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Abstract Extreme precipitation events can have a significant impact on the environment, agriculture, economy and safety, making close monitoring of their short‐ and long‐term trends essential for the development of effective mitigation and adaptation strategies. In this study, we analysed 16 in situ observation datasets from four different climate zones in Algeria, spanning from 1969 to 2021. The trend analysis was conducted using the original Mann–Kendall test and seven modified tests to eliminate the effects of short‐term persistence. Our findings reveal a significant increasing trend of extreme precipitation variability for most stations in the Warm Mediterranean climate zone, except for the Consecutive dry days index, which showed a negative trend for the same zone, while stations in the Cold/Warm semi‐arid climate and Cold desert climate (Bwk) zones showed a decreasing trend. Additionally, all index series with significant long‐term trends were affected by a significant shift in their means, which was confirmed by both the Lombard and Pettitt tests. However, when we used the modified MPT and the test eliminating the effects of long‐term persistence, the significance of the shifts and the trend decreased. Our results suggest that while extreme precipitation events have been increasing in some parts of Algeria; the trend may not be statistically significant in the long‐run, indicating the necessity of revisiting and refreshing the findings of previous studies for a more current perspective.

Summary Probable maximum snow accumulation (PMSA) is one of the key variables used to estimate the spring probable maximum flood (PMF). A robust methodology for evaluating the PMSA is imperative so the ensuing spring PMF is a reasonable estimation. This is of particular importance in times of climate change (CC) since it is known that solid precipitation in Nordic landscapes will in all likelihood change over the next century. In this paper, a PMSA methodology based on simulated data from regional climate models is developed. Moisture maximization represents the core concept of the proposed methodology; precipitable water being the key variable. Results of stationarity tests indicate that CC will affect the monthly maximum precipitable water and, thus, the ensuing ratio to maximize important snowfall events. Therefore, a non-stationary approach is used to describe the monthly maximum precipitable water. Outputs from three simulations produced by the Canadian Regional Climate Model were used to give first estimates of potential PMSA changes for southern Quebec, Canada. A sensitivity analysis of the computed PMSA was performed with respect to the number of time-steps used (so-called snowstorm duration) and the threshold for a snowstorm to be maximized or not. The developed methodology is robust and a powerful tool to estimate the relative change of the PMSA. Absolute results are in the same order of magnitude as those obtained with the traditional method and observed data; but are also found to depend strongly on the climate projection used and show spatial variability.

Excluding Antarctica and Greenland, 3.8% of the world’s glacier area is concentrated in Chile. The country has been strongly affected by the mega drought, which affects the south-central area and has produced an increase in dependence on water resources from snow and glacier melting in dry periods. Recent climate change has led to an elevation of the zero-degree isotherm, a decrease in solid-state precipitation amounts and an accelerated loss of glacier and snow storage in the Chilean Andes. This situation calls for a better understanding of future water discharge in Andean headwater catchments in order to improve water resources management in glacier-fed populated areas. The present study uses hydrological modeling to characterize the hydrological processes occurring in a glacio-nival watershed of the central Andes and to examine the impact of different climate change scenarios on discharge. The study site is the upper sub-watershed of the Tinguiririca River (area: 141 km2), of which nearly 20% is covered by Universidad Glacier. The semi-distributed Snowmelt Runoff Model + Glacier (SRM+G) was forced with local meteorological data to simulate catchment runoff. The model was calibrated on even years and validated on odd years during the 2008–2014 period and found to correctly reproduce daily runoff. The model was then forced with downscaled ensemble projected precipitation and temperature series under the RCP 4.5 and RCP 8.5 scenarios, and the glacier adjusted using a volume-area scaling relationship. The results obtained for 2050 indicate a decrease in mean annual discharge (MAD) of 18.1% for the lowest emission scenario and 43.3% for the most pessimistic emission scenario, while for 2100 the MAD decreases by 31.4 and 54.2%, respectively, for each emission scenario. Results show that decreasing precipitation lead to reduced rainfall and snowmelt contributions to discharge. Glacier melt thus partly buffers the drying climate trend, but our results show that the peak water occurs near 2040, after which glacier depletion leads to reducing discharge, threatening the long-term water resource availability in this region.

Cold region hydrology is conditioned by distinct cryospheric and hydrological processes. While snowmelt is the main contributor to both surface and subsurface flows, seasonally frozen soil also influences the partition of meltwater and rain between these flows. Cold regions of the Northern Hemisphere midlatitudes have been shown to be sensitive to climate change. Assessing the impacts of climate change on the hydrology of this region is therefore crucial, as it supports a significant amount of population relying on hydrological services and subjected to changing hydrological risks. We present an exhaustive review of the literature on historical and projected future changes on cold region hydrology in response to climate change. Changes in snow, soil, and streamflow key metrics were investigated and summarized at the hemispheric scale, down to the basin scale. We found substantial evidence of both historical and projected changes in the reviewed hydrological metrics. These metrics were shown to display different sensitivities to climate change, depending on the cold season temperature regime of a given region. Given the historical and projected future warming during the 21st century, the most drastic changes were found to be occurring over regions with near-freezing air temperatures. Colder regions, on the other hand, were found to be comparatively less sensitive to climate change. The complex interactions between the snow and soil metrics resulted in either colder or warmer soils, which led to increasing or decreasing frost depths, influencing the partitioning rates between the surface and subsurface flows. The most consistent and salient hydrological responses to both historical and projected climate change were an earlier occurrence of snowmelt floods, an overall increase in water availability and streamflow during winter, and a decrease in water availability and streamflow during the warm season, which calls for renewed assessments of existing water supply and flood risk management strategies.

