Improved Mekong Basin Runoff Estimate and Its Error Characteristics Using Pure Remotely Sensed Data Products

Abstract

Basin runoff is a quantity of river discharge per unit basin area monitored close to an estuary mouth, essential for providing information on the flooding and drought conditions of an entire river basin. Owing to a decreasing number of in situ monitoring stations since the late 1970s, basin runoff estimates using remote sensing have been advocated. Previous runoff estimates of the entire Mekong Basin calculated from the water balance equation were achieved through the hybrid use of remotely sensed and model-predicted data products. Nonetheless, these basin runoff estimates revealed a weak consistency with the in situ ones. To address this issue, we provide a newly improved estimate of the monthly Mekong Basin runoff by using the terrestrial water balance equation, purely based on remotely sensed water balance component data products. The remotely sensed water balance component data products used in this study included the satellite precipitation from the Tropical Rainfall Measuring Mission (TRMM), the satellite evapotranspiration from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the inferred terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE). A comparison of our new estimate and previously published result against the in situ runoff indicated a marked improvement in terms of the Pearson’s correlation coefficient (PCC), reaching 0.836 (the new estimate) instead of 0.621 (the previously published result). When a three-month moving-average process was applied to each data product, our new estimate further reached a PCC of 0.932, along with the consistent improvement revealed from other evaluation metrics. Conducting an error analysis of the estimated mean monthly runoff for the entire data timespan, we found that the usage of different evapotranspiration data products had a substantial influence on the estimated runoff. This indicates that the choice of evapotranspiration data product is critical in the remotely sensed runoff estimation.

Spatiotemporal Land Use and Land Cover Changes and Associated Runoff Impact in Itaperuna, Brazil

“…The integration of these technologies has led to significant progress in water management. Remote sensing data aids in flood monitoring and prediction, identification of aquifer recharge zones, and development of sustainable water management strategies [39,40]. Furthermore, GIS facilitates information exchange among government institutions, water resource management organizations, and researchers, fostering effective collaboration in addressing water-related crises [41].…”

Integration of Satellite-Derived and Ground-Based Soil Moisture Observations for a Precipitation Product over the Upper Heihe River Basin, China

“…Since the launch of the Tropical Rainfall Measuring Mission (TRMM) satellite in 1997, satellite remote-sensing-based precipitation products have matured [2]. The TRMM multisatellite precipitation analysis (TMPA) product [7] and its global successor, the integrated multisatellite retrievals for global precipitation measurement (GPM) product, have been used widely in many areas owing to their reasonably high accuracy and general applicability [12][13][14][15]. The GPM product provides rainfall estimates from merged multisensor information [7].…”