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The ocean is the main driver of internal climate variability. However, the relatively short length of the historical record limits our ability to study the impact of large-scale oscillations on the regional climate. This work uses the output from the Canadian Earth System Model large ensemble (CanESM2-LE) to study the impact of three large-scale oscillations on temperature and precipitation anomalies over North America. The 50-member ensemble provides data series covering 2500 years to study the superpositions between ENSO, AMO, and PDO over the 1961–2010 period. The main characteristics of all three oscillations are well reproduced by CanESM2-LE. The impact of each oscillation is considered independently (with the others in their neutral phases), as well as combined with the other two (e.g., all three in non-neutral phases). The results outline a dominant role of ENSO in annual precipitation and of AMO in temperature over most of North America. PDO has a minimal impact on precipitation and temperature. The dominant roles of ENSO on precipitation and AMO on temperature are enhanced by superpositions between these patterns. These combined impacts are consistent with their independent ones. Even though this study is conducted in a model world, the superpositions are mostly consistent with our understanding derived from observations. The results therefore extend our understanding of the relationship between large-scale oscillations and climate variability over North America and highlight the importance of considering the superpositions between oscillations to better understand internal hydroclimatic variability. [ABSTRACT FROM AUTHOR]

Bias correction methods have now emerged as the most commonly used approach when applying climate model outputs to impact studies. However, comparatively much fewer studies have looked at the limitations of bias correction caused by the very nature of the climate system. Two main sources of errors can affect the efficiency of bias correction over a future period: climate sensitivity and internal variability of the climate system. The former is related to differences in the forcing response between a climate model and the real climate system, whereas the latter results from the chaotic nature of the climate system. Using a "pseudo-reality" approach, this study investigates the contribution of these two sources of error to remaining biases of climate model after bias correction for future periods. The pseudo-reality approach uses one climate model as a reference dataset to correct other climate models. Results indicate that bias correction is beneficial over the reference period and in near future periods. However, large biases remain in future periods. The difference in climate sensitivities is the main contributor to the remaining biases in corrected data. Internal variability affects the near and far future similarly and may dominate in the near future, especially for precipitation. The impact of differences in climate sensitivity between the reference dataset and climate model data cannot be eliminated, while the impact of internal variability can be lessened by using a reference period for as long as possible to filter out low-frequency modes of variability. [ABSTRACT FROM AUTHOR]

The Hydrometeorological Sandbox - École de technologie supérieure (HYSETS) is a rich, comprehensive and large-scale database for hydrological modelling covering 14425 watersheds in North America. The database includes data covering the period 1950–2018 depending on the type and source of data. The data include a wide array of hydrometeorological data required to perform hydrological and climate change impact studies: (1) watershed properties including boundaries, area, elevation slope, land use and other physiographic information; (2) hydrometric gauging station discharge time-series; (3) precipitation, maximum and minimum daily air temperature time-series from weather station records and from (4) the SCDNA infilled gauge meteorological dataset; (5) the NRCan and Livneh gridded interpolated products' meteorological data; (6) ERA5 and ERA5-Land reanalysis data; and (7) the SNODAS and ERA5-Land snow water equivalent estimates. All data have been processed and averaged at the watershed scale, and provides a solid basis for hydrological modelling, climate change impact studies, model calibration assessment, regionalization method evaluation and essentially any study requiring access to large amounts of spatiotemporally varied hydrometeorological data. Measurement(s) elevation • temperature • hydrological precipitation process • Fluid Discharge • climate • physiographic feature Technology Type(s) digital curation Sample Characteristic - Environment watershed Sample Characteristic - Location North America Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12600281 [ABSTRACT FROM AUTHOR]

The potential impacts of floods are of significant concern to our modern society raising the need to identify and quantify all the uncertainties that can impact their simulations. Climate simulations at finer spatial resolutions are expected to bring more confidence in these hydrological simulations. However, the impact of the increasing spatial resolutions of climate simulations on floods simulations has to be evaluated. To address this issue, this paper assesses the sensitivity of summer–fall flood simulations to the Canadian Regional Climate Model (CRCM) grid resolution. Three climate simulations issued from the fifth version of the CRCM (CRCM5) driven by the ERA-Interim reanalysis at 0.44°, 0.22° and 0.11° resolutions are analysed at a daily time step for the 1981–2010 period. Raw CRCM5 precipitation and temperature outputs are used as inputs in the simple lumped conceptual hydrological model MOHYSE to simulate streamflows over 50 Quebec (Canada) basins. Summer–fall flooding is analysed by estimating four flood indicators: the 2-year, 5-year, 10-year and 20-year return periods from the CRCM5-driven streamflows. The results show systematic impacts of spatial resolution on CRCM5 outputs and seasonal flood simulations. Floods simulated with coarser climate datasets present smaller peak discharges than those simulated with the finer climate outputs. Smaller catchments show larger sensitivity to spatial resolution as more detail can be obtained from the finer grids. Overall, this work contributes to understanding the sensitivity of streamflow modelling to the climate model's resolution, highlighting yet another uncertainty source to consider in hydrological climate change impact studies. [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]

