• hydrology;
  • mountain pine beetle;
  • land-energy budget;
  • modeling


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The mountain pine beetle (MPB) epidemic in western North America is generating growing concern associated with aesthetics, ecology, and forest and water resources. Given the substantial acreage of prematurely dying forests within Colorado and Wyoming (~two million acres in 2008), MPB infestations have the potential to significantly alter forest canopy, impacting several aspects of the local water and land-energy cycle. Hydrologic processes that may be influenced include canopy interception of precipitation and radiation, snow accumulation, melt and sublimation, soil infiltration and evapotranspiration. To investigate the changing hydrologic and energy regimes associated with MPB infestations, we used an integrated hydrologic model coupled with a land surface model to incorporate physical processes related to energy at the land surface. This platform was used to model hillslope-scale hydrology and land-energy changes throughout the phases of MPB infestation through modification of the physical parameterisation that accounts for alteration of stomatal resistance and leaf area indices. Our results demonstrate that MPB infested watersheds will experience a decrease in evapotranspiration, an increase in snow accumulation accompanied by earlier and faster snowmelt and associated increases in runoff volume and timing. Impacts are similar to those projected under climate change, yet with a systematically higher snowpack. These results have implications for water resource management because of higher tendencies for flooding in the spring and drought in the summer. Copyright © 2011 John Wiley & Sons, Ltd.


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The mountain pine beetle (Dendroctonus ponderosae, MPB) epidemic in Western North America presents challenging forestry and water resource problems. Outbreaks of MPB have been historically endemic and result in about 2% tree mortality in affected watersheds. However, recent outbreaks have affected an unprecedented quantity of trees in the Rocky Mountain West and are resulting in close to 100% tree mortality (Samman and Logan, 2000; Logan et al., 2003; Safranyik and Wilson, 2006). Because infestation is common in major watersheds in the semi-arid West, the impact of the MPB on water quality and quantity are of great concern.

High tree mortality rates of recent MPB infestations will induce significant changes in forest canopy, which in turn may impact several aspects of the local water and energy cycle. Previous research has shown that alterations in the canopy can have considerable impacts on the water balance including canopy interception of precipitation and solar radiation (Barry et al., 1990; Hardy et al., 1997; Bales et al., 2006; Rinehart et al., 2008); canopy wind speed (Yamazaki and Kondo, 1992; Wigmosta et al., 1994; Gelfan et al., 2004); snow accumulation, snow-melt, sublimation, and evapotranspiration (ET) (Hardy et al., 1997; Hedstrom and Pomeroy, 1998; Pomeroy et al., 2002; Musselman et al., 2008). Changes in canopy cover and forest structure due to MPB infestations have the potential to result in more rapid snowmelt and drier soil in the summer. Impacts on snow processes are particularly important because in many areas susceptible to MPB, the dominant source of surface, soil and ground water is snowmelt (Barnett et al., 2005). Annual water yields and peakflows have been shown to increase following insect infestations (Bethlahmy, 1974; Potts, 1984), which can lead to increased particulate transport. Decreases in forest canopy will also result in decreased transpiration owing to widespread tree die off, and thus reduce soil water/groundwater uptake and potentially increase groundwater recharge (MacDonald and Stednick, 2003).

Field studies have recently begun to investigate the changing hydrologic regime owing to widespread tree mortality caused by MPB, and it is therefore imperative that we begin to model the changing subsurface and land-atmosphere fluxes to gain a better understanding of the phenomenon to guide observations and inform water and forest managers. Integrated hydrological models are an essential tool to understand the relationships between groundwater, surface water and land-energy feedbacks and hence compliment and focus field observations (Maxwell and Kollet, 2008; Kumar et al., 2009; Therrien et al., 2009; Ferguson and Maxwell, 2010; Leung et al., 2010; Sulis et al., 2010; Ferguson and Maxwell, 2011; Ferguson et al., 2011).

The goal of this study was to investigate how the surface energy balance and hydrologic regime would change throughout the course of an MPB infestation. Previous studies (not related to the MPB) have used integrated hydrologic models to look at the feedbacks between the hydrologic and the land-energy cycles under climate change (Wilby et al., 2006; Maxwell and Kollet, 2008; Brookfield et al., 2010; Ferguson and Maxwell, 2010), to gain insight into how the two influence each other. Here, we extend the idea that feedbacks between the local water and land-energy cycles are important by perturbing land-cover to represent an insect infestation of epidemic proportions.


