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A satisfactory performance of hydrological models under historical climate conditions is considered a prerequisite step in any hydrological climate change impact study. Despite the significant interest in global hydrological modeling, few systematic evaluations of global hydrological models (gHMs) at the catchment scale have been carried out. This study investigates the performance of 4 gHMs driven by 4 global observation-based meteorological inputs at simulating weekly discharges over 198 large-sized North American catchments for the 1971–2010 period. The 16 discharge simulations serve as the basis for evaluating gHM accuracy at the catchment scale within the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). The simulated discharges by the four gHMs are compared against observed and simulated weekly discharge values by two regional hydrological models (rHMs) driven by a global meteorological dataset for the same period. We discuss the implications of both modeling approaches as well as the influence of catchment characteristics and global meteorological forcing in terms of model performance through statistical criteria and visual hydrograph comparison for catchment-scale hydrological studies. Overall, the gHM discharge statistics exhibit poor agreement with observations at the catchment scale and manifest considerable bias and errors in seasonal flow simulations. We confirm that the gHM approach, as experimentally implemented through the ISIMIP2a, must be used with caution for regional studies. We find the rHM approach to be more trustworthy and recommend using it for hydrological studies, especially if findings are intended to support operational decision-making.

Abstract 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 studies Ensemble prediction systems are categorized into three classes: statistics‐based, climatology‐based and numerical weather prediction‐based hydrological ensemble prediction systems For each ensemble forecasting system, thorough technical information is provided

Summary Projected climate change effects on streamflow are investigated for the 2041–2070 horizon following the SRES A2 emissions scenario over two snowmelt-dominated catchments in Canada. A 16-member ensemble of SWAT hydrological model (HM) simulations, based on a comprehensive ensemble of the Canadian Regional Climate Model (CRCM) simulations driven by two global climate models (GCMs), with five realizations of the Canadian CGCM3 and three realizations of the German ECHAM5 is established per catchment. This study aims to evaluate, once model bias has been removed by statistical post-processing (SP), how the RCM-simulated climate changes differ from those of the parent GCMs, and how they affect the assessment of climate change-induced hydrological impacts at the catchment scale. The variability of streamflow caused by the use of different SP methods (mean-based versus distribution-based) within each statistical post-processing pathway of climate model outputs (bias correction versus perturbation) is also evaluated, as well as the uncertainty of natural climate variability. The simulations cover 1971–2000 in the reference period and 2041–2070 in the future period. For a set of criteria, results based on raw and statistically post-processed model outputs for the reference climate are compared with observations. This process demonstrates that SP is important not only for GCMs outputs, but also for CRCM outputs. SP leads to a high level of agreement between the CRCM and the driving GCMs in reproducing patterns of observed climate. The ensemble spread of the climate change signal on streamflow is large and varies with catchments and hydrological periods (winter/summer flows). The results of various hydrological indicators show that most of the uncertainty arises from the natural climate variability followed by the statistical post-processing. The uncertainty linked to the choice of statistical pathway is much larger than that associated with the choice of the method in quantifying the hydrological impacts. We find that the incorporation of dynamical downscaling of global models through the CRCM as an intermediate step in the GCM–RCM–SP–HM model chain does not lead to considerable differences in the assessment of the climate change impacts on streamflow for the study catchments.

Satellite-based broad-scale (i.e., global and continental) human settlement data are essential for diverse applications spanning climate hazard mitigation, sustainable development monitoring, spatial epidemiology and demographic modeling. Many human settlement products report exceptional detection accuracies above 85%, but there is a substantial blind spot in that product validation typically focuses on large urban areas and excludes rural, small-scale settlements that are home to 3.4 billion people around the world. In this study, we make use of a data-rich sample of 30 refugee settlements in Uganda to assess the small-scale settlement detection by four human settlement products, namely, Geo-Referenced Infrastructure and Demographic Data for Development settlement extent data (GRID3-SE), Global Human Settlements Built-Up Sentinel-2 (GHS-BUILT-S2), High Resolution Settlement Layer (HRSL) and World Settlement Footprint (WSF). We measured each product’s areal coverage within refugee settlement boundaries, assessed detection of 317,416 building footprints and examined spatial agreement among products. For settlements established before 2016, products had low median probability of detection and F1-score of 0.26 and 0.24, respectively, a high median false alarm rate of 0.59 and tended to only agree in regions with the highest building density. Individually, GRID3-SE offered more than five-fold the coverage of other products, GHS-BUILT-S2 underestimated the building footprint area by a median 50% and HRSL slightly underestimated the footprint area by a median 7%, while WSF entirely overlooked 8 of the 30 study refugee settlements. The variable rates of coverage and detection partly result from GRID3-SE and HRSL being based on much higher resolution imagery, compared to GHS-BUILT-S2 and WSF. Earlier established settlements were generally better detected than recently established settlements, showing that the timing of satellite image acquisition with respect to refugee settlement establishment also influenced detection results. Nonetheless, settlements established in the 1960s and 1980s were inconsistently detected by settlement products. These findings show that human settlement products have far to go in capturing small-scale refugee settlements and would benefit from incorporating refugee settlements in training and validating human settlement detection approaches.

