Abstract
Distributed hydrological models account for spatial heterogeneity by discretizing the watershed into unique units based on the watershed characteristics. However, parameter estimation is one of the major tasks in the application of distributed hydrological models. The existing calibration methods for distributed hydrological models do not consider the spatial variability of the parameters across the basin, and, therefore, do not guarantee good simulations on locations other than the calibration outlets. This study proposes a calibration approach which preserves the heterogeneity of the parameters across the basin. The basic simulation units of the distributed models are grouped in this approach based on the land use and soil type, and a random perturbation of the parameters is performed in these groups during calibration. The proposed method is demonstrated through a case study of two watersheds in the USA using soil and water assessment tool (SWAT) model. The results indicate that the calibrated model simulations in the upstream gauged locations (other than that used for calibration) are much better in the proposed approach, in contrast to the currently employed calibration method. Nonetheless, it is also observed that the proposed calibration approach would be more effective in watersheds that have higher spatial heterogeneity.
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Abbaspour KC,: SWAT-CUP Programme Version 2.1.5, http://www.eawag.ch/organisation/abteilungen/siam/software/swat/indexEN, last access: 11 October 2009.
Arabi M, Govindaraju RS, Engel B, Hantush M (2007) Multiobjective sensitivity analysis of sediment and nitrogen processes with a watershed model. Water Resour Res. https://doi.org/10.1029/2006WR005463
Arnold JG, Fohrer N (2005) SWAT2000: current capabilities and research opportunities in applied watershed modeling. Hydrol Process 19(3):563–572. https://doi.org/10.1002/hyp.5611
Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrologic modelling assessment: part I. Model development. J Am Water Resour Assoc 34(1):73–89
Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R, Santhi C, Harmel RD, van Griensven A, Van Liew MW, Kannan N, Jha MK (2012a) Swat: model use, calibration, and validation. Trans ASABE 55(4):1491–1508
Arnold JG, Moriasi DN, Gassman PW, Abbaspour KC, White MJ, Srinivasan R, Santhi C, Harmel RD, van Griensven A, Van Liew MW, Kannan N, Jha MK (2012b) SWAT: model use, calibration, and validation. Trans ASABE 55(4):1494–1508
Arnold JG, Youssef MA, Yen H, White MJ, Sheshukov AY, Sadeghi AM, Moriasi DN, Steiner JL, Amatya DM, Wayne Skaggs R, Haney EB, Jeong J, Arabi M, Gowda PH (2015) Hydrological processes and model representation: impact of soft data on calibration. Trans ASABE 58(6):1637–1660
Arnold JG, Engel BA, Srinivasan R (1993) Continuous-time, grid cell watershed model. Proc of the 18-19 June 1993 Conf. Spokane, Washington, 267–278
Aslam H, Laursen Andrew E (2017) SWAT modeling of hydrology, sediment and nutrients from the grand river Ontario. Water Qual Res J 52(4):243–257
Athira P, Sudheer KP, Cibin R, Chaubey I, (2011) Sensitivity analysis of stream flow generation parameters of SWAT model. Paper No. 1111731, Annual Conference of the ASABE, Louisville, KY
Athira P, Sudheer KP, Cibin R, Chaubey I, (2016) Regionalization of distributed hydrological models: a method to predict the streamflow and to quantify the predictive uncertainty. Stochastic environmental research and risk assessment, 30: 1131. (http://link.springer.com/article/https://doi.org/10.1007/s00477-015-1190-6).
Babar S, Ramesh H (2015) Streamflow response to land use-land cover change over the Nethravathi river basin India. J Hydrol Eng. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001177
Balascio CC, Palmeri DJ, Gao H (1998) Use of a genetic algorithm and multi-objective programming for calibration of a hydrologic model. Trans ASAE 41(3):615–619
Bingner RL, Garbrecht J, Arnold JG, Srinivasan R (1997) Effects of watershed subdivision on simulation runoff and fine sediment yields. Trans ASAE 40(5):1329–1335
Bosch NS (2008) The influence of impoundments on riverine nutrient transport: an evaluation using the soil and water assessment tool. J Hydrol 355:131–147
Cao W, Bowden WB, Davie T, Fenemor A (2006) Multi-variable and multi-site calibration and validation of SWAT in a large mountainous catchment with high spatial variability. Hydrol Proc 20(5):1057–1073
Cibin R, Sudheer KP, Chaubey I (2010) Sensitivity and identifiability of stream flow generation parameters of the SWAT model. Hydrol Proc 24(9):1133–1148
Coffey ME, Workman SR, Taraba JL, Fogle AW (2004) Statistical procedures for evaluating daily and monthly hydrologic model predictions. Trans ASAE 47(1):59
Das T, B´ardossyZeheHe AEY (2008) Comparison of conceptual model performance using different representations of spatial variability. J Hydrol 356:106–118. https://doi.org/10.1016/j.jhydrol.2008.04.008
Datta AP, Bolisetti T, (2015) Second-order autoregressive model-based likelihood functions for calibration and uncertainty analysis of SWAT model. J Hydrol Eng 20 (2).
