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Rivers are sensitive to natural climate change as well as to human impacts such as flow modification and land-use change. Climate change could cause changes to precipitation amounts, the intensity of cyclonic storms, the proportion of precipitation falling as rain, glacier mass balance, and the extent of permafrost; all of which affect the hydrology and morphology of river systems. Changes to the frequency and magnitude of flood flows present the greatest threat. Historically, wetter periods are associated with significantly higher flood frequency and magnitude. These effects are reduced in drainage basins with large lakes or glacier storage. Alluvial rivers with fine-grained sediments are most sensitive, but all rivers will respond, except those flowing through resistant bedrock. The consequences of changes in flow include changes in channel dimensions, gradient, channel pattern, sedimentation, bank erosion rates, and channel migration rates. The most sensitive and vulnerable regions are in southern Canada, particularly those regions at risk of substantial increases in rainfall intensity and duration. In northern rivers, thawing of permafrost and changes to river-ice conditions are important concerns. The type and magnitude of effects will be different between regions, as well as between small and large river basins. Time scales of change will range from years to centuries. These changes will affect the use that we make of rivers and their floodplains, and may require mitigative measures. Radical change is also possible. Climatic impacts will be ubiquitous and will be in addition to existing and future direct human impact on streamflow and rivers.

This paper presents an integrated assessment model for use with climate policy decision making in Canada. The feedback based integrated assessment model ANEMI_CDN represents Canada within the global society-biosphere-climate-economy-energy system. The model uses a system dynamics simulation approach to investigate the impacts of climate change in Canada and policy options for adapting to changing global conditions. The disaggregation techniques allow ANEMI_CDN to show results with various temporal resolutions. Two Canadian policy scenarios are presented as illustrative examples to map policy impacts on key model variables, including population, water-stress, food production, energy consumption, and emissions under changing climate over this century. The main finding is a significant impact of a carbon tax on energy consumption. Two policy scenario simulations provide additional insights to policy makers regarding the choice of adaptation/mitigation options along with their implementation time.

Modifications to land can serve to jointly reduce risks of floods and droughts to people and to ecosystems. Whether land modifications are implemented will depend on the willingness and ability of a diversity of actors. This article reviews the state of knowledge on land modification use in areas exposed to dual hydrologic risks and the land owners, managers, and users who directly make decisions about action on lands they control. The review presents a typology of land modifications and explains how land modifications interact with the hydrological cycle to reduce risks. It then addresses the roles and perspectives of the land owners, managers, and users undertaking land modifications, summarizing theories explaining motivations for, as well as barriers to and enablers of, land modification implementation. The analysis reveals geographical differences in narratives on land modifications as well as knowledge gaps regarding variation across actors and types of land modifications.

How decentralized government structure influences public service delivery has been a major focus of debate in the public finance literature. In this paper, we empirically examine the effect of fiscal decentralization on natural disaster damages across the U.S. states. We construct a unique measure of decentralization using state and local government expenditures on natural resources, which include investment in flood control and mitigation measures, among others. Using state‐level panel data from 1982 to 2011, we find that states that are more decentralized in natural resource expenditures have experienced more economic losses from floods and storms. This effect is only pronounced in states that are at higher risks of flooding. Our findings suggest that fiscal decentralization may lead to inefficient protection against natural disasters and provide implications for the assignment of disaster management responsibilities across different levels of government in the U.S. federal system.

The effects of wetlands on stream flows are well established, namely mitigating flow regimes through water storage and slow water release. However, their effectiveness in reducing flood peaks and sustaining low flows is mainly driven by climate conditions and wetland type with respect to their connectivity to the hydrographic network (i.e. isolated or riparian wetlands). While some studies have demonstrated these hydrological functions/services, few of them have focused on the benefits to the hydrological regimes and their evolution under climate change (CC) and, thus, some gaps persist. The objective of this study was to further advance our knowledge with that respect. The PHYSITEL/HYDROTEL modelling platform was used to assess current and future states of watershed hydrology of the Becancour and Yamaska watersheds, Quebec, Canada. Simulation results showed that CC will induce similar changes on mean seasonal flows, namely larger and earlier spring flows leading to decreases in summer and fall flows. These expected changes will have different effects on 20-year and 100-year peak flows with respect to the considered watershed. Nevertheless, conservation of current wetland states should: (i) for the Becancour watershed, mitigate the potential increase in 2-year, 20-year and 100-year peak flows; and (ii) for the Yamaska watershed, accentuate the potential decrease in the aforementioned indicators. However, any loss of existing wetlands would be detrimental for 7-day 2-year and 10-year as well as 30-day 5-year low flows.

ABSTRACTThis work explores the ability of two methodologies in downscaling hydrological indices characterizing the low flow regime of three salmon rivers in Eastern Canada: Moisie, Romaine and Ouelle. The selected indices describe four aspects of the low flow regime of these rivers: amplitude, frequency, variability and timing. The first methodology (direct downscaling) ascertains a direct link between large-scale atmospheric variables (the predictors) and low flow indices (the predictands). The second (indirect downscaling) involves downscaling precipitation and air temperature (local climate variables) that are introduced into a hydrological model to simulate flows. Synthetic flow time series are subsequently used to calculate the low flow indices. The statistical models used for downscaling low flow hydrological indices and local climate variables are: Sparse Bayesian Learning and Multiple Linear Regression. The results showed that direct downscaling using Sparse Bayesian Learning surpassed the other a...

