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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 10
Hydrol. Earth Syst. Sci., 20, 4391–4407, 2016
https://doi.org/10.5194/hess-20-4391-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 20, 4391–4407, 2016
https://doi.org/10.5194/hess-20-4391-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 28 Oct 2016

Research article | 28 Oct 2016

A pre-calibration approach to select optimum inputs for hydrological models in data-scarce regions

Esraa Tarawneh1, Jonathan Bridge1, and Neil Macdonald2 Esraa Tarawneh et al.
  • 1Centre for Engineering Sustainability, School of Engineering, University of Liverpool, Liverpool, L69 3GH, UK
  • 2School of Environmental Sciences, University of Liverpool, Liverpool, L69 7ZT, UK

Abstract. This study uses the Soil and Water Assessment Tool (SWAT) model to quantitatively compare available input datasets in a data-poor dryland environment (Wala catchment, Jordan; 1743 km2). Eighteen scenarios combining best available land-use, soil and weather datasets (1979–2002) are considered to construct SWAT models. Data include local observations and global reanalysis data products. Uncalibrated model outputs assess the variability in model performance derived from input data sources only. Model performance against discharge and sediment load data are compared using r2, Nash–Sutcliffe efficiency (NSE), root mean square error standard deviation ratio (RSR) and percent bias (PBIAS). NSE statistic varies from 0.56 to −12 and 0.79 to −85 for best- and poorest-performing scenarios against observed discharge and sediment data respectively. Global weather inputs yield considerable improvements on discontinuous local datasets, whilst local soil inputs perform considerably better than global-scale mapping. The methodology provides a rapid, transparent and transferable approach to aid selection of the most robust suite of input data.

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The study presents a rapid, robust and reproducible approach to select optimum inputs for hydrological models. We show significant variation in pre-calibrated model performance due to the choice of different combinations of land-use, soil and weather datasets, particularly in data-scarce regions most in need of computationally-efficient approaches to optimise reliability of models and support decision-making. Our case study is directly relevant to planners and decision-makers in Wala, Jordan.
The study presents a rapid, robust and reproducible approach to select optimum inputs for...
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