Inequality of household water security follows a Development Kuznets Curve.

Water security requires not only sufficient availability of and access to safe and acceptable quality for domestic uses, but also fair distribution within and across populations. However, a key research gap remains in understanding water security inequality and its dynamics, which in turn creates an impediment to tracking progress towards sustainable development. Therefore, we analyse the inequality of water security using data from 7603 households across 28 sites in 22 low- and middle-income countries, measured using the Household Water Insecurity Experiences Scale. Here we show an inverted-U shaped relationship between site water security and inequality of household water security. This Kuznets-like curve suggests a process that as water security grows, the inequality of water security first increases then decreases. This research extends the Kuznets curve applications and introduces the Development Kuznets Curve concept. Its practical implications support building water security and achieving more fair, inclusive, and sustainable development.

A systematic approach to understand hydrogeochemical dynamics in large river systems: Development and application to the River Ganges (Ganga) in India.

Large river systems, such as the River Ganges (Ganga), provide crucial water resources for the environment and society, yet often face significant challenges associated with cumulative impacts arising from upstream environmental and anthropogenic influences. Understanding the complex dynamics of such systems remains a major challenge, especially given accelerating environmental stressors including climate change and urbanization, and due to limitations in data and process understanding across scales. An integrated approach is required which robustly enables the hydrogeochemical dynamics and underpinning processes impacting water quality in large river systems to be explored. Here we develop a systematic approach for improving the understanding of hydrogeochemical dynamics and processes in large river systems, and apply this to a longitudinal survey (> 2500 km) of the River Ganges (Ganga) and key tributaries in the Indo-Gangetic basin. This framework enables us to succinctly interpret downstream water quality trends in response to the underpinning processes controlling major element hydrogeochemistry across the basin, based on conceptual water source signatures and dynamics. Informed by a 2019 post-monsoonal survey of 81 river bank-side sampling locations, the spatial distribution of a suite of selected physico-chemical and inorganic parameters, combined with segmented linear regression, reveals minor and major downstream hydrogeochemical transitions. We use this information to identify five major hydrogeochemical zones, characterized, in part, by the inputs of key tributaries, urban and agricultural areas, and estuarine inputs near the Bay of Bengal. Dominant trends are further explored by investigating geochemical relationships (e.g. Na:Cl, Ca:Na, Mg:Na, Sr:Ca and NO3:Cl), and how water source signatures and dynamics are modified by key processes, to assess the relative importance of controls such as dilution, evaporation, water-rock interactions (including carbonate and silicate weathering) and anthropogenic inputs. Mixing/dilution between sources and water-rock interactions explain most regional trends in major ion chemistry, although localized controls plausibly linked to anthropogenic activities are also evident in some locations. Temporal and spatial representativeness of river bank-side sampling are considered by supplementary sampling across the river at selected locations and via comparison to historical records. Limitations of such large-scale longitudinal sampling programs are discussed, as well as approaches to address some of these inherent challenges. This approach brings new, systematic insight into the basin-wide controls on the dominant geochemistry of the River Ganga, and provides a framework for characterising dominant hydrogeochemical zones, processes and controls, with utility to be transferable to other large river systems.

The Time Machine framework: monitoring and prediction of biodiversity loss.

Transdisciplinary solutions are needed to achieve the sustainability of ecosystem services for future generations. We propose a framework to identify the causes of ecosystem function loss and to forecast the future of ecosystem services under different climate and pollution scenarios. The framework (i) applies an artificial intelligence (AI) time-series analysis to identify relationships among environmental change, biodiversity dynamics and ecosystem functions; (ii) validates relationships between loss of biodiversity and environmental change in fabricated ecosystems; and (iii) forecasts the likely future of ecosystem services and their socioeconomic impact under different pollution and climate scenarios. We illustrate the framework by applying it to watersheds, and provide system-level approaches that enable natural capital restoration by associating multidecadal biodiversity changes to chemical pollution.

Estimation of river flow using CubeSats remote sensing.

