field is a valid solution across the image domain to the nonlinear system of equations obtained by minimizing a cost function inferred from the conservation constraint equations for multiple tracers. This is done by deriving an iteration equation for the velocity, based on the multiple-tracer displaced frame difference equations, and a local approximation to the velocity field. The number of velocity equations is greater than the number of velocity components, and thus overly constrain the solution. The iterative technique uses Gauss-Newton and Levenberg-Marquardt methods and our own algorithm of the progressive relaxation of the over-constraint.

We demonstrate the nonlinear multiple-tracer GOS technique with sequential multispectral Landsat and ASTER images over a portion of the Potomac River in MD/VA, and derive a dense field of accurate velocity vectors. We compare the GOS river velocities with those from over 12 years of data at four NOAA reference stations, and find good agreement. We discuss how to find the appropriate spatial and temporal resolutions to allow optimization of the technique for specific rivers.

A long-standing challenge for hydrologists has been a lack of observational data on global-scale basin hydrological behavior. With observations from NASA's Gravity Recovery And Climate Experiment (GRACE) mission, hydrologists are now able to study terrestrial water storage for large river basins (>200,000 km²), with monthly time resolution. Here we provide results of a time series model of basin-average GRACE terrestrial water storage anomaly and Global Precipitation Climatology Project (GPCP) precipitation for the world's largest basins. We address the short (10-year) length of the GRACE record by adopting a parametric spectral method to calculate frequency-domain transfer functions of storage response to precipitation forcing, and then generalize these transfer functions based on large-scale basin characteristics, such as percent forest cover and basin temperature. Among the parameters tested, results show that temperature, soil water-holding capacity and percent forest cover are important controls on relative storage variability, while basin area and mean terrain slope are less important. The derived empirical relationships were accurate (0. 54 ≤ E_f ≤ 0.84) in modeling global-scale water storage anomaly time series for the study basins using only precipitation, average basin temperature, and two land-surface variables, offering the potential for synthesis of basin storage time series beyond the GRACE observational period. Such an approach could be applied towards gap filling between current and future GRACE missions, and for predicting basin storage given predictions of future precipitation.

Air permeability is measured in the fractured crystalline rocks of the Roselend Natural Laboratory (France). Single hole pneumatic injection tests as well as differential barometric pressure monitoring are conducted on scales ranging from 1 to 50 m, in both shallow and deep boreholes, as well as in an isolated 60 m³ chamber at 55 m depth. The field experiments are interpreted using numerical simulations in equivalent homogeneous porous media with their real 3D geometry in order to estimate pneumatic parameters. For pneumatic injection tests, steady-state data first allow to estimate air permeability. Then, pressure recovery after a pneumatic injection test allows to estimate the air-filled porosity. Comparison between the various studied cases clarifies the influence of the boundary conditions on the accuracy of the often used 1D estimate of air permeability. It also shows that permeabilities correlate slightly with fracture density. In the chamber, a one order of magnitude difference is found between the air permeabilities obtained from pneumatic injection tests and from differential barometric pressure monitoring. This discrepancy is interpreted as a scale effect resulting from the approximation of the heterogeneous fractured rock by a homogeneous numerical model. The difference between the rock volumes investigated by pneumatic injection tests and by differential barometric pressure monitoring may also play a role. No clear dependence of air permeability on saturation has been found so far.

The U.S. Nuclear Regulatory Commission actively investigated climate and infiltration at Yucca Mountain for two decades to (i) understand important controls and uncertainties influencing percolation through the unsaturated zone on multimillennial time scales and (ii) provide flux boundary conditions for up to one million years in performance assessment models of the proposed Yucca Mountain repository. This second part of a two-part series describes site-scale model results for present and potential future conditions, and confirmatory analyses for present-day conditions. At both the grid-cell and site-average scale, the calculated uncertainty distribution for net infiltration is approximately lognormal and the coefficient of variation decreases with increasing net infiltration. Smaller relative but larger absolute responses to climate change occur where net infiltration is large. Comparisons of distributed model estimates with temperature and geochemical observations from the unsaturated zone suggest that average estimates are generally consistent but exhibit significant variability. An observed seepage event in the South Ramp of the Exploratory Studies Facility, combined with related subsurface observations across the site, suggests that subsurface spreading from zones of high infiltration to zones of low infiltration may occur in stratabound fractures, laterally extensive discontinuities, or at transitions between welded and nonwelded tuff units. Two conceptual models for unsaturated-zone flow each explain the subsurface observations, collectively providing bounding estimates for net infiltration. Model-predicted uncertainty distribution for decadal-average site-scale net infiltration is generally consistent with estimated percolation fluxes using the bounding hypotheses, suggesting that the model-calculated uncertainty is reasonably consistent with the uncertainty in interpreting site observations.

Diamond Lake in Minnesota is covered every winter with ice and snow providing a modified thermal insulation between water and air. Autonomous temperature sensors, data loggers, were placed in this lake so that hourly measurements could be obtained from the snow-covered ice and water. The sensors that became frozen measured damped and delayed thermal response from the air temperature fluctuation. Those sensors that were deeper within the snow-covered ice measured continuous, almost constant, temperature values near freezing. Several of them were within the liquid water and responded with a fluctuation of 24-hour periods of amplitudes up to 0.2°C. Our analysis of the vertical temperature profiles suggested that the source of periodic water heating comes from the lake bottom. Because of the absence of daily temperature variations of the snow covered ice, the influence of the air temperature fluctuation can be ruled out. We attribute the heating process to the periodic inflow of ground water to the lake and the cooling to the heat diffusion to the overlying ice cover. The periodic ground water inflow is interpreted as due to solid Earth tides, which cause periodic fluctuations of the ground water pressure head.

Quantification of the recharge in fractured aquifers is particularly challenging because of the multi-scale heterogeneity and the range of temporal scales involved. Here, we investigate the hydraulic response to recharge of a fractured aquifer, using a frequency domain approach. Transfer functions are calculated in a range of temporal scales from 1 day up to a few years, for a fractured crystalline-rock aquifer located in Ploemeur (S Brittany, France), using recharge and groundwater level fluctuations as input and output respectively. The spatial variability of the response to recharge (characteristic response time, amplitude, temporal scaling) is analyzed for ten wells sampling the different compartments of the aquifer. Some of the transfer functions follow the linear reservoir model behavior. On the contrary, others display a temporal scaling at high-frequency that cannot be represented by classic models. Large scale hydraulic parameters, estimated from the low-frequency response, are compared with those estimated from hydraulic tests at different scales. The variability of transmissivity and storage coefficient tends to decrease with scale, and the average estimates converge towards the highest values at large scale. The small scale variability of diffusivities, which implies the existence of a range of characteristic temporal scales associated with different pathways, is suggested to be at the origin of the unconventional temporal scaling of the hydraulic response to recharge at high-frequency.

In this study, an ecohydrological Cellular Automata Tree-Grass-Shrub Simulator (CATGraSS) is presented. CATGraSS is driven by pulses of rainfall and daily solar radiation. In the model, each cell can hold a single plant type (tree, shrub, tree seedlings, shrub seedlings, grass) or can be bare soil. Plant competition is modeled explicitly by keeping track of mortality and establishment of plants, both calculated probabilistically based on soil moisture stress. Topographic influence on incoming shortwave radiation is treated explicitly, which leads to spatial variations in potential evapotranspiration and soil moisture over storm and inter-storm time scales, and plant distribution over annual time scales. The model is implemented in a small basin (3.3 km²) in central New Mexico, USA, where north-facing slopes are characterized by a juniper pine and grass savanna, and south-facing slopes by creosotebush shrubs and grasses. Representing the current climate by a seasonal-varying Poisson Rectangular Pulse rainfall model, CATGraSS is calibrated with success against the existing plant patterns in the study catchment. The model is then used in a series of numerical experiments. CATGraSS is first run on flat terrain to examine the role of topography on plant patterns. Consistent with our observation in the region, this “flat run” gave a shrubland ecosystem with scattered grasses and trees. Model sensitivity to rainfall is investigated in a limited number of simulations by altering rainfall frequency-magnitude statistics, and seasonality. The sensitivity runs suggest that changes in the storm characteristics could lead to a dramatic reorganization of the plant composition on topography in central New Mexico. CATGraSS results underscore the importance of solar irradiance in determining vegetation composition over complex terrain under a water limited ecosystem.

In arid and semi-arid regions, irrigation water is scarce and often contains high concentrations of salts. To reduce negative effects on crop yields, the irrigated amounts must include water for leaching and therefore exceed evapotranspiration. The leachate (drainage) water returns to water sources such as rivers or groundwater aquifers and increases their level of salinity and the leaching requirement for irrigation water of any sequential user. We develop a conceptual sequential (upstream-downstream) model of irrigation that predicts crop yields and water consumption and tracks the water flow and level of salinity along a river dependent on irrigation management decisions. The model incorporates an agro-physical model of plant response to environmental conditions including feedbacks. For a system with limited water resources, the model examines the impacts of water scarcity, salinity and technically inefficient application on yields for specific crop, soil, and climate conditions. Moving beyond the formulation of a conceptual frame, we apply the model to the irrigation of Capsicum annum on Arava Sandy Loam soil. We show for this case how water application could be distributed between upstream and downstream plots or farms. We identify those situations where it is beneficial to trade water from upstream to downstream farms (assuming that the upstream farm holds the water rights). We find that water trade will improve efficiency except when loss levels are low. We compute the marginal value of water, i.e. the price water would command on a market, for different levels of water scarcity, salinity and levels of water loss.

The drainage systems of polythermal glaciers play an important role in high-latitude hydrology, and are determinants of ice flow rate. Flow-recession analysis and linear-reservoir simulation of runoff time series are here used to evaluate seasonal and inter-annual variability in the drainage system of the polythermal Finsterwalderbreen, Svalbard, in 1999 and 2000. Linear flow recessions are pervasive, with mean coefficients of a fast reservoir varying from 16 h (1999) to 41 h (2000), and mean coefficients of an intermittent, slow reservoir varying from 54 h (1999) to 114 h (2000). Drainage-system efficiency is greater overall in the first of the two seasons, the simplest explanation of which is more rapid depletion of the snow cover. Reservoir coefficients generally decline during each season (at 0.22 h d^–1 in 1999 and 0.52 h d^–1 in 2000), denoting an increase in drainage efficiency. However, coefficients do not exhibit a consistent relationship with discharge. Finsterwalderbreen therefore appears to behave as an intermediate case between temperate glaciers and other polythermal glaciers with smaller proportions of temperate ice. Linear-reservoir runoff simulations exhibit limited sensitivity to a relatively wide range of reservoir coefficients, although the use of fixed coefficients in a spatially-lumped model can generate significant sub-seasonal error. At Finsterwalderbreen, an ice-marginal channel with the characteristics of a fast reservoir, and a subglacial upwelling with the characteristics of a slow reservoir, both route meltwater to the terminus. This suggests that drainage-system components of significantly contrasting efficiencies can co-exist spatially and temporally at polythermal glaciers.