Bias correction is usually applied to climate model outputs before they are used as inputs to environmental models for impact studies. Every climate model is post-processed independently of others to account for biases originating from model structure and internal variability. To better understand the role of internal variability, multi-member ensembles (multiple runs of a single climate model, with identical forcing but different initial conditions) have now become common in the modeling community. Bias correcting such ensembles requires specific considerations. Correcting all members of such an ensemble independently would force all of them to the target distribution, thus removing the signature of natural variability over the calibration period. How this undesirable effect would propagate onto subsequent time periods is unknown. This study proposes three bias correction variants of a multi-member ensemble and compares their performances against an independent correction of each individual member of the ensemble. The comparison is based on precipitation and temperature, as well as on resulting streamflows simulated by a hydrological model. Two multi-member ensembles (5-member CanESM2 and 10-member CSIRO-MK3.6) were used for a subtropical monsoon watershed in China. The results show that all bias correction methods reduce precipitation and temperature biases for all ensemble members. As expected, independent correction reduces the spread of each ensemble over the calibration period. This is, however, followed by an overestimation of the spread over the subsequent validation period. Pooling all members to calculate common bias correction factors produces the best results over the calibration period; however, the difference among three bias correction variants becomes less clear over the validation period due to internal variability, and even less so when considering streamflows, as the impact model adds its own uncertainty. [ABSTRACT FROM AUTHOR]

Given the ever increasing number of climate change simulations being carried out, it has become impractical to use all of them to cover the uncertainty of climate change impacts. Various methods have been proposed to optimally select subsets of a large ensemble of climate simulations for impact studies. However, the behaviour of optimally-selected subsets of climate simulations for climate change impacts is unknown, since the transfer process from climate projections to the impact study world is usually highly non-linear. Consequently, this study investigates the transferability of optimally-selected subsets of climate simulations in the case of hydrological impacts. Two different methods were used for the optimal selection of subsets of climate scenarios, and both were found to be capable of adequately representing the spread of selected climate model variables contained in the original large ensemble. However, in both cases, the optimal subsets had limited transferability to hydrological impacts. To capture a similar variability in the impact model world, many more simulations have to be used than those that are needed to simply cover variability from the climate model variables' perspective. Overall, both optimal subset selection methods were better than random selection when small subsets were selected from a large ensemble for impact studies. However, as the number of selected simulations increased, random selection often performed better than the two optimal methods. To ensure adequate uncertainty coverage, the results of this study imply that selecting as many climate change simulations as possible is the best avenue. Where this was not possible, the two optimal methods were found to perform adequately. [ABSTRACT FROM AUTHOR]

Ensemble streamflow predictions (ESPs) offer great potential benefits for water resource management, as they contain key probabilistic information for analyzing prediction uncertainty. Ensemble weather forecasts (EWFs) are usually incorporated into ESPs to provide climate information. However, there is no simple way to combine both of them, since EWFs are generally biased and under-dispersed. This study presents a new short-term (1 to 7 lead days) probabilistic streamflow prediction system combining stochastic weather generation and EWFs. The bias and under-dispersion of EWFs were first corrected using a weather generator-based post-processing approach (GPP). The corrected weather forecasts were then coupled with a hydrological model for streamflow forecasts. The proposed GPP forecast was compared against two other forecasts, one using the raw EWF (GFS), and the other using a stochastic weather generator (WG). The comparison was carried out over two Quebec watersheds, using a set of deterministic and probabilistic verification metrics. The deterministic metrics showed that the GPP forecast is consistently the best at predicting the ensemble mean streamflow for both watersheds and for all the leads ranging between 1 and 7 days, followed by the WG forecast. The probabilistic metrics showed negative or near zero skill retained by the GFS forecast for the first 7 lead days. The WG system was much more skillful than the GFS. The GPP forecast consistently displayed the highest skill and reliability in terms of all the metrics applied. With increasing lead days, the skill and reliability of the GPP forecast tend to converge toward that of the WG forecast, indicating that the short-term GPP forecast could easily be linked to a pure WG forecast to extend the forecast horizon. [ABSTRACT FROM AUTHOR]

