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NASA NTRS2022

Predictive Integrated Stratigraphic Modeling Report, Center-wide Per- and Polyfluoroalkyl Substances, Potential Release Location 237, Kennedy Space Center, FL

The National Aeronautics and Space Administration (NASA) is investigating the occurrence of per- and polyfluoroalkyl substances (PFAS) Center-wide at Kennedy Space Center (KSC), Merritt Island, Florida (Figure 1-1) as Potential Release Location 237. AECOM Technical Services, Inc. (AECOM) conducted investigation activities in 2020 and 2021 to develop a better understanding of the fate and transport of PFAS in groundwater and surface water at KSC center-wide using PRedictive Integrated Stratigraphic Modeling (PRISM). PRISM uses best practices from the fields of geology, hydrology, and chemistry to acquire a holistic understanding of the subsurface and more accurately predict contaminant migration pathways. Based on the potential magnitude (frequency and areal distribution) of PFAS impacts at KSC, the analyses described herein were designed to provide tools to address the unique behavior of PFAS chemicals.

PFAS↗
DOE OSTI2022

Quantifying the City-Scale Impacts of Impervious Surfaces on Groundwater Recharge Potential: An Urban Application of WRF–Hydro

Decades of urbanization have created sprawling, complex, and vulnerable cities, half of which are located in water-scarce areas. With the looming effects of climate change, including increasing droughts and water shortages, there is an urgent need to better understand how urbanization impacts the water cycle at city scale. Impervious surfaces disrupt the natural flow of water, affecting groundwater recharge in water-scarce cities, such as Los Angeles, looking to harness local water resources. In the face of growing water demand, informing on opportunities to maximize potential groundwater recharge can help increase cities’ resilience. WRF–Hydro, a physics-based hydrological modeling system, capable of resolving atmospheric, land surface, and hydrological processes at city scale, is adapted to represent urban impervious surfaces. The modified model is used to assess the hydrological implications of historical urbanization. Pre- and post-urban scenarios are used to quantify the impacts of impervious surfaces on the local water budget. Our results show that urbanization in LA has vastly decreased the potential for groundwater recharge, with up to half of the water inflow being redirected from infiltration in highly urbanized watersheds, while doubling surface runoff’s share of the city’s water budget, from ~15% to 30%. This study not only sheds light on the role of imperviousness on groundwater recharge in water-scarce cities, but also offers a robust and transferable tool for the management of urban land and water resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Leveraging Groundwater Dynamics to Improve Predictions of Summer Low-Flow Discharges

Summer streamflow predictions are critical for managing water resources; however, warming-induced shifts from snow to rain regimes impact low-flow predictive models. Additionally, reductions in snowpack drive earlier peak flows and lower summer flows across the western United States increasing reliance on groundwater for maintaining summer streamflow. However, it remains poorly understood how groundwater contributions vary interannually. Here, we quantify recession limb groundwater (RLGW), defined as the proportional groundwater contribution to the stream during the period between peak stream flow and low flow, to predict summer low flows across three diverse western US watersheds. We ask (a) how do snow and rain dynamics influence interannual variations of RLGW contributions and summer low flows?; (b) which watershed attributes impact the effectiveness of RLGW as a predictor of summer low flows? Linear models reveal that RLGW is a strong predictor of low flows across all sites and drastically improves low-flow prediction compared to snow metrics at a rain-dominated site. Results suggest that strength of RLGW control on summer low flows may be mediated by subsurface storage. Subsurface storage can be divided into dynamic (i.e., variability saturated) and deep (i.e., permanently saturated) components, and we hypothesize that interannual variability in dynamic storage contribution to streamflow drives RLGW variability. In systems with a higher proportion of dynamic storage, RLGW is a better predictor of summer low flow because the stream is more responsive to dynamic storage contributions compared to deep-storage-dominated systems. Overall, including RLGW improved low-flow prediction across diverse watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Modeling the topographic influence on aboveground biomass using a coupled model of hillslope hydrology and ecosystem dynamics

