Abstract
The correct and reasonable delineation of actual hydrologic processes is a footstone for the effective simulation of pollutants in watershed models. In this study, a simple but comprehensive semidistributed modeling approach based on the generalized watershed loading function (GWLF) was modified to enable the accurate simulation of hydrology in watersheds. The frame of the original GWLF model (ORM), with a lumped hydrological parameter, was modified by adding channel routing processes, which made it possible to introduce the concept of subbasins. Then, the revised GWLF model was applied to the Luanhe watershed (30,000 km2) on a monthly bias in comparison with the ORM and the previously revised version. The sensitivity analysis and generalized likelihood uncertainty estimation (GLUE) uncertainty analysis were individually conducted to evaluate these modifications. Eventually, we compared four extreme conditions for the daily streamflow simulations of the three model versions in the Tunxi watershed but without calibration. All of the results indicated that the stability and accuracy of the model and the validity of the parameters were all enhanced and improved by the new revised version of the model, which provided reliable simulation results and indicated that it is a prospective tool to support watershed management.
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References
Arnold JG, Williams J, Nicks A, Sammons N (1990) SWRRB; a basin scale simulation model for soil and water resources management SWRRB; a basin scale simulation model for soil and water resources management
Beven K, Binley A (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Process 6:279–298
Beven K, Smith P, Freer J (2007) Comment on “hydrological forecasting uncertainty assessment: incoherence of the GLUE methodology” by Pietro Mantovan and Ezio Todini. J Hydrol 338:315–318. https://doi.org/10.1016/j.jhydrol.2007.02.023
Borah DK, Bera M (2004) Watershed-scale hydrologic and nonpoint-source pollution models: review of applications. Trans Asae 47:789–803
Borah DK, Yagow G, Saleh A, Barnes PL, Rosenthal W, Krug EC, Hauck LM (2006) Sediment and nutrient modeling for TMDL development and implementation. Trans ASABE 49:967–986
Chang HJ, Evans BM, Easterling DR (2001) The effects of climate change on stream flow and nutrient loading. J Am Water Resour Assoc 37:973–985. https://doi.org/10.1111/j.1752-1688.2001.tb05526.x
Chapra SC, Pelletier G (2003) QUAL2K: A modeling framework for simulating river and stream water quality: Documentation and users manual. Civil and Environmental Engineering Dept., Tufts University, Medford, MA
Evans BM, Lehning DW, Corradini KJ, Petersen GW, Nizeyimana E, Hamlett JM, Robillard PD (2002) A comprehensive GIS-based modeling approach for predicting nutrient loads in watersheds. J Spat Hydrol
Fu C, James AL, Yao H (2014) SWAT-CS: revision and testing of SWAT for Canadian shield catchments. J Hydrol 511:719–735. https://doi.org/10.1016/j.jhydrol.2014.02.023
Gleick PH (2003) Global freshwater resources: soft-path solutions for the 21st century. Science 302:1524–1528. https://doi.org/10.1126/science.1089967
Green WH, Ampt G (1911) Studies on soil physics. J Agric Sci 4:1–24
Haith D, Mandel R, Wu R (1992) GWLF: Generalized Watershed Loading Functions User’s Manual, Version 2.0. Cornell University
Haith D, Shoemaker L (1987) Generalized watershed loading functions for stream flow nutrients. Water Resour Bull 23:471–478
Hong B, Swaney DP (2007) Regional Nutrient Management (ReNuMa) Model, Version 1.0. User's Manual
Hong B, Swaney DP (2013) Regional Nutrient Management (ReNuMa) Model, Version 2.2. 1 User’s Manual Cornell University
Hong B, Swaney DP, Howarth RW (2010) A toolbox for calculating net anthropogenic nitrogen inputs (NANI). Environ Model Softw 26:623–633. https://doi.org/10.1016/j.envsoft.2010.11.012
Howarth RW, Fruci JR, Sherman D (1991) Inputs of sediment and carbon to an estuarine ecosystem: influence of land use. Ecol Appl Publ Ecol Soc Am 1:27–39. https://doi.org/10.2307/1941845
Jennings E, Allott N, Pierson DC, Schneiderman EM, Lenihan D, Samuelsson P, Taylor D (2009) Impacts of climate change on phosphorus loading from a grassland catchment: implications for future management. Water Res 43:4316–4326. https://doi.org/10.1016/j.watres.2009.06.032