Interactions among flow, geomorphic processes, and riparian vegetation can strongly influence both channel form and vegetation communities. To investigate such interactions, we took advantage of a series of dam-managed flood releases that were designed in part to maintain a native riparian woodland system on a sand-bed, dryland river, the Bill Williams River, Arizona, USA. Our resulting multi-year flow experiment examined differential mortality among native and nonnative riparian seedlings, associated flood hydraulics and geomorphic changes, and the temporal evolution of feedbacks among vegetation, channel form, and hydraulics. We found that floods produced geomorphic and vegetation responses that varied with distance downstream of a dam, with scour and associated seedling mortality closer to the dam and aggradation and burial-induced mortality in a downstream reach. We also observed significantly greater mortality among nonnative tamarisk (Tamarix) seedlings than among native willow (Salix gooddingii) seedlings, reflecting the greater first-year growth of willow relative to tamarisk. When vegetation was small early in our study period, the effects of vegetation on flood hydraulics and on mediating flood-induced channel change were minimal. Vegetation growth in subsequent years resulted in stronger feedbacks, such that vegetation's stabilizing effect on bars and its drag effect on flow progressively increased, muting the geomorphic effects of a larger flood release. These observations suggest that the effectiveness of floods in producing geomorphic and ecological changes varies not only as a function of flood magnitude and duration, but also of antecedent vegetation density and size.

Simultaneous scaling of soil water retention and hydraulic conductivity functions provides an effective means to characterize the heterogeneity and spatial variability of soil hydraulic properties in a given study area. The statistical significance of this approach largely depends on the number of soil samples collected. Unfortunately, direct measurement of the soil hydraulic functions is tedious, expensive and time-consuming. Here we present a simple and cost-effective hybrid scaling approach that combines the use of ancillary information (e.g. particle-size distribution and soil bulk density) with direct measurements of saturated soil water content and saturated hydraulic conductivity. Our results demonstrate that the presented approach requires far fewer laboratory measurements than conventional scaling methods to adequately capture the spatial variability of the soil hydraulic properties.

Among the numerous methods proposed over the past decades for solving reservoirs management problems, only a few are applicable on high dimensional reservoir systems (HDRSs). The progressive hedging algorithm (PHA) was rarely used for managing reservoir systems, but this method is a promising alternative to conventionally-used methods for managing HDRS (e.g. the stochastic dual dynamic programming). The PHA is especially well suited when a new stochastic optimization model must be built upon an existing deterministic optimization model (DOM). In such case, scenario subproblems can be resolved using an existing DOM with minor modifications. In previous studies, the PHA was rarely used and only tested on problems covering short-range planning horizons (2 months with 6 time periods) where a small number of non-anticipativity constraints (NACs) must be satisfied. Large reservoirs often need to be managed over a much longer planning horizon (1–5 years) containing many tens of time periods. In such case, convergence becomes much more difficult to achieve because of the larger number of NACs to be satisfied. Finding a non-anticipative solution becomes particularly difficult when the input scenarios differ drastically. In this study, we demonstrate the applicability of the PHA for managing HDRSs over long-term (more than a year) horizons in highly uncertain decision environments. We apply the PHA on Hydro-Québec's reservoir system over a 92 weeks (periods) horizon. We analyze the performance of the PHA for different penalty parameter values. Deterministic solutions are compared to stochastic solution.

We explore the permeability evolution of fractures in carbonate rock that results from the effects of mechanical stress and non-equilibrium chemistry (pH of fluid). Core plugs of Capitan Limestone are saw-cut to form a smooth axial fracture that is subsequently roughened to simulate a natural fracture with controlled surface topography. Aqueous solutions of ammonium chloride (pH 5 ~ 7) transit these plugs at confining stresses of 3 to 10 MPa with flow rates and mineral mass fluxes measured to constrain competing mechanisms of permeability evolution. The effluent calcium concentrations are always much lower than equilibrium calcium solubility, resulting in the dissolution-dominant permeability evolution in our experiments. Depending on the combination of confining stress and fluid pH the fracture apertures either gape (permeability increase) or close (permeability reduction). We quantitatively constrain the transition between gaping (pH<6.1) and closing (pH>6.5) with this transition independent of confining stress up to 10 MPa. A transitional regime (6.1<pH<6.5) of invariant aperture represents a balance between the two mechanisms of free-face dissolution and pressure solution at the bridging asperities. We employ a lumped-parameter model to interpret the dissolution-dominant evolution of permeability. By considering different dissolution rate constants between non-contacting asperities and the stagnant water film at the contacting asperities, this model replicates the principal characteristics of permeability evolution of the fracture. Observed rates of aperture change are ill-matched when the influent pH is 5-6 since wormhole formation is not accommodated in the model. These observations offer a promising pathway to index the switch from aperture gaping to aperture closing for reactive flow as reactivity is reduced and stress effects become more important.

The U.S. Nuclear Regulatory Commission investigated climate and infiltration at Yucca Mountain to (i) understand important controls and uncertainties influencing percolation through the unsaturated zone on multimillennial time scales and (ii) provide flux boundary conditions for up to one million years in performance assessment models of the proposed Yucca Mountain repository. This first part of a two-part series describes a procedure for abstracting the results from detailed numerical simulations of local-scale infiltration into a site-scale model considering uncertainty and variability in distributed net infiltration. Part 2 describes site-scale model results and corroboration. A detailed one-dimensional numerical model was used to estimate bare-soil net infiltration at the scales of hours and meters for 442 soil, bedrock, and climate combinations. The set of results are abstracted into three parametric response functions for decadal-average bare-soil infiltration given hydraulic and climatic parameters. The three abstractions describe deep soil, shallow soil over a coarser layer, and shallow soil over a finer layer. The site-scale model considers spatial variability and uncertainty of the input parameters on a 30-m grid, using the abstractions independently in each cell. Two additional abstractions account for overland flow and vegetation. The model uses Monte Carlo simulation, with all input parameters uncertain and spatially variable, to calculate the mean and standard deviation of net infiltration in each grid cell for selected climate states. Using abstractions rather than detailed simulations speeds calculation of infiltration realizations by many orders of magnitude relative to a detailed simulation.

Ideally, a seasonal streamflow forecasting system would ingest skilful climate forecasts and propagate these through calibrated hydrological models initialised with observed catchment conditions. At global scale, practical problems exist in each of these aspects. For the first time, we analysed theoretical and actual skill in bimonthly streamflow forecasts from a global Ensemble Streamflow Prediction (ESP) system. Forecasts were generated six times per year for 1979–2008 by an initialised hydrological model and an ensemble of 1º resolution daily climate estimates for the preceding 30 years. A post-ESP conditional sampling method was applied to 2.6% of forecasts, based on predictive relationships between precipitation and one of 21 climate indices prior to the forecast date. Theoretical skill was assessed against a reference run with historic forcing. Actual skill was assessed against streamflow records for 6192 small (<10,000 km²) catchments worldwide. The results show that initial catchment conditions provide the main source of skill. Post-ESP sampling enhanced skill in equatorial South America and Southeast Asia, particularly in terms of tercile probability skill, due to the persistence and influence of the El Niño Southern Oscillation. Actual skill was on average 54% of theoretical skill but considerably more for selected regions and times of year. The realized fraction of the theoretical skill probably depended primarily on the quality of precipitation estimates. Forecast skill could be predicted as the product of theoretical skill and historic model performance. Increases in seasonal forecast skill are likely to require improvement in the observation of precipitation and initial hydrological conditions.

The mathematical formulation of the Muskingum method, like that of many numerical schemes used for river routing, requires that all upstream river reaches be updated prior to updating the flow rate of any given reach. Due to this topological constraint, such numerical schemes have traditionally been solved in an upstream to downstream manner which imposes inherent limitations on the speedup that can be achieved in a parallel computing environment because each computing core has to wait for completion of all cores addressing upstream sub-basins prior to starting its own sub-basin. The research presented in this paper quantifies the exact influence among river reaches during the update step of the Muskingum method and shows that the influence decreases with increasing distance between two reaches until it becomes too small to be accounted for by floating-point arithmetic. A formal definition of the minimal distance from which the relative influence becomes numerically inexistent – the radius of influence – is presented. Based on this distance, expressed as a number of river reaches, a new estimate of the maximum theoretical speedup that can be achieved by the Muskingum method or by similar numerical schemes is presented and implies large potential gains in computing time when domains are much larger than the radius of influence. An application to the approximately 180,000 river reaches of the Upper Mississippi River Basin at a 15-minute time step over 2004 shows a radius of influence on the order of 150 river reaches. The speedup obtained for this application is much higher than previously thought possible, but also much lower than could be attained, suggesting that further investigations are necessary.

Water scarcity may appear to be a simple concept, yet it can be difficult to define for complex natural-human systems. The term ‘water scarcity’; has been used in a variety of ways, and it has given rise to a variety of related measurements and indices. Clarity on such a fundamental concept is needed as the theme of ‘water, sustainability and climate’; is advanced in many research programs. The purpose of this commentary is to highlight key aspects of water scarcity that alternative measures such as aggregate indices do not explicitly recognize. A general and succinct definition of water scarcity is that it is the marginal value of a unit of water. We develop a simple but robust definition of water scarcity and illustrate it with examples of the many system connections involving biophysical and socioeconomic factors. We make two main points. First, unlike the scarcity of many other goods, water scarcity is hugely variable across location, time, and a multitude of uses that are valued either directly or indirectly by society. This means that precise measures of water scarcity will often be elusive in practice, though this is a reflection of the complex role of water in natural-human systems, rather than a feature of scarcity per se. Secondly, scarcity is fundamentally an anthropocentric concept and, thus, is distinguished from the related notion of water deficit. While such an anthropocentric perspective may seem limiting, it can encompass the vast range of interests that society has in water.

