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The examination of primary risk assessment methodologies reveals a significant expansion in recent years, particularly toward encompassing ecosystem preservation and predictive models for environmental contaminant behavior. However, alongside this progress, new challenges have surfaced, such as engineered nanoparticles, cumulative impacts, and the risks associated with emerging contaminants of concern. This research aims to uncover fresh perspectives within the realm of global environmental risk assessment concerning the stress on water resources. Based on the results, the directions for studying water pollution’s environmental risks are highlighted. Special attention is given to water multi-stressor challenges with significant impact and therefore to multi-risk assessment of aquatic ecosystem components and human health. The foundational framework for the primary phases of risk assessment was delineated, taking into account the existing body of prior research. Drawing from the current state of knowledge, the notion of evaluating cumulative ecological risks (termed multi-risk) stemming from pollutant exposure, encompassing emerging contaminants among other factors, is introduced. This encompasses the phases of contaminant migration, transformation, and accumulation within the various components of the hydrosphere, specifically in surface water bodies, groundwater, and their eventual discharge into the sea and ocean, within a unified global water system. Furthermore, alternative approaches for incorporating additional factors, such as climate change, into the overarching risk assessment framework have been pinpointed, offering novel perspectives for future research endeavors in this domain.

Denitrification is the main process removing nitrate in river drainage basins and buffer input from agricultural land and limits aquatic ecosystem pollution. However, the identification of denitrification hotspots (for example, riparian zones), their role in a landscape context and the evolution oftheir overall removal capacity at the drainage basin scale are still challenging. The main approaches used (that is, mass balance method, denitrification proxies, and potential wetted areas) suffer from methodological drawbacks. We review these approaches and the key frameworks that have been proposed to date to formalize the understanding of the mechanisms driving denitrification: (i) Diffusion versus advection pathways of nitrate transfer, (ii) the biogeochemical hotspot, and (iii) the Damköhler ratio. Based on these frameworks, we propose to use high-resolution mapping of catchment topography and landscape pattern to define both potential denitrification sites and the dynamic hydrologic modeling at a similar spatial scale (<10 km2). It would allow the quantification of cumulative denitrification activity at the small catchment scale, using spatially distributed Damköhler and Peclet numbers and biogeochemical proxies. Integration of existing frameworks with new tools and methods offers the potential for significant breakthroughs in the quantification and modeling of denitrification in small drainage basins. This can provide a basis for improved protection and restoration of surface water and groundwater quality.

Ensuring sustainable management and an adequate supply of freshwater resources is a growing challenge around the world. Even in historically water abundant regions climate change together with population growth and economic development are processes that are expected to contribute to an increase in permanent and seasonal water scarcity in the coming decades. Previous studies have shown how policies to address water scarcity often fail to deliver lasting improvements because they do not account for how these processes influence, and are influenced by, human-water interactions shaping water supply and demand. Despite significant progress in recent years, place-specific understanding of the mechanisms behind human-water feedbacks remain limited, particularly in historically water abundant regions. To this end, we here present a Swedish case study where we, by use of a qualitative system dynamics approach, explore how human-water interactions have contributed to seasonal water scarcity at the local-to-regional scale. Our results suggest that the current approach to address water scarcity by inter-basin water transports contributes to increasing demand by creating a gap between the perceived and actual state of water resources among consumers. This has resulted in escalating water use and put the region in a state of systemic lock-in where demand-regulating policies are mitigated by increases in water use enabled by water transports. We discuss a combination of information and economic policy instruments to combat water scarcity, and we propose the use of quantitative simulation methods to further assess these strategies in future studies.

