Abstract
Global climate change is becoming an increasingly important issue that threatens the imperiled planet. Quantifying the impact of climate change on the streamflow has been an essential task for the proper management of water resources to mitigate this impact. This study aims to evaluate the skill of an artificial neural network (ANN) method in downscaling precipitation, maximum temperature, and minimum temperature and assess the potential impacts of climate change on the streamflow in the Wabash River Basin of the Midwestern United States (U.S.) using the Soil and Water Assessment Tool (SWAT). A statistical downscaling technique based on an ANN method was employed to estimate precipitation and temperature at a higher resolution. The downscaled climate projections from five general circulation models (GCMs) under the three representative concentration pathway (RCP) scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5) for the periods of 2026–2050 and 2075–2099 as well as the historical period were incorporated into the SWAT model to assess the potential impact of climate change on the Wabash River regime. Calibration and validation of the SWAT model indicated the streamflow simulations matched the observed results very well. The ANN method successfully reproduced the observed maximum/minimum temperature and precipitation; however, bias in precipitation was observed in regard to the frequency distribution. Compared with the simulated streamflow in the historical period, the predicted streamflow based on the RCP scenarios showed an obvious decreasing trend, where the annual streamflows will be decreased by 13.00%, 17.59%, and 6.91% in the midcentury periods and 25.29%, 27.61%, and 15.04% in the late-century periods under the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Climate warming dominated the streamflow decrease under the RCP2.6 and RCP4.5 scenarios. By contrast, under RCP8.5, the streamflow was affected by the joint actions of changes in temperature and precipitation.
Highlights
Global climate change, which is mainly characterized by climate warming, is an unequivocal reality
Calibration and Validation of Hydrological Model Using Observed Climate Datasets. e parameter uncertainty definitely leads to uncertainties in model simulation. e best-fit parameter values and the sensitivities of model parameters are listed in Table 3. e main sources of streamflow uncertainty were the SCS runoff curve number for moisture condition II (CN2), soil evaporation compensation factor (ESCO), groundwater “revap” coefficient (GW_REVAP), deep aquifer percolation fraction (RCHRG_DP), baseflow alpha factor for bank storage (ALPHA_BNK), groundwater delay time (GW_DELAY), moist bulk density (SOL_BD), effective hydraulic conductivity in the main channel alluvium (CH_K2), and threshold depth of water in the shallow aquifer required for the return flow to occur (GWQMN). e P values of these factors are less than 0.05. ese results were basically consistent with the study [15]
We applied climate change simulations generated by five general circulation models (GCMs) that contributed to Coupled Model Intercomparison Project Phase 5 (CMIP5) for simulating the midcentury and late-century behavior of streamflow conditions over the Wabash River Basin
Summary
Global climate change, which is mainly characterized by climate warming, is an unequivocal reality. E International Panel on Climate Change (IPCC) suggested a greater than 90% probability that global warming is caused by human activities, such as the build-up of heat-trapping gases in the atmosphere because of fossil fuel burning [3]. Global hydrological cycles are expected to be accelerated under the conditions of climate warming given that the capacity of the air to hold water vapor increases exponentially in a warming climate and precipitation will increase on average, which will reduce the increase of people who live under conditions of water stress [5, 7]. Advances in Meteorology the study demonstrated that climate change is projected to cause shifts in precipitation patterns and a likely increase in the frequency and distribution of floods and droughts
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