The biogeochemical cycles of iron (Fe) and manganese (Mn) in lakes and reservoirs have predictable seasonal trends, largely governed by stratification dynamics and redox conditions in the hypolimnion. However, short-term (i.e., sub-weekly) trends in Fe and Mn cycling are less well-understood, as most monitoring efforts focus on longer-term (i.e., monthly to yearly) time scales. The potential for elevated Fe and Mn to degrade water quality and impact ecosystem functioning, coupled with increasing evidence for high spatiotemporal variability in other biogeochemical cycles, necessitates a closer evaluation of the short-term Fe and Mn cycling dynamics in lakes and reservoirs. We adapted a UV-visible spectrophotometer coupled with a multiplexor pumping system and PLSR modeling to generate high spatiotemporal resolution predictions of Fe and Mn concentrations in a drinking water reservoir (Falling Creek Reservoir, Vinton, VA, USA) equipped with a hypolimnetic oxygenation (HOx) system. We quantified hourly Fe and Mn concentrations during two distinct transitional periods: reservoir turnover (Fall 2020) and initiation of the HOx system (Summer 2021). Our sensor system was able to successfully predict mean Fe and Mn concentrations as well as capture sub-weekly variability, ground-truthed by traditional grab sampling and laboratory analysis. During fall turnover, hypolimnetic Fe and Mn concentrations began to decrease more than two weeks before complete mixing of the reservoir occurred, with rapid equalization of epilimnetic and hypolimnetic Fe and Mn concentrations in less than 48 hours after full water column mixing. During the initiation of hypolimnetic oxygenation in Summer 2021, we observed that Fe and Mn were similarly affected by physical mixing in the hypolimnion, but displayed distinctly different responses to oxygenation, as indicated by the rapid oxidation of soluble Fe but not soluble Mn. This study demonstrates that Fe and Mn concentrations are highly sensitive to shifting DO and stratification and that their dynamics can substantially change on hourly to daily time scales in response to these transitions.
In a context of climate change, the stakes surrounding water availability are getting higher. Decomposing and quantifying the effects of climate on discharge allows to better understand their impact on water resources. We propose a methodology to separate the effect of change in annual mean of climate variables from the effect of intra-annual distribution of precipitations. It combines the Budyko framework with outputs from a Land Surface Model (LSM). The LSM is used to reproduces the behavior of 2134 reconstructed watersheds over Europe between 1902 and 2010, with climate inputs as the only source of change. We fit to the LSM outputs a one parameter approximation to the Budyko framework. It accounts for the evolution of annual mean in precipitation (P) and potential evapotranspiration (PET). We introduce a time-varying parameter in the equation which represents the effect of long-term variations in the intra-annual distribution of P and PET. To better assess the effects of changes in annual means or in intra-annual distribution of P, we construct synthetic forcings fixing one or the other. The results over Europe show that the changes in discharge due to climate are dominated by the trends in the annual averages of P. The second main climate driver is PET, except over the Mediterranean area where changes in intra-annual variations of P have a higher impact on discharge than trends in PET. Therefore the effects of changes in intra-annual distribution of climate variables are not to be neglected when looking at changes in annual discharge.
Coastal watersheds are vulnerable to compound flooding associated with intense rainfall, storm surge, and high tide. Coastal compound flooding (CCF) simulation, in particular for low-gradient coastal watersheds, requires a tight-coupling procedure to represent nonlinear and complex flood processes and interconnectivity among multidimensional hydraulics and hydrologic models. This calls for the development of a fully-coupled CCF modeling framework. Here, the modeling framework is centered around the development of interconnected meshes of the node-link-basin using the Interconnected Channel and Pond Routing (ICPR) model. The modeling framework has been built for a complex drainage network, consisting of tidal creeks, tidal channels, underground sewer networks, and detention ponds in Charleston Peninsula, SC. The floodplain dynamics of the urbanized peninsula are modeled by a high-resolution LiDAR-derived Digital Elevation Model (DEM) and Digital Surface Model (DSM), and overland flow is simulated by energy balance, momentum balance, or diffusive wave methods. The performance of the CCF model is tested for the 2015 SC major flood and 2021 tidal flood events. The momentum balance-based CCF model shows 98.35% efficiency in capturing street-level flooding location and the CCF model depicts that using the DSM potentially improves the simulation accuracy of the flood by 15-33% compared to LiDAR DEM. Moreover, it is found the momentum balance between surge arrival from a tidally influenced river and rainfall runoff plays an important role in flood dynamics in urbanized catchments. This study contributes to the existing knowledge of fine-scale floodplain dynamics in urban areas by enhancing the fully-coupled numerical representation of CCF processes.
