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Improvement of the SWAT Model for Snowmelt Runoff Simulation in Seasonal Snowmelt Area Using Remote Sensing Data

Hongling Zhao, Hongyan Li, Yunqing Xuan Orcid Logo, Changhai Li, Heshan Ni

Remote Sensing, Volume: 14, Issue: 22, Start page: 5823

Swansea University Author: Yunqing Xuan Orcid Logo

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DOI (Published version): 10.3390/rs14225823

Abstract

The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks to its ability of representing the effects of snowmelt and permafrost on runoff generation and confluence. However, a core method used in the SWAT model, the temperature index method, assumes both the dates for ma...

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Published in: Remote Sensing
ISSN: 2072-4292
Published: MDPI AG 2022
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa61952
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Abstract: The SWAT model has been widely used to simulate snowmelt runoff in cold regions thanks to its ability of representing the effects of snowmelt and permafrost on runoff generation and confluence. However, a core method used in the SWAT model, the temperature index method, assumes both the dates for maximum and minimum snowmelt factors and the snowmelt temperature threshold, which leads to inaccuracies in simulating snowmelt runoff in seasonal snowmelt regions. In this paper, we present the development and application of an improved temperature index method for SWAT (SWAT+) in simulating the daily snowmelt runoff in a seasonal snowmelt area of Northeast China. The improvements include the introduction of total radiation to the temperature index method, modification of the snowmelt factor seasonal variation formula, and changing the snowmelt temperature threshold according to the snow depth derived from passive microwave remote sensing data and temperature in the seasonal snowmelt area. Further, the SWAT+ model is applied to study climate change impact on future snowmelt runoff (2025–2054) under the climate change scenarios including SSP2.6, SSP4.5, and SSP8.5. Much improved snowmelt runoff simulation is obtained as a result, supported by several metrics, such as MAE, RE, RMSE, R2, and NSE for both the calibration and validation. Compared with the baseline period (1980–2019), the March–April ensemble average snowmelt runoff is shown to decrease under the SSP2.6, SSP4.5, and SSP8.5 scenario during 2025–2054. This study provides a valuable insight into the efficient development and utilization of spring water resources in seasonal snowmelt areas.
Keywords: snowmelt runoff; climate change; snowmelt flood; SWAT model; remote sensing; seasonal snowmelt area
College: College of Engineering
Funders: This study was supported by Key R&D project funding from Jilin Province Science and Technology Department, China (20200403070SF) and Key Program of National Natural Science Foundation of China (42230204).
Issue: 22
Start Page: 5823