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Recent studies have brought to attention how residual antibiotics from domestic, agricultural and industrial wastes have been found in alarming quantities in the world’s rivers. Evidence is growing that significant concentrations of these substances may lead to the gradual development of drug-resistant bacteria, among many other potential impacts. Still, there is a lack of observational data in the field since these substances are not typically included in routine monitoring programs, especially in developing regions. In this work, we develop a model to estimate the emission of various antibiotics and their subsequent transport in river networks at high spatial resolution and global scale, enabling first-time estimates of the surface-water concentrations of these compounds for virtually any river in the world.The transport in the river system is estimated using the contaminant fate module in the high-resolution, global river routing model HydroROUT. A key component of this research is the integration of three novel datasets in the modeling approach. These datasets include: (1) the average levels of consumption of antibiotics for each country in the world, which are used to estimate the release of the antibiotics in each region; (2) a global database of wastewater treatment plants (WWTPs); which are used to geo-locate point sources of the contaminant discharges into river networks and (3) a global compilation of measured pharmaceutical concentrations in river reaches that is used for model validation.The WWTP global database includes detailed information (mostly from official regional or national sources) on 58,502 individual plants such as their facility and discharge locations, population served, flow rate of wastewater discharge, and level of treatment of processed wastewaters. Being essential to spatially explicit water quality assessments, in cases where this information was not available, auxiliary datasets such as a satellite-derived population grid and a DEM-derived river network were used to estimate missing attributes.The high resolution (500-meters) predictions of the model can be used in a variety of subsequent applications. First, the model can be used to identify specific areas in river networks where high concentrations of contaminants are expected and where field studies should be focused. Secondly, scientists and regulators can use the model to develop screening methods to inform the development of guidelines or regulations designed to minimize the risks associated with the environmental release of pharmaceuticals. Thirdly, governments and operators of wastewater treatment facilities can use the model to set appropriate treatment standards for individual wastewater treatment plants and to ensure that advanced treatment technologies, which are inherently resource-intensive, are deployed only in areas where they are needed. And, finally, wastewater treatment technology providers can use the results to drive the development and deployment of new treatment technologies with potential global markets.

Dams, reservoirs, and other water management infrastructure provide benefits, but can also have negative impacts. Dam construction and removal affects progress toward the UN sustainable development goals at local to global scales. Yet, globally-consistent information on the location and characteristics of these structures are lacking, with information often highly localised, fragmented, or inaccessible. A freely available, curated, consistent, and regularly updated global database of existing dams and other instream infrastructure is needed along with open access tools to support research, decision-making and management needs. Here we introduce the Global Dam Watch (GDW) initiative (www.globaldamwatch.org ) whose objectives are: (a) advancing recent efforts to develop a single, globally consistent dam and instream barrier data product for global-scale analyses (the GDW database); (b) bringing together the increasingly numerous global, regional and local dam and instream barrier datasets in a directory of databases (the GDW directory); (c) building tools for the visualisation of dam and instream barrier data and for analyses in support of policy and decision making (the GDW knowledge-base) and (d) advancing earth observation and geographical information system techniques to map a wider range of instream structures and their properties. Our focus is on all types of anthropogenic instream barriers, though we have started by prioritizing major reservoir dams and run-of-river barriers, for which more information is available. Our goal is to facilitate national-scale, basin-scale and global-scale mapping, analyses and understanding of all instream barriers, their impacts and their role in sustainable development through the provision of publicly accessible information and tools. We invite input and partnerships across sectors to strengthen GDW’s utility and relevance for all, help define database content and knowledge-base tools, and generally expand the reach of GDW as a global hub of impartial academic expertise and policy information regarding dams and other instream barriers.

Non-technical summary There has been a long history of conflicts, studies, and debate over how to both protect rivers and develop them sustainably. With a pause in new developments caused by the global pandemic, anticipated further implementation of the Paris Agreement and high-level global climate and biodiversity meetings in 2021, now is an opportune moment to consider the current trajectory of development and policy options for reconciling dams with freshwater system health. Technical summary We calculate potential loss of free-flowing rivers (FFRs) if proposed hydropower projects are built globally. Over 260,000 km of rivers, including Amazon, Congo, Irrawaddy, and Salween mainstem rivers, would lose free-flowing status if all dams were built. We propose a set of tested and proven solutions to navigate trade-offs associated with river conservation and dam development. These solution pathways are framed within the mitigation hierarchy and include (1) avoidance through either formal river protection or through exploration of alternative development options; (2) minimization of impacts through strategic or system-scale planning or re-regulation of downstream flows; (3) restoration of rivers through dam removal; and (4) mitigation of dam impacts through biodiversity offsets that include restoration and protection of FFRs. A series of examples illustrate how avoiding or reducing impacts on rivers is possible – particularly when implemented at a system scale – and can be achieved while maintaining or expanding benefits for climate resilience, water, food, and energy security. Social media summary Policy solutions and development pathways exist to navigate trade-offs to meet climate resilience, water, food, and energy security goals while safeguarding FFRs.

In this paper, we investigate two deep learning approaches to recovering initial temperature profiles from thermographic images in non-destructive material testing. First, we trained a deep neural network (DNN) in an end-to-end fashion by directly feeding the surface temperature measurements to the DNN. Second, we turned the surface temperature measurements into virtual waves (a recently developed concept in thermography), which we then fed to the DNN. To demonstrate the effectiveness of these methods, we implemented a data generator and created a dataset comprising a total of 100,000 simulated temperature measurement images. With the objective of determining a suitable baseline, we investigated several state-of-the-art model-based reconstruction methods, including Abel transformation, curvelet denoising, and time- and frequency-domain synthetic aperture focusing techniques (SAFT). Additionally, a physical phantom was created to support evaluation on completely unseen real-world data. The results of several experiments suggest that both the end-to-end and the hybrid approach outperformed the baseline in terms of reconstruction accuracy. The end-to-end approach required the least amount of domain knowledge and was the most computationally efficient. The hybrid approach required extensive domain knowledge and was more computationally expensive than the end-to-end approach. However, the virtual waves served as meaningful features that convert the complex task of the end-to-end reconstruction into a less demanding undertaking. This in turn yielded better reconstructions with the same number of training samples compared to the end-to-end approach. Additionally, it allowed more compact network architectures and use of prior knowledge, such as sparsity and non-negativity. The proposed method is suitable for non-destructive testing in 2D where the amplitudes along the objects are considered to be constant (e.g. for metallic wires). To encourage the development of other deep-learning-based reconstruction techniques, we release both the synthetic and the real-world datasets along with the implementation of the deep learning methods to the research community. Code available athttps://git.silicon-austria.com/pub/confine/ThermUNet

We propose a hybrid method for reconstructing thermographic images by combining the recently developed virtual wave concept with deep neural networks. The method can be used to detect defects inside materials in a non-destructive way. We propose two architectures along with a thorough evaluation that shows a substantial improvement compared to state-of-the-art reconstruction procedures. The virtual waves are invariant of the thermal diffusivity property of the material. Consequently, we can use extremely compact architectures that require relatively little training data, and have very fast loss convergence. As a supplement of the paper [1], we provide the MATLAB and Python implementations along with the data set comprising 40,000 simulated temperature measurement images in total, and their corresponding defect locations. Thus, the presented results are completely reproducible.

