Document Type

Journal Article

Department/Unit

Department of Geography

Language

English

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.

Keywords

Mekong River, water level, remote sensing, PDSI, ENSO, hydrological extremes

Publication Date

1-2018

Source Publication Title

Water

Volume

10

Issue

1

Publisher

MDPI

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

DOI

10.3390/w10010058

Link to Publisher's Edition

https://doi.org/10.3390/w10010058

ISSN (print)

20734441

ISSN (electronic)

20734441

Included in

Geography Commons

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