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This thesis examines the main socio-environmental relationships related to recurrent floodings in the Sainte-Anne River watershed by mobilizing the history of land use, local knowledge and risk management policies. From a political ecology perspective, these relationships are part of both social representations of nature, power dynamics associated with the appropriation of ressources and multiple temporalities. By also mobilizing the theoreticals frameworks of the anthropology of disasters and amphibian anthropology, this study allow to retrace step by step what “flood zone” is as a socially constructed space by the authorities, and go back to the source of the advent of “flood” as a catastrophic event, where rising waters have been part of the characteristics of these territories since the beginning of the sedentarization of its inhabitants. Based on 93 semi-directed interviews (76 residents and 17 institutions stakeholders) realized between February to October 2019, non-participant observation and documentary research in municipal and regional archives, this ethnography of the Sainte-Anne River watershed allows a unique incursion with Quebec riverside residents who live with recurrent rising waters. Through four case studies (Saint-Raymond, Saint-Alban, Saint-Casimir and Sainte-Anne-de-la-Pérade), historical contexts of occupation of the territory were documented and significant events were described by focusing on local residents adaptation strategies and anticipated management by institutional stakeholders. The result is a portrait, in a temporal perspective, of the relationship of cohabitation between residents and the river and its overflows. This cohabitation is characterized by tensions and paradoxes associated with different social representations of water and temporality that coexist within the actors, as well as changes in power relations towards the environment. Cette thèse examine les principaux rapports socio-environnementaux liés aux inondations récurrentes dans le bassin versant de la rivière Sainte-Anne en mobilisant l’histoire de l’occupation du territoire, les savoirs locaux et les politiques de gestion des risques. Dans une perspective d’écologie politique, ces rapports s’inscrivent à la fois dans les représentations sociales de la nature, les dynamiques de pouvoir associées à l’appropriation des ressources et des temporalités multiples. En puisant également dans les cadres théoriques de l’anthropologie des catastrophes et de l’anthropologie amphibienne, cette étude permet notamment de retracer pas à pas ce qu’est la « zone inondable » en tant qu’espace construit socialement par les autorités, et de remonter à la source de l’avènement de « l’inondation » comme étant un événement catastrophique, alors que la montée des eaux fait partie des caractéristiques de ces territoires depuis le début de la sédentarisation des habitants. Basée sur 93 entrevues semi-dirigées (76 riverains et 17 acteurs institutionnels) menées de février à octobre 2019, de l’observation non participante et une recherche documentaire dans les archives municipales et régionales, cette ethnographie du bassin versant de la rivière Sainte-Anne permet une incursion unique auprès de citoyens québécois qui vivent avec la montée récurrente des eaux. Pour quatre municipalités riveraines (Saint-Raymond, Saint-Alban, Saint-Casimir et Sainte-Anne-de-la-Pérade), les contextes historiques de l’occupation du territoire ont été documentés et les événements significatifs ont été décrits en focalisant sur les stratégies d’adaptation des résidents et la gestion menée par des acteurs institutionnels. En résulte un portrait, dans une perspective temporelle, de la relation de cohabitation entre les riverains et la rivière et ses débordements. Cette cohabitation est caractérisée par des tensions et des paradoxes associés aux différentes représentations sociales de l’eau et de la temporalité qui coexistent au sein des acteurs, ainsi qu’aux changements dans les rapports de pouvoir envers l’environnement.

