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Ensemble forecasting applied to the field of hydrology is currently an established area of research embracing a broad spectrum of operational situations. This work catalogs the various pathways of ensemble streamflow forecasting based on an exhaustive review of more than 700 studies over the last 40 years. We focus on the advanced state of the art in the model‐based (dynamical) ensemble forecasting approaches. Ensemble streamflow prediction systems are categorized into three leading classes: statistics‐based streamflow prediction systems, climatology‐based ensemble streamflow prediction systems and numerical weather prediction‐based hydrological ensemble prediction systems. For each ensemble approach, technical information, as well as details about its strengths and weaknesses, are provided based on a critical review of the studies listed. Through this literature review, the performance and uncertainty associated with the ensemble forecasting systems are underlined from both operational and scientific viewpoints. Finally, the remaining key challenges and prospective future research directions are presented, notably through hybrid dynamical ‐ statistical learning approaches, which obviously present new challenges to be overcome in order to allow the successful employment of ensemble streamflow forecasting systems in the next decades. Targeting students, researchers and practitioners, this review provides a detailed perspective on the major features of an increasingly important area of hydrological forecasting. Key Points: This work summarizes the 40 years of research in the generation of streamflow forecasts based on an exhaustive review of studiesEnsemble prediction systems are categorized into three classes: statistics‐based, climatology‐based and numerical weather prediction‐based hydrological ensemble prediction systemsFor each ensemble forecasting system, thorough technical information is provided [ABSTRACT FROM AUTHOR]

The inter‐variable dependence of climate variables is usually not considered in many bias correction methods, even though it has been deemed important for various impact studies. Another possible approach is to forgo the bias correction of climate model outputs, and instead, post‐process the outputs of the impact model. This has the advantage of circumventing the difficulties associated with correcting the inter‐variable dependence of climate variables. Using a hydrological impact study as an example, this study investigates the feasibility of bias correcting impact model outputs by comparing the performance of the pre‐processing and post‐processing of hydrological model simulations when using bias correction methods. The performance over calibration and validation periods was used to assess the transferability of both approaches. The results show that both the pre‐processing and post‐processing procedures are capable of significantly reducing the bias of simulated streamflow time series for most global climate models (GCMs), even though their performances depend on GCM simulations, hydrological models, streamflow metrics and watersheds. Both approaches were likely to perform badly over the validation period when bias correction factors have a strong seasonal variability and are therefore sensitive to bias nonstationarity of climate model outputs and/or streamflow between the calibration and validation periods. This problem is found to be more acute for the post‐processing method because streamflows often have a seasonal pattern with more abrupt changes than precipitation and temperature. For this reason, pre‐processing is recommended as it is less likely to suffer from this problem. Key Points: The feasibility of bias correcting hydrological model simulations is tested by comparing the pre‐processing and post‐processing approachesBias nonstationarity of global climate model outputs and sharp seasonal correction factor gradients lead to performance deterioration of correction methods.Bias correction of climate model simulations is still recommended for hydrological impact studies [ABSTRACT FROM AUTHOR]

In spring 2011, an unprecedented flood hit the complex eastern United States (U.S.)–Canada transboundary Lake Champlain–Richelieu River (LCRR) Basin, destructing properties and inducing negative impacts on agriculture and fish habitats. The damages, covered by the Governments of Canada and the U.S., were estimated to C$90M. This natural disaster motivated the study of mitigation measures to prevent such disasters from reoccurring. When evaluating flood risks, long‐term evolving climate change should be taken into account to adopt mitigation measures that will remain relevant in the future. To assess the impacts of climate change on flood risks of the LCRR basin, three bias‐corrected multi‐resolution ensembles of climate projections for two greenhouse gas concentration scenarios were used to force a state‐of‐the‐art, high‐resolution, distributed hydrological model. The analysis of the hydrological simulations indicates that the 20‐year return period flood (corresponding to a medium flood) should decrease between 8% and 35% for the end of the 21st Century (2070–2099) time horizon and for the high‐emission scenario representative concentration pathway (RCP) 8.5. The reduction in flood risks is explained by a decrease in snow accumulation and an increase in evapotranspiration expected with the future warming of the region. Nevertheless, due to the large climate inter‐annual variability, short‐term flood probabilities should remain similar to those experienced in the recent past. [ABSTRACT FROM AUTHOR]