River flow characterizes the integrated response from watersheds, so it is essential to quantify to understand the changing water cycle and underpin the sustainable management of freshwaters. However, river gauging stations are in decline with ground-based observation networks shrinking. This study proposes a novel approach of estimating river flows using the Planet CubeSats constellation with the possibility to monitor on a daily basis at the sub-catchment scale through remote sensing. The methodology relates the river discharge to the water area that is extracted from the satellite image analysis. As a testbed, a series of Surface Reflectance PlanetScope images and observed streamflow data in Araguaia River (Brazil) were selected to develop and validate the methodology. The study involved the following steps: (1) survey of measurements of water level and river discharge using in-situ data from gauge-based Conventional Station (CS) and measurements of altimetry using remote data from JASON-2 Virtual Station (JVS); (2) survey of Planet CubeSat images for dates in step 1 and without cloud cover; (3) image preparation including clipping based on different buffer areas and calculation of the Normalized Difference Vegetation Index (NDVI) per image; (4) water bodies areas calculation inside buffers in the Planet CubeSat images; and (5) correlation analysis of CubeSat water bodies areas with JVS and CS data. Significant correlations between the water bodies areas with JVS (R2 = 88.83%) and CS (R2 = 96.49%) were found, indicating that CubeSat images can be used as a CubeSat Virtual Station (CVS) to estimate the river flow. This newly proposed methodology using CubeSats allows for more accurate results than the JVS-based method used by the Brazilian National Water Agency (ANA) at present. Moreover, CVS requires small areas of remote sensing data to estimate with high accuracy the river flow and the height variation of the water in different timeframes. This method can be used to monitor sub-basin scale discharge and to improve water management, particularly in developing countries where the presence of conventional stations is often very limited.

Macrophyte Controls on Urban Stream Microbial Metabolic Activity.

Urban rivers worldwide are affected directly by macrophyte growth, causing reduced flow velocity and risks of flooding. Therefore, cutting macrophytes is a common management practice to ensure free drainage. The impacts of macrophyte removal on transient storage dynamics and microbial metabolic activity of wastewater-fed urban streams are unknown, preventing any assessment of the hydrodynamic and biogeochemical consequences of this management practice. Slug tracer injections were performed with the conservative tracer uranine and the reactive tracer resazurin to quantify the implications of macrophyte cutting on stream flow dynamics and metabolism. Macrophyte cutting reduced mean tracer arrival times in managed stream reaches but did not significantly decrease whole-stream microbial metabolic activity. In fact, transient storage indices were found to have increased after cutting, suggesting that macrophyte removal and the resulting increase in flow velocity may have enhanced hyporheic exchange flow through streambed sediments. Our results evidence that macrophyte cutting in nutrient-rich urban streams does not necessarily lead to lower in-stream storage and metabolism but that the gain in hyporheic exchange and streambed microbial metabolic activity can compensate for reduced in-stream storage. Increased stream flow resulting from macrophyte removal may therefore even enhance nutrient and pollutant attenuation capacity of streambed sediments.

Combining in-situ fluorometry and distributed rainfall data provides new insights into natural organic matter transport dynamics in an urban river.

Urbanization alters the quality and quantity of Dissolved Organic Matter (DOM) fluxes to rivers potentially leading to water quality problems and impaired ecosystem function. Traditional synoptic and point sampling approaches are generally inadequate for monitoring DOM source dynamics. To identify links between spatial heterogeneity in precipitation and DOM dynamics, we used a unique approach combining high spatial and temporal resolution precipitation datasets featuring point, catchment, and land-cover weighted precipitation to characterise catchment transport dynamics. These datasets were linked to fluorescence records from an urban stream (Bourn Brook, Birmingham, UK). Humic-like fluorescence (HLF: Ex. 365 nm, Em. 490 nm) and Tryptophan-like fluorescence (TLF: Ex. 285 nm, Em. 340 nm) were measured, (plus river flow and turbidity) at 5 min intervals for 10 weeks during Autumn 2017. The relationship between discharge (Q) and concentration (C) for TLF and HLF were strongly chemodynamic at low Q (

Peak grain forecasts for the US High Plains amid withering waters.

Irrigated agriculture contributes 40% of total global food production. In the US High Plains, which produces more than 50 million tons per year of grain, as much as 90% of irrigation originates from groundwater resources, including the Ogallala aquifer. In parts of the High Plains, groundwater resources are being depleted so rapidly that they are considered nonrenewable, compromising food security. When groundwater becomes scarce, groundwater withdrawals peak, causing a subsequent peak in crop production. Previous descriptions of finite natural resource depletion have utilized the Hubbert curve. By coupling the dynamics of groundwater pumping, recharge, and crop production, Hubbert-like curves emerge, responding to the linked variations in groundwater pumping and grain production. On a state level, this approach predicted when groundwater withdrawal and grain production peaked and the lag between them. The lags increased with the adoption of efficient irrigation practices and higher recharge rates. Results indicate that, in Texas, withdrawals peaked in 1966, followed by a peak in grain production 9 y later. After better irrigation technologies were adopted, the lag increased to 15 y from 1997 to 2012. In Kansas, where these technologies were employed concurrently with the rise of irrigated grain production, this lag was predicted to be 24 y starting in 1994. In Nebraska, grain production is projected to continue rising through 2050 because of high recharge rates. While Texas and Nebraska had equal irrigated output in 1975, by 2050, it is projected that Nebraska will have almost 10 times the groundwater-based production of Texas.

