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Résumé L'hydrogéomorphologie étudie la dynamique des rivières en se concentrant sur les interactions liant la structure des écoulements, la mobilisation et le transport des sédiments et les morphologies qui caractérisent les cours d'eau et leur bassin‐versant. Elle offre un cadre d'analyse et des outils pour une meilleure intégration des connaissances sur la dynamique des rivières pour la gestion des cours d'eau au sens large, et plus spécifiquement, pour leur restauration, leur aménagement et pour l'évaluation et la prévention des risques liés aux aléas fluviaux. Au Québec, l'hydrogéomorphologie émerge comme contribution significative dans les approches de gestion et d'évaluation du risque et se trouve au cœur d'un changement de paradigme dans la gestion des cours d'eau par lequel la restauration des processus vise à augmenter la résilience des systèmes et des sociétés et à améliorer la qualité des environnements fluviaux. Cette contribution expose la trajectoire de l'hydrogéomorphologie au Québec à partir des publications scientifiques de géographes du Québec et discute des visées de la discipline en recherche et en intégration des connaissances pour la gestion des cours d'eau . , Abstract Hydrogeomorphology studies river dynamics, focusing on the interactions between flow structure, sediment transport, and the morphologies that characterize rivers and their watersheds. It provides an analytical framework and tools for better integrating knowledge of river dynamics into river management in the broadest sense, and more specifically, into river restoration as well as into the assessment and prevention of risks associated with fluvial hazards. In Quebec, hydrogeomorphology is emerging as a significant contribution to risk assessment and management approaches, and is at the heart of a paradigm shift in river management whereby process restoration aims to increase the resilience of fluvial systems and societies, and improve the quality of fluvial environments. This contribution outlines the trajectory of hydrogeomorphology in Quebec, based on scientific publications by Quebec geographers, and discusses the discipline's aims in research and knowledge integration for river management . , Messages clés Les géographes du Québec ont contribué fortement au développement des connaissances et outils de l'hydrogéomorphologie. L'hydrogéomorphologie a évolué d'une science fondamentale à une science où les connaissances fondamentales sont au service de la gestion des cours d'eau. L'hydrogéomorphologie et le cortège de connaissances et d'outils qu'elle promeut font de cette discipline une partenaire clé pour une gestion holistique des cours d'eau.

Climate change and more frequent severe storms have caused persistent flooding, storm surges, and erosion in the northeastern coastal region of the United States. These weather-related disasters have continued to generate negative environmental consequences across many communities. This study examined how coastal residents’ exposure to flood risk information and information seeking behavior were related to their threat appraisal, threat-coping efficacy, and participation in community action in the context of building social resilience. A random sample of residents of a coastal community in the Northeastern United States was selected to participate in an online survey (N = 302). Key study results suggested that while offline news exposure was weakly related to flood vulnerability perception, online news exposure and mobile app use were both weakly associated with flood-risk information seeking. As flood vulnerability perception was strongly connected to flood severity perception but weakly linked to lower self-efficacy beliefs, flood severity perception was weakly and moderately associated with response-efficacy beliefs and information seeking, respectively. Furthermore, self-efficacy beliefs, response efficacy beliefs, and flood-risk information seeking were each a weak or moderate predictor of collective efficacy beliefs. Lastly, flood risk information-seeking was a strong predictor and collective efficacy beliefs were a weak predictor of community action for flood-risk management. This study tested a conceptual model that integrated the constructs from risk communication, information seeking, and protection motivation theory. Based on the modeling results reflecting a set of first-time findings, theoretical and practical implications are discussed.

