Estimating river discharge (Q) is critical for ecosystems and water resource management. Traditionally, estimating Q has depended on a single rating curve or the Manning equation. In contrast to the single rating curve, several rating curves at different locations have been linearly combined in an ensemble learning regression method to estimate Q (ELQ) at the Brazzaville gauge station in the central Congo River in a previous study. In this study, we further tested the proposed ELQ and apply it to the Lower Mekong River Basin (LMRB) with three locations: Stung Treng, Kratie, and Tan Chau. Two major advancements for estimating Q with ELQ are presented. First, ELQ successfully estimated Q at Tan Chau, downstream of Kratie, where hydrodynamic complexities exist. Since the hydrologic characteristics downstream of Kratie are extremely diverse and complex in time and space, most previous studies have estimated Q only upstream from Kratie with hydrologic models and statistical methods. Second, we estimated Q over the LMRB using ELQ with water levels (H) obtained from two radar altimetry missions, Envisat and Jason-2, which made it possible to estimate Q seamlessly from 2003 to 2016. Owing to ELQ with multi-mission radar altimetry data, we have overcome the problems of a single rating curve: Locations for estimating Q have to be close to virtual stations, e.g., a few tens of kilometers, because the performance of the single rating curve degrades as the distance between the location of Q estimation and a virtual station increases. Therefore, most previous studies had not used Jason-2 data whose cross-track interval is about 315 km at the equator. On the contrary, several H obtained from Jason-2 altimetry were used in this study regardless of distances from in-situ Q stations since the ELQ method compensates for degradation in the performance for Q estimation due to the poor rating curve with virtual stations away from in-situ Q stations. In general, the ELQ-estimated Q ( ${\widehat{Q}}_{ELQ}$ ) showed more accurate results compared to those obtained from a single rating curve. In the case of Tan Chau, the root mean square error (RMSE) of ${\widehat{Q}}_{ELQ}$ decreased by 1504/1338 m³/s using Envisat-derived H for the training/validation datasets. We successfully applied ELQ to the LMRB, which is one of the most complex basins to estimate Q with multi-mission radar altimetry data. Furthermore, our method can be used to obtain finer temporal resolution and enhance the performance of Q estimation with the current altimetry missions, such as Sentinel-3A/B and Jason-3.

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.