Warmer temperatures are expected to raise mountain stream temperatures, affecting water quality and ecosystem health. We demonstrate the importance of climate-driven changes in hydrology as fundamental to understanding changes in the local water quality. In particular, we focus on changes in stream temperature, dissolved oxygen concentrations, and sediment transport in mountainous, snowmelt-dominated, and water limited systems, using the Sierra Nevada as our case study. Downscaled output from an ensemble of General Circulation Model projections for the A2 (higher greenhouse gas) emission scenario was used to drive the Soil and Water Assessment Tool (SWAT) with a new integrated stream temperature model on the subbasin scale. Spring and Summer stream temperature increase by 1 to 5.5 °C, with varying increases among subbasins. The highest projected stream temperatures are in the low-elevation subbasins of the southern Sierra Nevada, while the northern Sierra Nevada, with distinct impacts on snowmelt and subsurface flow contributions to streamflow, shows moderated increases. The spatial pattern of stream temperature changes were the result of differences in surface and subsurface hydrologic, snowmelt, and air temperature changes. Concurrent with stream temperature increases and decreases in Spring and Summer flows, simulations indicated decreases in DO (10%) and sediment (50%) concentrations by 2100. Stream temperature and DO concentrations for several major streams decline below survival thresholds for several native indicator species. These results highlight that climatic changes in water limited mountain systems may drive changes in water quality that have to be understood on the reach scale for developing adaptive management options.

Arid regions are characterized by high variability in the arrival of rainfall, and species found in these areas have adapted mechanisms to ensure the capture of this scarce resource. In particular, the rooting strategies employed by vegetation can be critical to their survival. However, land surface models currently prescribe rooting profiles as a function of only the plant functional type of interest with no consideration for the soil texture or rainfall regime of the region being modeled. Additionally, these models do not incorporate the ability of vegetation to dynamically alter their rooting strategies in response to transient changes in environmental forcings or competition from other plant species, and therefore tend to underestimate the resilience of these ecosystems.

To address the simplicity of the current representation of roots in land surface models, a new dynamic rooting scheme was incorporated into the framework of the distributed ecohydrologic model tRIBS+VEGGIE. The new scheme optimizes the allocation of carbon to the root zone to reduce the perceived stress of the vegetation, so that root profiles evolve based upon local climate and soil conditions. The strength of this scheme lies in its ability to optimize the rooting profile in a computationally-efficient manner, without requiring additional parameterization by the model user.

The ability of the new scheme to capture the complex dynamics of natural systems was evaluated by comparisons to hourly-timescale energy flux, soil moisture and vegetation growth observations from the Walnut Gulch Experimental Watershed, Arizona. Robust agreement was found between the model and observations, providing confidence that the improved model is able to capture the multidirectional interactions between climate, soil and vegetation at this site.

This study addresses a significant potential source of error that exists in radar-rainfall maps that are combined using data from multiple WSR-88D radars of the Next Generation Radars (NEXRAD) national network in the United States. This error stems from different radar calibration offsets that create a border within discontinuous rainfall fields at the equidistance zone among radars. The discontinuity in rainfall fields could lead to mis-estimation of rainfall over basins and, subsequently, to significant errors in streamflow predictions through a hydrologic model. In this study, we produce enhanced radar-rainfall estimates (HN3) based on a novel approach that allows us to reduce the effects of the relative radar calibration bias. We use the relative bias information previously presented in a radar reflectivity comparison study. To investigate the effects of the relative bias adjustment, we evaluate the HN3 and Stage IV radar-rainfall by comparing them with rain gauge data and analyzing their ability to simulate streamflow for an extreme flood case. While the HN3 estimates are statistically comparable to the Stage IV estimates in the rain gauge data comparison, the border line that identifies discontinuous rainfall fields disappears in the HN3 estimates. We performed hydrological simulations using a physically-based, data-intensive, calibration-free, hillslope-link hydrologic model called CUENCAS and demonstrated CUENCA's ability to accurately simulate flows by comparing results with observed and predicted streamflow generated by the Sacramento (SAC) model. SAC is the operational flood forecast model that has been used by the National Weather Service since 1969, and it was extensively calibrated based on historical data. The simulation results show that the adjustment improves streamflow predictions in the regions where the mis-estimation of rainfall quantity is considerable. We conclude that systematic error arising from different calibration offsets in rainfall fields can significantly affect hydrologic predictions.

Support for low-carbon energy and opposition to new large dams encourages global development of small hydropower facilities. This support is manifested in national and international energy and development policies designed to incentivize growth in the small hydropower sector while curtailing large dam construction. However, the preference of small to large dams assumes, without justification, that small hydropower dams entail fewer and less severe environmental and social externalities than large hydropower dams. With the objective to evaluate the validity of this assumption, we investigate cumulative biophysical effects of small (<50 MW) and large hydropower dams in China's Nu River basin, and compare effects normalized per megawatt of power produced. Results reveal that biophysical impacts of small hydropower may exceed those of large hydropower, particularly with regard to habitat and hydrologic change. These results indicate that more comprehensive standards for impact assessment and governance of small hydropower projects may be necessary to encourage low-impact energy development.

We study and explain the origin of early breakthrough and long tailing plume behavior by simulating solute transport through three-dimensional X-ray images of six different carbonate rock samples, representing geological media with a high degree of pore-scale complexity. A Stokes solver is employed to compute the flow field and the particles are then transported along streamlines to represent advection, while the random walk method is used to model diffusion. We compute the propagators (concentration vs. displacement) for a range of Peclet numbers (Pe) and relate it to the velocity distribution obtained directly on the images. There is a very wide distribution of velocity that quantifies the impact of pore structure on transport. In samples with a relatively narrow spread of velocities, transport is characterized by a small immobile concentration peak, representing essentially stagnant portions of the pore space, and a dominant secondary peak of mobile solute moving at approximately the average flow speed. On the other hand, in carbonates with a wider velocity distribution, there is a significant immobile peak concentration and an elongated tail of moving fluid. An increase in Pe – decreasing the relative impact of diffusion – leads to the faster formation of secondary mobile peak(s). This behavior indicates highly anomalous transport. The implications for modeling field-scale transport are discussed.

Parlange and Brutsaert [1987] derived a modified Boussinesq equation to account for the capillary effect on watertable dynamics in unconfined aquifers. Barry et al. [1996] solved this equation subject to a periodic boundary condition. Their solution shows significant influence of capillarity on watertable fluctuations, which evolve to finite-amplitude standing waves at the high frequency limit. Here, we propose a new governing equation for the watertable, which considers both horizontal and vertical flows in an unsaturated zone of finite thickness. An approximate analytical solution for periodic watertable fluctuations based on the new equation was derived. In agreement with previous results, the analytical solution shows that the unsaturated zone's storage capacity permits watertable fluctuations to propagate more readily than predicted by the Boussinesq equation. Furthermore, the new solution reveals a capping effect of the unsaturated zone on both the amplitude and phase of the watertable fluctuations as well as the watertable overheight. Due to the finite thickness of the unsaturated zone, the capillary effect on watertable fluctuations is modified mainly with reduced amplitude damping and phase shift.

Particle tracking methods are widely applied in subsurface hydrology to calculate advective transport within flow fields obtained from the numerical solution of the groundwater flow or Richards equation. These procedures are the standard to acquire travel or transit time distributions for a region of interest or to delineate well capture zones. For cell-centered, regular, structured grids, analytical solutions for the computation of particle pathlines inside grid cells are known for steady state flow fields and transient flow fields under the condition of a constant velocity gradient over a full time step. We extend particle pathline computation using an exact solution for fully transient flow that relaxes this condition and will even be valid for a complete reversal of the velocity gradient from one time step to the next.

The acquisition of reliable datasets representative of hydrological regimes and their variations is a critical concern for water resource assessment. For the subsurface, traditional approaches based on probe measurements, core analysis and well data can be laborious, expensive, and highly intrusive, while only yielding sparse data sets. For this study, an innovative field survey, merging relative microgravimetry, magnetic resonance soundings and hydrological measurements, was conducted to evaluate both surface and subsurface water storage variations in a semi-arid Sahelian area. The instrumental setup was implemented in the lower part of a typical hillslope feeding to a temporary pond. Weekly measurements were carried out using relative spring gravimeters during three months of the rainy season in 2009 over a 350 × 500 m² network of twelve microgravity stations. Gravity variations of small to medium amplitude (≤ 220 nm s²) were measured with accuracies better than 50 nm s^-2, revealing significant variations of the water storage at small time (from one week up to three months) and space (from a couple of meters up to a few hundred meters) scales. Consistent spatial organization of the water storage variations were detected, suggesting high infiltration at the outlet of a small gully. The comparison with hydrological measurements and magnetic resonance soundings involved that most of the microgravity variations came from the heterogeneity in the vadose zone. The results highlights the potential of time lapse microgravity surveys for detecting intraseasonal water storage variations and providing rich space-time datasets for process investigation or hydrological model calibration/evaluation.

We propose a novel analytical description of the streamflow probability distribution functions (pdfs) in Alpine catchments characterized by pronounced, snow-dominated winter low flows. Knowledge about such hydrological regimes is crucial for water resources management in mountain environments and the related wide range of socio-economic, environmental and ecological services. We use a stochastic framework, generalizing that employed by Botter et al. [2007b], to link precipitation (rain and snow) and streamflow dynamics. The effect of snow dynamics on the flow regime is specifically included by incorporating the temporary disconnection of high elevation areas that experience freezing conditions over the entire winter season, and the delay produced on streamflow formation by the temporary accumulation (and later melting) of snow at lower elevations. The novel analytical model employs four parameters that can be directly estimated from observed discharge, precipitation and air temperatures, and one calibration parameter (the elevation threshold z^* delimiting catchment areas with a permanent seasonal snow cover that is non-responsive during winter owing to snow accumulation without melt). We test the developed model for 14 catchments with contrasting hydroclimatic conditions, located in the Swiss and the Italian Alps. Overall, the proposed analytic model reproduces the observed streamflow pdfs remarkably well. Exceptions exist, though, and the possible origin of deviations between observed and modeled pdfs are discussed. We suggest that our approach marks a progress towards the general statistical characterization of catchment streamflow variability.