Abstract Groundwater quality modelling plays an important role in water resources management decision making processes. Accordingly, models must be developed to account for the uncertainty inherent in the modelling process, from the sample measurement stage through to the data interpretation stages. Artificial intelligence models, particularly fuzzy inference systems (FIS), have been shown to be effective in groundwater quality evaluation for complex aquifers. In the current study, fuzzy set theory is applied to groundwater-quality related decision-making in an agricultural production context; the Mamdani, Sugeno, and Larsen fuzzy logic-based models (MFL, SFL, and LFL, respectively) are used to develop a series of new, generalized, rule-based fuzzy models for water quality evaluation using widely accepted irrigation indices and hydrological data from the Sarab Plain, Iran. Rather than drawing upon physiochemical groundwater quality parameters, the present research employs widely accepted agricultural indices (e.g., irrigation criteria) when developing the MFL, SFL and LFL groundwater quality models. These newly-developed models, generated significantly more consistent results than the United States Soil Laboratory (USSL) diagram, addressed the inherent uncertainty in threshold data, and were effective in assessing groundwater quality for agricultural uses. The SFL model is recommended as it outperforms both MFL and LFL in terms of accuracy when assessing groundwater quality using irrigation indices.

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.

Cette thèse traite des aspects de la quantification de l'incertitude dans l'évaluation des ressources éoliennes avec les pratiques d'analyses d'incertitude et de sensibilité. Les objectifs de cette thèse sont d'examiner et d'évaluer la qualité des pratiques d'analyse de sensibilité dans l'évaluation des ressources éoliennes, de décourager l'utilisation d'une analyse de sensibilité à la fois, d'encourager l'utilisation d'une analyse de sensibilité globale à la place, d'introduire des méthodes d'autres domaines., et montrer comment les analyses d'incertitude et de sensibilité globale ajoutent de la valeur au processus d'aide à la décision. Cette thèse est organisée en quatre articles : I. Une revue des pratiques d'analyse de sensibilité dans l'évaluation des ressources éoliennes avec une étude de cas de comparaison d'analyses de sensibilité individuelles et globales du coût actualisé de l'énergie éolienne offshore ; II. Technique Quasi-Monte Carlo dans l'analyse de sensibilité globale dans l'évaluation des ressources éoliennes avec une étude de cas sur les Émirats Arabes Unis; III. Utilisation de la famille de distribution Halphen pour l'estimation de la vitesse moyenne du vent avec une étude de cas sur l'Est du Canada; IV. Étude d'évaluation des ressources éoliennes offshore du golfe Persique avec les données satellitaires QuikSCAT.Les articles I à III ont chacun donné lieu à une publication évaluée par des pairs, tandis que l'article IV - à une soumission. L'article I propose des classifications par variable de sortie d'analyse de sensibilité, méthode, application, pays et logiciel. L'article I met en évidence les lacunes de la littérature, fournit des preuves des pièges, conduisant à des résultats d'évaluation erronés et coûteux des ressources éoliennes. L'article II montre comment l'analyse de sensibilité globale offre une amélioration au moyen du quasi-Monte Carlo avec ses plans d'échantillonnage élaborés permettant une convergence plus rapide. L'article III introduit la famille de distribution Halphen pour l'évaluation des ressources éoliennes. Article IV utilise les données satellitaires SeaWinds/QuikSCAT pour l'évaluation des ressources éoliennes offshore du golfe Persique. Les principales contributions à l'état de l'art avec cette thèse suivent. À la connaissance de l'auteur, aucune revue de l'analyse de sensibilité dans l'évaluation des ressources éoliennes n'est actuellement disponible dans la littérature, l'article I en propose une. L'article II relie la modélisation mathématique et l'évaluation des ressources éoliennes en introduisant la technique de quasi-Monte Carlo dans l'évaluation des ressources éoliennes. L'article III présente la famille de distribution de Halphen, de l'analyse de la fréquence des crues à l'évaluation des ressources éoliennes. <br /><br />This dissertation deals with the aspects of quantifying uncertainty in wind resource assessment with the practices of uncertainty and sensitivity analyses. The objectives of this dissertation are to review and assess the quality of sensitivity analysis practices in wind resource assessment, to discourage the use of one-at-a-time sensitivity analysis, encourage the use of global sensitivity analysis instead, introduce methods from other fields, and showcase how uncertainty and global sensitivity analyses adds value to the decision support process. This dissertation is organized in four articles: I. Review article of 102 feasibility studies: a review of sensitivity analysis practices in wind resource assessment with a case study of comparison of one-at-a-time vs. global sensitivity analyses of the levelized cost of offshore wind energy; II. Research article: Quasi-Monte Carlo technique in global sensitivity analysis in wind resource assessment with a case study on United Arab Emirates; III. Research article: Use of the Halphen distribution family for mean wind speed estimation with a case study on Eastern Canada; IV. Application article: Offshore wind resource assessment study of the Persian Gulf with QuikSCAT satellite data. Articles I-III have each resulted in a peer-reviewed publication, while Article IV – in a submission. Article I offers classifications by sensitivity analysis output variable, method, application, country, and software. It reveals the lack of collective agreement on the definition of sensitivity analysis in the literature, the dominance of nonlinear models, the prevalence of one-at a-time sensitivity analysis method, while one-at-a-time method is only valid for linear models. Article I highlights gaps in the literature, provides evidence of the pitfalls, leading to costly erroneous wind resource assessment results. Article II shows how global sensitivity analysis offers improvement by means of the quasi-Monte Carlo with its elaborate sampling designs enabling faster convergence. Article III introduces the Halphen distribution family for the purpose of wind recourse assessment. Article IV uses SeaWinds/QuikSCAT satellite data for offshore wind resource assessment of the Persian Gulf. The main contributions to the state-of-the-art with this dissertation follow. To the best of author’s knowledge, no review of sensitivity analysis in wind resource assessment is currently available in the literature, Article I offers such. Article II bridges mathematical modelling and wind resource assessment by introducing quasi-Monte Carlo technique to wind resource assessment. Article III introduces the Halphen distribution family from flood frequency analysis to wind resource assessment.

