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Currently, there are no tools that measure improvements in levels of empathy among diverse water stakeholders participating in transboundary decision-making. In this study, we used an existing empathy scale from clinical psychology during an Experimental Decision Laboratory (EDL) where participants allocated water across a transboundary basin during minor and major drought conditions. We measured changes in empathy using a pre-test/post-test design and triangulated quantitative results with open-ended survey questions. Results were counter-intuitive. For most participants, levels of the four components of empathy decreased after participating in the EDL; however, significant demographically-driven differences emerged. Qualitative results confounded the problem through the capture of participant perceptions of increased overall empathy and perspective taking specifically. Implications for methodological tool development, as well as practice for water managers and researchers are discussed. Water empathy is a particularly sensitive construct that requires specialized intervention and measurement.

It is well established that the Earth's climate system has warmed significantly over the past several decades, and in association there have been widespread changes in various other Earth system components. This has been especially prevalent in the cold regions of the northern mid- to high latitudes. Examples of these changes can be found within the western and northern interior of Canada, a region that exemplifies the scientific and societal issues faced in many other similar parts of the world, and where impacts have global-scale consequences. This region has been the geographic focus of a large amount of previous research on changing climatic, cryospheric, and hydrological regimes in recent decades, while current initiatives such as the Changing Cold Regions Network (CCRN) introduced in this review seek to further develop the understanding and diagnosis of this change and hence improve the capacity to predict future change. This paper provides a comprehensive review of the observed changes in various Earth system components and a concise and up-to-date regional picture of some of the temporal trends over the interior of western Canada since the mid- or late 20th century. The focus is on air temperature, precipitation, seasonal snow cover, mountain glaciers, permafrost, freshwater ice cover, and river discharge. Important long-term observational networks and data sets are described, and qualitative linkages among the changing components are highlighted. Increases in air temperature are the most notable changes within the domain, rising on average 2 °C throughout the western interior since 1950. This increase in air temperature is associated with hydrologically important changes to precipitation regimes and unambiguous declines in snow cover depth, persistence, and spatial extent. Consequences of warming air temperatures have caused mountain glaciers to recede at all latitudes, permafrost to thaw at its southern limit, and active layers over permafrost to thicken. Despite these changes, integrated effects on stream flow are complex and often offsetting. Following a review of the current literature, we provide insight from a network of northern research catchments and other sites detailing how climate change confounds hydrological responses at smaller scales, and we recommend several priority research areas that will be a focus of continued work in CCRN. Given the complex interactions and process responses to climate change, it is argued that further conceptual understanding and quantitative diagnosis of the mechanisms of change over a range of scales is required before projections of future change can be made with confidence.

Globally, greenhouse gas budgets are dominated by natural sources, and aquatic ecosystems are a prominent source of methane (CH4) to the atmosphere. Beaver (Castor canadensis and Castor fiber) populations have experienced human-driven change, and CH4 emissions associated with their habitat remain uncertain. This study reports the effect of near extinction and recovery of beavers globally on aquatic CH4 emissions and habitat. Resurgence of native beaver populations and their introduction in other regions accounts for emission of 0.18–0.80 Tg CH4 year–1 (year 2000). This flux is approximately 200 times larger than emissions from the same systems (ponds and flowing waters that became ponds) circa 1900. Beaver population recovery was estimated to have led to the creation of 9500–42 000 km2 of ponded water, and increased riparian interface length of >200 000 km. Continued range expansion and population growth in South America and Europe could further increase CH4 emissions.

Nondestructive estimations of plant community characteristics are essential to vegetation monitoring programs. However, there is no universally accepted method for this purpose in the Arctic, partly because not all programs share the same logistical constraints and monitoring goals. Our aim was to determine the most efficient and effective method for long-term monitoring of alpine tundra vegetation. To achieve this, we established 12 vegetation-monitoring plots on a south-facing slope in the alpine tundra of southern Yukon Territory, Canada. Four observers assessed these plots for vascular plant species abundance employing three methods: visual cover (VC) and subplot frequency (SF) estimation and modified point-intercept (PI) (includes rare species present but not intersected by a pin). SF performed best in terms of time required per plot and sensitivity to variations in species richness. All methods were similarly poor at estimating relative abundance for rare species, but PI and VC were substantially better at high abundances. Differences among methods were larger than among observers. Our results suggest that SF is best when the monitoring focus is on rare species or species richness across extensive areas. However, when the focus is on monitoring changes in relative abundance of common species, VC or PI should be preferred.

During the formation of a frazil-generated ice cover, ice bridging can occur along sections of rivers that are geomorphologically suited (e.g. very sinuous, narrowing channel width, low gradient bed slope) to arrest the flow of ice pans. Bridging may occur at several locations along the river independently from one another with separate ice cover formations occurring simultaneously. Juxtaposition of incoming frazil ice and ice pans extending from the ice bridgings can cause backwater levels to rise upstream and cause flooding. The separate ice covers will eventually merge to completely cover the river sooner than if no additional bridging had occurred. This makes it difficult to predict the time required for a river to be completely ice covered and the locations and timing of related potential flood events. A geospatial model is introduced in this paper that applies a principal component analysis to cluster geomorphological features – such as sinuosity, channel width and channel slope – into typologies. Certain combinations of these typologies reveal predisposition of certain geomorphological features to ice bridging or non-bridging. The Dauphin River in Manitoba is used as a test case for the development of the model.

Geography and associated hydrological, hydroclimate and land-use conditions and their changes determine the states and dynamics of wetlands and their ecosystem services. The influences of these controls are not limited to just the local scale of each individual wetland but extend over larger landscape areas that integrate multiple wetlands and their total hydrological catchment – the wetlandscape. However, the data and knowledge of conditions and changes over entire wetlandscapes are still scarce, limiting the capacity to accurately understand and manage critical wetland ecosystems and their services under global change. We present a new Wetlandscape Change Information Database (WetCID), consisting of geographic, hydrological, hydroclimate and land-use information and data for 27 wetlandscapes around the world. This combines survey-based local information with geographic shapefiles and gridded datasets of large-scale hydroclimate and land-use conditions and their changes over whole wetlandscapes. Temporally, WetCID contains 30-year time series of data for mean monthly precipitation and temperature and annual land-use conditions. The survey-based site information includes local knowledge on the wetlands, hydrology, hydroclimate and land uses within each wetlandscape and on the availability and accessibility of associated local data. This novel database (available through PANGAEA https://doi.org/10.1594/PANGAEA.907398; Ghajarnia et al., 2019) can support site assessments; cross-regional comparisons; and scenario analyses of the roles and impacts of land use, hydroclimatic and wetland conditions, and changes in whole-wetlandscape functions and ecosystem services.

Typical machine learning regression applications aim to report the mean or the median of the predictive probability distribution, via training with a squared or an absolute error scoring function. The importance of issuing predictions of more functionals of the predictive probability distribution (quantiles and expectiles) has been recognized as a means to quantify the uncertainty of the prediction. In deep learning (DL) applications, that is possible through quantile and expectile regression neural networks (QRNN and ERNN respectively). Here we introduce deep Huber quantile regression networks (DHQRN) that nest QRNNs and ERNNs as edge cases. DHQRN can predict Huber quantiles, which are more general functionals in the sense that they nest quantiles and expectiles as limiting cases. The main idea is to train a deep learning algorithm with the Huber quantile regression function, which is consistent for the Huber quantile functional. As a proof of concept, DHQRN are applied to predict house prices in Australia. In this context, predictive performances of three DL architectures are discussed along with evidential interpretation of results from an economic case study.