A process-based mesh-distributed watershed model for water runoff and soil erosion simulation

References

• Abban, B., Lai, Y. G., & Greimann, B. P. (2021). Watershed Modeling: Hydrological Model Development and Validation. Project Report ENV-2021-061, Technical Service Center, U.S. Bureau of Reclamation, Denver, Colorado.
   Google Scholar
• Abbott, M. B., Bathurst, J. C., Cunge, J. A., O'Connell, P. E., & Rasmussen, J. (1986). An introduction to the European hydrological system - Systeme Hydrologique European, “SHE”, 1: History and philosophy of a physically-based, distributed modelling system. Journal of Hydrology, 87(1-2), 45–59. https://doi.org/10.1016/0022-1694(86)90114-9
   Web of Science ®Google Scholar
• Addor, N., & Melsen, L. A. (2019). Legacy rather than adequacy drives the selection of hydrological models. Water Resources Research, 55(1), 378–390. https://doi.org/10.1029/2018WR022958
   Web of Science ®Google Scholar
• Akan, A. O. (1985). Similarity solution of overland flow on pervious surface. Journal of Hydraulic Engineering, 111(7), 1057–1067. https://doi.org/10.1061/(ASCE)0733-9429(1985)111:7(1057)
   Web of Science ®Google Scholar
• Alexiades, V., Amiez, G., & Gremaud, P. (1996). Super-time-stepping acceleration of explicit schemes for parabolic problems. Communications in Numerical Methods in Engineering, 12(1), 31–42. https://doi.org/10.1002/(SICI)1099-0887(199601)12:1<31::AID-CNM950>3.0.CO;2-5
   Google Scholar
• Allen, R. (2005). Penman-Monteith equation. Encyclopedia of Soils in the Environment, 180–188, ISBN: 978-0-12-348530-4.
   Google Scholar
• ASCE (American Society of Civil Engineers). (1993). Criteria for evaluation of watershed models. Journal of Irrigation and Drainage Engineering, 119(3), 429–442. https://doi.org/10.1061/(ASCE)0733-9437(1993)119:3(429)
   Web of Science ®Google Scholar
• Bardossy, A. (2007). Calibration of hydrological model parameters for ungauged catchments. Hydrology and Earth System Sciences, 11(2), 703–710. https://doi.org/10.5194/hess-11-703-2007
   Web of Science ®Google Scholar
• Barthel, R., & Banzhaf, S. (2016). Groundwater and surface water interaction at the regional-scale – a review with focus on regional integrated models. Water Resources Management, 30(1), 1–32. https://doi.org/10.1007/s11269-015-1163-z
   Web of Science ®Google Scholar
• Bartholmes, J. C., Thielen, J., Ramos, M. H., & Gentilini, S. (2009). The European flood alert system EFAS - part 2: Statistical skill assessment of probabilistic and deterministic operational forecasts. Hydrology and Earth System Sciences, 13(2), 141–153. https://doi.org/10.5194/hess-13-141-2009
   Web of Science ®Google Scholar
• Berg, S., & Sudicky, E. A. (2019). Toward large-scale integrated surface and subsurface modeling. Groundwater, 57(1), 1–2. https://doi.org/10.1111/gwat.12844
   PubMed Web of Science ®Google Scholar
• Beven, K. J. (1985). Distributed models in hydrological forecasting. M.G. Anderson and T.P. Burt, John Wiley (eds), NY, 425–435.