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In this study, we used the integrated hydrologic model, ParFlow, to study the MPB-induced perturbations in plant processes on the complete, coupled, terrestrial water and energy balances in a forested Rocky Mountain watershed. In the configuration used here, ParFlow simulates three-dimensional, variably saturated groundwater flow, and fully integrated overland flow; subsurface and overland flow equations are solved simultaneously, allowing non-linear feedbacks between the surface and subsurface flows (Kollet and Maxwell, 2006; Sulis et al., 2010). ParFlow was coupled to the Common Land Model (CLM) (Dai et al., 2003; Maxwell and Miller, 2005), which calculates the water and energy fluxes at the land surface including evaporation from the canopy and ground surface, transpiration from vegetation, ground heat flux, and snow water equivalent (SWE). CLM is forced with atmospheric data such as precipitation, solar radiation, wind, temperature, humidity and pressure. Complete details on the equations coupled in CLM and ParFlow can be found in the literature (Maxwell and Miller, 2005; Maxwell and Kollet, 2008; Kollet et al., 2009), and a summary of the relevant equations can be found in the section on Coupled Numerical Model: Summary of Equations.

Coupled numerical model: summary of processes

ParFlow solves the variably saturated Richards equation in three spatial dimensions coupled to the kinematic wave equation for overland flow to determine the change in surface and subsurface water storage. This formulation allows for physically based runoff generation, topographic flow and a spatially variable water table that can interact with the land surface. In CLM, ET is the combination of ground evaporation, canopy evaporation (from wetted stems and leaves) and transpiration. Transpiration depends non-linearly on solar radiation, via the light response of stomata and can therefore be controlled by the stomatal resistance, which is directly adopted in CLM from the Land Surface Model (Bonan, 1996). The leaf area index (LAI) of the watershed is also an important part of the ET equations as it determines the amount of incoming radiation and precipitation reaching the forest floor. Precipitation is either intercepted by foliage or directly falls to the ground when the maximum water capacity of the canopy is reached (adopted from the Biosphere–Atmosphere Transfer Scheme (Dickinson et al., 1993)). All the hydrology in CLM used to calculate soil moisture, infiltration and runoff is replaced by the values in ParFlow, and ET calculated from CLM is transferred to ParFlow; ParFlow, in turn, provides CLM with subsurface pressure head and saturations to limit soil moisture and modify soil heat transfer properties.

Coupled numerical model: summary of equations

Although complete details of the solution approach that ParFlow uses to solve for coupled surface–subsurface flow are given in (Jones and Woodward, 2001; Kollet and Maxwell, 2006), a brief summary of the equations is presented here. Fundamentally, ParFlow solves the Richards equation for variably saturated flow in three spatial dimensions given as

  • display math(1)


  • display math(2)

In these expressions, h is the pressure head [unit is defined as length (L)], z is the vertical coordinate (L), Ks(x) is the saturated hydraulic conductivity tensor (length per unit time), k is the relative permeability (−), Ss is the specific storage coefficient (L−1), ϕ is the porosity (−), Sw is the relative saturation (−), and qr is a general source–sink term that represents transpiration, wells, and other fluxes (length per unit time). The specific volumetric (Darcy) flux is denoted by q (length per unit time).

Overland flow is represented in ParFlow by the two-dimensional kinematic wave equation included as the overland flow boundary condition resulting from application of continuity conditions for pressure and flux (Kollet and Maxwell, 2006):

  • display math(3)

where νsw is the two-dimensional, depth-averaged surface water velocity (length per unit time); h is the surface ponding depth (L), if h > 0; qr(x) is a general source–sink (e.g. rainfall, ET) rate (length per unit time); and k is the unit vector in the vertical. Note that ‖h, 0‖ indicates the greater value of the two quantities, that the lhs of Equation (3) represents vertical fluxes (e.g. in–exfiltration) across the land surface boundary, and that the overland flow condition assumes that pressure h represents both surface pressure and the ponding depth at the ground surface under saturated conditions (Kollet and Maxwell, 2006). Equation (3) represents the connection between surface and subsurface flow processes.

The primary equations used to represent evapotranspiration and interception in CLM are summarised succeedingly. A more comprehensive description of CLM and the equations used can be found in Dai et al., 2003. Total ET is taken as the sum of evaporation from bare ground Eg, evaporation from vegetation Ew (i.e. direct evaporation from wetted foliage), and plant transpiration Etr.

