Ecological Flow Management Identified as Leading Driver of Grassland Greening in the Gobi Desert Using Deep Learning

Abstract

Abstract This study develops a convolutional recurrent deep learning model to accurately predict fine‐resolution spatiotemporal changes in grass coverage in arid regions. Applying the model to the Gobi Desert reveals that ecological flow regulation contributes to 61.8% of the total increase in grass cover (130.6 km2) in the study area (40,423 km2) over 2005–2015, nearly triple the contribution of local climate change (+23.0%). The transboundary hydrological impact (+32.4%) and interactions between drivers (−17.2%) are also significant. In an intermediate future climate change scenario, we found no statistically significant trend for the total grass‐covered area due to the counteracting effects among different drivers. The study findings suggest that timely, adaptive and spatially heterogeneous ecological flow management is crucial for addressing grassland degradation in arid regions. This study provides a promising approach to land surface modeling under climate change and human disturbance and expands the existing understanding of the global greening process.

Keywords

• deep learning
• grassland
• ecological flow
• drylands
• ecohydrology
• artificial intelligence