Stochastic spatial disaggregation of extreme precipitation to validate a regional climate model and to evaluate climate change impacts over a small watershed

Abstract

Abstract. Regional climate models (RCMs) are valuable tools to evaluate impacts of climate change (CC) at regional scale. However, as the size of the area of interest decreases, the ability of a RCM to simulate extreme precipitation events decreases due to the spatial resolution. Thus, it is difficult to evaluate whether a RCM bias on localized extreme precipitation is caused by the spatial resolution or by a misrepresentation of the physical processes in the model. Thereby, it is difficult to trust the CC impact projections for localized extreme precipitation. Stochastic spatial disaggregation models can bring the RCM precipitation data at a finer scale and reduce the bias caused by spatial resolution. In addition, disaggregation models can generate an ensemble of outputs, producing an interval of possible values instead of a unique discrete value.The objective of this work is to evaluate whether a stochastic spatial disaggregation model applied on annual maximum daily precipitation (i) enables the validation of a RCM for a period of reference, and (ii) modifies the evaluation of CC impacts over a small area. Three simulations of the Canadian RCM (CRCM) covering the period 1961-2099 are used over a small watershed (130 km 2 ) located in southern Québec, Canada. The disaggregation model applied is based on Gibbs sampling and accounts for physical properties of the event (wind speed, wind direction, and convective available potential energy -CAPE), leading to realistic spatial distributions of precipitation. The results indicate that disaggregation has a significant impact on the validation. However, it does not provide a precise estimate of the simulation bias because of the difference in resolution between disaggregated values (4 km) and observations, and because of the underestimation of the spatial variability by the disaggregation model for the most convective events. Nevertheless, disaggregation illustrates that the simulations used mostly overestimated annual maximum precipitation depth in the study area during the reference period. Also, disaggregation slightly increases the signal of CC compared to the RCM raw simulations, highlighting the importance of spatial resolution in CC impact evaluation of extreme events.

Integrated assessment of climate change impact on surface runoff contamination by pesticides

“…(Tables 3 and 4). It is possibly due to an increase of convective rainfall events (Gagnon and Rousseau, 2014). Third, in some cases such as post-emergence herbicide applications in corn (Tables 1 and 2, Figure 5), bringing earlier in the future the application temporal window reduces the probability to "hit" a high precipitation event.…”

“…Resistance of crop enemies to pesticides could also increase under warmer conditions (Bloomfield et al 2006). Warmer conditions could also lead to more convective rainfall events (Gagnon and Rousseau, 2014), which are more intense and shorter than frontal precipitations. In this study, only daily precipitation depth was considered and the statistical method used to downscale climate model data (Mpelasoka and Chiew 2009) does not account for potential change in the proportion of convective and frontal precipitations.…”

Copula‐based downscaling of daily precipitation fields

“…Over the past years, several stochastic downscaling methods have been developed. Gagnon and Rousseau (2014) are expected based on the neighbours. Mehrotra and Sharma (2010) downscaled precipitation fields of a GCM with a Markov model that simulates conditional on atmospheric state variables, the past wetness state of each location and the fraction of wet values over an area around each location.…”

Spatial extremes modeling applied to extreme precipitation data in the state of Paraná

“…Pointwise and overall confidence bands can also be 20 derived from these simulations (Davison, 1997). Validation test can also be found in Gagnon and Rousseau (2014), the authors validated a regional transformed model and to evaluate climate change impacts over a watershed in the subwatershed of the Yamaska River, located south of the St. Lawrence River, Québec, Canada.…”