A two-stage sensitivity analysis for parameter identification and calibration of a physically-based distributed model in a river basin

References

• Abebe, N., Ogden, F., and Pradhan, N., 2010. Sensitivity and uncertainty analysis of the conceptual HBV rainfall–runoff model: implications for parameter estimation. Journal of Hydrology, 389 (3–4), 301–310. doi:10.1016/j.jhydrol.2010.06.007
   Web of Science ®Google Scholar
• Anderton, S., Latron, J., and Gallart, F., 2002. Sensitivity analysis and multi-response, multi-criteria evaluation of a physically based distributed model. Hydrological Processes, 16 (2), 333–353. doi:10.1002/hyp.336
   Web of Science ®Google Scholar
• Azim, F., Sattar, A., Habib-ur-Rehman, and Kanwal, A., 2016. Impact of climate change on sediment yield for Naran watershed. International Journal of Sediment Research, 31 (3), 212–219. Elsevier. doi:10.1016/j.ijsrc.2015.08.002
   Web of Science ®Google Scholar
• Babar, S. and Ramesh, H., 2015. Streamflow response to land use – land cover change over the Nethravathi river basin, India. Journal of Hydrologic Engineering, 20 (10), 05015002. doi:10.1061/(ASCE)HE.1943-5584.0001177
   Web of Science ®Google Scholar
• Bahremand, A. and De Smedt, F., 2008. Distributed hydrological modeling and sensitivity analysis in Torysa watershed, Slovakia. Water Resources Management, 22 (3), 393–408. doi:10.1007/s11269-007-9168-x
   Web of Science ®Google Scholar
• Bathurst, J., et al., 2006. Application of the SHETRAN basin-scale, landslide sediment yield model to the Llobregat basin, Spanish Pyrenees. Hydrological Processes, 20, 3119–3138. doi:10.1002/hyp.6151
   Web of Science ®Google Scholar
• Bathurst, J., et al., 2004. Validation of catchment models for predicting land-use and climate change impacts. 3. Blind validation for internal and outlet responses. Journal of Hydrology, 287 (1–4), 74–94. doi:10.1016/j.jhydrol.2003.09.021
   Web of Science ®Google Scholar
• Benedetti, L., Baets, B. De, Nopens, I., and Vanrolleghem, P.A., 2010. Multi-criteria analysis of wastewater treatment plant design and control scenarios under uncertainty. Environmental Modelling and Software, 25 (5), 616–621. Elsevier Ltd. doi:10.1016/j.envsoft.2009.06.003
   Web of Science ®Google Scholar
• Benke, K.K., Lowell, K.E., and Hamilton, A.J., 2008. Parameter uncertainty, sensitivity analysis and prediction error in a water-balance hydrological model. Mathematical and Computer Modelling, 47, 1134–1149. doi:10.1016/j.mcm.2007.05.017
   Web of Science ®Google Scholar
• Beven, K. and Binley, A., 1992. The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes, 6 (3), 279–298. doi:10.1002/(ISSN)1099-1085
   Web of Science ®Google Scholar
• Beven, K.J., Warren, R.O.S.S., and Zaoui, J., 1980. SHE: towards a methodology for physically-based distributed forecasting in hydrology. In: Hydrological forecasting (Proceedings of the Oxford Symposium, April 1980). Wallingford, UK: International Association of Hydrological Sciences, IAHS Publ. no. 129, 133–138. Available from: http://hydrologie.org/redbooks/a129/iahs_129_0133.pdf [Accessed 26 March 2019].
   Google Scholar
• Birkinshaw, S.J., 2010. Technical note : automatic river network generation for a physically-based river catchment model. Hydrology and Earth System Sciences, (14), 1767–1771. doi:10.5194/hess-14-1767-2010
   Web of Science ®Google Scholar
• Birkinshaw, S.J., et al., 2011. The effect of forest cover on peak flow and sediment discharge-an integrated field and modelling study in central-southern Chile. Hydrological Processes, 25 (8), 1284–1297. doi:10.1002/hyp.7900
   Web of Science ®Google Scholar
• Birkinshaw, S.J., et al., 2016. Climate change impacts on Yangtze river discharge at the three gorges dam. Hydrology and Earth System Sciences, 21 (4), 1–33. doi:10.5194/hess-2016-231
   Web of Science ®Google Scholar
• Blaney, H.F. and Criddle, W.D., 1950. Determining Water Requirements in Irrigated Areas from Climatological and Irrigation Data USDA-SCS-TP-96 Report 50. Washington, DC.