The maximum transpiration, Etrmax is given by

  • display math(4)

where froot,j, the root fraction within soil layer j is

  • display math(5)

and wLT is a factor that varies between 0 at the permanent wilting point to 1 at saturation.

  • display math(6)

σf is the fraction of vegetation excluding the part buried by snow, LAI is the leaf area index, and wLT is a function of φmax,sat,j the maximum, saturated and soil matrix potential at layer j.

When transpiration is not at a maximum, Etr is defined by

  • display math(7)

ET is now also a function of LSAI (the stem plus leaf area index), δ(Efpot) which is a step function and is equal to one for a positive Efpot (the potential ET) where

  • display math(8)

and zero when Efpot is zero or negative. ρa is the density of air, qfsat is the saturated specific humidity and qaf is the air specific humidity within the canopy space. Ld is the dry fraction of the foliage surface where

  • display math(9)


  • display math(10)

rb and rs the leaf boundary and stomatal resistances where rb is defined by

  • display math(11)

where Cf if the coefficient of transfer between the canopy air and underlying ground (0.01 m/s1/2), Uaf (the magnitude of the wind velocity incident on the leaves) is

  • display math(12)

and Df is the characteristic dimension of leaves in the wind direction. Va is the wind at reference height, Cd is the coefficient of drag and ram is the aerodynamic resistance for momentum.

ET is limited by the stomatal resistance rs, which is inversely related to leaf photosynthesis and is defined by

  • display math(13)

where m is an empirical parameter, A is leaf photosynthesis, cs is the CO2 concentration at the leaf surface, es is the vapour pressure at the leaf surface, ei is the saturation vapour pressure inside the leaf at the vegetation temperature, ps is the atmospheric pressure and b is the minimum stomatal conductance (2000 µmol m−2 s−1).

Evaporation from bare ground Eg and vegetation Ew are calculated by Equations (14) and (15), respectively:

  • display math(14)
  • display math(15)

where qg is the air specific humidity at the ground surface [kg/kg]; qa is the specific humidity at reference height zq, obtained from the prescribed atmospheric forcing [kg/kg]; and rd is the aerodynamic resistance of evaporation between the ground surface and canopy air, computed based on Monin–Obukhov similarity theory; inline image is the wetted fraction of the canopy [−].

Precipitation arriving at the top of the canopy is either intercepted by foliage, or directly falls through the gaps of leaves to the ground. In CLM, the rate of direct through-fall is proportional to P, the rate of precipitation (mm/s) and is given by

  • display math(16)

and the maximum water capacity of the canopy is given by

  • display math(17)

where the maximum storage of solid water is assumed to be the same as that of liquid water.

The land energy budget includes conduction of heat through both soil and snow layers. The snow-pack in CLM is modelled with up to five layers depending upon total snow depth. Three mechanisms are used for changing snow characteristics: destructive, overburden, and melt, and snow albedo decays over time owing to accumulation of dirt and growth of snow grain size. Previous simulations show the snow model compares well with observations of SWE and the land-energy model with ground temperature (Maxwell and Miller, 2005).

Problem domain and setup

Figure 1 shows the physical processes in our simulated hillslope. In our simulations, the water table begins 3 m below the ground surface on the left side of the domain and meets the ground surface on the right side of the domain. Bedrock is assumed to be 12.5 m below the ground surface and is a no flow boundary. Once water reaches the confines of the domain as either subsurface lateral flow or overland flow, it crosses the boundary and is no longer considered to be part of the domain, leading to subsurface storage losses or gains depending on the scenario.


Figure 1. Displays the governing processes in the three simulated watersheds. Arrow lengths indicate flux magnitudes. P is precipitation, ET is evapotranspiration, O is overland flow, and I is infiltration.

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This hillslope-scale study was simulated with a hypothetical domain, 500 m (x-direction) by 1000 m (y-direction) by 12.5 m (z-direction). The domain was discretised using Δx = 100 m, Δy = 200 m and Δz = 0.5 m. Three different simulations were run with topographic slopes of 0.01, 0.08 and 0.15 m/m (Figure 1) that were selected to represent the topography found both in high-elevation, mountainous, MPB-infested watersheds in the Rocky Mountain West, along with the flatter MPB-infested watersheds found in southern Wyoming. The saturated hydraulic conductivity was set to 0.1 m/h. Porosity and vanGenuchten parameters were assigned for the watershed. Porosity was held constant throughout at 0.390 (−) and the van Genuchten parameters were set as follows: α = 3.5 (1/m), n = 2 and Sres = 0.01 to represent an idealised, relatively fast draining, sandy clay loam soil. Soils in mountain hillslopes are of course very heterogeneous with great uncertainty about both point values and spatial distribution. The assumption of homogeneity invoked here is used to isolate the impacts of land-cover changes on the hydrology of the system, whereas minimising other confounding signals. Approaches that directly include heterogeneity at the hillslope scale (Rihani et al., 2010; Atchley and Maxwell, 2011) are beyond the scope of this current study and are excellent topics for future work.