Climate change impacts on water availability and hydrological extremes are major concerns as regards the Sustainable Development Goals. Impacts on hydrology are normally investigated as part of a modelling chain, in which climate projections from multiple climate models are used as inputs to multiple impact models, under different greenhouse gas emissions scenarios, which result in different amounts of global temperature rise. While the goal is generally to investigate the relevance of changes in climate for the water cycle, water resources or hydrological extremes, it is often the case that variations in other components of the model chain obscure the effect of climate scenario variation. This is particularly important when assessing the impacts of relatively lower magnitudes of global warming, such as those associated with the aspirational goals of the Paris Agreement. In our study, we use ANOVA (analyses of variance) to allocate and quantify the main sources of uncertainty in the hydrological impact modelling chain. In turn we determine the statistical significance of different sources of uncertainty. We achieve this by using a set of five climate models and up to 13 hydrological models, for nine large scale river basins across the globe, under four emissions scenarios. The impact variable we consider in our analysis is daily river discharge. We analyze overall water availability and flow regime, including seasonality, high flows and low flows. Scaling effects are investigated by separately looking at discharge generated by global and regional hydrological models respectively. Finally, we compare our results with other recently published studies. We find that small differences in global temperature rise associated with some emissions scenarios have mostly significant impacts on river discharge-however, climate model related uncertainty is so large that it obscures the sensitivity of the hydrological system.

The growing menace of global warming and restrictions on access to water in each region is a huge threat to global hydrological sustainability. Hence, the perspective at which hydrological studies are currently being carried out across the world to quantify and understand the water cycle modeling requires a further boost. In the past few decades, the theoretical understanding of machine learning (ML) algorithms for solving engineering issues, and the application of this method to practical problems have made very significant progress. In the field of hydrology, ML has been using for a better understanding of hydrological complexities. Then, using ML-based approaches for hydrological simulation have been a popular method for runoff modeling in recent years; it seems necessary to understand the application of ML in runoff modeling fully. Current research seeks to have an overview for rainfall–runoff modeling using ML approaches in recent years, including integrated and ordinary ML techniques (such as ANFIS, ANN, and SVM models). The main hydrological topics in this review study include surface hydrology, streamflow, rainfall–runoff, and flood modeling via ML approaches. Therefore, in this study, the author has critically reviewed the characteristics of machine learning models in runoff simulation, including advantages and disadvantages of three widely used machine learning models.

Human-induced expansion of oxygen-deficient zones can have dramatic impacts on marine systems and its resident biota. One example is the formation of the potent neurotoxic methylmercury (MeHg) that is mediated by microbial methylation of inorganic divalent Hg (HgII) under oxygen-deficient conditions. A negative consequence of the expansion of oxygen-deficient zones could be an increase in MeHg production due to shifts in microbial communities in favor of microorganisms methylating Hg. There is, however, limited knowledge about Hg-methylating microbes, i.e., those carrying hgc genes critical for mediating the process, from marine sediments. Here, we aim to study the presence of hgc genes and transcripts in metagenomes and metatranscriptomes from four surface sediments with contrasting concentrations of oxygen and sulfide in the Baltic Sea. We show that potential Hg methylators differed among sediments depending on redox conditions. Sediments with an oxygenated surface featured hgc-like genes and transcripts predominantly associated with uncultured Desulfobacterota (OalgD group) and Desulfobacterales (including Desulfobacula sp.) while sediments with a hypoxic-anoxic surface included hgc-carrying Verrucomicrobia, unclassified Desulfobacterales, Desulfatiglandales, and uncharacterized microbes. Our data suggest that the expansion of oxygen-deficient zones in marine systems may lead to a compositional change of Hg-methylating microbial groups in the sediments, where Hg methylators whose metabolism and biology have not yet been characterized will be promoted and expand.

Achieving an operational compromise between spatial coverage and temporal resolution in national scale river water quality monitoring is a major challenge for regulatory authorities, particularly where chemical concentrations are hydrologically dependent. The efficacy of flow-weighted composite sampling (FWCS) approaches for total phosphorus (TP) sampling (n = 26–52 analysed samples per year), previously applied in monitoring programmes in Norway, Sweden and Denmark, and which account for low to high flow discharges, was assessed by repeated simulated sampling on high resolution TP data. These data were collected in three research catchments in Ireland over the period 2010–13 covering a base-flow index range of 0.38 to 0.69. Comparisons of load estimates were also made with discrete (set time interval) daily and sub-daily sampling approaches (n = 365 to >1200 analysed samples per year). For all years and all sites a proxy of the Norwegian sampling approach, which is based on re-forecasting discharge for each 2-week deployment, proved most stable (median TP load estimates of 87–98%). Danish and Swedish approaches, using long-term flow records to set a flow constant, were only slightly less effective (median load estimates of 64–102% and 80–96%, respectively). Though TP load estimates over repeated iterations were more accurate using the discrete approaches, particularly the 24/7 approach (one sample every 7 h in a 24 bottle sampler - median % load estimates of 93–100%), composite load estimates were more stable, due to the integration of multiple small samples (n = 100–588) over a deployment.

