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Conference slide deck for participation in the Produced Water Society 2024 Annual Meeting research round table with a demonstration of a produced water market clearing tool that is presently in development. Our demonstration illustrates how an alternative approach to produced water management can reduce costs for operators and increase produced water recycling rates.
Abstract Hydroelectric power is playing a new and often expanded role in the world’s major power grids, offering low carbon generating capacity in industrializing, dam-building economies while providing reserve and flexibility to co-manage fledgling wind and solar resources in high income countries. Driven by river flows, conventional hydropower is exposed to the vagaries of weather and climate, motivating drought and climate change hydropower impact studies at large spatial scales. Here we review methods of climate-driven hydropower simulation at large spatial scales, specifically multi-basin regions to global. We identify four types of approach based on complexity of tools and richness of data applied to the problem. Since the earliest attempts to model climate-driven hydropower at continental scale almost two decades ago, the field has transitioned from one of scientific curiosity to practical application, with studies increasingly motivated by the need to inform power grid expansion planning and operation. As the hydrological and water management models used in large-scale hydropower studies become more sophisticated, new opportunities will emerge to study the impacts of changing hydropower on power system reliability and performance at large power grid scale. To grasp these opportunities, the water resources community must continue to enhance data and models for representing river flows and anthropogenic water use and management at subcontinental to global scales.
This study focuses on an agricultural region in California’s Central Valley, USA, where Managed Aquifer Recharge (MAR) is widely implemented to mitigate groundwater depletion under increasing water demand and climate variability. A deep learning and machine learning framework was developed to identify infiltration-MAR locations using satellite imagery and environmental data. The framework integrates surface water detection from Sentinel-2 imagery, geospatial delineation of water bodies, spatiotemporal tracking of water body dynamics, and supervised classification using meteorological, environmental, and topographic variables. The framework was applied to a 2379 km² study area southwest of Fresno, where 765 water bodies were detected, including 139 identified MAR sites based on publicly available datasets and expert knowledge. The classification model achieved an accuracy of 0.94 and an F1 score of 0.85. Feature importance analysis indicates that cropland, normalized difference vegetation index (NDVI), and evaporation are among the most influential predictors for infiltration-MAR. Notably, the framework suggests that engineered water management in infiltration-MAR systems can disrupt or even reverse the expected positive correlation between surface water extent and precipitation. These findings provide physically interpretable insights into the characteristics of existing infiltration-MAR facilities and demonstrate the potential of the proposed framework as a reproducible, interpretable, and potentially transferable tool for data-driven infiltration-MAR identification and inventory development under growing climatic and hydrological uncertainty.
Describing flow resistance using the physical properties of an underlying surface is a recalcitrant problem in overland flow models. If discharge measurements are available, an equivalent roughness (e.g., Manning’s n) can be calibrated to represent the effects of surface properties within the domain with a single numerical value. Alternatively, the flow resistance can be estimated from discharge and velocity measured at a point, typically a runoff plot outlet. However, such experimental estimates are often inconsistent with the equivalent roughness determined from calibration to discharge, even if both derive from the same dataset. For example, if Manning’s equation is used to parameterize flow resistance, the Manning’s n obtained by calibrating a model to discharge differs from the value of n calculated from measured flow and velocity at the hillslope outlet. Here, this discrepancy is resolved by deriving a correction factor relating experimentally-determined flow resistance to the equivalent roughness. The derived correction factor is tested for four commonly-used resistance formulations using 129 rainfall simulator experiments. The correction factor is necessary to reproduce measured velocities, and yields minor improvements in discharge prediction. Plain Language Summary: Accurate runoff prediction is needed for land and water management in dryland regions, where sporadic and limited rainfall necessitate efficient water use and drought mitigation strategies. The skill of runoff models is known to be hindered by out ability to estimate flow resistance, which is the quantity that describes how energy is lost from flowing water to the underlying surface. Typically, models represent flow resistance with an equivalent roughness, e.g., Manning’s n, that is adjusted until the model can reproduce available discharge observations at watershed scale. However, the flow resistance measured in plot-scale experiments (1–10 m) often exceeds equivalent roughness coefficients by a factor of 10. This means that the direct use of plot-scale experimental data to parameterize runoff models could cause errors in discharge and runoff velocity predictions. Here, we resolve these differences by deriving an analytic correction factor that relates flow resistance to the equivalent roughness required for models to reproduce experimental velocity and discharge data. This correction factor is tested using rainfall simulator data from 129 experiments performed in the US Southwest covering a wide range of precipitation intensities, soil textures and vegetation types. Use of the correction factor substantially improves model prediction of flow velocity, which is needed for reproducing the timing of flood events and the estimation of erosion.
