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• W3100892439 abstract "Precipitation occurs in two basic forms defined as liquid state and solid state. Different from rain-fed watershed, modeling snow processes is of vital importance in snow-dominated watersheds. The seasonal snowpack is a natural water reservoir, which stores snow water in winter and releases it in spring and summer. The warmer climate in recent decades has led to earlier snowmelt, a decline in snowpack, and change in the seasonality of river flows. The Soil and Water Assessment Tool (SWAT) could be applied in the snow-influenced watershed because of its ability to simultaneously predict the streamflow generated from rainfall and from the melting of snow. The choice of parameters, reference data, and calibration strategy could significantly affect the SWAT model calibration outcome and further affect the prediction accuracy. In this study, SWAT models are implemented in four upland watersheds in the Tulare Lake Basin (TLB) located across the Southern Sierra Nevada Mountains. Three calibration scenarios considering different calibration parameters and reference datasets are applied to investigate the impact of the Parallel Energy Balance Model (ParBal) snow reconstruction data and snow parameters on the streamflow and snow water-equivalent (SWE) prediction accuracy. In addition, the watershed parameters and lapse rate parameters-led equifinality is also evaluated. The results indicate that calibration of the SWAT model with respect to both streamflow and SWE reference data could improve the model SWE prediction reliability in general. Comparatively, the streamflow predictions are not significantly affected by differently lumped calibration schemes. The default snow parameter values capture the extreme high flows better than the other two calibration scenarios, whereas there is no remarkable difference among the three calibration schemes for capturing the extreme low flows. The watershed and lapse rate parameters-induced equifinality affects the flow prediction more (Nash-Sutcliffe Efficiency (NSE) varies between 0.2–0.3) than the SWE prediction (NSE varies less than 0.1). This study points out the remote-sensing-based SWE reconstruction product as a promising alternative choice for model calibration in ungauged snow-influenced watersheds. The streamflow-reconstructed SWE bi-objective calibrated model could improve the prediction reliability of surface water supply change for the downstream agricultural region under the changing climate." @default.
• W3100892439 created "2020-11-23" @default.
• W3100892439 creator A5036399361 @default.
• W3100892439 creator A5079801042 @default.
• W3100892439 creator A5086629244 @default.
• W3100892439 date "2020-11-14" @default.
• W3100892439 modified "2023-10-16" @default.
• W3100892439 title "Enhancing Soil and Water Assessment Tool Snow Prediction Reliability with Remote-Sensing-Based Snow Water Equivalent Reconstruction Product for Upland Watersheds in a Multi-Objective Calibration Process" @default.
• W3100892439 cites W1532059138 @default.
• W3100892439 cites W1560807794 @default.
• W3100892439 cites W1566832875 @default.
• W3100892439 cites W1860168626 @default.
• W3100892439 cites W1927614401 @default.
• W3100892439 cites W1948837742 @default.
• W3100892439 cites W1955691646 @default.
• W3100892439 cites W1989193553 @default.
• W3100892439 cites W1998840704 @default.
• W3100892439 cites W2003125845 @default.
• W3100892439 cites W2019538185 @default.
• W3100892439 cites W2022992475 @default.
• W3100892439 cites W2030995226 @default.
• W3100892439 cites W2033365776 @default.
• W3100892439 cites W2041307865 @default.
• W3100892439 cites W2059646894 @default.
• W3100892439 cites W2075466272 @default.
• W3100892439 cites W2080481825 @default.
• W3100892439 cites W2080505591 @default.
• W3100892439 cites W2081346522 @default.
• W3100892439 cites W2087348992 @default.
• W3100892439 cites W2088876159 @default.
• W3100892439 cites W2090915513 @default.
• W3100892439 cites W2093525923 @default.
• W3100892439 cites W2094809497 @default.
• W3100892439 cites W2097083345 @default.
• W3100892439 cites W2099501065 @default.
• W3100892439 cites W2101511525 @default.
• W3100892439 cites W2103497845 @default.
• W3100892439 cites W2108416032 @default.
• W3100892439 cites W2118456616 @default.
• W3100892439 cites W2124738823 @default.
• W3100892439 cites W2127858221 @default.
• W3100892439 cites W2133209872 @default.
• W3100892439 cites W2144215816 @default.
• W3100892439 cites W2159769116 @default.
• W3100892439 cites W2161913772 @default.
• W3100892439 cites W2171761316 @default.
• W3100892439 cites W2173199232 @default.
• W3100892439 cites W2204998988 @default.
• W3100892439 cites W2273064028 @default.
• W3100892439 cites W2280203836 @default.
• W3100892439 cites W2291349597 @default.
• W3100892439 cites W2334309988 @default.
• W3100892439 cites W2341977879 @default.
• W3100892439 cites W2347466222 @default.
• W3100892439 cites W2509458264 @default.
• W3100892439 cites W2516683110 @default.
• W3100892439 cites W2519376232 @default.
• W3100892439 cites W2549808807 @default.
• W3100892439 cites W2592856387 @default.
• W3100892439 cites W2605676989 @default.
• W3100892439 cites W2711211533 @default.
• W3100892439 cites W2741755810 @default.
• W3100892439 cites W2752630431 @default.
• W3100892439 cites W2776495033 @default.
• W3100892439 cites W2790689076 @default.
• W3100892439 cites W2794956048 @default.
• W3100892439 cites W2795004811 @default.
• W3100892439 cites W2799775089 @default.
• W3100892439 cites W2885080709 @default.
• W3100892439 cites W2885109349 @default.
• W3100892439 cites W2886897318 @default.
• W3100892439 cites W2895988889 @default.
• W3100892439 cites W2897850984 @default.
• W3100892439 cites W2921860422 @default.
• W3100892439 cites W2932511276 @default.
• W3100892439 cites W2958473689 @default.
• W3100892439 cites W2969738668 @default.
• W3100892439 cites W2970028348 @default.
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• W3100892439 doi "https://doi.org/10.3390/w12113190" @default.
• W3100892439 hasPublicationYear "2020" @default.
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