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The Penman-Monteith reference evapotranspiration (ET0) formulation was forced with humidity, radiation, and wind speed (HRW) fields simulated by four reanalyses in order to simulate hydrologic processes over six mid-sized nivo-pluvial watersheds in southern Quebec, Canada. The resulting simulated hydrologic response is comparable to an empirical ET0 formulation based exclusively on air temperature. However, Penman-Montheith provides a sounder representation of the existing relations between evapotranspiration fluctuations and climate drivers. Correcting HRW fields significantly improves the hydrologic bias over the pluvial period (June to November). The latter did not translate into an increase of the hydrologic performance according to the Kling-Gupta Efficiency (KGE) metric. The suggested approach allows for the implementation of physically-based ET0 formulations where HRW observations are insufficient for the calibration and validation of hydrologic models and a potential reinforcement of the confidence affecting the projection of low flow regimes and water availability.

Abstract In water resources applications (e.g., streamflow, rainfall‐runoff, urban water demand [UWD], etc.), ensemble member selection and ensemble member weighting are two difficult yet important tasks in the development of ensemble forecasting systems. We propose and test a stochastic data‐driven ensemble forecasting framework that uses archived deterministic forecasts as input and results in probabilistic water resources forecasts. In addition to input data and (ensemble) model output uncertainty, the proposed approach integrates both ensemble member selection and weighting uncertainties, using input variable selection and data‐driven methods, respectively. Therefore, it does not require one to perform ensemble member selection and weighting separately. We applied the proposed forecasting framework to a previous real‐world case study in Montreal, Canada, to forecast daily UWD at multiple lead times. Using wavelet‐based forecasts as input data, we develop the Ensemble Wavelet‐Stochastic Data‐Driven Forecasting Framework, the first multiwavelet ensemble stochastic forecasting framework that produces probabilistic forecasts. For the considered case study, several variants of Ensemble Wavelet‐Stochastic Data‐Driven Forecasting Framework, produced using different input variable selection methods (partial correlation input selection and Edgeworth Approximations‐based conditional mutual information) and data‐driven models (multiple linear regression, extreme learning machines, and second‐order Volterra series models), are shown to outperform wavelet‐ and nonwavelet‐based benchmarks, especially during a heat wave (first time studied in the UWD forecasting literature). , Key Points A stochastic data‐driven ensemble framework is introduced for probabilistic water resources forecasting Ensemble member selection and weighting uncertainties are explicitly considered alongside input data and model output uncertainties Wavelet‐based model outputs are used as input to the framework for an urban water demand forecasting study outperforming benchmark methods

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.

The slide of granular material in nature and engineering can happen under air (subaerial), under a liquidlike water (submerged), or a transition between these two regimes, where a subaerial slide enters a liquid and becomes submerged. Here, we experimentally investigate these three slide regimes (i.e., subaerial, submerged, and transitional) in two dimensions, for various slope angles, material types, and bed roughness. The goal is to shed light on the complex morphodynamics and flow structure of these granular flows and also to provide comprehensive benchmarks for the validation and parametrization of the numerical models. The slide regime is found to be a major controller of the granular morphodynamics (e.g., shape evolution and internal flow structure). The time history of the runout distance for the subaerial and submerged cases present a similar three-phase trend (with acceleration, steady flow, and deceleration phases) tough with different spatiotemporal scales. Compared to the subaerial cases, the submerged cases show longer runout time and shorter final runout distances. The transitional trends, however, show additional deceleration and reacceleration. The observations suggest that the impact of slide angle, material type, and bed roughness on the morphodynamics is less significant where the material interacts with water. Flow structure, extracted using a granular particle image velocimetry technique, shows a relatively power-law velocity profile for the subaerial condition and strong circulations for the submerged condition. An unsteady theoretical model based on the µ(I) rheology is developed and is shown to be effective in the prediction of the average velocity of the granular mass.