Abstract Debris-covered glaciers are an important component of the mountain cryosphere and influence the hydrological contribution of glacierized basins to downstream rivers. This study examines the potential to make estimates of debris thickness, a critical variable to calculate the sub-debris melt, using ground-based thermal infrared radiometry (TIR) images. Over four days in August 2019, a ground-based, time-lapse TIR digital imaging radiometer recorded sequential thermal imagery of a debris-covered region of Peyto Glacier, Canadian Rockies, in conjunction with 44 manual excavations of debris thickness ranging from 10 to 110 cm, and concurrent meteorological observations. Inferring the correlation between measured debris thickness and TIR surface temperature as a base, the effectiveness of linear and exponential regression models for debris thickness estimation from surface temperature was explored. Optimal model performance ( R 2 of 0.7, RMSE of 10.3 cm) was obtained with a linear model applied to measurements taken on clear nights just before sunrise, but strong model performances were also obtained under complete cloud cover during daytime or nighttime with an exponential model. This work presents insights into the use of surface temperature and TIR observations to estimate debris thickness and gain knowledge of the state of debris-covered glacial ice and its potential hydrological contribution.

Abstract Worldwide, there has been an increase in the presence of potentially toxic cyanobacterial blooms in drinking water sources and within drinking water treatment plants (DWTPs). The objective of this study is to validate the use of in situ probes for the detection and management of cyanobacterial breakthrough in high and low-risk DWTPs. In situ phycocyanin YSI EXO2 probes were devised for remote control and data logging to monitor the cyanobacteria in raw, clarified, filtered, and treated water in three full-scale DWTPs. An additional probe was installed inside the sludge holding tank to measure the water quality of the surface of the sludge storage tank in a high-risk DWTP. Simultaneous grab samplings were carried out for taxonomic cell counts and toxin analysis. A total of 23, 9, and 4 field visits were conducted at the three DWTPs. Phycocyanin readings showed a 93-fold fluctuation within 24 h in the raw water of the high cyanobacterial risk plant, with higher phycocyanin levels during the afternoon period. These data provide new information on the limitations of weekly or daily grab sampling. Also, different moving averages for the phycocyanin probe readings can be used to improve the interpretation of phycocyanin signal trends. The in situ probe successfully detected high cyanobacterial biovolumes entering the clarification process in the high-risk plant. Grab sampling results revealed high cyanobacterial biovolumes in the sludge for both high and low-risk plants.

Northern hardwoods are susceptible to a wide range of defects that can reduce the amount of sound wood with desirable qualities, such as the clear sapwood of sugar maple trees. Yet, the rate at which trees decline in quality due to the development of such defects has never been quantified in northern hardwood forests due to a dearth of repeat inventories that record the appearance of defects over time. As a result, it remains uncertain whether, and how, selection management reduces the probability of decline in quality. In this study, we quantify the rate at which trees decline in quality due to the development of defects, and we test several hypotheses regarding the influence of selection management on quality. Our results show that (1) the probability of decline in quality increases as trees grow larger; (2) crown dieback also increases the probability of decline in quality; (3) the probability of decline in quality is slightly lower in managed stands than in unmanaged stands, and (4) the probability of decline in quality increases with the mean annual temperature of the site. Finally, we combined our estimates of the probability of decline in quality with previous estimates of the probability of mortality to assess the overall risk associated with retaining trees of different species, sizes, and vigour profiles. The resulting metric can inform efforts to improve the management of northern hardwood forests by providing an integrated estimate of the risk that the value of a tree will be reduced, or eliminated, due to mortality or decline in quality.

Performing a complete silvicultural diagnosis before a silvicultural treatment generally requires assessing the state of regeneration with the help of an inventory by sampling, particularly for stands dominated by sugar maple (Acer saccharum Marsh.) or yellow birch (Betula alleghaniensis Britt.), in which partial cuts are recommended. This inventory may then be compared to the standard or used in a growth model for saplings (trees for which the diameter measured at 1.3 m above the ground (DBH) varies from 1.1 cm to 9.0 cm). Some of these tools are based on sapling density, while others are based on the stocking of the saplings or on the stocking of total regeneration (combining saplings and seedlings with a DBH ≤ 1.0 cm). We assessed the number of plots required to estimate the density and the stocking of saplings with a given margin of error in 28 stands. The results show that more plots are required than usual in practice to inventory sapling density. The stocking is much easier to estimate precisely.

