The Gravity Recovery and Climate Experiment (GRACE) data help to determine the total water storage anomalies (TWS) across the global scale. The various other important components such as Groundwater storage (GWS) and evapotranspiration for the region of South –East Asia have been determined. With the study of the gravity variation across the globe the long-term changes in the hydrological cycle can be determined which can be related to climate science or the influence of anthropogenic activities. The variation between the Groundwater storage (GWS) and the Total water storage (TWS) of the study area has been calculated for the pre and post-monsoon season of the study area. The variation between groundwater storage and total water storage can be visualized through geospatial analysis. Therefore, the regions with a substantial decrease in water storage can be related to various climate and anthropogenic factors hence implying a sustainable use of groundwater as a resource. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.

Through machine learning and remote sensing, a high-end model with a finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found by the application of Random Forest regression followed by the implication of the water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is noted through random forest regression. Thus, the model can be used for various regions of the dataset specifically for the area where there is a lack of reach for data. It can be successfully used to form a sophisticated end-to-end ML model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.

Machine Learning and Remote sensing method to determine the relationship between Climate and Groundwater Recharge. Adya Aiswarya Dash1, Abhijit Mukherjee1,2,3. 1Department of Geology and Geophysics, Indian Institute of Technology Kharagpur, West Bengal 721302, India 2School of Environmental Science and Engineering, Indian Institute of Technology Kharagpur, West Bengal 721302, India 3Applied Policy Advisory for Hydrogeoscience (APAH) Group, Indian Institute of Technology Kharagpur, West Bengal 721302, India Abstract Through machine learning and remote sensing, a high-end model with a finer resolution for groundwater recharge has been developed for the region of South-East Asia. The groundwater recharge coefficient can be found by the application of Random Forest regression followed by the implication of the water budget method to calculate the Groundwater Recharge values. Climatic factors such as precipitation and actual evapotranspiration to map Groundwater Recharge has been framed with a sophisticated machine learning method to be considered as a scale predicting model. A comprehensive visualization of the dataset has been done; the accuracy of the model is noted through random forest regression. Thus, the model can be used for various regions of the dataset specifically for the area where there is a lack of reach for data. It can be successfully used to form a sophisticated end-to-end ML model. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.

Sustainably managing resources in a transboundary freshwater basin is a complex problem, particularly when considering the compounding impacts of climate change, hydropower development, and evolving water governance paradigms. In this study, we used a mixed methods approach to analyze potential impacts of climate change on regional hydrology, the ability of dam operation rules to keep downstream flow within acceptable limits, and the present state of water governance in Laos, Vietnam, and Cambodia. Our results suggest that future river flows in the 3S river system could move closer to natural (i.e., pre-development) conditions during the dry season and experience increased floods during the wet season. This anticipated new flow regime in the 3S region would require a shift in the current dam operations, from maintaining minimum flows to reducing flood hazards. Moreover, our Governance and Stakeholders survey assessment results revealed that existing water governance systems in Laos, Vietnam, and Cambodia are ill-prepared to address such anticipated future water resource management problems. Our results indicate that the solution space for addressing these complex issues in the 3S river basins will be highly constrained unless major deficiencies in transboundary water governance, strategic planning, financial capacity, information sharing, and law enforcement are remedied in the next decade. This work is part of an ongoing research partnership between the National Aeronautical and Space Agency (NASA) and the Conservation International (CI) dedicated to improving natural resources assessment for conservation and sustainable management.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.

The combined utilization of spatiotemporal clustering and deep learning neural network models were designed to evaluate the applicability of the multi-year and multi-sites precipitation δ18O forecasting method based on the precipitation isotope data of 10 stations in Germany from 1988 to 2012. In the overall forecasting, the performance of single-site multi-year forecasting is in the order of the Bi-directional Long Short-Term Memory (Bi-LSTM), CNN-Bi-LSTM, and the Convolutional Neural Network (CNN), with CNN-Bi-LSTM being the optimal model for multi-site multi-year forecasts. The seasonal forecasting does not demonstrate a significant improvement compared to the overall forecasting. For forecasting based on spatiotemporal clustering, cluster 1 improved accuracy, and cluster 2 improved error reduction and variance consistency. Nevertheless, the accuracy of forecasts depends solely on the amount of input data when the proportion of forecasting increases to a certain level. Overall, the seasonal forecasting is more appropriate for improving forecasting within a specific season, while spatiotemporal clustering can improve forecasting accuracy to some degree. In addition, optimal solutions exist for the type and number of model clusters. In terms of model types, CNN-Bi-LSTM generally has better forecasting performance than CNN and Bi-LSTM.