Hourly meteorological forcings were taken from the North American Land Data Assimilation System (NLDAS), forcing dataset (Cosgrove et al., 2003) for the 2008 water year (1 September 2007 to 31 August 2008) at Pennsylvania Gulch, Blue River, Colorado. The climate during 2008 was typical of an average year for this region by comparing precipitation with seasonal averages, and therefore a good representation of what a climatological weather pattern will be in the Rocky Mountains, yet still including high-frequency (hourly) variability. The model was run for each phase for 3 years, re-running the 2008 meteorological data for each year, to minimise the influence of initial conditions (but not running the model to equilibrium) on simulated results with results focusing on the third year.

In the model simulations, we defined the process of mortality in an affected tree to have four distinct phases: (1) green phase: the tree is alive and transpiring; (2) red phase: the tree has been attacked and has ceased transpiring and interception has slightly decreased; (3) grey phase: the tree is dead, has no remaining needles, transpiration has ceased, and interception is significantly decreased; and (4) dieback phase: the tree has fallen to the ground and begun decomposing as new vegetation begins to take its place. During an actual MPB infestation, the vegetation distribution throughout the four phases is likely to be heterogeneous; however, for the sake of simplicity and to understand the magnitudes of difference in the hydrologic and energy regimes between each phase, we are assuming a homogeneous distribution of vegetation during each phase.

The hydrologic and land-energy impacts of each of the four phases of MPB infestation were simulated by perturbing two vegetative parameters: stomatal resistance and leaf area index. Table 1 shows how we defined each phase of MPB infestation, along with its corresponding land surface classification, maximum and minimum LAI (depending on season, recall that LAI is dynamically calculated in CLM) and the minimum stomatal conductance. When the MPB infects a stand of trees, it introduces a blue-stain fungi (Ceratostomella montia and Europhium clavigerum) that essentially clogs the xylem and phloem tubes (Amman, 1978). This renders the tree unable to take up water and nutrients from its roots. Stomatal resistance was manipulated to represent this phenomenon during the red and grey stages of infestation by increasing the minimum stomatal conductance until it was maximised and transpiration approached zero. During the red and grey phases, it is assumed that there is no new undergrowth in the pine stands, transpiration approaches zero in the entire watershed and bare-ground evaporation is the only energy land-surface flux occurring.

Table 1. Parameters used in Parflow-CLM for distinguishing the four different mountain pine beetle phases.
PhaseLand surface classificationaMaximum LAIMinimum LAIMinimum stomatal conductance (µmol m−2 s−1)
  1. a

    More detailed information on the differences in the land surface classifications can be found in the IGBP (IGBP, 1990).

GreenEvergreen needleleaf forest652000
RedEvergreen needleleaf forest542.0E+06
GreyEvergreen needleleaf forest112.0E+06
DiebackOpen shrubland612000


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The yearly water balance for each phase of (MPB-induced) tree mortality under three different slopes is summarised in Table 2. This table shows that the difference between precipitation and ET is a consistent pattern throughout the progression of an MPB infestation for each of the three hill slope scale scenarios: as ET decreases for each phase of infestation whereas precipitation remains the same. Subsurface storage increases over the year for shallow slopes, but decreases over the year for steeper slopes, as more water leaves the domain through either subsurface lateral flow or overland flow (process shown in Figure 1). Through each phase of infestation, the change in storage increases, because of less ET occurring and more water becoming available for infiltration. Total yearly overland flow also increases with infestation progression owing to decreased interception and enhanced snowmelt.

Table 2. The domain-integrated water balance for each phase of infestation.a
PhaseSlopeΣ (PET)VoverlandΔS
  1. a

    Variables are defined as follows: P = precipitation, ET = evapotranspiration, Voverland = overland flow and ΔS = change in storage from 1 September 2007 to 31 August 2008. Values are in mm and are a yearly total.