   Google Scholar
• Bonell, M., et al., 2010. The impact of forest use and reforestation on soil hydraulic conductivity in the Western Ghats of India : implications for surface and sub-surface hydrology. Journal of Hydrology, 391 (1–2), 47–62. Elsevier B.V. doi:10.1016/j.jhydrol.2010.07.004
   Web of Science ®Google Scholar
• Bovolo, C.I., et al., 2009. A distributed framework for multi-risk assessment of natural hazards used to model the effects of forest fire on hydrology and sediment yield. Computers and Geosciences, 35 (5), 924–945. doi:10.1016/j.cageo.2007.10.010
   Web of Science ®Google Scholar
• Brazier, R.E., et al., 2000. Equifinality and uncertainty in physically based soil erosion models: application of the GLUE methodology to WEPP - the water erosion prediction project - for sites in the UK and USA. Earth Surface Processes and Landforms, 25, 825–845. doi:10.1002/(ISSN)1096-9837
   Web of Science ®Google Scholar
• Campolongo, F., Cariboni, J., and Saltelli, A., 2007. An effective screening design for sensitivity analysis of large models. Environmental Modelling and Software, 22 (10), 1509–1518. doi:10.1016/j.envsoft.2006.10.004
   Web of Science ®Google Scholar
• Carpenter, T.M., Georgakakos, K.P., and Sperfslagea, J.A., 2001. On the parametric and NEXRAD-radar sensitivities of a distributed hydrologic model suitable for operational use. Journal of Hydrology, 253 (253), 169–193. doi:10.1016/S0022-1694(01)00476-0
   Web of Science ®Google Scholar
• Central Water Commission, 2006. Integrated hydrological data book. India: New Delhi.
   Google Scholar
• Chow, V.T., 1959. Open-channel hydraulics. New York: McGraw-Hill.
   Google Scholar
• Cibin, R., Sudheer, K.P., and Chaubey, I., 2010. Sensitivity and identifiability of stream flow generation parameters of the SWAT model sensitivity and identifiability of stream flow generation. Hydrological Processes, 24 (9), 1133–1148. doi:10.1002/hyp.7568
   Web of Science ®Google Scholar
• Demaria, E.M., Nijssen, B., and Wagener, T., 2007. Monte Carlo sensitivity analysis of land surface parameters using the variable infiltration capacity model. Journal of Geophysical Research, 112, 1–15. doi:10.1029/2006JD007534
   Web of Science ®Google Scholar
• Đukić, V. and Radić, Z., 2016. Sensitivity analysis of a physically based distributed model. Water Resources Management, 30. doi:10.1007/s11269-016-1243-8
   Web of Science ®Google Scholar
• Dunne, T. and Black, R.D., 1970. Partial area contributions to storm runoff in a small New England watershed. Water Resources Research, 6 (5), 1296–1311. doi:10.1029/WR006i005p01296
   Web of Science ®Google Scholar
• Elliott, A.H., et al., 2012. Sediment modelling with fine temporal and spatial resolution for a hilly catchment. Hydrological Processes, 26 (24), 3645–3660. doi:10.1002/hyp.8445
   Web of Science ®Google Scholar
• Ewen, J. and Parkin, G., 1996. Validation of catchment models for predicting land-use and climate change impacts. 1. Method. Journal of Hydrology, 175 (1–4), 583–594. doi:10.1016/S0022-1694(96)80026-6
   Web of Science ®Google Scholar
• Ewen, J., Parkin, G., and Connell, P.E., 2000. SHETRAN: distributed river basin flow modeling system. Journal of Hydrologic Engineering, 5 (3), 250–258. doi:10.1061/(ASCE)1084-0699(2000)5:3(250)
   Web of Science ®Google Scholar
• FAO/IIASA/ISRIC/ISSCAS/JRC, 2012. Harmonized world soil database (version 1.2).FAO. Available from http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/en/
   Google Scholar
• Francos, A., et al., 2003. Sensitivity analysis of distributed environmental simulation models : understanding the model behaviour in hydrological studies at the catchment scale. Reliability Engineering and System Safety, 79, 205–218. doi:10.1016/S0951-8320(02)00231-4
   Web of Science ®Google Scholar
• Gan, Y., et al., 2014. Environmental modelling and software A comprehensive evaluation of various sensitivity analysis methods : A case study with a hydrological model. Environmental Modelling and Software, 51, 269–285. doi:10.1016/j.envsoft.2013.09.031
   Web of Science ®Google Scholar
• Guerreiro, S.B., et al., 2017. Dry getting drier − the future of transnational river basins in Iberia. Journal of Hydrology, Regional Studies, 12, 238–252. Elsevier. doi:10.1016/j.ejrh.2017.05.009
   Google Scholar
• Gupta, H.V., Sorooshian, S., and Yapo, P.O., 1998. Toward improved calibration of hydrologic models : multiple and noncommensurable measures of information. Water Resources Research, 34 (4), 751–763. doi:10.1029/97WR03495
   Web of Science ®Google Scholar
• Herman, J.D., et al., 2013. Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models. Hydrology and Earth System Sciences, 17 (7), 2893–2903. doi:10.5194/hess-17-2893-2013
   Web of Science ®Google Scholar
• Hewlett, J.D. and Troendle, C., 1975. Non point and diffused water sources: a variable source area problem. In: Symposium on watershed management. Logan, UT: Utah State University, ASCE, 21–46.