Graphene or graphene-based nanomaterials have emerged as novel scaffolds for developing robust bio-catalytic systems and a fast-developing promising contender for bioremediation. The interaction of bacteria and graphene is such an elusive issue that its implication in environmental biotechnology is unclear. The complexity and recalcitrant nature of the dyes make the conventional techniques inadequate and remain a challenge for industrial effluent treatment. Many scientists have developed hybrid processes and hybrid materials to enhance the treatment processes to satisfy increasingly stringent laws and criteria related to effluent discharge. The current study explicitly focuses on immobilization and growth of dye-degrading marine bacterial isolates on graphene oxide and their application in methylene blue dye degradation. The synergistic effects of adsorption and biodegradation achieved a unique clean-up performance that the counterpart-free bacteria could not fulfill. Further, toxicity analysis of intermediates also confirmed the non-toxic nature of the intermediates formed after synergistic treatment. This work has the potential to lead to zero effluent treatment processes.
Camera-based rainfall observation is a useful technology that contributes to the densification of rainfall observation networks because it can measure rainfall with high spatio-temporal resolution and low cost. To develop of practical camera-based rainfall observation technology, using the extinction coefficient as a clue, this study proposed relational Equations representing the relationship between image information, rainfall intensity, and scene depth by linking the theoretically derived rainfall intensity with a technique proposed in the computer vision field for removing static weather effects. Then, the proposed Equations were applied to outdoor images taken by commercial interval cameras at the observation site in a mountainous watershed in Japan. As a result, it was confirmed that transmission calculated from the image information decreases exponentially according to the increase in rainfall intensity and scene depth, as assumed in the proposed Equations. Therefore, the proposed Equations are generally valid even for outdoor images, and extremely important findings that will improve camera-based rainfall observation techniques were obtained. On the other hand, the calculated extinction coefficient tended to be overestimated in patches with a small scene depth, and overestimation of the extinction coefficient due to aerosol effects was also observed in the images taken during no rainfall. Although there are issues at present that need to be resolved for the technology proposed in this study, this technology has the potential to help the development of a camera-based rainfall observation technology that is accurate, robust, versatile, and accessible.
Pressure transient and tracer testing are conventional methods employed to investigate physical properties associated with fluid flow and/or storage in subsurface reservoirs or aquifers. These methods have been adopted independently to investigate from different physical aspects. Here, to quantify the heterogeneity of pore distributions containing subsurface fluid, a novel concept, which combines the pressure and tracer concentration responses obtained during pressure transient and tracer tests, respectively, has been proposed and validated. Herein, the key parameter is the difference between the apertures of the equivalent planar fractures estimated from the pressure and tracer concentration responses. In particular, this difference is attributed to the fact that the pressure and tracer concentration responses obey different physical mechanisms, diffusion and advection–dispersion problems, respectively, that generate dissimilar responses to the heterogeneity of pore distribution. The concept was successfully validated using laboratory experiments and reservoir simulations conducted at multiple scales. As observed, the apparent pore volume estimated from the pressure responses tended to be larger than the actual value owing to the delay in pressure responses during propagation through the pore. By quantifying the existence of undiscovered permeable structures in a reservoir simulation model, the proposed concept provides an insightful guide for successful decision-making in explorational and developmental geothermal projects. Furthermore, the concept provides a scale for assessing the accuracy of a reservoir simulation model in expressing an actual heterogeneous permeable structure.
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a scientific mission that collects high spatio-temporal resolution (~70 m, 1-5 days average revisit time) thermal images since its launch on 29 June 2018. As a predecessor of future missions, one of the main objectives of ECOSTRESS is to retrieve and understand the spatio-temporal variations in terrestrial evapotranspiration (ET) and its responses to soil water availability. In the European ECOSTRESS Hub (EEH), by taking advantage of land surface temperature retrievals, we generated ECOSTRESS ET products over Europe and Africa using three structurally contrasting models, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the non-parametric Surface Temperature Initiated Closure (STIC) model. A comprehensive evaluation of the EEH ET products was conducted with respect to flux measurements from 19 eddy covariance sites over 6 different biomes with diverse aridity levels. Results revealed comparable performances of STIC and SEBS (RMSE of ~70 W m-2). However, the relatively complex TSEB model produced a higher RMSE of ~90 W m-2. Comparison between STIC ET estimate and the operational ECOSTRESS ET product from NASA PT-JPL model showed a difference in RMSE between the two ET products around 50 W m-2. Substantial overestimation (>80 W m-2) was noted in PT-JPL ET estimates over shrublands and savannas presumably due to the weak constraint of LST in the model. Overall, the EEH is promising to serve as a support to the Land Surface Temperature Monitoring (LSTM) mission.