One approach to regional water balance modeling is to constrain rainfall-runoff models with a synthetic regionalized hydrologic response. For example, the Large Basin Runoff Model (LBRM), a cornerstone of hydrologic forecasting in the Laurentian Great Lakes basin, was calibrated to a synthetic discharge record resulting from a drainage area ratio method (ARM) for extrapolating beyond gaged areas. A challenge of such approaches is the declining availability of observations for development of synthetic records. To advance efficient use of the declining gage network in the context of regional water balance modeling, we present results from an assessment of ARM. All possible combinations of “most-downstream” gages were used to simulate runoff at the gaged outlet of Michigan's Clinton River watershed in order to determine the influence of gages' drainage area and other physical characteristics on model skill. For nearly all gage combinations, ARM simulations resulted in good model skill. However, the gages' catchment area relative to that of the outlet's catchment is not an unquestionable predictor of model performance. Results indicate that combinations representing less than 30% of the total catchment area (less than 10% in some cases) can provide very good discharge simulations, but that similarity of the gaged catchments' developed and cultivated area, stream density, and permeability relative to the outlet's catchment is also important for successful simulations. Recognition of thresholds on the relationship between the number of gages and their relative value in simulating flow over large area provides an opportunity for improving historical records for regional hydrologic modeling.

Many hydrological systems exhibit complex subsurface flow and storage behavior. Runoff observations often only provide insufficient information for unique process identification. Quantitative modeling of water and solute fluxes presents a potentially more powerful avenue to explore whether hypotheses about system functioning can be rejected or conditionally accepted. In this study we developed and tested four hydrological model structures, based on different hypotheses about subsurface flow and storage behavior, to identify the functioning of a large Mediterranean karst system. Using eight different system signatures, i.e. indicators of particular hydrodynamic and hydrochemical characteristics of the karst system, we applied a novel model evaluation strategy to identify the best conceptual model representation of the karst system within our set of possible system representations. Our approach to test model realism consists of three stages: (1) evaluation of model performance with respect to system signatures using automatic calibration, (2) evaluation of parameter identifiability using Sobol's sensitivity analysis, and (3) evaluation of model plausibility by combining the results of stages (1) and (2). These evaluation stages eliminated three out of four model structures and lead to a unique hypothesis about the functioning of the studied karst system. We used the estimated parameter values to further quantify subsurface processes. The chosen model is able to simultaneously provide high performances for eight system signatures with realistic parameter values. Our approach demonstrates the benefits of interpreting different tracers in a hydrologically meaningful way during model evaluation and identification.

Given the range of future uncertainty, there is increasing interest in developing and evaluating water management strategies that are robust to an uncertain future. As part of a process termed “decision scaling”, a climate response function was developed to isolate the impact of climate change on a water system in terms of hazards identified by stakeholders. The climate response function was then used to evaluate system performance over a wide range of climate conditions and to define robustness indicators. The robustness indicators, which measure system performance as a function of climate state, are conditioned on explicit assumptions about climate variable probability distributions. To illustrate this process, it is applied to the Upper Great Lakes to evaluate system robustness related to water management decisions and assess the impact of climate probability assumptions. The robustness indicators were used to identify decisions that outperformed other courses of action regardless of assumptions of future climate probabilities.

An earlier infiltration equation relied on curve fitting of infiltration data for the determination of one of the parameters, which limits its usefulness in practice. This handicap is removed here and the parameter is now evaluated by linking it directly to soil-water properties. The new predictions of infiltration using this evaluation are quite accurate. Positions and shapes of soil-water profiles are also examined in detail and found to be predicted analytically with great precision.

This study reports on two strategies for accelerating posterior inference of a highly parameterized and CPU-demanding groundwater flow model. Our method builds on previous stochastic collocation approaches [e.g., Marzouk and Xiu, 2009; Marzouk and Najm, 2009] and uses generalized polynomial chaos (gPC) theory to emulate the output of a large-scale groundwater flow model. The resulting surrogate model is CPU-efficient and serves to explore the posterior distribution at a much lower computational cost using two-stage MCMC simulation. The case study reported in this paper demonstrates a 2-5 times speed up in sampling efficiency.

The information contained in hyetographs and hydrographs is often synthesised by using key properties such as the peak or maximum value X_p, volume V, duration D and average intensity I. These variables play a fundamental role in hydrologic engineering as they are used, for instance, to define design hyetographs and hydrographs as well as to model and simulate the rainfall and stream flow processes. Given their inherent variability and the empirical evidence of the presence of a significant degree of association, such quantities have been studied as correlated random variables suitable to be modelled by multivariate joint distribution functions. The advent of copulas in geosciences simplified the inference procedures allowing for splitting the analysis of the marginal distributions and the study of the so-called dependence structure or copula. However, the attention paid to the modelling task has overlooked a more thorough study of the true nature and origin of the relationships that link X_p, V, D and I. In this study, we apply a set of ad hoc bootstrap algorithms to investigate these aspects by analysing the hyetographs and hydrographs extracted from 282 daily rainfall series from central eastern Europe, three 5-min rainfall series from central Italy, 80 daily stream flow series from the continental United States and two sets of 200 simulated universal multifractal time series. Our results show that all the pairwise dependence structures between X_p, V, D and I exhibit some key properties that can be reproduced by simple bootstrap algorithms that rely on a standard univariate resampling without resort to multivariate techniques. Therefore, the strong similarities between the observed dependence structures and the agreement between the observed and bootstrap samples suggest the existence of a numerical generating mechanism based on the superposition of the effects of sampling data at finite time steps and the process of summing realizations of independent random variables over random durations. We also show that the pairwise dependence structures are weakly dependent on the internal patterns of the hyetographs and hydrographs, meaning that the temporal evolution of the rainfall and runoff events marginally influences the mutual relationships of X_p, V, D and I. Finally, our findings point out that subtle and often overlooked deterministic relationships between the properties of the event hyetographs and hydrographs exist. Confusing these relationships with genuine stochastic relationships can lead to an incorrect application of multivariate distributions and copulas and to misleading results.

Floods are risky events ranging from small to catastrophic. Although expected flood damages are frequently used for economic policy analysis, alternative measures such as option price and cumulative prospect value exist. The empirical magnitude of these measures whose theoretical preference is ambiguous is investigated using case study data from Baltimore City. The outcome for the base case option price measure increases mean willingness to pay over the expected damage value by about 3 percent, a value which is increased with greater risk aversion, reduced by increased wealth, and only slightly altered by higher limits of integration. The base measure based on cumulative prospect theory is about 46 percent less than expected damages with estimates declining when alternative parameters are used. The method of aggregation is shown to be important in the cumulative prospect case which can lead to an estimate up to 41 percent larger than expected damages. Expected damages remain a plausible and the most easily computed measure for analysts.

Regional frequency analysis is a valuable and well-known method which allows using all the information at the regional scale to improve the actual estimation of the probability of occurrence of extreme events at a given site. In the framework of the index flood method, a local index, representing the local specificities of a given site, is used to normalize at-site observations for the estimation of the regional distribution. It is an essential feature of this model, contrasting with common characteristics shared between the sites of the homogenous region. However, the specification of the local index can be a crucial point. In particular, the performance of the quantile estimator derived from a regional frequency analysis can depend on the specification of the local index. Four regionalization models are proposed, where the local index is specified by different statistics in each model, and their performances are assessed through Monte Carlo simulations of several regional scenarios. Some guidelines are provided for the selection of the local index which is most adapted to the observed situation (including regional scenarios characterized by some degrees of asymmetry, homogeneity and inter-site correlation). A practical application on extreme skew storm surges is provided to illustrate the results.

Optimal operation models for a hydropower system using new fuzzy multi-objective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer non-linear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multi-objective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multi-objective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multi-objective optimization framework and confirm its applicability to a variety of multi-objective water resources problems.

We seek to improve scientific understanding of urban storm event hydrologic response through analyses of high-resolution rainfall and discharge data for the Baltimore metropolitan region. High-resolution radar rainfall fields, developed at 1 km² spatial resolution and 15-minute time resolution, and high-resolution, one- to five-minute, discharge data provide the detail necessary to accurately characterize storm event hydrologic response in small urban basins. We examine flood-producing rainfall properties and storm event hydrologic response for nine drainage basins in the Baltimore region, each with drainage area of approximately 10 km². The sample of watersheds includes seven urbanized basins, a forested reference basin, and a predominantly agricultural basin. Analyses are performed for the 50 largest flood events during a ten-year period in each of the nine basins. We find expected contrasts in flood peak distributions and storm event runoff production between the urban and non-urban watersheds, but we also find a spectrum of storm event hydrologic response among the urban watersheds. Moores Run and Dead Run are end-members of this urban spectrum, with Moores Run producing anomalously large flood peak magnitudes and Dead Run producing anomalously large storm event runoff ratios. Hydrologic response to the storm events for each basin is summarized through relationships between maximum basin-averaged rainfall rates and peak discharges. Analyses show that runoff production and timing of hydrologic response are linked to stormwater management infrastructure and play a central role in the spectrum of storm event response. Detention basins in these watersheds appear to operate as intended by original stormwater legislation to lower peak discharges but not necessarily lower runoff volumes. Antecedent moisture does not appear to significantly impact storm event hydrologic response in the urban or non-urban basins. Clustering of flood events is an important element of urban flood hydrology in the Baltimore region. The rainfall climatology of flood-producing storms varies from urban to non-urban watersheds with urban watershed flood frequency displaying a pronounced warm season maximum while the non-urban watershed flood frequency exhibits a more uniform intra-annual distribution. The diurnal cycle of flood occurrences exhibits a stronger evening maximum in urban watersheds than in non-urban watersheds, highlighting the central role of warm season thunderstorm systems for urban flooding in Baltimore.

Runoff generation in Alpine regions is typically affected by snow processes. Snow accumulation, storage, redistribution, and ablation control the availability of water. In this study, several robust parameterizations describing snow processes in Alpine environments were implemented in a fully distributed, physically based hydrological model. Snow cover development is simulated using different methods from a simple temperature index approach, followed by an energy balance scheme, to additionally accounting for gravitational and wind-driven lateral snow redistribution. Test site for the study is the Berchtesgaden National Park (Bavarian Alps, Germany) which is characterized by extreme topography and climate conditions. The performance of the model system in reproducing snow cover dynamics and resulting discharge generation is analyzed and validated via measurements of snow water equivalent and snow depth, satellite-based remote sensing data, and runoff gauge data. Model efficiency (Nash-Sutcliffe coefficient) for simulated runoff increases from 0.57 to 0.68 in a high Alpine headwater catchment and from 0.62 to 0.64 in total with increasing snow model complexity. In particular, the results show that the introduction of the energy balance scheme reproduces daily fluctuations in the snowmelt rates that trace down to the channel stream. These daily cycles measured in snowmelt and resulting runoff rates could not be reproduced by using the temperature index approach. In addition, accounting for lateral snow transport changes the seasonal distribution of modeled snowmelt amounts, which leads to a higher accuracy in modeling runoff characteristics.

