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, an...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.展开更多
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 compani...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.展开更多
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 si...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).展开更多
"Filtered(0.2μm)and unfiltered samples were analyzed for gross photoreduction,gross photooxidation,and net reduction rates of mercury using pseudo first-order curves.
We explored the effects of land-cover configuration, body size and trophic diversity in determining avian species richness on Prince Edward Island, Canada. Data on avian species richness were obtained from the Maritim...We explored the effects of land-cover configuration, body size and trophic diversity in determining avian species richness on Prince Edward Island, Canada. Data on avian species richness were obtained from the Maritime Breeding Bird Atlas data. Prince Edward Island was divided into 97 sampling cells of 10 × 10 km. Land-cover metrics were calculated using a forest inventory database, Fragstats and ArcView version 8.1. The relationships between avian species richness and explanatory variables were explored using correlation analysis, mixed forward-backward stepwise analysis, generalized linear models and Akaike’s information criterion. Models predicted between 27% and 63% of the variability in species richness, attributing substantial explanatory power to both the average body size and the range of body size spanned by the avian community. The body-size frequency distribution showed that avian communities were dominated by species weighing between 50 and 80 g. Habitat metrics associated with forests were more important to the avifauna than those related to agriculture. Avian species richness also decreased with both the fragmentation and isolation of wetlands. The total area covered by the human infrastructure land-cover and its subdivision were also important. Clearly, body size plays a key role in determining the diversity of birds on Prince Edward Island. In particular, species weighing 50 - 80 g appear to have sufficient resources to be successful on Prince Edward Island’s landscapes. Our findings also highlighted the importance of controlling the expansion of human infrastructure and both the fragmentation and reduction in size of wetlands to maintain avian species richness patterns.展开更多
Given the short duration of growing season in the Arctic, a strong correlation between plant productivity and growing season length (GSL) is conventionally assumed. Will this assumption hold true under a warming clima...Given the short duration of growing season in the Arctic, a strong correlation between plant productivity and growing season length (GSL) is conventionally assumed. Will this assumption hold true under a warming climate? In this study, we addressed the question by investigating the relationship between net primary productivity of leaves (NPP<sub>leaf</sub>) and GSL for various tundra ecosystems. We quantified NPP<sub>leaf</sub> and GSL using long-term satellite data and field measurements. Our results indicated that the relationship was not significant (i.e., decoupled) for 44% to 64% of tundra classes in the southern Canadian Arctic, but significant for all classes in the northern Canadian Arctic. To better understand the causes of the decoupling, we further decomposed the relationship into two components: the correspondence of interannual variations and the agreement of long- term trends. We found that the longer the mean GSL for a tundra class, the poorer the correspondence between their interannual variations. Soil moisture limitation further decoupled the relationship by deteriorating the agreement of long-term trends. Consequently, the decoupling between NPP<sub>leaf</sub> and GSL would be more likely to occur under a warming climate if the tundra class had a mean GSL > 116 (or 123) days with a dry (or moist) soil moisture regime.展开更多
The North Water Polynya(NOW)is one of the largest and most productive polynyas in the Arctic.Compared to the surrounding sea ice,the combination of high winds and cold air,together with the thin ice or open water surf...The North Water Polynya(NOW)is one of the largest and most productive polynyas in the Arctic.Compared to the surrounding sea ice,the combination of high winds and cold air,together with the thin ice or open water surface of the NOW,produces large turbulent heat fluxes(THFs).The accurate estimation of these parameters requires high-resolution atmospheric data,which can be provided by the reanalysis products from different sources.In this study,we calculated the winter latent heat flux(LHF)and sensible heat flux(SHF)over the NOW and its surrounding sea ice area from 2005/2006 to 2015/2016 using high-resolution(15 km)Arctic System Reanalysis version 2(ASRv2)data and low-resolution(30 km)European Centre for Medium-Range Weather Forecasts ERA5 data.Results show that the LHF/SHF over the surrounding sea ice is about 82%/88%lower than over the NOW,as estimated using either dataset.Furthermore,within each area,the difference in the THFs estimated from the two datasets is small.The spatial distribution of the LHF/SHF estimated from both data sources is similar to that of sea ice concentration.The average LHF/SHF in the polynya obtained using ASRv2 data is only 5%/7%higher than that from the values obtained using ERA5 data.This is because the wind speed and air temperature from the ASRv2 data are higher than those of ERA5,and their effects on the THFs can cancel each other out.Furthermore,the estimated THFs do not necessarily improve with the refined resolution of ASRv2.展开更多
文摘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.