Eawag. 2009. SWAT-CUP. Dübendorf, Switzerland: Swiss Federal Institute of Aquatic Science and Technology. Available at: www.eawag.ch/organisation/abteilungen/siam/software/ swat/index_EN
Eckhardt K, Arnold JG (2001) Automatic calibration of a distributed catchment model. J Hydrol 251(1–2):103–109
Eckhardt K, Fohrer N, Frede H-G (2005) Automatic model calibration. Hydrol Proc. https://doi.org/10.1002/hyp.5613
Farida D, Javier B, Ahmed S (2012) SWAT application in intensive irrigation systems: model modification, calibration and validation. J Hydrol 470:227–238
Fu ML, Fan T, Ding Z, Salih AQ, Al-Ansari N, Yaseen ZM (2020) Deep learning data-intelligence model based on adjusted forecasting window scale: application in daily streamflow simulation. IEEE ACCESS 8:32632–32651
Gassman PW, Reyes MR, Green CH, Arnold JG (2007) The soil and water assessment tool: historical development, applications, and future research directions. Trans ASABE 50(4):1211–1250
Gassman PW, Sadeghi AM, Srinivasan R (2014) Applications of the SWAT model special section: overview and insights. J Environ Qual 43:1–8
Gupta HV, Wagener T, Liu Y (2008) Reconciling theory with observations: elements of a diagnostic approach to model evaluation. Hydrol Proc 22(18):3802–3813
Hasan MA, Pradhanang SM (2017) Estimation of flow regime for a spatially varied Himalayan watershed using improved multi-site calibration of the soil and water assessment tool (SWAT) model. Environ Earth Sci 76:787. https://doi.org/10.1007/s12665-017-7134-3
Haverkamp S, Srinivasan R, Frede HG, Santhi C (2007) Sub-watershed spatial analysis tool: discretization of a distributed hydrologic model by statistical criteria. J Am Water Resour Assoc. https://doi.org/10.1111/j.1752-1688.2002.tb04377.x
Haw Y, White MJ, Arnold JG, Conor Keitzer S, Johnson M-V, Atwood JD, Daggupati P, Herbert ME, Sowa SP, Ludsin SA, Robertson DM, Srinivasan R, Rewa CA (2016) Western lake Erie basin: soft-data-constrained, NHDPlus resolution watershed modeling and exploration of applicable conservation scenarios. Sci Total Environ 569:1265–1281
Jalel A, Benabdallah S, Chabaâne ZL, Cudennec C (2016) Evaluation of potential evapotranspiration assessment methods for hydrological modelling with SWAT—application in data-scarce rural Tunisia. Agric Water Manag 174:39–51
Jing Y, Peter Reichert KC, Abbaspour JX, Yang H (2008) Comparing uncertainty analysis techniques for a SWAT application to the Chaohe basin in China. J Hydrol 358(1–2):1–23
Khalid K, Ali MF, Abd Rahman NF, Mispan MR, Haron SH, Othman Z, Bachok MF (2016) Sensitivity Analysis in Watershed Model Using SUFI-2 Algorithm. Procedia Eng 162:441–447
Krause P, Boyle DP, Bäse F (2005) Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5:89–97
Krysanova V, Arnold JG (2010) Advances in ecohydrological modelling with SWAT—a review. Hydrol Sci J 53(5):939–947
Krysanova V, Srinivasan R (2015) Assessment of climate and land use change impacts with SWAT. Reg Environ Change 15(3):431–434
Kumar R, Samaniego L, Attinger S (2010) The effects of spatial discretization and model parameterization on the prediction of extreme runoff characteristics. J Hydrol 392(1–2):54–69
Kumar R, Samaniego L, Attinger S (2013) Implications of distributed hydrologic model parameterization on water fluxes at multiple scales and locations. Water Resour Res 49(1):360–379
Manoj J, Gassman PW, Secchi S, Roy Gu, Arnold J (2004) Effect of watershed subdivision on swat flow, sediment, and nutrient predictions. J Am Water Resour Assoc 40(3):811–825
Moriasi DN, Gitau MW, Pai N, Daggupati P (2015) Hydrologic and water quality models: performance measures and evaluation criteria. Trans ASABE 58(6):1763–1785