Improved understanding of the complex dynamics associated with spatially and temporally variable runoff response is needed to better understand the hydrology component of interdisciplinary problems. The objective of this study was to quantitatively characterize the environmental controls on runoff generation for the range of different streamflow-generation mechanisms illustrated in the classic Dunne diagram. The comprehensive physics-based model of coupled surface-subsurface flow, InHM, is employed in a heuristic mode. InHM has been employed previously to successfully simulate the observed hydrologic response at four diverse, well-characterized catchments, which provides the foundation for this study. The C3 and CB catchments are located within steep, forested terrain; the TW and R5 catchments are located in gently sloping rangeland. The InHM boundary-value problems for these four catchments provide the corner-stones for alternative simulation scenarios designed to address the question of how runoff begins (and ends). Simulated rainfall-runoff events are used to systematically explore the impact of soil-hydraulic properties and rainfall characteristics. This approach facilitates quantitative analysis of both integrated and distributed hydrologic responses at high spatial and temporal resolution over the wide range of environmental conditions represented by the four catchments. The results from 140 unique simulation scenarios illustrate how rainfall intensity / depth, subsurface permeability contrasts, characteristic curve shapes, and topography provide important controls on the hydrologic-response dynamics. The processes by which runoff begins (and ends) are shown, in large part, to be defined by the relative rates of rainfall, infiltration, lateral flow convergence, and storage dynamics within the variably-saturated soil layers.

Water resource management agencies have traditionally relied upon past observations of historical hydrologic records for long-term planning. This assumption of stationarity, that the past is representative of the future, may no longer be valid under changing climate conditions. The Gunnison River Basin contributes approximately 16% of the annual natural streamflow within the Upper Colorado River Basin, affecting water supply availability over the entire Colorado River Basin. Recent studies indicate that streamflow over the Gunnison River Basin, a subbasin within the Colorado River Basin, may decrease on the order of 15% through the year 2099. Further study has developed a methodology to statistically characterize the risk of regime shifts using observations of past streamflow through the use of a two-parameter gamma distribution. In this study, regime characteristics derived using a paleo-reconstruction of streamflow over the Gunnison River Basin are compared regime characteristics developed using 112 projections of future hydrology to better understand how the frequency and duration of persistent dry and wet periods may change as the impacts of climate change are realized over the subbasin. Results indicate that under changing climate conditions, similar regime characteristics may be expected through 2039. However, between 2040 and 2099, more frequent and persistent dry regimes increase on the order of 50%. Conversely, wet regimes are expected to be shorter and less frequent than observed over the paleoclimatic record, decreasing in frequency by as much as 50%.

There is widespread recognition that the groundwater-surface water interface can have significant influence on the pattern and form of the transfer of nutrient-rich groundwater to rivers. Characterizing and quantifying this influence is critical for successful management of water resources in many catchments, particularly those threatened by rising nitrate levels in groundwater. Building on previous experimental investigations in one such catchment: the River Leith, UK, we report on a multi-measurement, multi-scale program aimed at developing a conceptualization of groundwater-surface water flow pathways along a 200m reach. Key to this conceptualization is the quantification of vertical and horizontal water fluxes, which is achieved through a series of Darcian flow estimates coupled with in-stream piezometer tracer dilution tests. These data, enhanced by multi-level measurements of chloride concentration in river bed pore water and water-borne geophysical surveying, reveal a contrast in the contribution of flow components along the reach. In the upper section of the reach, a localized connectivity to regional groundwater, that appears to suppress the hyporheic zone, is identified. Further downstream, horizontal (lateral and longitudinal) flows appear to contribute more to the total subsurface flow at the groundwater-surface water interface. Although variation in hydraulic conductivity of the river bed is observed, localized variation that can account for the spatial variability in flow pathways is not evident. The study provides a hydrological conceptualization for the site, which is essential for future studies which address biogeochemical processes, in relation to nitrogen retention/release. Such a conceptualization would not have been possible without a multi-experimental program.

During in situ remediation, a treatment solution is often injected into a contaminated aquifer to degrade the groundwater contaminant. Since contaminant degradation reactions occur only at locations where the treatment solution and groundwater contaminant overlap, mixing of the treatment solution and the contaminated groundwater is necessary for reaction to occur. Mixing results from molecular diffusion and pore scale dispersion, which operate over small length scales; thus, mixing during in situ remediation can only occur where the separation distance between the treatment solution and contaminated groundwater is small. To promote mixing, advection can be used to spread the treatment solution into the contaminated groundwater to increase the extent of the region where the two solutions coexist. A certain degree of passive spreading is the natural consequence of aquifer heterogeneity, which is manifested as macrodispersion. An alternative mechanism is active spreading, in which unsteady flows lead to stretching and folding of plumes. Active spreading can be accomplished by engineered injection and extraction (EIE), in which clean water is injected and extracted at wells surrounding a contaminant plume to create unsteady flow fields that stretch and fold the treatment solution and contaminant plumes. For a model system in which nested plumes of two reactants undergo scalar transport and instantaneous reaction, the simulation results reported here indicate that EIE enhances degradation of groundwater contamination in homogeneous and heterogeneous aquifers compared to baseline models without EIE. Furthermore, this study shows that the amount of reaction provided by the spreading due to EIE is greater than the amount of reaction due to spreading from heterogeneity alone.

This study examines the impact of endmember (i.e., hot and cold extremes) selection on the performance and mechanisms of error propagation in satellite-based spatial variability models for estimating actual evapotranspiration, using the triangle, Surface Energy Balance Algorithm for Land (SEBAL), and Mapping Evapotranspiration with high Resolution and Internalized Calibration (METRIC) models. These models were applied to the Soil Moisture-Atmosphere Coupling Experiment site in central Iowa on two Landsat Thematic Mapper/Enhanced Thematic Mapper Plus acquisition dates in 2002. Evaporative fraction (EF, defined as the ratio of latent heat flux to availability energy) estimates from the three models at field and watershed scales were examined using varying endmembers. Results show that the endmembers fundamentally determine the magnitudes of EF retrievals at both field and watershed scales. The hot and cold extremes exercise a similar impact on the discrepancy between the EF estimates and the ground-based measurements, i.e., given a hot (cold) extreme, the EF estimates tend to increase with increasing temperature of cold (hot) extreme, and decrease with decreasing temperature of cold (hot) extreme. The coefficient of determination between the EF estimates and the ground-based measurements depends principally on the capability of remotely sensed surface temperature (T_s) to capture EF (i.e., depending on the correlation between T_s and EF measurements), being slightly influenced by the endmembers. Varying the endmembers does not substantially affect the standard deviation and skewness of the EF frequency distributions from the same model at the watershed scale. However, different models generate markedly different EF frequency distributions due to differing model physics, especially the limiting edges of EF defined in the remotely sensed vegetation fraction (f_c) and T_s space. In general, the endmembers cannot be properly determined because: (1) they do not necessarily exist within a scene, varying with the spatial extent, resolution, and quality of satellite images being used; and/or (2) different operators can select different endmembers. Furthermore, the limiting edge of EF=0 in the f_c-T_s space varies with the model, with SEBAL-type models having inherently an increasing curvilinear limiting edge of EF=0 with f_c. The spatial variability models therefore require careful calibration in order to deduce reasonable EF limiting edges and then confine the magnitudes of EF estimates.

The laboratory experiments of Gramling et al. [2002] showed that incomplete mixing at the pore scale exerts a significant impact on transport of reactive solutes and that assuming complete mixing leads to overestimation of product concentration in bimolecular reactions. Successively, several attempts have been made to model this experiment, either considering spatial segregation of the reactants, non-Fickian transport applying a Continuous Time Random Walk (CTRW) or an effective upscaled time-dependent kinetic reaction term. Previous analyses of these experimental results showed that, at the Darcy scale, conservative solute transport is well described by a standard advection dispersion equation, which assumes complete mixing at the pore scale. However, reactive transport is significantly affected by incomplete mixing at smaller scales, i.e. within a reference elementary volume (REV). We consider here the family of equilibrium reactions for which the concentration of the reactants and the product can be expressed as a function of the mixing ratio, the concentration of a fictitious non reactive solute. For this type of reactions we propose, in agreement with previous studies, to model the effect of incomplete mixing at scales smaller than the Darcy scale assuming that the mixing ratio is distributed within an REV according to a Beta distribution. We compute the parameters of the Beta model by imposing that the mean concentration is equal to the value that the concentration assumes at the continuum Darcy scale, while the variance decays with time as a power law. We show that our model reproduces the concentration profiles of the reaction product measured in the Gramling et al. [2002] experiments using the transport parameters obtained from conservative experiments and an instantaneous reaction kinetic. The results are obtained applying analytical solutions both for conservative and for reactive solute transport, thereby providing a method to handle the effect of incomplete mixing on multispecies reactive solute transport, which is simpler than other previously developed methods.

Seepage at the sediment-water interface in several lakes, a large river, and an estuary exhibits substantial temporal variability when measured with temporal resolution of 1 minute or less. Already substantial seepage rates changed by 7 and 16 percent in response to relatively small rain events at two lakes in the northeastern USA, but did not change in response to two larger rain events at a lake in Minnesota. However, seepage at that same Minnesota lake changed by 10 percent each day in response to withdrawals from evapotranspiration. Seepage increased by more than an order of magnitude when a seiche occurred in the Great Salt Lake, Utah. Near the head of a fjord in Puget Sound, Washington, seepage in the inter-tidal zone varied greatly from -115 to +217 cm/day in response to advancing and retreating tides when the time-averaged seepage was upward at +43 cm/day. At all locations, seepage variability increased by one to several orders of magnitude in response to wind and associated waves. Net seepage remained unchanged by wind unless wind also induced a lake seiche. These examples from sites distributed across a broad geographic region indicate that temporal variability in seepage in response to common hydrological events is much larger than previously realized. At most locations, seepage responded within minutes to changes in surface-water stage and within minutes to hours to groundwater recharge associated with rainfall. Likely implications of this dynamism include effects on water residence time, geochemical transformations, and ecological conditions at and near the sediment-water interface.

