Remote Sensing-Based Long-Term Assessment of Water Dynamics and Influencing Factors in Abaya and Chamo Lakes, East African Rift Valley, Ethiopia

Abstract

Lakes are essential to global freshwater systems, supporting ecosystem services and ecological processes, but they are increasingly impacted by climate change and human activities. This study examined the long-term dynamics of Lakes Abaya and Chamo in the Ethiopian Rift Valley using Landsat images, altimetry-derived lake level data, and a machine learning method within Google Earth Engine, specifically a Multi-Index-based Random Forest (MIRF) classifier. The MIRF classifier achieved high accuracy, ranging from 97.58% to 99.13%, in extracting lake surface water. Substantial fluctuations in lake areas and lake levels were observed: Lake Abaya’s area decreased from 2000 to 2005 at a rate of 6.67 km²/year, then expanded until 2022 at the same increasing rate; Lake Chamo’s area decreased from 2000 to 2010 at a rate of 1.62 km²/year, then expanded until 2022 at a rate of 2.88 km²/year. The correlation analysis between lake areas and environmental factors such as Rainfall (RF), Temperature (TEMP), Normalized Difference Vegetation Index (NDVI), Groundwater Storage (GWS), Terrestrial Water Storage (TWS), and Soil Moisture (SM) revealed important associations. For Lake Abaya, strong correlations were identified with NDVI and TWS, suggesting that vegetation cover and terrestrial water significantly influence its area changes. In contrast, for Lake Chamo, NDVI emerged as the key driver, indicating that vegetation dynamics play a crucial role in the lake’s fluctuations. Furthermore, higher-order polynomial regression models were developed to better capture the complex relationships between lake area and water levels for both lakes. In general, this study integrates remote sensing, machine learning, and cloud computing, offering valuable insights into the lakes’ long-term characteristic and providing critical information for future water resource management strategies.