Au Québec, les conditions printanières extraordinaires de 2017 et 2019 ont incité le gouvernement provincial à commander une mise à jour des cartes des zones inondables. La plupart des cartes existantes ne reflètent pas adéquatement l’aménagement actuel du territoire, ni l’aléa associé. Généralement, pour la cartographie, les modèles hydrodynamiques tel que HEC-RAS sont utilisés, mais ces outils nécessitent une expertise significative, des données hydrométriques et des relevés bathymétriques à haute résolution. Étant donnée la nécessité de mettre à jour ces cartes tout en réduisant les coûts financiers associés, des méthodes conceptuelles simplifiées ont été développées. Ces approches, y compris l’approche géomatique HAND (Height above the nearest drainage), qui reposent uniquement sur un modèle numérique d’élévation (MNE), sont de plus en plus utilisées. HAND permet de calculer la hauteur d’eau nécessaire pour inonder chaque pixel du MNE selon la différence entre son élévation et celle du pixel du cours d’eau dans lequel il se déverse. Les informations sur la géométrie hydraulique dérivées par HAND ainsi que l’application de l’équation de Manning permettent la construction d’une courbe de tarage synthétique (CTS) pour chaque tronçon de rivière homogène. Dans la littérature, cette méthode a été appliquée pour établir une cartographie de la zone inondable de première instance de grands fleuves aux États-Unis avec un taux de correspondance de 90% par rapport à l’utilisation de HEC-RAS. Elle n’a toutefois pas été appliquée sur de petits bassins versants, car ceux-ci engendrent des défis méthodologiques substantiels. Ce projet s’attaque à ces défis sur deux bassins versants Québécois, ceux des rivières à la Raquette et Delisle. Les conditions frontières des modèles sont dérivées d’un traitement statistique empirique des séries de débits simulés avec le modèle hydrologique HYDROTEL. Étant donnée l’absence de stations météorologiques sur le territoire à l’étude, des chroniques du système Canadien d’Analyse de la précipitation (CaPA) ont été utilisées pour cette modélisation hydrologique. Les résultats de ce projet pointent vers des performances satisfaisantes de l’approche géomatique HAND-CTS en comparaison avec le modèle hydrodynamique HEC-RAS (1D/2D et 2D au complet), avec des taux de correspondance entre les étendues des inondations supérieurs à 60 % pour les bassins versants de Delisle et à la Raquette. Les comparaisons étaient effectuées sur une gamme de débit allant d’un débit de période de retour de 2 ans jusqu’à un débit de plus de 350 ans. On notera que l’application sur la rivière à la Raquette a été développée dans les règles de l’art, incluant un processus de calage développé dans le cadre d’un projet de maitrise en sciences de l’eau connexe à ce mémoire, relativement à la longueur du tronçon, le calage vertical de la CTS en considérant la hauteur d’eau présente dans le cours d’eau lors du relevé LiDAR et sa précision verticale. Les résultats ont montré que le coefficient de précision globale le plus bas était de 98 % pour un débit de 350 ans, avec une précision de plus que 99 % pour les autres périodes de retour, ce qui représente une très bonne performance du modèle. Et par ailleurs, le coefficient de Kappa conditionnel humide variait entre 58 % et 28 %. Alors, que pour la rivière Delisle, l’application se veut naïve, c’est-à-dire sans calage préalable de la méthode HANDCTS. La précision globale a varié entre 83 % et 96 %, ce qui est considéré comme "très approprié" et une variation du coefficient Kappa conditionnel humide de 35,2 à 64,3 %. Alors que pour une différence d’élévations d'eau entre les élévations de référence et simulées, la performance était quantifiée par un RMSE qui variait pour les périodes de retour de 100 ans et de 350 ans respectivement de 4,5 m et de 7,1 m. Enfin, la distribution spatiale des différences d’élévations montre une distribution gaussienne avec une moyenne qui est à peu près égale à 0 où la plupart des erreurs se situent entre -0,34 m et 1,1 m La cartographie des zones inondables dérivée de HAND-CTS présente encore certains défis associés notamment à la présence d’infrastructures urbaines complexes (ex. : ponceaux, ponts et seuils) dont l’influence hydraulique n’est pas considérée. Dans le contexte où l’ensemble du Québec (529 000 km²) dispose d’une couverture LiDAR, les résultats de ce mémoire permettront de mieux comprendre les sources d’incertitude associées à la méthode HAND-CTS tout en démontrant son potentiel pour les bassins versants dépourvus de données bathymétriques et hydrométéorologiques. <br /><br />The 2017 and 2019 extraordinary spring conditions prompted the Quebec government to update flood risk maps, as most of them do not adequately reflect current land use and associated hazard. Generally, hydrodynamic models such as HEC-RAS are used for flood mapping, but they require significant expertise, hydrometric data, and high-resolution bathymetric surveys. Given the need to update these maps while reducing the associated financial costs, simplified conceptual methods have been developed over the last decade. These methods are increasingly used, including HAND (height above the nearest drainage), which relies on a Digital Elevation Model (DEM) to delineate the inundation area given the water height in a river segment. Furthermore, the river geometry derived from HAND data and the application of Manning’s equation allow for the construction of a synthetic rating curve (SRC) for each homogeneous river segment. In the scientific literature, this framework has been applied to produce first-instance floodplain mapping of large rivers. For example, in the Continental United States 90% match rates were achieved when compared to the use of HEC-RAS. However, this framework has not been validated for small watersheds, as substantial methodological challenges are anticipated. This project addresses these underlying challenges in two Quebec watersheds, the à la Raquette and Delisle watersheds. The boundary conditions of the HECRAS models were derived from an empirical statistical treatment of flow time series simulated by HYDROTEL, a hydrological model, using Canadian Precipitation Analysis Product (CaPA) time series. The results of this project point towards satisfactory performances, with match rates greater than 60 % for both watersheds. It should be noted that the application on the Delisle River is naive, that is without prior calibration of the HAND-SRC method. The overall accuracy ranged from 83.4 % to 96.2 % while the water surface elevation difference was quantified by an RMSE that was for the 100-year and 350-year return periods of 4.5 m and 7.1 m respectively and where most errors are between -0.34 m and 1.1 m representing a very good model comparing to similar studies. For à la Raquette, the application showed an overall accuracy coefficient of 98 % for a 350-year flow, with an accuracy of over 99 % for other return periods. The mapping of flood risk areas using HAND-SRC still faces certain challenges, notably the presence of complex urban infrastructures (e.g., culverts, bridges, and weirs) whose hydraulic influences are not considered by this geomatic approach. Given that most of Quebec (529,000 km²) topography has been digitized using LiDAR data, the results conveyed in this MSc thesis will allow for a better understanding of the sources of uncertainty associated with the application of the HAND-SRC method while demonstrating its potential for watersheds lacking hydrometeorological and high-resolution bathymetric data.