Abstract The increased frequency of mild rain‐on‐snow (R.O.S.) events in cold regions associated with climate change is projected to affect snowpack structure and hydrological behaviour. The ice layers that form in a cold snowpack when R.O.S. events occur have been shown to influence flowthrough processes and liquid water retention, with consequences for winter floods, groundwater recharge, and water resources management. This study explores interconnections between meteorological conditions, ice layer formation, and lateral flows during R.O.S. events throughout the 2018–2019 winter in meridional Quebec, Canada. Automated hydro‐meteorological measurements, such as water availability for runoff, snow water equivalent, and snowpit observations, are used to compute water and energy balances, making it possible to characterize a snowpack's internal conditions and flowthrough regimes. For compatibility assessment, water and energy balances‐based flowthrough scenarios are then compared to different hydro‐meteorological variables', such as water table or streamlet water levels. The results show an association between highly variable meteorological conditions, frequent R.O.S. events, and ice layer formation. Lateral flows were mainly observed during the early stage of the ablation period. The hydrologically significant lateral flows observed in the study are associated with winter conditions that are predicted to become more frequent in a changing climate, stressing the need for further evaluation of their potential impact at the watershed scale.

Abstract. Accurate knowledge of snow depth distributions in forested regions is crucial for applications in hydrology and ecology. In such a context, understanding and assessing the effect of vegetation and topographic conditions on snow depth variability is required. In this study, the spatial distribution of snow depth in two agro-forested sites and one coniferous site in eastern Canada was analyzed for topographic and vegetation effects on snow accumulation. Spatially distributed snow depths were derived by unmanned aerial vehicle light detection and ranging (UAV lidar) surveys conducted in 2019 and 2020. Distinct patterns of snow accumulation and erosion in open areas (fields) versus adjacent forested areas were observed in lidar-derived snow depth maps at all sites. Omnidirectional semi-variogram analysis of snow depths showed the existence of a scale break distance of less than 10 m in the forested area at all three sites, whereas open areas showed comparatively larger scale break distances (i.e., 11–14 m). The effect of vegetation and topographic variables on the spatial variability in snow depths at each site was investigated with random forest models. Results show that the underlying topography and the wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. These results highlight the importance of including and better representing these processes in physically based models for accurate estimates of snowpack dynamics.

This study assesses the performance of UAV lidar system in measuring high-resolution snow depths in agro-forested landscapes in southern Québec, Canada. We used manmade, mobile ground control points in summer and winter surveys to assess the absolute vertical accuracy of the point cloud. Relative accuracy was determined by a repeat flight over one survey block. Estimated absolute and relative errors were within the expected accuracy of the lidar (~5 and ~7 cm, respectively). The validation of lidar-derived snow depths with ground-based measurements showed a good agreement, however with higher uncertainties observed in forested areas compared with open areas. A strip alignment procedure was used to attempt the correction of misalignment between overlapping flight strips. However, the significant improvement of inter-strip relative accuracy brought by this technique was at the cost of the absolute accuracy of the entire point cloud. This phenomenon was further confirmed by the degraded performance of the strip-aligned snow depths compared with ground-based measurements. This study shows that boresight calibrated point clouds without strip alignment are deemed to be adequate to provide centimeter-level accurate snow depth maps with UAV lidar. Moreover, this study provides some of the earliest snow depth mapping results in agro-forested landscapes based on UAV lidar.

Objectives. To assess the environmental justice implications of flooding from Hurricane Harvey in Greater Houston, Texas, we analyzed whether the areal extent of flooding was distributed inequitably with respect to race, ethnicity, and socioeconomic status, after controlling for relevant explanatory factors.Methods. Our study integrated cartographic information from Harvey’s Inundation Footprint, developed by the US Federal Emergency Management Agency, with sociodemographic data from the 2012–2016 American Community Survey. Statistical analyses were based on bivariate correlations and multivariate generalized estimating equations.Results. The areal extent of Harvey-induced flooding was significantly greater in neighborhoods with a higher proportion of non-Hispanic Black and socioeconomically deprived residents after we controlled for contextual factors and clustering.Conclusions. Results provide evidence of racial/ethnic and socioeconomic injustices in the distribution of flooding and represent an importa...

Abstract The present study analyses the impacts of past and future climate change on extreme weather events for southern parts of Canada from 1981 to 2100. A set of precipitation and temperature‐based indices were computed using the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) multi‐model ensemble projections at 8 km resolution over the 21st Century for two representative concentration pathway (RCP) scenarios: RCP4.5 and RCP8.5. The results show that this region is expected to experience stronger warming and a higher increase in precipitation extremes in future. Generally, projected changes in minimum temperature will be greater than changes in maximum temperature, as shown by respective indices. A decrease in frost days and an increase in warm nights will be expected. By 2100 there will be no cool nights and cool days. Daily minimum and maximum temperatures will increase by 12 and 7°C, respectively, under the RCP8.5 scenario, when compared with the reference period 1981–2000. The highest warming in minimum temperature and decrease in cool nights and days will occur in Ontario and Quebec provinces close to the Great Lakes and Hudson Bay. The highest warming in maximum temperature will occur in the southern parts of Alberta and Saskatchewan. Annual total precipitation is expected to increase by about 16% and the occurrence of heavy precipitation events by five days. The highest increase in annual total precipitation will occur in the northern parts of Ontario and Quebec and in western British Columbia.

ABSTRACTTrends in indices based on daily temperature and precipitation are examined for two periods: 1948–2016 for all stations in Canada and 1900–2016 for stations in the south of Canada. These in...