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
•GSDM applied on reference data generates accurate and precise rainfall fields.•GSDM inherits of the forecast bias.•Global ensemble forecasts (GEPS) are not consistent, with or without GSDM.•GSDM ...performance was better with regional deterministic forecasts (RDPS).•Neighboring pixels should be considered when producing high-resolution ensembles.
Several businesses and industries rely on rainfall forecasts to support their day-to-day operations. To deal with the uncertainty associated with rainfall forecast, some meteorological organisations have developed products, such as ensemble forecasts. However, due to the intensive computational requirements of ensemble forecasts, the spatial resolution remains coarse. For example, Environment and Climate Change Canada’s (ECCC) Global Ensemble Prediction System (GEPS) data is freely available on a 1-degree grid (about 100km), while those of the so-called High Resolution Deterministic Prediction System (HRDPS) are available on a 2.5-km grid (about 40 times finer). Potential users are then left with the option of using either a high-resolution rainfall forecast without uncertainty estimation and/or an ensemble with a spectrum of plausible rainfall values, but at a coarser spatial scale. The objective of this study was to evaluate the added value of coupling the Gibbs Sampling Disaggregation Model (GSDM) with ECCC products to provide accurate, precise and consistent rainfall estimates at a fine spatial resolution (10-km) within a forecast framework (6-h). For 30, 6-h, rainfall events occurring within a 40,000-km2 area (Québec, Canada), results show that, using 100-km aggregated reference rainfall depths as input, statistics of the rainfall fields generated by GSDM were close to those of the 10-km reference field. However, in forecast mode, GSDM outcomes inherit of the ECCC forecast biases, resulting in a poor performance when GEPS data were used as input, mainly due to the inherent rainfall depth distribution of the latter product. Better performance was achieved when the Regional Deterministic Prediction System (RDPS), available on a 10-km grid and aggregated at 100-km, was used as input to GSDM. Nevertheless, most of the analyzed ensemble forecasts were weakly consistent. Some areas of improvement are identified herein.
Despite a well-developed structural framework, orogenic gold deposits from the Malartic-Val-d’Or Camp (MVC) report ages that span nearly 345 million years (~ 2705–2360 Ma), significantly post-dating ...the tectono-metamorphic history of the Abitibi-Wawa Orogeny, and are in conflict with the structural setting along the retrograde path of the orogenic cycle. Gold in the MVC is commonly associated with three events that form pre- and syn-/late-regional penetrative fabric (S2), developed during major N-S shortening (D2, ~ 2669–2643 Ma): (1) pre-D2 quartz-carbonate veins; (2) deformed quartz-carbonate stockworks, entrained in S2; (3) laminated brittle-ductile quartz-tourmaline-carbonate reverse-shear and tension veins, sub-parallel to S2, which contain the bulk of the gold. Here, we present in situ U–Pb geochronological and rare earth element (REE) data from 85 xenotime grains, hosted by quartz-carbonate veins (Kiena S50) and quartz-tourmaline-carbonate veins (Goldex, Triangle, Plug #4, Beaufor, Pascalis Gold Trend, and Akasaba West). Xenotime in textural equilibrium with Au-hosting pyrite of pre-D2 veins yields a subtle negative Eu anomaly and elevated medium-REE contents, and records an auriferous hydrothermal event at 2686 ± 15 Ma (
n
= 19; 11 grains; MSWD = 2.8), consistent with cross-cutting relationships with dated intrusions. Late-D2 veins record two subsequent hydrothermal events, based on texture and chemistry of xenotime. Xenotime in textural equilibrium with gold-bearing pyrite contains elevated medium-REE values (Nd>1000 ppm; Sm>2490 ppm) and constrains the timing of vein emplacement to 2643 ± 3 Ma (
n
= 92; 44 grains; MSWD = 1.2), dating the main phase of quartz-tourmaline-carbonate vein formation and major gold precipitation. Xenotime in textural disequilibrium with auriferous sulfides, located at dissolved sulfide grain boundaries and in sulfide fractures, contains lower medium-REE values (Nd<1000 ppm; Sm<2490 ppm), and yields an age of 2607 ± 5 Ma (
n
= 63; 30 grains; MSWD = 1.9), indicating a second period of gold mineralization. Our data imply that district-scale gold mineralization in the MVC formed during at least two short-lived hydrothermal periods with the bulk of orogenic gold introduced along the retrograde path of the orogenic cycle, ~25–30 million years post-peak metamorphism.
