Mapping Groundwater-Dependent Ecosystems in the Urumqi River and Chai Wo-pu Basins Using Geospatial Technologies and Field Data

Abstract. Groundwater-Dependent Ecosystems (GDEs) are widely distributed in arid and semi-arid regions and serve as critical ecological safety barriers. However, the precise identification and mapping of GDEs has long posed a challenge for researchers, as the integration of geospatial technologies and field measurement techniques has remained insufficient. This study selected the final year of a prolonged drought period and the current year for analysis. Key indicators, including vegetation cover (FVC), the difference between evapotranspiration and precipitation (ET-P), Terrain Wetness Index (TWI) and the vegetation groundwater uptake index (VGUI), were employed. The K-means clustering algorithm was applied for classification, and spatial overlay analysis was performed in the Urumqi River and Chai Wo-pu Basin in Xinjiang, China, to assess the spatial distribution and temporal variations of GDEs. Additionally, the results were validated through the integration of wetland distribution and field investigations. The findings indicate that areas classified as "likely" or "very likely" GDEs are predominantly concentrated around Dongdao Haizi, Chai Wo-pu Lake, salt lakes, and adjacent regions, covering 45.4 % of the potential rea. Over time, the area classified as "unlikely" and "highly unlikely" for GDEs shows an increasing trend. By introducing field data of the VGUI, the study eliminates the interference from factors such as human irrigation in the identification of GDEs in typical areas, achieving refined mapping. This work provides valuable insights for the precise identification of GDEs in arid and semi-arid regions.