   Google Scholar
• Camporese, M., Paniconi, C., Putti, M., & Orlandini, S. (2010). Surface-subsurface flow modeling with path-based runoff routing, boundary condition-based coupling, and assimilation of multisource observation data. Water Resources Research, 46(2), W02512. https://doi.org/10.1029/2008WR007536
   Web of Science ®Google Scholar
• Daniel, E. B., Camp, J. V., LeBoeuf, E. J., Penrod, J. R., Abkowitz, M. D., & Dobbins, J. P. (2010). Watershed modeling using GIS technology: A critical review. Journal of Spatial Hydrology, 10(2), 13–28. https://scholarsarchive.byu.edu/josh/vol10/iss2/4
   Google Scholar
• De Mello, C. R., Norton, L. D., Pinto, L. C., Beskow, S., & Curi, N. (2016). Agricultural watershed modeling: A review for hydrology and soil erosion processes. Ciência e Agrotecnologia, 40(1), 7–25. https://doi.org/10.1590/S1413-70542016000100001
   Web of Science ®Google Scholar
• Devi, G. K., Ganasri, B. P., & Dwarakish, G. S. (2015). A review on hydrological models. Aquatic Procedia, 4, 1001–1007. https://doi.org/10.1016/j.aqpro.2015.02.126
   Google Scholar
• Di Giammarco, P., Todini, E., & Lamberti, P. (1996). A conservative finite element approach to overland flow: The control volume finite element formulation. Journal of Hydrology, 175(1-4), 267–291. https://doi.org/10.1016/S0022-1694(96)80014-X
   Web of Science ®Google Scholar
• Dottori, F., Kalas, M., Salamon, P., Bianchi, A., Alfieri, L., & Feyen, L. (2017). An operational procedure for rapid flood risk assessment in Europe. Natural Hazards and Earth System Sciences, 17(7), 1111–1126. https://doi.org/10.5194/nhess-17-1111-2017
   Google Scholar
• EEA, European Environmental Agency. (2010). Mapping the impacts of natural hazards and technological accidents in Europe: An overview of the last decade. EEA Technical Report. European Environment Agency, Copenhagen, 144.
   Google Scholar
• Engel, B., Storm, D., White, M., & Arnold, J. (2007). A hydrologic/water quality model application protocol. Journal of the American Water Resources Association, 43(5), 1223–1236. https://doi.org/10.1111/j.1752-1688.2007.00105.x
   Web of Science ®Google Scholar
• Ewen, J., Parkin, G., & O’Connell, P. E. (2000). SHETRAN: Distributed river basin flow and transport modeling system. ASCE Journal Hydrologic Engineering, 5(3), 250–258. https://doi.org/10.1061/(ASCE)1084-0699(2000)5:3(250)
   Web of Science ®Google Scholar
• Fatichi, S., Vivoni, E., Ogden, F., Ivanov, V., Mirus, G., Downer, D., Camporese, C., Davison, M., Ebel, J., Jones, B., Kim, N., Mascaro, J., Niswonger, G., Restrepo, R., Rigon, P., Shen, R., Sulis, C., & Tarboton D, M. (2016). An overview of current applications, challenges, and future trends in distributed process-based models in hydrology. Journal of Hydrology, 537, 45–60. https://doi.org/10.1016/j.jhydrol.2016.03.026
   Web of Science ®Google Scholar
• FHWA, Federal Highway Administration. (2002). User’s Manual for FESWMS Flo2DH, Publication No. FHWA-RD-03-053, Federal Highway Administration, U.S. Department of Transportation.
   Google Scholar
• Gao, L., & Li, D. (2014). A review of hydrological/water-quality models. Frontiers of Agricultural Science and Engineering, 1(4), 267–276. https://doi.org/10.15302/J-FASE-2014041
   Google Scholar
• Gayathri, K. D., Ganasri, B. P., & Dwarakish, G. S. (2015). A review on hydrological models. Aquatic Procedia, 4(2015), 1001–1007. https://doi.org/10.1016/j.aqpro.2015.02.126
   Google Scholar
• Gupta, H. V., Sorooshian, S., & Yapo, P. (1999). Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration. Journal of Hydrologic Engineering, 4(2), 135–143. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135)
   Web of Science ®Google Scholar
• Guy, B. T., Rudra, R. P., Dickenson, W. T., & Sohrabi, T. M. (2009). Empirical model for calculating sediment-transport capacity in shallow overland flow: Model development. Biosystem Engineering, 103(1), 105–115. https://doi.org/10.1016/j.biosystemseng.2009.02.002
   Web of Science ®Google Scholar
• Islam, Z. (2011). A review on physically based hydrologic modeling. Technical Report, University of Alberta, Canada, 45 p.
   Google Scholar
• Jenkerson, C. B., Maiersperger, T. K., & Schmidt, G. L. (2010). eMODIS - a user-friendly data source. U.S. Geological Survey (USGS) Open-File Report, Reston, VA, USA. 2010-1055.