MiSeon L, Geunae P, MinJi P, JongYoonb P, JiWan L, SeongJoon K (2010) Evaluation of non-point source pollution reduction by applying best management practices using a SWAT model and QuickBird high resolution satellite imagery. J Environ Sci 22(6):826–833
Monireh F, Abbaspour KC, Schulin R, Yang H (2008) Modelling blue and green water resources availability in Iran. Hydrol Proc. https://doi.org/10.1002/hyp.7160
Moriasi DN, Arnold JG, Liew MWV, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50(3):885–900
Moriasi D, Wilson B, Douglas Mankin K, Arnold J, Gowda P (2012) Hydrologic and water quality models: use calibration and validation. Trans ASABE 10(13031/2013):42265
Muleta MK, Nicklow JW, Bekele EG (2007) Sensitivity of a distributed watershed simulation model to spatial scale. J Hydrol Eng ASCE 12(2):163–172
Neitsch SL, Arnold JG, Kiniry JR, Williams JR (2005) Soil and water assessment tool theoretical documentation, version 2005. Temple, tex.: USDA‐ARS Grassland, Soil and Water Research Laboratory. Available at: www.brc.tamus.edu/swat/doc.html. Accessed 1 November 2006
Ng TL, Eheart JW, Cai XM (2010) Comparative calibration of a complex hydrologic model by stochastic methods GLUE and PEST. Trans ASABE 53(6):1773–1786
Ning J, Gao Z, Lu Q (2015) Runoff simulation using a modified SWAT model with spatially continuous HRUs. Environ Earth Sci 74:5895–5905
Peng S, Hou Y, Xie Y, Chen C, Chen Xi, Li Q, Simin Qu, Fang X, Srinivasan R (2013) Application of a SWAT model for hydrological modeling in the Xixian watershed China. J Hydrol Eng 18(11):1522–1529
Rahbeh M, Chanasyk D, Miller J (2011) Two-way calibration-validation of SWAT model for a small prairie watershed with short observed record. Canadian Water Resour J 36(3):247–270
Razavi S, Tolson BA, Matott LS, Thomson NR, MacLean A, Seglenieks FR (2010) Reducing the computational cost of automatic calibration through model pre-emption. Water Resour Res 46:W11523. https://doi.org/10.1029/2009WR008957
Savvidou E, Efstratiadis A, Koussis AD, Koukouvinos A, Skarlatos D (2018) The curve number concept as a driver for delineating hydrological response units. Water 10:194. https://doi.org/10.3390/w10020194
Seibert Jan, Jeffrey McDonnell (2002) On the dialog between experimentalist and modeller in catchment hydrology: use of soft information for multi-criteria model calibration. Water Resour Res 38(11):1241–1252
Setegn Shimelis G, Ragahavan Srinivasan, Melesse AM, Bijan Dargahi (2009) SWAT model application and prediction uncertainty analysis in the Lake Tana Basin Ethiopia. Hydrol Proc. https://doi.org/10.1002/hyp.7457
Shamshirband S, Hashemi S, Salimi H, Samadianfard S, Asadi E, Shadkani S, Kargar K, Mosavi A, Nabipour N, Chau K-W (2020) Predicting standardized streamflow index for hydrological drought using machine learning model. Eng Appl Comput Fluid Mech 14(1):339–350
Shi P, Hou Y, Xie Y, Chen C, Chen X, Li Q, Qu S, Fang X, Srinivasan R (2013) Application of a swat model for hydrological modelling in the Xixian watershed China. J Hydrol Eng. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000578
Singh V, Goyal MK, Chu X (2016) Multicriteria evaluation approach for assessing parametric uncertainty during extreme peak and low flow conditions over snow glaciated and inland catchments. J Hydrol Eng. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001217
Sinha RK, Eldho TI (2018) Effects of historical and projected land use/cover change on runoff and sediment yield in the Netravati river basin, Western Ghats. India Environ Earth Sci 77:111. https://doi.org/10.1007/s12665-018-7317-6
Sobol IM (1993) Sensitivity estimates for nonlinear mathematical models. Math Model Comput Exp 1:404–414
Spruill C, Workman S, Taraba J (2000) Simulation of daily and monthly stream discharge from small watersheds using the SWAT model. Trans ASAE 43:1431–1439