Predicting water imbibition into porous materials is important in a wide variety of fields, and hydraulic fracturing of low permeability hydrocarbon reservoirs has emerged as an application that is imposing a large water footprint. Furthermore, reliable predictions of imbibition are needed to better manage water use, yet are challenging because of uncertainties in both the permeability and capillary pressure driving force. Here, this uncertainty is reduced through evaluating correlations between the permeability and the effective capillary pressure associated with the wetting front, P c,f . These correlations allow elimination of P c,f from the Green and Ampt equation and concentrate all uncertainties in fluxes on the effective permeability k . Over a wide range of k and porosities n , imbibition scales approximately with k 1/3 . Although Leverett k 1/4 scaling for predicting P c,f is shown to be inferior when tested with data spanning a wide range of n , it nevertheless predicted imbibition fairly well. From simple imbibition measurements, both the empirical and Leverett scaling approaches allow estimates of k that have root-mean-square deviations of about one order of magnitude relative to measurements that ranged over 10 orders of magnitude in k .
Climate change and human activities alter the global freshwater cycle, causing nonstationary processes as its distribution shifting over time, yet a comprehensive understanding of these changes remains elusive. Here, we develop a remote sensing–informed terrestrial reanalysis and assess the nonstationarity of and interconnections among global water cycle components from 2003 to 2020. We highlight 20 hotspot regions where terrestrial water storage exhibits strong nonstationarity, impacting 35% of the global population and 45% of the area covered by irrigated agriculture. Emerging long-term trends dominate the most often (48.2%), followed by seasonal shifts (32.8%) and changes in extremes (19%). Notably, in mid-latitudes, this encompasses 34% of Asia and 27% of North America. The patterns of nonstationarity and their dominant types differ across other water cycle components, including precipitation, evapotranspiration, runoff, and gross primary production. These differences also manifest uniquely across hotspot regions, illustrating the intricate ways in which each component responds to climate change and human water management. Our findings emphasize the importance of considering nonstationarity when assessing water cycle information toward the development of strategies for sustainable water resource usage, enhancing resilience to extreme events, and effectively addressing other challenges associated with climate change.
Evaluating extreme precipitation events (EPEs) is essential for building climate-resilient water management strategies, but it remains a major challenge in ungauged basins. Using the 26,070 km 2 Wadi al-Rummah basin in central Saudi Arabia as a case study, we developed an alternative, reliable, cost-effective satellite-based framework that combines empirically derived EPE thresholds, imagery-calibrated 2D hydrodynamic modeling, GRACE water-storage diagnostics, and bias-corrected CMIP6 projections to assess flood hazards and recharge potential under current and future climate scenarios in ungauged basins. The integrated approach and the resulting findings followed four key steps: (1) Identified a 22.5 mm EPE threshold, the 80th percentile of 3-day GPM/IMERG rainfall (2000–2024), aligned with flood-triggering events (Nov 2018: 23–28 mm; Apr, 2023: 42 mm); (2) Developed and calibrated a RiverFlow2D model using Sentinel-2 and PlanetScope imagery for the November 2018 flood, accurately reproducing flood depth and extent (RMSE ≤0.31 m; fuzzy-Dice ≥0.91), and estimating runoff (41 %), infiltration (25 %), and evaporation (34 %); (3) Independently validated the model with the April 2023 event (RMSE ≤0.35 m; fuzzy-Dice ≥0.86); (4) Conducted climate projections (2025–2100) from five bias-corrected NEX-GDDP CMIP6 models that revealed a 34 % increase in EPE intensity under SSP2-4.5 and 48 % under SSP5-8.5 scenarios, relative to 20th-century baselines. Our findings indicate that while intensifying extremes in the 21st century increase flood risk, the results highlight the potential for episodic recharge if effective retention strategies are employed, and offer a transferable model for climate-informed planning in data-scarce arid regions.