The planetary boundaries framework has proven useful for many global sustainability contexts, but is challenging to apply to freshwater, which is spatially heterogeneous, part of complex socio-ecological systems and often dominated by local dynamics. To date, the planetary boundary for water has been simplistically defined by as the global rate of blue water consumption, functioning as a proxy for water partitioning in the global hydrological cycle, and considering impacts on rivers’ environmental flow requirements. We suggest the current planetary boundary should be replaced since it does not adequately represent the influence of water in critical Earth System functions such as regional climate and biodiversity. We review the core functions of water in the Earth System and set out a roadmap towards a more robust, holistic, and locally applicable water planetary boundary. We propose defining the boundary using four core functions of water (hydroclimatic regulation, hydroecological regulation, storage, and transport) in conjunction with five water stores (surface water, atmospheric water, soil moisture, groundwater and frozen water). Through the functions, the stores are inextricably interconnected with the atmosphere, land, ocean and biosphere. The roadmap outlined here suggests how to move towards setting six new water planetary sub-boundaries. This ambitious scientific and policy Grand Challenge that could substantially improve our understanding and management of water cycle modifications in the Earth System and provide a complementary approach to existing water management tools.

It is well recognized that there are currently limitations in the spatial and temporal resolution of environmental exposure models due to significant variabilities and uncertainties in model inputs and parameters. Here we present the updated Pangea multi-scale multimedia model based on the more spatially resolved, catchment-based hydrological HydroBASINS dataset covering the entire globe. We apply it to predict spatially-explicit exposure concentrations of linear alkylbenzene sulphonate (LAS) and triclosan (TCS) as two chemicals found in homecare (HC) and personal care (PC) products in river catchments across Asia, and test its potential for identifying/prioritizing catchments with higher exposure concentrations. In addition, we also identify the key parameters in the model framework driving higher concentrations and perform uncertainty analyses by applying Monte Carlo simulations on emissions and other non-spatial model inputs.The updated combination of Pangea with the HydroBASINS hydrological data represents a substantial improvement from the previous model with the gridded hydrological dataset (WWDRII) for modelling substance fate, with higher resolution and improved coverage in regions with lower flows, with the results demonstrating good agreement with monitored concentrations for TCS in both the freshwater (R2 = 0.55) and sediment (R2 = 0.81) compartments. The ranking of water basins by Predicted Environmental Concentrations (PECs) was similar for both TCS and LAS, with highest concentrations (Indus, Huang He, Cauvery, Huai He and Ganges) being one to two orders of magnitude greater than the water basins with lowest predicted PECs (Mekong and Brahmaputra). Emissions per unit volume of each catchment, chemical persistence, and river discharge were deemed to be the most influential factors on the variation of predicted PECs. Focusing on the Huang He (Yellow River) water basin, uncertainty confidence intervals (factor 31 for LAS and 6 for TCS) are much lower than the variability of predicted PECs across the Huang He catchments (factors 90,700 for LAS and 13,500 for TCS). Keywords: Multimedia modelling, LAS, Triclosan, Household chemicals, Ecosystem exposure

The contamination of freshwater systems arises in many river basins due to industrialization and population growth, posing risks to ecosystems and human health. Despite these concerns, the fate and potential impact of many emerging pollutants are currently unknown, especially when the compounds are released into surface waters from populations distributed across large spatial scales. In order to address this shortcoming, a spatially-explicit contaminant fate model was developed as an extension of the global, vector-based river routing model HydroROUT. HydroROUT operates at very high spatial resolution (∼500 m), simulates river and stream chemical transport with in-stream removal, and contains links to a set of lakes and reservoirs, which act as a partial sink during the transport. The chemical fate model was applied to China and includes a consumption and release module based on county-level population demographics, considers point-source contributions from 2547 wastewater treatment plants, and accumulates contributions of rural and urban populations not connected to sewage treatment plants. As a case study, the sources and fates of the estrogens estrone (E1), 17β-estradiol (E2), estriol (E3), as well as the synthetic estrogenic steroid hormone 17α-ethinylestradiol (EE2) were modelled in Chinese surface water bodies. Preliminary validation of the results showed predictions to be within the ranges of concentrations reported in literature, with exception of EE2. The total estrogenic mass in the entire river and lake system amounted to 370 tonnes of estrogens, with about 1.3 tonnes per year discharged to the ocean, neighboring countries or to inland sinks. Under a selected baseline scenario, eco-toxicological risk—i.e., contaminant concentrations in excess of the predicted no effect concentration (PNEC)—is found in 23.6% of all analyzed rivers of China with an average flow > 0.1 m3/s. Out of these, about 4% of rivers showed a high level of risk of 10 times or more above PNEC. Medium-to-large rivers are disproportionally affected; for example, 23.6%, 37.3%, 29.0% and 21.6% of river length are at risk in rivers of 1–10, 10–100, 100–1,000, and 1,000–10,000 m3/s of discharge, respectively, whereas no risk was predicted in the largest rivers (i.e., >10,000 m3/s) of China. Wastewater treatment plants process 22.5% of the total hormone load and thus play an important role in water quality control by reducing the risk in substantial portions of the river network, which would otherwise show elevated risk. Releases from untreated population dominate by far the overall contribution to risk.

The HydroATLAS database provides a standardized compendium of descriptive hydro-environmental information for all watersheds and rivers of the world at high spatial resolution. Version 1.0 of HydroATLAS offers data for 56 variables, partitioned into 281 individual attributes and organized in six categories: hydrology; physiography; climate; land cover & use; soils & geology; and anthropogenic influences. HydroATLAS derives the hydro-environmental characteristics by aggregating and reformatting original data from well-established global digital maps, and by accumulating them along the drainage network from headwaters to ocean outlets. The attributes are linked to hierarchically nested sub-basins at multiple scales, as well as to individual river reaches, both extracted from the global HydroSHEDS database at 15 arc-second (~500 m) resolution. The sub-basin and river reach information is offered in two companion datasets: BasinATLAS and RiverATLAS. The standardized format of HydroATLAS ensures easy applicability while the inherent topological information supports basic network functionality such as identifying up- and downstream connections. HydroATLAS is fully compatible with other products of the overarching HydroSHEDS project enabling versatile hydro-ecological assessments for a broad user community., Measurement(s)hydro-environmental characteristics • watershed • drainage basin • river • stream • hydrographic featureTechnology Type(s)digital curationSample Characteristic - Environmentfreshwater environment • aquatic environmentSample Characteristic - LocationEarth (planet) Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.10059551

