Abstract
This study investigates different sources of uncertainty in the assessment of the climate change impacts on total monthly precipitation in the Campbell River basin, British Columbia, Canada. Four global climate models (GCMs), three greenhouse gas emission scenarios (RCPs) and six downscaling methods (DSMs) are used in the assessment. These sources of uncertainty are analyzed separately for two future time periods (2036 to 2065 and 2066 to 2095). An uncertainty metric is calculated based on the variation in simulated precipitation due to choice of GCMs, emission scenarios and downscaling models. The results show that the selection of a downscaling method provides the largest amount of uncertainty when compared to the choice of GCM and/or emission scenario. However, the choice of GCM provides a significant amount of uncertainty if downscaling methods are not considered. This assessment work is conducted at ten different locations in the Campbell River basin.
Highlights
Climate change due to greenhouse gas (GHGs) emissions is impacting the global hydrological cycle as well as regional hydrology across the world, and it will continue in the future [1]
This study found that the uncertainty in precipitation projection due to the choice of global climate models (GCMs) is larger than that due to the choice of emission scenarios for different temporal scales
This study addressed different sources of uncertainty, GCM data is available from the CMIP5 and the conclusion is conflicting with other regional climate impact studies [7] [14]
Summary
Climate change due to greenhouse gas (GHGs) emissions is impacting the global hydrological cycle as well as regional hydrology across the world, and it will continue in the future [1]. A large number of climate change assessment studies on hydrology have been conducted so far on different temporal and spatial scales [2]-[4]. The outputs of global climate models (GCMs) are used for regional climate change impact assessment. GCMs simulate time series of global climate variables (e.g. sea level pressure, temperature, specific humidity) considering different emission scenarios of GHGs. GCM outputs are coarsely gridded (>100 km2) and often fail to capture non-smooth fields such as precipitation [5]. Spatial downscaling is required for better understanding and assessment of future hydrologic conditions at watershed scales under different climate change scenarios
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