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The European Center for Medium-Range Weather Forecasts (ECMWF) has recently released its most advanced reanalysis product, the ERA5 dataset. It was designed and generated with methods giving it multiple advantages over the previous release, the ERA-Interim reanalysis product. Notably, it has a finer spatial resolution, is archived at the hourly time step, uses a more advanced assimilation system and includes more sources of data. This paper aims to evaluate the ERA5 reanalysis as a potential reference dataset for hydrological modelling by considering the ERA5 precipitation and temperatures as proxies for observations in the hydrological modelling process, using two lumped hydrological models over 3138 North-American catchments. This study shows that ERA5-based hydrological modeling performance is equivalent to using observations over most of North-America, with the exception of the Eastern half of the US, where observations lead to consistently better performance. ERA5 temperature and precipitation biases are consistently reduced compared to ERA-Interim and systematically more accurate for hydrological modelling. Differences between ERA5, ERA-Interim and observation datasets are mostly linked to precipitation, as temperature only marginally influences the hydrological simulation outcome. [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]

Hydrological models are commonly used to forecast streamflow and for climate change impact studies. There is a wide range of hydrology models using lumped conceptual approaches all the way to more physically based distributed algorithms. Most of these models come with a steep learning curve before they can be used efficiently by the end user, and they can be tricky to calibrate appropriately. Only a small number of hydrology models can be considered easy to set up and use, and even fewer provide their source code for easy modification to be tailored to individual needs. These drawbacks make it difficult to use these models in educational applications. The goal of this paper is to introduce a very simple, yet efficient, lumped-conceptual hydrological model designed to address the above problems. The MATLAB-based HMETS hydrological model is simple and can be easily and quickly set up on a new watershed, including automatic calibration using state of the art optimization algorithms. Despite its simplicity, the model has proved to perform well against two other lumped-conceptual hydrological models over 320 watersheds. HMETS obtained a median Nash-Sutcliffe Efficiency of 0.72 in validation, compared to 0.64 for MOHYSE (similar structure) and 0.77 for HSAMI (more complex structure). The model's source code is freely available and includes an optional simplified user interface. A climate change impacts simulation tool using the constant scaling downscaling method is also incorporated to the interface. HMETS has been tested in the Construction Engineering Final-Year Project for a group of 60 undergraduate students. [ABSTRACT FROM AUTHOR]

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Weather data are usually required at several locations over a large watershed, especially when using distributed models for hydrological simulations. In many applications, spatially correlated weather data can be provided by a multisite stochastic weather generator, which considers the spatial correlation of weather variables. Prior to using a multisite weather generator for hydrological modeling, its ability to adequately represent the proper hydrological response needs to be assessed. This study assesses the effectiveness of a new multisite weather generator (MulGETS) for hydrological modeling over a Canadian watershed in the Province of Québec. Prior to hydrological modeling, MulGETS is first evaluated with respect to reproducing the spatial correlation and statistical characteristics of precipitation and temperature for the studied watershed. Hydrological simulations obtained from MulGETS-generated precipitation and temperature are then compared with those obtained from a single-site weather generator (WeaGETS) and a WeaGETS-based lumped approach (WeaGETS-lumped) that averages the climate series over all stations in a watershed before running the single-site weather generator. The hydrology is simulated using two hydrological models: the conceptually lumped model HSAMI and the physically based distributed model CEQUEAU. When using the conceptually lumped model, the weather time series is first averaged over all stations in the watershed. The results show that the monthly mean discharge is accurately represented by both MulGETS-generated and WeaGETS-lumped-generated precipitation and temperature, whereas it is considerably underestimated by WeaGETS data for the snowmelt period. The MulGETS and WeaGETS-lumped data also show significant advantages in representing the monthly streamflow variability, which is underestimated by the WeaGETS outputs. Additionally, MulGETS and WeaGETS-lumped consistently perform better than WeaGETS for simulating extreme flows (snowmelt high flows and summer-autumn high and low flows). However, no obvious difference in performance was found between MulGETS and WeaGETSlumped data for hydrological modeling. Moreover, the use of a physically based distributed model with MulGETS did not result in any significant performance gain compared with the much simpler combination of WeaGETS-lumped with a lumped hydrological model for the studied watershed. Overall, this study indicates that a single-site weather generator combined with a lumped hydrological model is sufficient for accurate hydrological simulations, even in the case of a large watershed. [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]

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]

