Prospects for Reconstructing Daily Runoff from Individual Upstream Remotely-Sensed Climatic Variables

Basin water supply, planning, and its allocation requires runoff measurements near an estuary mouth. However, insufficient financial budget results in no further runoff measurements at critical in situ stations. This has recently promoted the runoff reconstruction via regression between the runoff a...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-02, Vol.14 (4), p.999
Main Authors: Fok, Hok Sum, Chen, Yutong, Zhou, Linghao
Format: Article
Language:English
Subjects:
Climate
Climate change
daily runoff forecast
El Nino
ENSO
Estuaries
GRACE gravimetry
Hydraulics
Hydrology
Mekong Basin
Neural networks
Precipitation
Rain
Rainy season
Regression
Regression analysis
Remote sensing
Rivers
Runoff
Seasons
Standardized precipitation index
Time series
TRMM precipitation
Upstream
Water supply
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Summary:Basin water supply, planning, and its allocation requires runoff measurements near an estuary mouth. However, insufficient financial budget results in no further runoff measurements at critical in situ stations. This has recently promoted the runoff reconstruction via regression between the runoff and nearby remotely-sensed variables on a monthly scale. Nonetheless, reconstructing daily runoff from individual basin-upstream remotely-sensed climatic variables is yet to be explored. This study investigates standardized data regression approach to reconstruct daily runoff from the individual remotely-sensed climatic variables at the Mekong Basin’s upstream. Compared to simple linear regression, the daily runoff reconstructed and forecasted from the presented approach were improved by at most 5% and 10%, respectively. Reconstructed runoffs using neural network models yielded ~0.5% further improvement. The improvement was largely a function of the reduced discrepancy during dry and wet seasons. The best forecasted runoff obtained from the basin-upstream standardized precipitation index, yielded the lowest normalized root-mean-square error of 0.093.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14040999