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If research on attribution of extreme weather events is to inform emerging climate change policies, it needs to diagnose all of the components of risk.

The KnnCAD Version 4 weather generator algorithm for nonparametric, multisite simulations of temperature and precipitation data is presented. The K-nearest neighbor weather generator essentially reshuffles the historical data, with replacement. In KnnCAD Version 4, a block resampling scheme is introduced to preserve the temporal correlation structure in temperature data. Perturbation of the reshuffled variable data is also added to enhance the generation of extreme values. The Upper Thames River Basin in Ontario, Canada isused as a case study and the model is shown to simulate effectively the historical characteristics at the site. The KnnCAD Version 4 approach is shown to improve on the previous versions of the model and offers a major advantage over many parametric and semiparametric weather generators in that multisite use can be easily achieved without making statistical assumptions dealing with the spatial correlations and probability distributions of each variable.

Climate change has a significant influence on streamflow variation. The aim of this study is to quantify different sources of uncertainties in future streamflow projections due to climate change. For this purpose, 4 global climate models, 3 greenhouse gas emission scenarios (representative concentration pathways), 6 downscaling models, and a hydrologic model (UBCWM) are used. The assessment work is conducted for 2 different future time periods (2036 to 2065 and 2066 to 2095). Generalized extreme value distribution is used for the analysis of the flow frequency. Strathcona dam in the Campbell River basin, British Columbia, Canada, is used as a case study. The results show that the downscaling models contribute the highest amount of uncertainty to future streamflow predictions when compared to the contributions by global climate models or representative concentration pathways. It is also observed that the summer flows into Strathcona dam will decrease, and winter flows will increase in both future time periods. In addition to these, the flow magnitude becomes more uncertain for higher return periods in the Campbell River system under climate change.

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

Canada has experienced some of the most rapid warming on Earth over the past few decades with a warming rate about twice that of the global mean temperature since 1948. Long-term warming is observed in Canada’s annual, winter and summer mean temperatures, and in the annual coldest and hottest daytime and nighttime temperatures. The causes of these changes are assessed by comparing observed changes with climate model simulated responses to anthropogenic and natural (solar and volcanic) external forcings. Most of the observed warming of 1.7°C increase in annual mean temperature during 1948–2012 [90% confidence interval (1.1°, 2.2°C)] can only be explained by external forcing on the climate system, with anthropogenic influence being the dominant factor. It is estimated that anthropogenic forcing has contributed 1.0°C (0.6°, 1.5°C) and natural external forcing has contributed 0.2°C (0.1°, 0.3°C) to the observed warming. Up to 0.5°C of the observed warming trend may be associated with low frequency variability of the climate such as that represented by the Pacific decadal oscillation (PDO) and North Atlantic oscillation (NAO). Overall, the influence of both anthropogenic and natural external forcing is clearly evident in Canada-wide mean and extreme temperatures, and can also be detected regionally over much of the country.

Abstract This study detected, for the first time, the long term annual and seasonal rainfall trends over Bihar state, India, between 1901 and 2002. The shift change point was identified with the cumulative deviation test (cumulative sum – CUSUM), and linear regression. After the shift change point was detected, the time series was subdivided into two groups: before and after the change point. Arc-Map 10.3 was used to evaluate the spatial distribution of the trends. It was found that annual and monsoon rainfall trends decreased significantly; no significant trends were observed in pre-monsoon, monsoon, post-monsoon and winter rainfall. The average decline in rainfall rate was –2.17 mm·year −1 and –2.13 mm·year −1 for the annual and monsoon periods. The probable change point was 1956. The number of negative extreme events were higher in the later period (1957–2002) than the earlier period (1901–1956).