• Stochastic weather generator is used a main computational tool for taking climate non-stationarity into account. • Parameters of stochastic weather generator are conditioned on the most relevant subset of large-scale climate indices. • Long-term streamflow forecast can be improved, particularly around spring flood. This paper aims at improving long-term streamflow forecasts by implementing a novel technique based on conditioning the parameters of a stochastic weather generator on large-scale climate indices, with varying lengths of training periods during the establishment of correlations. The most important climate indices are identified by looking at yearly correlations between a set of 40 indices and meteorological data (precipitation and temperature) at the watershed scale. A linear model is then constructed to identify precipitation and temperature anomalies to induce perturbations in the stochastic weather generator. Time windows of 5, 10, 15, 20 and 30 years are used in determining the optimal linear model. The performance of the proposed approach is assessed against that of a resampling of past climatology and using the same stochastic weather generator unconditioned on climate indices. Each member of the ensemble weather forecast is then fed to a hydrological model to create the Ensemble Streamflow Forecasts (ESF) with a one-year forecasting horizon. The three approaches are tested in hindcast mode over a 30-year period at 12 forecast dates. Results show that temperatures are significantly correlated with large-scale climate indices, whereas precipitation is only weakly related to the same indices. The length of the time window has a considerable impact on the prediction ability of the linear models. The precipitation models based on short duration time windows performed better than those based on longer windows, while the reverse was found for the temperature models. A comparison between all three Ensemble Streamflow Forecast approaches is assessed using the Continuous Ranked Probability Score (CRPS) metric. Results show that the proposed method improves long-term streamflow forecasting, particularly the volumetric bias and the peak flows during the spring flood. [ABSTRACT FROM AUTHOR]

Many studies have reported projected increases in the frequency and intensity of extreme precipitation events in awarmer future climate. These results challenge the assumption of climate stationarity, a standard hypothesis in the estimation of extreme precipitation quantiles (e.g., 100-yr return period) often used as key design criteria for many infrastructures. In this work, changes in hourly to 5-day precipitation extremes occurring between the 1980-99 and 2080-99 periods are investigated using three large ensembles (LE) of climate simulations. The first two are the global CanESM2 50-member ensemble at a 2.88 resolution and the global CESM1 40-member ensemble at a 18 resolution. The third is the regional CRCM5 50-member ensemble at a 0.118 resolution, driven at its boundaries by the 50-member CanESM2 ensemble over the northeastern North America (NNA) and Europe (EU) domains. Results indicate increases in the frequency of future extreme events, and, accordingly, a reduction of the return period of current extreme events for all tested spatial resolutions and temporal scales. Agreement between the three ensembles suggests that extreme precipitations, corresponding to the 100-yr return period over the reference period, become 4-5 (2-4) times more frequent on average for the NNA (EU) domain for daily and 5-day annual maximum precipitation. Projections by CRCM5-LE show even larger increases for subdaily precipitation extremes. Considering the life-span of many public infrastructures, these changesmay have important implications on service levels and the design of many water infrastructures and for public safety, and should therefore be taken into consideration in establishing design criteria. [ABSTRACT FROM AUTHOR]

Weather generators are usually used to produce an ensemble of climate time series for vulnerability assessments and impact studies in hydrological and agricultural communities. Multisite and multivariate weather generators (MMWGs) have many advantages over single-site counterparts in terms of coupling with distributed models for the assessment of spatial variability in various impact sectors. However, the existing MMWGs usually suffer from limitations in preserving multisite and multivariate dependencies at multiple time scales, as well as preserving the low-frequency variability of climate variables. This study proposes a new MMWG which preserves low-frequency climate variability by coupling annual, monthly and daily weather generators into a single model. Specifically, the daily precipitation and temperature time series generated by a widely used multisite daily weather generator is adjusted using monthly and annual climate time series generated by a first-order linear autoregressive model. This combination preserves the multisite and multivariate attributes, as well as low-frequency variability at monthly and annual scales. The performance of the proposed MMWG was evaluated by comparing the baseline model against that of its variants with monthly or annual adjustments for climate generation. The performance was also assessed using a hydrological model over two watersheds with different hydroclimatic characteristics. The results show that the proposed weather generator performs well with respect to reproducing the marginal distributional attributes, multisite and multivariate dependencies, and climate variability at the daily, monthly and annual scales. Weather generators with either monthly or annual adjustments (and not both) only improve the simulations in multisite and multivariate dependencies and low-frequency variability at the corresponding time scales. In terms of hydrological modeling, the proposed model consistently performs better than the baseline model and its variants with only monthly or annual adjustments in representing the mean and variance of monthly and annual streamflows. It also performs better in representing the frequency distribution of mean and extreme streamflow events. Overall, the proposed MMWG can effectively produce multisite and multivariate climate time series with low-frequency variability and has a strong potential for use in climate change impact studies. [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.