Uncertainty in Precipitation Projection under Changing Climate Conditions: A Regional Case Study

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.

Quantification of uncertainty in the assessment of future streamflow under changing climate conditions

“…Hence, it is important to use multiple DSMs during climate change impact assessment. Mandal et al () reached a similar conclusion in the study of Pr projection under changing climatic conditions. It is to be expected that if the Pr pattern is affected, then the streamflow will change too.…”

“…According to Prudhomme and Davies (), selection of AOGCMs creates more uncertainty in the downscaling process compared to the choice of emission scenarios or model parameterization. However, its also found that downscaling methods might be a significant source of uncertainty in hydrologic projections compared to the choice of climate models and emission scenarios that are a much less significant source of uncertainty (Bürger, Sobie, Cannon, Werner, & Murdock, ; Mandal, Breach, & Simonovic, ). All the studies mentioned above they investigated only changes in climatic variables, for example, temperature or precipitation (Pr).…”

Uncertainty assessment for climate change impact on intense precipitation: how many model runs do we need?

“…In the case of future concentration scenarios, this study made use of the precipitation simulations for all four RCP scenarios (RCP2.6, RCP4.5, RCP6.0, RCP8.5). However, all RCPs have not been mostly considered in hydrological impact studies of climate change (Booth et al, 2013;Mohammeda et al, 2015;Fatichi et al, 2016;Mandal et al, 2016;Monjo et al, 2016) and for simplifying and based on the research purpose, the climate model outputs for only one RCP (RCP8.5 representing the extreme future conditions or RCP4.5 representing the medium future conditions) or two RCPs (RCP4.5 & RCP8.5) have been used. From uncertainty analysis point of view, all RCPs are needed to cover the total ensemble uncertainty of climate change.…”

“…The application of multi‐model ensembles to quantify the projection uncertainty of climate change has recently increased in hydrological studies (Minville et al ., ; Chen et al ., ; Hughes et al ., ; Kingston et al ., ; Dessu and Melesse, ; Mandal et al ., ). The larger the ensemble size of independent climate models considered, the better the quantification of climate change uncertainty would be.…”

“…Therefore, the relative magnitude of each of the uncertainty sources might be defined to achieve the minimal uncertainties to extreme precipitation and construction of future IDF curves. The application of multi-model ensembles to quantify the projection uncertainty of climate change has recently increased in hydrological studies (Minville et al, 2008;Chen et al, 2011;Hughes et al, 2011;Kingston et al, 2011;Dessu and Melesse, 2013;Mandal et al, 2016). The larger the ensemble size of independent climate models considered, the better the quantification of climate change uncertainty would be.…”