Geographically-weighted water balance approach for satellite-hydrologic runoff estimation in Mekong Basin under ENSO

• DOI: 10.1016/j.jag.2023.103234
• Publication Date: 2023-02-24T23:20:48Z
• Authors: Hok Sum Fok; Yutong Chen; Zhongtian Ma; Vagner G. Ferreira; Robert Tenzer
• Keywords: Environmental sciences; Physical geography; GRACE; 0207 environmental engineering; GE1-350; 02 engineering and technology; ENSO; Water balance; Geographical weighting; Basin runoff; GB3-5030

Total basin runoff has been estimated via satellite-hydrologic observations since 2000 due to the high maintenance cost of in situ runoff measurements and the existence of many ungauged river basins. Previous estimates of the satellite-hydrologic basin runoff have been done by simply averaging each gridded runoff within a river basin generated from satellite-hydrologic data products via the water balance equation. Nonetheless, geographical heterogeneities within the river basin are present, which should be considered when estimating satellite-hydrologic runoff. This study proposes a novel geographically-weighted water balance approach to estimate the satellite-hydrologic basin runoff. Our runoff estimates in the Mekong Basin show an improvement of 50–60 % (by means of reducing a root-mean-square error) when compared with published results, which is also consistent with other evaluation statistics. Despite hydrological characteristics for the floodplain of Tonlé Sap Lake in Cambodia differing from the mainstream of Mekong Basin, the runoff basin-averaged, including those areas, also reveal a comparable result against in situ runoff located at the Mekong delta based on the proposed method. We also acquire that the impact of ENSO variability on satellite-hydrologic runoff estimates is substantial, which has not previously been reported. Our error analysis indicates that space-borne water storage is one of the critical remaining error sources. We also anticipate that the runoff estimates could further be improved by combining different versions of space-borne water storage datasets.