Singing voice detection, also referred to as vocal detection (VD), aims at automatically identifying the regions in a music recording where at least one person sings. It is highly challenging due to the timbral and expressive richness of the human singing voice, as well as the practically endless variety of interfering instrumental accompaniment. Additionally, certain instruments have an inherent risk of being misclassified as vocals due to similarities of the sound production system. In this paper, we present a machine learning approach that is based on our previous work for VD, which is specifically designed to deal with those challenging conditions. The contribution of this paper is threefold: First, we present a new method for VD that passes a compact set of features to a long short-term memory recurrent neural network classifier that obtains state-of-the-art results. Second, we thoroughly evaluate the proposed method along with related approaches to really probe the weaknesses of the methods. In order to allow for such a thorough evaluation, we make a curated collection of datasets available to the research community. Finally, we focus on a specific problem that was not obvious and had not been discussed in the literature so far. The reason for this is precisely because limited evaluations had not revealed this as a problem: the lack of loudness invariance. We will discuss the implications of utilizing loudness-related features and show that our method successfully deals with this problem due to the specific set of features it uses.

The parameter uncertainty in the eco-hydrological model Soil and Water Assessment Tool (SWAT) was estimated using non-unique parameter sets for the Altmuhl watershed (Bavaria, Germany). The Sequential Uncertainty Fitting Algorithm (SUFI-2) was used to calibrate SWAT. The non-unique parameter sets found were subsequently applied to SWAT concurrently with climate change simulations to determine the variables of streamflow, nitrate nitrogen (NO3 −-N) and total phosphorus (TP). A suite of seven bias corrected climate change simulations provided reference (1970–2000) and future (2041–2070) climate data. The non-unique behavioural parameter sets that met an objective function of NSE >0.6 during calibration were applied to SWAT with the reference climate and with the future climate simulations. The best parameter set was also propagated through SWAT with each reference and future climate simulation in turn. Combining the non-unique behavioural parameter sets for estimating uncertainty bounds with an ensemble of climate change simulations led to a wider mean monthly spread (difference between maximum and minimum) of simulated NO3 −-N and TP than using the best run with the future climate simulations. More monthly data was considered using the non-unique approach, resulting in statistical significances for more months of the year and overall lower interquartile ranges. The study quantifies the non-unique behavioural parameter set contributions to the modelling prediction, which assists in making more informed decisions based on available knowledge, with its limitations, of the future simulations. We outline a simple approach that can easily be replicated for similar hydrological modelling studies.

The fragmentation of free-flowing rivers (FFRs) through major infrastructure development remains a pivotal threat to global freshwater biodiversity. More than 3400 large hydropower dams (>1 MW) are either planned or under construction, posing threats to freshwater megafauna (i.e., freshwater animals that can reach 30 kg). Here, we investigate the global patterns of river connectivity within distribution ranges of freshwater megafauna and explore how these patterns could change in the future. We found that the susceptibility of freshwater megafauna to reduced river connectivity in different dimensions depends on their life history. Freshwater megafauna taxa with a short accumulated length or low percentage of FFRs in their distribution ranges are more likely to be threatened. On average, there is higher freshwater megafauna richness in FFRs with proposed dams than in rivers that would remain free-flowing in the future or that are already fragmented. If all the proposed dams were built, 94 (18.9%) out of 497 remaining FFRs >500 km that provide habitat for freshwater megafauna would lose their free-flowing status. Our results highlight the reduced connectivity of rivers currently inhabited by freshwater megafauna and the future intensification of river fragmentation in megafauna-rich basins. Research and conservation efforts, including life-history studies, identification of critical habitats, updates of IUCN Red List assessments, advanced communication of the potential impacts on ecosystem services and livelihoods of affected communities, and strategic siting, design, and operations of proposed dams are urgently needed to safeguard freshwater megafauna and overall freshwater biodiversity.

Global wetlands and floodplains offer benefits and perils alike for human society. For example, humans rely on natural flood cycles for fisheries and agriculture, yet flooding also caused nearly one trillion USD in damage in the past 30 years and impacts millions of people every year. Looking forward, altered flow regimes or increased drought conditions are expected to affect the natural inundation cycle and its ecosystem services. The current and potential future impacts of flooding and drying events warrant increasing efforts to quantify our dependence and exposure within flooded areas, since any change from current inundation patterns is expected to have consequences for those who rely on regular flood occurrences. This paper provides a baseline global assessment of the dependence and exposure of human populations, urban areas, roads, and agriculture on current inundation patterns. The analysis uses a spatially explicit inundation map at ∼500 m resolution (GIEMS-D15) derived from satellite remote sensing to represent flooding extents and overlays it with current population and land use maps. We find that 35% of the analyzed population, or 2.0 billion people, live inside areas that are prone to inland flooding, which comprise only 12% of the land surface area (excluding marine coastal areas), confirming that population densities within inundation zones are about three-times above global average. Likewise, 35% of urban areas potentially experience regular, seasonal, or infrequent flooding. Agriculture shows a similar pattern with 24% of the world’s cropland in areas of recurring inundation. Finally, we estimate that 18% of the global road network is exposed to inundation during high water periods. These global estimates demonstrate a preferential tendency of human populations, infrastructure, and agriculture to be co-located within inundation areas, making related anthropogenic activities highly susceptible to future changes in flood regimes. The results are intended to offer a suite of first-order estimates as partial input to more holistic risk and vulnerability assessments and to ultimately improve environmental planning and policy at large scales.

An atlas of European river barriers has been made, by curating and correcting existing records, and by surveying 2,700 kilometres of waterways. It reveals that rivers are fragmented by an amazing number of obstructions. At atlas of European river barriers.

Wetlands have been extensively modified by human activities worldwide. We provide a global-scale portrait of the threats and protection status of the world's inland wetlands by combining a global map of inundation extent derived from satellite images with data on threats from human influence and on protected areas. Currently, seasonal inland wetlands represent approximately 6% of the world's land surface, and about 89% of these are unprotected (as defined by protected areas IUCN I–VI and Ramsar sites). Wetland protection ranges from 20% in Central and 18% in South America to only 8% in Asia. Particularly high human influence was found in Asia, which contains the largest wetland area of the world. High human influence on wetlands even within protected areas underscores the urgent need for more effective conservation measures. The information provided here is important for wetland conservation planning and reveals that the current paradigm of wetland protection may be inadequate.