This paper examines climate change impacts on the water resources system of the Manicouagan River (Québec, Canada). The objective is to evaluate the performance of existing infrastructures under future climate projections and the associated uncertainties. The main purpose of the water resources system is hydropower production. A reservoir optimization algorithm, Sampling Stochastic Dynamic Programming (SSDP), was used to derive weekly operating decisions for the existing system subject to reservoir inflows reflecting future climate, for optimum hydropower production. These projections are simulations from the SWAT hydrologic model for climate change scenarios for the period from 2010 to 2099. Results show that the climate change will alter the hydrological regime of the study area: earlier timing of the spring flood, reduced spring peak flow, and increased annual inflows volume in the future compared to the historical climate. The SSDP optimization algorithm adapted the operating policy to the future hydrological regime by adjusting water reservoir levels in the winter and spring, and increasing the release through turbines, which in the end increased power generation. However, there could be more unproductive spills for some power plants, which would decrease the overall efficiency of the existing water resources system. [ABSTRACT FROM AUTHOR]

This paper discusses the possibility for a privately managed hydro-power system to adapt to a projected increase in water flow in their central-Québec watersheds by adding power generation potential. Runoffs simulated by a lumped rainfall-runoff model were fed into a stochastic dynamic programming (SDP) routine to generate reservoir operating rules. These rules were optimized for maximum power generation under maximal and minimal reservoir level constraints. With these optimized rules, a power generation simulator was used to predict the amount of generated hydropower. The same steps, excluding calibration, were performed on 60 climate projections (from 23 general circulation models and 3 greenhouse gas emission scenarios) for future horizons 2036-2065 and 2071-2100. Reservoir operation rules were optimized for every climate change projection for the 3 power plants in the system. From these simulations, it was possible to determine hydropower numbers for both horizons. The same steps were performed under a modified system in which an additional turbine was added to each power plant. Results show that both the non-structural (optimizing reservoir rules) and structural (adding turbines) adaptation measures allow for increased power production, but that adapting operating rules is sufficient to reap the most of the benefits of increased water availability. [ABSTRACT FROM AUTHOR]

The resolution of General Circulation Models (GCMs) is too coarse for climate change impact studies at the catchment or site-specific scales. To overcome this problem, both dynamical and statistical downscaling methods have been developed. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. This paper evaluates the improvement in statistical downscaling (SD) predictive power when using predictors from a Regional Climate Model (RCM) over a GCM for downscaling site-specific precipitation. Our approach uses mixed downscaling, combining both dynamic and statistical methods. Precipitation, a critical element of hydrology studies that is also much more difficult to downscale than temperature, is the only variable evaluated in this study. The SD method selected here uses a stepwise linear regression approach for precipitation quantity and occurrence (similar to the well-known Statistical Downscaling Model (SDSM) and called SDSM-like herein). In addition, a discriminant analysis (DA) was tested to generate precipitation occurrence, and a weather typing approach was used to derive statistical relationships based on weather types, and not only on a seasonal basis as is usually done. The existing data record was separated into a calibration and validation periods. To compare the relative efficiency of the SD approaches, relationships were derived at the same sites using the same predictors at a 300km scale (the National Center for Environmental Prediction (NCEP) reanalysis) and at a 45km scale with data from the limited-area Canadian Regional Climate Model (CRCM) driven by NCEP data at its boundaries. Predictably, using CRCM variables as predictors rather than NCEP data resulted in a much-improved explained variance for precipitation, although it was always less than 50 % overall. For precipitation occurrence, the SDSM-like model slightly overestimated the frequencies of wet and dry periods, while these were well-replicated by the DA-based model. Both the SDSM-like and DA-based models reproduced the percentage of wet days, but the wet and dry statuses for each day were poorly downscaled by both approaches. Overall, precipitation occurrence downscaled by the DA-based model was much better than that predicted by the SDSM-like model. Despite the added complexity, the weather typing approach was not better at downscaling precipitation than approaches without classification. Overall, despite significant improvements in precipitation occurrence prediction by the DA scheme, and even going to finer scales predictors, the SD approach tested here still explained less than 50 % of the total precipitation variance. While going to even smaller scale predictors (10-15 km) might improve results even more, such smaller scales would basically transform the direct outputs of climate models into impact models, thus negating the need for statistical downscaling approaches. [ABSTRACT FROM AUTHOR]

The behaviour of the water resource system of the Peribonka River (Quebec, Canada) exploited for hydropower is evaluated under various hydrological regimes, using different climate change scenarios. The hydrological regime of the recent past and the regimes of 30 climate projections are considered. The potential hydrological regimes are simulated for climate projections from five general circulation models (GCM) for two greenhouse gas emission scenarios and three temporal horizons (2020, 2050 and 2080). For each hydrological regime, weekly reservoir operating rules are calculated with a dynamic and stochastic optimization model. Simulations of the water resource system with adapted operating rules in these climate change contexts are compared with the management of the water resource system at the control period (1961–1990). For the majority of climate projections, the analysis of simulations in the context of climate change shows an increase in hydropower and in annual unproductive spills. These increases reach 22% and 300%, respectively, compared to the control period. Also, the reliability of a reservoir is compromised for half of the climate projections, with annual probabilities reaching above the maximum operating levels, up to 0.3%, whereas these probabilities were null for the control period. Despite the rise in production, the annual efficiency of the power plants would fluctuate between −5 to +8%, depending on the power plant, the climate projection and the horizon. [ABSTRACT FROM AUTHOR]