Les printemps 2017 et 2019 auront frappé l’imaginaire collectif en raison de l’ampleur des crues ayant touché de nombreuses rivières du Québec et des dommages qui leur sont associés. En 2019, près de 6700 résidences localisées dans 51 municipalités et distribuées dans presque toutes les principales régions du Québec ont été inondées, sans compter les nombreuses autres résidences qui se sont retrouvées isolées en raison de routes submergées et de glissements de terrain. Le bilan en 2017 était similaire, avec 5371 maisons inondées dans 261 municipalités et 4066 personnes évacuées. Les débits dans plusieurs rivières ont excédé les valeurs mesurées depuis que les stations de jaugeage ont été installées. À titre d’exemple, en 2019, le débit journalier dans la rivière Rouge à la hauteur du Barrage de la Chute-Bell, où Hydro-Québec a craint pour la stabilité de l’ouvrage, a atteint 975 m3/s, la plus forte valeur jamais enregistrée depuis 1964. Une analyse statistique révèle qu’un tel débit a une chance d’être dépassé en moyenne une fois tous les 175 ans. Il s’agit d’un événement exceptionnel. Pourtant, un autre événement extrême se produisait au même endroit en 1998, cette fois-ci avec un débit maximal journalier de 914 m3/s. Deux crues printanières majeures en 20 ans : est-ce la conséquence des changements climatiques ? Cet article propose une genèse des événements hydrologiques extrêmes, puis présente des projections climatiques aux horizons 2050 et 2080 pour différentes rivières au Sud et au Nord du fleuve Saint-Laurent. Puis, est exposée la démarche générale employée pour caractériser le régime hydrologique des bassins versants en climat futur.

Soil moisture is often considered a direct way of quantifying agricultural drought since it is a measure of the availability of water to support crop growth. Measurements of soil moisture at regional scales have traditionally been sparse, but advances in land surface modelling and the development of satellite technology to indirectly measure surface soil moisture has led to the emergence of a number of national and global soil moisture data sets that can provide insight into the dynamics of agricultural drought. Droughts are often defined by normal conditions for a given time and place; as a result, data sets used to quantify drought need a representative baseline of conditions in order to accurately establish a normal. This presents a challenge when working with earth observation data sets which often have very short baselines for a single instrument. This study assessed three soil moisture data sets: a surface satellite soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission operating since 2010; a blended surface satellite soil moisture data set from the European Space Agency Climate Change Initiative (ESA-CCI) that has a long history and a surface and root zone soil moisture data set from the Canadian Meteorology Centre (CMC)’s Regional Deterministic Prediction System (RDPS). An iterative chi-squared statistical routine was used to evaluate each data set’s sensitivity to canola yields in Saskatchewan, Canada. The surface soil moisture from all three data sets showed a similar temporal trend related to crop yields, showing a negative impact on canola yields when soil moisture exceeded a threshold in May and June. The strength and timing of this relationship varied with the accuracy and statistical properties of the data set, with the SMOS data set showing the strongest relationship (peak X2 = 170 for Day of Year 145), followed by the ESA-CCI (peak X2 = 89 on Day of Year 129) and then the RDPS (peak X2 = 65 on Day of Year 129). Using short baseline soil moisture data sets can produce consistent results compared to using a longer data set, but the characteristics of the years used for the baseline are important. Soil moisture baselines of 18–20 years or more are needed to reliably estimate the relationship between high soil moisture and high yielding years. For the relationship between low soil moisture and low yielding years, a shorter baseline can be used, with reliable results obtained when 10–15 years of data are available, but with reasonably consistent results obtained with as few as 7 years of data. This suggests that the negative impacts of drought on agriculture may be reliably estimated with a relatively short baseline of data.

Floods are some of the most dangerous and most frequent natural disasters occurring in the northern region of Iran. Flooding in this area frequently leads to major urban, financial, anthropogenic, and environmental impacts. Therefore, the development of flood susceptibility maps used to identify flood zones in the catchment is necessary for improved flood management and decision making. The main objective of this study was to evaluate the performance of an Evidential Belief Function (EBF) model, both as an individual model and in combination with Logistic Regression (LR) methods, in preparing flood susceptibility maps for the Haraz Catchment in the Mazandaran Province, Iran. The spatial database created consisted of a flood inventory, altitude, slope angle, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), distance from river, rainfall, geology, land use, and Normalized Difference Vegetation Index (NDVI) for the region. After obtaining the required information from various sources, 151 of 211 recorded flooding points were used for model training and preparation of the flood susceptibility maps. For validation, the results of the models were compared to the 60 remaining flooding points. The Receiver Operating Characteristic (ROC) curve was drawn, and the Area Under the Curve (AUC) was calculated to obtain the accuracy of the flood susceptibility maps prepared through success rates (using training data) and prediction rates (using validation data). The AUC results indicated that the EBF, EBF from LR, EBF-LR (enter), and EBF-LR (stepwise) success rates were 94.61%, 67.94%, 86.45%, and 56.31%, respectively, and the prediction rates were 94.55%, 66.41%, 83.19%, and 52.98%, respectively. The results showed that the EBF model had the highest accuracy in predicting flood susceptibility within the catchment, in which 15% of the total areas were located in high and very high susceptibility classes, and 62% were located in low and very low susceptibility classes. These results can be used for the planning and management of areas vulnerable to floods in order to prevent flood-induced damage; the results may also be useful for natural disaster assessment.