The long, open-ended period of recovery from a disaster event is the phase of a disaster that the interdisciplinary field of disaster studies struggles to understand. In the process of rebuilding, places do not simply reset – they transform, often in ways that confound any reduction of disaster risk, instead making people and settings more vulnerable to future hazard events. Reducing disaster risk is regarded as a global priority, but policies intended to reduce disaster risk have been largely ineffective. This obduracy represents a grand challenge in disaster studies. Here, I propose that the correlated trends of runaway economic costs of disaster events, growing social inequity, environmental degradation, and resistance to policy intervention in disaster settings are hallmark indicators of a system trap – a dynamic in which self-reinforcing feedbacks drive a system toward an undesirable and seemingly inescapable state, with negative consequences that tend to amplify each other over time. I offer that these trends in disaster settings are the collective expression of an especially powerful and distinct kind of system trap, which here I term the “disaster trap” – a new theoretical concept to help explain and address runaway disaster risk. I suggest that disaster traps are likely strongest in tourism-dominated coastal settings with high exposure to tropical cyclones and colonial histories of racial capitalism, which I explore with an empirical illustration from Antigua & Barbuda. Formalising a linkage between gilded and safe-development traps matters because their effects likely compound each other nonlinearly, such that disaster risk only increases and disaster risk reduction becomes increasingly difficult to achieve. Addressing traps requires understanding them as dynamic systems, described as fundamentally and completely as possible – their components, mechanisms, drivers, and structure – in order to reveal when and where interventions might be most effective at reducing disaster risk.

Precipitation and temperature are among major climatic variables that are used to characterize extreme weather events, which can have profound impacts on ecosystems and society. Accurate simulation of these variables at the local scale is essential to adapt urban systems and policies to future climatic changes. However, accurate simulation of these climatic variables is difficult due to possible interdependence and feedbacks among them. In this paper, the concept of copulas was used to model seasonal interdependence between precipitation and temperature. Five copula functions were fitted to grid (approximately 10 km × 10 km) climate data from 1960 to 2013 in southern Ontario, Canada. Theoretical and empirical copulas were then compared with each other to select the most appropriate copula family for this region. Results showed that, of the tested copulas, none of them consistently performed the best over the entire region during all seasons. However, Gumbel copula was the best performer during the winter season, and Clayton performed best in the summer. More variability in terms of best copula was found in spring and fall seasons. By examining the likelihoods of concurrent extreme temperature and precipitation periods including wet/cool in the winter and dry/hot in the summer, we found that ignoring the joint distribution and confounding impacts of precipitation and temperature lead to the underestimation of occurrence of probabilities for these two concurrent extreme modes. This underestimation can also lead to incorrect conclusions and flawed decisions in terms of the severity of these extreme events.

Extreme events are widely studied across the world because of their major implications for many aspects of society and especially floods. These events are generally studied in terms of precipitation or temperature extreme indices that are often not adapted for regions affected by floods caused by snowmelt. The rain on snow index has been widely used, but it neglects rain-only events which are expected to be more frequent in the future. In this study, we identified a new winter compound index and assessed how large-scale atmospheric circulation controls the past and future evolution of these events in the Great Lakes region. The future evolution of this index was projected using temperature and precipitation from the Canadian Regional Climate Model large ensemble (CRCM5-LE). These climate data were used as input in Precipitation Runoff Modelling System (PRMS) hydrological model to simulate the future evolution of high flows in three watersheds in southern Ontario. We also used five recurrent large-scale atmospheric circulation patterns in north-eastern North America and identified how they control the past and future variability of the newly created index and high flows. The results show that daily precipitation higher than 10 mm and temperature higher than 5 ∘C were necessary historical conditions to produce high flows in these three watersheds. In the historical period, the occurrences of these heavy rain and warm events as well as high flows were associated with two main patterns characterized by high Z500 anomalies centred on eastern Great Lakes (HP regime) and the Atlantic Ocean (South regime). These hydrometeorological extreme events will still be associated with the same atmospheric patterns in the near future. The future evolution of the index will be modulated by the internal variability of the climate system, as higher Z500 on the east coast will amplify the increase in the number of events, especially the warm events. The relationship between the extreme weather index and high flows will be modified in the future as the snowpack reduces and rain becomes the main component of high-flow generation. This study shows the value of the CRCM5-LE dataset in simulating hydrometeorological extreme events in eastern Canada and better understanding the uncertainties associated with internal variability of climate.

This chapter presents current knowledge of observed and projected impacts from extreme weather events, based on recorded events and their losses, as well as studies that project future impacts from anthropogenic climate change. The attribution of past changes in such impacts focuses on the three key drivers: changes in extreme weather hazards that can be due to natural climate variability and anthropogenic climate change, changes in exposure and vulnerability, and risk reduction efforts. The chapter builds on previous assessments of attribution of extreme weather events, to drivers of changes in weather hazard, exposure and vulnerability. Most records of losses from extreme weather consist of information on monetary losses, while several other types of impacts are underrepresented, complicating the assessment of losses and damages. Studies into drivers of losses from extreme weather show that increasing exposure is the most important driver through increasing population and capital assets. Residual losses (after risk reduction and adaptation) from extreme weather have not yet been attributed to anthropogenic climate change. For the Loss and Damage debate, this implies that overall it will remain difficult to attribute this type of losses to greenhouse gas emissions. For the future, anthropogenic climate change is projected to become more important for driving future weather losses upward. However, drivers of exposure and especially changes in vulnerability will interplay. Exposure will continue to lead to risk increases. Vulnerability on the other hand may be further reduced through disaster risk reduction and adaptation. This would reduce additional losses and damages from extreme weather. Yet, at the country scale and particularly in developing countries, there is ample evidence of increasing risk, which calls for significant improvement in climate risk management efforts.