The impact of snow-atmosphere coupling on climate variability and extremes over North America is investigated using modeling experiments with the fifth generation Canadian Regional Climate Model (CRCM5). To this end, two CRCM5 simulations driven by ERA-Interim reanalysis for the 1981–2010 period are performed, where snow cover and depth are prescribed (uncoupled) in one simulation while they evolve interactively (coupled) during model integration in the second one. Results indicate systematic influence of snow cover and snow depth variability on the inter-annual variability of soil and air temperatures during winter and spring seasons. Inter-annual variability of air temperature is larger in the coupled simulation, with snow cover and depth variability accounting for 40–60% of winter temperature variability over the Mid-west, Northern Great Plains and over the Canadian Prairies. The contribution of snow variability reaches even more than 70% during spring and the regions of high snow-temperature coupling extend north of the boreal forests. The dominant process contributing to the snow-atmosphere coupling is the albedo effect in winter, while the hydrological effect controls the coupling in spring. Snow cover/depth variability at different locations is also found to affect extremes. For instance, variability of cold-spell characteristics is sensitive to snow cover/depth variation over the Mid-west and Northern Great Plains, whereas, warm-spell variability is sensitive to snow variation primarily in regions with climatologically extensive snow cover such as northeast Canada and the Rockies. Furthermore, snow-atmosphere interactions appear to have contributed to enhancing the number of cold spell days during the 2002 spring, which is the coldest recorded during the study period, by over 50%, over western North America. Additional results also provide useful information on the importance of the interactions of snow with large-scale mode of variability in modulating temperature extreme characteristics.

This study focuses on the evaluation of daily precipitation and temperature climate indices and extremes simulated by an ensemble of 12 Regional Climate Model (RCM) simulations from the ARCTIC-CORDEX experiment with surface observations in the Canadian Arctic from the Adjusted Historical Canadian Climate Dataset. Five global reanalyses products (ERA-Interim, JRA55, MERRA, CFSR and GMFD) are also included in the evaluation to assess their potential for RCM evaluation in data sparse regions. The study evaluated the means and annual anomaly distributions of indices over the 1980–2004 dataset overlap period. The results showed that RCM and reanalysis performance varied with the climate variables being evaluated. Most RCMs and reanalyses were able to simulate well climate indices related to mean air temperature and hot extremes over most of the Canadian Arctic, with the exception of the Yukon region where models displayed the largest biases related to topographic effects. Overall performance was generally poor for indices related to cold extremes. Likewise, only a few RCM simulations and reanalyses were able to provide realistic simulations of precipitation extreme indicators. The multi-reanalysis ensemble provided superior results to individual datasets for climate indicators related to mean air temperature and hot extremes, but not for other indicators. These results support the use of reanalyses as reference datasets for the evaluation of RCM mean air temperature and hot extremes over northern Canada, but not for cold extremes and precipitation indices.

AbstractSoil moisture–atmosphere interactions play a key role in modulating climate variability and extremes. This study investigates how soil moisture–atmosphere coupling may affect future extreme events, particularly the role of projected soil moisture in modulating the frequency and maximum duration of hot spells over North America, using the fifth-generation Canadian Regional Climate Model (CRCM5). With this objective, CRCM5 simulations, driven by two coupled general circulation models (MPI-ESM and CanESM2), are performed with and without soil moisture–atmosphere interactions for current (1981–2010) and future (2071–2100) climates over North America, for representative concentration pathways (RCPs) 4.5 and 8.5. Analysis indicates that, in future climate, the soil moisture–temperature coupling regions, located over the Great Plains in the current climate, will expand farther north, including large parts of central Canada. Results also indicate that soil moisture–atmosphere interactions will play an imp...