Moving beyond the Technology: A Socio-technical Roadmap for Low-Cost Water Sensor Network Applications.

In this paper, we critically review the current state-of-the-art for sensor network applications and approaches that have developed in response to the recent rise of low-cost technologies. We specifically focus on water-related low-cost sensor networks, and conceptualize them as socio-technical systems that can address resource management challenges and opportunities at three scales of resolution: (1) technologies, (2) users and scenarios, and (3) society and communities. Building this argument, first we identify a general structure for building low-cost sensor networks by assembling technical components across configuration levels. Second, we identify four application categories, namely operational monitoring, scientific research, system optimization, and community development, each of which has different technical and nontechnical configurations that determine how, where, by whom, and for what purpose low-cost sensor networks are used. Third, we discuss the governance factors (e.g., stakeholders and users, networks sustainability and maintenance, application scenarios, and integrated design) and emerging technical opportunities that we argue need to be considered to maximize the added value and long-term societal impact of the next generation of sensor network applications. We conclude that consideration of the full range of socio-technical issues is essential to realize the full potential of sensor network technologies for society and the environment.

River temperature research and practice: Recent challenges and emerging opportunities for managing thermal habitat conditions in stream ecosystems.

There is growing evidence that river temperatures are increasing under climate change, which is expected to be exacerbated by increased abstractions to satisfy human water demands. Water temperature research has experienced crucial advances, both in terms of developing new monitoring and modelling tools, as well as understanding the mechanisms of temperature feedbacks with biogeochemical and ecological processes. However, water practitioners and regulators are challenged with translating the widespread and complex technological, modelling and conceptual advances made in river temperature research into improvements in management practice. This critical review provides a comprehensive overview of recent advances in the state-of-the-art monitoring and modelling tools available to inform ecological research and practice. In so doing, we identify pressing research gaps and suggest paths forward to address practical research and management challenges. The proposed research directions aim to provide new insights into spatio-temporal stream temperature dynamics and unravel drivers and controls of thermal river regimes, including the impacts of changing temperature on metabolism and aquatic biogeochemistry, as well as aquatic organisms. The findings of this review inform future research into ecosystem resilience in the face of thermal degradation and support the development of new management strategies cutting across spatial and temporal scales.

Diatoms as indicators of the effects of river impoundment at multiple spatial scales.

River impoundment constitutes one of the most important anthropogenic impacts on the World's rivers. An increasing number of studies have tried to quantify the effects of river impoundment on riverine ecosystems over the past two decades, often focusing on the effects of individual large reservoirs. This study is one of the first to use a large-scale, multi-year diatom dataset from a routine biomonitoring network to analyse sample sites downstream of a large number of water supply reservoirs (n = 77) and to compare them with paired unregulated control sites. We analysed benthic diatom assemblage structure and a set of derived indices, including ecological guilds, in tandem with multiple spatio-temporal variables to disclose patterns of ecological responses to reservoirs beyond the site-specific scale. Diatom assemblage structure at sites downstream of water supply reservoirs was significantly different to control sites, with the effect being most evident at the regional scale. We found that regional influences were important drivers of differences in assemblage structure at the national scale, although this effect was weaker at downstream sites, indicating the homogenising effect of river impoundment on diatom assemblages. Sites downstream of reservoirs typically exhibited a higher taxonomic richness, with the strongest increases found within the motile guild. In addition, Trophic Diatom Index (TDI) values were typically higher at downstream sites. Water quality gradients appeared to be an important driver of diatom assemblages, but the influence of other abiotic factors could not be ruled out and should be investigated further. Our results demonstrate the value of diatom assemblage data from national-scale biomonitoring networks to detect the effects of water supply reservoirs on instream communities at large spatial scales. This information may assist water resource managers with the future implementation of mitigation measures such as setting environmental flow targets.

Drone-based Structure-from-Motion provides accurate forest canopy data to assess shading effects in river temperature models.

