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

• Main Authors: Donghwan Kim, Hyongki Lee, Chi-Hung Chang, Duong Du Bui, Susantha Jayasinghe, Senaka Basnayake, Farrukh Chishtie, Euiho Hwang
• Format: Article
• Language: English
• Published: MDPI AG 2019-11-01
• Series: Remote Sensing
• Subjects: river discharge; ensemble learning regression; elq; mekong river; altimetry
• Online Access: https://www.mdpi.com/2072-4292/11/22/2684

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 (<inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>Q</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>E</mi> <mi>L</mi> <mi>Q</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula>) 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 <inline-formula> <math display="inline"> <semantics> <mrow> <msub> <mover accent="true"> <mi>Q</mi> <mo stretchy="false">^</mo> </mover> <mrow> <mi>E</mi> <mi>L</mi> <mi>Q</mi> </mrow> </msub> </mrow> </semantics> </math> </inline-formula> 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.