Abstract
The consequence of climate variations on hydrology remains the greatest challenging aspect of managing water resources. This research focused on the quantitative approach of the uncertainty in variations of climate influence on drought pattern of the Cheongmicheon watershed by assigning weights to General Circulation Models (GCMs) based on model performances. Three drought indices, Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI) are used for three durations 3-, 6- and 9-months. This study included 27 GCMs from Coupled Model Intercomparison Project 5 (CMIP5) and considered three future periods (2011–2040, 2041–2070 and 2071–2100) of the concentration scenario of Representation Concentration Pathway (RCP) 4.5. Compared to SPEI and SDI, SPI identified more droughts in severe or extreme categories of shorter time scales than SPEI or SDI. The results suggested that the discrepancy in temperature plays a significant part in characterizing droughts. The Reliability Ensemble Averaging (REA) technique was used to give a mathematical approximation of associated uncertainty range and reliability of future climate change predictions. The uncertainty range and reliability of Root Mean Square Error (RMSE) varied among GCMs and total uncertainty ranges were between 50% and 200%. This study provides the approach for realistic projections by incorporating model performance ensemble averaging based on weights from RMSE.
Highlights
The variability in climate change is a crucial element in the hydrologic cycle
The models show a positive bias in mean indicating that the Coupled Model Intercomparison Project 5 (CMIP5) general circulation models (GCMs) tend to overestimate the observed mean precipitation
The coefficient of determination (R2) is negative, indicating that the models were weakly correlated. This may be due to poor model behavior and outliners in model simulation results from software. This demonstrates the limited value of R2 alone for model performance quantification; these negative values do not affect the objective of this study as the performance evaluation were based almost entirely in the bias and Root Mean Square Error (RMSE) weighting for model uncertainty prediction
Summary
The variability in climate change is a crucial element in the hydrologic cycle. Slight discrepancies in climate can alter variations in the hydrologic processes of the hydrologic cycle [1]. The effects of climate change are diverse, and they vary locally and internationally with their intensity and duration Challenged with this realism of varying climate, law makers in expanded diverse institutions are progressively searching for quantitative descriptions of climate forecasting. They require projections of regional and climate changes that will influence humans, economies and ecosystems [2]. The hydrological influence of a changing climate on hydrology is usually analyzed using various climatological models with climate change events obtained from GCMs forced with emission scenarios These results have been rarely used in management of water resources because of the existence of uncertainties in both future climate change projections from GCMs and assessments of climate variation effects on hydrology
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