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

Abstract Reference evapotranspiration (ETo) is one of the most important factors in the hydrologic cycle and water balance studies. In this study, the performance of three simple and three wavelet hybrid models were compared to estimate ETo in three different climates in Iran, based on different combinations of input variables. It was found that the wavelet-artificial neural network was the best model, and multiple linear regression (MLR) was the worst model in most cases, although the performance of the models was related to the climate and the input variables used for modeling. Overall, it was found that all models had good accuracy in terms of estimating daily ETo. Also, it was found in this study that large numbers of decomposition levels via the wavelet transform had noticeable negative effects on the performance of the wavelet-based models, especially for the wavelet-adaptive network-based fuzzy inference system and wavelet-MLR, but in contrast, the type of db wavelet function did not have a detectable effect on the performance of the wavelet-based models.

Compound dry-hot events enlarge homogenously due to teleconnected land-atmosphere feedbacks. , Using over a century of ground-based observations over the contiguous United States, we show that the frequency of compound dry and hot extremes has increased substantially in the past decades, with an alarming increase in very rare dry-hot extremes. Our results indicate that the area affected by concurrent extremes has also increased significantly. Further, we explore homogeneity (i.e., connectedness) of dry-hot extremes across space. We show that dry-hot extremes have homogeneously enlarged over the past 122 years, pointing to spatial propagation of extreme dryness and heat and increased probability of continental-scale compound extremes. Last, we show an interesting shift between the main driver of dry-hot extremes over time. While meteorological drought was the main driver of dry-hot events in the 1930s, the observed warming trend has become the dominant driver in recent decades. Our results provide a deeper understanding of spatiotemporal variation of compound dry-hot extremes.

Watershed management efforts in agriculturally dominated landscapes of North America face nearly two centuries of laws and policies that encouraged habitat destruction. Although streams and wetlands in these landscapes are actively being restored using designs that incorporate science and engineering, watershed drainage laws can constrain action or impact passively restored or naturalized habitat. In general, drainage laws require removal of any riparian vegetation or wood deemed to obstruct flow in streams regulated as drains. We use a case study from Indiana (USA) to introduce the shortcomings of drainage laws for allowing large wood, which is an important habitat feature, to remain in stream ecosystems. Removals of large wood from monitored stream reaches in a regulated drain were associated with subsequent declines in fish biomass. Such legal activities represent an important environmental management problem that exists under drainage laws which apply to streams over a widespread geographic region of North America. Recent litigation in Wisconsin (USA) suggests that if state legislatures fail to update these antiquated laws, the courts may act in favour of science-based management of drains. The statutes and regulations that govern agricultural drainage warrant careful consideration if streams within drainage districts are to be managed to improve ecological function. © 2020 John Wiley & Sons, Ltd.

Abstract Within the Copernicus Climate Change Service (C3S), ECMWF is producing the ERA5 reanalysis which, once completed, will embody a detailed record of the global atmosphere, land surface and ocean waves from 1950 onwards. This new reanalysis replaces the ERA‐Interim reanalysis (spanning 1979 onwards) which was started in 2006. ERA5 is based on the Integrated Forecasting System (IFS) Cy41r2 which was operational in 2016. ERA5 thus benefits from a decade of developments in model physics, core dynamics and data assimilation. In addition to a significantly enhanced horizontal resolution of 31 km, compared to 80 km for ERA‐Interim, ERA5 has hourly output throughout, and an uncertainty estimate from an ensemble (3‐hourly at half the horizontal resolution). This paper describes the general set‐up of ERA5, as well as a basic evaluation of characteristics and performance, with a focus on the dataset from 1979 onwards which is currently publicly available. Re‐forecasts from ERA5 analyses show a gain of up to one day in skill with respect to ERA‐Interim. Comparison with radiosonde and PILOT data prior to assimilation shows an improved fit for temperature, wind and humidity in the troposphere, but not the stratosphere. A comparison with independent buoy data shows a much improved fit for ocean wave height. The uncertainty estimate reflects the evolution of the observing systems used in ERA5. The enhanced temporal and spatial resolution allows for a detailed evolution of weather systems. For precipitation, global‐mean correlation with monthly‐mean GPCP data is increased from 67% to 77%. In general, low‐frequency variability is found to be well represented and from 10 hPa downwards general patterns of anomalies in temperature match those from the ERA‐Interim, MERRA‐2 and JRA‐55 reanalyses.