Carbon dioxide (CO₂) storage in deep geological formations can lead to significant reductions in anthropogenic CO₂ emissions if large amounts of CO₂ can be stored. Estimates of the storage capacity are therefore essential to the evaluation of individual storage sites as well as the feasibility of the technology. One important limitation on the storage capacity is the radius of review, the lateral extent of the pressure perturbation, of the storage project. We show that pressure dissipation into ambient mudrocks retards lateral pressure propagation significantly and therefore increases the storage capacity. For a three-layer model of a reservoir surrounded by thick mudrocks, the far-field pressure is approximated well by a single-phase model. Through dimensional analysis and numerical simulations, we show that the lateral extent of the pressure front follows a power-law that depends on a single dissipation parameter , where R_k and R_S are the ratios of mudrock to reservoir permeability and specific storage, and R_l is the aspect ratio of the confined pressure plume. Both the coefficient and the exponent of the power-law are sigmoid decreasing functions of M. The M-values of typical storage sites are in the region where the power-law changes rapidly. The combination of large uncertainty in mudrock properties and the sigmoid shape leads to wide and strongly skewed probability distributions for the predicted radius of review and storage capacity. Therefore, if the lateral extent of the pressure front limits the storage capacity, the determination of the mudrock properties is an important component of the site characterization.

A colloid transport model is introduced that is conceptually simple yet captures the essential features of colloid transport and retention in saturated porous media when colloid retention is dominated by the secondary minimum because an electrostatic barrier inhibits substantial deposition in the primary minimum. This model is based on conventional colloid filtration theory (CFT) but eliminates the empirical concept of attachment efficiency. The colloid deposition rate is computed directly from CFT by assuming all predicted interceptions of colloids by collectors result in at least temporary deposition in the secondary minimum. Also, a new paradigm for colloid re-entrainment based on colloid population heterogeneity is introduced. To accomplish this, the initial colloid population is divided into two fractions. One fraction, by virtue of physiochemical characteristics (e.g., size and charge), will always be re-entrained after capture in a secondary minimum. The remaining fraction of colloids, again as a result of physiochemical characteristics, will be retained “irreversibly” when captured by a secondary minimum. Assuming the dispersion coefficient can be estimated from tracer behavior, this model has only two fitting parameters: (1) the fraction of the initial colloid population that will be retained “irreversibly” upon interception by a secondary minimum, and (2) the rate at which reversibly retained colloids leave the secondary minimum. These two parameters were correlated to the depth of the Derjaguin-Landau-Verwey-Overbeek (DLVO) secondary energy minimum and pore water velocity, two physical forces that influence colloid transport. Given this correlation, the model serves as a heuristic tool for exploring the influence of physical parameters such as surface potential and fluid velocity on colloid transport.

In general, preferential flow occurring in forest catchments cannot be reasonably described using classical partial differential equations. As a result, linear or non-linear reservoir models are widely used in hillslope and catchment hydrology. Currently, few studies have simulated the hydrological threshold behavior that has been observed in many experimental catchments. In this study, five models with different structures were constructed using linear reservoir method to explore the inherent mechanisms of threshold behavior and to analyze the uncertainty of model structure in threshold simulations.

According to the model results, the average bedrock depression storage over the study catchment (0.99 km²), which can be represented using the height of the lowest lateral outlet from the reservoir bottom (h₁), was 1.5–5.1 mm. Substantial movable water percolated into the fissure bedrock and was discharged into the streamflow as base flow after storm events, illustrating why the slope of the linear relationships between the total event precipitation plus the antecedent soil moisture index (P + ASI) and the event quick flow depth above the rising threshold was less than one. Streamflow was simulated effectively by all five models with (h₁ > 0) or without (h₁ = 0) bedrock depression storage, however, different ratios of annual infiltration into bedrock to annual interflow discharged to the stream were obtained by models with h₁ > 0 (2.5–2.8) and h₁ = 0 (1.8–1.9). Namely, the calculated infiltration into bedrock was larger by models with h₁ > 0 than that by models with h₁ = 0. At the storm event scale, the simulated total bedrock flow was larger than the interflow for models with h₁ > 0 by a ratio of 1:0.7, whereas for models with h₁ = 0 the ratio was ~1:1.5.

Understanding key controls on hydrologic dynamics is important for effectively allocating resources for data collection, reducing model dimensionality and making modeling decisions. This work seeks to elucidate the physical factors responsible for the observed hydrologic patterns of a watershed in Michigan using a process-based model, PAWS+CLM. The model is tested using observed data for streamflows, soil temperature, groundwater table depths and satellite-based observations of evapo-transpiration (ET) and leaf area index (LAI). Numerical experiments are carried out to lump the effects of key controls, including land use types, nitrogen levels, groundwater redistribution and soil texture, into different process indices. Using Analysis of Variance (ANOVA), we quantitatively determine the strengths of these controls on ET, net primary production (NPP) and other important variables. Groundwater flow is found to be the major control on runoff and infiltration, with soil texture ranking next, while vegetation type and nitrogen levels are found to dominate NPP, top soil temperature and transpiration. Soil texture and groundwater are found to have comparable influence on soil moisture, which is in agreement with analysis of field data in the literature. All controls are found to co-limit ET, which serves as the nexus for ecosystem-hydrology interactions. From the simulations results, we find that nitrogen significantly controls transpiration, through which it influences other hydrologic fluxes. While there is room for improving descriptions of the nitrogen cycle in the current version of CLM, these novel results call for an understanding of the interplay between hydrology and biogeochemistry. Additional analysis shows that the relative strengths of the controls examined in this work are fairly robust with respect to changes in parameters and spatial resolution.

An experimental granitic catchment in southern China was instrumented to continuously monitor the hydrologic processes, within which two plots (5 × 10 m) were equipped. Observed rainfall and corresponding responses of surface water, soil water and groundwater were used to examine the hydrological threshold behaviors. New hydrological thresholds were determined on the spatial scales of plot and catchment and on the temporal scales of storm event and month, namely, those between (1) the sum of total event precipitation (P) and the antecedent soil moisture index (ASI) (hereafter P + ASI) and the total event interflow at plots A (soil depth: 200–250 cm ) and B (soil depth: 50–90 cm), and between P + ASI and deep interflow at plot A, within which an aquitard exists, (2) P + ASI and event peak flow, (3) the normalized maximum well water level (NMWWL) and event peak flow at the catchment scale, and (4) P + ASI and monthly stream flow depth. In addition, thresholds between P + ASI / NMWWL and event quick flow depth were identified, in accordance with findings of previous studies. Based on the hydrometric observations, we believed that the soil moisture deficit and bedrock depression storage were the main reasons for the threshold behavior in the study catchment. Below a specific threshold of P+ASI, none or little interflow was generated on hillslopes and contributed to streamflow, while interflow generated at the soil-bedrock interface on hillslopes started to contribute to streamflow when it was above the threshold, and the runoff contributing areas expanded from near-stream zone to hillslopes.

Space-time rainfall simulation is useful to study questions like for instance the propagation of rainfall-measurement uncertainty in hydrological modeling. This study adapts a classical Gaussian field simulation technique, the turning band method, in order to produce sequences of rainfall fields satisfying three key features of actual precipitation systems: i) the skewed point distribution and the space-time structure of non-zero rainfall (NZR); ii) the average probability and the space-time structure of intermittency; iii) a prescribed advection field. The acronym of our simulator is SAMPO, for Simulation of Advected Mesoscale Precipitations and their Occurrence.

SAMPO assembles various theoretical developments available from the literature. The concept of back-trajectories introduces a priori any type of advection field in the heart of the TBM. TBM outputs transformation into rainfall fields with a desired structure is controlled using Chebyshev-Hermite polynomial expansion. The intermittency taken as a binary process statistically independent of the NZR process allows the use of a common algorithm for both processes. The 3-D simulation with a space-time anisotropy captures important details of the precipitation kinematics summarized by the Taylor velocity of both NZR and intermittency.

A case study based on high-resolution weather radar data serves as an example of model inference. Illustrative simulations revisit some classical questions about rainfall variography like the influence of advection or intermittency. They also show the combined role of Taylor's and advection velocities.

An increasing demand for agricultural goods affects the pressure on global water resources over the coming decades. In order to quantify these effects we have developed a new agro-economic water scarcity indicator considering explicitly economic processes in the agricultural system. The indicator is based on the water shadow price generated by an economic land use model linked to a global vegetation-hydrology model. Irrigation efficiency is implemented as a dynamic input depending on the level of economic development. We are able to simulate the heterogeneous distribution of water supply and agricultural water demand for irrigation through the spatially explicit representation of agricultural production. This allows to identify regional hot spots of blue water scarcity and explicit shadow prices for water.

We generate scenarios based on moderate policies regarding future trade liberalization and the control of livestock-based consumption, dependent on different population and GDP projections. Results indicate increased water scarcity in the future, especially in South Asia, the Middle East, and North Africa. In general, water shadow prices decrease with increasing liberalization, foremost in South Asia, South-East Asia, and the Middle East. Policies to reduce livestock consumption in developed countries do not only lower the domestic pressure on water but also alleviate to a large extent water scarcity in developing countries. It is shown that one of the two policy options would be insufficient for most regions to retain water scarcity in 2045 on levels comparable to 2005.

We used a combination of diffusion theory and neutron transport simulations to estimate the lateral footprint for a cosmic-ray soil moisture probe. The footprint is radial and can be described by an exponential function. Our theory assumes, and our simulations confirm that the corresponding exponential folding length is closely related to the moderation length in air, which in this work is defined as the average net displacement experienced by neutrons while traveling from the point of emission from soil to the point of detection in air. These simulations indicate that the effective moderation length is 150 m at sea level, and that this value is fairly constant over a wide range of detection energies–from 10⁰ to 10⁵ eV. If we define the lateral footprint as the area encompassing two e-fold distances, i.e. the area from which 86% of the recorded neutrons originate, then the footprint diameter is nearly 600 m in dry air. Both theory and simulations indicate that the footprint is inversely proportional to air density and linearly proportional to the height of the sensor above the ground for heights up to 125 m. Futhermore, our simulations indicate that the dependence on soil moisture is small, but the dependence on atmospheric humidity is significant, with a decrease in the footprint diameter of 40 m for every 0.01 kg kg^-1 increase in specific humidity. The good agreement between our theory and transport simulations suggests that the lateral footprint is determined mainly by the properties of air.