Abstract. Topographic heterogeneity and lateral subsurface flow at the hillslope scale of ≤1 km may have outsized impacts on tropical forest through their impacts on water available to plants under water-stressed conditions. However, vegetation dynamics and finer-scale hydrologic processes are not concurrently represented in Earth system models. In this study, we integrate the Energy Exascale Earth System Model (E3SM) land model (ELM) that includes the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), with a three-dimensional hydrology model (ParFlow) to explicitly resolve hillslope topography and subsurface flow and perform numerical experiments to understand how hillslope-scale hydrologic processes modulate vegetation along water availability gradients at Barro Colorado Island (BCI), Panama. Our simulations show that groundwater table depth (WTD) can play a large role in governing aboveground biomass (AGB) when drought-induced tree mortality is triggered by hydraulic failure. Analyzing the simulations using random forest (RF) models, we find that the domain-wide simulated AGB and WTD can be well predicted by static topographic attributes, including surface elevation, slope, and convexity, and adding soil moisture or groundwater table depth as predictors further improves the RF models. Different model representations of mortality due to hydraulic failure can change the dominant topographic driver for the simulated AGB. Contrary to the simulations, the observed AGB in the well-drained 50 ha forest census plot within BCI cannot be well predicted by the RF models using topographic attributes and observed soil moisture as predictors, suggesting other factors such as nutrient status may have a larger influence on the observed AGB. The new coupled model may be useful for understanding the diverse impact of local heterogeneity by isolating the water availability and nutrient availability from the other external and internal factors in ecosystem modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

GCAM–GLORY v1.0: representing global reservoir water storage in a multi-sector human–Earth system model

Abstract. Reservoirs play a significant role in modifying the spatiotemporal availability of surface water to meet multi-sector human demands, despite representing a relatively small fraction of the global water budget. Yet the integrated modeling frameworks that explore the interactions among climate, land, energy, water, and socioeconomic systems at a global scale often contain limited representations of water storage dynamics that incorporate feedbacks from other systems. In this study, we implement a representation of water storage in the Global Change Analysis Model (GCAM) to enable the exploration of the future role (e.g., expansion) of reservoir water storage globally in meeting demands for, and evolving in response to interactions with, the climate, land, and energy systems. GCAM represents 235 global water basins, operates at 5-year time steps, and uses supply curves to capture economic competition among renewable water (now including reservoirs), non-renewable groundwater, and desalination. Our approach consists of developing the GLObal Reservoir Yield (GLORY) model, which uses a linear programming (LP)-based optimization algorithm and dynamically linking GLORY with GCAM. The new coupled GCAM–GLORY approach improves the representation of reservoir water storage in GCAM in several ways. First, the GLORY model identifies the cost of supplying increasing levels of water supply from reservoir storage by considering regional physical and economic factors, such as evolving monthly reservoir inflows and demands, and the leveled cost of constructing additional reservoir storage capacity. Second, by passing those costs to GCAM, GLORY enables the exploration of future regional reservoir expansion pathways and their response to climate and socioeconomic drivers. To guide the model toward reasonable reservoir expansion pathways, GLORY applies a diverse array of feasibility constraints related to protected land, population, water sources, and cropland. Finally, the GLORY–GCAM feedback loop allows evolving water demands from GCAM to inform GLORY, resulting in an updated supply curve at each time step, thus enabling GCAM to establish a more meaningful economic value of water. This study improves our understanding of the sensitivity of reservoir water supply to multiple physical and economic dimensions, such as sub-annual variations in climate conditions and human water demands, especially for basins experiencing socioeconomic droughts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)