UNDRR report published to mark the International Day for Disaster Risk Reduction on October 13, 2020, confirms how extreme weather events have come to dominate the disaster landscape in the 21st century.

Abstract As losses from extreme weather events grow, many governments are looking to privatize the financing and incentivization of climate adaptation through insurance markets. In a pure market approach to insurance for extreme weather events, individuals become responsible for ensuring they are adequately covered for risks to their own properties, and governments no longer contribute funds to post‐disaster recovery. Theoretically, insurance premiums signal the level of risk faced by each household, and incentivize homeowners to invest in adaptive action, such as retrofitting, or drainage work, to reduce premiums. Where risk is considered too high by insurance markets, housing is devalued, in theory leading to retreat from risky areas. In this review article, we evaluate the suitability of private insurance as a mechanism for climate adaptation at a household and community level. We find a mismatch between social understandings of responsibility for climate risks, and the technocratic, market‐based home insurance products offered by private insurance markets. We suggest that by constructing increasingly individualized, technical, and calculative evaluations of risk, market‐based models of insurance for extreme weather events erode the solidaristic and collective discourses and practices that support adaptive behavior. This article is categorized under: Vulnerability and Adaptation to Climate Change > Institutions for Adaptation

Reliable long-term streamflow forecast is essential in water resources management and plays a key role in reservoir management and hydropower generation. Properly framing the uncertainty is the key issue in providing a reliable long-term streamflow forecast, and probabilistic forecasts have been used to this effect. In a probabilistic approach, each observed historical data is taken as a possible realization of the future. Non stationarity of hydrometeorological variables, either due to the climate internal variability or anthropogenic change, is another important problem for long-term streamflow forecasts as it is becoming increasingly clearer that past historical data may not adequately represent the current climate. Therefore, there is a need to develop flexible approaches taking into account non-stationarity for long-term streamflow forecasts. Resampling past historical time series is the main approach used for probabilistic long term streamflow forecasts. However, non-stationarity is a key issue of resampling approaches. One possible approach is to make use of a stochastic weather generator coupled to a hydrological model to generate long-term probabilistic streamflow forecasts. Weather generators can easily be modified to account for climatic trends and therefore have the potential to take non-stationarity into account. However, before weather generators can be modified to account for climate non-stationarity, it is first necessary to evaluate whether the modeling chain consisting of a stochastic weather generator and a hydrological model can generate probabilistic streamflow forecasts with a performance similar to that of more traditional resampling approaches. The first objective of this study is therefore, to compare the performance of a stochastic weather generator against that of resampling historical meteorological time series in order to produce ensemble streamflow forecasts. Results indicate that while there are differences between both methods, they nevertheless largely both perform similarly, thus showing that weather generators can be used as substitutes to resampling the historical past. Based on these results, two approaches for taking non-stationarity into account have been proposed. Both approaches are based on a climate-based perturbation of the stochastic weather generator parameters. The first approach explored a simple perturbation method in which the entire length of the historical record is used to quantify internal variability, while a subset of recent years is used to characterize mean climatic values for precipitation, minimum and maximum temperatures. Results show that the approach systematically improves long-term streamflow forecasts accuracy, and that results are dependent on the time window used to estimate current mean climatic estimates. The second approach conditioned the parameters of a stochastic weather generator on largescale climate indices. In this approach, the most important climate indices are identified by looking at yearly correlations between a set of 40 indices and precipitation and temperature. A linear model is then constructed to identify precipitation and temperature anomalies which are then used to induce perturbations in the stochastic weather generator. Five different time windows are defined to determine the optimal linear model. Results show that temperatures are significantly correlated with large-scale climate indices, whereas precipitation is only weakly related to the same indices. The length of the time window has a considerable impact on the prediction ability of the linear models. The precipitation models based on short-duration time windows performed better than those based on longer windows, while the reverse was found for the temperature models. Results show that the proposed method improves long-term streamflow forecasting, particularly around the spring flood.

While impressive results have been achieved in the well-known fields where Deep Learning allowed for breakthroughs such as computer vision, its impact on different older areas is still vastly unexplored. In Computational Fluid Dynamics and especially in Flood Modeling, many phenomena are very high-dimensional, and predictions require the use of numerical simulations, which can be, while very robust and tested, computationally heavy and may not prove useful in the context of real-time predictions. This issue led to various attempts at developing Reduced-Order Modeling techniques, both intrusive and non-intrusive. One recent relevant addition is a combination of Proper Orthogonal Decomposition with Deep Neural Networks (POD-NN). Yet, to our knowledge, little has been performed in implementing uncertainty-aware regression tools in the example of the POD-NN framework. In this work, we aim at comparing different novel methods addressing uncertainty quantification in Neural Networks, pushing forward the POD-NN concept with Deep Ensembles and Bayesian Neural Networks, which we first test on benchmark problems, and then apply to a real-life application: flooding predictions in the Mille-Iles river in Laval, QC, Canada. Building a non-intrusive surrogate model, able to know when it doesn’t know, is still an open research area as far as neural networks are concerned.