Abstract. Dissolved organic carbon (DOC) trends, predominantly showing long-term increases in concentration, have been observed across many regions of the Northern Hemisphere. Elevated DOC concentrations are a major concern for drinking water treatment plants, owing to the effects of disinfection byproduct formation, the risk of bacterial regrowth in water distribution systems, and treatment cost increases. Using a unique 30-year data set encompassing both extreme wet and dry conditions in a eutrophic drinking water reservoir in the Great Plains of North America, we investigate the effects of changing source-water and in-lake water chemistry on DOC. We employ novel wavelet coherence analyses to explore the coherence of changes in DOC with other environmental variables and apply a generalized additive model to understand predictor–DOC responses. We found that the DOC concentration was significantly coherent with (and lagging behind) flow from a large upstream mesotrophic reservoir at long (> 18-month) timescales. DOC was also coherent with (lagging behind) sulfate and in phase with total phosphorus, ammonium, and chlorophyll a concentrations at short (≤ 18-month) timescales across the 30-year record. These variables accounted for 56 % of the deviance in DOC from 1990 to 2019, suggesting that water-source and in-lake nutrient and solute chemistry are effective predictors of the DOC concentration. Clearly, climate and changes in water and catchment management will influence source-water quality in this already water-scarce region. Our results highlight the importance of flow management to shallow eutrophic reservoirs; wet periods can exacerbate water quality issues, and these effects can be compounded by reducing inflows from systems with lower DOC. These flow management decisions address water level and flood risk concerns but also have important impacts on drinking water treatability.

