Abstract
Understanding the soil and hydrologic processes in agricultural watersheds are vital for reliable assessments of water quantity and quality to support integrated river basin management. However, deriving hydrology-relevant information is complicated in flat data-scarce agricultural watersheds due to constraints in watershed delineation, flat topography, poor natural drainage, and irregular irrigation schedules by human intervention. The study aimed to improve the applicability of the Soil and Water Assessment Tool (SWAT) model to simulate daily flow and NO3 concentrations in a flat data-scarce agricultural watershed in the Lower Seyhan Plain (LSP) in Turkey. Refined digitized stream networks, discharge data derived from fully equipped gauging stations, and satellite data (Landsat 7 ETM+, Aster GDEM, etc.) had to be integrated into the modeling process to improve the simulation quality. The model was calibrated using a 2-year (2011–2012) dataset of streamflow and NO3 using the Sequential Uncertainty Fitting (SUFI-2) approach and validated from 2013 to 2018. Daily water yields were predicted with a reasonable simulation accuracy (E values ranging from 0.53 to 0.82 and percent bias (PBIAS) from 0 to +4.1). The results proved that integrating redefined stream networks to SWAT within a Geographic Information System (GIS) environment increases the simulation capability of flow and nitrate dynamics efficiently. Automated delineation of these networks and sub-basins at low topographic transitions limits the SWAT accuracy.
Highlights
Mesoscale hydrological models can be useful tools to answer land use and water management related questions in agricultural regions worldwide, mostly with mountainous and hilly topography [1].the application of hydrological models in flat agricultural watersheds is still challenging due to anthropogenic disturbance of drainage systems and plain topography
Using the stream networks derived from the digital elevation model (DEM) in automated delineation procedures in hydraulic-hydrological models can lead to inaccuracies in low-relief regions [3,4,5]
By redefining digitized stream networks, using discharge data derived from fully equipped gauging stations, and integrating Landsat 7 ETM+, Aster GDEM satellite images into the modeling procedure, we show how to overcome data scarcity and the problems caused by the flat topography of the study region
Summary
Mesoscale hydrological models can be useful tools to answer land use and water management related questions in agricultural regions worldwide, mostly with mountainous and hilly topography [1]. Digitized stream networks derived by a stream burning method to artificially lower and fit the relief data along the existing channels in the catchment can account for this problem recently [15] This technique can increase the accuracy of representing the input channels in the model—in case the stream networks may not be consistent with the DEM due to the flat topography of the study area [3]. The study shows ways to improve the applicability of SWAT and similar models in flat data-scarce agricultural regions in the Mediterranean to predict flows and nutrient loads It contributes to integrated river basin management in such areas
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