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Satellite-based precipitation products (SPPs) have great potential in water-related applications, especially in ungauged/poor-gauged basins. Three SPPs, namely integrated multisatellite retrieval for the global precipitation measurement (GPM) mission, tropical rainfall measuring mission multisatellite precipitation analysis version 7, and precipitation estimation from remotely sensed information using artificial neural networks—climate data record, were evaluated over the lower Mekong river basin (LMB) from January 4, 2014 to February 28, 2017 at daily and monthly scales. Daily rainfall data collected from 119 rain gauges in the LMB were used to conduct a pixel-point comparison. Daily discharge observations at six stream gauges, together with a well-calibrated distributed hydrological model, were used to evaluate the hydrological utilities of the three SPP s. The results convey that: integrated multisatellite retrieval for the GPM mission shows more stable and precise estimation of precipitation in pixel-point comparisons (for both all rainfall events and only heavy rain events) than the other two SPP s; precipitation estimation from remotely sensed information using artificial neural networks—climate data record overestimates the rainfall amounts in LMB seriously by 17%; and integrated multisatellite retrieval for the GPM mission performs better than other two SPP s when forcing hydrological model to simulate discharges with more stable and accurate discharge results (daily Nash–Sutcliffe efficiency coefficient larger than 0.73 and monthly Nash–Sutcliffe efficiency coefficient larger than 0.84).

The Tonle Sap Lake (TSL) Basins of the Lower Mekong are one of the world’s most productive ecosystems and have recently been disturbed by climate change. The SWAT (Soil & Water Assessment Tool) hydrological model is utilized to investigate the effect of future climate scenarios. This study focused on two climate scenarios (RCP2.6 and RCP8.5) with three GCMs (GFDL-CM3, GISS-E2-R-CC, and IPSL-CM5A-MR) and their impact on the hydrological process and extremes in the Sen River Basin, the largest tributary of the TSL basin. The annual precipitation, surface runoff, lateral flow, groundwater flow, and total water yield are projected to decrease in both the near-future (2020–2040) and mid-future period (2050–2070), while actual evapotranspiration is projected to increase by 3.3% and 5.3%. Monthly precipitation is projected to increase by 11.2% during the rainy season and decrease by 7.5% during the dry season. Two climate models (GISS and IPSL model) lead to decreases in 1-day, 3-day, 7-day, 30-day, and 90-day maximum flows and minimum flows flow. Thus, the prediction results depend on the climate model used.

Study Region Mekong River Basin and surrounding areas. Study Focus This study investigated the impacts of climate change on future meteorological and hydrological droughts in the Mekong River Basin and its surrounding areas. Our work is based on the output of five global climate models (GCMs) and simulations using the geomorphology-based hydrological model (GBHM) for the historical (1975–2004), near future (2010–2039), middle future (2040–2069), and far future (2070–2099) periods. The meteorological droughts in the study area were measured using SPI and SPEI, while the hydrological droughts were measured using SSI. New Hydrological Insights for the Region The results suggest that droughts will generally reduce in the future over most of the study area, but will be more unevenly distributed with an eastward migration as compared to the historical period. Both meteorological and hydrological droughts will intensify in the near future, but will then reduce in intensity. Meteorological droughts will increase in the northeastern areas in the near future, followed by migration towards the south. Hydrological droughts showed similar aggravation followed by reduction, with upstream areas showing greater variability. In the general context of drought alleviation, southwestern China and the Mekong River estuary may suffer from a continuously increasing drought intensity in the future. This finding is based on 100-year extreme drought events.

Advances in near real-time rainstorm prediction using remote sensing have offered important opportunities for effective disaster management. However, this information is subject to several sources of systematic errors that need to be corrected. Temporal and spatial characteristics of both satellite and in-situ data can be combined to enhance the quality of storm estimates. In this study, we present a spatiotemporal object-based method to bias correct two sources of systematic error in satellites: displacement and volume. The method, Spatiotemporal Contiguous Object-based Rainfall Analysis for Bias Correction (ST-CORAbico), uses the spatiotemporal rainfall analysis ST-CORA incorporated with a multivariate kernel density storm segmentation for describing the main storm event characteristics (duration, spatial extension, volume, maximum intensity, centroid). Displacement and volume are corrected by adjusting the spatiotemporal structure and the intensity distribution, respectively. ST-CORAbico was applied to correct the early version of the Integrated Multi-satellite Retrievals for the Global Precipitation Mission (GPM-IMERG) over the Lower Mekong basin in Thailand during the monsoon season from 2014 to 2017. The performance of ST-CORABico is compared against the Distribution Transformation (DT) and Gamma Quantile Mapping (GQM) probabilistic methods. A total of 120 storm events identified over the study area were classified into short and long-lived storms by using a k-means cluster analysis method. Examples for both storm event types describe the error reduction due to location and magnitude by ST-CORAbico. The results showed that the displacement and magnitude correction made by ST-CORAbico considerably reduced RMSE and bias of GPM-IMERG. In both storm event types, this method showed a lower impact on the spatial correlation of the storm event. In comparison with DT and GQM, ST-CORAbico showed a superior performance, outperforming both approaches. This spatiotemporal bias correction method offers a new approach to enhance the accuracy of satellite-derived information for near real-time estimation of storm events.

Inundated areas of the Mekong Delta are the most important regions for agricultural production in both Cambodia and Vietnam. With population growth and increasing water demand in the dry season, effectively managing available water in the region is vital for crop production. This study is aimed at developing a sub-area based modeling of flood inundation model in order to analyze flood inundation processes in the Cambodian Mekong Delta as a basis for introducing semi-flood control for new cropping systems such as double-rice cropping. The simulated results of the flood inundation model from 2002 to 2003 were utilized for estimating land appearance in the dividing sub-area, and the potential of securing land for double-rice cropping was determined based on the period of land appearance and the cropping schedule. In order to realize the available water for double cropping, consideration of the effect of operating canal gates for controlling the early and receding inundation are crucially necessary. By considering the operation of control gates facilities, the potential land for securing double-rice cropping exceeded about 50 km2 compared to the 34 km2 of the actual land used for recession rice (about 30 and 20% of the total selected area, respectively). The study confirms that the sub-area based modeling of inundation model can be a helpful tool for water management in the Cambodia Mekong Delta.