Prediction of Vegetation Indices Series Based on SWAT-ML: A Case Study in the Jinsha River Basin

Abstract

Vegetation dynamics significantly influence watershed ecohydrological processes. Physically based hydrological models often have general plant development descriptions but lack vegetation dynamics data for ecohydrological simulations. Solar-induced chlorophyll fluorescence (SIF) and the Normalized Difference Vegetation Index (NDVI) are widely used in monitoring vegetation dynamics and ecohydrological research. Accurately predicting long-term SIF and NDVI dynamics can support the monitoring of vegetation anomalies and trends. This study proposed a SWAT-ML framework, combining the Soil and Water Assessment Tool (SWAT) and machine learning (ML), in the Jinsha River Basin (JRB). The lag effects that vegetation responds to using hydrometeorological elements were considered while using SWAT-ML. Based on SWAT-ML, SIF and NDVI series from 1982 to 2014 were reconstructed. Finally, the spatial and temporal characteristics of vegetation dynamics in the JRB were analyzed. The results showed the following: (1) the SWAT-ML framework can simulate ecohydrological processes in the JRB with satisfactory results (NS > 0.68, R2 > 0.79 for the SWAT; NS > 0.77, MSE max, SIFave, NDVImax, and NDVIave, respectively). In the middle and lower reaches, the connection between the SIF and hydrometeorological factors is stronger than that of the NDVI. This research developed a new framework and can provide a reference for complex ecohydrological simulation.

Keywords

• vegetation dynamics
• vegetation prediction
• SWAT
• machine learning
• SIF
• NDVI