   Google Scholar
• Holvoet, K., et al., 2005. Sensitivity analysis for hydrology and pesticide supply towards the river in SWAT. Physics and Chemistry of the Earth, Parts A/B/C, 30 (8–10), 518–526. doi:10.1016/j.pce.2005.07.006
   Web of Science ®Google Scholar
• Hornberger, G. and Spear, R., 1981. An approach to the preliminary analysis of environmental systems. Journal of Environmental Management, 12 (1), 7–18.
   Web of Science ®Google Scholar
• Kelleher, C., Wagener, T., and McGlynn, B., 2015. Model based analysis of the influence of catchment properties on hydrologic partitioning across five mountain headwater subcatchments. Water Resources Research, 51, 4109–4136. doi:10.1002/2014WR016147
   PubMed Web of Science ®Google Scholar
• Lee, M.T. and Delleur, J., 1976. A variable source area model of the rainfall‐runoff process based on the watershed stream network. Water Resources Research, 12 (5), 1029–1036.
   Web of Science ®Google Scholar
• Lenhart, T., et al., 2002. Comparison of two different approaches of sensitivity analysis. Physics and Chemistry of the Earth, Parts A/B/C, 27, 645–654.
   Web of Science ®Google Scholar
• Madusudhanan, C., Eldho, T.I., and Pai, D., 2015 .Hydrological impacts of climate change in a humid tropical River basin. In: Proc. 36th IAHR World Congr. Hague, Netherlands.
   Google Scholar
• Matthews, C.J., et al., 2007. Analysing the sensitivity behaviour of two hydrology models. Environmental Modelling and Assessment, 12, 27–41. doi:10.1007/s10666-006-9049-3
   Web of Science ®Google Scholar
• Moriasi, D.N., et al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the American Society of Agricultural and Biological Engineers, 50 (3), 885–900. doi:10.13031/2013.23153
   Web of Science ®Google Scholar
• Morris, M.D., 1991. Factorial sampling plans for preliminary computational experiments. Technometrics, 33 (2), 161–174. doi:10.1080/00401706.1991.10484804
   Web of Science ®Google Scholar
• Mourato, S., Moreira, M., and Corte-Real, J., 2015. Water resources impact assessment under climate change scenarios in Mediterranean watersheds. Water Resources Management, 29 (7), 2377–2391. doi:10.1007/s11269-015-0947-5
   Web of Science ®Google Scholar
• Muleta, M.K., 2012. Improving model performance using season-based evaluation. Journal of Hydrologic Engineering, 17, 191–200.
   Web of Science ®Google Scholar
• Muleta, M.K. and Nicklow, J.W., 2005. Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model. Journal of Hydrology, 306, 127–145. doi:10.1016/j.jhydrol.2004.09.005
   Web of Science ®Google Scholar
• Nash, J.E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10 (3), 282–290.
