Understanding Uncertainty in Probabilistic Floodplain Mapping in the Time of Climate Change - Zahmatkesh, Zahra; Han, Shasha; Coulibaly, Paulin (Water, 2021)
An integrated framework was employed to develop probabilistic floodplain maps, taking into account hydrologic and hydraulic uncertainties under climate change impacts. To develop the maps, several scenarios representing the individual and compounding effects of the models’ input and parameters uncertainty were defined. Hydrologic model calibration and validation were performed using a Dynamically Dimensioned Search algorithm. A generalized likelihood uncertainty estimation method was used for quantifying uncertainty. To draw on the potential benefits of the proposed methodology, a flash-flood-prone urban watershed in the Greater Toronto Area, Canada, was selected. The developed floodplain maps were updated considering climate change impacts on the input uncertainty with rainfall Intensity–Duration–Frequency (IDF) projections of RCP8.5. The results indicated that the hydrologic model input poses the most uncertainty to floodplain delineation. Incorporating climate change impacts resulted in the expansion of the potential flood area and an increase in water depth. Comparison between stationary and non-stationary IDFs showed that the flood probability is higher when a non-stationary approach is used. The large inevitable uncertainty associated with floodplain mapping and increased future flood risk under climate change imply a great need for enhanced flood modeling techniques and tools. The probabilistic floodplain maps are beneficial for implementing risk management strategies and land-use planning.

Climate indices to characterize climatic changes across southern Canada - Wazneh, Hussein; Arain, M. Altaf; Coulibaly, Paulin (Meteorological Applications, 2020)
Abstract The present study analyses the impacts of past and future climate change on extreme weather events for southern parts of Canada from 1981 to 2100. A set of precipitation and temperature‐based indices were computed using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) multi‐model ensemble projections at 8 km resolution over the 21st Century for two representative concentration pathway (RCP) scenarios: RCP4.5 and RCP8.5. The results show that this region is expected to experience stronger warming and a higher increase in precipitation extremes in future. Generally, projected changes in minimum temperature will be greater than changes in maximum temperature, as shown by respective indices. A decrease in frost days and an increase in warm nights will be expected. By 2100 there will be no cool nights and cool days. Daily minimum and maximum temperatures will increase by 12 and 7°C, respectively, under the RCP8.5 scenario, when compared with the reference period 1981–2000. The highest warming in minimum temperature and decrease in cool nights and days will occur in Ontario and Quebec provinces close to the Great Lakes and Hudson Bay. The highest warming in maximum temperature will occur in the southern parts of Alberta and Saskatchewan. Annual total precipitation is expected to increase by about 16% and the occurrence of heavy precipitation events by five days. The highest increase in annual total precipitation will occur in the northern parts of Ontario and Quebec and in western British Columbia.

Future shift in winter streamflow modulated by the internal variability of climate in southern Ontario - Champagne, Olivier; Arain, M. Altaf; Leduc, Martin; Coulibaly, Paulin; McKenzie, Shawn (Hydrology and Earth System Sciences, 2020)
Fluvial systems in southern Ontario are regularly affected by widespread early-spring flood events primarily caused by rain-on-snow events. Recent studies have shown an increase in winter floods in this region due to increasing winter temperature and precipitation. Streamflow simulations are associated with uncertainties mainly due to the different scenarios of greenhouse gas emissions, global climate models (GCMs) or the choice of the hydrological model. The internal variability of climate, defined as the chaotic variability of atmospheric circulation due to natural internal processes within the climate system, is also a source of uncertainties to consider. Uncertainties of internal variability can be assessed using hydrological models fed by downscaled data of a global climate model large ensemble (GCM-LE), but GCM outputs have too coarse of a scale to be used in hydrological modeling. The Canadian Regional Climate Model Large Ensemble (CRCM5-LE), a 50-member ensemble downscaled from the Canadian Earth System Model version 2 Large Ensemble (CanESM2-LE), was developed to simulate local climate variability over northeastern North America under different future climate scenarios. In this study, CRCM5-LE temperature and precipitation projections under an RCP8.5 scenario were used as input in the Precipitation Runoff Modeling System (PRMS) to simulate streamflow at a near-future horizon (2026–2055) for four watersheds in southern Ontario. To investigate the role of the internal variability of climate in the modulation of streamflow, the 50 members were first grouped in classes of similar projected change in January–February streamflow and temperature and precipitation between 1961–1990 and 2026–2055. Then, the regional change in geopotential height (Z500) from CanESM2-LE was calculated for each class. Model simulations showed an average January–February increase in streamflow of 18 % (±8.7) in Big Creek, 30.5 % (±10.8) in Grand River, 29.8 % (±10.4) in Thames River and 31.2 % (±13.3) in Credit River. A total of 14 % of all ensemble members projected positive Z500 anomalies in North America's eastern coast enhancing rain, snowmelt and streamflow volume in January–February. For these members the increase of streamflow is expected to be as high as 31.6 % (±8.1) in Big Creek, 48.3 % (±11.1) in Grand River, 47 % (±9.6) in Thames River and 53.7 % (±15) in Credit River. Conversely, 14 % of the ensemble projected negative Z500 anomalies in North America's eastern coast and were associated with a much lower increase in streamflow: 8.3 % (±7.8) in Big Creek, 18.8 % (±5.8) in Grand River, 17.8 % (±6.4) in Thames River and 18.6 % (±6.5) in Credit River. These results provide important information to researchers, managers, policymakers and society about the expected ranges of increase in winter streamflow in a highly populated region of Canada, and they will help to explain how the internal variability of climate is expected to modulate the future streamflow in this region.