The management adaptation potential of the Peribonka River water resource system (Quebec, Canada) is investigated in the context of the evolution of climate change. The objective of this study is to evaluate the impacts on hydropower, power plant efficiency, unproductive spills and reservoir reliability due to changes in the hydrological regimes. The climate change projections used here are from the Canadian regional climate model (CRCM) nested by the Canadian-coupled global climate model (CGCM3) forced with the SRES A2 greenhouse gas emission scenario. The hydrological regimes were simulated with the distributed hydrological model Hydrotel. They were incorporated into a dynamic and stochastic optimization model in order to adapt the operating rules of the water resource system annually, according to the evolution of the climate. The impacts were analyzed over the years 1961-2099, split into four periods for comparison purposes: control period (1961-1990), horizon 2020 (2010-2039), horizon 2050 (2040-2069) and horizon 2080 (2070-2099). The main results indicate that annual mean hydropower would decrease by 1.8% for the period 2010-2039 and then increase by 9.3% and 18.3% during the periods 2040-2069 and 2070-2099, respectively. The trend to increase is statistically significant starting from 2061 (Mann-Kendall with p = 5%). The change in the mean annual production is statistically significant for the 2040-2069 and 2070-2099 periods (t-test with p = 5%). Also, the change in the variance is significant for the periods 2010-2039, 2040-2069 and 2070-2099 (F-test). Annual mean unproductive spills would increase from 1961-2099, but the trend is not statistically significant. However, the changes in the variance of the annual mean spills are significant in the periods 2010-2039, 2040-2069 and 2070-2099. Overall, the reliability of a reservoir would decrease and the vulnerability increase as the climate changes. [ABSTRACT FROM AUTHOR]

Spatial autocorrelation is a correlation between the values of a single variable, considering their geographical locations. This concept has successfully been used for multi-site generation of daily precipitation data (Khalili et al. in J Hydrometeorol 8(3):396–412, 2007). This paper presents an extension of this approach. It aims firstly to obtain an accurate reproduction of the spatial intermittence property in synthetic precipitation amounts, and then to extend the multi-site approach to the generation of daily maximum temperature, minimum temperature and solar radiation data. Monthly spatial exponential functions have been developed for each weather station according to the spatial dependence of the occurrence processes over the watershed, in order to fulfill the spatial intermittence condition in the synthetic time series of precipitation amounts. As was the case for the precipitation processes, the multi-site generation of daily maximum temperature, minimum temperature and solar radiation data is realized using spatially autocorrelated random numbers. These random numbers are incorporated into the weakly stationary generating process, as with the Richardson weather generator, and with no modifications made. Suitable spatial autocorrelations of random numbers allow the reproduction of the observed daily spatial autocorrelations and monthly interstation correlations. The Peribonca River Basin watershed is used to test the performance of the proposed approaches. Results indicate that the spatial exponential functions succeeded in reproducing an accurate spatial intermittence in the synthetic precipitation amounts. The multi-site generation approach was successfully applied for the weather data, which were adequately generated, while maintaining efficient daily spatial autocorrelations and monthly interstation correlations. [ABSTRACT FROM AUTHOR]

The ability of turbulent nuées ardentes (surges) to transport coarse pyroclasts has been questioned on the basis that settling velocities of coarse fragments in the deposits are much too high for them to have been supported by turbulence in a dilute gas suspension. A computer model is used to evaluate the settling velocity of pyroclasts in suspensions of varying concentration and temperature. Since suspension of grains in low-concentration surges occurs if the shear velocity exceeds the settling velocity, the shear velocities related to the 16th and 84th percentiles, and the mean of the grain-size distribution are compared in surge deposits of the Vulsini, with the shear velocity necessary to move the coarsest grain on the bed surface (the Shields criterion). The results show that the settling velocities do not vary significantly in gaseous suspensions having volume concentrations lower than 15%, and that an increase in concentration to 25% is not sufficient to decrease the settling velocity of the coarser fraction, if it represents flow shear velocity. It is shown that the settling velocity of the mean grain size ( M) best depicts the shear velocity of a dilute turbulent suspension. Applying the results to the May 1902 paroxysmal nuées ardentes of Mount Pelée shows that the estimated mean velocities are well within the observed velocities, and sufficient to support all the clasts in dilute, turbulent suspensions. [ABSTRACT FROM AUTHOR]