A data set containing hydrometeorological and hydroclimatological data for 3,540 watersheds in North America is described. The data set contains four main parts: (a) observed hydrometeorological data including daily streamflow observations, precipitation, minimum temperature, and maximum temperature; (b) 20 bias‐corrected climate model projections for two Representative Concentration Pathway (RCP) scenarios and five bias correction methods; (c) hydrological model calibration parameters and simulated streamflow for 4 hydrological models, 2 objective functions, and 10 calibration parameter sets for the reference period; and (d) hydrological simulations for each of the combinations of the abovementioned elements of the climate change impact study chain, for a total of 16,000 combinations. The data set also contains simulations and bias‐corrected climate for 30‐year horizons corresponding to 1.5°C and 2°C temperature increases for a subset of the climate models, for an additional 8,000 combinations. All simulations in the reference period are also provided. Fifty‐one precomputed hydrological indices are made available for each simulation. Overall, 2.89 × 1012 years of simulations are classified, analyzed, compressed, and made available for all researchers. This data set can be used to evaluate the uncertainty of various components in the impact study chain, to establish relationships between catchment properties and hydrological response to climate change, and to evaluate the spatial distribution of hydrological change according to a multitude of hydrological indices. Key Points: A data set containing hydrometeorological observations and hydroclimatological simulations for 3,540 watersheds in North America is presentedThe data set is the first to provide large‐sample hydrological simulations and indices in the context of climate change impacts on hydrologyThis data set can be used for multiple purposes including impact assessment, uncertainty, and hydrological process analysis over large scales [ABSTRACT FROM AUTHOR]

Multi‐member ensembles of climate models are used to study the role of internal climate variability and its potential impact on climate change impact studies. The reliability of such ensembles with respect to representing the internal climate variability is generally implicitly accepted. Using the latest version of the Climate Research Unit (CRU) data set as a baseline, this study verifies the reliability of multi‐member ensembles in estimating the internal precipitation and temperature variability at the multi‐decadal scale. To achieve this, multi‐decadal variability calculated using climate model multi‐member ensembles is first compared to that calculated using CRU data. The inter‐member variability of a single climate model is then compared to the multi‐decadal variability of CRU precipitation and temperature. Three climate models, with the number of members ranging between 5 and 40, are used to investigate whether the reliability is dependent upon the number of members of a climate model. The results show that multi‐member ensembles are capable of capturing the observed spatial pattern of multi‐decadal variability for both precipitation and temperature at the global scale. However, multi‐member ensembles perform better over regions with smaller variability. For the variables and timescale investigated, all three multi‐member ensembles show similar performances, suggesting that a five‐member ensemble may be sufficient to estimate the internal multi‐decadal climate variability for the chosen variables. Overall, this study indicates that the multi‐member ensemble of a climate model can be used to estimate the internal climate variability of annual and seasonal precipitation and temperature at the multi‐decadal and regional scales, if long historical records are not available. Climate model multi‐member ensembles are capable of capturing the observed spatial pattern of MDV for both precipitation and temperature. Climate model multi‐member ensembles perform better over regions with smaller variability. Performance of multi‐member ensembles did not depend on the number of members within each ensemble for annual and seasonal precipitation at the multi‐decadal scale. In the figure, multi‐decadal variability of mean annual precipitation estimated using CRU data and different climate model ensembles. Different colors represent the standard deviation of precipitation in millimeters. [ABSTRACT FROM AUTHOR]

