Abstract
In watershed hydrology, it is challenging to physically monitor various aspects that influence the hydrological processes. To quantify these watershed processes in a basin with changing spatial and temporal characteristics, public domain hydrological models incorporating inverse modeling are considered. The quantified processes aid in the decision-making, design, and development of hydrological units. But the first confusion that arises in modeling these processes is which hydrological model should be considered and what methods should be adopted to quantify the best hydrological parameters. Even though a best model is considered hydrologists assumption of parameter insensitivity and uniqueness over varying climatic conditions and space, the conditionality of model calibration with unique technique and performance indicator is prone to the poor performance of the model. Betterment of model performance can be achieved by switching parameters sensitive to varying climatic conditions and reprieving the conditionality of model calibration. Hence, the purpose of this paper is to review (i) different hydrological models available around the globe, (ii) the selection criteria for the hydrological model and the superiority of the SWAT model, (iii) the description of the SWAT model, followed by sensitivity analysis and calibration techniques involved in SWAT output, and (iv) summaries of season-based SWAT evaluation.
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References
Te Chow V, Maidment DR, Mays LW (1988) Applied hydrology. McGraw-Hill, USA
Crawford NH, Linsley RK (1966) Digital simulation in hydrology: Stanford watershed model IV. In: Contemporary hydrology, pp 157–158
Arnold JG, Allen PM, Bernhardt G (1993) A comprehensive surface-groundwater flow model. J Hydrol 142(1–4):47–69. https://doi.org/10.1016/0022-1694(93)90004-S
Bhanja SN, Coon ET, Lu D, Painter SL (2022) Evaluation of distributed process-based hydrologic model performance using only a priori information to define model inputs. J Hydrol 618(June):129176. https://doi.org/10.1016/j.jhydrol.2023.129176
van Griensven A, Meixner T, Grunwald S, Bishop T, Diluzio M, Srinivasan R (2006) A global sensitivity analysis tool for the parameters of multi-variable catchment models. J Hydrol 324(1–4):10–23. https://doi.org/10.1016/j.jhydrol.2005.09.008
Li Q, Yu X, Xin Z, Sun Y (2013) Modeling the effects of climate change and human activities on the hydrological processes in a semiarid watershed of Loess Plateau. J Hydrol Eng 18(4):401–412. https://doi.org/10.1061/(asce)he.1943-5584.0000629
White ED, Feyereisen GW, Veith TL, Bosch DD (2008) Improving daily water yield estimates in the little river watershed: SWAT adjustments. Am Soc Agric Biol Eng Annu Int Meet ASABE 7(January):4309–4327. https://doi.org/10.13031/2013.25948
Cai Y et al (2023) Enhancing SWAT model with modified method to improve eco-hydrological simulation in arid region. J Clean Prod 403:136891. https://doi.org/10.1016/j.jclepro.2023.136891
Rafiei Emam A, Kappas M, Hoang Khanh Nguyen L, Renchin T (2016) Hydrological modeling in an ungauged basin of central Vietnam using SWAT model. Hydrol Earth Syst Sci Discuss 1–33. https://doi.org/10.5194/hess-2016-44
Raju K, Nandagiri L (2015) Application and test of the SWAT model in the upper Cauvery river basin, Karnataka, India. In: 4th international engineering symposium, pp 2–8. https://doi.org/10.13140/RG.2.1.1263.3129
Adhikary PP et al (2019) Effect of calibration and validation decisions on streamflow modeling for a heterogeneous and low runoff-producing river basin in India. J Hydrol Eng 24(7):05019015. https://doi.org/10.1061/(asce)he.1943-5584.0001792
Muleta MK (2012) Improving model performance using season-based evaluation. J Hydrol Eng 17(1):191–200. https://doi.org/10.1061/(asce)he.1943-5584.0000421
Shin MJ, Guillaume JHA, Croke BFW, Jakeman AJ (2013) Addressing ten questions about conceptual rainfall-runoff models with global sensitivity analyses in R. J Hydrol 503:135–152. https://doi.org/10.1016/j.jhydrol.2013.08.047
Box G, Jenkins G (1976) Time series analysis: forecasting and control. In: Holden Day, revised edn. San Francisco, p 575
Van Griensven A, Francos A, Bauwens W (2002) Sensitivity analysis and auto-calibration of an integral dynamic model for river water quality. Water Sci Technol 45(9):325–332. https://doi.org/10.2166/wst.2002.0271
Devak M, Dhanya CT (2017) Sensitivity analysis of hydrological models: review and way forward. J Water Clim Change 8(4):557–575. https://doi.org/10.2166/wcc.2017.149
Khalid K et al (2016) Sensitivity analysis in watershed model using SUFI-2 algorithm. Proc Eng 162:441–447. https://doi.org/10.1016/j.proeng.2016.11.086
Santos L, Andersson JCM, Arheimer B (2022) Evaluation of parameter sensitivity of a rainfall-runoff model over a global catchment set. Hydrol Sci J 67(3):342–357. https://doi.org/10.1080/02626667.2022.2035388
Chang CH, Cai LY, Lin TF, Chung CL, Van Der Linden L, Burch M (2015) Assessment of the impacts of climate change on the water quality of a small deep reservoir in a humid-subtropical climatic region. Water 7(4):1687–1711. https://doi.org/10.3390/w7041687
Jahanshahi A, Golshan M, Afzali A (2017) Simulation of the catchments hydrological processes in arid, semi-arid and semi-humid areas. Desert 22(1):1–10. https://doi.org/10.22059/jdesert.2017.62295
