Article open access publication

Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI

Hydrology and Earth System Sciences, Copernicus Publications, ISSN 1607-7938

Volume 21, 12, 2017

DOI:10.5194/hess-21-6235-2017, Dimensions: pub.1099618483,

Authors

Koch, Julian (3) (4)

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Affiliations

Organisations

  1. (1) Swedish University of Agricultural Sciences, grid.6341.0
  2. (2) Polytechnic University of Valencia, grid.157927.f
  3. (3) Geological Survey of Denmark and Greenland, grid.13508.3f
  4. (4) University of Copenhagen, grid.5254.6, KU
  5. (5) University of Basilicata, grid.7367.5
  6. (6) University of California, Santa Barbara, grid.133342.4

Description

Abstract. Ecohydrological modeling studies in developing countries, such as sub-Saharan Africa, often face the problem of extensive parametrical requirements and limited available data. Satellite remote sensing data may be able to fill this gap, but require novel methodologies to exploit their spatio-temporal information that could potentially be incorporated into model calibration and validation frameworks. The present study tackles this problem by suggesting an automatic calibration procedure, based on the empirical orthogonal function, for distributed ecohydrological daily models. The procedure is tested with the support of remote sensing data in a data-scarce environment – the upper Ewaso Ngiro river basin in Kenya. In the present application, the TETIS-VEG model is calibrated using only NDVI (Normalized Difference Vegetation Index) data derived from MODIS. The results demonstrate that (1) satellite data of vegetation dynamics can be used to calibrate and validate ecohydrological models in water-controlled and data-scarce regions, (2) the model calibrated using only satellite data is able to reproduce both the spatio-temporal vegetation dynamics and the observed discharge at the outlet and (3) the proposed automatic calibration methodology works satisfactorily and it allows for a straightforward incorporation of spatio-temporal data into the calibration and validation framework of a model.

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Times Cited: 6

Field Citation Ratio (FCR): 2

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