Abstract The flood-prone Saint John River (SJR, Wolastoq), which lies within a drainage basin of 55 110 km 2 , flows a length of 673 km from its source in northern Maine, United States, to its mouth in southern New Brunswick, Canada. Major industries in the basin include forestry, agriculture, and hydroelectric power. During the 1991–2020 reference period, the SJR basin (SJRB) experienced major spring flood events in 2008, 2018, and 2019. As part of the Saint John River Experiment on Cold Season Storms, the objective of this research is to characterize and contrast these three major spring flood events. Given that the floods all occurred during spring, the hypothesis being tested is that rapid snowmelt alone is the dominant driver of flooding in the SJRB. There were commonalities and differences regarding the contributing factors of the three flood years. When averaged across the upper basin, they showed consistency in terms of positive winter and spring total precipitation anomalies, positive snow water equivalent anomalies, and steep increases in April cumulative runoff. Rain-on-snow events were a prominent feature of all three flood years. However, differences between flood years were also evident, including inconsistencies with respect to ice jams and high tides. Certain factors were present in only one or two of the three flood years, including positive total precipitation anomalies in spring, positive heavy liquid precipitation anomalies in spring, positive heavy solid precipitation anomalies in winter, and positive temperature anomalies in spring. The dominant factor contributing to peak water levels was rapid snowmelt.

Abstract Overcooled talus slopes are generally described as islands of sporadic permafrost below the lower alpine limit of permafrost. The negative thermal anomaly of the ground is mainly consecutive to the internal ventilation of the deposit, but it is also conditioned by multiple factors as topography, slope aspect and incline, openwork structure and coarseness of the deposit, air temperature, solar radiation and wind regime. Therefore, the study of the spatiotemporal dynamics of ventilation processes allows a better understanding of the phenomenon. At Cannon Cliff, New Hampshire (USA), several field visits and environmental monitoring allowed us to describe the varying nature and significance of the ventilation mechanisms that can be observed at the ground surface and associated with both the intensity and direction of the airflows in a talus debris accumulation/protalus rampart system. The thermal negative anomalies are strong enough to lower the ground temperature to the point of preserving ice during the late spring and summer seasons. The monitoring of the gradient between external (air) and internal (talus) temperatures coupled with several dendroecological and geomorphological analyses provided a complete environmental picture of the impacts, feedback and extent of the phenomenon.

