Abstract
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.
Highlights
Arid and semi-arid regions of the world suffer from water scarcity exacerbated by growing populations, increasing per capita water consumption and agricultural intensification
The results show that simulating higher discharge rates, which is usually associated with larger watersheds, introduces greater uncertainty in Soil and Water Assessment Tool (SWAT) discharge estimates and the study states that very good model performance is achieved for monthly stream-flow estimation, while the outputs of daily simulation are only within acceptable range
We suggest the reason for this is the continuity and consistency of the Climate Forecast System Reanalysis (CFSR) dataset, which is provided by the National Centre for Environmental Prediction (NCEP) reanalysis climate data derived from global satellite imagery for a grid of statistically interpolated points (Saha et al, 2010)
Summary
Arid and semi-arid regions of the world suffer from water scarcity exacerbated by growing populations, increasing per capita water consumption and agricultural intensification. Effective water management is crucial and relevant decision making can be assisted by approximating the complex hydrologic systems of arid and semi-arid regions through modelling. This enables scenario-testing and forecasting to inform decisionmaking in water and land management (Tessema, 2011; Wheater et al, 2008a). Data available to underpin models may, vary significantly, both in quality and quantity (Pilgrim et al, 1988) This encourages the use of modelling “rules of thumb” or estimations based on spatially or temporally aggregated data for the modelled area, or data obtained from comparably better-studied regions (Gee and Hillel, 1988; Nyong et al, 2007; Tingsanchali and Gautam, 2000)
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