Managing produced water from oil and gas wells constitutes a significant portion of the costs of operating a well. Here, in this work, we have designed two different centralized water treatment facilities capable of managing produced water from oil and gas wells in Texas and Louisiana, both of which convert the produced water into the following valuable resources: ten-pound brine and fresh water. The two main designs each use commercially available technology with varying levels of establishment in treating produced water. Both treatment processes remove oil and grease and suspended solids, reduce the divalent ion concentrations, and concentrate the brines to a near-saturation state. The baseline design uses chemical precipitation to remove the divalent ions to meet the reuse specifications, whereas the advanced design uses nanofiltration (NF) membranes to separate divalent ions and uses reverse osmosis (RO) membranes to partially concentrate the brine. Both models use mechanical vapor recompression to concentrate the brine up to NaCl saturation. The baseline process is shown to be cost-effective for low-hardness brines. In the case of high hardness, the chemical precipitation step is cost-prohibitive. We find that NF membranes are a promising alternative to chemical precipitation as a means of separating monovalent and divalent ions.
This study (completed by US Department of Energy, National Energy Technology Laboratory) investigates the performance, cost, and generating capacity impacts of switching from wet cooling towers to dry cooling systems to reduce consumptive water use at existing fossil fuel-fired power plants in a water-stressed region. Here, retrofit analysis of dry cooling is conducted on a unit-by-unit basis for both coal-fired and gas-fired power plants in several western U.S. states. Based on regional averages, retrofitting dry cooling systems in lieu of wet cooling towers decreases total annual plant water consumption at existing coal- and gas-fired power plants by 93% and 100%, respectively, while increasing the levelized cost of electricity generation by approximately 12% and 18%, respectively. Over the year, the change in monthly net regional generating capacity from dry cooling retrofits exhibits a seasonal pattern, with the largest shortfalls occurring in July. Dry cooling retrofits thus lead to tradeoffs in water savings, cost, and capacity shortfalls, which vary with power plant characteristics. The tradeoffs identified here can inform electric power industry water management plans and decision-making for alternative cooling options in water-stressed regions.
Water use at Los Alamos National Laboratory (LANL) has become a top priority due to New Mexico’s arid climate. LANL is taking steps to reduce its current water footprint and become a national leader in water efficiency. LANL used approximately 271 million gallons of water in FY 2021. Despite the large water footprint, the Lab has reduced its water use by 2% each year since a 2007 baseline. To continue this positive trend of water conservation, the water management team at Utilities & Infrastructure – Operations Support & Improvement (UI-OSI) studied water usage across campus and developed several water reduction strategies.
Fukushima Daiichi Nuclear Power Station was severely damaged by a tsunami induced by the earthquake on March 11, 2011. A large amount of water treatment secondary waste has been generated from the purification work of contaminated water that consists of the water flowing into the reactor building. The Mid and Long-Term Road map specifies that solid waste at Fukushima Daiichi Nuclear Power Station should be stored safely and reasonably according to its characteristics. In order to meet this goal, a versatile treatment solution is needed, and the In-Can vitrification seems to be a suitable solution. The DEM and MELT In-Can vitrification process is being developed and qualified in France to treat waste arising from decommissioning and dismantling operations. This qualification is carried out through the consortium of the DEM and MELT project coordinated by CEA which gathers Orano, ECM technologies and Andra. The DEM and MELT In-Can vitrification process is a robust, simple and versatile in situ vitrification process. It is designed for high or intermediate level waste and is compact enough to be implemented in an existing facility or close to the waste to be treated. It is developed to treat liquid and solid waste, to produce a small amount of secondary waste and to minimize investment and operating costs. The DEM and MELT In-Can vitrification process is also developed with a modular design in order to be adapted to nuclear operators needs and requirements. The In-Can melting process has already been developed and fully qualified by the CEA for Alpha liquid waste and can be rated TRL 7 for this scope. The In-Can vitrification technology is considered as a qualified solution that could be applied for the treatment of waste resulting from the D and D work in Fukushima Daiichi Nuclear Power Station. The feasibility evaluation of the DEM and MELT In-Can vitrification process for Fukushima Daiichi water treatment secondary waste is a project led by Orano, CEA and ANADEC. This R and D project is performed by the funds from the Ministry of Economy, Trade and Industry as The Subsidy Program 'Project of Decommissioning and Contaminated Water Management'. This paper presents the first results of this feasibility evaluation, from the scenarios and glass formulation definition to the industrial implementation feasibility. (authors)
