Possible Impacts of Climate Change on Daily Streamflow and Extremes at Local Scale in Ontario, Canada. Part II: Future Projection

The paper forms the second part of an introduction to possible impacts of climate change on daily streamflow and extremes in the Province of Ontario, Canada. Daily streamflow simulation models developed in the companion paper (Part I) were used to project changes in frequency of future daily streamflow events. To achieve this goal, future climate information (including rainfall) at a local scale is needed. A regression-based downscaling method was employed to downscale eight global climate model (GCM) simulations (scenarios A2 and B1) to selected weather stations for various meteorological variables (except rainfall). Future daily rainfall quantities were projected using daily rainfall simulation models with downscaled future climate information. Following these projections, future daily streamflow volumes can be projected by applying daily streamflow simulation models. The frequency of future daily high-streamflow events in the warm season (May–November) was projected to increase by about 45%-55% late this century from the current condition, on average of eight-GCM A2 projections and four selected river basins. The corresponding increases for future daily low-streamflow events and future daily mean streamflow volume could be about 25%-90% and 10%-20%, respectively. In addition, the return values of annual one-day maximum streamflow volume for various return periods were projected to increase by 20%-40%, 20%-50%, and 30%-80%, respectively for the periods 2001-50, 2026-75, and 2051-2100. Inter-GCM and interscenario uncertainties of future streamflow projections were quantitatively assessed. On average, the projected percentage increases in frequency of future daily high-streamflow events are about 1.4-2.2 times greater than inter-GCM and interscenario uncertainties.

Possible Impacts of Climate Change on Daily Streamflow and Extremes at Local Scale in Ontario, Canada. Part I: Historical Simulation

The paper forms the first part of an introduction to possible impacts of climate change on daily streamflow and extremes in the Province of Ontario, Canada. In this study, both conceptual and statistical streamflow simulation modeling theories were collectively applied to simulate daily streamflow volumes. Based on conceptual rainfall-runoff modeling principle, the predictors were selected to take into account several physical factors that affect streamflow, such as (1) current and previous quantities of rainfall over the watershed, (2) an index of pre-storm moisture conditions, (3) an index of pre-storm evapotranspiration capacities, and (4) a seasonal factor representing seasonal variation of streamflow volume. These rainfall-runoff conceptual factors were applied to an autocorrelation correction regression procedure to develop a daily streamflow simulation model for each of the four selected river basins. The streamflow simulation models were validated using a leave-one-year-out cross-validation scheme. The simulation models identified that the explanatory predictors are consistent with the physical processes typically associated with high-streamflow events. Daily streamflow simulation models show that there are significant correlations between daily streamflow observations and model validations, with model R2s of 0.68-0.71, 0.61-0.62, 0.71-0.74, and 0.95 for Grand, Humber, Upper Thames, and Rideau River Basins, respectively. The major reason for the model performance varying across the basins might be that rainfall-runoff response time and physical characteristics differ significantly among the selected river basins. The results suggest that streamflow simulation models can be used to assess possible impacts of climate change on daily streamflow and extremes at a local scale, which is major objective of a companion paper (Part II).

Climate Change and Heavy Rainfall-Related Water Damage Insurance Claims and Losses in Ontario, Canada

The objective of this paper was to project possible impacts of climate change on heavy rainfall-related water damage insurance claims and incurred losses for four selected cites (Kitchener-Waterloo, London, Ottawa, and Toronto) located at Ontario, Canada. To achieve this goal, the future climate change scenarios and rainfall simulations, at local scale, were needed. A statistical downscaling method was used to downscale five global climate model (GCM) scenarios to selected weather stations. The downscaled meteorological variables included surface and upper-air hourly temperature, dew point, west-east and south-north winds, air pressure, and total cloud cover. These variables are necessary to project future daily rainfall quantities using within-weather-type rainfall simulation models. A model result verification process has been built into the whole exercise, including rainfall simulation modeling and the development of downscaling transfer functions. The results of the verification, based on historical observations of the outcome variables simulated by the models, showed a very good agreement. To effectively evaluate heavy rainfall-related water damage insurance claims and incurred losses, a rainfall index was developed considering rainfall intensity and duration. The index was evaluated to link with insurance data as to determination of a critical threshold of the rainfall index for triggering high numbers of rainfall-related water damage insurance claims and incurred losses. The relationship between rainfall index and insurance data was used with future rainfall simulations to project changes in future heavy rainfall-related sewer flood risks in terms of water damage insurance claims and incurred losses. The modeled results showed that, averaged over the five GCM scenarios and across the study area, both the monthly total number of rainfall-related water damage claims and incurred losses could increase by about 13%, 20% and 30% for the periods 2016-2035, 2046-2065, and 2081-2100, respectively (from the four-city seasonal average of 12 ± 1.7 thousand claims and $88 ± $21 million during April-September 1992-2002). Within the context of this study, increases in the future number of insurance claims and incurred losses in the study area are driven by only increases in future heavy rainfall events.

Changes in Seasonal Patterns of Temperature and Precipitation in China During 1971-2000

Many studies have shown evidence for significant changes in surface climate in different regions of the world and during different seasons over the past 100 years. Based on daily temperature and precipitation data from 720 climate stations in China, cluster analysis was used to identify regions in China that have experienced similar changes in the seasonal cycle of temperature and precipitation during the 1971-2000 climate normal period. Differences in 11-day averages of daily mean temperature and total precipitation between the first （1971-1985） and second （1986-2000） halves of the record were analyzed using the Mann- Whitney U test and the global κ-means clustering algorithm. Results show that most parts of China experienced significant increases in temperature between the two periods, especially in winter, although some of this warming may be attributable to the urban heat island effect in large cities. Most of western China experienced more precipitation in 1986-2000, while precipitation decreased in the Yellow River valley. Changes in the summer monsoon were also evident, with decreases in precipitation during the onset and decay phases, and increases during the wettest period.