Stream confluences are ubiquitous interfaces in freshwater networks and serve as junctions of previously independent landscapes. However, few studies have investigated how confluences influence transport, mixing, and fate of organic matter and inorganic nutrients at the scale of river networks. To understand how network biogeochemical fluxes may be altered by confluences, we conducted two sampling campaigns at five confluences in summer and fall 2021 spanning the extent of a mixed land use stream network. We sampled the confluence mainstem and tributary reaches as well as throughout the mixing zone downstream. We predicted that biologically reactive solutes would mix non-conservatively downstream of confluences and that alterations to downstream biogeochemistry would be driven by differences in chemistry and size of the tributary and upstream reaches. In our study, confluences were geomorphically distinct downstream compared to reaches upstream of the confluence. Dissolved organic matter and nutrients mixed non-conservatively downstream of the five confluences. Biogeochemical patterns downstream of confluences were only partially explained by contributing reach chemistry and drainage area. We found that the relationship between geomorphic variability, water residence time, and microbial respiration differed between reaches upstream and downstream of confluences. The lack of explanatory power from network-scale drivers suggests that non-conservative mixing downstream of confluences may be driven by biogeochemical processes within the confluence mixing zone. The unique geomorphology, non-conservative biogeochemistry, and ubiquity of confluences highlights a need to account for the distinct functional role of confluences in water resource management in freshwater networks.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.

Mountain System Recharge (MRS) processes are the natural recharge pathways in arid and semi-arid mountainous regions. However, MSR processes are often poorly understood and characterized in hydrologic models. Mountains are the primary source of water supply to valley aquifers via multiple pathways including lateral groundwater flow from the mountain block (Mountain-block Recharge, MBR) and focused recharge from mountain streams contributing to mountain front recharge (MFR) at the piedmont zone. Here, we present a multi-tool isogeochemical approach to characterize mountain flow paths and MSR processes in the northern Tulare basin, California. We used groundwater chemistry data to delineate hydrochemical facies and explain the chemical evolution of groundwater from the Sierra Nevada to the Central Valley aquifer. Isotope tracers helped to validate MSR processes. Novel application of End-Member Mixing Analysis (EMMA) using conservative chemical components revealed three MSR end-members: (1) evaporated Ca-HCO3 water type associated with MFR, (2) non-evaporated Ca-HCO3 and Na-HCO3 water types with short residence times associated with shallow MBR, and (3) Na-HCO3 groundwater type with long residence time associated with deep MBR. We quantified the contribution of each MSR process to the valley aquifer using mixing ratio calculation (MIX). Our results show that deep MBR is a significant component of recharge representing more than 50% of the valley groundwater. Greater hydraulic connectivity between the Sierra Nevada and Central Valley has significant implications for parameterizing Central Valley groundwater flow models and improving groundwater management. Our framework is useful for understanding MSR processes in other snow-dominated mountain watersheds.

Earth Observations (EO) systems aim to monitor nearly all aspects of the global Earth environment. Observations of Essential Water Variables (EWVs) together with advanced data assimilation models, could provide the basis for systems that deliver integrated information for operational and policy level decision making that supports the Water-Energy-Food-Nexus (EO4WEF), and concurrently the UN Sustainable Development Goals (SDGs), and UN Framework Convention on Climate Change (UNFCCC). Implementing integrated EO for GEO-WEF (EO4WEF) systems requires resolving key questions regarding the selection and standardization of priority variables, the specification of technologically feasible observational requirements, and a template for integrated data sets. This paper presents a concise summary of EWVs adapted from the GEO Global Water Sustainability (GEOGLOWS) Initiative and consolidated EO observational requirements derived from the GEO Water Strategy Report (WSR). The UN-SDGs implicitly incorporate several other Frameworks and Conventions such as The Sendai Framework for Disaster Risk Reduction; The Ramsar Convention on Wetlands; and the Aichi Convention on Biological Diversity. Primary and Supplemental EWVs that support WEF Nexus & UN-SDGs, and Climate Change are specified. The EO-based decision-making sectors considered include water resources; water quality; water stress and water use efficiency; urban water management; disaster resilience; food security, sustainable agriculture; clean & renewable energy; climate change adaptation & mitigation; biodiversity & ecosystem sustainability; weather and climate extremes (e.g., floods, droughts, and heat waves); transboundary WEF policy.