Abstract: Bias correction methods are usually applied to climate model outputs before using these outputs for hydrological climate change impact studies. However, the use of a bias correction procedure is debatable, due to the lack of physical basis and the bias nonstationarity of climate model outputs between future and historical periods. The direct use of climate model outputs for impact studies has therefore been recommended in a few studies. This study investigates the possibility of using reanalysis‐driven regional climate model (RCM) outputs directly for hydrological modelling by comparing the performance of bias‐corrected and nonbias‐corrected climate simulations in hydrological simulations over 246 watersheds in the Province of Québec, Canada. When using RCM outputs directly, the hydrological model is specifically calibrated using RCM simulations. Two evaluation metrics (Nash–Sutcliffe efficiency [NSE] and transformed root mean square error [TRMSE]) and three hydrological indicators (mean, high, and low flows) are used as criteria for this comparison. Two reanalysis‐driven RCMs with resolutions of 45 km and 15 km are used to investigate the scale effect of climate model simulations and bias correction approaches on hydrology modelling. The results show that nonbias‐corrected simulations perform better than bias‐corrected simulations for the reproduction of the observed streamflows when using NSE and TRMSE as criteria. The nonbias‐corrected simulations are also better than or comparable with the bias‐corrected simulations in terms of reproducing the three hydrological indicators. These results imply that the raw RCM outputs driven by reanalysis can be used directly for hydrological modelling with a specific calibration of hydrological models using these datasets when gauged observations are scarce or unavailable. The nonbias‐corrected simulations (at a minimum) should be provided to end users, along with the bias‐corrected ones, especially for studying the uncertainty of hydrological climate change impacts. This is especially true when using an RCM with a high resolution, since the scale effect is observed when the RCM resolution increases from a 45‐km to a 15‐km scale. [ABSTRACT FROM AUTHOR]

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041-2065) over the reference (1971-1995), simulated using bias-corrected and nonbias-corrected simulations, is compared using mean flow, spring high flow, and summer-autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias-corrected or nonbias-corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias-corrected results should be provided to end users along with the bias-corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions. [ABSTRACT FROM AUTHOR]

Precipitation and temperature time series suffer from many problems, such as short time, inadequate spatial coverage, missing data, and biases from various causes, which are particularly critical in remote areas such as Northern Canada. The development of alternative datasets for using as proxies for inadequate/missing weather data represents a key research area. In this paper, the performance of 6 alternative datasets is evaluated for hydrological modelling over 12 watersheds located across Canada and the contiguous United States. The datasets can be classified into 3 distinct categories: (a) interpolated gridded data, (b) reanalysis data, and (c) climate model outputs. Hydrological simulations were carried out using a lumped conceptual hydrological model calibrated using standard weather data and compared against results using a calibration specific to each alternative dataset. Prior to the hydrological simulations, the alternative datasets were all evaluated with respect to their ability to reproduce gridded daily precipitation and temperature characteristics over North America. The results show that both the reanalysis data and climate model data adequately represent the spatial pattern of daily precipitation and temperature over North America. The North American Regional Reanalysis (NARR) dataset consistently shows the best performance. With respect to hydrological modelling, the observed discharges are accurately represented by both the gridded and NARR datasets, and more so for the NARR data. The National Centers for Environmental Prediction dataset consistently performs worst as it is unable to even capture the seasonal pattern of observed streamflow for 3 out of the 12 watersheds. These results indicate that the NARR dataset could be used as a proxy for gauged precipitation and temperature for hydrological modelling over watersheds where observational datasets are deficient. The results also illustrate the ability of climate model data to be used for performing hydrological modelling when driven by reanalysis data at their boundaries, and especially so for high-resolution models. [ABSTRACT FROM AUTHOR]

