A self-calibrating runoff and streamflow remote sensing model for ungauged basins using open-access earth observation data

Ate Poortinga, Wim Bastiaanssen, Gijs Simons, David Saah, Gabriel Senay, Mark Fenn, Brian Bean, John Kadyszewski

Research output: Contribution to journalArticleScientificpeer-review

15 Citations (Scopus)

Abstract

Due to increasing pressures on water resources, there is a need to monitor regional water resource availability in a spatially and temporally explicit manner. However, for many parts of the world, there is insufficient data to quantify stream flow or ground water infiltration rates. We present the results of a pixel-based water balance formulation to partition rainfall into evapotranspiration, surface water runoff and potential ground water infiltration. The method leverages remote sensing derived estimates of precipitation, evapotranspiration, soil moisture, Leaf Area Index, and a single F coefficient to distinguish between runoff and storage changes. The study produced significant correlations between the remote sensing method and field based measurements of river flow in two Vietnamese river basins. For the Ca basin, we found R2 values ranging from 0.88-0.97 and Nash-Sutcliffe efficiency (NSE) values varying between 0.44-0.88. The R2 for the Red River varied between 0.87-0.93 and NSE values between 0.61 and 0.79. Based on these findings, we conclude that the method allows for a fast and cost-effective way to map water resource availability in basins with no gauges or monitoring infrastructure, without the need for application of sophisticated hydrological models or resource-intensive data.

Original languageEnglish
Article number86
JournalRemote Sensing
Volume9
Issue number1
DOIs
Publication statusPublished - 2017

Keywords

  • Ca basin
  • Groundwater
  • Hydrological model
  • Red river basin
  • Surface flow
  • Ungauged river basin
  • Vietnam

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