Free-flowing rivers (FFRs) support diverse, complex and dynamic ecosystems globally, providing important societal and economic services. Infrastructure development threatens the ecosystem processes, biodiversity and services that these rivers support. Here we assess the connectivity status of 12 million kilometres of rivers globally and identify those that remain free-flowing in their entire length. Only 37 per cent of rivers longer than 1,000 kilometres remain free-flowing over their entire length and 23 per cent flow uninterrupted to the ocean. Very long FFRs are largely restricted to remote regions of the Arctic and of the Amazon and Congo basins. In densely populated areas only few very long rivers remain free-flowing, such as the Irrawaddy and Salween. Dams and reservoirs and their up- and downstream propagation of fragmentation and flow regulation are the leading contributors to the loss of river connectivity. By applying a new method to quantify riverine connectivity and map FFRs, we provide a foundation for concerted global and national strategies to maintain or restore them. A comprehensive assessment of the world’s rivers and their connectivity shows that only 37 per cent of rivers longer than 1,000 kilometres remain free-flowing over their entire length.

Groundwater, the largest available store of global freshwater1, is relied upon by more than two billion people2. It is therefore important to quantify the spatiotemporal interactions between groundwater and climate. However, current understanding of the global-scale sensitivity of groundwater systems to climate change3,4—as well as the resulting variation in feedbacks from groundwater to the climate system5,6—is limited. Here, using groundwater model results in combination with hydrologic data sets, we examine the dynamic timescales of groundwater system responses to climate change. We show that nearly half of global groundwater fluxes could equilibrate with recharge variations due to climate change on human (~100 year) timescales, and that areas where water tables are most sensitive to changes in recharge are also those that have the longest groundwater response times. In particular, groundwater fluxes in arid regions are shown to be less responsive to climate variability than in humid regions. Adaptation strategies must therefore account for the hydraulic memory of groundwater systems, which can buffer climate change impacts on water resources in many regions, but may also lead to a long, but initially hidden, legacy of anthropogenic and climatic impacts on river flows and groundwater-dependent ecosystems.

Natural lakes and reservoirs are important, yet not well constrained sources of greenhouse gasses to the atmosphere. In particular for N2O emissions, a huge variability is observed in the few, observation‐driven flux estimates that have been published so far. Recently, a process‐based, spatially explicit model has been used to estimate global N2O emissions from more than 6,000 reservoirs based on nitrogen (N) and phosphorous inflows and water residence time. Here, we extend the model to a dataset of 1.4 million standing water bodies comprising natural lakes and reservoirs. For validation, we normalized the simulated N2O emissions by the surface area of each water body and compared them against regional averages of N2O emission rates taken from the literature or estimated based on observed N2O concentrations. We estimate that natural lakes and reservoirs together emit 4.5±2.9 Gmol N2O‐N yr‐1 globally. Our global scale estimate falls in the far lower end of existing, observation‐driven estimates. Natural lakes contribute only about half of this flux, although they contribute 91% of the total surface area of standing water bodies. Hence, the mean N2O emission rates per surface area are substantially lower for natural lakes than for reservoirs with 0.8±0.5 mmol N m‐2yr‐1 vs. 9.6±6.0 mmol N m‐2yr‐1, respectively. This finding can be explained by on average lower external N inputs to natural lakes. We conclude that upscaling based estimates, which do not distinguish natural lakes from reservoirs, are prone to important biases. Funding agencies: European UnionEuropean Union (EU) [776810]; Swedish Research Council VRSwedish Research Council; Swedish Research Council FORMASSwedish Research CouncilSwedish Research Council Formas; European Research Council (ERC Grant)European Research Council (ERC) Accepted version on author's personal web page https://liu.se/dfsmedia/dd35e243dfb7406993c1815aaf88a675/35054-source/options/download/natural-lakes

Inland waters, including streams and rivers, are active components of the global carbon cycle. Despite the large areal extent of the world’s mountains, the role of mountain streams for global carbon fluxes remains elusive. Using recent insights from gas exchange in turbulent streams, we found that areal CO2 evasion fluxes from mountain streams equal or exceed those reported from tropical and boreal streams, typically regarded as hotspots of aquatic carbon fluxes. At the regional scale of the Swiss Alps, we present evidence that emitted CO2 derives from lithogenic and biogenic sources within the catchment and delivered by the groundwater to the streams. At a global scale, we estimate the CO2 evasion from mountain streams to 167 ± 1.5 Tg C yr−1, which is high given their relatively low areal contribution to the global stream and river networks. Our findings shed new light on mountain streams for global carbon fluxes., SCOPUS: ar.j, info:eu-repo/semantics/published

Effectively conserving ecosystem services in order to maintain human wellbeing is a global need that requires an understanding of where ecosystem services are produced by ecosystems and where people benefit from these services. However, approaches to effectively identify key locations that have the capacity to supply ecosystem services and actually contribute to meeting human demand for those services are lacking at broad spatial scales. We developed new methods that integrate measures of the capacity of ecosystems to provide services with indicators of human demand and ability to access these services. We then identified important areas for three ecosystem services currently central to protected area management in Canada—carbon storage, freshwater, and nature-based recreation—and evaluated how these hotspots align with Canada’s current protected areas and resource development tenures. We find that locations of ecosystem service capacity overlap only weakly (27–36%) with actual service providing areas (incorporating human access and demand). Overlapping hotspots of provision for multiple ecosystem services are also extremely limited across Canada; only 1.2% (∼56 000 km2) of the total ecosystem service hotspot area in Canada consists of overlap between all three ecosystem services. Canada’s current protected area network also targets service capacity to a greater degree than provision. Finally, one-half to two-thirds of current ecosystem service hotspots (54–66%) overlap with current and planned resource extraction activities. Our analysis demonstrates how to identify areas where conservation and ecosystem service management actions should be focused to more effectively target ecosystem services to ensure that critical areas for ecosystem services that directly benefit people are conserved. Further development of these methods at national scales to assess ecosystem service capacity and demand and integrate this with conventional biodiversity and conservation planning information will help ensure that both biodiversity and ecosystem services are effectively safeguarded.

Summary The hydrological model SWAT was applied to the upper Altmühl watershed to examine the simulated 10th and 90th percentiles of streamflow, nitrate nitrogen (NO3 --N), and total phosphorus loads (TP), using an ensemble of reference climate (1975–2000) and future climate (2046–2070) simulations. A comparison between the two periods showed that in the future, the 90th percentiles of the NO3 --N loads increase in all seasons which indicates a trend to increasing diffuse pollution in the mid-term future. Conversely, the 90th percentile TP loads diminished in winter and otherwise remained similar to the reference period. The 10th and 90th percentile changes in the future streamflow followed changes in precipitation, and did not have any apparent influence on extreme nutrient transport events.

