Multivariate assimilation of satellite-based leaf area index and ground-based river streamflow for hydrological modelling of irrigated watersheds using SWAT

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 m 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 m. 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.

Automated summary

Vegetation dynamics play a crucial role in the terrestrial water cycle, but their effects on hydrological models of irrigated watersheds have been rarely studied. In this study, a combinatorial approach using Sentinel-2 data was proposed and applied to an irrigated watershed in Iran. The results showed that the approach improved the predictive accuracy of the model and had a significant influence on the estimation of irrigation volume and timing.