Abstract. Spaceborne microwave remote sensing (300 MHz–100 GHz) provides a valuable method for characterizing environmental changes, especially in Arctic–boreal regions (ABRs) where ground observations are generally spatially and temporally scarce. Although direct measurements of carbon fluxes are not feasible, spaceborne microwave radiometers and radar can monitor various important surface and near-surface variables that affect terrestrial carbon cycle processes such as respiratory carbon dioxide (CO2) fluxes; photosynthetic CO2 uptake; and processes related to net methane (CH4) exchange including CH4 production, transport and consumption. Examples of such controls include soil moisture and temperature, surface freeze–thaw cycles, vegetation water storage, snowpack properties and land cover. Microwave remote sensing also provides a means for independent aboveground biomass estimates that can be used to estimate aboveground carbon stocks. The microwave data record spans multiple decades going back to the 1970s with frequent (daily to weekly) global coverage independent of atmospheric conditions and solar illumination. Collectively, these advantages hold substantial untapped potential to monitor and better understand carbon cycle processes across ABRs. Given rapid climate warming across ABRs and the associated carbon cycle feedbacks to the global climate system, this review argues for the importance of rapid integration of microwave information into ABR terrestrial carbon cycle science.

Abstract The Paris Agreement made net zero emissions a global target. In response, net zero carbon building standards have proliferated, making net zero a popular target for buildings. But to meaningfully contribute to global decarbonization efforts, net zero standards and the organizations who promote them, must be deemed legitimate. Given the building industry’s reputation for being highly fragmented and slow to change, how has this legitimacy been constructed? What are the implications of this legitimation process? This article seeks to answer these questions by exploring the narratives used by the World Green Building Council (WGBC) to legitimate Net Zero Carbon Buildings (NZCB) from 2015 to 2021. Our analysis is based on over 100 documents produced by the WGBC and 22 interviews with WGBC and Green Building Council representatives, policymakers, and industry actors. Results reveal six main storylines adopted by the WGBC to extend the legitimacy of sustainable green building movement actors to the new net zero governance space. This legitimation process allows the WGBC to develop and implement net zero standards quickly, but also creates tensions between efficiency and procedural integrity, potential and proven results, corporate and collective value. While NZCB are here to stay, these tensions highlight barriers to their wide-scale adoption and question their ability to deliver an economically viable, socially just, environmental, net zero transition.

The identification of bedforms has an important role in the study of seafloor morphology. The presence of these dynamic structures on the seafloor represents a hazard for navigation. They also influence the hydrodynamic simulation models used in the context, for example, of coastal flooding. Generally, multiBeam EchoSounders (MBES) are used to survey these bedforms. Unfortunately, the coverage of the MBES is limited to small areas per survey. Therefore, the analysis of large areas of interest (like navigation channels) requires the integration of different datasets acquired over overlapping areas at different times. The presence of spatial and temporal inconsistencies between these datasets may significantly affect the study of bedforms, which are subject to many natural processes (e.g. tides; flow). This paper proposes a novel approach to integrate multisource bathymetric datasets to study bedforms. The proposed approach is based on consolidating multisource datasets and applying the Empirical Bayesian Kriging interpolation for the creation of a multisource Digital Bathymetric Model (DBM). It has been designed to be adapted for estuarine areas with a high dynamism of the seafloor, characteristic of the fluvio-marine regime of the Estuary of the Saint-Lawrence River. This area is distinguished by a high tidal cycle and the presence of fields of dunes. The study involves MBES data that was acquired daily over a field of dunes in this area over the span of four days for the purpose of monitoring the morphology and migration of dunes. The proposed approach performs well with a resulting surface with a reduced error relative to the original data compared to existing approaches and the conservation of the dune shape through the integration of the data sets despite the highly dynamic fluvio-marine environments.