The impacts of climate and land use changes that may affect the quality of surface water in the future were quantified to identify the relative importance of each change. Sustainable field management practices were examined for their effectiveness to improve water quality in the future under the combined changes, to integrate the strategies into water management plans. In this modelling study, scenarios of climate change and land use change were therefore applied alone, and in tandem, in the hydrological model SWAT (Soil and Water Assessment Tool) to simulate the impacts on streamflow, sediments, total phosphorus (TP) and nitrate-nitrogen (NO 3 − -N) for a 2050 time horizon in the 629 km 2 Pike River watershed located in southern Quebec/northern Vermont. The combined climate and land use change scenarios simulated TP concentrations to be higher during January and February, but to be lower during April, compared to the reference simulation (1971–2000). Yet, the TP water quality criterion of 0.02 mg/L was always surpassed. On the other hand, the NO 3 – N concentrations were lower than the reference simulation during 8 months out of the year, only rarely exceeding the water quality criterion of 10 mg/L. When field management strategies were simulated to reduce the impacts, the mean annual sediments and TP loads were reduced particularly in winter. Despite the load reductions, the 0.02 mg TP/L criterion remained exceeded in each month (similar to values in the reference simulation). The future NO 3 − -N concentrations remained below 10 mg/L. This study demonstrates that both climate and land use change should be considered together to account for synergistic impacts to water quality, and that sustainable management strategies are effective adaptations to safeguard water quality from future changes that may occur in a watershed.

In this paper, we investigate two deep learning approaches to recovering initial temperature profiles from thermographic images in non-destructive material testing. First, we trained a deep neural network (DNN) in an end-to-end fashion by directly feeding the surface temperature measurements to the DNN. Second, we turned the surface temperature measurements into virtual waves (a recently developed concept in thermography), which we then fed to the DNN. To demonstrate the effectiveness of these methods, we implemented a data generator and created a dataset comprising a total of 100 000 simulated temperature measurement images. With the objective of determining a suitable baseline, we investigated several state-of-the-art model-based reconstruction methods, including Abel transformation, curvelet denoising, and time- and frequency-domain synthetic aperture focusing techniques. Additionally, a physical phantom was created to support evaluation on completely unseen real-world data. The results of several experiments suggest that both the end-to-end and the hybrid approach outperformed the baseline in terms of reconstruction accuracy. The end-to-end approach required the least amount of domain knowledge and was the most computationally efficient one. The hybrid approach required extensive domain knowledge and was more computationally expensive than the end-to-end approach. However, the virtual waves served as meaningful features that convert the complex task of the end-to-end reconstruction into a less demanding undertaking. This in turn yielded better reconstructions with the same number of training samples compared to the end-to-end approach. Additionally, it allowed more compact network architectures and use of prior knowledge, such as sparsity and non-negativity. The proposed method is suitable for non-destructive testing (NDT) in 2D where the amplitudes along the objects are considered to be constant (e.g., for metallic wires). To encourage the development of other deep-learning-based reconstruction techniques, we release both the synthetic and the real-world datasets along with the implementation of the deep learning methods to the research community.

To develop meaningful land use scenarios, drivers that affect changes in the landscape are required. In this study, driving factors that influence farmers to change crops on their farm were determined. A questionnaire was administered to four independent groups of farmers who identified and ranked influencing factors pertaining to their choices of crops. The farmers were located in two mid-latitude agricultural watersheds (in Germany and Canada). The ranked influencing factors were used to develop a "farmer driven" scenario to 2040 in both watersheds. Results showed that the most important influencing factors for farmers to change crops were the "economic return of the crop" and "market factors". Yet, when the drivers of crop land use change were grouped into two categories of "financial" and "indirectly-related financial" factors, the "financial" factors made up approximately half of the influencing factors. For some responses, the "indirectly-related financial" factors (i.e. "access to farm equipment", the "farm experience", and "climate") ranked higher than or just as high as the financial factors. Overall, in the four farmer groups the differences between the rankings of the influencing factors were minor, indicating that drivers may be transferable between farms if the farmers are full-time and the farming regions have comparable growing seasons, access to markets, similar technology, and government programs for farm income. In addition to the "farmer driven" scenario, a "policy driven" scenario was derived for each watershed based only on available information on the financial incentives provided to farmers (i.e. agricultural subsidies, income support, crop insurance). The influencing factors ranked by the farmers provided in-depth information that was not captured by the "policy driven" scenario and contributed to improving predictions for crop land use development. This straight-forward method to rank qualitative data provided by farmers can easily be replicated in other watersheds to improve environmental impact modelling.

In Acoustic Scene Classification (ASC) two major approaches have been followed . While one utilizes engineered features such as mel-frequency-cepstral-coefficients (MFCCs), the other uses learned features that are the outcome of an optimization algorithm. I-vectors are the result of a modeling technique that usually takes engineered features as input. It has been shown that standard MFCCs extracted from monaural audio signals lead to i-vectors that exhibit poor performance, especially on indoor acoustic scenes. At the same time, Convolutional Neural Networks (CNNs) are well known for their ability to learn features by optimizing their filters. They have been applied on ASC and have shown promising results. In this paper, we first propose a novel multi-channel i-vector extraction and scoring scheme for ASC, improving their performance on indoor and outdoor scenes. Second, we propose a CNN architecture that achieves promising ASC results. Further, we show that i-vectors and CNNs capture complementary information from acoustic scenes. Finally, we propose a hybrid system for ASC using multi-channel i-vectors and CNNs by utilizing a score fusion technique. Using our method, we participated in the ASC task of the DCASE-2016 challenge. Our hybrid approach achieved 1 st rank among 49 submissions, substantially improving the previous state of the art.

Modelling land surface water flow is of critical importance for simulating land surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL (TOPography-based hydrological MODEL), and the most important parameter of this model is the well-known topographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically conditioned HydroSHEDS (Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales) data using the GA2 algorithm (GRIDATB 2). At 15 arcsec resolution, these layers are 4 times finer than the resolution of the previously best-available topographic index layers, the compound topographic index of HYDRO1k (CTI). For the largest river catchments occurring on each continent we found that, in comparison with CTI our revised values were up to 20% lower in, e.g. the Amazon. We found the highest catchment means were for the Murray–Darling and Nelson–Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. For the majority of large catchments, however, the spread of our new GA2 index values is very similar to those of CTI, yet with more spatial variability apparent at fine scale. We believe these new index layers represent greatly improved global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.

Large hydropower schemes have recently gained renewed interest as a provider of efficient and renewable energy, particularly in developing countries. However, some dams may have widespread effects on hydrological and ecosystem integrity, which reach beyond the scales addressed by typical environmental impact assessments. In this paper we address two main ecological impacts—reduced river connectivity and changes in the natural flow regime—at the scale of the entire Mekong River Basin as an important component of dam evaluations. The goal is to improve our understanding of the effect of individual dams as well as clusters of dams at a very large scale. We introduce two new indices, the River Connectivity Index (RCI) as a tool to measure network connectivity, and the River Regulation Index (RRI) as a measure of flow alteration, and calculate the individual and cumulative impact of 81 proposed dams using HydroROUT, a graph-theory based river routing model. Furthermore, we demonstrate how quantitative weighting, e.g. based on river habitat characterizations or species distribution models, may be included in dam impact assessments. A global comparison of large rivers shows that the Mekong would experience strong deterioration in the fragmentation and flow regulation indices if all dams that are currently under consideration in the basin were built, placing it among other heavily impounded rivers in the world. The results illustrate the importance of considering the location of dams, both relative in the network and relative to other already existing dams. Our approach may be used as an index-based ranking system for individual dams, or to compare basin-wide development scenarios, with the goal of providing guidance for decision makers wishing to select locations for future dams with less environmental impacts and to identify and develop potential mitigation strategies.