Climatic warming will increase river temperature globally, with consequences for cold water-adapted organisms. In regions with low forest cover, elevated river temperature is often associated with a lack of bankside shading. Consequently, river managers have advocated riparian tree planting as a strategy to reduce temperature extremes. However, the effect of riparian shading on river temperature varies substantially between locations. Process-based models can elucidate the relative importance of woodland and other factors driving river temperature and thus improve understanding of spatial variability of the effect of shading, but characterising the spatial distribution and height of riparian tree cover necessary to parameterise these models remains a significant challenge. Here, we document a novel approach that combines Structure-from-Motion (SfM) photogrammetry acquired from a drone to characterise the riparian canopy with a process based temperature model (Heat Source) to simulate the effects of tree shading on river temperature. Our approach was applied in the Girnock Burn, a tributary of the Aberdeenshire Dee, Scotland. Results show that SfM approximates true canopy elevation with a good degree of accuracy (R2 = 0.96) and reveals notable spatial heterogeneity in shading. When these data were incorporated into a process-based temperature model, it was possible to simulate river temperatures with a similarly-high level of accuracy (RMSE <0.7 °C) to a model parameterised using 'conventional' LiDAR tree height data. We subsequently demonstrate the utility of our approach for quantifying the magnitude of shading effects on stream temperature by comparing simulated temperatures against another model from which all riparian woodland has been removed. Our findings highlight drone-based SfM as an effective tool for characterising riparian shading and improving river temperature models. This research provides valuable insights into the effects of riparian woodland on river temperature and the potential of bankside tree planting for climate change adaptation.

Streambed Organic Matter Controls on Carbon Dioxide and Methane Emissions from Streams.

Greenhouse gas (GHG) emissions of carbon dioxide (CO2) and methane (CH4) from streambeds are currently understudied. There is a paucity of research exploring organic matter (OM) controls on GHG production by microbial metabolic activity in streambeds, which is a major knowledge gap given the increased inputs of allochthonous carbon to streams, especially in agricultural catchments. This study aims to contribute to closing this knowledge gap by quantifying how contrasting OM contents in different sediments affect streambed GHG production and associated microbial metabolic activity. We demonstrate, by means of an incubation experiment, that streambed sediments have the potential to produce substantial amounts of GHG, controlled by sediment OM quantity and quality. We observed streambed CO2 production rates that can account for 35% of total stream evasion estimated in previous studies, ranging between 1.4 and 86% under optimal conditions. Methane production varied stronger than CO2 between different geologic backgrounds, suggesting OM quality controls between streambed sediments. Moreover, our results indicate that streambed sediments may produce much more CO2 than quantified to date, depending on the quantity and quality of the organic matter, which has direct implications for global estimates of C fluxes in stream ecosystems.

Macroinvertebrate community responses to river impoundment at multiple spatial scales.

River impoundment by the construction of dams potentially modifies a wide range of abiotic and biotic factors in lotic ecosystems and is considered one of the most significant anthropogenic impacts on rivers globally. The past two decades have witnessed a growing body of research centred on quantifying the effects of river impoundment, with a focus on mitigating and managing the effects of individual large dams. This study presents a novel multi-scale comparison of paired downstream and control sites associated with multiple water supply reservoirs (n = 80) using a spatially extensive multi-year dataset. Macroinvertebrate community structure and indices were analysed in direct association with spatial (e.g. region) and temporal variables (e.g. season) to identify consistent patterns in ecological responses to impoundment. Macroinvertebrate communities at monitoring sites downstream of water supply reservoirs differed significantly from those at control sites at larger spatial scales, both in terms of community structure and taxa richness. The effect was most significant at the regional scale, while biogeographical factors appeared to be important drivers of community differences at the national scale. Water supply reservoirs dampened natural seasonal patterns in community structure at sites downstream of impoundments. Generally, taxonomic richness was higher and %EPT richness lower at downstream sites. Biomonitoring indices used for river management purposes were able to detect community differences, demonstrating their sensitivity to river regulation activities. The results presented improve our understanding of the spatially extensive and long-term effects of water supply reservoirs on instream communities and provide a basis for the future implementation of mitigation measures on impounded rivers and heavily modified waterbodies.

Thermal sensitivity of CO2 and CH4 emissions varies with streambed sediment properties.

