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Rainstorm‐generated sediment yield model based on soil moisture proxies ( SMP )
Author(s) -
Gupta Sushindra Kumar,
Singh Pushpendra K.,
Tyagi Jaivir,
Sharma Gunwant,
Jethoo Ajay Singh
Publication year - 2020
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.13789
Subject(s) - mean squared error , statistics , mathematics , goodness of fit , storm , hydrology (agriculture) , antecedent moisture , environmental science , soil science , sediment , meteorology , watershed , geology , computer science , runoff curve number , geomorphology , geotechnical engineering , physics , machine learning
This study develops improved Soil Moisture Proxies (SMP) based suspended sediment yield (SMPSY) models corresponding to three antecedent moisture conditions (AMCs) (i.e., AMC‐I‐AMC‐III) by coupling the improved initial abstraction (I a ‐λ) model, the SMA procedure and the SMP concept for modelling the rainfall generated suspended sediment yield. The SMPSY models specifically incorporate a watershed storage index (S) model to accentuate the transformation from storm to storm and to avoid the sudden jumps in sediment yield computation. The workability of the SMPSY models is tested using a large dataset of rainfall and sediment yield (98 storm events) from twelve small watersheds and a comparison has been made with the existing MSY model. The goodness‐of‐fit (GOF) statistics is evaluated in terms of the Nash Sutcliffe efficiency (NSE), and error indices, i.e., root mean square error (RMSE), normalized root mean square error (nRMSE), standard error (SE), mean absolute error (MAE), and RMSE‐observations standard deviation ratio (RSR). The NSE values vary from 74.31% to 96.57% and from 75.21% to 91.78%, respectively for the SPMSY and MSY model. The NSE statistics indicate that the SMPSY model has lower uncertainty in simulating sediment yield as compared to the MSY model. The error indices are lower for the SMPSY model than the MSY model for most of the watersheds. These results show that the SMPSY model has less uncertainty and performs better than the MSY model. A sensitivity analysis of the SMPSY model shows that the parameter β is most sensitive followed by parameter S, α and A. Overall, the results show that the characterization of soil moisture variability in terms of SMPs and incorporation of improved delivery ratio and runoff coefficient relationship improves the simulation of the erosion and sediment yield generation process.