Flood Occurrence and Impact Models for Socioeconomic Applications over Canada and the United States
Authors: Grenier Manuel , Boudreault Mathieu , Carozza David A. , Boudreault Jérémie , Raymond Sébastien 
Published: February 2023

Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.
Large-scale socioeconomic studies of the impacts of floods are difficult
and costly for countries such as Canada and the United States due to the
large number of rivers and size of watersheds. Such studies are however
very important to analyze spatial patterns and temporal trends to inform
large-scale flood risk management decisions and policies. In this paper,
we present different flood occurrence and impact models based upon
statistical and machine learning methods over 31,000 watersheds spread
across Canada and the US. The models can be quickly calibrated and
thereby easily run predictions over thousands of scenarios in a matter
of minutes. As applications of the models, we present the geographical
distribution of the modelled average annual number of people displaced
due to flooding in Canada and the US, as well as various scenario
analyses. We find for example that an increase of 10% in average
precipitation yields an increase of population displaced of 18% in
Canada and 14% in the U.S. The model can therefore be used by a broad
range of end-users ranging from climate scientists to economists who
seek to translate climate and socioeconomic scenarios into flood
probabilities and impacts measured in terms of population displaced.