SEARCH

SEARCH BY CITATION

References

  • Allen, R. G. (2006), Evaporation modeling: Potential, in Encyclopedia of Hydrological Sciences, edited by M. G. Anderson, p. 11, John Wiley, Hoboken, N. J.
  • Ballard, C. E. (2011), The Role of Physics Based Models for Simulating Runoff Responses to Rural Land Management Scenarios, 340 pp., Imp. Coll. London, London.
  • Ballard, C. E., N. McIntyre, H. S. Wheater, J. Holden, and Z. E. Wallage (2011), Hydrological modelling of drained blanket peatland, J. Hydrol., 407(1–4), 8193.
  • Ballard, C. E., N. McIntyre, and H. S. Wheater (2012), Effects of peatland drainage management on peak flows, Hydrol. Earth Syst. Sci., 16, 22992310.
  • Barton, R. R. (1998), Simulation metamodels, in Proceedings of the 30th Conference on Winter Simulation, edited by D. J. Medeiros and E. F. Watson, pp. 167176, IEEE Press, Washington, D. C.
  • Beven, K. (1989), Changing ideas in hydrology—The case of physically-based models, J. Hydrol., 105(1–2), 157172.
  • Beven, K. (1993), Prophecy, reality and uncertainty in distributed hydrological modelling, Adv. Water Resour., 16(1), 4151.
  • Beven, K. (2001), How far can we go in distributed hydrological modelling?, Hydrol. Earth Syst. Sci., 5(1), 112.
  • Boussinesq, J. (1867), Etude sur la propagation de la chaleur dans les milieux homogenes, Fac. des Sci. de Paris, Paris.
  • Bulygina, N., N. McIntyre, and H. Wheater (2011), Bayesian conditioning of a rainfall-runoff model for predicting flows in ungauged catchments and under land use changes, Water Resour. Res., 47, W02503, doi:10.1029/2010WR009240.
  • Bulygina, N., C. Ballard, N. McIntyre, G. O'Donnell, and H. Wheater (2012), Integrating different types of information into hydrological model parameter estimation: Application to ungauged catchments and land use scenario analysis, Water Resour. Res., 48, W06519, doi:10.1029/2011WR011207.
  • Carrillo, G., P. A. Troch, M. Sivapalan, T. Wagener, C. Harman, and K. Sawicz (2011), Catchment classification: Hydrological analysis of catchment behavior through process-based modeling along a climate gradient, Hydrol. Earth Syst. Sci., 15, 34113430, doi:10.5194/hess-15–3411-2011.
  • Castelletti, A., S. Galelli, M. Ratto, R. Soncini-Sessa, and P. C. Young (2012a), A general framework for dynamic emulation modelling in environmental problems, Environ. Modell. Software, 34, 58.
  • Castelletti, A., S. Galelli, M. Restelli, and R. Soncini-Sessa (2012b), Data-driven dynamic emulation modelling for the optimal management of environmental systems, Environ. Modell. Software, 34, 3043.
  • Dobler, C., S. Hagemann, R. L. Wilby, and J. Stotter (2012), Quantifying different sources of uncertainty in hydrological projections in an Alpine watershed, Hydrol. Earth Syst. Sci., 16, 43434360.
  • Dunn, S. M., and R. Mackay (1996), Modelling the hydrological impacts of open ditch drainage, J. Hydrol., 179(1–4), 3766.
  • Ebel, B. A., and K. Loague (2006), Physics-based hydrologic-response simulation: Seeing through the fog of equifinality, Hydrol. Processes, 20(13), 28872900.
  • Evans J. P., and Jakeman A. J., (1998), Development of a simple, catchment-scale, rainfall–evapotranspiration–runoff model, Environ. Modell. Software., 13(3–4), 385393.
  • Ewen, J. (1997), “Blueprint” for the UP modelling system for large scale hydrology, Hydrol. Earth Syst. Sci., 1(1), 5569.
  • Ewen, J., G. O'Donnell, A. Burton, and E. O'Connell (2006), Errors and uncertainty in physically-based rainfall-runoff modelling of catchment change effects, J. Hydrol., 330(3–4), 641650.
  • Ewen, J., G. O'Donnell, W. M. Mayes, J. Geris, and E. O'Connell (2008), Multiscale experimentation, monitoring and analysis of long-term land use change and flood risk (EA Project SC060092): Experimental design, monitoring design and project record, Report, 22 pp., Newcastle Univ., Newcastle.