This paper proposes a strategy for improving the computational efficiency of model inversion using the method of anchored distributions by “bundling” similar model parametrizations in the likelihood function. Inferring the likelihood function typically requires a large number of forward model simulations for each possible model parametrization; as a result, the process is quite expensive. To ease this prohibitive cost, we present an approximation for the likelihood function – called “bundling” – that relaxes the requirement for high quantities of forward model simulations. This approximation redefines the conditional statement of the likelihood function as the probability of a set of similar model parametrizations – “bundle” – replicating field measurements, which we show is neither a model-reduction nor a sampling approach to improving the computational efficiency of model inversion. To evaluate the effectiveness of these modifications, we compare the quality of predictions and computational cost of bundling relative to a baseline method of anchored distributions inversion of three-dimensional flow and transport model parameters. Additionally, to aid understanding of the implementation we provide a tutorial for bundling- in the form of a sample data set and script for the R statistical computing language. For our synthetic experiment, bundling achieved a 35% reduction in overall computational cost and had a limited negative impact on predicted probability distributions of the model parameters. Strategies for minimizing error in the bundling approximation, for enforcing similarity amongst the sets of model parametrizations, and for identifying convergence of the likelihood function are also presented.

The Surface Water and Ocean Topography (SWOT) radar interferometer satellite mission will provide unprecedented global measurements of water surface elevation (h) for inland water bodies. However, like most remote sensing technologies SWOT will not observe river channel bathymetry below the lowest observed water surface, thus limiting its value for estimating river depth and/or discharge. This study explores if remotely-sensed observations of river inundation width and h alone, when accumulated over time, may be used to estimate this unmeasurable flow depth. To test this possibility, synthetic values of h and either cross-sectional flow width (w) or effective width (W_e, inundation area divided by reach length) are extracted from 1,495 previously surveyed channel cross-sections for the Upper Mississippi, Illinois, Rio Grande, and Ganges-Brahmaputra river systems, and from 62 km of continuously acquired sonar data for the Upper Mississippi. Two proposed methods (called “Linear” and “Slope-Break”) are tested that seek to identify a small subset of geomorphically "optimal" locations where w or W_e co-vary strongly with h, such that they may be usefully extrapolated to estimate mean cross-sectional flow depth (d). While the simplest Linear Method is found to have considerable uncertainty, the Slope-Break Method, identifying locations where two distinct hydraulic relationships are identified (one for moderate to high flows and one for low flows), holds promise. Useful slope-breaks were discovered in all four river systems, ranging from 6 (0.04%) to 242 (16%) of the 1,495 studied cross-sections, assuming channel bathymetric exposures ranging from 20% to 95% of bankfull conditions, respectively. For all four rivers, the derived depth estimates from the Slope-break Method have root mean squared errors (RMSEs) of <20% (relative to bankfull mean depth) assuming channel bathymetry exposures of ~25% or more. Based on historic discharge records and HEC-RAS hydraulic modeling, the Upper Mississippi and Rio Grande rivers experience adequate channel exposures at least ~60% and ~42% of the time, respectively. For the Upper Mississippi, so-called “reach-averaging” (spatial averaging along some predetermined river length) of native-resolution h and W_e values reduces both RMSE and longitudinal variability in the derived depth estimates, especially at reach-averaging lengths of ~1000-2000 m. These findings have positive implications for SWOT and other sensors attempting to estimate river flow depth and/or discharge solely from incomplete, remotely-sensed hydraulic variables, and suggest that useful depth retrievals can be obtained within the spatial and temporal constraints of satellite observations.

Accurate estimation of the characteristics of the winter snowpack is crucial for prediction of available water supply, flooding, and climate feedbacks. Remote sensing of snow has been most successful for quantifying the spatial extent of the snowpack, although satellite estimation of snow water equivalent, fractional snow covered area, and snow depth is improving. Here we show that GPS observations of vertical land surface loading reveal seasonal responses of the land surface to the total weight of snow, providing information about the stored snow water equivalent. We demonstrate that the seasonal signal in SOPAC GPS vertical land surface position time series at six locations in the western United States is driven by elastic loading of the crust by the snowpack. GPS observations of land surface deformation are then used to predict the water load as a function of time at each location of interest and compared for validation to nearby SNOTEL observations of snow water equivalent. Estimates of soil moisture are included in the analysis and result in considerable improvement in the prediction of snow water equivalent.

The original review of macropores and water flow in soils by Beven and Germann is now thirty years old and has become one of the most highly cited papers in hydrology. This paper attempts to review the progress in observations and theoretical reasoning about preferential soil water flows over the intervening period. It is suggested that the topic has still not received the attention that its importance deserves, in part because of the ready availability of software packages rooted firmly in the Richards domain, albeit that there is convincing evidence that this may be predicated on the wrong experimental method for natural conditions. There is still not an adequate physical theory linking all types of flow, and there are still not adequate observational techniques to support the scale dependent parameterisations that will be required at practical field and hillslope scales of application. Some thoughts on future needs to develop a more comprehensive representation of soil water flows are offered.

The complexity of predicting surface runoff from hydrological models is compounded by uncertainties associated with the model structure, parameters and inputs. A hierarchical mixture of experts (HME) is recognised as one of the ways of incorporating model structural uncertainty into hydrological simulations. In this article, a framework capable of incorporating parameter and structural uncertainties is implemented via the use of a hierarchical mixture of experts together with sequential Monte Carlo parameter sampling. The use of a HME enables aggregation of multiple constituent models at the same instance, mixed to different extents in a dynamic manner as specified by a gating function, allowing the modeller to better characterise the uncertainty associated with the obtained predictions.

This article presents a mechanism for better specifying the structure of the gating function used for combining models in a HME approach, by investigating the combination of predictor variables that allows the best model mixing. These predictors exist in various forms, each of which represents information on the catchment. We apply three different types of predictors to a case study, the Never Never River catchment in Australia. The outcomes from this case study consistently demonstrate improved Bayesian Information Criterion (BIC) readings for the HME especially when used with a combination of predictors. The predictor coefficients are further used for regionalisation with the Manning River catchment, having similar characteristics to the Never Never River catchment, and also demonstrate satisfactory improvement in BIC when compared with a single structure model.

We develop a probabilistic model to estimate the rate of flood-induced losses for a set of properties distributed over a large geographical region (for example a portfolio of insured properties within a country). The use of detailed physically based models over large areas becomes difficult due to the vast amount of data needed and the high implementation cost. The proposed model allows one to incorporate results from such detailed models, but can be used also in regions that have not been studied in much detail. Minimal required information includes the rate and spatial extent of severe precipitation, the topography and river network from which regions at risk of flooding can be identified, and information on historical floods with an approximate delineation of the flooded area and associated aggregate losses for at least a few major events. An application to river flood loss from residential buildings in Belgium is presented.

The accumulation of discharge along a stream valley is frequently assumed to be the primary control on solute transport processes. Relationships of both increasing and decreasing transient storage, and decreased gross losses of stream water have been reported with increasing discharge; however, we have yet to validate these relationships with extensive field study. We conducted transient storage and mass recovery analysis of artificial tracer studies completed for 28 contiguous 100-m reaches along a stream valley, repeated under four baseflow conditions. We calculated net and gross gains and losses, temporal moments of tracer breakthrough curves, and best-fit transient storage model parameters (with uncertainty estimates) for 106 individual tracer injections. Results supported predictions that gross loss of channel water would decrease with increased discharge through time. However, results showed no clear relationship between discharge and transient storage, and our further analysis of solute tracer methods demonstrated that the lack of this relation may be explained by uncertainty and equifinality in the transient storage model framework. Furthermore, comparison of water balance and transient storage approaches reveals complications in clear interpretation of either method due to changes in advective transport time, which sets a the temporal boundary separating transient storage and channel water balance. We have little ability to parse this limitation of solute tracer methods from the physical processes we seek to study. We suggest the combined analysis of both transient storage and channel water balance more completely characterizes transport of solutes in stream networks than can be inferred from either method alone.

River flow synthesizing and downscaling are required for the analysis of risks associated with water resources management plans and for regional impact studies of climate change. This paper presents a probabilistic model that synthesizes and downscales monthly river flow by estimating the joint distribution of flows of two adjacent months conditional on covariates. The covariates may consist of lagged and aggregated flow variables (synthesizing), or exogenous climatic variables (downscaling), or combinations of these two types. The joint distribution is constructed by connecting two marginal distributions in terms of copulas. The relationship between covariates and distribution parameters is approximated by an artificial neural network, which is calibrated using the principle of maximum likelihood. Outputs of the neural network yield parameters of the joint distribution. From the estimated joint distribution, a conditional distribution of river flow of current month given the estimation of the previous month can be derived. Depending on the different types of covariate information, this conditional distribution may serve as the ‘engine’ for synthesizing or downscaling river flow sequences. The idea of the proposed model is illustrated using three case studies. The first case deals with synthetic data and shows that the model is capable of fitting a non-stationary joint distribution. Second, the model is utilized to synthesize monthly river flow at four sample stations on the main stream of the Colorado River. Results reveal that the model reproduces essential evaluation statistics fairly well. Third, a simple illustrative example for river flow downscaling is presented. Analysis indicates that the model can be a viable option to downscale monthly river flow as well.

Simplification is an unavoidable aspect of model usage. Even complex, physically based models are simplifications of reality. More profound simplification is required to construct the “lumped parameter” models of semi-physical basis that are often employed for simulation of large-scale processes operative over one or many watersheds. Simplification can lead to model predictive error beyond that which would be expected on the basis of study-area information deficits alone. Building on a recently developed mathematical description of the model simplification process, this work employs linear subspace methods to analyze in detail the nature and ramifications of that process when applied to a one-dimensional, Richards equation-based unsaturated zone model used to predict recharge to a groundwater system. Two simplified versions of this model are examined. The first achieves simplification through assuming vertical parameter uniformity. The second achieves simplification through use of a lumped parameter model in place of the Richards equation-based model. Relationships between parameters employed by the complex model and those used by each of the simplified models are analyzed. The nature of predictive errors incurred through simplification is explored. Also explored is the ability of the calibration process to decrease the propensity for model error in making some predictions, while increasing the propensity for model error in the making of others – an outcome that may be considered counter-intuitive from a Bayesian perspective, but which is a natural consequence of suboptimal simplification.