This data release provides all data and code used in the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)" to model stream temperature, evaluate, and assess results. The associated manuscript explores current open questions in prediction in ungauged and unmonitored basins concerning top-down versus bottom-up approaches, tradeoffs between data available and input requirements, and the appropriate representation of catchment attributes as inputs to deep learning models. Modeling was done primarily with long short-term memory (LSTM) models, and stream site coverage spans 1362 locations across the conterminous United States. The data is organized into these items items:Code repository and data for the paper " "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models" Willard et al. (2024)".Code: stream_temp_ml_regionalization.zip contains the code repositoryData to run the code: - data_dir.zip -- contains all files that should be moved to the "DATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- metadata_dir.zip -- contains all files that should be moved to the "METADATA_DIR" variable defined in the "set_env_vars.sh" script in the code repository- error_analysis_attribute_and_groundwater_dir.zip - workflows for the extended error analysis by stream attribute and groundwater influenceData produced by the code and used in the paper:- outputs_dir.zip - contains model output and results (outputs_dir/results), model weights (outputs_dir/models), and all other outputs used for the paper including feature importances.To cite this code, please use the following BibTeX or MLA entries:bibtex:@misc{willard2024streamdata, author = {Jared Willard and Fabio Ciulla and Helen Weierbach and Vipin Kumar and Charuleka Varadharajan}, title = {Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models"}, year = {2024}, doi = {10.15485/2448016}, publisher = {ESS-DIVE Repository}, url = {https://doi.org/10.15485/2448016}}MLA: Willard, Jared, et al. Dataset for "Evaluating Deep Learning Approaches for Predictions in Unmonitored Basins with Continental-scale Stream Temperature Models". 2024. ESS-DIVE Repository, doi:10.15485/2448016.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux?

Temperature-based methods have been developed to infer 1D vertical exchange flux between a stream and the subsurface. Current analyses rely on fitting physically based analytical and numerical models to temperature time series measured at multiple depths to infer daily average flux. These methods have seen wide use in hydrologic science despite strong simplifying assumptions including a lack of consideration of model structural error or the impacts of multidimensional flow or the impacts of transient streambed hydraulic properties. We performed a “perfect-model experiment” investigation to examine whether regression trees, with and without gradient boosting, can extract sufficient information from model-generated subsurface temperature time series, with and without added measurement error, to infer the corresponding exchange flux time series at the streambed surface. Using model-generated, synthetic data allowed us to assess the basic limitations to the use of machine learning; further examination of real data is only warranted if the method can be shown to perform well under these ideal conditions. We also examined whether the inherent feature importance analyses of tree-based machine learning methods can be used to optimize monitoring networks for exchange flux inference.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Data-driven modeling of municipal water system responses to hydroclimate extremes

Sustainable western US municipal water system (MWS) management depends on quantifying the impacts of supply and demand dynamics on system infrastructure reliability and vulnerability. Systems modeling can replicate the interactions but extensive parameterization, high complexity, and long development cycles present barriers to widespread adoption. To address these challenges, we develop the Machine Learning Water Systems Model (ML-WSM) – a novel application of data-driven modeling for MWS management. We apply the ML-WSM framework to the Salt Lake City, Utah water system, where we benchmark prediction performance on the seasonal response of reservoir levels, groundwater withdrawal, and imported water requests to climate anomalies at a daily resolution against an existing systems model. The ML-WSM accurately predicts the seasonal dynamics of all components; especially during supply-limiting conditions (KGE > 0.88, PBias < ±3%). Extreme wet conditions challenged model skill but the ML-WSM communicated the appropriate seasonal trends and relationships to component thresholds (e.g., reservoir dead pool). The model correctly classified nearly all instances of vulnerability (83%) and peak severity (100%), encouraging its use as a guidance tool that complements systems models for evaluating the influences of climate on MWS performance.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Distribution of Infiltration in the 216-U-10, 216-B-3 Pond, and 216-T-4 Pond Systems 1944-1997