In time series of essential climatological variables, many discontinuities are created not by climate factors but changes in the measuring system, including relocations, changes in instrumentation, exposure or even observation practices. Some of these changes occur due to reorganization, cost-efficiency or innovation. In the last few decades, station movements have often been accompanied by the introduction of an automatic weather station (AWS). Our study identifies the biases in daily maximum and minimum temperatures using parallel records of manual and automated observations. They are selected to minimize the differences in surrounding environment, exposition, distance and difference in elevation. Therefore, the type of instrumentation is the most important biasing factor between both measurements. The pairs of weather stations are located in Piedmont, a region of Italy, and in Gaspe Peninsula, a region of Canada. They have 6years of overlapping period on average, and 5110 daily values. The approach implemented for the comparison is divided in four main parts: a statistical characterization of the daily temperature series; a comparison between the daily series; a comparison between the types of events, heat wave, cold wave and normal events; and a verification of the homogeneity of the difference series. Our results show a higher frequency of warm (+10%) and extremely warm (+35%) days in the automated system, compared with the parallel manual record. Consequently, the use of a composite record could significantly bias the calculation of extreme events.

In response to extreme flood events and an increasing awareness that traditional flood control measures alone are inadequate to deal with growing flood risks, spatial flood risk management strategies have been introduced. These strategies do not only aim to reduce the probability and consequences of floods, they also aim to improve local and regional spatial qualities. To date, however, research has been largely ignorant as to how spatial quality, as part of spatial flood risk management strategies, can be successfully achieved in practice. Therefore, this research aims to illuminate how spatial quality is achieved in planning practice. This is done by evaluating the configurations of policy instruments that have been applied in the Dutch Room for the River policy program to successfully achieve spatial quality. This policy program is well known for its dual objective of accommodating higher flood levels as well as improving the spatial quality of the riverine areas. Based on a qualitative comparative analysis, we identified three successful configurations of policy instruments. These constitute three distinct management strategies: the “program‐as‐guardian”, the “project‐as‐driver,” and “going all‐in” strategies. These strategies provide important leads in furthering the development and implementation of spatial flood risk management, both in the Netherlands and abroad.