As all kinds of physics-based and data-driven models are emerging in the fields of hydrologic and hydraulic engineering, Bayesian model averaging (BMA) is one of the popular multi-model methods used to account for the various uncertainty sources in the flood modeling process and generate robust ensemble predictions based on multiple competitive candidate models. The reliability of BMA parameters (weights and variances) determines the accuracy of BMA predictions. However, the uncertainty in the BMA parameters with fixed values, which are usually obtained from the Expectation-Maximization (EM) algorithm, has not been adequately investigated in BMA-related applications over the past few decades. Given the limitations of the commonly used EM algorithm, the Metropolis-Hastings (M-H) algorithm, which is one of the most widely used algorithms in the Markov Chain Monte Carlo (MCMC) method, is proposed to estimate the BMA parameters and quantify their associated uncertainty. Both numerical experiments and the one-dimensional HEC-RAS models are employed to examine the applicability of the M-H algorithm with multiple independent Markov chains. The performances of the EM and M-H algorithms in the BMA analysis are compared based on the daily water stage predictions from 10 model configurations. The results show that the BMA weights estimated from both algorithms are comparable, while the BMA variances obtained from the M-H MCMC algorithm are closer to the given variances in the numerical experiment. Moreover, the normal proposal distribution used in the M-H algorithm can yield narrower distributions for the BMA weights than those from the uniform prior. Overall, the MCMC approach with multiple chains can provide more information associated with the uncertainty of BMA parameters and its prediction performance is better than the default EM algorithm in terms of multiple evaluation metrics as well as algorithm flexibility.

Evaluation of the performance of hydrologic and hydraulic models is a crucial step in the modeling process. Considering the limitations of single statistical metrics, such as the Nash Sutcliffe efficiency (NSE), the Kling Gupta efficiency (KGE), and the coefficient of determination (R2), which are widely used in the evaluation of model performance, an evaluation framework that incorporates multiple criteria and based on the generalized likelihood uncertainty estimation (GLUE) is proposed to demonstrate the uncertainty in the evaluation criteria and hence to quantify the overall uncertainty of flood models in a comprehensive way. This framework is applied to the one-dimensional HEC-RAS models of six reaches located in States of Indiana and Texas of the United States to quantify the uncertainty associated with the channel roughness and upstream flow input. Specifically, the effects of different prior distributions of the uncertainty sources, multiple high-flow scenarios, and various types of measurement errors (white noise, positive bias, and negative bias) in observations on the evaluation metrics are investigated by using the bootstrapping method and Monte Carlo simulations. The results show that the model performances based on the uniform and normal priors are comparable. The distributions of all the evaluation metrics in the framework are significantly different for the flood model under different high-flow scenarios, and it further indicates that the metrics are essentially random statistical variables. Additionally, the white-noise error in observations has the least impact on the metrics, while the positive and the negative biases would have opposite impacts, which depends on whether the model overestimated or underestimated the hydrologic variable.

Natural hazards such as floods, hurricanes, heatwaves, and wildfires cause significant economic losses (e.g., agricultural and property damage) as well as a high number of fatalities. Natural hazards are often driven by univariate or multivariate hydrometeorological drivers. Therefore, it is crucial to understand how and which hydrometeorological variables (i.e., drivers) combine to contribute to the impacts of these hazards. Additionally, when multiple drivers are associated with a hazard, traditional univariate risk assessment approaches are insufficient to cover the full spectrum of impact-relevant conditions originating from different combinations of multiple drivers. Based on historical socioeconomic loss data, we develop an impact-based approach to assess the influence of different hydrometeorological drivers on the impacts caused by different hazard event types. We use the Spatial Hazard Events and Losses Database for the United States (SHELDUS™) to identify the historical hazard events that caused socioeconomic impacts (property and crop damage, injuries, and fatalities) in our case study area, Miami-Dade County, in south Florida. For 9 different hazard types, we obtained data for 13 hydrometeorological drivers from historical in-situ observations and reanalysis products corresponding to the timing and locations of the hazard events found in the SHELDUS database. The relative importance of each hazard driver in generating impacts and the frequency of multiple drivers was then assessed. We found that many high-impact events were caused by multiple hydrometeorological drivers (i.e., compound events). For example, 61% of the recorded flooding events were compound events rather than univariate hazards and these contributed 99% of total property damage and 98.2% of total crop damage in Miami-Dade County. For several hazards, such as hurricanes/tropical storms and wildfires, all the events that caused damage are classified as compound events in our framework. Our findings emphasize the benefit of including socioeconomic impact information when analyzing hazard events, as well as the importance of analyzing all relevant hydrometeorological drivers to identify compound events.