  • Ewen, J., G. O'Donnell, W. M. Mayes, J. Geris, and E. O'Connell (2009), Multiscale experimentation, monitoring and analysis of long-term land use change and flood risk (EA Project SC060092), Interim Science Report, 94 pp., Newcastle Univ., Newcastle.
  • Ewen, J., G. O'Donnell, W. M. Mayes, J. Geris, and E. O'Connell (2010), Multiscale experimentation, monitoring and analysis of long-term land use change and flood risk (EA Project SC060092), Final Science Report, 133 pp., Newcastle Univ., Newcastle.
  • Ewen, J., G. O'Donnell, N. Bulygina, C. Ballard, and E. O'Connell (2013), Towards understanding links between rural land management and the catchment flood hydrograph, Q. J. R. Meteorol. Soc., 139(671), 350357.
  • Forsman, Å., and A. Grimvall (2003), Reduced models for efficient simulation of spatially integrated outputs of one-dimensional substance transport models, Environ. Modell. Software, 18(4), 319327.
  • Freer, J. E., H. McMillan, J. J. McDonnell, and K. J. Beven (2004), Constraining dynamic TOPMODEL responses for imprecise water table information using fuzzy rule based performance measures, J. Hydrol., 291(3–4), 254277.
  • Fuller, R. M., G. M. Smith, J. M. Sanderson, R. A. Hill, and A. G. Thomson (2002), The UK land cover map 2000: Construction of a parcel-based vector map from satellite images, Cartogr. J., 39(1), 1525.
  • Galelli, S., C. Gandolfi, R. Soncini-Sessa, and D. Agostani (2010), Building a metamodel of an irrigation district distributed-parameter model, Agric. Water Manage., 97(2), 187200.
  • Gupta, H. V., S. Sorooshian, and P. O. Yapo (1998), Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34(4), 751763, doi:10.1029/97WR03495.
  • Hall, J. and M. Anderson (2002), Handling uncertainty in extreme or unrepeatable hydrological processes—The need for an alternative paradigm, Hydrol. Processes, 16, 18671870.
  • Jackson, B. M., H. S. Wheater, N. R. McIntyre, J. Chell, O. J. Francis, Z. Frogbrook, M. Marshall, B. Reynolds, and I. Solloway (2008), The impact of upland land management on flooding: Insights from a multiscale experimental and modelling programme, J. Flood Risk Manage., 1(2), 7180.
  • Loague, K., and J. E. VanderKwaak (2004), Physics-based hydrologic response simulation: Platinum bridge, 1958 Edsel, or useful tool, Hydrol. Processes, 18(15), 29492956.
  • Martina, M. L. V., and E. Todini (2008), Watershed hydrological modelling: Toward physically meaningful processes representation, in Hydrological Modelling and the Water Cycle, edited by S. Sorooshian et al., pp. 229241, Springer, Netherlands.
  • Martina, M. L. V., E. Todini, and Z. Liu (2011), Preserving the dominant physical processes in a lumped hydrological model, J. Hydrol., 399(1–2), 121131.
  • McIntyre, N., P. Young, B. Orellana, M. Marshall, B. Reynolds, and H. S. Wheater (2011), Identification of nonlinearity in rainfall-flow response using data-based mechanistic modeling, Water Resour. Res., 47, W03515, doi:10.1029/2010WR009851.
  • Nandakumar, N., and R. G. Mein (1997), Uncertainty in rainfall-runoff model simulations and the implications for predicting the hydrologic effects of land-use change, J. Hydrol., 192(1–4), 211232.
  • NSRI (2011), LandIS Digital Soil Datasets, edited by C. Keay, Cranfield, U. K. [Available at http://www.landis.org.uk/data/index.cfm.]
  • O'Connell, P. E., et al. (2004), Review of impacts of rural land use and management on flood generation, R&D Tech. Rep. FD2114/TR, 152 pp., Department for Food, Environment and Rural Affairs, London.
  • O'Connell, P. E., J. Ewen, G. O'Donnell, and P. Quinn (2007), Is there a link between agricultural land-use management and flooding?, Hydrol. Earth Syst. Sci., 11(1), 96107.
  • O'Donnell, G., J. Ewen, and P. E. O'Connell (2011), Sensitivity maps for impacts of land management on an extreme flood in the Hodder catchment, UK, Phys. Chem. Earth, 36(13), 630637.
  • O'Hagan, A., J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith (1999), Uncertainty analysis and other inference tools for complex computer codes, in Bayesian Statistics 6, edited by J. M. Bernardo et al., Oxford Univ. Press, Oxford, U. K.