High-resolution morphological modeling of fluvial processes with complex, rapidly varying flows has been limited so far by model accuracy or computational efficiency. One of the most widely used numerical algorithms is based on the Total Variation Diminishing (TVD) method, solved by either upwind or centered approaches. An upwind scheme preserves high accuracy but is complex and computationally demanding, whereas the simplicity and efficiency of a centered approach compromise the accuracy. The present paper extends a recent upwind-biased centered scheme originally developed for clear water and scalar transport over a rigid bed, to sediment-laden flows over an erodible bed. It does so by developing a fully coupled 2-D mathematical model using a finite volume method for structured grids. The complete set of non-capacity based governing equations, involving the effects of bed deformation and sediment density variation, as well as the influences of turbulence and sediment diffusion, and the temporal and spatial scales needed for sediment adaptation, is solved at one time to obtain synchronous solutions for the entire computational domain. For stability, a two-stage splitting approach together with a 2^nd order Runge-Kutta method is employed for the source terms. The model is verified in a number of tests covering a wide range of complex (sediment-laden) flows. The model is demonstrated to accurately simulate shock waves and reflection waves, but also rapid bed deformations at high sediment transport rates. The combination of high numerical accuracy and computational efficiency makes the model an important tool to forecast flood events in morphologically complex areas.

Land loss in the Mississippi River delta caused by subsidence and erosion has resulted in habitat loss and increased exposure of settled areas to storm surge risks. There is debate over the most cost-efficient and geomorphologically feasible projects to build land by river diversions, namely, whether a larger number of small, or a lesser number of large engineered diversions provide the most efficient outcomes. This study uses an optimization framework to identify portfolios of diversions that are efficient for three general restoration objectives: maximize land built and minimize cost and water diverted. The framework links the following models: 1) a hydraulic water and sediment diversion model that, for a given structural design for a diversion, estimates the volume of water and sediment diverted, 2) a geomorphological land building model that estimates the amount of land built over a time period, given the volume of water and sediment, and 3) a statistical model of investment cost as a function of diversion depth and width. An efficient portfolio is found by optimizing one objective subject to constraints on achievement of the other two; then by permuting those constraints, we find distinct portfolios that represent tradeoffs among the objectives. Although the analysis explores generic relationships among size, cost, and land building (and thus does not consider specific project proposals or locations), the results demonstrate that large-scale land building (>200 km²) programs that operate over a time span of 50 years require deep diversions because of the enhanced efficiency of sand extraction per unit water. This conclusion applies whether or not there are significant scale economies or diseconomies associated with wider and deeper diversions.

We present a model order reduction technique that overcomes the computational burden associated with the application of Monte Carlo methods to the solution of the groundwater flow equation with random hydraulic conductivity. The method is based on the Galerkin projection of the high-dimensional model equations onto a subspace, approximated by a small number of pseudo-optimally chosen basis functions (principal components). To obtain an efficient reduced order model, we develop an offline algorithm for the computation of the parameter-independent principal components. Our algorithm combines a greedy algorithm for the snapshot selection in the parameter space and an optimal distribution of the snapshots in time. Moreover, we introduce a residual-based estimation of the error associated with the reduced model. This estimation allows a considerable reduction of the number of full system model solutions required for the computation of the principal components. We demonstrate the robustness of our methodology by way of numerical examples, comparing the empirical statistics of the ensemble of the numerical solutions obtained using the traditional Monte Carlo method and our reduced model. The numerical results show that our methodology significantly reduces the computational requirements (CPU time and storage) for the solution of the Monte Carlo simulation, ensuring a good approximation of the mean and variance of the head. The analysis of the empirical probability density functions at the observation wells suggests that our reduced model produces good results and is most accurate in the regions with large drawdown.

The transport experiment at the MADE site (a highly heterogeneous aquifer) was investigated extensively in the last 25 years. The longitudinal mass distribution m(x,t) of the observed solute plume differed from the Gaussian shape and displayed strong asymmetry. This is in variance with the prediction of stochastic models of flow and transport in weakly heterogeneous aquifers. In the last decade we have forwarded a model coined as MIM (multi-indicator), in which the heterogeneous structure consists of blocks of different of different and independent random lognormal K. Thus the structure is completely characterized by K_G (the geometric mean), (the logconducitvity variance) and the integral scale I. Flow (uniform in the mean) and advective transport were solved by the semi analytical SCA (self consistent approximation). The SCA models the travel time of a solute parcel from an injection to a control plane as a sequence of independent time steps, each resulting from the simple solution for isolated blocks surrounded by a uniform matrix. The aim of the article is to determine whether the model could predict the observed mass distribution of MADE ( based on the most recent direct-push injection logger data), by using the recently collected detailed K data and the observed mean head gradient. It was found that the agreement with the measured plume is quite satisfactory, differences related to incomplete mass recovery, injection condition and ergodicity notwithstanding. It is concluded that the physical mechanism of advection, modeled by the local ADE, and the heterogeneity of K, are able to explain the MADE plume behavior and the stochastic model could predict it.

We present an un-mixing method, based on genetic algorithm – soil-vegetation-atmosphere-transfer (SVAT) modeling, to extract sub-grid information of soil and vegetation from remotely sensed soil moisture (downscaled) e.g., soil hydraulic properties, area fractions of soil-vegetation combinations and un-mixed soil moisture time series, that most land surface models use. The un-mixing method was evaluated using numerical experiments comprising of mixed-pixels with simple and complex soil-vegetation combinations, in idealized case studies (with or without uncertainty) and under actual field conditions (Walnut Creek (WC11) field, Soil Moisture Experiment 2005 (SMEX05), Iowa). Additional validation experiments were conducted at an air-borne remote sensing footprint (Little Washita (LW21) site, Southern Great Plains hydrology campaign 1997 (SGP97), Oklahoma) using Electronically Scanning Thin Array Radiometer (ESTAR). Results of the idealized experiments suggest that the un-mixing method can extract optimal or near-optimal solutions to the inverse problem under different hydrologic and climatic conditions. Errors in soil moisture data, initial and boundary conditions can compound uncertainty in the solution. The solutions generated under actual field conditions (WC11 field) were able to match soil moisture observations. Analysis showed that typical soil moisture retention curves of catalogued dominant soils in WC11 field did not match well with measurements, but those derived from actual field-scale soil moisture inversion matched better. The un-mixing method performed well in replicating soil hydraulic behavior at the ESTAR footprint. Unlike in WC11 field, the typical soil moisture retention curves of catalogued soils in LW21 field matched better with measurements. We envisaged that the un-mixing method can provide quick and easy way of extracting sub-grid soil moisture variability and soil-vegetation information in a pixel.

Natural mixed ecosystems (grass and woody vegetation) and managed grasslands are the dominant contrasting ecosystems of semi-arid regions. These two types of land covers are known to differ in their responses to water stress, as the trees demonstrate both greater stress resistance and greater ability to tap into deeper water sources. In this study we examine the contrasting influences of vegetation differences (grassland vs. mixed ecosystems) and soil differences (deeper alluvial valley soils vs. shallow upland soils) on evapotraspiration and CO₂ exchange dynamics. We draw on data from two representative case study sites within the Flumendosa river basin on Sardinia. At both sites land-surface and CO₂ fluxes are estimated by eddy covariance instruments on micrometeorological towers. Fluxes at the two ecosystems are compared, and the effect of the vegetation cover is examined with the help of an ecohydrologic model to control for the different soil influences. The results show that the water and carbon fluxes in these ecosystems are more controlled by soil differences during the late spring, when the deeper soil depth leads to a doubling of the available moisture and an increase of 48%in the mixed natural vegetation transpiration. The system then switches to vegetation control in the summer as the presence or absence of drought tolerant trees is the dominant imprint on continued transpiration and photosynthesis. In fact, total grassland evapotranspiration in the summer is only 20% as large as the mixed vegetation evapotranspiration in the summer.

Groundwater discharge to streams can be distributed variably in space due to the heterogeneous composition of the subsurface. Fiber optic distributed temperature sensing (DTS) has been applied to detect and quantify spatially concentrated groundwater discharge to streams. However a systematic uncertainty assessment for this approach with respect to changing boundary conditions is missing and limits of detection are unclear. In this study artificial point sources with controlled inflow rates to a natural first-order stream were used to quantitatively test the approach for inflow rates in the range of <1% to ∼19% of upstream discharge and varying temperature differences between stream water and inflowing water. Even small inflow fractions down to ∼2% of upstream discharge could be detected with the DTS. Inflow fractions calculated from DTS based stream temperature observations and independently measured inflow temperatures were comparable to measured inflow fractions. Average uncertainty estimation based on error propagation calculations ranged between 9% and 22% for experiments well above the detection limits of the DTS, but ranged up to 147% for experiments close to the lower end of the detectable range.

We evaluate the results of dynamically downscaled winter precipitation over Western Montana using the Weather Research and Forecasting (WRF) model through comparison with estimates from the observationally based Parameter-elevation Regressions on Independent Slopes Model (PRISM). Seven years (6 winters) from 2000-2006 are simulated at 4 km resolution to assess the similarities and differences between the two models as well as the implications for hydrologic modeling. Inherent biases in both approaches are apparent, highlighting the difficulty in climate model validation. Results show general agreement between the two models in the spatial distribution of winter precipitation. A principal component analysis shows similar spatial patterns between models in the leading six components suggesting that the main processes that drive the spatial distribution of precipitation were properly captured. The first component explains almost 70\% of total variance and the first three components explain more than 85\% in both datasets. The largest differences between the two datasets exist in areas at high elevation and upstream of the continental divide where observations are sparse. In these areas, WRF consistently predicts higher amounts of precipitation and larger interannual variability than PRISM. We suggest that these results are realistic for impingement of moist air masses on topography and, if correct, could have significant implications in flood forecasting, water resource management, and climate change studies.

In this technical note, I present a steady-state analytical solution concentrations of a parent solute reacting to a daughter solute, both of which are undergoing transport and multirate mass transfer. While the governing equations are complicated, the resulting solution can be expressed in simple terms. A function of the ratio of concentrations, , can be used as a metric of mass transfer and reaction if the reactions are mostly confined to the immobile domain, where P is the ratio of production of daughter to decay of parent. I apply this metric with the resazurin – resorufin (Raz-Rru) tracer system in a stream to obtain an integrated measure of respiration that occurs on or in a stream bed. The slope of the graph of vs. advective travel time is a function of the strength of surface – bed interaction and respiration. This graph can be used for rapid comparison of different experiments and streams.