   Google Scholar
• Norton, J., 2015. An introduction to sensitivity assessment of simulation models. Environmental Modelling and Software, 69, 166–174. doi:10.1016/j.envsoft.2015.03.020
   Web of Science ®Google Scholar
• Nossent, J., Elsen, P., and Bauwens, W., 2011. Sobol’ sensitivity analysis of a complex environmental model. Environmental Modelling and Software, 26 (12), 1515–1525. doi:10.1016/j.envsoft.2011.08.010
   Web of Science ®Google Scholar
• Op de Hipt, F., et al., 2017. Applying SHETRAN in a TropicalWest African catchment (Dano, Burkina Faso)—calibration, validation, uncertainty assessment. Water, 9 (2), 101. doi:10.3390/w9020101
   Web of Science ®Google Scholar
• Osuch, M., Romanowicz, R.J., and Booij, M.J., 2015. The influence of parametric uncertainty on the relationships between HBV model parameters and climatic characteristics HBV model parameters and climatic characteristics. Hydrological Sciences Journal, 60 (7–8), 1299–1316. doi:10.1080/02626667.2014.967694
   Web of Science ®Google Scholar
• Pan, S., et al., 2017. A two-step sensitivity analysis for hydrological signatures in Jinhua River Basin, East China. Hydrological Sciences Journal, 62 (15), 2511–2530. doi:10.1080/02626667.2017.1388917
   Web of Science ®Google Scholar
• Pujol, G., Iooss, B., and Janon, A., 2015. Sensitivity analysis package, R package version 1.11.1. Available from: http://cran.r-project.org/web/packages/sensitivity/index.html [Accessed 12 June 2015].
   Google Scholar
• Putty, M.R.Y., 1994. The mechanisms of streamflow generation in the Sahyadri Ranges (Western Ghats) of South India. Thesis (PhD). Indian Institute of Science, Bangalore.
   Google Scholar
• Putty, M.R.Y. and Prasad, R., 1994. Streamflow generation in the Western Ghats. In: Procedings Sixth National Sympsium Hydrology. Shillong, India, 189–194.
   Google Scholar
• Putty, M.R.Y. and Prasad, R., 2000a. Understanding runoff processes using a watershed model — a case study in the Western Ghats in South India. Journal of Hydrology, 228 (3–4), 215–227.
   Web of Science ®Google Scholar
• Putty, M.R.Y. and Prasad, R., 2000b. Runoff processes in headwater catchments — an experimental study in Western Ghats, South India. Journal of Hydrology, 235 (1–2), 63–71.
   Web of Science ®Google Scholar
• Rakovec, O., et al., 2014. Distributed evaluation of local sensitivity analysis (DELSA), with application to hydrologic models. Water Resources Research, 50 (1), 409–426. doi:10.1002/2013WR014063
   Web of Science ®Google Scholar
• Ratto, M., et al., 2007. Uncertainty, sensitivity analysis and the role of data based mechanistic modeling in hydrology. Hydrology and Earth System Sciences, 11, 1249–1266.
   Web of Science ®Google Scholar
• Refsgaard, J.C., 1997. Parameterisation, calibration and validation of distributed hydrological models. Journal of Hydrology, 198, 69–97.
   Web of Science ®Google Scholar
• Ruano, M.V., et al., 2011. Application of the Morris method for screening the in fl uential parameters of fuzzy controllers applied to wastewater treatment plants. Water Sience Technology, 63 (10), 2199–2206. doi:10.2166/wst.2011.442
   PubMed Web of Science ®Google Scholar
• Saltelli, A., Tarantola, S., and Campolongo, F., 2000. Sensitivity analysis as an ingredient of modeling. Statistical Science, 15 (4), 377–395.
   Web of Science ®Google Scholar
• Saltelli, A., et al., 2004. Sensitivity analysis in practice: a guide to assessing scientific models. Chichester, England: Wiley.
   Google Scholar
• Sarrazin, F., Pianosi, F., and Wagener, T., 2016. Global sensitivity analysis of environmental models: convergence and validation. Environmental Modelling and Software, 79, 135–152. doi:10.1016/j.envsoft.2016.02.005
   Web of Science ®Google Scholar
• Shetkar, R. and Mahesha, A., 2011. Tropical, seasonal river basin development : hydrogeological analysis. Journal of Hydrologic Engineering, 16 (3), 280–291. doi:10.1061/(ASCE)HE.1943-5584.0000328
   Web of Science ®Google Scholar
• Sieber, A. and Uhlenbrook, S., 2005. Sensitivity analyses of a distributed catchment model to verify the model structure. Journal of Hydrology, 310 (1–4), 216–235. doi:10.1016/j.jhydrol.2005.01.004
   Web of Science ®Google Scholar
• Sinha, R.K. and Eldho, T.I., 2018. Effects of historical and projected land use/cover change on runoff and sediment yield in the Netravati river basin, Western Ghats, India. Environmental Earth Sciences, 77 (3), 111. Springer Berlin Heidelberg. doi:10.1007/s12665-018-7317-6
   Web of Science ®Google Scholar
• Sobol‘, M., 1993. Sensitivity estimates for nonlinear mathematical models. Mathematical Modelling in Civil Engineering (MMCE), 1 (4), 407–414.
