Assessing the spatiotemporal distributions of evapotranspiration in the Three Gorges Reservoir Region of China using remote sensing data
Journal of Mountain Science
Evapotranspiration (ET) is a critical component of the global hydrological cycle, and it has a large impact on water resource management as it affects the availability of freshwater resources. It is important to understand the hydrological cycle for the water resources planning and management. This study used Moderate Resolution Imaging Spectroradiometer (MODIS) satellite derived ET, and potential evapotranspiration (PET) and Tropical Rainfall Measuring Mission (TRMM) satellite derived precipitation datasets to assess the spatial and temporal distributions of ET, PET, and precipitation during the study period at Three Gorges Reservoir (TGR) region. Based on the topographic variations and land-use/land-cover distributions, the study region which includes five counties of Hubei Province and nineteen counties of Chongqing Municipality was divided into four study zones. The ET and precipitation data were evaluated using in situ observations. The ET, PET, and precipitation data were compared to analyze the spatial and long-term (2001-2016) temporal distributions of average annual ET, PET, and precipitation, and to understand the relationships between them in the study region. The results showed that each selected zone had highest ET at the counties with the Yangtze River passing through whereas lowest at the counties which were located away from the river. Results also showed increasing trends in ET and PET from south-west to north-east in the study region. Analysis showed TGR had a significant impact on spatial and temporal distributions of ET and PET in the study region. Therefore, this study helps to understand the impact of TGR on spatial and temporal distributions of ET and PET during and after the construction.
Ma, Z., Ray, R., & He, Y. (2018). Assessing the spatiotemporal distributions of evapotranspiration in the Three Gorges Reservoir Region of China using remote sensing data. Journal of Mountain Science, 15 (12), 2676-2692. https://doi.org/10.1007/s11629-018-5180-2