Next Article in Journal
A Livelihood Resilience Measurement Framework for Dam-Induced Displacement and Resettlement
Previous Article in Journal
A WCSPH Particle Shifting Strategy for Simulating Violent Free Surface Flows
Article

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

by 1, 2,* and 1
1
Department of Land, Air and Water Resource, University of California-Davis, Davis, CA 95616, USA
2
Department of Civil and Environmental Engineering, University of California-Merced, Merced, CA 95344, USA
*
Author to whom correspondence should be addressed.
Water 2020, 12(11), 3190; https://doi.org/10.3390/w12113190
Received: 21 October 2020 / Revised: 10 November 2020 / Accepted: 12 November 2020 / Published: 14 November 2020
(This article belongs to the Section Hydrology and Hydrogeology)
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. View Full-Text
Keywords: snow water equivalent; Soil and Water Assessment Tool; multi-objective calibration; upland watershed; equifinality; Southern Sierra Nevada snow water equivalent; Soil and Water Assessment Tool; multi-objective calibration; upland watershed; equifinality; Southern Sierra Nevada
Show Figures

Figure 1

MDPI and ACS Style

Liu, Z.; Yin, J.; E. Dahlke, H. 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. Water 2020, 12, 3190. https://doi.org/10.3390/w12113190

AMA Style

Liu Z, Yin J, E. Dahlke H. 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. Water. 2020; 12(11):3190. https://doi.org/10.3390/w12113190

Chicago/Turabian Style

Liu, Zhu; Yin, Jina; E. Dahlke, Helen. 2020. "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" Water 12, no. 11: 3190. https://doi.org/10.3390/w12113190

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop