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The goal of this study is to compare the seasonal variability of 12 physicochemical characteristics of waters in the Ottawa and St. Lawrence Rivers (SLR). Water samples were collected on board the research vessel Lampsillis in the spring (May), summer (August), and fall (October) of 2006 at four stations located downstream from the confluence of the two rivers. Temperature and total nitrogen values varied significantly for the three seasons. In contrast, seasonal values of light extinction coefficient and turbidity do not show any significant variation. The values of the other characteristics varied significantly only for one season. Comparison of these data with those measured in 1994–1996 reveals a net warming of the waters and a significant increase in nitrite-nitrate concentrations due to the increasing use of nitrogen-bearing fertilizers by farmers in Quebec. Concentrations of these two substances are higher than the limits set by the government of Quebec for water quality in rivers.

Abstract Numerous studies have examined the impact of prairie pothole wetlands on overall watershed dynamics. However, very few have looked at individual wetland dynamics across a continuum of alteration status using subdaily hydrometric data. Here, the importance of surface and subsurface water storage dynamics in the prairie pothole region was documented by (1) characterizing surface fill–spill dynamics in intact and consolidated wetlands; (2) quantifying water‐table fluctuations and the occurrence of overland flow downslope of fully drained wetlands; (3) assessing the relation (or lack thereof) between intact, consolidated or drained wetland hydrological behaviour, and stream dynamics; and (4) relating wetland hydrological behaviour to landscape characteristics. Focus was on southwestern Manitoba, Canada, where ten intact, three consolidated, seven fully drained wetlands, and a nearby creek were monitored over two years with differing antecedent storage conditions. Hourly hydrological time series were used to compute behavioural metrics reflective of year‐specific and season‐specific wetland dynamics. Behavioural metrics were then correlated to wetland physical characteristics to identify landscape controls on wetland hydrology. Predictably, more frequent spillage or overland flow was observed when antecedent storage was high. Consolidated wetlands had a high degree of water permanence and a greater frequency of fill–spill events than intact wetlands. Shallow and highly responsive water tables were present downslope of fully drained wetlands. Potential wetland–stream connectivity was also inferred via time‐series analysis, while some landscape characteristics (e.g., wetland surface, catchment area, and storage volume) strongly correlated with wetland behavioural metrics. The nonstationarity of dominant processes was, however, evident through the lack of consistent correlations across seasons. This, therefore, highlights the importance of combining multiyear high‐frequency hydrometric data and detailed landscape analyses in wetland hydrology studies.

The inherent complexity of planning at sea, called maritime spatial planning (MSP), requires a planning approach where science (data and evidence) and stakeholders (their engagement and involvement) are integrated throughout the planning process. An increasing number of innovative planning support systems (PSS) in terrestrial planning incorporate scientific models and data into multi-player digital game platforms with an element of role-play. However, maritime PSS are still early in their innovation curve, and the use and usefulness of existing tools still needs to be demonstrated. Therefore, the authors investigate the serious game, MSP Challenge 2050, for its potential use as an innovative maritime PSS and present the results of three case studies on participant learning in sessions of game events held in Newfoundland, Venice, and Copenhagen. This paper focusses on the added values of MSP Challenge 2050, specifically at the individual, group, and outcome levels, through the promotion of the knowledge co-creation cycle. During the three game events, data was collected through participant surveys. Additionally, participants of the Newfoundland event were audiovisually recorded to perform an interaction analysis. Results from survey answers and the interaction analysis provide evidence that MSP Challenge 2050 succeeds at the promotion of group and individual learning by translating complex information to players and creating a forum wherein participants can share their thoughts and perspectives all the while (co-) creating new types of knowledge. Overall, MSP Challenge and serious games in general represent promising tools that can be used to facilitate the MSP process.

This study evaluates projected changes to rain-on-snow (ROS) characteristics (i.e., frequency, rainfall amount, and runoff) for the future 2041–2070 period with respect to the current 1976–2005 period over North America using six simulations, based on two Canadian RCMs, driven by two driving GCMs for RCP4.5 and 8.5 emission pathways. Prior to assessing projected changes, the two RCMs are evaluated by comparing ERA-Interim driven RCM simulations with available observations, and results indicate that both models reproduce reasonably well the observed spatial patterns of ROS event frequency and other related features. Analysis of current and future simulations suggest general increases in ROS characteristics during the November–March period for most regions of Canada and for northwestern US for the future period, due to an increase in the rainfall frequency with warmer air temperatures in future. Future ROS runoff is often projected to increase more than future ROS rainfall amounts, particularly for northeastern North America, during snowmelt months, as ROS events usually accelerate snowmelt. The simulations show that ROS event is a primary flood generating mechanism over most of Canada and north-western and -central US for the January–May period for the current period and this is projected to continue in the future period. More focused analysis over selected basins shows decreases in future spring runoff due to decreases in both snow cover and ROS runoff. The above results highlight the need to take into consideration ROS events in water resources management adaptation strategies for future climate.