The source of the Richelieu River is Lake Champlain, located between the states of New York, Vermont, and Québec. In 2011, the lake and the Richelieu River reached historical flood levels, raising questions about the influence of climate change on the watershed. The objectives of this work are to model the hydrology of the watershed, construct a reservoir model for the lake and to analyze flooding trends using climate simulations. The basin was modeled using the HSAMI lumped conceptual model from Hydro-Québec with a semi-distributed approach in order to estimate the inflows into Lake Champlain. The discharge at the Richelieu River was computed by using a mass balance equation between the inputs and outputs of Lake Champlain. Future trends were estimated over the 2041-2070 and 2071-2100 periods using a large number of outputs from general circulation models and regional climate models downscaled with constant scaling and daily translation methods. While there is a certain amount of uncertainty as to future trends, there is a decreasing tendency in the magnitude of the mean spring flood. A flood frequency analysis showed most climate projections indicate the severity of most extreme spring floods may be reduced over the two future periods although results are subject to a much larger uncertainty than for the mean spring flood. On the other hand, results indicate summer-fall extreme events such as caused by hurricane Irene in August 2011 may become more frequent in the future. [ABSTRACT FROM AUTHOR]

ABSTRACT Stochastic weather generators are computer models used to simulate synthetic weather time series based on statistical characteristics of observed weather time series for a location of interest. The performance of weather generators should always be evaluated when applied over a new region, especially in a different climate zone. The Loess Plateau of China suffers from extensive soil erosion problems, mostly resulting from extreme precipitation events in an otherwise dry climate. The generation of accurate synthetic climate data (including the extremes) is the key to adequately assess the effect of soil and water conservation measures, especially in a changing climate. Accordingly, this work compares the ability of five commonly used stochastic weather generators ( WGEN, CLIMGEN, CLIGEN, WeaGETS and LARSWG) to simulate daily precipitation and temperature for the Loess Plateau. The results show that the first-order Markov chain-based model satisfactorily simulates the precipitation occurrence, even though slightly better results can be achieved by using the second- and third-order Markov chain-based models. The semi-empirical distribution-based model shows a slightly better performance than the higher-order Markov chain-based models at generating dry spells, while it gives a worse performance in generating wet spells. The semi-empirical distribution-based LARSWG is consistently better than the parametric distribution-based weather generators at simulating daily precipitation amounts, and the three-parameter distribution-based models are consistently better than two-parameter distribution-based models, especially for generating extreme precipitation events. Overall, the performance of WeaGETS is generally superior to that of the other weather generators; it can be used for simulating daily precipitation, Tmax and Tmin for the Loess Plateau. While CLIGEN is not consistently a top performer, its reasonable representation of extreme precipitation events makes it suitable for soil erosion studies. [ABSTRACT FROM AUTHOR]

This paper focuses on evaluating the uncertainty of three common regionalization methods for predicting continuous streamflow in ungauged basins. A set of 268 basins covering 1.6 million km2 in the province of Quebec was used to test the regionalization strategies. The multiple linear regression, spatial proximity, and physical similarity approaches were evaluated on the catchments using a leave-one-out cross-validation scheme. The lumped conceptual HSAMI hydrological model was used throughout the study. A bootstrapping method was chosen to further estimate uncertainty due to parameter set selection for each of the parameter set/regionalization method pairs. Results show that parameter set selection can play an important role in regionalization method performance depending on the regionalization methods (and their variants) used and that equifinality does not contribute significantly to the overall uncertainty witnessed throughout the regionalization methods applications. Regression methods fail to consistently assign behavioral parameter sets to the pseudoungauged basins (i.e., the ones left out). Spatial proximity and physical similarity score better, the latter being the best. It is also shown that combining either physical similarity or spatial proximity with the multiple linear regression method can lead to an even more successful prediction rate. However, even the best methods were shown to be unreliable to an extent, as successful prediction rates never surpass 75%. Finally, this paper shows that the selection of catchment descriptors is crucial to the regionalization strategies' performance and that for the HSAMI model, the optimal number of donor catchments for transferred parameter sets lies between four and seven. [ABSTRACT FROM AUTHOR]