Around the world, water is considered a fundamental factor, and plays a role in public health and economic growth. Both the water development rates and the proportion of the population are directly related to water needs. Water quality regarding physiochemistry and microbiology is important in dietary needs. Drinking water is considered one of the most important food products. Therefore, the water should meet the recommended quality standards. So, it should be free of bacteria, parasites, all kinds of microorganisms, and chemical substances which are dangerous to human health. This research focused on five cities of the Alkalaa Municipal Community, which forms 43% of the inhabitants of this community, in the Bint Jbeil district south of Lebanon. The goal of this study is to determine the fundamental physicochemical and microbiological water properties of eight distinct sites, as well as the amount of pollution. These tests were carried out in accordance with World Health Organization criteria (WHO). The collected data were utilized to assess the level of pollution in the examined zone.
Lebanon’s natural water resources are facing serious problems and approaches exhaustion. One of these issues is deteriorating performance, which is linked to unregulated resource planning and rising demand. There are many different types of consumption, such as residential, industrial, and irrigation. Surface and groundwater are both referred to designate water resources. However, due to the obvious accessibility of exploitation, surface water resources such as rivers, lakes, and basins are primarily used. The Ras El-Ain basin is 6 km far south of Tyr, Lebanon. The Lebanese state dedicated it, along with other reservoirs, to supply potable water for Tyr and the surrounding villages. Today, these basins’ water quality has deteriorated significantly because of unrestricted liquid and soil waste dumping. As a result, contaminants develop in the basin water. Aside from laboratory testing for water quality, contamination can be seen through direct observations, odors, watercolors, and patterns. The purpose of this study is to assess the level of pollution in the Ras El-Ain basin. This basin has been progressively subjected to a variety of quality degradation characteristics. This includes the most important physiochemical properties. As a result, the physicochemical and microbiological water characteristics of five selected samples from each basin were tested. These tests were performed in accordance with European Standard Methods and World Health Organization guidelines (WHO). The effect of pollutant disposal in the Ras El-Ain basin was studied using multivariate approaches. The obtained results were used to evaluate the pollution degree in various regions of the basin.
Poem to imagine the “essence” of water as it circulates through the Earth universe, synergistically supporting all environments and living ecosystems, forming, and shaping land and life. The poem links key elements of the interactive global water cycle and international programs to sustainably manage natural, and socioeconomic resources, given the challenge of climate change. It is in awareness of: –Essential Water Variables (EWVs) of the Group on Earth Observations (GEO) Global Water Sustainability (GEOGLOWS) initiative; Earth Observations (EO) for the Water-Energy-Food Nexus (EO4WEF) community activity; UN Sustainable Development Goals (UN SDGs), UNFCCC–Climate Change. The poem hopes to bring water to the forefront of consciousness. Readers are invited to comment on the intangible “feelings” evoked by the poem.
A major challenge in the inversion of subsurface parameters is the ill-posedness issue caused by the inherent subsurface complexities and the generally spatially sparse data. Appropriate simplifications of inversion models are thus necessary to make the inversion process tractable and meanwhile preserve the predictive ability of the inversion results. In the present study, we investigate the effect of model complexity on the inversion of fracture aperture distribution as well as the prediction of long-term thermal performance in a field-scale single-fracture EGS model. Principal component analysis (PCA) was used to map the original cell-based aperture field to a low-dimensional latent space. The complexity of the inversion model was quantitatively represented by the percentage of total variance in the original aperture fields preserved by the latent space. Tracer, pressure and flow rate data were used to invert for fracture aperture through an ensemble-based inversion method, and the inferred aperture field was then used to predict thermal performance. We found that an over-simplified aperture model could not reproduce the inversion data and the predicted thermal response was biased. A complex aperture model could reproduce the data but the thermal prediction showed significant uncertainty. A model with moderate complexity, although not resolving many fine features in the “true” aperture field, successfully matched the data and predicted the long-term thermal behavior. The results provide important insights into the selection of model complexity for effective subsurface reservoir inversion and prediction.