Time series of monthly, cumulative ET and overland flow, the change in storage and average monthly SWE for each slope scenario and each phase of MPB infestation are shown in Figure 2. Row A of this figure depicts a general trend of decreasing ET with advancing phase of infestation. As expected, annual ET is greatest during the summer and lowest during the winter. During the winter months, the green and red phases experience similar values of ET as transpiration is low and the snowpack limits ET. However, during the summer when the green phase begins actively transpiring, the differences in ET between it and other phases are magnified. The red and grey phases experience a drastic decrease in ET near July owing to summer moisture stress, whereas the green phase ET continues to increase because of transpiration despite the drier top surface layer. Model simulation of ET assumes that in the green phase, the trees are able to extract water from deep in the subsurface, whereas in the red/grey phases, bare-soil ET is solely dependent on the soil moisture in the top surface layer.


Figure 2. The complete water balance and average monthly snow water equivalent for the four phases of infestation at 1%, 8% and 15% slopes. Row A is total monthly ET, row B is total monthly overland flow, row C is the change in storage from the beginning to the end of that month, and row D is the monthly average snow water equivalent. Values are all in mm.

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The same trend in runoff throughout the phases of infestation was observed regardless of slope; however, the steeper slopes have a magnified runoff response to snowmelt for the grey phase (Figure 2, Table 2). This can be linked to needle loss, where a greater amount of precipitation reaches the forest floor at an increased rate causing an increase in overland flow when infiltration capacity is surpassed. The timing of the peak overland flow is significantly earlier in the grey and dieback phases than in the red and green phases owing to earlier and faster snowmelt. Higher soil moisture earlier in the spring results in earlier onset of runoff because infiltration capacity is exceeded sooner. With the highest rate of snowmelt, the grey and dieback phases also have the largest increase in subsurface storage during the spring melt. The differences in overland flow and subsurface storage are more exaggerated between the phases in shallower slopes because of land cover being more influential than slope in determining runoff rates.

The monthly snowpack changes were consistent for all slopes as illustrated in Figure 2(D). In these plots, we see two distinct trends as infestation progresses: greater snowpack and a shorter snow season. Greater snow accumulation associated with needle loss can be explained by decreases in canopy interception of precipitation. In CLM, changes in slope do not result in aspect changes that are large enough to cause any differences in SWE. Essentially, CLM handles aspect by a consistently facing slope for all cases. However, in an actual mountainous watershed, the slope-aspect ratio would cause differences in SWE. The canopy is able to hold a specific amount of snow for each storm that then melts at a faster rate than through-fall snow because of lower albedo in the canopy than on the snow-covered ground. Despite increased snow accumulation for individual storms, this does not always ensure a greater snowpack in domains without needles because increased radiation penetration through the canopy causes earlier and faster snowmelt. This phenomenon is seen during both periods of transition, fall and spring, in association with the snow season.

Surface saturation increases through each phase of infestation, but the differences are most apparent during times of moisture stress (Figure 3). In steeper watersheds, the difference in surface saturation is not as drastic because soils are drier owing to increased baseflow runoff and subsequent drainage of shallow soils. Saturation also drives many of the other hydrologic processes. For example, during November, ET decreases in the red and green phases partly because of the dry top surface layer (Figures 2(A) and 3) with a minimum in mid-winter months. In the 1% slope case, the dieback phase's ET is able to rebound in the summer months, surpassing the grey phase ET. This is not seen for the steeper slopes and is due to the fact that the shallow, surface soil moisture is higher for the 1% slope (Figure 3(A)) than for the other two slopes, allowing more ET to occur in the dieback phase. This relationship between dieback rebound and slope exists because the deeper roots of the phreatophytic lodgepole pine are able to access groundwater whereas the shrub vegetation cannot. The shrub vegetation used in the dieback phase has roots that extend less than 3.1 m into the subsurface. For the steeper slopes, the water table is deeper than 3.1 m, thereby limiting the amount of water the plants can transpire.


Figure 3. Top layer saturation, averaged hourly over the entire domain for (A) 1%, (B) 8%, and (C) 15% slopes.

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For example, when looking at the monthly differences in ET during the late fall, ET does not follow the typical yearly pattern (Figure 2(A)): the grey phase exhibits an increase in ET whereas all the other phases experience a decrease in ET. To further explore this phenomenon, we compared the daily average ground temperature, SWE and ET for the green and grey phases during a 2-month period (Figure 4). Because the land during the grey phase has a small LAI, more radiation reaches the ground and warms the surface layers (Figure 4(A)). The warming of the ground causes snow to melt and infiltrate, increasing saturation in the top surface layer (Figure 3) and contributing to the spike in ET above the other phases during that period (Figure 4(C)). Once the ground temperature falls below freezing and snow begins to accumulate in early December (indicated by the arrow in Figure 4(B)), ET in the grey phase drops back below the green and red phases and the ground temperatures from all phases equilibrate.