A three-dimensional large eddy simulation model is used to simulate the turbulent flow dynamics around a circular pier in live-bed and clear-water scour conditions. The Navier–Stokes equations are transformed into a σ-coordinate system and solved using a second-order unstructured triangular finite-volume method. We simulate the bed evolution by solving the Exner-Polya equation assisted by a sand-slide model as a correction method. The bedload transport rate is based on the model of Engelund and Fredsœ. The model was validated for live-bed conditions in a wide channel and clear-water conditions in a narrow channel against the experimental data found in the literature. The in-house model NSMP3D can successfully produce both the live-bed and clear-water scouring throughout a stable long-term simulation. The flow model was used to study the effects of the blockage ratio in the flow near the pier in clear-water conditions, particularly the contraction effect at the zone where the scour hole starts to form. The scour depth in the clear water simulations is generally deeper than the live-bed simulations. In clear-water, the results show that the present model is able to qualitatively and quantitatively capture the hydrodynamic and morphodynamic processes near the bed. In comparison to the wide channel situation, the simulations indicate that the scour rate is faster in the narrow channel case.

Ice control structures (ICSs) play a vital role in preventing ice jams and safeguarding communities by either stabilizing ice cover or relocating jams upstream. Understanding and modeling the interaction between ice floes and these structures is crucial for assessing their effectiveness and optimizing their designs. However, simulating these complex multi-physics systems poses challenges for numerical techniques. In this paper, we introduce and evaluate a fully-Lagrangian mesh-free continuum-discrete model based on the Smoothed Particles Hydrodynamics (SPH) method and Discrete Element Method (DEM) for three-dimensional (3D) simulation of ice interactions with control structures. To validate and parameterize the numerical model, we conduct two sets of experiments using real and artificial ice materials: (1) dam-break wave-ice-structure interaction and (2) ice-ICS interaction in an open channel. By comparing numerical and experimental results we demonstrate the capability and relative accuracy of our model. Our findings indicate that real ice generally exhibits faster jam evolution and ice passage through the ICS compared to artificial ice. Moreover, we identify the Froude number and ice material type as important factors influencing jam formation, evolution, and ICS effectiveness. Through sensitivity analysis of material properties, we highlight the significant impact of friction and restitution coefficients.

Abstract. This study investigates the ability of long short-term memory (LSTM) neural networks to perform streamflow prediction at ungauged basins. A set of state-of-the-art, hydrological model-dependent regionalization methods are applied to 148 catchments in northeast North America and compared to an LSTM model that uses the exact same available data as the hydrological models. While conceptual model-based methods attempt to derive parameterizations at ungauged sites from other similar or nearby catchments, the LSTM model uses all available data in the region to maximize the information content and increase its robustness. Furthermore, by design, the LSTM does not require explicit definition of hydrological processes and derives its own structure from the provided data. The LSTM networks were able to clearly outperform the hydrological models in a leave-one-out cross-validation regionalization setting on most catchments in the study area, with the LSTM model outperforming the hydrological models in 93 % to 97 % of catchments depending on the hydrological model. Furthermore, for up to 78 % of the catchments, the LSTM model was able to predict streamflow more accurately on pseudo-ungauged catchments than hydrological models calibrated on the target data, showing that the LSTM model's structure was better suited to convert the meteorological data and geophysical descriptors into streamflow than the hydrological models even calibrated to those sites in these cases. Furthermore, the LSTM model robustness was tested by varying its hyperparameters, and still outperformed hydrological models in regionalization in almost all cases. Overall, LSTM networks have the potential to change the regionalization research landscape by providing clear improvement pathways over traditional methods in the field of streamflow prediction in ungauged catchments.