Agriculture is the traditional and leading field of economy of Tetritskaro Municipality, but faces the challenge of changing climate. The study investigates male and female farmers’ perception of climate change issues in Tetritskaro, their main source of information, adaptation measures choosen and their needs. Climate change data available in Tetritskaro focused on characteristic extreme weather events coupled with face-to-face interviews from 254 farmers (male - 53%, female - 47%) was analyzed. The study revealed that men and women have more or less similar perceptions of climate change issues. For male farmers, the main source of information on climate, seasonal prediction and weather forecast is conversations with fellow farmers, and for female farmers it is indigenous knowledge of the local environment. Male and female farmers, have adapted to the changes in climate similarly applying measures such as pesticides, fertilizer and irrigation, early sowing, and earlier harvest, while the exchange of information between fellow farmers, use of various hail protection products and crop diversification techniques is more frequent among male farmers. Farmers expressed the need for low interest loans to purchase agricultural products, equipment and restore/create windbreak zones. Most of the male farmers indicate the need for introduction new technologies, while female farmers are more in need of information and training in agricultural activities. The study shows the need for development of climate change adaptation policies and interventions in Tetritskaro. Obtained results can be used not only in other agricultural regions of Georgia, but in other countries with the same problems. , Agriculture is the traditional and leading field of economy of Tetritskaro Municipality, but faces the challenge of changing climate. The study investigates male and female farmers’ perception of climate change issues in Tetritskaro, their main source of information, adaptation measures choosen and their needs. Climate change data available in Tetritskaro focused on characteristic extreme weather events coupled with face-to-face interviews from 254 farmers (male - 53%, female - 47%) was analyzed. The study revealed that men and women have more or less similar perceptions of climate change issues. For male farmers, the main source of information on climate, seasonal prediction and weather forecast is conversations with fellow farmers, and for female farmers it is indigenous knowledge of the local environment. Male and female farmers, have adapted to the changes in climate similarly applying measures such as pesticides, fertilizer and irrigation, early sowing, and earlier harvest, while the exchange of information between fellow farmers, use of various hail protection products and crop diversification techniques is more frequent among male farmers. Farmers expressed the need for low interest loans to purchase agricultural products, equipment and restore/create windbreak zones. Most of the male farmers indicate the need for introduction new technologies, while female farmers are more in need of information and training in agricultural activities. The study shows the need for development of climate change adaptation policies and interventions in Tetritskaro. Obtained results can be used not only in other agricultural regions of Georgia, but in other countries with the same problems.

With the growing realization that crystallization processes may evolve through a sequence of different solid forms, including amorphous precursor phases, the development of suitable in-situ experimental probes is essential for comprehensively mapping the time-evolution of such processes. Here we demonstrate that the CLASSIC NMR (Combined Liquid- And Solid-State In-situ Crystallization NMR) strategy is a powerful technique for revealing the transitory existence of amorphous phases during crystallization processes, applying this technique to study crystallization of dl-menthol and l-menthol from their molten liquid phases. The CLASSIC NMR results provide direct insights into the conditions (including the specific time period) under which the molten liquid phase, transitory amorphous phases and final crystalline phases exist during these crystallization processes.

