Daily River Discharge Estimation Using Multi-Mission Radar Altimetry Data and Ensemble Learning Regression in the Lower Mekong River Basin

Abstract

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 (QELQ) 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 QELQ 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.

A data‐oriented strategy to support water resource managers and researchers

“…One study explored the use of Earth observations to improve the management of irrigation water reservoirs in the upper Indus River Basin using methods reported in Hill et al (2020), while a second study used improved snowmelt estimates to provide more accurate predictions of flows into irrigation water reservoirs. In the Lower Mekong River Basin, a system for monitoring surface water storage using integrated multi-satellite products is providing new tools for water management agencies in Viet Nam and Cambodia (Kim et al, 2019). The need for more funds to support research and development has been partially addressed through the World Meteorological…”

Streamflow prediction in “geopolitically ungauged” basins using satellite observations and regionalization at subcontinental scale

“…This algorithm was based on Kmeans clustering for the automatic detection of outliers. Their method was found to be computationally effective compared to other methods, such as Kalman filter approach by Schwatke et al (2015) and applicable in the Mekong river basin (Kim et al, 2019b). No -Envisat-"ungauged" catchment (EU117) Note: "Geopolitically ungauged" catchments are catchments that actually have historical observations of daily streamflow and water level for only cross-validating the proposed method (not used at all for calibration) (Table 1).…”

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

“…(4). An actively sensed water level (or so-called stage) measured from satellite altimetry is directly correlated with nearby in situ RD either via rating curves (e.g., [25][26][27]) or an ensemble-learning regression technique [28]. This method is direct; however, the nominal size of the radar altimetric footprint (i.e., between 3.5 km and 5 km (e.g., [29])) is normally larger than the river width.…”