Weather generators based on resampling simulate new time series of weather variables by reordering the observed values such that the statistics of the simulated data are coherent with the observed ones. These weather generators are fully data-driven and simple to implement, do not rely on parametric distributions, and can reproduce the dynamics among the weather variables under analysis. However, although the simulated time series is new, the produced weather fields at arbitrary timesteps are copies of the weather fields found in the training dataset. Consequently, the spatial variability of simulations is restricted. Furthermore, these weather generators cannot create weather fields with out-of-sample extreme values because the scope of the resampling method is constrained to the observed values. In this work, we embedd the Direct Sampling algorithm — a data-driven method for producing simulations — into resampling-based weather generators to improve the spatial variability of the produced weather fields, and for generating extreme weather fields. We increase the spatial variability by applying Direct Sampling as a post-processing step on the weather generator outputs. Furthermore, we produce out-of-sample extreme weather fields using Direct Sampling in two ways: 1) applying quantile mappings on the Direct Sampling simulations for a given return period, and 2) using a set of control points jointly with Direct Sampling with values informed by return period analysis. We validate our approach using precipitation, temperature, and cloud cover weather-fields time series datasets, for a region in northwest India. The results are analyzed using a set of statistical and connectivity metrics.

Sustainable river management can be supported by models predicting long-term morphological developments. Even for one-dimensional morphological models, run times can be up to several days for simulations over multiple decades. Alternatively, analytical tools yield metrics that allow estimation of migration celerity and damping of bed waves, which have potential for being used as rapid assessment tools to explore future morphological developments. We evaluate the use of analytical relations based on linear stability analyses of the St. Venant-Exner equations, which apply to bed waves with spatial scales much larger than the water depth. With a one-dimensional numerical morphological model, we assess the validity range of the analytical approach. The comparison shows that the propagation of small bed perturbations is well-described by the analytical approach. For Froude numbers over 0.3, diffusion becomes important and bed perturbation celerities reduce in time. A spatial-mode linear stability analysis predicts an upper limit for the bed perturbation celerity. For longer and higher bed perturbations, the dimensions relative to the water depth and the backwater curve length determine whether the analytical approach yields realistic results. For higher bed wave amplitudes, non-linearity becomes important. For Froude numbers ≤0.3, the celerity of bed waves is increasingly underestimated by the analytical approach. The degree of underestimation is proportional to the ratio of bed wave amplitude to water depth and the Froude number. For Froude numbers exceeding 0.3, the net impact on the celerity depends on the balance between the decrease due to damping and the increase due to non-linear interaction.

Total evaporation from the vast terrain of the Tibetan Plateau (TP) may strongly influence downwind regions. However, the ultimate fate of this moisture remains unclear. This study tracked and quantified TP-originating moisture. The results show that the TP moisture participation in downwind regions’ precipitation is the strongest around the eastern edge of the TP and then weakens gradually toward the east. Consequently, TP moisture in the composition of precipitation over the central-eastern TP is the largest of over 30%. 44.9-46.7% of TP annual evaporation is recycled over the TP, and about 2/3 of the TP evaporation is reprecipitated over terrestrial China. Moisture cycling of TP origin shows strong seasonal variation, with seasonal patterns largely determined by precipitation, evaporation and wind fields. High levels of evaporation and precipitation over the TP in summer maximize local recycling intensity and recycling ratios. Annual precipitation of TP origin increased mainly around the northeastern TP during 2000-2020. This region consumed more than half of the increased TP evaporation. Further analyses showed that changes in reprecipitation of TP origin were consistent with precipitation trends in nearby downwind areas: when intensified TP evaporation meets intensified precipitation, more TP moisture is precipitated out. The model estimated an annual precipitation recycling ratio (PRR) of 26.9-30.8% in forward moisture tracking. However, due to the non-closure issue of the atmospheric moisture balance equation, the annual PRR in backward tracking can be ~6% lower.

