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Articles

Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models

Approche par modélisation hybride de l’hydrologie des Prairies : fusion de modèles hydrologiques dirigés par les données et basés sur les processus

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Pages 1473-1489 | Received 12 Jul 2013, Accepted 13 Feb 2014, Published online: 22 Jun 2015

REFERENCES

  • Abbaspour, K.C., et al., 2007. Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. Journal of Hydrology, 333 (2–4), 413–430. doi:10.1016/j.jhydrol.2006.09.014
  • Abrahart, R.J., et al., 2012. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Progress in Physical Geography, 36 (4), 480–513. doi:10.1177/0309133312444943
  • Altunkaynak, A., 2007. Forecasting surface water level fluctuations of lake van by artificial neural networks. Water Resources Management, 21 (2), 399–408. doi:10.1007/s11269-006-9022-6
  • Anderson, E.A., 1976. A point energy and mass balance of a snow cover. NOAA Technical Report NWS-19, US Department of Commerce, Washington, DC.
  • Arnold, J.G., Allen, P.M., and Bernhardt, G., 1993. A comprehensive surface-groundwater flow model. Journal of Hydrology, 142, 47–69. doi:10.1016/0022-1694(93)90004-S
  • Arnold, J.G., et al., 1998. Large area hydrologic modelling and assessment part I: model development. Journal of the American Water Resources Association, 34 (1), 73–89. doi:10.1111/j.1752-1688.1998.tb05961.x
  • Chen, J. and Adams, B.J., 2006. Integration of artificial neural networks with conceptual models in rainfall-runoff modelling. Journal of Hydrology, 318 (1–4), 232–249. doi:10.1016/j.jhydrol.2005.06.017
  • Chua, L.H.C. and Wong, T.S.W., 2010. Improving event-based rainfall–runoff modelling using a combined artificial neural network–kinematic wave approach. Journal of Hydrology, 390 (1–2), 9–107.
  • Corzo, G.A., et al., 2009. Combining semi-distributed process-based and data-driven models in flow simulation: a case study of the Meuse river basin. Hydrology and Earth System Sciences, 13, 1619–1634. doi:10.5194/hess-13-1619-2009
  • Coulibaly, P., 2010. Reservoir computing approach to Great Lakes water level forecasting. Journal of Hydrology, 381, 76–88. doi:10.1016/j.jhydrol.2009.11.027
  • Cunge, J.A., 1969. On the subject of a flood propagation computation method (Musklngum method). Journal of Hydraulic Research, 7 (2), 205–230. doi:10.1080/00221686909500264
  • Ehsanzadeh, E., et al., 2012. On the behaviour of dynamic contributing areas and flood frequency curves in North American Prairie watersheds. Journal of Hydrology, 414–415, 364–373. doi:10.1016/j.jhydrol.2011.11.007
  • Elshorbagy, A., Simonovic, S.P., and Panu, U.S., 2000. Performance evaluation of artificial neural networks for runoff prediction. Journal of Hydrologic Engineering, 5 (4), 424–427. doi:10.1061/(ASCE)1084-0699(2000)5:4(424)
  • Elshorbagy, A.A., et al., 2010. Experimental investigation of the predictive capabilities of data driven modelling techniques in hydrology—Part 1: concepts and methodology. Hydrology and Earth System Sciences, 14, 1931–1941. doi:10.5194/hess-14-1931-2010
  • Environment Canada, 2009. Canadian climate normals 1971-2000 [online]. Available from: http://www.climate.weatheroffice.ec.gc.ca/climate_normals/index_e.html. [Accessed: 10 December 2011].
  • Fang, X., et al., 2008. Drought impacts on Canadian prairie wetland snow hydrology. Hydrological Processes, 22 (15), 2858–2873. doi:10.1002/hyp.7074
  • Gassman, P.W., et al., 2007. The soil and water assessment tool: historical development, applications, and future research directions. Transactions of the ASABE, 50 (4), 1211–1250. doi:10.13031/2013.23637
  • Gray, D.M. and Landine, P.G., 1988. An energy-budget snowmelt model for the Canadian Prairies. Canadian Journal of Earth Sciences, 25, 1292–1303. doi:10.1139/e88-124
  • Green, W.H. and Ampt, G.A., 1911. Studies on soil physics, 1. The flow of air and water through soils. Journal of Agricultural Sciences, 4, 11–24.
  • Guez, A., Protopopsecu, V., and Barhen, J., 1988. On the stability, storage capacity, and design of nonlinear continuous neural networks. IEEE Transactions on Systems, Man, and Cybernetics, 18 (1), 80–87. doi:10.1109/21.87056