A modeling system to simulate fate and transport of contaminant plumes in groundwater beneath the Central Plateau to support the Cumulative Impact Evaluation (CIE) and Composite Analysis (CA) is being developed. The CIE will evaluate the cumulative effect of multiple Central Plateau radiological and chemical sources on groundwater quality. This will enable cleanup decisions to be evaluated in the context of all Central Plateau waste sites and existing groundwater conditions. The purpose of this Environmental Calculation File (ECF) is to partition the infiltration of wastewater and contaminant inventory between the main pond and each expansion lobe of the 216-B-3 Pond system, and to partition the infiltration of wastewater and contaminant inventory between the ditches and ponds of the 216-U-10 Pond and 216-T-4 Pond systems.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Initial Uncertainty Analysis of Carbon Tetrachloride Contamination and Remediation in the Ringold A and Lower Mud Units at the Central Plateau

The long-term effectiveness of groundwater cleanup at the Hanford Site Central Plateau depends on predictive models that can capture key uncertainties in contaminant fate and transport. Carbon tetrachloride (CCl 4 ), a persistent and toxic compound, presents particular challenges due to variability in degradation rates, uncertainty in initial plume distribution, and subsurface heterogeneity. These uncertainties directly influence plume persistence, migration pathways, and remedy performance, and thus must be systematically evaluated to support long-term remediation planning. To address these gaps, a large-scale Monte Carlo analysis was conducted using the Plateau to River (P2R) model framework. The modeling approach parameterized three primary uncertainty factors: (1) degradation rate, (2) initial plume distribution, and (3) hydraulic conductivity. Degradation was represented as a first-order process, with half-lives ranging from 70 to 700 years. Initial plume distributions were created using a geostatistical simulation method (sgsim), which generates many equally plausible versions of how contaminants might be distributed underground. From this, 100 different scenarios were mapped onto the P2R grid. Variability in hydraulic conductivity was represented in a similar way, with 100 scenarios each for the Ringold Lower Mud and Ringold A units (layers 6 and 7), based on fitted exponential variograms and conditioned to well data. In total, more than 1000 realizations were simulated to assess plume behavior under uncertainty. Results demonstrate that degradation kinetics exert the strongest control over plume persistence: Shorter half-lives produced rapid mass reduction, while longer half-lives yielded persistent plumes with limited attenuation. A nonlinear response was observed, with steep mass reductions at half-lives greater than 200 years and near-linear declines beyond this threshold, reflecting interactions between degradation and pumping. The initial plume distribution strongly influenced early transport patterns, with broader sources generating larger plume footprints, although pump-and-treat operations constrained plume migration to managed areas. By comparison, hydraulic conductivity variability in the Ringold units had only a secondary influence, modifying spreading behavior without altering the dominant migration pathways governed by source configuration and hydraulic controls. Overall, the analysis highlights that uncertainty in degradation rate and initial plume configuration are the primary drivers of variability in plume predictions, while conductivity heterogeneity plays a limited role. These findings underscore the need for improved site-specific data on degradation processes and source characterization to enhance the reliability of long-term performance assessments and to better inform remedial decision-making at the Central Plateau.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2024

Hydrocarbon Burn Facility, SWMU 007 Per- and Polyfluoroalkyl Substances (PFAS) Assessment Report Addendum