Avalanches and other hazardous mass movements pose a danger to the population and critical infrastructure in alpine areas. Hence, understanding and continuously monitoring mass movements is crucial to mitigate their risk. We propose to use Distributed Acoustic Sensing (DAS) to measure strain rate along a fiber-optic cable to characterize ground deformation induced by avalanches. We recorded 12 snow avalanches of various dimensions at the Vallée de la Sionne test site in Switzerland, utilizing existing fiber-optic infrastructure and a DAS interrogation unit during the winter 2020/2021. By training a Bayesian Gaussian Mixture Model, we automatically characterize and classify avalanche-induced ground deformations using physical properties extracted from the frequency-wavenumber and frequency-velocity domain of the DAS recordings. The resulting model can estimate the probability of avalanches in the DAS data and is able to differentiate between the avalanche-generated seismic near-field, the seismo-acoustic far-field and the mass movement propagating on top of the fiber. By analyzing the mass-movement propagation signals, we are able to identify group velocity packages within an avalanche that propagate faster than the phase velocity of the avalanche front, indicating complex internal structures. Importantly, we show that the seismo-acoustic far-field can be detected before the avalanche reaches the fiber-optic array, highlighting DAS as a potential research and early warning tool for hazardous mass movements.

Extraction of critical hydrologic features from high-resolution topographic data is challenging using existing grid coarsening approaches, such as surface flow path, river network, and slope, which limits the application of hydrological models. In this research, the influence of various grid coarsening techniques on the prediction outcomes was measured by a numerical experiment based on the integrated hydrological model ParFlow-Common Land Model (ParFlow.CLM). Three grid coarsening methods (Nearest Neighbor Coarsening, Majority Coarsening, Hydrography-Driven Coarsening) were applied to simulate evapotranspiration(E), soil temperature(ST), streamflow, soil moisture(SM) and latent(LE) heat fluxes in central China’s Sixi Valley, a classic example of a karstic basin. As a result, the three grid coarsening methods perform uniform in simulating latent heat fluxes and soil temperature. However, their ability to predict soil moisture surface flow and evapotranspiration are more diverging. The hydrography-driven coarsening extracts significantly more accurate valleys, rivers network, and slopes closer to the actual terrain than existing coarsening strategies. Slopes derived from hydrography-driven coarsening methods can be used to predict more accurately the top soil moisture, evapotranspiration, and streamflow dynamics processes. This study stresses that a hydrography-driven coarsening strategy is advocated for all those cases in which topographic slope extracted using a coarse-grid digital elevation model is an important influence on the ParFlow.CLM simulation of essential hydrographic features.

To determine plausible groundwater recharge rates on early Mars, we develop analytic and numerical solutions for an unconfined steady-state aquifer beneath the southern highlands. We show that the aquifer’s mean hydraulic conductivity, $K$, is the primary constraint on the plausible magnitude of mean steady recharge, $r$. By restricting groundwater upwelling to Arabia Terra, using a mean hydraulic conductivity of, $K$ ${\sim}10^{-7}$ m/s, and varying shoreline elevations and recharge distributions, the mean recharge must be order of $10^{-2}$ mm/yr. Recharge for other values of $K$ can be estimated as $r$ ${\sim}10^{-5}\,K$. Our value is near the low end of previous recharge estimates and two orders-of-magnitude below the smallest precipitation estimates. This suggests that, for a steady hydrologic cycle, most precipitation forms runoff, not groundwater recharge. It is also plausible the transient aquifer response to recharge is sufficiently slow that no upwelling occurs prior to cessation of climatic excursions causing precipitation.

The usefulness of extended records of streamflow from tree-ring based hydrologic reconstructions seems obvious- a longer record provides a broader range of the variability of extremes and allows recent and/or ongoing events to be evaluated in a long-term context. The information from these centuries-long records may have clear implications for water resource management, but it is often unclear exactly how this information can be applied to management. In this presentation, I will discuss some of the challenges I have observed that are involved in using streamflow reconstructions in management decisions. These range from issues related to an agency’s capacity to use new types of data to mismatches between what is needed (e.g., daily resolution, a network of gage inputs) and what reconstruction data provide. The skillfulness of a streamflow reconstruction also has a bearing on its perceived credibility in terms of useable data. In spite of these challenges, there is a variety of ways that these data have been used by water resource managers in the western US. The uses are often not immediately evident, but can take the form of, for example, sensitively assessment, awareness raising, and shifts in prior assumptions. Relationship building between researchers and resource managers can yield mutual respect and understanding that lead to both interesting research questions and relevant and valuable information, even if the application to management is not tangible or immediate.

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.