This paper assesses linear regression-based methods in downscaling daily precipitation from the general circulation model (GCM) scale to a regional climate model (RCM) scale (45- and 15-km grids) and down to a station scale across North America. Traditional downscaling experiments (linking reanalysis/dynamical model predictors to station precipitation) as well as nontraditional experiments such as predicting dynamic model precipitation from larger-scale dynamic model predictors or downscaling dynamic model precipitation from predictors at the same scale are conducted. The latter experiments were performed to address predictability limit and scale issues. The results showed that the downscaling of daily precipitation occurrence was rarely successful at all scales, although results did constantly improve with the increased resolution of climate models. The explained variances for downscaled precipitation amounts at the station scales were low, and they became progressively better when using predictors from a higher-resolution climate model, thus showing a clear advantage in using predictors from RCMs driven by reanalysis at its boundaries, instead of directly using reanalysis data. The low percentage of explained variances resulted in considerable underestimation of daily precipitation mean and standard deviation. Although downscaling GCM precipitation from GCM predictors (or RCM precipitation from RCM predictors) cannot really be considered downscaling, as there is no change in scale, the exercise yields interesting information as to the limit in predictive ability at the station scale. This was especially clear at the GCM scale, where the inability of downscaling GCM precipitation from GCM predictors demonstrates that GCM precipitation-generating processes are largely at the subgrid scale (especially so for convective events), thus indicating that downscaling precipitation at the station scale from GCM scale is unlikely to be successful. Although results became better at the RCM scale, the results indicate that, overall, regression-based approaches did not perform well in downscaling precipitation over North America. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The resolution of general circulation models ( GCMs) is too coarse to assess the site-specific impacts of climate change. Downscaling approaches have been developed to meet this requirement. As the resolution of climate model increases, it is imperative to know whether the finer resolution of regional climate models ( RCMs) would result in any improvement of statistical downscaling quality at the station scale for particular downscaling methods. The objective of this study is to assess the effects of climate model resolutions on statistical downscaling quality of precipitation using the generator for point climate change ( GPCC) model. The downscaling is conducted across three scales, from GCM, and mid- and high-resolution RCMs to a station scale for two Canadian stations in the Quebec province. Observed precipitation gridded to the corresponding scales is also studied in parallel, totalling six downscaling experiments. The results show that the statistics of downscaled precipitation are somewhat overestimated for the Sept-Iles station for all six downscaling experiments with the relative error of mean daily, monthly, and annual precipitation ranging between 1.1 and 5.0%, between 2.7 and 5.9% and between 0.5 and 6.2%, respectively, but underestimated for the Bonnard station with the relative error ranging between −4.1 and −10.2%, between −3.2 and −13.3%, and between −2.6 and −12.0%, respectively. The number of wet days per year is well preserved with the difference between observed and downscaled data ranging between −2.9 and 6 d across all downscaling experiments and two stations. The quality of downscaled precipitation is similar between using gridded observations and model-simulated data for both stations. Furthermore, there is no noteworthy scale effect on downscaling quality when downscaling climate model output with GPCC, indicating that for regions without RCM projections, high-quality daily series at stations can be derived directly from GCM projections with this particular downscaling model. © 2013 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This work compares the performance of six bias correction methods for hydrological modeling over 10 North American river basins. Four regional climate model (RCM) simulations driven by reanalysis data taken from the North American Regional Climate Change Assessment Program intercomparison project are used to evaluate the sensitivity of bias correction methods to climate models. The hydrological impacts of bias correction methods are assessed through the comparison of streamflows simulated by a lumped empirical hydrology model (HSAMI) using raw RCM-simulated and bias-corrected precipitation time series. The results show that RCMs are biased in the simulation of precipitation, which results in biased simulated streamflows. All six bias correction methods are capable of improving the RCM-simulated precipitation in the representation of watershed streamflows to a certain degree. However, the performance of hydrological modeling depends on the choice of a bias correction method and the location of a watershed. Moreover, distribution-based methods are consistently better than mean-based methods. A low coherence between the temporal sequences of observed and RCM-simulated (driven by reanalysis data) precipitation was observed over 5 of the 10 watersheds studied. All bias corrections methods fail over these basins due to their inability to specifically correct the temporal structure of daily precipitation occurrence, which is critical for hydrology modeling. In this study, this failure occurred on basins that were distant from the RCM model boundaries and where topography exerted little control over precipitation. These results indicate that bias correction performance is location dependent and that a careful validation should always be performed, especially on studies over new regions. [ABSTRACT FROM AUTHOR]