This document discusses the Per-and Polyfluoroalkyl Substances (PFAS) assessment activities performed from May through August 2023 at the Hydrocarbon Burn Facility (HBF) located at Kennedy Space Center (KSC), Florida. HBF was used for firefighting training between 1966 and 1994. Aqueous film-forming foams (AFFFs) that contained PFAS were used as suppressants for fighting petroleum fuel fires during training. PFAS sampling was first completed at HBF in 2015. This PFAS Assessment Report Addendum (ARA) is a continuation of sampling efforts, with an overall objective to further delineate PFAS concentrations in groundwater to the pGCTLs and the United States Environmental Protection Agency (EPA) Tapwater Regional Screening Levels (RSLs) dated November 2023 and better understand groundwater to surface water interaction at the Site. The field activities presented in this PFAS ARA were conducted between May and August 2023 and included the collection of 132 direct push groundwater samples and the collection of five colocated surface water, sediment, and pore water samples from locations south/southeast of HBF in August 2023. Exceedances of the pGCTLs and Tapwater RSLs were observed in the direct push groundwater results; however, groundwater has been delineated to the pGCTLs. Exceedances of the Florida provisional Surface Water Screening Levels (pSWSLs) were observed in surface water and porewater, and concentrations of PFAS in these media confirm PFAS migration within surface channels to the south of the HBF site area. Maximum sediment concentrations, which correlate with the maximum surface water and porewater concentrations, demonstrate that PFAS impacted surface water is the source of sediment impacts. Overall, elevated concentrations of PFAS are centered on the HBF site area that trend north and south along the surface water filled swales and along the edge of Banana River. Based on analytical results, not all surface water features in this area exhibit evidence of transport. PFAS migration/transport appears to be more dominant in surface water features during the wet season. Discharges to the Banana River primarily occur where surface water facilitates transport. In contrast, groundwater transport is more effective during the dry season. During precipitation events in either the dry season or the wet season, there appears to be migration from the intermediate groundwater zone to the shallow zone into the surface water features. Additional sampling and data collection is recommended to support further refinement of PFAS groundwater delineation, a remedial alternative evaluation, and the fate and transport model.

Jennifer Buel↗
NASA NTRS2022

Bryce Canyon Water Resources: Monitoring Vegetation Health and Water Availability in Bryce Canyon National Park for Drought Stress Mitigation Planning

Bryce Canyon National Park is home to groundwater-dependent ecosystems (GDEs) that are threatened by a multidecadal drought and increased groundwater extraction due to a spike in tourism. These ecosystems contain unique species that are only found in areas where near-surface groundwater is present, such as aspen groves and fens. These species contribute to the high biodiversity found in Bryce Canyon, which boosts an ecosystem’s productivity and the services it provides to the park. Unfortunately, many of these GDEs are too small to identify with traditional Earth observation platforms and are difficult to physically reach for monitoring purposes. This project partnered with the National Park Service to identify springs and seeps as a proxy for GDEs within Bryce Canyon from 2013–2022. Furthermore, this project tested the feasibility of various methods to detect and monitor springs and seeps and therefore facilitate the partner’s efforts to conserve these ecologically valuable GDEs in Bryce Canyon. The team mapped groundwater discharge with high resolution National Agriculture Imagery Program (NAIP) and assessed park vegetation trends with Landsat 8 Operational Land Imager (OLI) and PlanetScope imagery. In-situ precipitation data and the Western Land Data Assimilation System (WLDAS) were used to produce time series of climatic variables. Seeps and spring locations were predicted using random forest classification and maximum entropy machine learning models.

Groundwater dependent ecosystems↗
DOE OSTI2021

Natural Systems Data Package for the Active Trenches of the Low-Level Burial Grounds, Hanford Site, Washington

This document summarizes the available information regarding the natural system (vadose zone and saturated zone) at the active trenches of the 200 West Area and 200 East Area Low-Level Burial Grounds (LLBGs). This information is relevant for developing the natural system fate and transport model for the update of the active trenches performance assessment. This document presents a description of the major features, events, and processes of the natural system at the active trenches of the 200 West Area and 200 East Area LLBGs that involves the geologic features or framework, and the vadose zone and groundwater flow and transport system. The description of the geologic features includes the following items: geologic setting, major structural features, major hydrostratigraphic units, and geologic framework. The discussion concerning vadose zone flow and transport at the active trenches of the 200 West Area and 200 East Area LLBGs includes the following topics: vadose zone features, events, and processes relevant to the development of the vadose zone conceptual model; hydrogeologic units and conditions; factors affecting moisture movement; and flow and transport properties of major units. The discussion concerning the groundwater flow and transport system in the vicinity of the active trenches of the 200 West Area and 200 East Area LLBGs includes the following topics: saturated zone features, events, and processes relevant to the saturated zone conceptual model; hydrogeologic units; groundwater flow conditions; and flow and transport properties.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Influence of physical and chemical hydrology on bioremediation of a U-contaminated aquifer informed by reactive transport modeling incorporating 238 U/ 235 U ratios