Natural disasters have been demonstrated to cause devastating effects on economic and social development in China, but little is known about the relationship between natural disasters and income at the household level. This study explores the impact of natural disasters on household income, expenditure, and inequality in China as the first study of this nature for the country. The empirical analysis is conducted based on a unique panel dataset that contains six waves of the Chinese Household Income Project (CHIP) survey data over the 1988–2018 period, data on natural disasters, and other social and economic status of households. By employing the fixed effects models, we find that disasters increase contemporaneous levels of income inequality, and disasters that occurred in the previous year significantly increase expenditure inequality. Natural disasters increase operating income inequality but decrease transfer income inequality. Poor households are found to be more vulnerable to disasters and suffer significant income losses. However, there is no evidence to suggest that natural disasters significantly reduce the income of upper- and middle-income groups. These findings have important implications for policies aimed at poverty alleviation and revenue recycling, as they can help improve economic justice and enhance resilience to natural disasters.

Agricultural activities can result in the contamination of surface runoff with pathogens, pesticides, and nutrients. These pollutants can enter surface water bodies in two ways: by direct discharge into surface waters or by infiltration and recharge into groundwater, followed by release to surface waters. Lack of financial resources makes risk assessment through analysis of drinking water pollutants challenging for drinking water suppliers. Inability to identify agricultural lands with a high-risk level and implement action measures might lead to public health issues. As a result, it is essential to identify hazards and conduct risk assessments even with limited data. This study proposes a risk assessment model for agricultural activities based on available data and integrating various types of knowledge, including expert and literature knowledge, to estimate the levels of hazard and risk that different agricultural activities could pose to the quality of withdrawal waters. To accomplish this, we built a Bayesian network with continuous and discrete inputs capturing raw water quality and land use upstream of drinking water intakes (DWIs). This probabilistic model integrates the DWI vulnerability, threat exposure, and threats from agricultural activities, including animal and crop production inventoried in drainage basins. The probabilistic dependencies between model nodes are established through a novel adaptation of a mixed aggregation method. The mixed aggregation method, a traditional approach used in ecological assessments following a deterministic framework, involves using fixed assumptions and parameters to estimate ecological outcomes in a specific case without considering inherent randomness and uncertainty within the system. After validation, this probabilistic model was used for four water intakes in a heavily urbanized watershed with agricultural activities in the south of Quebec, Canada. The findings imply that this methodology can assist stakeholders direct their efforts and investments on at-risk locations by identifying agricultural areas that can potentially pose a risk to DWIs.

This study aimed at evaluating the efficacy of an online CBT intervention with limited therapist contact targeting a range of posttraumatic symptoms among evacuees from the 2016 Fort McMurray wildfires. One hundred and thirty-six residents of Fort McMurray who reported either moderate PTSD symptoms (PCL-5 ≥ 23) or mild PTSD symptoms (PCL-5 ≥ 10) with moderate depression (PHQ-9 ≥ 10) or subthreshold insomnia symptoms (ISI ≥ 8) were randomized either to a treatment (n = 69) or a waitlist condition (n = 67). Participants were on average 45 years old, and mostly identified as White (82%) and as women (76%). Primary outcomes were PTSD, depression, and insomnia symptoms. Secondary outcomes were anxiety symptoms and disability. Significant Assessment Time × Treatment Condition interactions were observed on all outcomes, indicating that access to the treatment led to a decrease in posttraumatic stress (F[1,117.04] = 12.128, p = .001; d = .519, 95% CI = .142–.895), depression (F[1,118.29] = 9.978, p = .002; d = .519, 95% CI = .141–.898) insomnia (F[1,117.60] = 4.574, p = .035; d = .512, 95% CI = .132–.892), and anxiety (F[1,119.64] = 5.465, p = .021; d = .421, 95% CI = .044–.797) symptom severity and disability (F[1,111.55] = 7.015, p = .009; d = .582, 95% CI = .200–.963). Larger effect sizes (d = 0.823–1.075) were observed in participants who completed at least half of the treatment. The RESILIENT online treatment platform was successful to provide access to specialized evidence-based mental health care after a disaster.