Li, C., Lu, T., Wang, S. and Xu, J. (2023) Coupled Thorens and Soil Conservation Service models for soil erosion assessment in a Loess Plateau watershed, China. Remote Sensing, 15(3), 803. (doi: 10.3390/rs15030803)
Text
290995.pdf - Published Version Available under License Creative Commons Attribution. 3MB |
Abstract
Assessing soil erosion in China’s severely eroded Loess Plateau is urgently needed but is usually limited by suitable erosion models and long-term field measurements. In this study, we coupled the Thorens and Soil Conservation Service (SCS) models to evaluate runoff and sediment yield during the 1980s and 2010s in the Xiaolihe watershed on the Loess Plateau. Results showed the proposed model framework had a satisfactory performance in modelling spatially distributed runoff and sediment yield. The Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS) and the root mean square error-measured standard deviation ratio (RSR) were 0.93, 4.42% and 0.27 for monthly runoff; and 0.31, 62.31% and 0.82 for monthly sediment yield. The effects of land use changes on runoff and sediment yield were well captured by the SCS and Thorens models. The proposed modelling framework is distributed with a simple structure, requires relatively little data that can be obtained from public datasets, and can be used to predict runoff and sediment yield in other similar ungagged or poorly monitored watersheds. This work has important implications for runoff and erosion assessment in other arid and semi-arid regions, to derive runoff and erosion rates across large areas with scarce field measurements.
Item Type: | Articles |
---|---|
Additional Information: | This research was jointly funded by the National Natural Science Foundation of China Project (grant U2243601, 42007052), and the Fundamental Research Funds for the Central Universities. |
Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Xu, Dr Jiren |
Creator Roles: | |
Authors: | Li, C., Lu, T., Wang, S., and Xu, J. |
College/School: | College of Social Sciences > School of Social & Environmental Sustainability |
Journal Name: | Remote Sensing |
Publisher: | MDPI |
ISSN: | 2072-4292 |
ISSN (Online): | 2072-4292 |
Copyright Holders: | Copyright © 2023 The Authors |
First Published: | First published in Remote Sensing 15(3):803 |
Publisher Policy: | Reproduced under a Creative Commons License |
University Staff: Request a correction | Enlighten Editors: Update this record