Microbially-catalyzed reductive immobilization of aqueous uranium (U) as a solid phase has been proposed as a U remediation technique. Both laboratory and field experiments have demonstrated that this reduction reaction alters the 238 U/ 235 U ratio, producing a 238 U-enriched U(IV) solid. In contrast, other major U reactive transport processes fractionate these isotopes much less. This suggests the potential to quantify the extent of bioreduction occurring in groundwater containing U using the 238 U/ 235 U ratio, which would substantially improve upon current practices largely relying on U concentration measurements alone. Current reactive transport models for uranium dynamics include only concentration measurements, which are strongly influenced by highly coupled reactive transformation and aqueous transport processes. The complex physical and chemical behavior of U potentially compromises such quantitative analysis of U storage and release. Here we report the first numerical reactive transport model which explicitly incorporates variations in the 238 U/ 235 U ratio of U and demonstrates improved interpretation of the principal chemical reactions and groundwater transport processes affecting the subsurface mobility and distribution of this widespread contaminant. Recent U bioreduction studies performed in a contaminated aquifer in Rifle, Colorado, USA applied Rayleigh distillation models to interpret U stable isotope fractionation observed as a result of acetate amendment. These simplified models were unable to resolve the spatiotemporal pattern of U isotope fractionation recorded in the aqueous solutes. Here, we employ the multi-component, isotope-enabled CrunchTope reactive transport software to interpret these measured U isotope ratios, and demonstrate accurate reproduction of observed trends in both geochemistry and 238 U/ 235 U ratios for two consecutive years of field experiments. Overall, our results indicate that accurately modeling both the U concentration and isotope ratio distributions greatly constrains the parameter space of the model. We find that the transport properties of U in the Rifle aquifer are governed by the presence of low-permeability regions, which the isotopes are uniquely sensitive to. When U reduction is spatially constrained by these low-permeability regions, the shift in the 238 U/ 235 U ratio becomes more muted. Accurate modeling of observed U isotope ratios thus provides a powerful means to better understand bioremediation, and the current study serves to advance the application of this novel method.

238U/235U↗
DOE OSTI2020

Revised Kimberlina Groundwater Simulations

Kimberlina Groundwater Qaulity Simulations (Version 3: 2015 October) by Lawrence Livermore National Labratory. Submitted to the EDX team at the National Energy Technology Laboratory for research use. This dataset contains 562 updated groundwater quality simulations based on the Kimberlina site. It is a six-layer model including the aquifers and aquitards inbetween the ground surface and the reservoir. The input files and associated leakage information for these simulations are included in the zip file "nrap_kim_well31_fluxes and inputs.tar.gz". These 562 simulations are the successful results out of 1000 monte carlo simulations, with time length greater than 5 years. Each simulation consists 6 output files, therefore, there are 3372 files (6*562) in total. One single example simulation of full time length (200 year) is also provided in the zip file "single_simulation_200yr_sim0012.tar.gz". These 6 groundwater quality parameters are: - pH (use absolute value) - TDS (mg/kg) - Pressure (Pa) - CO2 Liquid Saturation (fraction range 0.0-1.0) - CO2 Gas Concentration (mol/L) - CO2 Liquid Concentration (mol/kg) Their correponding file names, take the first simulation for example, are: ph0001.ntab (for pH) , tds0001.ntab (for TDS), usnt0001.p.ntab (for pressure), usnt0001.s_liquid.ntab (for liquid saturation), usnt0001.c_co2_gas.ntab (for CO2 gas concentration) and usnt0001.c_co2_liquid.ntab (for CO2 liquid concentration). Each output file contains at least 19 columns (variables). The total number of columns depends on the simulation time length. The column data include the information of numerical records, geologic location and sizes and the simulated parameter values over time. The first 13 variables are about numerical records and relative geologic information for a simulation grid: 1. index: simulation index 2. i: the ith grid of x-axis 3. j: the jth grid of y-axis 4. k: the kth grid of z-axis 5. element_ref: element type reference 6. nuft_ind: nuft index 7. x: grid location in the x axis direction 8. y: grid location in the y axis direction 9. z: grid location in the z axis direction 10. dx: grid length in the x axis direction 11. dy: grid length in the y axis direction 12. dz: grid length in the z axis direction 13. volume: volume of the simulation grid The rest (14, 15, 16...) variables are the simulated parameter values over time, take pH as an example, are: 14. ph000y: initial pH value. 15. ph001y: simulated pH value at the end of the 1st year. 16. ph002y: simulated pH value at the end of the 2nd year. ... (and so on)... The zip input files have the input fluxes of brine and CO2 (in kg/s) allocated to model layers 1,2,3 and 5 (layers 4 and 6 are assumed to be no-flow aquitards) through the following nodes: (src1 WELL 7 7 25 25 10 32) #Etchegoin (src2 WELL 7 7 25 25 33 72) #Macoma Chanac (src3 WELL 7 7 25 25 73 83) #Santa Margarita Mclure (src4 WELL 7 7 25 25 84 89) #Fruitvale Round Mountain (aquitard) (src5 WELL 7 7 25 25 90 96) #Olcese Sand (thief zone) (src6 WELL 7 7 25 25 97 99) #tremblor Freeman (aquitard) ex: brn1 is assigned to src 1 from i=(7,7), j=(25,25), k=(10,32) Edited by Ya-Mei Yang at National Energy Technology Laboratory