Abstract. Climate change impact studies require a reference climatological dataset providing a baseline period to assess future changes and post-process climate model biases. High-resolution gridded precipitation and temperature datasets interpolated from weather stations are available in regions of high-density networks of weather stations, as is the case in most parts of Europe and the United States. In many of the world's regions, however, the low density of observational networks renders gauge-based datasets highly uncertain. Satellite, reanalysis and merged product datasets have been used to overcome this deficiency. However, it is not known how much uncertainty the choice of a reference dataset may bring to impact studies. To tackle this issue, this study compares nine precipitation and two temperature datasets over 1145 African catchments to evaluate the dataset uncertainty contribution to the results of climate change studies. These deterministic datasets all cover a common 30-year period needed to define the reference period climate. The precipitation datasets include two gauge-only products (GPCC and CPC Unified), two satellite products (CHIRPS and PERSIANN-CDR) corrected using ground-based observations, four reanalysis products (JRA55, NCEP-CFSR, ERA-I and ERA5) and one merged gauged, satellite and reanalysis product (MSWEP). The temperature datasets include one gauged-only (CPC Unified) product and one reanalysis (ERA5) product. All combinations of these precipitation and temperature datasets were used to assess changes in future streamflows. To assess dataset uncertainty against that of other sources of uncertainty, the climate change impact study used a top-down hydroclimatic modeling chain using 10 CMIP5 (fifth Coupled Model Intercomparison Project) general circulation models (GCMs) under RCP8.5 and two lumped hydrological models (HMETS and GR4J) to generate future streamflows over the 2071–2100 period. Variance decomposition was performed to compare how much the different uncertainty sources contribute to actual uncertainty. Results show that all precipitation and temperature datasets provide good streamflow simulations over the reference period, but four precipitation datasets outperformed the others for most catchments. They are, in order, MSWEP, CHIRPS, PERSIANN and ERA5. For the present study, the two-member ensemble of temperature datasets provided negligible levels of uncertainty. However, the ensemble of nine precipitation datasets provided uncertainty that was equal to or larger than that related to GCMs for most of the streamflow metrics and over most of the catchments. A selection of the four best-performing reference datasets (credibility ensemble) significantly reduced the uncertainty attributed to precipitation for most metrics but still remained the main source of uncertainty for some streamflow metrics. The choice of a reference dataset can therefore be critical to climate change impact studies as apparently small differences between datasets over a common reference period can propagate to generate large amounts of uncertainty in future climate streamflows.

Climate anomalies, such as floods and droughts, as well as gradual temperature changes have been shown to adversely affect economies and societies. Although studies find that climate change might increase global inequality by widening disparities across countries, its effects on within-country income distribution have been little investigated, as has the role of rainfall anomalies. Here, we show that extreme levels of precipitation exacerbate within-country income inequality. The strength and direction of the effect depends on the agricultural intensity of an economy. In high-agricultural-intensity countries, climate anomalies that negatively impact the agricultural sector lower incomes at the bottom end of the distribution and generate greater income inequality. Our results indicate that a 1.5-SD increase in precipitation from average values has a 35-times-stronger impact on the bottom income shares for countries with high employment in agriculture compared to countries with low employment in the agricultural sector. Projections with modeled future precipitation and temperature reveal highly heterogeneous patterns on a global scale, with income inequality worsening in high-agricultural-intensity economies, particularly in Africa. Our findings suggest that rainfall anomalies and the degree of dependence on agriculture are crucial factors in assessing the negative impacts of climate change on the bottom of the income distribution.

Anthropogenic climate change is currently driving environmental transformation on a scale and at a pace that exceeds historical records. This represents an undeniably serious challenge to existing social, political, and economic systems. Humans have successfully faced similar challenges in the past, however. The archaeological record and Earth archives offer rare opportunities to observe the complex interaction between environmental and human systems under different climate regimes and at different spatial and temporal scales. The archaeology of climate change offers opportunities to identify the factors that promoted human resilience in the past and apply the knowledge gained to the present, contributing a much-needed, long-term perspective to climate research. One of the strengths of the archaeological record is the cultural diversity it encompasses, which offers alternatives to the solutions proposed from within the Western agro-industrial complex, which might not be viable cross-culturally. While contemporary climate discourse focuses on the importance of biodiversity, we highlight the importance of cultural diversity as a source of resilience.

