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Water 2018, 10(1), 58; https://doi.org/10.3390/w10010058

Application of ENSO and Drought Indices for Water Level Reconstruction and Prediction: A Case Study in the Lower Mekong River Estuary

1
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
2
Institute of Marine Science and Technology, Wuhan University, Wuhan 430079, China
3
Department of Geography, Hong Kong Baptist University, Hong Kong, China
4
State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan 430077, China
5
School of Environment and Sustainability, University of Saskatchewan, Saskatoon, SK S7N 3H5, Canada
*
Authors to whom correspondence should be addressed.
Received: 19 October 2017 / Revised: 22 December 2017 / Accepted: 9 January 2018 / Published: 11 January 2018
(This article belongs to the Special Issue Impact of Climate on Hydrological Extremes)
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Abstract

Water level monitoring is important for understanding the global hydrological cycle. Remotely-sensed indices that capture localized instantaneous responses have been extensively explored for water level reconstruction during the past two decades. However, the potential usage of the Palmer’s Drought Severity Index (PDSI) and El Niño Southern Oscillation (ENSO) indices for water level reconstruction and prediction has not been explored. This paper examines the relationship between observed water level and PDSI based on a soil-moisture water balance model and three ENSO indices for the lower Mekong River estuary on a monthly temporal scale. We found that the time-lagged information between the standardized water level and the ENSO indices that enabled us to reconstruct the water level using the ENSO indices. The influence of strong ENSO events on the water level can help capture the hydrological extremes during the period. As a result, PDSI-based water level reconstruction can be further improved with the assistance of ENSO information (called ENSO-assisted PDSI) during ENSO events. The water level reconstructed from the PDSI and ENSO indices (and that of remote sensing) compared to observed water level shows a correlation coefficient of around 0.95 (and <0.90), with an RMS error ranging from 0.23 to 0.42 m (and 0.40 to 0.79 m) and an NSE around 0.90 (and <0.81), respectively. An external assessment also displayed similar results. This indicates that the usage of ENSO information could lead to a potential improvement in water level reconstruction and prediction for river basins affected by the ENSO phenomenon and hydrological extremes. View Full-Text
Keywords: Mekong River; water level; remote sensing; PDSI; ENSO; hydrological extremes Mekong River; water level; remote sensing; PDSI; ENSO; hydrological extremes
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Fok, H.S.; He, Q.; Chun, K.P.; Zhou, Z.; Chu, T. Application of ENSO and Drought Indices for Water Level Reconstruction and Prediction: A Case Study in the Lower Mekong River Estuary. Water 2018, 10, 58.

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