brine leakage↗
DOE OSTI2023

Data-model files associated with the manuscript titled "The Importance of Explicitly Representing the Streambed in Watershed Models" (Shuai et al., 2023 HP)

This data package contains the model inputs and outputs used in the manuscript titled "The Importance of Explicitly Representing the Streambed in Watershed Models" (Shuai et al., 2023 HP). The data.zip file contains the data used to drive the Advanced Terrestrial Simulator (ATS) model simulations. The model.zip file contains the XML input file for ATS. The notebook.zip file contains the Jupyter notebooks for pre- and post- processing model results. The figures.zip file contains the raw figures associated with the manuscript. We generated this data package in support of the manuscript and research reproducibility. The background of this study is that the streambed itself has not been explicitly represented in watershed models, although the streambed characteristics are significantly different from those of its surrounding soil. We aim to answer the following questions: 1) How do streambed properties including hydraulic conductivity, thickness and resolution impact the groundwater-surface water exchange fluxes across the streambed? 2) Is an explicit representation of streambed important in watershed modeling?; 3) Is a high-resolution streambed needed for watershed simulations?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Intensifying tropical cyclones in the Arabian Sea replenish depleting aquifers

Tropical cyclones intensified globally in recent decades, delivering extreme precipitation deeper inland. While much research has focused on the role of climate change in tropical cyclone intensification, less is known about their contribution to groundwater recharge, especially in arid regions where freshwater is scarce and aquifers are being depleted. Here we quantify cyclone-driven groundwater recharge across the Arabian Peninsula from 2002 to 2021 using satellite-based total water storage and hydrodynamic modeling. Findings show that cyclones contributed up to 60% of total precipitation in the southern Arabian Peninsula. Cyclone Mekunu (2018) alone delivered 30 km 3 of precipitation inland, resulting in a net groundwater recharge of 3.2 ± 1.2 km 3 in the Najd subbasin. These findings reveal that tropical cyclones play a crucial role in replenishing groundwater resources in arid regions. Our approach provides a framework for quantifying recharge in ungauged arid basins worldwide, offering valuable insights for climate-resilient water resource management.

Saleh, Hassan [Western Michigan Univ., Kalamazoo M↗
DOE OSTI2023

Predicting Short-Term Deformation in the Central Valley Using Machine Learning

Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers’ integrity.

58 GEOSCIENCES↗