  • Hargreaves, G.L., Hargreaves, G.H., and Riley, J.P., 1985. Agricultural benefits for Senegal River Basin. Journal of Irrigation and Drainage Engineering, 111 (2), 113–124. doi:10.1061/(ASCE)0733-9437(1985)111:2(113)
  • Hornik, K., Stinchcombe, M., and White, H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2 (5), 359–366. doi:10.1016/0893-6080(89)90020-8
  • Izadifar, Z. and Elshorbagy, A., 2010. Prediction of hourly actual evapotranspiration using neural networks, genetic programming, and statistical models. Hydrological Processes, 24, 3413–3425. doi:10.1002/hyp.7771
  • Jain, A. and Srinivasulu, S., 2006. Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques. Journal of Hydrology, 317 (3–4), 291–306. doi:10.1016/j.jhydrol.2005.05.022
  • Kişi, Ö., 2006. Evapotranspiration estimation using feed-forward neural networks. Nordic Hydrology, 37 (3), 247–260. doi:10.2166/nh.2006.010
  • Lemmen, D., et al., 2008. From impacts to adaptation: Canada in a changing climate 2007. Ottawa: Natural Resources Canada.
  • Levenberg, K., 1944. A method for the solution of certain problems in least squares. The Quarterly of Applied Mathematics, 2, 164–168.
  • Lévesque, É., et al., 2008. Evaluation of streamflow simulation by SWAT model for two small watersheds under snowmelt and rainfall. Hydrological Sciences Journal, 53 (5), 961–976. doi:10.1623/hysj.53.5.961
  • Liu, Y.B., Yang, W., and Wang, X., 2008. Development of a SWAT extension module to simulate riparian wetland hydrologic processes at a watershed scale. Hydrological Processes, 22 (16), 2901–2915. doi:10.1002/hyp.6874
  • Mackay, D.J.C., 1992. A practical Bayesian framework for backpropagation networks. Neural Computation, 4, 448–472. doi:10.1162/neco.1992.4.3.448
  • Maier, H.R. and Dandy, G.C., 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling & Software, 15 (1), 101–124. doi:10.1016/S1364-8152(99)00007-9
  • Marquardt, D., 1963. An algorithm for least squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11 (2), 431–441. doi:10.1137/0111030
  • Monteith, J.L., 1965. Evaporation and the environment. In the state and movement of water in living organisms. 19th Symposia of the Society for Experimental Biology. Cambridge University Press, London, UK, p. 205–234.
  • Morris, M.D., 1991. Factorial sampling plans for preliminary computational experiments. Technometrics, 33, 161–174. do i:10.1080/00401706.1991.10484804
  • Nazemi, A.-R., Hosseini, S.M., and Akbarzadeh-T, M.-R., 2006. Soft computing-based nonlinear fusion algorithms for describing non-Darcy flow in porous media. Journal of Hydraulic Research, 44 (2), 269–282. doi:10.1080/00221686.2006.9521681
  • Nazemi, A.R., et al., 2008. On the quality of multi-objective calibration results in conceptual rainfall-runoff models. In Proceedings of the European Conference on Flood Risk Management: Research into Practice, FloodRisk 2008, 30 September –2 October, Oxford, UK.
  • Neitsch, S.L., et al., 2011. Soil and water assessment tool (SWAT) user’s manual: version 2009. Temple, TX: U.S. Department of Agriculture, Agricultural Research Service, Grassland, Soil, and Water Research Laboratory.
  • Panagopoulos, I., Mimikou, M., and Kapetanaki, M., 2007. Estimation of nitrogen and phosphorus losses to surface water and groundwater through the implementation of the SWAT model for Norwegian soils. Journal Soils Sediments, 7 (4), 223–231. doi:10.1065/jss2007.04.219
  • Phillips, R.W., Spence, C., and Pomeroy, J.W., 2011. Connectivity and runoff dynamics in heterogeneous basins. Hydrological Processes, 25, 3061–3075.
  • Prairie Farm Rehabilitation Administration, 2008. Prairie farm rehabilitation administration (PFRA) watershed project–- areas of non-contributing drainage. Canada: Agriculture and Agri-Food Canada.
  • Priestley, C.H.B. and Taylor, R.J., 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review, 100, 81–92. doi:10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2
  • Pryke, A., Mostaghim, S., and Nazemi, A., 2007. Heatmap visualization of population based multi objective algorithms. In: S. Obayashi, et al. eds. Evolutionary multi-criterion optimization. Berlin: Springer, 361–375. doi:10.1007/978-3-540-70928-2_29