L'évaluation de l'impact hydrologique du changement climatique présente une importance particulière pour les bassins de la Méditerranée, qui sont très sensibles aux événements hydrologiques extrêmes. La modélisation des systèmes aussi complexes pour la gestion des ressources hydriques est un défi difficile. L'objectif global de ce travail est de contribuer au développement d'une approche de modélisation qui permette l'évaluation de l'impact hydrologique du changement climatique sur deux bassins de la Méditerranée, localisés en Sardaigne. Cette contribution se concentre sur deux sujets principaux: comprendre comment la représentation physique des modèles hydrologiques grave sur l'évaluation de l'impact hydrologique dû au changement climatique sur un bassin avec un climat semi-aride, le Rio Mannu di San Sperate, et montrer comme le modélisation avancé puisse aider à définir de mesures de modération et adaptation dans un système complexe enclin aux événements hydrique extrêmes, le Flumendosa, en conditions de changement climatique. Pour atteindre cet objectif le travail s'articule en trois phases. Les effets du changement climatique sur le bassin du Rio Mannu sont évalués à travers la comparaison des résultats de cinq modèles hydrologiques, CATchment HYdrology (CATHY), Soil and Water Assessment Tool (SWAT), TIN-based Real time Integrated Basin Simulator (tRIBS), TOPographic Kinematic APproximation and Integration-eXtended (TOPKAPI-X), and Water flow and balance Simulation Model (WASIM), en utilisant comme forçage atmosphérique les données de quatre combinaisons de modèles climatiques globaux (GCM) et régionaux (RCM). Pour évaluer les incertitudes une métrique récemment proposée est utilisée: les résultats des modèles sont comparés pendant une période de référence et future, en utilisant l'index de corrélation de Pearson et le bias de Duveiller. Même si certaines différences existent, en tout les modèles hydrologiques montrent une bonne concordance, et ils répondent de manière semblable à la réduction de la précipitation et à l'accroissement de la température prévu par les modèles climatiques. Il s'attend donc que le bassin dans l'avenir sera sujet à une réduction de la disponibilité de ressource hydrique, avec des conséquences négatives en particulier pour le secteur agricole. Une comparaison détaillée des réponses obtenue sur le même bassin avec trois modèles hydrologique à base physique avec différent degré pour ce qui concerne la représentation des procès physiques et des caractéristiques du terrain, CATHY, TOPKAPI-X, tRIBS, est effectué dans le but de tester la transférabilité des paramètres entre les trois modèles hydrologiques, avec une attention particulière sur les difficultés relevées dans les périodes de calibrage et validation. Tandis que les trois modèles ont répondu de manière semblable pendant la période de calibrage, significatives différences ont été relevées pendant la période de validation, caractérisé par un climat très sec, avec le modèle CATHY, qu'il a produit un très bas décharge. En conséquence, pour obtenir résultats satisfaisants avec le modèle CATHY, l’hypothèse de croûtage de sol a été assumée, sur la base dont la couche premier de sol a été modelée avec une conductibilité hydraulique saturée réduite. Finalement le modèle TOPKAPI-X est implémenté sur un des principaux bassins de la Sardaigne, d'importance stratégique pour le système hydrique de la région, le Flumendosa, afin d’évaluer les effets du changement climatique à plus grande échelle. Le modèle répond avec une diminution des valeurs de décharge, contenu hydrique et évapotranspiration réelle à la réduction de la précipitation et accroissement des températures prévus par les modèles climatiques en donnant aussi support à une scène future de carence de la ressource hydrique dans ce bassin de la zone Méditerranéenne.<br /><br />Assessing the hydrologic impacts of climate change is of great importance in the Mediterranean basins, which are heavily sensitive to climate variability, with significant impacts on water resources and hydrologic extremes. Modeling such complex systems to manage water resources and predict hydrologic extremes is a difficult task. The overall aim of the work described in this thesis is to bring a contribution in developing a modeling approach that allows evaluation of local hydrologic impacts of climate changes in two Mediterranean catchments located in Sardinia. This contribution revolves around two main themes: understanding how physical representation of hydrologic models can affect hydrologic impact assessment under climate change on a semi-arid basin of the Mediterranean region, the Rio Mannu catchment, and demonstrating how advanced hydrologic modeling can help in defining adaptation measures in a complex water system, the Flumendosa basin, under climate change. The work to achieve the general objective is elaborated into three stages. The effects of climate change are evaluated on the Rio Mannu catchment through comparison of the results from five hydrologic models, CATchment HYdrology (CATHY), Soil and Water Assessment Tool (SWAT), TIN-based Real time Integrated Basin Simulator (tRIBS), TOPographic Kinematic APproximation and Integration-eXtended (TOPKAPI-X), and Water flow and balance Simulation Model (WASIM), and using as atmospheric input outputs of four climate global (GCM) and regional (RCM) model combinations. In order to evaluate uncertainties, a recently proposed metric is used: climate and hydrologic models results are compared in terms of agreement with each other in reference and future periods using Pearson correlation values and Duveiller bias. Notwithstanding some differences, overall the five hydrologic models show good agreement, and they respond similarly to the reduced precipitation and increased temperatures predicted by the climate models, lending strong support to a future scenario of increased water shortages for this region of the Mediterranean, with negative consequences especially for the agricultural sector. Detailed comparison of the responses obtained with three physically based hydrologic models, but to varying degrees as regards physical processes and terrain features representation – CATHY, tRIBS, and TOPKAPI-X – on the same catchment is carried out, with the aim to test the transferability of parameters between the three hydrologic models, focusing in particular on the calibration and validation difficulties. While the three hydrologic models responded similarly during the calibration year, significant differences were found for the drier validation period for the CATHY model, which produced very low streamflow. To obtain satisfactory results for the CATHY model, an hypothesis of soil crusting was assumed and the first soil layer was modeled with a lower saturated hydraulic conductivity. Finally, the TOPKAPI-X model is applied on a large Sardinian basin prone to extreme flood events, the Flumendosa basin, to assess the hydrologic impact of climate change at much larger scale. The model responds with decreasing value of discharge, soil water content, and actual evapotranspiration to the reduced precipitation and increased temperature predicted by the climate models, lending strong support to a future scenario of increased water shortages also in this basin of the Mediterranean region.<br /><br />La valutazione dell’impatto idrologico del cambiamento climatico riveste particolare importanza per i bacini del Mediterraneo, sensibili ad eventi idrologici estremi. Modellizzare dei sistemi così complessi per la gestione della risorse idriche è una sfida difficile. L’obiettivo globale di questo lavoro è contribuire allo sviluppo di un approccio modellistico che consenta la valutazione dell’impatto idrologico del cambiamento climatico su due bacini del Mediterraneo localizzati in Sardegna. Questo contributo si focalizza su due temi principali: capire come la rappresentazione fisica dei modelli idrologici incida sulla valutazione dell’impatto idrologico dovuto al cambiamento climatico su un bacino con un clima semi-arido, il Rio Mannu di San Sperate, e dimostrare come la modellizzazione avanzata possa aiutare nel definire misure di adattamento in un sistema idrico complesso incline ad eventi estremi, il Flumendosa, in condizioni di cambiamento climatico. Per raggiungere tale obiettivo il lavoro si articola in tre fasi. Gli effetti del cambiamento climatico sul bacino del Rio Mannu sono stati valutati attraverso il confronto dei risultati di cinque modelli idrologici, CATchment HYdrology (CATHY), Soil and Water Assessment Tool (SWAT), TIN-based Real time Integrated Basin Simulator (tRIBS), TOPographic Kinematic APproximation and Integration-eXtended (TOPKAPI-X), and Water flow and balance Simulation Model (WASIM), utilizzando come forzante atmosferica gli output di quattro combinazioni di modelli climatici globali (GCM) e regionali (RCM). Per valutare le incertezze è stata utilizzata una metrica recentemente proposta: i risultati dei modelli sono stati comparati durante un periodo di riferimento e futuro, utilizzando l’indice di correlazione di Pearson e il bias di Duveiller. Pur con qualche differenza, complessivamente i modelli idrologici mostrano una buona concordanza tra loro, e rispondono in maniera simile alla riduzione della precipitazione e all’incremento della temperatura previsti dai modelli climatici. Ci si aspetta pertanto che il bacino nel futuro sarà soggetto ad una riduzione della disponibilità di risorsa idrica, con conseguenze negative in particolare per il settore agricolo. È stato effettuato un confronto dettagliato delle risposte ottenute sullo stesso bacino con tre modelli idrologici fisicamente basati di diverso grado per quanto riguarda la rappresentazione dei processi fisici e delle caratteristiche del terreno, CATHY, TOPKAPI-X, tRIBS, con lo scopo di testare la trasferibilità dei parametri tra i tre modelli idrologici, concentrandosi sulle difficoltà riscontrate nei periodi di calibrazione e validazione. Mentre i tre modelli hanno risposto in maniera simile durante il periodo di calibrazione, sono state riscontrate significative differenze durante il periodo di validazione, caratterizzato da un clima molto secco, con il modello CATHY, che ha prodotto una portata molto bassa. Pertanto, per ottenere risultati soddisfacenti con il modello CATHY, è stata assunta l’ipotesi di soil crusting, sulla base della quale il primo strato di suolo è stato modellato con una ridotta conducibilità idraulica satura. Infine il modello TOPKAPI-X è stato implementato su uno dei principali bacini sardi di importanza strategica per il sistema idrico della regione, il Flumendosa, per valutare gli effetti del cambiamento climatico a scala maggiore. Il modello risponde con una diminuzione dei valori di portata, contenuto idrico ed evapotraspirazione reale alla riduzione della precipitazione ed incremento della temperature previsto dai modelli climatici, dando supporto ad uno scenario futuro di carenza della risorsa idrica anche in questo bacino dell’area Mediterranea.

