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Shifts in hydroclimatic regimes associated with global climate change may impact freshwater availability and quality. In high latitudes of the northern hemisphere, where vast quantities of carbon are stored terrestrially, explaining landscape-scale carbon (C) budgets and associated pollutant transfer is necessary for understanding the impact of changing hydroclimatic regimes. We used a dynamic modelling approach to simulate streamflow, DOC concentration, and DOC export in a northern Canadian catchment that has undergone notable climate warming, and will continue to for the remainder of this century. The Integrated Catchment model for Carbon (INCA-C) was successfully calibrated to a multi-year period (2012-2016) that represents a range in hydrologic conditions. The model was subsequently run over 30-year periods representing baseline and two future climate scenarios. Average discharge is predicted to decrease under an elevated temperature scenario (22-27 % of baseline) but increase (116-175 % of baseline) under an elevated temperature and precipitation scenario. In the latter scenario the nival hydroclimatic regime is expected to shift to a combined nival and pluvial regime. Average DOC flux over 30 years is predicted to decrease (24-27 % of baseline) under the elevated temperature scenario, as higher DOC concentrations are offset by lower runoff. Under the elevated temperature and precipitation scenario, results suggest an increase in carbon export of 64-81 % above baseline. These increases are attributed to greater connectivity of the catchment. The largest increase in DOC export is expected to occur in early winter. These predicted changes in DOC export, particularly under a climate that is warmer and wetter could be part of larger ecosystem change and warrant additional monitoring efforts in the region., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

A dynamic soil chemistry model, SAFE, was applied to boreal stands located in three regions of Quebec (Canada) to investigate the relative contributions of forest harvesting methods (whole-tree and stem-only harvesting), natural disturbances (wildfire and spruce budworm outbreak) and atmospheric deposition on base cation fluxes and the soil acid–base status. Model parameterization was done using measured and published soil and vegetation data, published trends of nutrient dynamics, governmental databases on atmospheric deposition and climate, the reconstruction method MAKEDEP and throughfall equations that we developed for coniferous stands. The model suggested that forest disturbances, both anthropogenic and natural, increased base saturation, and thus base cation availability, to different degrees for periods of one to five decades. However, such disturbances, no matter their interval of return and their intensity, did not appear to be the main driving force of soil chemistry in the long term. On the other hand, simulation results showed that the B horizon at all sites has undergone acidification due to acid atmospheric deposition during the 20th century. Results suggest that atmospheric deposition is the main driver of long-term trends of soil chemistry for the study sites and that the issue of sustainability of soil productivity depends more on air pollution abatement policies than on forest management strategies. [Copyright &y& Elsevier]

The vast majority of models developed for wastewater treatment and receiving water systems have been of the distributed-parameter and state-space (lumped-parameter) forms. On the other hand, most control system design methods and, for that matter, methods of system identification refer to the black box class of models and in particular the time series models. In the current study two modelling techniques of the black box class of models were used to model the data collected from full-scale treatment operations at the Gold Bar Wastewater Treatment Plant (GBWWTP), the largest plant in the Edmonton area (Alberta, Canada). An artificial neural network was trained to make short-term predictions of the quantity of wastewater entering the plant during storm events using rainfall data collected from eight gauges covering the parts of the city that are serviced by combined sewers. After training, the model was able to generalize very well when tested against an unseen set of data. Transfer function time series models were used to model the quality data collected from a primary sedimentation tank at the plant. The models were able to make hourly predictions of the total suspended solids and chemical oxygen demand in the primary effluent. The presented models have the predictiveness and adaptiveness requirements needed for models that could be utilized as part of a real-time control system. Key words: dynamic modelling, artificial neural networks, transfer-function models, wastewater inflow, primary sedimentation.La grande majorité des modèles développés pour les systèmes de traitement des eaux usées et des milieux récepteurs sont du style paramètres distribués et états-espaces (paramètres localisés). Par contre, les méthodes de conception de systèmes de contrôle et, par le fait même, les méthodes d'identification de systèmes font référence à la classe de modèles à boîte noire et, en particulier, aux modèles à séries chronologiques. L' étude actuelle comporte deux techniques de modélisation de classe de modèles à boîte noire qui ont été utilisées pour modéliser les données obtenues des opérations de traitement à pleine échelle à la station de traitement des eaux usées Gold Bar (GBWWTP), la plus grande station de traitement des eaux usées dans la région d'Edmonton (Alberta, Canada). Un réseau neural artificiel a été conçu pour prédire à court terme la quantité d'eaux usées pénétrant dans la station de traitement durant des tempêtes en utilisant les données de précipitations provenant de huit jauges couvrant les régions de la ville couvertes par le réseau unitaire d'assainissement. Une fois développé, le modèle a réussi à très bien généraliser lorsque ses données ont été confrontées à un nouvel ensemble de données. Des séries chronologiques de fonction de transfert ont été utilisées pour modéliser les données qualitatives recueillies dans le bassin de décantation primaire de la station de traitement. Les modèles ont prédit les solides totaux en suspension et la demande chimique en oxygène dans l'effluent primaire, selon une base horaire. Les modèles présentés possèdent les critères de prédiction et d'adaptation requis pour être utilisés dans un système de contrôle en temps réel. Mots clés : modélisation dynamique, réseaux neuraux artificiels, modèles de fonctions de transfert, arrivée des eaux usées, décantation primaire. [Traduit par la rédaction] [ABSTRACT FROM AUTHOR]