Calibration of a parsimonious distributed ecohydrological daily model in a data-scarce basin by exclusively using the spatio-temporal variation of NDVI
1Department of Crop Production Ecology, Swedish University of
Agricultural Sciences, Uppsala, Sweden
2Department of Hydrology, Geological Survey of Denmark and Greenland,
Copenhagen, Denmark
3Department of Geosciences and Natural Resources Management, University
of Copenhagen, Copenhagen Denmark
4Department of European and Mediterranean Cultures, University of
Basilicata, Matera, Italy
5Bren School of Environmental Science and Management, Department of
Geography, UC Santa Barbara, Santa Barbara, California, USA
6Research Group of Hydrological and Environmental Modelling (GIHMA),
Research Institute of Water and Environmental Engineering, Universitat
Politècnica de València, Valencia, Spain
1Department of Crop Production Ecology, Swedish University of
Agricultural Sciences, Uppsala, Sweden
2Department of Hydrology, Geological Survey of Denmark and Greenland,
Copenhagen, Denmark
3Department of Geosciences and Natural Resources Management, University
of Copenhagen, Copenhagen Denmark
4Department of European and Mediterranean Cultures, University of
Basilicata, Matera, Italy
5Bren School of Environmental Science and Management, Department of
Geography, UC Santa Barbara, Santa Barbara, California, USA
6Research Group of Hydrological and Environmental Modelling (GIHMA),
Research Institute of Water and Environmental Engineering, Universitat
Politècnica de València, Valencia, Spain
Received: 04 Nov 2016 – Discussion started: 08 Nov 2016 – Revised: 03 Nov 2017 – Accepted: 07 Nov 2017 – Published: 08 Dec 2017
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.
Plants are shaping the landscape and controlling the hydrological cycle, particularly in arid and semi-arid ecosystems. Remote sensing data appears as an appealing source of information for vegetation monitoring, in particular in areas with a limited amount of available field data. Here, we present an example of how remote sensing data can be exploited in a data-scarce basin. We propose a mathematical methodology that can be used as a springboard for future applications.
Plants are shaping the landscape and controlling the hydrological cycle, particularly in arid...