  • Ritchie, J.T., 1972. A model for predicting evaporation from a row crop with incomplete cover. Water Resources Research, 8, 1204–1213. doi:10.1029/WR008i005p01204
  • Saskatchewan Watershed Authority, 2005. Background report: Assiniboine River watershed. Regina: Assiniboine Watershed. Technical committee, 123.
  • Shaw, D.A., 2010. The influence of contributing area on the hydrology of the prairie pothole region of North America. Thesis (PhD). Department of Geography, University of Saskatchewan, Saskatoon, Canada.
  • Shields, J.A., et al., 1991. Soil Landscapes of Canada—Procedures Manual and User’s Handbook—1991. LRRC Contribution Number 88-29, Land Resource Research Centre, Research Branch, Agriculture Canada, Ottawa. 74.
  • Shook, K.R., et al., 2011. Memory effects of depressional storage in Northern Prairie hydrology. Hydrological Processes, 25 (25), 3890–3898. doi:10.1002/hyp.8381
  • Shrestha, R.R., Dibike, Y.B., and Prowse, T.D., 2012. Modeling climate change impacts on hydrology and nutrient loading in the Upper Assiniboine Catchment. JAWRA Journal of the American Water Resources Association, 48 (1), 74–89. doi:10.1111/j.1752-1688.2011.00592.x
  • Solomatine, D.P. and Xue, Y., 2004. M5 model trees and neural networks: application to flood forecasting in the upper reach of the Huai River in China. Journal of Hydrologic Engineering, 9 (6), 491–501. doi:10.1061/(ASCE)1084-0699(2004)9:6(491)
  • Sophocleous, M.A., et al., 1999. Integrated numerical modelling for basin-wide water management: the case of the Rattlesnake Creek basin in south-central Kansas. Journal of Hydrology, 214, 179–196. doi:10.1016/S0022-1694(98)00289-3
  • Spence, C., 2007. On the relation between dynamic storage and runoff: a discussion on thresholds, efficiency, and function. Water Resources Research, 43 (12), 1944–7973. doi:10.1029/2006WR005645
  • Spence, C., 2010. A paradigm shift in hydrology: storage thresholds across scales influence catchment runoff generation. Geography Compass, 4, 819–833. doi:10.1111/j.1749-8198.2010.00341.x
  • Spence, C., et al., 2010. Storage dynamics and streamflow in a catchment with a variable contributing area. Hydrological Processes, 24, 2209–2221. doi:10.1002/hyp.7492
  • Srivastava, P., McNair, J.N., and Johnson, T.E., 2006. Comparison of process-based and artificial neural network approaches for streamflow modelling in an agricultural watershed. Journal of the American Water Resources Association (JAWRA), 42 (3), 545–563. doi:10.1111/j.1752-1688.2006.tb04475.x
  • Talebizadeh, M., et al., 2010. Uncertainty analysis in sediment load modelling using ANN and SWAT model. Water Resources Management, 24, 1747–1761. doi:10.1007/s11269-009-9522-2
  • USDA Soil Conservation Service, 1972. National engineering handbook section 4 hydrology. Chapters 4–10. Washington, DC: U.S. Government Printing Office.
  • Van Der Kamp, G., Hayashi, M., and Gallen, D., 2003. Comparing the hydrology of grassed and cultivated catchments in the semi-arid Canadian prairies. Hydrological Processes, 17, 559–575. doi:10.1002/hyp.1157
  • van Griensven, A. and Meixner, T., 2007. A global and efficient multi-objective autocalibration and uncertainty estimation method for water quality catchment models. Journal of Hydroinformatics, 9 (4), 277–291. doi:10.2166/hydro.2007.104
  • Wagener, T., Howard, S.W., and Hoshin, V.G., 2004. Rainfall-runoff modelling in gauged and ungauged catchments. England: Imperial College Print.
  • Wen, L., et al., 2011. Reconstructing sixty year (1950-2009) daily soil moisture over the Canadian Prairies using the variable infiltration capacity model. Canadian Water Resources Journal, 36 (1), 83–102. doi:10.4296/cwrj3601083
  • Williams, J.R., 1969. Flood routing with variable travel time or variable storage coefficients. Transactions of the ASAE, 12 (1), 100–103. doi:10.13031/2013.38772
  • Zhang, G.P., 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175. doi:10.1016/S0925-2312(01)00702-0
  • Zhang, X., Srinivasan, R., and Hao, F., 2007. Predicting hydrologic response to climate change in the Luohe river basin using the SWAT model. American Social Agricultural Biologic Engineering, 50 (3), 901–910.

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