文摘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.
文摘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).
文摘"Filtered(0.2μm)and unfiltered samples were analyzed for gross photoreduction,gross photooxidation,and net reduction rates of mercury using pseudo first-order curves.
文摘We explored the effects of land-cover configuration, body size and trophic diversity in determining avian species richness on Prince Edward Island, Canada. Data on avian species richness were obtained from the Maritime Breeding Bird Atlas data. Prince Edward Island was divided into 97 sampling cells of 10 × 10 km. Land-cover metrics were calculated using a forest inventory database, Fragstats and ArcView version 8.1. The relationships between avian species richness and explanatory variables were explored using correlation analysis, mixed forward-backward stepwise analysis, generalized linear models and Akaike’s information criterion. Models predicted between 27% and 63% of the variability in species richness, attributing substantial explanatory power to both the average body size and the range of body size spanned by the avian community. The body-size frequency distribution showed that avian communities were dominated by species weighing between 50 and 80 g. Habitat metrics associated with forests were more important to the avifauna than those related to agriculture. Avian species richness also decreased with both the fragmentation and isolation of wetlands. The total area covered by the human infrastructure land-cover and its subdivision were also important. Clearly, body size plays a key role in determining the diversity of birds on Prince Edward Island. In particular, species weighing 50 - 80 g appear to have sufficient resources to be successful on Prince Edward Island’s landscapes. Our findings also highlighted the importance of controlling the expansion of human infrastructure and both the fragmentation and reduction in size of wetlands to maintain avian species richness patterns.
文摘Given the short duration of growing season in the Arctic, a strong correlation between plant productivity and growing season length (GSL) is conventionally assumed. Will this assumption hold true under a warming climate? In this study, we addressed the question by investigating the relationship between net primary productivity of leaves (NPP<sub>leaf</sub>) and GSL for various tundra ecosystems. We quantified NPP<sub>leaf</sub> and GSL using long-term satellite data and field measurements. Our results indicated that the relationship was not significant (i.e., decoupled) for 44% to 64% of tundra classes in the southern Canadian Arctic, but significant for all classes in the northern Canadian Arctic. To better understand the causes of the decoupling, we further decomposed the relationship into two components: the correspondence of interannual variations and the agreement of long- term trends. We found that the longer the mean GSL for a tundra class, the poorer the correspondence between their interannual variations. Soil moisture limitation further decoupled the relationship by deteriorating the agreement of long-term trends. Consequently, the decoupling between NPP<sub>leaf</sub> and GSL would be more likely to occur under a warming climate if the tundra class had a mean GSL > 116 (or 123) days with a dry (or moist) soil moisture regime.
基金supported by the National Key Research and Development Program of China(Grant no.2024YFB3908004).
文摘The North Water Polynya(NOW)is one of the largest and most productive polynyas in the Arctic.Compared to the surrounding sea ice,the combination of high winds and cold air,together with the thin ice or open water surface of the NOW,produces large turbulent heat fluxes(THFs).The accurate estimation of these parameters requires high-resolution atmospheric data,which can be provided by the reanalysis products from different sources.In this study,we calculated the winter latent heat flux(LHF)and sensible heat flux(SHF)over the NOW and its surrounding sea ice area from 2005/2006 to 2015/2016 using high-resolution(15 km)Arctic System Reanalysis version 2(ASRv2)data and low-resolution(30 km)European Centre for Medium-Range Weather Forecasts ERA5 data.Results show that the LHF/SHF over the surrounding sea ice is about 82%/88%lower than over the NOW,as estimated using either dataset.Furthermore,within each area,the difference in the THFs estimated from the two datasets is small.The spatial distribution of the LHF/SHF estimated from both data sources is similar to that of sea ice concentration.The average LHF/SHF in the polynya obtained using ASRv2 data is only 5%/7%higher than that from the values obtained using ERA5 data.This is because the wind speed and air temperature from the ASRv2 data are higher than those of ERA5,and their effects on the THFs can cancel each other out.Furthermore,the estimated THFs do not necessarily improve with the refined resolution of ASRv2.
