Regionalizing the ACEA Model with Remote Sensing for Water Footprints in the Upper Syr Darya Basin

Water consumption in the upper Syr Darya Basin, Central Asia, is primarily driven by the agricultural sector, emphasizing the need for precise water footprint (WF) assessments. This study addresses this critical gap by incorporating a dynamic crop calendar into the AquaCrop-Earth@lternatives (ACEA) model – a process-based model designed to simulate crop growth and water productivity in the context of agro-hydrology. To enhance the model's regional relevance, we integrated remote sensing data, specifically the MODIS Normalized Difference Vegetation Index (NDVI), to estimate planting dates and growing season lengths, which serve as proxies for crop calendar information. Two model runs were conducted: one using the original ACEA settings and another with satellite-derived crop calendar inputs. The study focuses on simulating yield, evapotranspiration (ET), and the unit crop WF for irrigated cotton between 2003 and 2019, analysing how variations in crop calendar data affect both spatial and temporal outcomes. To evaluate model performance, we compared simulated ET with remote sensing-based ET data from sources such as WaPOR, GLEAM, and MODIS, and matched simulated yield with reported statistics. The findings of this study can improve global models of crop WF, enhancing their applicability to specific regions, and advancing our understanding of agricultural water use in the Syr Darya Basin.