Changes in society's vulnerability to natural hazards are important to understand, as they determine current and future risks, and the need to improve protection. Very large impacts including high numbers of fatalities occur due to single storm surge flood events. Here, we report on impacts of global coastal storm surge events since the year 1900, based on a compilation of events and data on loss of life. We find that over the past, more than eight thousand people are killed and 1.5 million people are affected annually by storm surges. The occurrence of very substantial loss of life (g10000 persons) from single events has however decreased over time. Moreover, there is a consistent decrease in event mortality, measured by the fraction of exposed people that are killed, for all global regions, except South East Asia. Average mortality for storm surges is slightly higher than for river floods, but lower than for flash floods. We also find that for the same coastal surge water level, mortality has decreased over time. This indicates that risk reduction efforts have been successful, but need to be continued with projected climate change, increased rates of sea-level rise and urbanisation in coastal zones.

The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state of the art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. Here, we present an assessment from the CanSISE Network on trends in the historical record of snow cover (fraction, water equivalent) and sea ice (area, concentration, type, and thickness) across Canada. We also assess projected changes in snow cover and sea ice likely to occur by mid-century, as simulated by the Coupled Model Intercomparison Project Phase 5 (CMIP5) suite of Earth system models. The historical datasets show that the fraction of Canadian land and marine areas covered by snow and ice is decreasing over time, with seasonal and regional variability in the trends consistent with regional differences in surface temperature trends. In particular, summer sea ice cover has decreased significantly across nearly all Canadian marine regions, and the rate of multi-year ice loss in the Beaufort Sea and Canadian Arctic Archipelago has nearly doubled over the last 8 years. The multi-model consensus over the 2020–2050 period shows reductions in fall and spring snow cover fraction and sea ice concentration of 5–10% per decade (or 15–30% in total), with similar reductions in winter sea ice concentration in both Hudson Bay and eastern Canadian waters. Peak pre-melt terrestrial snow water equivalent reductions of up to 10% per decade (30% in total) are projected across southern Canada.

Abstract A quantitative and qualitative understanding of the anticipated climate-change-driven multi-scale spatio-temporal shifts in precipitation and attendant river flows is crucial to the development of water resources management approaches capable of sustaining and even improving the ecological and socioeconomic viability of rain-fed agricultural regions. A set of homogeneity tests for change point detection, non-parametric trend tests, and the Sen’s slope estimator were applied to long-term gridded rainfall records of 27 newly formed districts in Chhattisgarh State, India. Illustrating the impacts of climate change, an analysis of spatial variability, multi-temporal (monthly, seasonal, annual) trends and inter-annual variations in rainfall over the last 115 years (1901–2015 mean 1360 mm·y −1 ) showed an overall decline in rainfall, with 1961 being a change point year (i.e., shift from rising to declining trend) for most districts in Chhattisgarh. Spatio-temporal variations in rainfall within the state of Chhattisgarh showed a coefficient of variation of 19.77%. Strong inter-annual and seasonal variability in regional rainfall were noted. These rainfall trend analyses may help predict future climate scenarios and thereby allow planning of effective and sustainable water resources management for the region.

Generalized Additive Models (GAMs) are introduced in this study for the regional estimation of low-flow characteristics at ungauged basins and compared to other approaches commonly used for this purpose. GAMs provide more flexibility in the shape of the relationships between the response and explanatory variables in comparison to classical models such as multiple linear regression (MLR). Homogeneous regions are defined here using the methods of hierarchical cluster analysis, canonical correlation analysis and region of influence. GAMs and MLR are then used within the delineated regions and also for the whole study area. In addition, a spatial interpolation method is also tested. The different models are applied for the regional estimation of summer and winter low-flow quantiles at stations in Quebec, Canada. Results show that for a given regional delineation method, GAMs provide improved performances compared to MLR.

Abstract Flow duration curves (FDC) are used to obtain daily streamflow series at ungauged sites. In this study, functional multiple regression (FMR) is proposed for FDC estimation. Its natural framework for dealing with curves allows obtaining the FDC as a whole instead of a limited number of single points. FMR assessment is performed through a case study in Quebec, Canada. FMR provides a better mean FDC estimation when obtained over sites by considering simultaneously all FDC quantiles in the assessment of each given site. However, traditional regression provides a better mean FDC estimation when obtained over given FDC quantiles by considering all sites in the assessment of each quantile separately. Mean daily streamflow estimation is similar; yet FMR provides an improved estimation for most sites. Furthermore, FMR represents a more suitable framework and provides a number of practical advantages, such as insight into descriptor influence on FDC quantiles. Hence, traditional regression may be preferred if only few FDC quantiles are of interest; whereas FMR would be more suitable if a large number of FDC quantiles is of interest, and therefore to estimate daily streamflows.

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).