Cette thèse vise à améliorer notre compréhension du modèle hédonique et de son application sur les données des biens immobiliers afin d'étudier l'impact d'un événement / externalité / environnementale liée à la présence d'inondation sur la valeur des propriétés résidentielles. Étant donné que les données immobilières sont réparties dans l'espace et dans le temps, des "corrections" temporelles et spatiales sont nécessaires dans le processus de modélisation économétrique. La recherche prend appui sur l’équation de prix hédonique. L’analyse empirique recours également à l’estimateur de type différence de différences spatio-temporelles (STDID) afin d’étudier l’effet d’une inondation survenue en 1998 sur le prix des résidences dans la ville de Laval au Canada entre 1995-2007. Les résultats suggèrent que l’utilisation des informations sur les zones inondables dans le but d’évaluer l’impact des inondations sur les valeurs résidentielles n’est pas une approche nécessairement appropriée. Les conclusions suggèrent que la grande hétérogénéité des résultats notés dans la littérature n’est probablement pas étrangère à la façon de définir les résidences touchées par les inondations. Cela signifie que les recherches empiriques sur les effets des inondations sur la valeur immobilière mesurent en réalité la valeur liée à la perception du risque d'inondation et non l’effet réel de l'inondation. Les résultats suggèrent que les applications futures dans la littérature devront porter une attention particulière à la manière de définir les zones inondables et d’identifier les résidences réellement touchées.

Abstract. The ongoing warming of cold regions is affecting hydrological processes, causing deep changes, such as a ubiquitous increase in river winter discharges. The drivers of this increase are not yet fully identified mainly due to the lack of observations and field measurements in cold and remote environments. In order to provide new insights into the sources generating winter runoff, the present study explores the possibility of extracting information from icings that form over the winter and are often still present early in the summer. Primary sources detection was performed using time-lapse camera images of icings found in both proglacial fields and upper alpine meadows in June 2016 in two subarctic glacierized catchments in the upper part of the Duke watershed in the St. Elias Mountains, Yukon. As images alone are not sufficient to entirely cover a large and hydrologically complex area, we explore the possibility of compensating for that limit by using four supplementary methods based on natural tracers: (a) stable water isotopes, (b) water ionic content, (c) dissolved organic carbon, and (d) cryogenic precipitates. The interpretation of the combined results shows a complex hydrological system where multiple sources contribute to icing growth over the studied winter. Glaciers of all sizes, directly or through the aquifer, represent the major parent water source for icing formation in the studied proglacial areas. Groundwater-fed hillslope tributaries, possibly connected to suprapermafrost layers, make up the other detectable sources in icing remnants. If similar results are confirmed in other cold regions, they would together support a multi-causal hypothesis for a general increase in winter discharge in glacierized catchments. More generally, this study shows the potential of using icing formations as a new, barely explored source of information on cold region winter hydrological processes that can contribute to overcoming the paucity of observations in these regions.

