Search

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).

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.