Estimation of irrigation return flow on monthly time resolution using SWAT model under limited data availability

ABSTRACT

This study is an assessment of the return flow ratio of an irrigation abstraction using the flow records of a downstream stream-gauging station, with the example of the Kozluk scheme irrigated by diverted water from Garzan Creek flowing through the southeastern region of Turkey. In the planning reports of the major dam projects of the region, an unverified return ratio was assumed in eliminating the influence of this irrigation on the flow measurements of Garzan Creek. The correctness of this assumed return ratio is evaluated by analysing the monthly streamflow measurements of the Besiri station through a Soil and Water Assessment Tool (SWAT) model constructed with the coarse-scale topography, land use and soil data from open source databases. The results show the necessity of irrigation project-based return flow analyses using regional fine-scale datasets, instead of rule-of-thumb assumptions, to determine the effects of irrigation activities on flow regimes more accurately.     

Assessing the performance and uncertainty analysis of the SWAT and RBNN models for simulation of sediment yield in the Nagwa watershed,India

Abstract

The process-based Soil and Water Assessment Tool (SWAT) model and the data-driven radial basis neural network (RBNN) model were evaluated for simulating sediment load for the Nagwa watershed in Jharkhand, India, where soil erosion is a severe problem. The SWAT model calibration and uncertainty analysis were performed with the Sequential Uncertainty Fitting algorithm version 2 and the bootstrap technique was applied on the RBNN model to analyse uncertainty in model output. The percentage of data bracketed by the 95% prediction uncertainty (95PPU) and the r factor were the two measures used to assess the goodness of calibration. Comparison of the results of the two models shows that the value of r factor (r = 0.41) in the RBNN model is less than that of SWAT model (r = 0.79), which means there is a wider prediction interval for the SWAT model results. More values of observed sediment yield were bracketed by the 95PPU in the RBNN model. Thus, the RBNN model estimates the sediment yield values more accurately and with less uncertainty.

Editor D. Koutsoyiannis; Associate editor H. Aksoy

Citation Singh, A., Imtiyaz, M., Isaac, R.K., and Denis, D.M., 2014. Assessing the performance and uncertainty analysis of the SWAT and RBNN models for simulation of sediment yield in the Nagwa watershed, India. Hydrological Sciences Journal, 59 (2), 351–364.