   Google Scholar
• Jomaa, S., Barry, D. A., Heng, B. C. P., Brovelli, A., Sander, G. C., & Parlange, J.-Y. (2012). Influence of rock fragment coverage on soil erosionand hydrological response: Laboratory flume experiments and modeling. Water Resources Research, 48(5), W05535. https://doi.org/10.1029/2011WR011255
   Google Scholar
• Julien, P. Y. (2002). River mechanics. Cambridge University Press, p. 434.
   Google Scholar
• 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. Environmental Modelling & Software, 75(2016), 68–76. https://doi.org/10.1016/j.envsoft.2015.09.009
   Google Scholar
• Kavetski, D., Kuczera, G., & Franks, S. W. (2006). Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory. Water Resources Research, 42, W03407. https://mountainscholar.org/bitstream/handle/10217/61574/HydrologyPapers_n63.pdf
   Web of Science ®Google Scholar
• Kilinc, M. Y., & Richardson, E. V. (1973). Mechanics of soil erosion from overland flow generated by simulated rainfall. Hydrology Papers Number 63. Colorado State University, Fort Collins, Colorado.
   Google Scholar
• Kollet, S., Sulis, M., Maxwell, R. M., Paniconi, C., Putti, M., Bertoldi, G., Coon, E. T., Cordano, E., Endrizzi, S., Kikinzon, E., Mouche, E., Mügler, C., Park, Y.-J., Refsgaard, J. C., Stisen, S., & Sudicky, E. (2017). The integrated hydrologic model intercomparison project, IH-MIP2: A second set of benchmark results to diagnose integrated hydrology and feedbacks. Water Resources Research, 53(1), 867–890. https://doi.org/10.1002/2016WR019191
   Web of Science ®Google Scholar
• Lai, Y. G. (2020). A two-dimensional depth-averaged sediment transport mobile-bed model with polygonal meshes. Water, 12(4), 1032. https://doi.org/10.3390/w12041032
   Web of Science ®Google Scholar
• Lai, Y. G., Greimann, B. P., & Politano, M. (2019). Watershed Erosion Modeling: Literature Review and SRH-W Design. Project Report ENV-2019-033. Technical Service Center, U.S. Bureau of Reclamation, Denver, Colorado.
   Google Scholar
• Legates, D. R., & McCabe, G. J. (1999). Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233–241. https://doi.org/10.1029/1998WR900018
   Web of Science ®Google Scholar
• Li, Q., Unger, A. J. A., Sudicky, E. A., Kassenaar, D., Wexler, E. J., & Shikaze, S. (2008). Simulating the multi-seasonal response of a large-scale watershed with a 3D physically-based hydrologic model. Journal of Hydrology, 357(3-4), 317–336. https://doi.org/10.1016/j.jhydrol.2008.05.024
   Web of Science ®Google Scholar
• Li, X., Cheng, G., Lin, H., Cai, X., Fang, M., Ge, Y., Hu, X., Chen, M., & Li, W. (2018). Watershed system model: The essentials to model complex human-nature system at the river basin scale. Journal of Geophysical Research: Atmospheres, 123(6), 3019–3034. https://doi.org/10.1002/2017JD028154
   Web of Science ®Google Scholar
• Maxwell, R. M., Putti, M., Meyerhoff, S., Delfs, J. O., Ferguson, I. M., Ivanov, V., Kim, J., Kolditz, O., Kollet, S. J., & Kumar, M. (2014). Surface-subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks. Water Resources Research, 50(2), 1531–1549. https://doi.org/10.1002/2013WR013725
   Web of Science ®Google Scholar
• Moges, E., Demissie, Y., Larsen, L., & Yassin, F. (2020). Review: Sources of hydrological model uncertainties and advances in their analysis. Water, 13(1), 28. https://doi.org/10.3390/w13010028
   Web of Science ®Google Scholar
• Moriasi, D., Arnold, J., Van Liew, M., Bingner, R., Harmel, R., & Veith, T. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153
   Web of Science ®Google Scholar
• Moriasi, D., Gitau, M., Pai, N., & Daggupati, P. (2015). Hydrologic and water quality models: Performance measures and evaluation criteria. Transactions of the ASABE (American Society of Agricultural and Biological Engineers), 58(6), 1763–1785. https://doi.org/10.13031/trans.58.10715
   Web of Science ®Google Scholar
• Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models: Part 1. A discussion of principles. Journal of Hydrology, 10(3), 282–290. https://doi.org/10.1016/0022-1694(70)90255-6
   Google Scholar
• Nord, G., Esteves, M., Lapetite, J. M., & Hauet, A. (2009). Effect of particle density and inflow concentration of suspended sediment on bedload transport in rill flow. Earth Surface Processes and Landforms, 34(2), 253–263. https://doi.org/10.1002/esp.1710
   Google Scholar
• NRC, National Research Council. (2001). Assessing the TMDL approach to water quality management. Committee to assess the scientific basis of the TMDL approach to water pollution reduction. National Academy Press.
