Multivariate Assimilation of Satellite-based Leaf Area Index and Ground-based River Streamflow for Hydrological Modelling of Irrigated Watersheds using SWAT+
基于卫星的叶面积指数和基于地面的河流流量的多变量同化，用于使用 SWAT+ 进行灌溉流域的水文建模
- 关键词：
- 来源：
- ScienceDirect
- 类型：
- 学术文献
- 语种：
- 英语
- 原文发布日期：
- 2022-06-03
- 摘要：
- Vegetation dynamics have different effects on the terrestrial water cycle and therefore play an important role in catchment-scale biophysical and hydrological modeling. Meanwhile, validation of hydrological models of irrigated watersheds concerning vegetation dynamics has rarely been studied. In this study, we propose a combinatorial approach for modelling irrigated watersheds; at first, based on Sentinel-2 (S2), we provide crop/land-use mapping; then we retrieve S2-based leaf area index (LAI) through the neural network algorithm and the PROSAIL model; finally, we employ sequential particle filter (PF) technique to explore the benefits of jointly assimilating high-resolution S2-LAI as well as in-situ river discharge observations for improving the predictive accuracy of SWAT+ model. Moreover, we explain how SWAT+ source code must be modified to implement the assimilation procedure. The developed methodology is applied to an irrigated watershed in Iran; we provide 66 raster LAI maps with a spatial resolution of 20 meters for the study area related to January to December of 2019 as well as crop/land-use mapping of 2019 with a spatial resolution of 10 meters. Results show that improvements in the hydrological simulation of LAI, evapotranspiration, and river discharge are achieved when we apply multivariate assimilation of S2-LAI and streamflow observations, compared to univariate assimilation scenarios. Results also reveal that LAI assimilation has a significant influence on the estimation of irrigation volume and timing.
- 所属专题：
- 135