Stream temperature (Ts) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate Ts predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions of Ts in unmonitored stream reaches can enable decision makers to be responsive to changes caused by unforeseen disturbances. In this study, we demonstrate the use of classical machine learning (ML) models, support vector regression and gradient boosted trees (XGBoost), for monthly Ts predictions in 78 pristine and human-impacted catchments of the Mid-Atlantic and Pacific Northwest hydrologic regions spanning different geologies, climate, and land use. The ML models were trained using long-term monitoring data from 1980–2020 for three scenarios: (1) temporal predictions at a single site, (2) temporal predictions for multiple sites within a region, and (3) spatiotemporal predictions in unmonitored basins (PUB). In the first two scenarios, the ML models predicted Ts with median root mean squared errors (RMSE) of 0.69–0.84 °C and 0.92–1.02 °C across different model types for the temporal predictions at single and multiple sites respectively. For the PUB scenario, we used a bootstrap aggregation approach using models trained with different subsets of data, for which an ensemble XGBoost implementation outperformed all other modeling configurations (median RMSE 0.62 °C).The ML models improved median monthly Ts estimates compared to baseline statistical multi-linear regression models by 15–48% depending on the site and scenario. Air temperature was found to be the primary driver of monthly Ts for all sites, with secondary influence of month of the year (seasonality) and solar radiation, while discharge was a significant predictor at only 10 sites. The predictive performance of the ML models was robust to configuration changes in model setup and inputs, but was influenced by the distance to the nearest dam with RMSE <1 °C at sites situated greater than 16 and 44 km from a dam for the temporal single site and regional scenarios, and over 1.4 km from a dam for the PUB scenario. Our results show that classical ML models with solely meteorological inputs can be used for spatial and temporal predictions of monthly Ts in pristine and managed basins with reasonable (<1 °C) accuracy for most locations.
Hydrograph separation is one of many approaches used to analyse shifts in source water contributions to stream flow resulting from climate change in remote watersheds. Understanding these shifts is vital, as shifts in source water contributions to a stream can shape water management decisions. Because remote watersheds are often inaccessible and have poorly characterized contributing water sources, or end-members, it is critical to understand the implications of using different hydrograph separation techniques in these data-limited environments. To explore the uncertainty associated with different techniques, results from two hydrograph separation techniques, mass balance and principle component analysis, were compared using 3 years of aqueous geochemical data from the East River watershed located in the Elk Mountains of Central Colorado. Solute concentrations of the end-members were characterized by both a limited set of direct chemical measurements of different sources and detailed seasonal instream chemistry to examine the influences of uncertain end-member compositions in a data-limited environment. Annual volumetric end-member contributions to stream flow had relatively good agreement across separation techniques. Large variations in time were observed in the hydrograph separations, depending on the end-member type, and estimated flow contributions varied between the selected solutes. End-member concentrations characterized by stream chemistry showed several limitations including a reduced number of distinguishable end-members and differences in timing of flow contributions. Here the results highlight the benefits of using multiple hydrograph separation techniques by providing a ‘weight-of-evidence’ approach to environments with limited end-member concentration data.
Gas diffusion layers (GDLs) play a crucial role in heat transfer and water management of cathode catalyst layers in polymer electrolyte fuel cells (PEFCs). Thermal and water gradients can accelerate electrocatalyst degradation and therefore the selection of GDLs can have a major influence on PEFC durability. Currently, the role of GDLs in electrocatalyst degradation is poorly studied. Here, we perform electrocatalyst accelerated stress test studies on membrane electrode assemblies (MEAs) prepared using three most commonly used GDLs. The effect of GDLs on electrocatalyst degradation is evaluated in both nitrogen (non-reactive) and air (reactive) gas environments at 100% relative humidity. In situ electrochemical characterization and extensive physical characterization is performed to understand the subtle differences in electrocatalyst degradation and correlated to the use of different GDLs. Overall, no difference is observed in the electrocatalyst degradation due to GDLs based on polarization curves at the end of life. But interestingly, MEA with a cracked microporous layer (MPL) in the GDL exhibited a higher electrocatalyst loading loss, which resulted in a lower and more heterogeneous increase in the average electrocatalyst nanoparticle size.