Zettam A, Taleb A, Sauvage S, Boithias L, Belaidi N, Sánchez-Pérez JM (2017) Modelling hydrology and sediment transport in a semi-arid and anthropized catchment using the swat model: the case of the Tafna River (Northwest Algeria). Water 9(3). https://doi.org/10.3390/w9030216
Gao X, Chen X, Biggs TW, Yao H (2018) Separating wet and dry years to improve calibration of SWAT in Barrett watershed, Southern California. Water 10(3):1–13. https://doi.org/10.3390/w10030274
Zhang D, Chen X, Yao H, Lin B (2015) Improved calibration scheme of SWAT by separating wet and dry seasons. Ecol Modell 301:54–61. https://doi.org/10.1016/j.ecolmodel.2015.01.018
Yang J, Reichert P, Abbaspour KC, Xia J, Yang H (2008) Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China. J Hydrol 358(1–2):1–23. https://doi.org/10.1016/j.jhydrol.2008.05.012
Abbaspour KC et al (2007) Modelling hydrology and water quality in the pre-alpine/alpine Thur watershed using SWAT. J Hydrol 333(2–4):413–430. https://doi.org/10.1016/j.jhydrol.2006.09.014
Brooks KN, Ffolliott PF, Magner JA (1998) Hydrology and the management of watersheds, vol 27, no. 6
Wagener T et al (2010) The future of hydrology: an evolving science for a changing world. Water Resour Res 46(5). https://doi.org/10.1029/2009WR008906
Feldman AD (2000) Hydrologic modeling system technical reference manual. Hydrol Model Syst HEC-HMS Tech Ref Man 148
Metcalf and Eddy (1971) Storm water management model, vol I—final report, p 338
Mastin MC, Thanh L (2002) User’s guide to SSARRMENU
Liang X, Lettenmaier DP, Wood EF, Burges SJ (1994) A simple hydrologically based model of land surface water and energy fluxes for general circulation models. J Geophys Res 99(D7). https://doi.org/10.1029/94jd00483
Arnold JG, Srinivasan R, Muttiah RS, Williams JR (1998) Large area hydrological modeling and assessment part I: model development. J Am water Resour Assoc 34(1):73–89
Beven KJ, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci Bull 24(1):43–69
DHI (2002) MIKE II: a modelling system for rivers and channels. reference manual, DHI software 2002. Horsholm, Denmark
Refsgaard JC, Storm B (1995) MIKE SHE. In: Singh VP (ed), Computer models of watershed hydrology. Water Resources Publications Color., pp 809–847
M. G. McDonald and A. W. Harbaugh, “A modular three-dimensional finite-difference ground-water flow model,” 1984.
Kauffeldt A, Wetterhall F, Pappenberger F, Salamon P, Thielen J (2016) Technical review of large-scale hydrological models for implementation in operational flood forecasting schemes on continental level. Environ Model Softw 75(January):68–76. https://doi.org/10.1016/j.envsoft.2015.09.009
US Soil Conservation Service and U. D. of Agriculture (1985) National engineering handbook. Section 4, Hydrology. U.S. Dept. of Agriculture, Soil Conservation Service, Washington, D.C
Monteith JL (1965) Evaporation and environment. The stage and movement of water in living organisms. In: 19th symposia of the society for experimental biology. Cambridge University Press
Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evaporation using large-scale parameters. Mon Weather Rev 100(2):81–92. https://doi.org/10.1175/1520-0493(1972)100%3c0081:otaosh%3e2.3.co;2
Green W, Ampt G (1911) Studies on soil physics. J Agric Sci 4(1):1–24. https://www.cambridge.org/core/journals/journal-of-agricultural-science/article/abs/studies-on-soil-phyics/6EE03D61E70FCEFD6EAE4D59BFCC1FF9
Seibert J, Staudinger M, van Meerveld HJ (2019) Validation and over-parameterization—experiences from hydrological modeling. Springer International Publishing
Morris MD (1991) Factorial sampling plans for preliminary computational experiments. Technometrics 33(2):161–174. https://doi.org/10.1177/001872086700900503
Mckay MD, Beckman RJ, Conover WJ (1979) A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 42(1):55–61. http://www.jstor.org/stable/1271432
Medina Y, Muñoz E (2020) Analysis of the relative importance of model parameters in watersheds with different hydrological regimes. Water 12(9). https://doi.org/10.3390/W12092376
Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6(3):279–298. https://doi.org/10.1002/hyp.3360060305
Duan Q, Sorooshian S, Gupta V (1992) Effective and efficient global optimization. Water Resour Res 28(4):1015–1031
Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087–1092. https://doi.org/10.1063/1.1699114
Sharannya TM, Mudbhatkal A, Mahesha A (2018) Assessing climate change impacts on river hydrology—a case study in the Western Ghats of India. J Earth Syst Sci 127(6):1–11. https://doi.org/10.1007/s12040-018-0979-3
van Werkohoven K, Wagener T, Reed P, Tang Y (2008) Rainfall characteristics define the value of streamflow observations for distributed watershed model identification. Geophys Res Lett 35(11):1–6. https://doi.org/10.1029/2008GL034162
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Kumar, H.K.Y., Kumble, V. (2024). Toward Selection and Improving the Performance of the SWAT Hydrological Model: A Review. In: Menon, N.V.C., Kolathayar, S., Rodrigues, H., Sreekeshava, K.S. (eds) Recent Advances in Civil Engineering for Sustainable Communities. IACESD 2023. Lecture Notes in Civil Engineering, vol 459. Springer, Singapore. https://doi.org/10.1007/978-981-97-0072-1_28
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