Preprints
https://doi.org/10.5194/hess-2017-251
https://doi.org/10.5194/hess-2017-251

  06 Jun 2017

06 Jun 2017

Review status: this preprint has been withdrawn by the authors.

SWAT-CUP for Calibration of Spatially Distributed Hydrological Processes and Ecosystem Services in a Vietnamese River Basin Using Remote Sensing

Lan T. Ha1,2, Wim G. M. Bastiaanssen1,3, Ann van Griensven3,4, Albert I. J. M. van Dijk5,6, and Gabriel B. Senay7 Lan T. Ha et al.
  • 1Delft University of Technology, 2628 CN Delft, The Netherlands
  • 2Institute of Water Resources Planning, Hanoi, Vietnam
  • 3IHE Delft Institute for Water Education, 2611 AX Delft, The Netherlands
  • 4Vrije Universiteit Brussel, Brussel, 1050, Belgium
  • 5Fenner School of Environment & Society, Australian National University, Canberra, ACT 2601, Australia
  • 6Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, Australia
  • 7U.S. Geological Survey Earth Resources Observation Science Center, North Central Climate Science Center, Fort Collins, Colorado, CO 80526, USA

Abstract. Distributed hydrological models are usually calibrated against the measured outflow of a certain drainage area, provided flow data is available. A close match with flow does however not mean that the spatially distributed hydrological processes are properly understood and simulated. In this paper, remotely sensed precipitation, evapotranspiration (ET) and leaf area index (LAI) from open access data sources were used to calibrate the SWAT model for the Day Basin, a tributary of the Red River in Vietnam. The efficacy of the SWAT-CUP parameter sensitivity and optimization model developed by Abbaspour (2015) was tested with spatial remote sensing input parameters. The innovation is that the parameters of the soil-vegetation processes were optimized for every Hydrological Response Unit for which remotely sensed monthly ET and LAI values were available. Such level of detail cannot be achieved from flow measurements, which are the integrated result of many processes over large areas. A total of 15 soil-vegetation process parameters were calibrated. The SUFI2 algorithm in SWAT-CUP appeared to be an adequate practical tool for the calibration process. Simulated monthly ET correlations with remote sensing estimates showed an R2 = 0.71 and NSE = 0.65 while monthly LAI showed correlations of R2 = 0.59 and NSE = 0.57 over a five year validation period. Accumulated modelled ET over the 5-year calibration period amounted to 5713 mm compared to 6015 mm of remotely sensed ET: a non-significant difference of 302 mm (5.3 %). Because river flow was not optimized during the calibration process, it could be used as an independent validation of the calibrated model simulations. The monthly flow at two flow measurement stations were adequately estimated (R2 = 0.78 and 0.55, NSE = 0.71 and 0.63 for Phu Ly and Ninh Binh, respectively). The estimated total water withdrawal from the Red River was 1.934 billion m3/yr with a peak flow of approximately 200 m3/s during the months of February and July. The availability of a reliable set of parameters will make SWAT a useful tool for optimizing water conservation, agricultural outputs, and ecosystem services such as reduced soil erosion, better water quality standards, carbon sequestration, micro-climate cooling amongst others. Such calibrated distributed eco-hydrological models can be used for appraising scenarios of green growth.

This preprint has been withdrawn.

Lan T. Ha et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Lan T. Ha et al.

Lan T. Ha et al.

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This preprint has been withdrawn.

Short summary
The paper shows a new approach in calibrating hydrological model using remote sensing data from open access sources. The innovation is that the parameters of the soil-vegetation processes were optimized that will make SWAT a useful tool for optimizing water conservation, agricultural outputs, and ecosystem services such as reduced soil erosion, better water quality standards, carbon sequestration, micro-climate cooling and appraising scenarios of green growth.