Abstract
This study demonstrates the use of spatially downscaled, monthly general circulation model (GCM) rainfall and temperature data to drive the established HyMOD hydrological model to evaluate the prospective effects of climate change on the fluvial run-off of the River Derwent basin in the UK. The evaluation results of this monthly hydrological model using readily available, monthly GCM data are consistent with studies on nearby catchments employing high-temporal resolution data, indicating that useful hydro-climatic planning studies may be possible using standard datasets and modest computational resources. HyMOD was calibrated against 5 km2 gridded UK Climate Projections dataset data and then driven using monthly spatially interpolated (~5 km2) outputs from Hadley Centre Coupled Model, version 3 and the Canadian Centre for Climate Modelling and Analysis for Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC-SRES) A2a and B2a covering the 2020s, 2050s and 2080s. Results for both GCMs project a decrease in annual run-off in both GCM models and scenarios with higher values in the summer/autumn months, whereas an increase in the later winter months. Both Hadley Centre Coupled Model, version 3 and the Canadian Centre for Climate Modelling and Analysis show higher ranges of uncertainty during the winter season with higher values of run-off associated with December in all three simulation periods and two scenarios. A seasonal comparison of run-off simulations shows that both GCMs give similar results in summer and autumn, whereas disparities due to GCM uncertainties are more conspicuous in winter and spring. In this study, both the GCMs under A2a scenario have demonstrated the high possibility of time shift in monthly average peak run-offs in the Derwent River by 2080s in comparison with the early 21st century. Copyright © 2013 John Wiley & Sons, Ltd.
Highlights
The results from Global Climate Model (GCM) projections and recent studies (Parker et al 1992; Fowler et al 2005a; Hulme et al 2002) suggest that anthropogenic climate change will result in changes to regional temperatures and other climatic processes yielding corresponding changes in rainfall intensity, variability and spatial distribution
The Derwent catchment has showed reductions in autumn and spring, which are critical recharge periods in groundwater point of view of the region. It indicates need of efficient water resources management strategies to counter its effect on groundwater aquifer, local water industries, local water treatments and dilution of wastewater effluent, agricultural and ecological water demands and management. 3.4.2 B2a Scenario This study evaluates the seasonal comparison of HadCM3 and CCCMA models under the B2a scenario
Table 7; The seasonal and annual variation of future average runoff in Derwent catchment along with corresponding upper and lower boundaries of uncertainty ranges
Summary
The results from Global Climate Model (GCM) projections and recent studies (Parker et al 1992; Fowler et al 2005a; Hulme et al 2002) suggest that anthropogenic climate change will result in changes to regional temperatures and other climatic processes yielding corresponding changes in rainfall intensity, variability and spatial distribution. A conceptual rainfall-runoff model operating at a monthly time-step (auxiliary HyMOD) has been driven using spatially downscaled (~5km) monthly precipitation, temperature and potential evaporation (obtained from Blaney-Criddle method) data from two major general circulation models: CCCMA and HadCM3, and two driving scenarios: A2a and B2a. 3 A further aim of the study was to identify the disparity in different GCM outputs when 4 combined with conceptual hydrological models to simulate the future distribution of monthly 5 and seasonal river flows, Comparing the performance of two GCMs, both CCCMA and 6 HadCM3 predict similar steam flow changes for summer and autumn under both A2a and 7 B2a scenarios. This study does provide a baseline demonstration of the 2 type of results that may be obtained using readily available monthly GCM datasets utilizing 3 properly biased corrected monthly conceptual hydrological models. 4
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