   Google Scholar
• Overton, D. E., & Brakensiek, D. L. (1973). A kinematic model of surface runoff response. IAHS Pub, 9, 110–112.
   Google Scholar
• Paik, K., & Kumar, P. (2010). Optimality approaches to describe characteristic fluvial patterns on landscapes. Philosophical Transactions of the Royal Society B: Biological Sciences, 365(1545), 1387–1395. https://doi.org/10.1098/rstb.2009.0303
   PubMed Web of Science ®Google Scholar
• Panday, S., & Huyakorn, P. S. (2004). A fully coupled physically-based spatially-distributed model for evaluating surface/subsurface flow. Advance Water Resources, 27(4), 361–382. https://doi.org/10.1016/j.advwatres.2004.02.016
   Web of Science ®Google Scholar
• Parajuli, P. B., & Ouyang, Y. (2013). Chapter 3: Watershed-scale hydrological modeling methods and applications. In P. M. Bradley (Ed.), Current perspectives in contaminant hydrology and water resources sustainability. InTech. https://doi.org/10.5772/47884
   Google Scholar
• Peel, M. C., & McMahon, T. A. (2020). Historical development of rainfall-runoff modeling. Wiley Interdisciplinary Reviews: Water, 7(5). https://doi.org/10.1002/wat2.1471
   Google Scholar
• Peric, M., & Ferguson, S. (2005). The advantage of polyhedral meshes. Dynamics - Issue 24 (Spring 2005), 4–5.
   Google Scholar
• Post, D. E., & Votta, L. G. (2005). Computational science demands a new paradigm. Physics Today, 58(1), 35–41. https://doi.org/10.1063/1.1881898
   Web of Science ®Google Scholar
• Ramsankaran, R., Kothyari, U. C., Ghosh, S. K., Malcherek, A., & Murugesan, A. (2013). Physically-based distributed soil erosion and sediment yield model (DREAM) for simulating individual storm events. Hydrological Sciences Journal, 58(4), 872–891. https://doi.org/10.1080/02626667.2013.781606
   Web of Science ®Google Scholar
• Rocscience. (2017). Phase² - 2D finite element program for stress analysis and support design around excavations in soil and rock. Groundwater Verification Manual, Part 1, Rocscience Inc, Toronto, Ontario, Canada.
   Google Scholar
• Scherer, U., & Zehe, E. (2015). Predicting land use and soil controls on erosion and sediment redistribution in agricultural loess areas: Model development and cross scale verification. Hydrology and Earth System Sciences, 12, 3527–3592. https://doi.org/10.5194/hessd-12-3527-2015
   Google Scholar
• Sharika, U., Senarath, S., Ogdon, F. L., Downer, C. W., & Sharif, H. O. (2000). On the calibration and verification of two-dimensional, distributed, Hortonian, continuous watershed models. Water Resources Research, 36(6), 1495–1510. https://doi.org/10.1029/2000WR900039
   Web of Science ®Google Scholar
• Singh, J., Knapp, H., & Demissie, M. (2004). Hydrologic modelling of the Iroquois River watershed using HSPF and SWAT. ISWS CR 2004-08. Champaign, Ill.: Illinois State Water Survey.