Tritium, a radioactive isotope of hydrogen, is typically confined within one or more confinement barriers. Tritium readily reacts with oxygen to form tritiated water or can exchange with protium or deuterium atoms in water to create tritiated water which is around 10,000 times more toxic than elemental tritium. Primary tritium confinement systems can create small volumes of highly tritiated water. Secondary confinement systems, like inert gloveboxes, will produce larger volumes of lower concentration tritiated water. Much larger volumes of tritiated water of very low tritium content are produced by tritium permeation/diffusion. Tritium from inside primary and secondary confinement barriers will diffuse from these systems into cooling water systems. Closed loop, recirculating cooling water systems will slowly accumulate tritium and create concentrations which exceed drinking water (or permitted discharge) concentrations. The cooling water systems designed as series of interconnected cooling loops that can slowly transfer tritium from one cooling loop to another cooling loop through heat exchangers. These large volumes of tritiated water may be above federal discharge limits and also lack convenient disposition pathways. This document will discuss tritiated water management strategies from the perspective of large volume, low tritium concentrations that exceed drinking water or permitted discharge limits. Applicable US regulatory requirements for a Molybdenum-99 (Mo-99) production facility concerning tritiated water are also cited. Tritiated water disposition options, based on typical waste minimization strategies will guide dispositions options. A Case Study example is presented to illustrate the pros and cons of several disposition options. The report does not recommend any particular disposition pathway but discusses strategies to be explored by a producer to guide selection of processes which best fits the needs of a particular waste stream.
Fermilab’s program to monitor and manage low levels of tritium in surface and sewer water on the laboratory site.
The Three Mile Island, Unit Two (TMI-2) pressurized water reactor core melted down in 1979 due to an untimely combination of maintenance problems that led to a loss of feedwater, followed by a series of operational misunderstandings and errors. The melted core recovery process required development of a wide array of tools. After approximately three years of water management and other cleanup actions, the first views of the core revealed a much higher degree of damage than previously expected. Approximately 62 metric tons of the core had melted, leaving only 42 of the 177 fuel assemblies standing with a majority of rods intact. The core to be recovered was composed of loose debris and a solidified mass of formerly molten fuel. Robotic tools that had been designed for the task were of limited effectiveness due to the range of material types and phases. Over a period of several years, the central melt was broken up, primarily by use of a core-boring drill originally designed to acquire samples through the depth of the debris field. The broken pieces were loaded into specially designed debris canisters by use of long-handled pick-and-place tools and suctioned into baffled knockout canisters using an airlift vacuum system. A remotely operable underwater plasma arc torch was developed for removal of the lower core support structure to be able to retrieve the fuel pieces and secondary melt that had accumulated on the bottom reactor vessel head. Canister design played a central role in defining the initial retrieval process and later affected transportation, interim wet and its current interim dry storage. Due to concerns about the potential for radiolysis of residual water in the debris and other canister material, the canisters were fitted with hydrogen recombiner catalyst units to prevent a buildup of flammable gas and potential pressurization. To confirm the effectiveness of the recombiners during shipping, eight dewatered canisters were kept sealed for up to 205 days with periodic sampling of the headspace gas. The highest hydrogen value (9 vol%) was observed in a canister held for 147 days while the longest stored canister resulted in a 5% hydrogen concentration. The oxygen concentration never exceeded 0.5 %, and the primary backfill was >80% argon, meaning there was no flammability risk. Radiolytic hydrogen was also observed in wet pool storage, where the vented canisters discharged a portion of the water backfill as a result of gas production. The 344 canisters (268 fuel debris, 62 filter, and 12 knockout type) were dewatered, loaded in groups of seven into a double barrier shipping cask, and transported to the U.S. Department of Energy site in Idaho for 10 years of pool storage.
Mining provides significant economic value while often impacting local water supplies and environments because of freshwater usage and waste disposal practices. In this paper we identify current practices in mine water, including how water is used in mining, influent and effluent water quality, treatment technologies, and end uses with the goal of informing future research on implementable, reliable, and cost-effective advanced water treatment in the mining sector. This study also reviews the available literature to broadly evaluate mining in the United States and performs a techno-economic assessment on water use and disposal for three detailed case studies applicable to lithium, uranium, and copper mines. These case studies highlight specific industry examples of distinct extraction methods, geographical regions, and mined commodities. Hypothetical scenarios based on case study baselines revealed potential impacts to mine water available for beneficial reuse through the use of novel water treatment technologies and alternate water management strategies. Finally, an assessment of national level impacts resulting from the reuse of treated mine source water is presented.