Figure 4. Daily average (A) ground temperature, (B) snow water equivalent, and (C) evapotranspiration (ET) in the green and grey phases for a 1% slope during a 2-month period. The arrows indicate where the ground temperatures in both phases equalise, snow begins to accumulate in the grey phase, and the grey phase's ET permanently dips below that of the green phase.

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However, the trend mentioned earlier is not consistent throughout the year. For example, the grey and dieback phases do not initially accumulate as much snow as the red and green phases owing to increased shortwave radiation and higher ground temperatures. As snow begins to accumulate in mid-October, despite the grey phase's reduced snow interception, the red and green phases accumulate more snow on the forest floor. Until the snowpack becomes permanent, the grey phase has a more intermittent snow pack owing to increased radiation reaching the forest floor and melting the snow. This enhanced melting trend in the grey phase continues until both the air and the ground temperatures remain below freezing (seen in Figure 4). At this point, snowpack accumulation in the grey phase accelerates and ultimately accumulates more snow than its needled counterparts owing to the reduced interception of snow. Overall, the grey and dieback phases accumulate more snow than the red and green phases, but have a shorter period of snow-covered ground, leading to a more magnified runoff response in the spring. Similar to climate change impacts, the infested watersheds will experience a more magnified runoff response occurring earlier with more saturated soils and greater groundwater recharge.


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This work uses an integrated hydrologic model to simulate the effects of vegetation changes on water and energy fluxes from the ongoing MPB epidemic in the Rocky Mountain West using a semi-idealised forested hillslope. We arrived at three main conclusions relating to alterations in the hydrologic and energy budgets in MPB impacted regions:

  1. ET decreases as the infestation progresses except for 1 month in the fall when the grey phase's ET increases due to increased ground saturation and evaporation.
  2. Snow accumulation is accelerated and snowmelt occurs earlier as infestation progresses, which results in a significantly shorter snow season. However, peak SWE increases as infestation progresses.
  3. As the infestation progresses, the rate of snowmelt increases resulting in increased peak runoff.

In this integrated hydrologic modelling study, we also confirmed that decreases in forest canopy do result in decreased evapotranspiration, reduced soil/groundwater uptake and thus, increased groundwater recharge.

A large amount of research has suggested that climate change is also a primary driver of earlier snowmelts and earlier peak flows (Stone et al., 2002; Stewart et al., 2004; Barnett et al., 2008). A key difference between our findings and those attributed to changes in temperature is that MPB infestation may both increase the snowpack and result in earlier melts, whereas climate change impacts will decrease the snowpack at lower elevations and initiate an earlier melt season. Thus, in watersheds with MPB susceptible forests, climate models should include the impacts of the MPB infestation. The consequences of an earlier and faster melt season could include extremes such as flooding during late spring and a longer duration of low flow and dry conditions in the summer.

Although these model simulations show the general trends and changes of the water and energy budgets because of the MPB infestation under typical climates, they do not currently extend to include climate change scenarios that may also be encountered. Changes in climate may further exacerbate trends seen through the phases of MPB infestation and necessitate changes to water management practices. Forest management practices may also need to take into account the increased risk of fire and possible slope stability issues owing to widespread tree mortality. It will also be necessary to conduct high-resolution model simulations of the Rocky Mountain region (Kollet et al., 2010) to scale up changes in ET because of large-scale MPB and to look at the large-scale atmospheric feedbacks using integrated groundwater-atmosphere models (Maxwell et al., 2007; Goderniaux et al., 2009; Leung et al., 2010; Maxwell et al., 2011). Field observations in MPB-impacted catchments have been initiated and are now necessary to validate our modelling predictions and quantify the actual degree of perturbations to overland flow, top layer saturation, ET, and snowpack.


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This research was developed under STAR Fellowship Assistance Agreement No. FP91735401 awarded by the US Environmental Protection Agency (EPA). We wish to thank John Williams for his computational help using Parflow and Dr. Justin Lawrence for his insightful review that contributed to the clarity of this work. Although the research described in the article has been funded wholly or in part by the US Environmental Protection Agency's STAR program through Grant RD-83438701-0, it has not been subjected to any EPA review and therefore does not necessarily reflect the views of the Agency, and no official endorsement should be inferred.


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