   Google Scholar
• Spruill, C.A., Workman, S.R., and Taraba, J.L., 2000. Simulation of daily and monthly stream discharge from small watersheds using the SWAT model. Transactions of the ASAE, 43 (6), 1431–1439.
   Google Scholar
• Sun, X.Y., et al., 2012. Three complementary methods for sensitivity analysis of a water quality model. Environmental Modelling and Software, 37, 19–29. doi:10.1016/j.envsoft.2012.04.010
   Web of Science ®Google Scholar
• Tang, Y., et al., 2007. Advancing the identification and evaluation of distributed rainfall-runoff models using global sensitivity analysis. Water Resources Research, 43, 1–14. doi:10.1029/2006WR005813
   PubMed Web of Science ®Google Scholar
• Tarantola, S., et al., 2002. Can global sensitivity analysis steer the implementation of models for environmental assessments and decision-making? Stochastic Environmental Research and Risk Assessment, 16 (1), 63–76.
   Web of Science ®Google Scholar
• Tong, C., 2010. Self-validated variance-based methods for sensitivity analysis of model outputs. Reliability Engineering and System Safety, 95 (3), 301–309. Elsevier. doi:10.1016/j.ress.2009.10.003
   Web of Science ®Google Scholar
• van Griensven, A., et al., 2006. A global sensitivity analysis tool for the parameters of multi-variable catchment models. Journal of Hydrology, 324 (324), 10–23. doi:10.1016/j.jhydrol.2005.09.008
   Web of Science ®Google Scholar
• Van Werkhoven, K., et al., 2008. Characterization of watershed model behavior across a hydroclimatic gradient. Water Resources Research, 44, 1–16. doi:10.1029/2007WR006271
   Web of Science ®Google Scholar
• Wagener, T., 2003. Evaluation of catchment models. Hydrological Processes, 17, 3375–3378. doi:10.1002/hyp.5158
   Web of Science ®Google Scholar
• Wagener, T. and Kollat, J., 2007. Visual and numerical evaluation of hydrologic and environmental models using the Monte Carlo analysis toolbox (MCAT) numerical and visual evaluation of hydrological and environmental models using the Monte Carlo analysis toolbox. Environmental Modelling and Software, 22, 1021–1033. doi:10.1016/j.envsoft.2006.06.017
   Web of Science ®Google Scholar
• Yang, J., 2011. Convergence and uncertainty analyses in Monte-Carlo based sensitivity analysis. Environmental Modelling and Software, 26 (4), 444–457. doi:10.1016/j.envsoft.2010.10.007
   Web of Science ®Google Scholar
• Yang, J., et al., 2012. Multi-objective sensitivity analysis of a fully distributed hydrologic model WetSpa. Water Resources Management, 26, 109–128. doi:10.1007/s11269-011-9908-9
   Web of Science ®Google Scholar
• Ye, Y., et al., 2014. Parameter identification and calibration of the Xin’anjiang model using the surrogate modeling approach. Frontiers of Earth Science, 8 (2), 264–281. doi:10.1007/s11707-014-0424-0
   Web of Science ®Google Scholar
• Zelelew, M. and Alfredsen, K., 2013. Sensitivity-guided evaluation of the HBV hydrological model parameterization. Journal of Hydroinformatics, 15 (3). doi:10.2166/hydro.2012.011
   Web of Science ®Google Scholar
• Zhang, C., Chu, J., and Fu, G., 2013a. Sobol’s sensitivity analysis for a distributed hydrological model of Yichun River Basin, China. Journal of Hydrology, 480, 58–68. Elsevier B.V. doi:10.1016/j.jhydrol.2012.12.005
   Web of Science ®Google Scholar
• Zhang, R., et al., 2013b. Automatic calibration of the SHETRAN hydrological modelling system using MSCE. Water Resources Management, 27, 4053–4068. doi:10.1007/s11269-013-0395-z
   Web of Science ®Google Scholar