Restoration of wetlands is a high priority world-wide. Peat extraction areas can be restored by rewetting, however affecting the environment. It could be expected to turn the drained peat-cutover area from a source to a sink of most elements. This study examined effects of such rewetting on peat, hydrology and water chemistry over 15 years at two sites in Sweden; the nutrient-poor Porla peatland and the nutrient-rich Västkärr peatland. Rewetting caused minor changes to peat chemistry, but at the Västkärr site ammonium concentrations increased in superficial peat layers while nitrate decreased. In terms of hydrology, rewetting of the Porla site decreased annual runoff and both high and low discharges. Water pH at the Porla site stayed fairly stable, but at the Västkärr site pH, after an initial 4 years dip, gradually increased to higher values than before rewetting. Water colour and organic matter content were fairly stable, but slightly lower values were found after 15 years than in initial 4–5 years. The concentrations of base cations and of inorganic N were lower after rewetting, while total P was higher. However, these impacts could change from an initial phase as the wetlands in the long-term perspective develop into mires.

Groundwater accounting is becoming increasingly important for sustainable societal development. To gain insights into the long-term spatiotemporal changes from 1992 to 2015 regarding groundwater resources in Kumamoto, southern Japan, we analyzed climatological time series, discharge from the groundwater-fed lake (Ezu Lake), and groundwater levels from 94 wells. To explicitly detect temporal changes in these variables and assess potential drivers of change, we used three different trend analyses: the Mann-Kendall Test (MKT), Seasonal Mann-Kendall Test (SMKT), and Innovative Trend Analysis (ITA). Our results revealed a consistently increasing trend in monthly mean temperature, monthly total precipitation, and maximum hourly rainfall. However, a decreasing trend was detected for the discharge rate from the Ezu Lake from 1992 to 2015. Among the 94 investigated groundwater wells, 64 wells showed an upward trend (p ≤ 0.05), while 23 wells showed a downward trend (p ≤ 0.05) for groundwater level. The observed decline in groundwater level is related to the decline in Ezu Lake discharge. However, the increase for the majority of groundwater wells is related to the increased precipitation and applied artificial groundwater recharge as well as a decrease in the groundwater abstraction rate. The ITA outperformed both SMKT and MKT, particularly in detecting minor variations in hydrological time-series data. The results and approach presented in this study can provide a scientific basis for improve groundwater accounting and water resources management in Kumamoto area and other regions with similar climate and socioeconomic conditions.

Restoration of channelized streams by returning coarse sediment from stream edges to the wetted channel has become a common practice in Sweden. Yet, restoration activities do not always result in the return of desired biota. This study evaluated a restoration project in the Vindel River in northern Sweden in which practitioners further increased channel complexity of previously restored stream reaches by placing very large boulders (> 1 m), trees (> 8 m), and salmonid spawning gravel from adjacent upland areas into the channels. One reach restored with basic methods and another with enhanced methods were selected in each of ten different tributaries to the main channel. Geomorphic and hydraulic complexity was enhanced but the chemical composition of riparian soils and the communities of riparian plants and fish did not exhibit any clear responses to the enhanced restoration measures during the first 5 years compared to reaches restored with basic restoration methods. The variation in the collected data was among streams instead of between types of restored reaches. We conclude that restoration is a disturbance in itself, that immigration potential varies across landscapes, and that biotic recovery processes in boreal river systems are slow. We suggest that enhanced restoration has to apply a catchment-scale approach accounting for connectivity and availability of source populations, and that low-intensity monitoring has to be performed over several decades to evaluate restoration outcomes.