   Google Scholar
• Singh, V. P., & Woolhiser, D. A. (2002). Mathematical modeling of watershed hydrology. Journal of Hydrologic Engineering, 7(4), 270–292. https://doi.org/10.1061/(ASCE)1084-0699(2002)7:4(270)
   Web of Science ®Google Scholar
• Sosnowski, M., Krzywanski, J., Grabowska, K., & Gnatowska, R. (2018). Polyhedral meshing in numerical analysis of conjugate heat transfer. EPJ Web of Conferences 180, 02096 (2018). https://doi.org/10.1051/epjconf/201818002096EFM2017.
   Google Scholar
• Stern, M., Flint, L., Minear, J., Flint, A., & Wright, S. (2016). Characterizing changes in streamflow and sediment supply in the Sacramento River Basin, California, using hydrological simulation program - FORTRAN (HSPF). Water, 8(10), 1–21. https://doi.org/10.3390/w8100432
   Web of Science ®Google Scholar
• Swets, D. L., Reed, B. C., Rowland, J. R., & Marko, S. E. (1999). A weighted least-squares approach to temporal smoothing of NDVI. In Proceedings of the 1999 ASPRS Annual Conference, from Image to Information, Portland, Oregon, May 17–21, Bethesda, Maryland, American Society for Photogrammetry and Remote Sensing, CD-ROM, 1 disc.
   Google Scholar
• USEPA, U.S. Environmental Protection Agency. (2010). National summary of impaired waters and TMDL information. U.S. Environmental Protection Agency. Washington, D.C.. Report available at: http://iaspub.epa.gov/waters10/attains_nation_cy.control?p_report_type = T.
   Google Scholar
• USGS, United States Geological Survey. (2014). National Geospatial-Intelligence Agency (NGA), and National Aeronautics and Space Administration (NASA). Shuttle Radar Topography Mission 1 Arc-Second Global. Retrieved 10 October, 2017, from https://earthexplorer.usgs.gov.
   Google Scholar
• van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44(5), 892–898. https://doi.org/10.2136/sssaj1980.03615995004400050002x
   Web of Science ®Google Scholar
• van Rijn, L. C. (2007). Unified view of sediment transport by currents and waves. III: Graded beds. Journal of hydraulic engineering. ASCE, 133(7), 761–775. https://doi.org/10.1061/(ASCE)0733-9429(2007)133:7(761)
   Google Scholar
• Velleux, M. L., England, J. F., & Julien, P. Y. (2005). TREX watershed modeling framework user’s manual: Model theory and description. Colorado State University, Dept. Civil and Environmental Engineering, Fort Collins, CO.
   Google Scholar
• Versteeg, H. K., & Malalasekera, W. (2007). An introduction to computational fluid dynamics: The finite volume method, 2nd edition. Prentice-Hall.
   Google Scholar
• Wang, J., Stern, M., Alpers, C. N., King, V. M., Webster, J., Quinn, N. W. T., & Lai, Y. (2019). Research and Development of a Watershed-Scale Model/Tool for Simulating Effects of Wildfires on Mercury Contamination of Land and Water, Report No. ST-2019-7112-01, Research and Development Office, Bureau of Reclamation, USA.
   Google Scholar
• Wicks, J. M., & Bathurst, J. C. (1996). SHESED: A physically based distributed erosion and sediment yield component for the SHE hydrological modelling system. Journal of Hydrology, 175(1-4), 213–236. https://doi.org/10.1016/S0022-1694(96)80012-6
   Web of Science ®Google Scholar
• Willmott, C. J. (1981). On the validation of models. Physical Geography, 2(2), 184–194. https://doi.org/10.1080/02723646.1981.10642213
   Google Scholar
• Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall-erosion losses. Agricultural handbook number 537. United States Department of Agriculture-Science and Education Administration.
   Google Scholar
• Yuan, L., Sinshaw, T., & Forshay, K. J. (2020). Review of watershed-scale water quality and nonpoint source pollution models. Geosciences, 10(25), 1–36. https://doi.org/10.3390/geosciences10010025
   PubMedGoogle Scholar
• Zhang, G. H., Wang, L. L., Tang, K. M., Luo, R. T., & Zhang, X. C. (2011). Effects of sediment size on transport capacity of overland flow on steep slopes. Hydrological Sciences Journal, 56(7), 1289–1299. https://doi.org/10.1080/02626667.2011.60917
   Web of Science ®Google Scholar