The water cycle is an important component of the earth system and it plays a key role in many facets of society, including energy production, agriculture, and human health and safety. In this study, the Energy Exascale Earth System Model version 1 (E3SMv1) is run with low-resolution (roughly 110 km) and high-resolution (roughly 25 km) configurations — as established by the High Resolution Model Intercomparison Project protocol — to evaluate the atmospheric and terrestrial water budgets over the conterminous United States (CONUS) at the large watershed scale. The water cycle slows down in the HR experiment relative to the LR, with decreasing fluxes of precipitation, evapotranspiration, atmospheric moisture convergence, and runoff. The reductions in these terms exacerbate biases for some watersheds, while reducing them in others. For example, precipitation biases are exacerbated at HR over the Eastern and Central CONUS watersheds, while precipitation biases are reduced at HR over the Western CONUS watersheds. The most pronounced changes to the water cycle come from reductions in precipitation and evapotranspiration, the latter of which results from decreases in evaporative fraction. While the HR simulation is warmer than the LR, moisture convergence decreases despite the increased atmospheric water vapor, suggesting circulation biases are an important factor. Additional exploratory metrics show improvements to water cycle extremes (both in precipitation and streamflow), fractional contributions of different storm types to total precipitation, and mountain snowpack.
Stable carbon (δ¹³C) and oxygen (δ¹⁸O) isotope measurements in lacustrine ostracodes are widely used to infer past climatic conditions. Previous work has used individual ostracode valves to resolve seasonal and subdecadal climate signals, yet environmental controls on geochemical variability within co-occurring specimens from modern samples are poorly constrained. Here we focus on individual ostracode valves in modern-aged Lake Turkana sediments, an alkaline desert lake in tropical East Africa. We present individual ostracode valve analyses (IOVA) of δ¹³C and δ¹⁸O measurements (n = 329) of extant species Sclerocypris clavularis from 17 sites spanning the entire lake (n-avg ~19 specimens per site). We demonstrate that the pooled statistics of individual valve measurements at each site overcome inter-specimen isotopic variance and are driven by hydrological variability in the lake. Mean IOVA-δ¹³C and -δ¹⁸O across the sites exhibit strong spatial trends with higher values at more southerly latitudes, modulated by distance from the inflow of the Omo River. Whereas the latitudinal δ¹³C gradient reflects low riverine δ¹³C and decreasing lacustrine productivity towards the southern part of the lake, the δ¹⁸O gradient is controlled by evaporation superimposed on the waning influence of low-δ¹⁸O Omo River waters, sourced from the Ethiopian highlands. We show that ostracode δ¹⁸Oproximal to Omo River inflow is deposited under near-equilibrium conditions and that inter-specimen δ¹⁸O variability across the basin is consistent with observed temperature and lake water δ¹⁸O variability. IOVA can provide skillful constraints on high-frequency paleoenvironmental signals and, in Omo-Turkana sediments, yield quantitative insights into East African paleohydrology.
The way Alpine rivers mobilize, convey and store coarse material during high-magnitude events is poorly understood, notably because it is difficult to obtain measurements of bedload transport at the watershed scale. Seismic sensor data, evaluated with appropriate seismic physical models, can provide that missing link by yielding absolute time-series of bedload transport. Low cost and ease of installation allows for networks of sensors to be deployed, providing continuous, watershed-scale insights into bedload transport dynamics. Here, we deploy a network of 24 seismic sensors to capture the motion of coarse material in a 13.4 km2 Alpine watershed during a high-magnitude bedload transport event. First, we benchmark the seismic inversion routine with an independent time-series obtained with a calibrated acoustic system. Then, we apply the procedure to the other seismic sensors across the watershed. Spatially-distributed time-series of bedload transport reveal a relative inefficiency of Alpine watersheds in evacuating coarse material, even during a relatively infrequent high-magnitude bedload transport event. Significant inputs measured for some tributaries were rapidly attenuated as the main river crossed less hydraulically-efficient reaches, and only a comparatively negligible proportion of the total amount of material mobilized in the watershed was exported at the outlet. Cross-correlation analysis of the time-series suggests that a faster moving water wave (re-)mobilizes local material and bedload is expected to move slower, and over shorter distances. Multiple periods of competent flows are likely to be necessary to evacuate the coarse material produced throughout the watershed during individual source-mobilizing bedload transport events.