Abstract Current flood risk mapping, relying on historical observations, fails to account for increasing threat under climate change. Incorporating recent developments in inundation modelling, here we show a 26.4% (24.1–29.1%) increase in US flood risk by 2050 due to climate change alone under RCP4.5. Our national depiction of comprehensive and high-resolution flood risk estimates in the United States indicates current average annual losses of US$32.1 billion (US$30.5–33.8 billion) in 2020’s climate, which are borne disproportionately by poorer communities with a proportionally larger White population. The future increase in risk will disproportionately impact Black communities, while remaining concentrated on the Atlantic and Gulf coasts. Furthermore, projected population change (SSP2) could cause flood risk increases that outweigh the impact of climate change fourfold. These results make clear the need for adaptation to flood and emergent climate risks in the United States, with mitigation required to prevent the acceleration of these risks.

Abstract Flood exposure has been linked to shifts in population sizes and composition. Traditionally, these changes have been observed at a local level providing insight to local dynamics but not general trends, or at a coarse resolution that does not capture localized shifts. Using historic flood data between 2000-2023 across the Contiguous United States (CONUS), we identify the relationships between flood exposure and population change. We demonstrate that observed declines in population are statistically associated with higher levels of historic flood exposure, which may be subsequently coupled with future population projections. Several locations have already begun to see population responses to observed flood exposure and are forecasted to have decreased future growth rates as a result. Finally, we find that exposure to high frequency flooding (5 and 20-year return periods) results in 2-7% lower growth rates than baseline projections. This is exacerbated in areas with relatively high exposure to frequent flooding where growth is expected to decline over the next 30 years.

Abstract Flooding remains a major problem for the United States, causing numerous deaths and damaging countless properties. To reduce the impact of flooding on communities, the U.S. government established the Community Rating System (CRS) in 1990 to reduce flood damages by incentivizing communities to engage in flood risk management initiatives that surpass those required by the National Flood Insurance Program. In return, communities enjoy discounted flood insurance premiums. Despite the fact that the CRS raises concerns about the potential for unevenly distributed impacts across different income groups, no study has examined the equity implications of the CRS. This study thus investigates the possibility of unintended consequences of the CRS by answering the question: What is the effect of the CRS on poverty and income inequality? Understanding the impacts of the CRS on poverty and income inequality is useful in fully assessing the unintended consequences of the CRS. The study estimates four fixed‐effects regression models using a panel data set of neighborhood‐level observations from 1970 to 2010. The results indicate that median incomes are lower in CRS communities, but rise in floodplains. Also, the CRS attracts poor residents, but relocates them away from floodplains. Additionally, the CRS attracts top earners, including in floodplains. Finally, the CRS encourages income inequality, but discourages income inequality in floodplains. A better understanding of these unintended consequences of the CRS on poverty and income inequality can help to improve the design and performance of the CRS and, ultimately, increase community resilience to flood disasters.

Abstract Autism spectrum disorder prevalence more than quadrupled in the United States between 2000 and 2020. Ice storm-related prenatal maternal stress (PNMS) predicts autistic-like trait severity in children exposed early in gestation. The objective was to determine the extent to which PNMS influences the severity and trajectory of autistic-like traits in prenatally flood-exposed children at ages 4–7 years and to test moderation by sex and gestational timing. Soon after the June 2008 floods in Iowa, USA, 268 women pregnant during the disaster were assessed for objective hardship, subjective distress, and cognitive appraisal of the experience. When their children were 4, 5½, and 7 years old, mothers completed the Social Communication Questionnaire (SCQ) to assess their children’s autistic-like traits; 137 mothers completed the SCQ for at least one age. The final longitudinal multilevel model showed that the greater the maternal subjective distress, the more severe the child’s autistic-like traits, controlling for objective hardship. The effect of PNMS on rate of change was not significant, and there were no significant main effects or interactions involving sex or timing. Prenatal maternal subjective distress, but not objective hardship or cognitive appraisal, predicted more severe autistic-like traits at age 4, and this effect remained stable through age 7.