Google Earth Engine application evaluates the relationship between storage changes and flow fluctuations in the Saigon-Dong Nai River basin

This research utilized the Google Earth Engine (GEE) platform to estimate and analyze water storage dynamics within the Saigon-Dong Nai River basin in Vietnam, leveraging advanced remote sensing datasets related to evapotranspiration (ET) and precipitation (P). Specifically, two primary sources of input data were employed: evapotranspiration estimates derived from MODIS (Moderate Resolution Imaging Spectroradiometers) data, which provided comprehensive spatial coverage across the basin, and precipitation data obtained from the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) satellite product. These datasets enabled a detailed examination of hydrological changes over the period from 2000 to 2019. The research further explored the correlation between fluctuations in stream-flow and changes in water storage during this timeframe. The results of the study revealed distinct seasonal patterns in water storage. Each year, from January to April, water storage exhibited a negative trend, primarily due to increased rates of evaporation coupled with reduced precipitation. Conversely, the period from May to October, which corresponds to the rainy season, saw a substantial increase in water storage variation. This positive change was attributed to higher precipitation, along with decreased solar radiation and lower temperatures, which collectively reduced evapotranspiration losses. Statistical analysis demonstrated a positive correlation between precipitation and evapotranspiration, as well as between water storage and stream-flow. The correlation coefficient between storage and stream-flow variability was found to be relatively low (R=0.38), yet it was statistically significant (p<0.05). The methodological approach adopted in this study can be readily applied to similar hydrological investigations in other river basins. Furthermore, the insights gained from these findings offer valuable support for water resource managers in developing and implementing more effective and sustainable water management policies.