[1] This study is focused on assessing a summer-fall probable maximum precipitation (PMP) under recent climate conditions and then applying it under a future projected climate using output information from a regional climate model. The estimated PMPs are forced into a hydrological model to investigate potential changes in probable maximum flood (PMF) values. The PMP method is based on the moisture maximization method developed by the World Meteorological Organization. Precipitable water amounts are evaluated using data from the Canadian Regional Climate Model. The approach was tested on the Manic-5 River basin in Canada. Results show the PMP intensity could increase by 0.5-6% for 48 h and 72 h PMP values by the 2071-2100 horizon. These PMP values were used to assess projected PMF values. A lumped conceptual hydrological model was calibrated under recent climate values and model parameters were kept unchanged for modeling future hydrological regimes. Hydrological modeling results indicate modest changes of the PMF values for future climate projections, with no clearly identified upward or downward trends. The study also highlighted the need for a coherent approach to estimate PMF values under future climate conditions using projected PMP estimates, since the current practice of simulating the PMF value by inserting half of a PMP six days prior to the PMP event in a meteorological time series did not produce consistent results. An approach based on a random insertion of the PMP into a meteorological time series is a promising avenue to explore. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) and greenhouse gas emissions scenarios (GGES) are generally considered to be the two major sources of uncertainty in quantifying the climate change impacts on hydrology. Other sources of uncertainty have been given less attention. This study considers overall uncertainty by combining results from an ensemble of two GGES, six GCMs, five GCM initial conditions, four downscaling techniques, three hydrological model structures, and 10 sets of hydrological model parameters. Each climate projection is equally weighted to predict the hydrology on a Canadian watershed for the 2081-2100 horizon. The results show that the choice of GCM is consistently a major contributor to uncertainty. However, other sources of uncertainty, such as the choice of a downscaling method and the GCM initial conditions, also have a comparable or even larger uncertainty for some hydrological variables. Uncertainties linked to GGES and the hydrological model structure are somewhat less than those related to GCMs and downscaling techniques. Uncertainty due to the hydrological model parameter selection has the least important contribution among all the variables considered. Overall, this research underlines the importance of adequately covering all sources of uncertainty. A failure to do so may result in moderately to severely biased climate change impact studies. Results further indicate that the major contributors to uncertainty vary depending on the hydrological variables selected, and that the methodology presented in this paper is successful at identifying the key sources of uncertainty to consider for a climate change impact study. [ABSTRACT FROM AUTHOR]

Laforce, Serge, Marie-Claude Simard, Robert Leconte, and François Brissette, 2011. Climate Change and Floodplain Delineation in Two Southern Quebec River Basins. Journal of the American Water Resources Association (JAWRA) 47(4):785-799. DOI: 10.1111/j.1752-1688.2011.00560.x Abstract: A methodology is presented for mapping the flooded extent of rivers under projected climate change. The methodology follows a top-down modeling approach, where future climate projections generated by global climate models (GCMs) are downscaled to the watershed scale and used as input to hydrological and hydrodynamic models for predicting future river flows and associated open water levels. A range of possible future climate responses are taken into account, allowing quantification of flood-mapping uncertainties resulting from GCM structure and greenhouse gas emission scenarios (GHGES). Probabilistic projections of future flood zones are developed by assuming that all GCMs and GHGES be equally weighted. The proposed methodology was applied to two river basins located in southern Quebec, Canada, for the time horizons 2020 and 2080. Twenty- and hundred-year floods were computed and corresponding flood maps have been produced. Results indicate that there is a general trend toward an increased spring peak discharge for the Châteauguay River Basin and a decrease for the du Nord River Basin at the 2020 horizon. A less obvious trend was observed for the 2080 horizon, some GCM-GHGES producing an increase in spring peak flows, whereas others would result in a less severe spring flood. These uncertainties in flood flows have cascaded into uncertainties in the corresponding flooded extent and represented as probabilistic flood maps. [ABSTRACT FROM AUTHOR]