Taormina R, Kwok-Wing C (2015) ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS. Eng Appl Artif Intell 45:429–440
Teshager AD, Gassman PW, Secchi S, Schoof JT, Misgna G (2016) Modeling agricultural watersheds with the soil and water assessment tool (SWAT): calibration and validation with a novel procedure for spatially explicit HRUs. Environ Manage 57:894–911
Wang X, Melesse AM (2005) Evaluation of the SWAT model’s snowmelt hydrology in a Northwestern Minnesota watershed. Trans ASAE 48(4):1359–1376
White KL, Chaubey I (2005) Multi-site and multi-variable calibration of the SWAT model. J Am Water Resour Assoc 41(5):1077–1089
White MJ, Daren Harmel R, Arnold JG, William JR (2012) SWAT check: a screening tool to assist users in the identification of potential model application problems. J Environ Qual. https://doi.org/10.2134/jeq2012.0039
Williams JR, Arnold JG, Kiniry JR, Gassman PW, Green CH (2008) History of model development at temple Texas. Hydrol Sci J 53(5):948–960. https://doi.org/10.1623/hysj.53.5.948
Willmott CJ, Robeson SM, Matsuura K (2011) A refined index of model performance. Int J Climatol 13:2088–2094
Worku T, Khare D, Tripathi SK (2017) Modelling runoff–sediment response to land use/land cover changes using integrated GIS and SWAT model in the Beressa watershed. Environ Earth Sci 76:550. https://doi.org/10.1007/s12665-017-6883-3
Wu CL, Chau KW (2013) Prediction of rainfall time series using modular soft computing methods. Eng Appl Artif Intell 26(3):997–1007
Wu Y, Liu S (2012a) Automating calibration, sensitivity and uncertainty analysis of complex models using the R package flexible modelling environment (FME): SWAT as an example. Environ Model Software 31:1364–8152
Wu Y, Liu S (2012b) Automating calibration, sensitivity and uncertainty analysis of complex models using the R package flexible modelling environment (FME): SWAT as an example. Environ Model Software 31:99–109
Xiaomeng S, Jianyun Z, Zhan C, Xuan Y, Ye M, Chonggang Xu (2015) Global sensitivity analysis in hydrological modeling: review of concepts, methods, theoretical framework, and applications. J Hydrol 523:739–757
Yilmaz KK, Gupta HV, Wagener T (2008) A process-based diagnostic approach to model evaluation: application to the NWS distributed hydrologic model. Water Resour Res 44(W09417):2008. https://doi.org/10.1029/2007WR006716
Yong C, Marek GW, Marek TH, Brauer DK, Srinivasan R (2017) Assessing the efficacy of the SWAT auto-irrigation function to simulate irrigation, evapotranspiration, and crop response to management strategies of the Texas high plains. Water 9(7):509
Zhang X, Srinivasan R, Liew MV (2008) Multi-site calibration of the SWAT model for hydrologic modeling. Trans ASABE 51(6):2039–2049
Zhang X, Srinivasan R, Van Liew M (2010) On the use of multi-algorithm, genetically adaptive multi-objective method for multi-site calibration of the SWAT model. Hydrol Proc 24:955–969. https://doi.org/10.1002/hyp.7528
Zhang X, Beeson P, Link R, Manowitz D, Izaurralde RC, Sadeghi A, Thomson AM, Sahajpal R, Srinivasan R, Arnold JG (2013) Efficient multi-objective calibration of a computationally intensive hydrologic model with parallel computing software in Python. Environ Model Software 46:208–218
Zhang J, Li Q, Guo B, Gong H (2015) The comparative study of multi-site uncertainty evaluation method based on SWAT model. Hydrol Proc 29:2994–3009
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Athira, P., Sudheer, K.P. Calibration of distributed hydrological models considering the heterogeneity of the parameters across the basin: a case study of SWAT model. Environ Earth Sci 80, 131 (2021). https://doi.org/10.1007/s12665-021-09434-8
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DOI: https://doi.org/10.1007/s12665-021-09434-8