A B S T R A C T Urban growth is increasing the demand for freshwater resources, yet surprisingly the water sources of the world’s large cities have never been globally assessed, hampering efforts to assess the distribution and causes of urban water stress. We conducted the first global survey of the large cities’ water sources, and show that previous global hydrologic models that ignored urban water infrastructure significantly overestimated urban water stress. Large cities obtain 78 � 3% of their water from surface sources, some of which are far away: cumulatively, large cities moved 504 billion liters a day (184 km 3 yr � 1 ) a distance of 27,000 � 3800 km, and the upstream contributing area of urban water sources is 41% of the global land surface. Despite this infrastructure, one in four cities, containing $4.8 � 0.7 trillion in economic activity, remain water stressed due to geographical and financial limitations. The strategic management of these cities’ water sources is therefore important for the future of the global economy.

Projected climate and environmental change are expected to increase the pressure on global freshwater resources. To prepare for and cope with the related risks, stakeholders need to devise plans for sustainable management of river systems, which in turn requires the identification of management-appropriate operational units, such as groups of rivers that share similar environmental and biological characteristics. Ideally, these units are of a manageable size, and are biotically or abiotically distinguishable across a variety of river types. Here, we aim to address this need by presenting a new global river classification framework (GloRiC) to establish a common vocabulary and standardized approach to the development of globally comprehensive and integrated river classifications that can be tailored to different goals and requirements. We define the GloRiC conceptual framework based on five categories of variables: (1) hydrology; (2) physiography and climate; (3) fluvial geomorphology; (4) water chemistry; and (5) aquatic biology. We then apply the framework using hydro-environmental attributes provided by a seamless high-resolution river reach database to create initial instances of three sub-classifications (hydrologic, physio-climatic, and geomorphic) which we ultimately combine into 127 river reach types at the global scale. These supervised classifications utilize a mix of statistical analyses and expert interpretation to identify the classifier variables, the number of classes, and their thresholds. In addition, we also present an unsupervised, multivariable k-means statistical clustering of all river reaches into 30 groups. These first-of-their-kind global river reach classifications at high spatial resolution provide baseline information for a total of 35.9 million kilometers of rivers that have been assessed in this study, and are expected to be particularly useful in remote or data-poor river basins. The GloRiC framework and associated data are primarily designed for broad and rapid applicability in assessments that require stratified analyses of river ecosystem conditions at global and regional scales; smaller-scale applications could follow the same conceptual framework yet use more detailed data sources.

Governments, development banks, corporations, and nonprofits are increasingly considering the potential contribution of watershed conservation activities to secure clean water for cities and to reduce flood risk. These organizations, however, often lack decision-relevant, initial screening information across multiple cities to identify which specific city-watershed combinations present not only water-related risks but also potentially attractive opportunities for mitigation via natural infrastructure approaches. To address this need, this paper presents a novel methodology for a continental assessment of the potential for watershed conservation activities to improve surface drinking water quality and mitigate riverine and stormwater flood risks in 70 major cities across Latin America. We used publicly available geospatial data to analyze 887 associated watersheds. Water quality metrics assessed the potential for agricultural practices, afforestation, riparian buffers, and forest conservation to mitigate sediment and phosphorus loads. Flood reduction metrics analyzed the role of increasing infiltration, restoring riparian wetlands, and reducing connected impervious surface to mitigate riverine and stormwater floods for exposed urban populations. Cities were then categorized based on relative opportunity potential to reduce identified risks through watershed conservation activities. We find high opportunities for watershed activities to mitigate at least one of the risks in 42 cities, potentially benefiting 96 million people or around 60% of the urbanites living in the 70 largest cities in Latin America. We estimate water quality could be improved for 72 million people in 27 cities, riverine flood risk mitigated for 5 million people in 13 cities, and stormwater flooding mitigated for 44 million people in 14 cities. We identified five cities with the potential to simultaneously enhance water quality and mitigate flood risks, and in contrast, six cities where conservation efforts are unlikely to meaningfully mitigate either risk. Institutions investing in natural infrastructure to improve water security in Latin America can maximize their impact by focusing on specific watershed conservation activities either for cleaner drinking water or flood mitigation in cities identified in our analysis where these interventions are most likely to reduce risk.

Despite significant recent advancements, global hydrological models and their input databases still show limited capabilities in supporting many spatially detailed research questions and integrated assessments, such as required in freshwater ecology or applied water resources management. In order to address these challenges, the scientific community needs to create improved large-scale datasets and more flexible data structures that enable the integration of information across and within spatial scales; develop new and advanced models that support the assessment of longitudinal and lateral hydrological connectivity; and provide an accessible modeling environment for researchers, decision makers, and practitioners. As a contribution, we here present a new modeling framework that integrates hydrographic baseline data at a global scale (enhanced HydroSHEDS layers and coupled datasets) with new modeling tools, specifically a river network routing model (HydroROUT) that is currently under development. The resulting ‘hydro-spatial fabric’ is designed to provide an avenue for advanced hydro-ecological applications at large scales in a consistent and highly versatile way. Preliminary results from case studies to assess human impacts on water quality and the effects of dams on river fragmentation and downstream flow regulation illustrate the potential of this combined data-and-modeling framework to conduct novel research in the fields of aquatic ecology, biogeochemistry, geo-statistical modeling, or pollution and health risk assessments. The global scale outcomes are at a previously unachieved spatial resolution of 500 m and can thus support local planning and decision making in many of the world's large river basins. Copyright © 2013 John Wiley & Sons, Ltd.

Orthognathic surgery has always been a classical focus of maxillofacial surgery. Since more than 100 years, various surgical techniques for mandibular repositioning have been developed and clinically tested. Since the establishment of plate and screw osteosynthesis, orthognathic surgery became more stable and safe. Nowadays, different surgical methods for mobilising the mandible are existing. This international multicenter analysis (n = 51 hospitals) is providing first evidence based data for the current use of different surgical methods. The dominating techniques were Obwegeser/dal Pont (61%) followed by Hunsuck/Epker (37%) and Perthes/Schlössmann (29%). The main osteosynthesis materials were plates (82%), bicortical screws (23.5%), or a combination of both (5.9%). 47% of all centers reported to use several surgical methods at the same time, depending on the anatomical problem and the surgeon's preference. This shows that different surgical methods seem to work as comparable, safe, and reliable procedures in everydays clinical practise. On this basis, further prospective studies could evaluate possible advantages for our patients.