Khalili, Malika, François Brissette, and Robert Leconte, 2011. Effectiveness of Multi-site Weather Generator for Hydrological Modeling. Journal of the American Water Resources Association (JAWRA) 1-12. DOI: 10.1111/j.1752-1688.2010.00514.x A multi-site weather generator has been developed using the concept of spatial autocorrelation. The multi-site generation approach reproduces the spatial autocorrelations observed between a set of weather stations as well as the correlations between each pair of stations. Its performance has been assessed in two previous studies using both precipitation and temperature data. The main objective of this paper is to assess the efficiency of this multi-site weather generator compared to a uni-site generator with respect to hydrological modeling. A hydrological model, known as Hydrotel, was applied over the Chute du Diable watershed, located in the Canadian province of Quebec. The distributed nature of Hydrotel accounts for the spatial variations throughout the watershed, and thus allows a more in-depth assessment of the effect of spatially dependent meteorological input on runoff generation. Simulated streamflows using both the multi-site and uni-site generated weather data were statistically compared to flows modeled using observed data. Overall, the hydrological modeling using the multi-site weather generator significantly outperformed that using the uni-site generator. This latter combined to Hydrotel resulted in a significant underestimation of extreme streamflows in all seasons. [ABSTRACT FROM AUTHOR]

Over the past few years, many international initiatives and collaborations were launched to improve and share knowledge in hydrology research. Large databases allowed finding patterns and relationships across regions and scales. This paper introduces the Canadian model parameter experiment (CANOPEX) database, which is adapted from the US MOPEX project data and methods. The CANOPEX database includes meteorological and hydrometric data as well as watershed boundaries for 698 basins. Two sets of basin-averaged meteorological data (Maximum and minimum temperature and precipitation) are provided. The first dataset is directly taken from Environment Canada's weather stations whereas the second is extracted from the Natural Resources Canada gridded climate data product. Data are provided in MOPEX and Matlab formats. CANOPEX watersheds are well distributed over Canada, which allows investigating a variety of physiological and climatological conditions. The CANOPEX database can be used in a variety of hydrologic research projects such as climate change impact studies, model comparisons, multi-modelling, ensemble streamflow prediction and model parameter estimation. CANOPEX could be used to generalize findings to other cold climate catchments as well as assess the robustness of research methodologies and procedures. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An approach for mapping near-surface soil moisture at the watershed scale from RADARSAT-1 synthetic aperture radar (SAR) data was developed and tested on seven RADARSAT-1 SAR images acquired over the northern portion of the Châteauguay River Basin in southwestern Quebec, Canada, dominated by agricultural and herbaceous fields. A soil surface roughness map was first retrieved from a SAR image by inverting an empirical backscatter model with known (or assumed) soil moisture. The resulting map was then used with the backscatter model to recover near-surface soil moisture for the remaining SAR images. Field campaigns were conducted concurrent to SAR image acquisitions to measure soil moisture and surface roughness in 24 fields. Good agreement was observed between watershed-scale soil moisture values and measurements averaged for all sampled fields, with a correlation coefficient of 0.96 and an RMS error of 2.2%. However, considerable scatter was found between observed and SAR-derived soil moisture estimates at the field scale. Although the generated maps reveal reasonable small-scale soil moisture variability, no definitive conclusions could be drawn as to whether or not the proposed approach can quantify soil moisture at the field or within field scale due to insufficient ground measurements. Furrows and herbaceous and crop vegetation, which are known to affect the radar signal, appeared to have little influence on the ability to retrieve soil moisture at the watershed scale for the images analyzed in this study. [ABSTRACT FROM AUTHOR]

Examines the impact of climate change on seasonal floods of a southern Quebec River Basin. Generation of 24-hour rainfall hyetographs using region-specific cumulative distributions provided by the Canadian Atmospheric Environment Service; Use of the hyetographs as inputs to study hydrology-hydraulics model to simulate hydrographs; Possibility of increase in the volume of runoff with climatic change.