En analysant les principaux aspects de la structure organisationnelle et les transformations des rapports socioéconomiques et socioorganisationnels qui ont marqué l'évolution de l'industrie ...d'exploitation forestière, l'auteur nous présente les différents acteurs de ce secteur d'activités et décrit leur rôle, leurs conditions de travail ainsi que leurs environnements familial et social.
This article discusses the value and limitations of using microbial indicators to predict occurrence of enteric pathogens in water. Raw or treated sewage is a primary source of fecal contamination of ...the receiving surface water or groundwater; hence, understanding the relationship between pathogens and indicators in sewage is an important step in understanding the correlation in receiving waters. This article presents three different datasets representing different concentrations of pathogens and microbial indicators: sewage containing high concentrations of pathogens and indicators, surface water with variable concentrations, and groundwater with low concentrations. In sewage, even with very high levels of microorganisms, no mathematical correlation can predict the type or concentration of any pathogen. After discharge in the environment, direct correlation becomes biologically improbable as dilution, transport, and different inactivation rates occur in various environments. In surface waters, advanced statistical methods such as logistic regression have provided some level of predictability of the occurrence of pathogens but not specific counts. In groundwater, the continuous absence of indicators indicates an improbable occurrence of pathogen. In contrast, when these indicators are detected, pathogen occurrence probability increases significantly. In groundwater, given the nature and dissemination pattern of pathogenic microorganisms, a direct correlation with fecal microbial indicators is not observed and should not be expected. However, the indicators are still useful as a measure of risk. In summary, many pathogens of public health importance do not behave like fecal microbial indicators, and there is still no absolute indicator of their presence, only a probability of their co-occurrence.
En décrivant les perceptions et le vécu des Sourds et de leurs proches, particulièrement en termes de communication et de langue, les auteures souhaitent donner aux parents entendants d'un enfant ...sourd la possibilité de mieux comprendre le vécu de leur enfant (famille, enfance, école, travail, etc.).
••Significant differences between properties of alluvial and non-alluvial soils.••Alluvial Soil depletion is attributable to thin litter caused by frequent floods.••Some tree species respond ...significantly to soil variables and flood recurrence.
Soils and tree species are studied in mixed temperate forests along the Coaticook River (Southern Québec, Canada) to examine the effect of frequent floods on soil properties and vegetation. The soils and tree stands are located in the flood zones (FZ) and no-flood zones (NFZ) in 49 quadrats. Different statistical analyses were carried out on soils and tree data, including ANOVA, Kruskal-Wallis, Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) ordinations. The variables used for soil property analysis were pH, total organic carbon, total nitrogen, C/N ratio, total sulfur, Fe + Al, texture, bulk density, soil moisture, and soil biomass (litter), while the vegetation variables consisted of the number of tree species, their density, and the diameter and measurement of the basal area. The soil property analyses show significant differences between alluvial and non-alluvial soils, including organic carbon, nitrogen, pH, Fe + Al, soil moisture, texture, and plant litter thickness (cm). The PCA ordination realized on soil variables shows two distinct axes, including a first axis (PC1) grouping TOC, TN, TS, soil moisture and pH variables, and accounting for 35% of the total variation, and a second axis (PC2) that includes textural variables (clay and silt) and representing 21% of the total variability. The species richness and diversity analyses did not show statistical differences for the tree species between the two zones that were analyzed (FZ and NFZ), although some tree species are associated with the specific edaphic conditions. The results of the CCA ordination conducted on soil and vegetation data revealed that the tree communities have a significant response to soil variables and flood frequency. All the variables that were studied account for 40% of the variation in species composition based on soil properties. This study shows that successive floods affect soil properties, including depletion of soil organic carbon and nitrogen, and that floods and soil conditions can have a direct effect on the distribution of certain tree species.
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