Inhabitants of mountainous regions in least developed countries are recognized to be among the most vulnerable to climate change globally. Despite this, human dimensions work is in its infancy in mountain regions where we have limited understanding of who is vulnerable (or adaptable), to what stresses, and why. This study develops a baseline understanding of vulnerability to climate-related hydrological changes in the mountainous Khumbu region of eastern Nepal. Using a vulnerability approach, 80 interviews combining fixed and open-ended questions were conducted in four communities representing the geographic and livelihood variability of the region. The study identifies four region-wide vulnerabilities currently affecting residents: reduced water access for household uses, declining crop yields, reduced water access for meeting the high water demands of tourists, and reduced hydro-electricity generation. These vulnerabilities are widespread among the population but arrange spatially as a function of varying exposure-sensitivity to hydrological change, livelihood opportunities, and access to foreign financial assistance. Our findings indicate that precipitation change (not glacial change) is the greatest biophysical driver of vulnerability.

An analysis of streamflow characteristics (i.e. mean annual and seasonal flows and extreme high and low flows) in current and future climates for 21 watersheds of north-east Canada covering mainly the province of Quebec is presented in this article. For the analysis, streamflows are derived from a 10-member ensemble of Canadian Regional Climate Model (CRCM) simulations, driven by the Canadian Global Climate Model simulations, of which five correspond to current 1970–1999 period, while the other five correspond to future 2041–2070 period. For developing projected changes of streamflow characteristics from current to future periods, two different approaches are used: one based on the concept of ensemble averaging while the other approach is based on merged samples of current and similarly future simulations following multiple comparison tests. Verification of the CRCM simulated streamflow characteristics for the 1970–1999 period suggests that the model simulated mean hydrographs and high flow characteristics compare well with those observed, while the model tends to underestimate low flow extremes. Results of projected changes to mean annual streamflow suggest statistically significant increases nearly all over the study domain, while those for seasonal streamflow show increases/decreases depending on the season. Two- and 5-year return levels of 15-day low flows are projected to increase significantly over most part of the study domain, though the changes are small in absolute terms. Based on the ensemble averaging approach, changes to 10- and 30-year return levels of high flows are not generally found significant. However, when a similar analysis is performed using longer samples, significant increases to high flow return levels are found mainly for northernmost watersheds. This study highlights the need for longer samples, particularly for extreme events in the development of robust projections.

Land use scenarios are of primordial importance when implementing a hydrological model for the purpose of determining the future quality of water in a watershed. This paper provides the background for researching potential agricultural land use changes that may take place in a mesoscale watershed, for water quality research, and describes why studying the farm scale is important. An on-going study in Bavaria examining the local drivers of change in land use is described.

Summary This paper introduces an approach to route discharge with a variable river flow velocity based on the Manning–Strickler formula within large scale hydrological models. The approach has been developed for the global scale hydrological model WaterGAP and model results have been analyzed focusing on Europe. The goal was to find a method that is simple enough to derive the required parameters from globally available data while being sophisticated enough to deliver realistic flow velocity estimates for a large variety of environmental conditions. The river bed roughness (Manning’s n ) is approximated in a spatially explicit way based on topography, the location of urban population, and river sinuosity. The hydraulic radius is estimated from actual river discharge, and river bed slope is derived by combining a high resolution DEM, a 5 arc min drainage direction map, and river sinuosity. The modeled river flow velocity has been validated against data of US gauging stations. The representation of lateral transport has clearly been improved compared to the constant flow velocity applied in older model versions. The effect of incorporating variable flow velocities as compared to a constant flow velocity is largest on flood discharge, which generally increases in large rivers. The impact on monthly discharge hydrographs is marginal only. WaterGAP has been driven by three climate change projections for the 2050s to assess climate change impacts on flow velocity, and on the residence time of water in the European river system. Results indicate a decrease in residence times for Northern Europe and an increase for parts of the Mediterranean.

Despite the recognized importance of reservoirs and dams, global datasets describing their characteristics and geographical distribution are largely incomplete. To enable advanced assessments of the role and effects of dams within the global river network and to support strategies for mitigating ecohydrological and socioeconomic costs, we introduce here the spatially explicit and hydrologically linked Global Reservoir and Dam database (GRanD). As of early 2011, GRanD contains information regarding 6862 dams and their associated reservoirs, with a total storage capacity of 6197 km3. On the basis of these records, we estimate that about 16.7 million reservoirs larger than 0.01 ha – with a combined storage capacity of approximately 8070 km3 – may exist worldwide, increasing Earth's terrestrial surface water area by more than 305 000 km2. We find that 575 900 river kilometers, or 7.6% of the world's rivers with average flows above 1 cubic meter per second (m3 s−1), are affected by a cumulative upstream reservoir capacity that exceeds 2% of their annual flow; the impact is highest for large rivers with average flows above 1000 m3 s−1, of which 46.7% are affected. Finally, a sensitivity analysis suggests that smaller reservoirs have substantial impacts on the spatial extent of flow alterations despite their minor role in total reservoir capacity. Read More: http://www.esajournals.org/doi/abs/10.1890/100125

Anthropogenic climate change and increased human water use are widely expected to place great stress on available water resources across Africa and Madagascar, but the effects of these changes on freshwater biodiversity have only begun to be considered. We used a comprehensive species database and a global hydrologic model to examine the exposure of freshwater biodiversity to discharge and runoff alterations across Africa and Madagascar. Our results show that by the 2050s, ecoregions containing over 80% of freshwater fish species and several outstanding ecological and evolutionary phenomena are likely to experience hydrologic conditions substantially different from the present, with alterations in annual discharge or runoff of more than 10%. We recommend early action to buffer these species and systems from the expected changes, including reducing nonclimate stresses and implementing measures that buy species time to adjust to novel ecosystem characteristics.

The complex floodplains of large rivers offer a striking example of how geomorphology, by dictating patterns in the frequency and duration of soil saturation and surface flooding, influences ecosystem structure and function. This study draws on multiple sources of data from remote sensing, together with ground observations and water sampling, to distinguish floodplain ecosystems in the Madre de Dios River, a tributary of the Amazon River in Peru. This remote tropical river meanders across sub-Andean alluvial deposits in a tectonically active region and creates floodplain surfaces of varying ages, including terraces that are above the reach of present-day river floods. Data from Landsat ETM+ (optical multispectral), JERS-1 (L-band radar), and the Shuttle Radar Topography Mission (C-band interferometric Digital Elevation Models) were integrated in an object-oriented image analysis approach to distinguish five classes of floodplain vegetation. Vegetation classes generally correspond with successional age and reflect the activity of the riverine meander belt. Stage data for the river show erratic fluctuations and an annual range exceeding 8 m, but the maximum depth of floodplain inundation varied from > 1 m close to the river to approximately 0.1 m on more elevated terraces. The major ion composition of standing waters on the floodplain during the dry season indicated the importance of emergence of local groundwater in maintaining saturated soils, particularly further from the river, where backswamp vegetation is distinct and includes palm swamps. Thus, a hydrological continuum exists from deep but sporadic river inundation near the river to constant soil saturation by groundwater emergence in distal backswamps, reflecting the geomorphological origin and age of the floodplain deposits. This hydrogeomorphic continuum results in fundamental ecological differences. The exceptionally rich biodiversity of the sub-Andean region may be ascribed in part to the enhanced biodiversity associated with fluvial geomorphological features, and, thus, conservation planning must account for the diverse landforms created by fluvial dynamics.

