Impact of climate change on streamflow in selected river basins in Ghana     

Kwabena Kankam-Yeboah  Emmanuel Obuobie  Barnabas Amisigo  Yaw Opoku-Ankomah 《水文科学杂志》2013,58(4):773-788

Abstract

This study uses the Soil and Water Assessment Tool (SWAT) and downscaled climate projections from the ensemble of two global climate models (ECHAM4 and CSIRO) forced by the A1FI greenhouse-gas scenario to estimate the impact of climate change on streamflow in the White Volta and Pra river basins, Ghana. The SWAT model was calibrated for the two basins and subsequently driven by downscaled future climate projections to estimate the streamflow for the 2020s (2006–2035) and 2050s (2036–2075). Relative to the baseline, the mean annual streamflow estimated for the White Volta basin for the 2020s and 2050s showed a decrease of 22 and 50%, respectively. Similarly, the estimated streamflow for the 2020s and 2050s for the Pra basin showed a decrease of 22 and 46%, respectively. These results underscore the need to put in place appropriate adaptation measures to foster resilience to climate change in order to enhance water security within the two basins.

Citation Kankam-Yeboah, K., Obuobie, E., Amisigo, B., and Opoku-Ankomah, Y., 2013. Impact of climate change on streamflow in selected river basins in Ghana. Hydrological Sciences Journal, 58 (4), 773–788.  相似文献   

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

Ajai Singh  Mohd. Imtiyaz  R.K. Isaac  D.M. Denis 《水文科学杂志》2013,58(2):351-364

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.  相似文献   

Assessing drought threats to agricultural water supplies under climate change by combining the SWAT and MODSIM models for the Geum River basin,South Korea     

So Ra Ahn  Jae Hak Jeong 《水文科学杂志》2013,58(15):2740-2753

ABSTRACT

The impacts of future climate change on the agricultural water supply capacities of irrigation facilities in the Geum River basin (9645.5 km²) of South Korea were investigated using an integrated modeling framework that included a water balance network model (MODSIM) and a watershed-scale hydrologic model (Soil and Water Assessment Tool, SWAT). The discharges and baseflows from upland drainage areas were estimated using SWAT, and the predicted flow was used to feed agricultural reservoirs and multipurpose dams in subwatersheds. Using a split sampling method, we calibrated the daily streamflows and dam inflows at three locations using data from 6 years, including 3 years of calibration data (2005–2007) followed by 3 years of validation data (2008–2010). In the MODSIM model, the entire basin was divided into 14 subwatersheds in which various agricultural irrigation facilities such as agricultural reservoirs, pumping stations, diversions, culverts and groundwater wells were defined as a network of hydraulic structures within each subwatershed. These hydraulic networks between subwatersheds were inter-connected to allow watershed-scale analysis and were further connected to municipal and industrial water supplies under various hydrologic conditions. Projected climate data from the HadGEM3-RA RCP 4.5 and 8.5 scenarios for the period of 2006–2099 were imported to SWAT to calculate the water yield, and the output was transferred to MODSIM in the form of time-series boundary conditions. The maximum shortage rate of agricultural water was estimated as 38.2% for the 2040s and 2080s under the RCP 4.5 scenario but was lower under the RCP 8.5 scenario (21.3% in the 2040s and 22.1% in the 2080s). Under the RCP 4.5 scenario, the projected shortage rate was higher than that during the measured baseline period (1982–2011) of 25.6% and the RCP historical period (1982–2005) of 30.1%. The future elevated drought levels are primarily attributed to the increasingly concentrated rainfall distribution throughout the year under a monsoonal climate, as projected by the IPCC climate scenarios.