Globally, rivers and streams are important sources of carbon dioxide and methane, with small rivers contributing disproportionately relative to their size. Previous research on greenhouse gas (GHG) emissions from surface water lacks mechanistic understanding of contributions from streambed sediments. We hypothesise that streambeds, as known biogeochemical hotspots, significantly contribute to the production of GHGs. With global climate change, there is a pressing need to understand how increasing streambed temperatures will affect current and future GHG production. Current global estimates assume linear relationships between temperature and GHG emissions from surface water. Here we show non-linearity and threshold responses of streambed GHG production to warming. We reveal that temperature sensitivity varies with substrate (of variable grain size), organic matter (OM) content and geological origin. Our results confirm that streambeds, with their non-linear response to projected warming, are integral to estimating freshwater ecosystem contributions to current and future global GHG emissions.

Functional diversity and community assembly of river invertebrates show globally consistent responses to decreasing glacier cover.

Global change threatens invertebrate biodiversity and its central role in numerous ecosystem functions and services. Functional trait analyses have been advocated to uncover global mechanisms behind biodiversity responses to environmental change, but the application of this approach for invertebrates is underdeveloped relative to other organism groups. From an evaluation of 363 records comprising >1.23 million invertebrates collected from rivers across nine biogeographic regions on three continents, consistent responses of community trait composition and diversity to replicated gradients of reduced glacier cover are demonstrated. After accounting for a systematic regional effect of latitude, the processes shaping river invertebrate functional diversity are globally consistent. Analyses nested within individual regions identified an increase in functional diversity as glacier cover decreases. Community assembly models demonstrated that dispersal limitation was the dominant process underlying these patterns, although environmental filtering was also evident in highly glacierized basins. These findings indicate that predictable mechanisms govern river invertebrate community responses to decreasing glacier cover globally.

Improving representation of riparian vegetation shading in a regional stream temperature model using LiDAR data.

Modelling river temperature at the catchment scale is needed to understand how aquatic communities may adapt to current and projected climate change. In small and medium rivers, riparian vegetation can greatly reduce maximum water temperature by providing shade. It is thus important that river temperature models are able to correctly characterise the impact of this riparian shading. In this study, we describe the use of a spatially-explicit method using LiDAR-derived data for computing the riparian shading on direct and diffuse solar radiation. The resulting data are used in the T-NET one-dimensional stream temperature model to simulate water temperature from August 2007 to July 2014 for 270km of the Loir River, an indirect tributary of the Loire River (France). Validation is achieved with 4 temperature monitoring stations spread along the Loir River. The vegetation characterised with the LiDAR approach provides a cooling effect on maximum daily temperature (Tmax) ranging from 3.0°C (upstream) to 1.3°C (downstream) in late August 2009. Compared to two other riparian shading routines that are less computationally-intensive, the use of our LiDAR-based methodology improves the bias of Tmax simulated by the T-NET model by 0.62°C on average between April and September. However, difference between the shading routines reaches up to 2°C (monthly average) at the upstream-most station. Standard deviation of errors on Tmax is not improved. Computing the impact of riparian vegetation at the hourly timescale using reach-averaged parameters provides results close to the LiDAR-based approach, as long as it is supplied with accurate vegetation cover data. Improving the quality of riparian vegetation data should therefore be a priority to increase the accuracy of stream temperature modelling at the regional scale.

A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland's Atlantic salmon rivers under climate change.

The thermal suitability of riverine habitats for cold water adapted species may be reduced under climate change. Riparian tree planting is a practical climate change mitigation measure, but it is often unclear where to focus effort for maximum benefit. Recent developments in data collection, monitoring and statistical methods have facilitated the development of increasingly sophisticated river temperature models capable of predicting spatial variability at large scales appropriate to management. In parallel, improvements in temporal river temperature models have increased the accuracy of temperature predictions at individual sites. This study developed a novel large scale spatio-temporal model of maximum daily river temperature (Twmax) for Scotland that predicts variability in both river temperature and climate sensitivity. Twmax was modelled as a linear function of maximum daily air temperature (Tamax), with the slope and intercept allowed to vary as a smooth function of day of the year (DoY) and further modified by landscape covariates including elevation, channel orientation and riparian woodland. Spatial correlation in Twmax was modelled at two scales; (1) river network (2) regional. Temporal correlation was addressed through an autoregressive (AR1) error structure for observations within sites. Additional site level variability was modelled with random effects. The resulting model was used to map (1) spatial variability in predicted Twmax under current (but extreme) climate conditions (2) the sensitivity of rivers to climate variability and (3) the effects of riparian tree planting. These visualisations provide innovative tools for informing fisheries and land-use management under current and future climate.