Kim RJ, Loucks DP, Stedinger JR (2012) Artificial neural network models of watershed nutrient loading. Water Resour Manag 26:2781–2797. https://doi.org/10.1007/s11269-012-0045-x
Korfmacher KS (2001) The politics of participation in watershed modeling. Environ Manag 27:161–176
Lee KY, Fisher TR, Jordan TE, Correll DL, Weller DE (2000) Modeling the hydrochemistry of the Choptank River Basin using GWLF and arc/info: 1. Model calibration and validation. Biogeochemistry 49:143–173. https://doi.org/10.1023/a:1006375530844
Lee KY, Fisher TR, Rochelle-Newall E (2001) Modeling the hydrochemistry of the choptank river basin using GWLF and arc/info: 2. Model validation and application. Biogeochemistry 56:311–348. https://doi.org/10.1023/a:1013169027082
Li X, Weller DE, Jordan TE (2010) Watershed model calibration using multi-objective optimization and multi-site averaging. J Hydrol 380:277–288. https://doi.org/10.1016/j.jhydrol.2009.11.003
Li Z, Liu M, Zhao Y, Liang T, Sha J, Wang Y (2014) Application of regional nutrient management model in Tunxi catchment: in support of the trans-boundary eco-compensation in eastern China. Clean Soil, Air, Water 42:1729–1739. https://doi.org/10.1002/clen.201300380
Mantovan P, Todini E (2006) Hydrological forecasting uncertainty assessment: incoherence of the GLUE methodology. J Hydrol 330:368–381. https://doi.org/10.1016/j.jhydrol.2006.04.046
Nash JE, Sutcliffe JV (1970) River flow forecasting through conceptual models part I—A discussion of principles. J Hydrol 10:282–290
Ning SK, Chang NB, Jeng KY, Tseng YH (2006) Soil erosion and non-point source pollution impacts assessment with the aid of multi-temporal remote sensing images. J Environ Manag 79:88–101. https://doi.org/10.1016/j.jenvman.2005.05.019
Overton DE (1966) Muskingum flood routing of upland streamflow. J Hydrol 4:185–200
Qi Z, Kang G, Chu C, Qiu Y, Xu Z, Wang Y (2017) Comparison of SWAT and GWLF model simulation performance in humid south and semi-arid north of China. Water 9:567. https://doi.org/10.3390/w9080567
Schneiderman EM, Pierson DC, Lounsbury DG, Zion MS (2002) Modeling the hydrochemistry of the Cannonsville watershed with generalized watershed loading functions (GWLF). J Am Water Resour Assoc 38:1323–1347. https://doi.org/10.1111/j.1752-1688.2002.tb04350.x
SCS U (1986) Urban hydrology for small watersheds US Soil Conservation Service Technical Release 55:13
Sha J, Liu M, Wang D, Swaney DP, Wang Y (2013) Application of the ReNuMa model in the Sha He river watershed: tools for watershed environmental management. J Environ Manag 124:40–50. https://doi.org/10.1016/j.jenvman.2013.03.030
Sha J, Swaney DP, Hong B, Wang J, Wang Y, Wang Z-L (2014) Estimation of watershed hydrologic processes in arid conditions with a modified watershed model. J Hydrol 519:3550–3556. https://doi.org/10.1016/j.jhydrol.2014.10.063
Tuo Y, Chiogna G, Disse M (2015) A multi-criteria model selection protocol for practical applications to nutrient transport at the catchment scale. Water 7:2851–2880. https://doi.org/10.3390/w7062851
Williams JR (1969) Flood routing with variable travel time or variable storage coefficients. Trans ASAE 12:100–103
Wu R-S, Lin IW (2014) Modification of generalized watershed loading functions (GWLF) for daily flow simulation. Paddy Water Environ 13:269–279. https://doi.org/10.1007/s10333-014-0438-y
Acknowledgements
We wish to thank the Hai River Conservancy Commission of the Ministry of Water Resources and the Environmental Protection and Environment Monitoring Station of Huangshan City for providing the hydrology data. We would also like to acknowledge the National Science Data Share Project - Data Sharing Infrastructure of Earth System Science (China) for the data support. Finally, we are thankful for the investment of the Major Science and Technology Program for Water Pollution Control and Treatment (2017ZX07301-001-06).
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Qi, Z., Kang, G., Shen, M. et al. The Improvement in GWLF Model Simulation Performance in Watershed Hydrology by Changing the Transport Framework. Water Resour Manage 33, 923–937 (2019). https://doi.org/10.1007/s11269-018-2149-4
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DOI: https://doi.org/10.1007/s11269-018-2149-4