The transport of meltwater through porous snow is a fundamental process in hydrology that remains poorly understood but essential for more robust prediction of how the cryosphere will respond under climate change. Here we propose a continuum model that resolves the nonlinear coupling of preferential melt flow and the nonequilibrium thermodynamics of ice-melt phase change at the Darcy scale. We assume that the commonly observed unstable melt infiltration is due to the gravity fingering instabililty, and capture it using the modified Richards equation that is extended with a higher-order term in saturation. Our model accounts for changes in porosity and the thermal budget of the snowpack caused by melt refreezing at the continuum scale, based on a mechanistic estimate of the ice-water phase change kinetics formulated at the pore scale. We validate the model in 1D against field data and laboratory experiments of infiltration in snow and find generally good agreement. Compared to existing theory of stable melt infiltration, our 2D simulation results show that preferential infiltration delivers melt faster to deeper depths, and as a result, changes in porosity and temperature can occur at deeper parts of the snow. The simulations also capture the formation of vertical low porosity annulus known as ice pipes, which have been observed in the field but lack mechanistic understanding to date. Our results demonstrate how melt refreezing and unstable infiltration reshape the porosity structure of snow and impacts thermal and mass transport in highly nonlinear ways, which are not captured by simpler models.
Surface water nutrient pollution, the primary cause of eutrophication, remains a major environmental concern in Western Lake Erie despite intergovernmental efforts to regulate nutrient sources. The Maumee River Basin has been the largest nutrient contributor. The two primary nutrients sources are inorganic fertilizer and livestock manure applied to croplands, which are later carried to the streams via runoff and soil erosion. Prior studies on nutrient source attribution have focused on large watersheds or counties at long time scales. Source attribution at finer spatiotemporal scales, which enables more effective nutrient management, remains a substantial challenge. This study aims to address this challenge by developing a portable network model framework for phosphorus source attribution at the subwatershed (HUC-12) scale. Since phosphorus release is uncertain, we combine excess phosphorus derived from manure and fertilizer application and crop uptake data, flow dynamics simulated by the SWAT model, and in-stream water quality measurements into a probabilistic framework and apply Approximate Bayesian Computation to attribute phosphorus contributions from subwatersheds. Our results show significant variability in subwatershed-scale phosphorus release that is lost in coarse-scale attribution. Phosphorus contributions attributed to the subwatersheds are on average lower than the excess phosphorus estimated by the nutrient balance approach adopted by environmental agencies. Phosphorus release is higher during spring planting than the growing period, with manure contributing more than inorganic fertilizer. By enabling source attribution at high spatiotemporal resolution, our lightweight and portable model framework is suitable for broad applications in environmental regulation and enforcement for other regions and pollutants.
El Niño‐Southern Oscillation (ENSO) is often considered as a source of long-term predictability for extreme events via its teleconnection patterns. However, given that its characteristic cycle varies from two to seven years, it is difficult to obtain statistically significant conclusions based on observational periods spanning only a few decades. To overcome this, we apply the global flood risk modeling framework developed by Carozza and Boudreault to an equivalent of 1600 years of bias-corrected GCM outputs. The results show substantial anomalies in flood occurrences and impacts for El Niño and La Niña when compared to the all-year baseline. We were able to obtain a larger global coverage of statistically significant results than previous studies limited to observational data. Asymmetries in anomalies for both ENSO phases show a larger global influence of El Niño than La Niña on flood hazard and risk.
Urban water systems are composed of subsystems (gully pots, storm sewers and surface water), each with its own system dynamics. Engineers balance the functioning of the systems based on storage and discharge capacity of the subsystems. The load on, and capacity are influenced by e.g. ageing, urbanization, climate change. Consequently, the performance of and demand put on subsystems varies over time, potentially resulting in disturbances in the balance between the storage and discharge capacity of the subsystems. The Graph Based Weakest Link Method (GBWLM) is developed to analyse the behaviour of urban water systems to identify potential limitations due to deterioration, and/or changes in load. The proposed GBWLM is based on the structure of the networks. In addition, Graph theory is applied as alternative for series of hydraulic calculations. The GBWLM allows for an integrated performance assessments of urban water systems using multi-decades rainfall series. The results are sufficiently accurate to be able to determine the extent and frequency of urban flooding in order to compare the performance of the subsystems for various degrees of available discharge capacity. Keywords Criticality, flow paths analysis, Graph theory, linearised hydrodynamics, urban water systems, Weakest Link Method Highlights 1. Method for the analysis of urban water systems based on Graph theory 2. The use of linearised hydrodynamics in flow path analyses