Abstract Low flow conditions are governed by short-to-medium term weather conditions or long term climate conditions. This prompts the question: given climate scenarios, is it possible to assess future extreme low flow conditions from climate data indices (CDIs)? Or should we rely on the conventional approach of using outputs of climate models as inputs to a hydrological model? Several CDIs were computed using 42 climate scenarios over the years 1961–2100 for two watersheds located in Quebec, Canada. The relationship between the CDIs and hydrological data indices (HDIs; 7- and 30-day low flows for two hydrological seasons) were examined through correlation analysis to identify the indices governing low flows. Results of the Mann-Kendall test, with a modification for autocorrelated data, clearly identified trends. A partial correlation analysis allowed attributing the observed trends in HDIs to trends in specific CDIs. Furthermore, results showed that, even during the spatial validation process, the methodological framework was able to assess trends in low flow series from: (i) trends in the effective drought index (EDI) computed from rainfall plus snowmelt minus PET amounts over ten to twelve months of the hydrological snow cover season or (ii) the cumulative difference between rainfall and potential evapotranspiration over five months of the snow free season. For 80% of the climate scenarios, trends in HDIs were successfully attributed to trends in CDIs. Overall, this paper introduces an efficient methodological framework to assess future trends in low flows given climate scenarios. The outcome may prove useful to municipalities concerned with source water management under changing climate conditions.