Using the 160,000-km2 drainage basin of the Madre de Dios and Orthon rivers in the southwest Amazon as a test case, we piloted an approach for large-scale conservation planning for freshwater systems characterized by a near-complete lack of biological and physical data. We used newly available spatial and remote sensing datasets, including spaceborne optical and radar observations, and new techniques of spatial data analysis to generate subbasin (⩾100 km2), stream, and floodplain and wetland habitat classifications. We then generated a preliminary plan for a network of conservation areas to protect the most intact examples of representative habitat types while maximizing longitudinal and lateral connectivity. Proposed additions for freshwater conservation complement existing reserves and build on earlier conservation planning efforts for terrestrial ecosystems. In the resulting integrated plan, at least 20% of the area of each major freshwater habitat type is represented and two continuous corridors exist from the mouth of the Madre de Dios to its headwaters in the Andes. In total, we highlighted 84 currently unprotected subbasins to fulfill our representation and connectivity goals. About two-thirds of these subbasins were considered relatively undisturbed and are identified as Level I (critical management zones) or Level II (indigenous territories), whereas one-third are potentially degraded and thus were designated as Level III (threat mitigation zones). This exercise provides an example of how newly available remote-sensing datasets and analytical tools may be used to advance freshwater conservation planning, particularly in data-poor regions.

Declining trends in the integrity of freshwater systems demand exploration of all possible conservation solutions. Freshwater protected areas have received little attention, despite the prominence of protected areas as conservation interventions for terrestrial and more recently marine features. We argue that a dialogue on freshwater protected areas has been neglected both because few models of good protected area design exist, and because traditional notions of protected areas translate imperfectly to the freshwater realm. Partly as a result of this conceptual disconnect, freshwaters have been largely ignored in protected area accounting schemes, even though a number of existing freshwater conservation strategies could qualify according to general protected area definitions. Rather than impose terrestrially-motivated ideas about protected areas onto freshwaters, we propose new vocabulary –freshwater focal area, critical management zone, and catchment management zone – that can be used in conjunction with IUCN protected area categories and that recognize the special ecological dynamics of freshwaters, and in particular the critical role of fluvial processes. These terms, which attempt to diffuse concerns about locking away essential ecosystem goods and services, move us toward consideration of protected areas for freshwaters. This conceptual shift, which acknowledges that freshwater conservation may occur remotely from freshwater features, opens the door for improved integration of freshwater, terrestrial, and marine concerns in protected area design and management.

Chemicals released into freshwater systems threaten ecological functioning and may put aquatic life and the health of humans at risk. We developed a new contaminant fate model (CFM) that follows simple, well-established methodologies and is unique in its cross-border, seamless hydrological and geospatial framework, including lake routing, a critical component in northern environments. We validated the model using the pharmaceutical Carbamazepine and predicted eco-toxicological risk for 15 pharmaceuticals in the Saint-Lawrence River Basin, Canada. The results indicated negligible to low environmental risk for the majority of tested chemicals, while two pharmaceuticals showed elevated risk in up to 13% of rivers affected by municipal effluents. As an integrated model, our CFM is designed for application at very large scales with the primary goal of detecting high risk zones. In regulatory frameworks, it can help screen existing or new chemicals entering the market regarding their potential impact on human and environmental health. Due to its high geospatial resolution, our CFM can also facilitate the prioritization of actions, such as identifying regions where reducing contamination sources or upgrading treatment plants is most pertinent to achieve targeted pollutant removal or to protect drinking water resources.

Large-scale estimates of the area of terrestrial surface waters have greatly improved over time, in particular through the development of multi-satellite methodologies, but the generally coarse spatial resolution (tens of kms) of global observations is still inadequate for many ecological applications. The goal of this study is to introduce a new, globally applicable downscaling method and to demonstrate its applicability to derive fine resolution results from coarse global inundation estimates. The downscaling procedure predicts the location of surface water cover with an inundation probability map that was generated by bagged derision trees using globally available topographic and hydrographic information from the SRTM-derived HydroSHEDS database and trained on the wetland extent of the GLC2000 global land cover map. We applied the downscaling technique to the Global Inundation Extent from Multi-Satellites (GIEMS) dataset to produce a new high-resolution inundation map at a pixel size of 15 arc-seconds, termed GIEMS-D15. GIEMS-D15 represents three states of land surface inundation extents: mean annual minimum (total area, 6.5 x 10(6) km(2)), mean annual maximum (12.1 x 10(6) km(2)), and long-term maximum (173 x 10(6) km(2)); the latter depicts the largest surface water area of any global map to date. While the accuracy of GIEMS-D15 reflects distribution errors introduced by the downscaling process as well as errors from the original satellite estimates, overall accuracy is good yet spatially variable. A comparison against regional wetland cover maps generated by independent observations shows that the results adequately represent large floodplains and wetlands. GIEMS-D15 offers a higher resolution delineation of inundated areas than previously available for the assessment of global freshwater resources and the study of large floodplain and wetland ecosystems. The technique of applying inundation probabilities also allows for coupling with coarse-scale hydro-climatological model simulations. (C) 2014 Elsevier Inc All rights reserved.

Most studies on the impact of climate change on regional water resources focus on long-term average flows or mean water availability, and they rarely take the effects of altered human water use into account. When analyzing extreme events such as floods and droughts, the assessments are typically confined to smaller areas and case studies. At the same time it is acknowledged that climate change may severely alter the risk of hydrological extremes over large regional scales, and that human water use will put additional pressure on future water resources. In an attempt to bridge these various aspects, this paper presents a first-time continental, integrated analysis of possible impacts of global change (here defined as climate and water use change) on future flood and drought frequencies for the selected study area of Europe. The global integrated water model WaterGAP is evaluated regarding its capability to simulate high and low-flow regimes and is then applied to calculate relative changes in flood and drought frequencies. The results indicate large ‘critical regions’ for which significant changes in flood or drought risks are expected under the proposed global change scenarios. The regions most prone to a rise in flood frequencies are northern to northeastern Europe, while southern and southeastern Europe show significant increases in drought frequencies. In the critical regions, events with an intensity of today's 100-year floods and droughts may recur every 10–50 years by the 2070s. Though interim and preliminary, and despite the inherent uncertainties in the presented approach, the results underpin the importance of developing mitigation and adaptation strategies for global change impacts on a continental scale.