Abstract This study aims to isolate and quantify the role of shrinking glaciers in recent hydrological changes in eight watersheds in the southwestern Yukon (Canada) by using an original dual approach that consists of (i) watershed hydrological regime identification, followed by a trend analysis of discharge time series, and (ii) a model‐based peak water (PW) analysis using glacier cover change measurements. A distinction between hydrological regimes is a necessary add‐up to commonly used trend attribution methods as the lake runoff regime shares common characteristics with the glacier regime. Results show a link between shrinking glaciers and hydrological changes in the region, but the link is complex, and glacier retreat does not explain all the observed changes. Model outputs show that the two watersheds with a glacierized area exceeding 30% and one watershed with 2.9% glacierized area have not reached PW, whereas a 9.2% glacierized watershed and another watershed with 2.1% glacierized area have already passed it. These results suggest that glacierized area alone cannot explain short‐term changes related to watershed current position in terms of PW, and the rate of glacier retreat must be considered. By contrast, the actual rate of glacier retreat does not influence long‐term changes, such as the magnitude of PW and of the consequent drop in discharge. Once glaciers will have retreated to a point close to extinction, declines in summer discharge from 10% to 70% and proportional to the actual glacier cover are anticipated at watersheds that are currently more than 9% glacierized. , Plain Language Summary In this study, we aim to understand how shrinking glacier cover affects river discharges. In conditions of continuous retreat, glaciers produce an initial increase in runoff as they lose mass. The discharge then reaches a turning point, a plateau called peak water, and subsequently declines as the volume of glacial ice continues to decrease. When analyzing eight watersheds with different glacier covers in the southwestern Yukon, we found that two watersheds that are 30% covered by glaciers have not yet reached this plateau, and therefore, the discharge will continue to increase. Several watersheds with smaller glacierized portions have passed peak water, which means that the discharge will now continue to decrease. We were also able to estimate the magnitudes of these changes in discharge. We show that two watersheds with 30% glacierized area can still experience a 1.5‐ to 2‐fold increase in discharge and that watersheds currently more than 9% glacierized are predicted to show noticeable changes after peak water, with the possibility of discharge decreasing by a factor of 3 to 5 by the time glaciers have retreated to a point when their hydrological influence at the watershed scale becomes insignificant. , Key Points Noticeable acceleration of glacier retreat occurred in southwestern Yukon since 1999 with measured consequences for the regional hydrology Various hydrological changes have been detected at the study watersheds. Glacier retreat explains many but not all of those changes Long‐term hydrological changes are glacier cover dependent while decadal‐scale changes are driven by glacier retreat rate

Cyanobacterial bloom events produce toxins and taste and odor issues, disturbing drinkable water quality. Vacuum UV (VUV) is a promising advanced oxidation process used to treat impacted water, with potential applicability in small and remote communities. , Cyanobacterial blooms are a growing concern around the world. A feasible approach for small treatment plants fed by sources contaminated with cyanobacteria is vacuum UV (VUV). VUV is a promising advanced oxidation process used to treat water impacted by cyanobacterial blooms, with potential applicability in small and remote communities because of its simplicity. In this work, water samples from three Canadian lakes periodically affected by cyanobacteria were used to assess the impact of natural and algal organic matter (NOM/AOM) on treatment with VUV. NOM and AOM were characterized before and after VUV treatment by size exclusion chromatography (SEC) and fluorescence emission–excitation matrix (FEEM). FEEM spectra were analyzed with the parallel factor analysis (PARAFAC) tool. As a result, we found seven principal components describing the whole dataset. Disinfection by-product (DBP) formation after VUV treatment was analyzed and trihalomethanes (THM) yield was calculated. THM yield increased by 15–20% after VUV treatment. Regarding DBP formation and NOM/AOM fractions from SEC, we found that humic substances are the most important fraction causing the increase in DBP formation with at least 3 times higher yield than the other fractions: biopolymers, building blocks, low weight molecular acids and neutrals.

Multi-cohort forest management in northern hardwood stands may well be the best way to successfully regenerate tree species of intermediate shade tolerance, such as yellow birch (Betula alleghaniensis Britt.). The creation of large enough gaps in the canopy favors increased light availability within the opening, while soil scarification provides suitable germination seedbeds. Evidence of these methods’ success nonetheless remains mostly the purview of experimental studies rather than operational tests. In Quebec, Canada, the multi-cohort methods promoted include group selection cutting and patch cutting. The present study tested their implementation at an operational scale and over a large territory in both hardwood-dominated and mixedwood stands. We assessed their efficacy in promoting natural regeneration of commercial hardwood trees, notably yellow birch and sugar maple (Acer saccharum Marsh.). We conducted regeneration surveys at 2, 5, 10, and 15 years after harvest. Overall, group selection and patch cuttings were successful in regenerating the target species. Yellow birch, for instance, showed a mean stocking around 60% and a mean sapling density around 3400 stems ha−1 after 15 years. We compared several variables for measuring regeneration in early years, and found that the relative abundance, the stocking based on one stem per sampling unit, and the mean maximum height were good predictors of the relative presence of yellow birch and sugar maple in 15-year-old canopy openings. Using smaller sampling units (6.25 m2 rather than 25 m2) and waiting until year 5 may be more useful for making such predictions. In addition, there was an important turnover in vertical dominance in these openings. Non-commercial woody competitors were frequently dominant in early years but were often replaced by commercial hardwoods, notably yellow birch. We propose certain thresholds for assessing the success of post-harvest regeneration and for evaluating the need for a cleaning treatment.