Pesticide transport by surface runoff depends on climate, agricultural practices, topography, soil characteristics, crop type, and pest phenology. To accurately assess the impact of climate change, these factors must be accounted for in a single framework by integrating their interaction and uncertainty. This paper presents the development and application of a framework to assess the impact of climate change on pesticide transport by surface runoff in southern Quebec (Canada) for the 1981-2040 period. The crop enemies investigated were: weeds for corn (Zea mays); and for apple orchard (Malus pumila), three insect pests (codling moth (Cydia pomonella), plum curculio (Conotrachelus nenuphar) and apple maggot (Rhagoletis pomonella)) and two diseases (apple scab (Venturia inaequalis) and fire blight (Erwinia amylovora)). A total of 23 climate simulations, 19 sites, and 11 active ingredients were considered. The relationship between climate and phenology was accounted for by bioclimatic models of the Computer Centre for Agricultural Pest Forecasting (CIPRA) software. Exported loads of pesticides were evaluated at the edge-of-field scale using the Pesticide Root Zone Model (PRZM), simulating both hydrology and chemical transport. A stochastic model was developed to account for PRZM parameter uncertainty. Results of this study indicate that for the 2011-2040 period, application dates would be advanced from 3 to 7 days on average with respect to the 1981-2010 period. However, the impact of climate change on maximum daily rainfall during the application window is not statistically significant, mainly due to the high variability of extreme rainfall events. Hence for the studied sites and crop enemies considered, climate change impact on pesticide transported in surface runoff is not statistically significant throughout the 2011-2040 period.

Wetlands play an important role in preventing extreme low flows in rivers and groundwater level drawdowns during drought periods. This hydrological function could become increasingly important under a warmer climate. Links between peatlands, aquifers, and rivers remain inadequately understood. The objective of this study was to evaluate the hydrologic functions of the Lanoraie peatland complex in southern Quebec, Canada, under different climate conditions. This peatland complex has developed in the beds of former fluvial channels during the final stages of the last deglaciation. The peatland covers a surface area of ~76 km2 and feeds five rivers. Numerical simulations were performed using a steady-state groundwater flow model. Results show that the peatland contributes on average to 77% of the mean annual river base flow. The peatland receives 52% of its water from the aquifer. Reduced recharge scenarios (−20 and −50% of current conditions) were used as a surrogate of climate change. With these scenarios, the simulated mean head decreases by 0.6 and 1.6 m in the sand. The mean river base flow decreases by 16 and 41% with the two scenarios. These results strongly underline the importance of aquifer-peatland-river interactions at the regional scale. They also point to the necessity of considering the entire hydrosystem in conservation initiatives.