  • Orellana, B., I. Pechlivanidis, N. McIntyre, H. Wheater, and T. Wagener (2008), A toolbox for the identification of Parsimonious Semi-Distributed Rainfall-Runoff Models: Application to the Upper Lee Catchment, paper presented at International Congress on Environmental Modelling and Software, Barcelona, Spain.
  • Piñeros Garcet, J. D., A. Ordoñez, J. Roosen, and M. Vanclooster (2006), Metamodelling: Theory, concepts and application to nitrate leaching modelling, Ecol. Modell., 193(3–4), 629644.
  • Ratto, M., A. Castelletti, and A. Pagano (2012), Emulation techniques for the reduction and sensitivity analysis of complex environmental models, Environ. Modell. Software, 34, 14.
  • Razavi, S., B. A. Tolson, and D. H. Burn (2012a), Review of surrogate modeling in water resources, Water Resour. Res., 48, W07401, doi:10.1029/2011WR011527.
  • Razavi, S., B. A. Tolson, and D. H. Burn (2012b), Numerical assessment of metamodelling strategies in computationally intensive optimization, Environ. Modell. Software, 34, 6786.
  • Richards, L. A. (1931), Capillary conduction of liquids through porous mediums, Physics, 1(5), 318.
  • Rutter, A. J., A. J. Morton, and P. C. Robins (1975), A predictive model of rainfall interception in forests. II. Generalization of the model and comparison with observations in some Coniferous and hardwood stands, J. Appl. Ecol., 12(1), 367380.
  • Singh, V. P. (1996), Kinematic Wave Modeling in Water Resources. Surface-Water Hydrology, 1399 pp., John Wiley, Chichester, U. K.
  • Thompson, D. (2007), National soil resources institute: Information paper—The national soil map and soil classification, Report, 9 pp., Natl. Soil Resour. Inst., Cranfield Univ., Cranfield, U. K.
  • Viana, F., R. Haftka, and V. Steffen (2009), Multiple surrogates: How cross-validation errors can help us to obtain the best predictor, Struct. Multidisciplinary Optim., 39(4), 439457.
  • Wagener, T., M. J. Lees, and H. S. Wheater (2001), Rainfall-Runoff Modelling Toolbox User Manual, p. 61, Imp. Coll. London, London.
  • Wagener, T., M. J. Lees, and H. S. Wheater (2002), A toolkit for the development and application of parsimonious hydrological models, in Mathematical Models of Small Watershed Hydrology, vol. 1, edited by V. P. Singh and D. K. Frevert, pp. 87–136, Water Resources Publishers, Highlands Reach, Colo.
  • Wagener T., H. S. Wheater, and H. V. Gupta, (2004), Rainfall-Runoff Modelling in Gauged and Ungauged Catchments, 306 pp., Imperial College Press, London.
  • Wheater, H. S., B. Reynolds, N. McIntyre, M. Marshall, B. Jackson, Z. Frogbrook, I. Solloway, O. J. Francis, and J. Chell (2008), Impacts of upland land management on flood risk: Multi-scale modelling methodology and results from the Pontbren experiment, FRMRC Res. Rep. UR 16, 163 pp., Imp. Coll. & CEH Bangor, London, U. K.
  • Wheater, H. S., C. Ballard, N. Bulygina, N. McIntyre, and B. M. Jackson (2012), Modelling environmental change: Quantification of impacts of land use and land management change on UK flood risk, in System Identification, Environmental Modelling, and Control System Design, edited by L. Wang and H. Garnier, pp. 449481, Springer, London, doi:10.1007/978-0-85729-974-1_22.
  • Wigmosta, M., and R. Prasad (2006), Upscaling and downscaling—Dynamic models, in Encyclopedia of Hydrological Sciences, edited by M. G. Anderson, p. 12, John Wiley, Hoboken, N. J.
  • Woolhiser, D. A. (1996), Search for physically based runoff model—A hydrologic El Dorado, J. Hydraul. Eng., 122(3), 122129.
  • Young, P. C. (2001), Data-based mechanistic modelling and validation of rainfall-flow processes, in Model Validation: Perspectives in Hydrological Science, edited by M. G. Anderson and P. D. Bates, pp. 117161, John Wiley, Chichester, U. K.
  • Young, P. C. (2013), Hypothetico-inductive data-based mechanistic modeling of hydrological systems, Water Resour. Res., 49, 915935, doi:10.1002/wrcr.20068.
  • Young, P. C., and M. Ratto (2009), A unified approach to environmental systems modeling, Stochastic Environ. Res. Risk Assess., 23(7), 10371057.
  • Young P. C., and M. Ratto, (2011), Statistical emulation of large linear dynamic models, Technometrics, 53(1), 2943.