Abstract Centennial‐to‐millennial temperature records of the past provide a context for the interpretation of current and future changes in climate. Quaternary climates have been relatively well studied in north‐east North America and the adjacent Atlantic Ocean over the last decades, and new research methods have been developed to improve reconstructions. We present newly inferred reconstructions of sea surface temperature for the north‐western Atlantic region, together with a compilation of published temperature records. The database thus comprises a total of 86 records from both marine and terrestrial sites, including lakes, peatlands, ice and tree rings, each covering at least part of the Holocene. For each record, we present details on seasons covered, chronologies and information on radiocarbon dates and analytical time steps. The 86 records contain a total of 154 reconstructions of temperature and temperature‐related variables. Main proxies include pollen and dinocysts, while summer was the season for which the highest number of reconstructions were available. Many records covered most of the Holocene, but many dinocyst records did not extend to the surface, due to sediment mixing, and dendroclimate records were limited to the last millennium. The database allows for the exploration of linkages between sea ice and climate and may be used in conjunction with other palaeoclimate and palaeoenvironmental records, such as wildfire records and peatland dynamics. This inventory may also aid the identification of gaps in the geographic distribution of past temperature records thus guiding future research efforts.

Abstract Groundwater quality modelling plays an important role in water resources management decision making processes. Accordingly, models must be developed to account for the uncertainty inherent in the modelling process, from the sample measurement stage through to the data interpretation stages. Artificial intelligence models, particularly fuzzy inference systems (FIS), have been shown to be effective in groundwater quality evaluation for complex aquifers. In the current study, fuzzy set theory is applied to groundwater-quality related decision-making in an agricultural production context; the Mamdani, Sugeno, and Larsen fuzzy logic-based models (MFL, SFL, and LFL, respectively) are used to develop a series of new, generalized, rule-based fuzzy models for water quality evaluation using widely accepted irrigation indices and hydrological data from the Sarab Plain, Iran. Rather than drawing upon physiochemical groundwater quality parameters, the present research employs widely accepted agricultural indices (e.g., irrigation criteria) when developing the MFL, SFL and LFL groundwater quality models. These newly-developed models, generated significantly more consistent results than the United States Soil Laboratory (USSL) diagram, addressed the inherent uncertainty in threshold data, and were effective in assessing groundwater quality for agricultural uses. The SFL model is recommended as it outperforms both MFL and LFL in terms of accuracy when assessing groundwater quality using irrigation indices.

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).

Abstract There is increasing interest in the magnitude of the flow of freshwater to the Arctic Ocean due to its impacts on the biogeophysical and socio‐economic systems in the north and its influence on global climate. This study examines freshwater flow based on a dataset of 72 rivers that either directly or indirectly contribute flow to the Arctic Ocean or reflect the hydrologic regime of areas contributing flow to the Arctic Ocean. Annual streamflow for the 72 rivers is categorized as to the nature and location of the contribution to the Arctic Ocean, and composite series of annual flows are determined for each category for the period 1975 to 2015. A trend analysis is then conducted for the annual discharge series assembled for each category. The results reveal a general increase in freshwater flow to the Arctic Ocean with this increase being more prominent from the Eurasian rivers than from the North American rivers. A comparison with trends obtained from an earlier study ending in 2000 indicates similar trend response from the Eurasian rivers, but dramatic differences from some of the North American rivers. A total annual discharge increase of 8.7 km 3 /y/y is found, with an annual discharge increase of 5.8 km 3 /y/y observed for the rivers directly flowing to the Arctic Ocean. The influence of annual or seasonal climate oscillation indices on annual discharge series is also assessed. Several river categories are found to have significant correlations with the Arctic Oscillation, the North Atlantic Oscillation, or the Pacific Decadal Oscillation. However, no significant association with climate indices is found for the river categories leading to the largest freshwater contribution to the Arctic Ocean.

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.

AbstractIn this time of a changing climate, it is important to know whether lake levels will rise, potentially causing flooding, or river flows will dry up during abnormally dry weather. The Great Lakes region is the largest freshwater lake system in the world. Moreover, agriculture, industry, commerce, and shipping are active in this densely populated region. Environment and Climate Change Canada (ECCC) recently implemented the Water Cycle Prediction System (WCPS) over the Great Lakes and St. Lawrence River watershed (WCPS-GLS version 1.0) following a decade of research and development. WCPS, a network of linked models, simulates the complete water cycle, following water as it moves from the atmosphere to the surface, through the river network and into lakes, and back to the atmosphere. Information concerning the water cycle is passed between the models. WCPS is the first short-to-medium-range prediction system of the complete water cycle to be run on an operational basis anywhere. It currently produces ...