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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Recent Progress on Surface Water Quality Models Utilizing Machine Learning Techniques

Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at one time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models.

machine learning (ML)↗
DOE OSTI2025

The Water Table Model (WTM) (v2.0.1): coupled groundwater and dynamic lake modelling

Abstract. Ice-free land comprises 26 % of the Earth's surface and holds liquid water that delineates ecosystems, affects global geochemical cycling, and modulates sea levels. However, we currently lack the capacity to simulate and predict these terrestrial water changes across the full range of relevant spatial (watershed to global) and temporal (monthly to millennial) scales. To address this knowledge gap, we present the Water Table Model (WTM), which integrates coupled components to compute dynamic lake and groundwater levels. The groundwater component solves the 2D horizontal groundwater flow equation using non-linear equation solvers from the C++ PETSc (Portable, Extensible Toolkit for Scientific Computation) library. The dynamic lake component makes use of the Fill–Spill–Merge (FSM) algorithm to move surface water into lakes, where it may evaporate or affect groundwater flow. In a proof-of-concept application, we demonstrate the continental-scale capabilities of the WTM by simulating the steady-state climate-driven water table for the present day and the Last Glacial Maximum (LGM; 21 000 calendar years before present) across the North American continent. During the LGM, North America stored an additional 14.98 cm of sea-level equivalent (SLE) in lakes and groundwater compared to the climate-driven present-day scenario. We compare the present-day result to other simulations and real-world data. Open-source code for the WTM is available on GitHub and Zenodo.

Callaghan, Kerry L. (ORCID:0000000226740838)↗
DOE OSTI2021

Physics-Informed Learning for Predictive Multi-Scale Modeling of Water Cycle and Extreme Events

Climate and earth system models have limited skill in predicting water cycle and associated extremes such as intense rain- and wind-producing storms and resulting hazards across all timescales. Water cycle predictions are inherently limited by the predictability of the chaotic system, but large potential for improving predictions remains untapped due to limited use of data-model integration to improve predictive modeling. This white paper proposes a hybrid data-physics paradigm based on physics-informed neural networks (PINNs) to improve the Energy Exascale Earth System Model (E3SM). In particular, we aim to predict and quantify uncertainties of water cycle processes and low probability high impact events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Topical Report on Water Balance Model Development and Initial Application

This topical report summarizes results from developing a generic dynamic water balance tool which can be used by coal-fired power plant operators to evaluate FGD water management strategies. EPRI is using a statistical software model created on the GoldSim platform and data from coal-fired energy plants to determine the fraction of power plant water needs that can be satisfied by electrodialysis reversal treatment and, correspondingly, the discharge volume and/or byproducts that still need to be managed. The generic model is designed to accept a number of easily obtainable process variables and generate probabilities of resultant water characteristics. The model is also constructed as a universal process flow; one that can be adjusted to accommodate most major components of an FGD blowdown handling system. The generic model is dynamic and utilizes user-inputted operational variation in water quality and quantity to best provide a probabilistic representation of potential performance outcomes. In order to account for complexities of water composition and treatment, a separate chemical modeling software is used to evaluate water chemistry and unit operation performance. The thermodynamic modeling software, OLI Studio, is employed to convert ion data from laboratory analyses into balanced molecular concentrations that are classified as dissolved and suspended solids. The dynamic water model is designed to import a total of 11 species commonly found in FGD blowdown water as molecular concentrations.Water management unit operations are used in the model and categorized by either physical unit operation blocks or chemical unit operations blocks. As the name implies, the physical water treatment blocks are unit operations that are exclusively physical changes to the system (e.g., separations and volume changes). The chemical water treatment blocks are unit operations where chemical changes occur and require the use of OLI Studio to characterize subsequent streams. Water and material balances from three unique coal-fired power plants were obtained and preliminary modeling on the dynamic water balance tool was performed using the extracted data from these power plants. These three power plants were chosen due to their unique configurations and water profiles that represented a variety of operational changes. Results from using the dynamic tool with the three plant configurations are presented and discussed in the context of the existing tool and planned future capabilities.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Molecular Dynamics Simulations of Supercritical Carbon Dioxide and Water using TraPPE and SWM4-NDP Force Fields

The increased levels of carbon dioxide (CO 2 ) emissions due to the combustion of fossil fuels and the consequential impact on global climate change have made CO 2 capture, storage, and utilization a significant area of focus for current research. In most electrochemical CO 2 applications, water is used as a proton donor due to its high availability and mobility and use as a polar solvent. Additionally, supercritical CO 2 is a promising avenue for electrochemical applications due to its unique chemical and physical properties. Consequently, understanding the interactions between water and supercritical CO 2 is of great importance for future electrochemical applications. Molecular dynamics (MD) simulation is a powerful tool that enables atomistic-resolution dynamics of molecular systems, which can complement and guide future experimental investigations. This study employed atomistic MD to study the cosolubilities, codiffusivities, and structure of supercritical CO 2 and water systems, with a polarizable water model (SWM4-NDP) and a nonpolarizable CO 2 model (TraPPE). Additionally, ab initio MD simulations were used to better understand how atomistic polarizable/nonpolarizable models compare to explicit modeling of electron densities. The polarizable water model exhibited substantial improvement in water-associated properties. In conclusion, we anticipate the development of a compatible polarizable CO 2 model to yield similar improvement, providing a pathway for realizing novel high-pressure electrochemical systems.

25 ENERGY STORAGE↗
DOE OSTI2021

Modeling Liquid Water by Climbing up Jacob’s Ladder in Density Functional Theory Facilitated by Using Deep Neural Network Potentials

Within the framework of Kohn–Sham density functional theory (DFT), the ability to provide good predictions of water properties by employing a strongly constrained and appropriately normed (SCAN) functional has been extensively demonstrated in recent years. Here, we further advance the modeling of water by building a more accurate model on the fourth rung of Jacob’s ladder with the hybrid functional, SCAN0. In particular, we carry out both classical and Feynman path-integral molecular dynamics calculations of water with the SCAN0 functional and the isobaric–isothermal ensemble. To generate the equilibrated structure of water, a deep neural network potential is trained from the atomic potential energy surface based on ab initio data obtained from SCAN0 DFT calculations. For the electronic properties of water, a separate deep neural network potential is trained by using the Deep Wannier method based on the maximally localized Wannier functions of the equilibrated trajectory at the SCAN0 level. The structural, dynamic, and electric properties of water were analyzed. The hydrogen-bond structures, density, infrared spectra, diffusion coefficients, and dielectric constants of water, in the electronic ground state, are computed by using a large simulation box and long simulation time. For the properties involving electronic excitations, we apply the GW approximation within many-body perturbation theory to calculate the quasiparticle density of states and bandgap of water. Compared to the SCAN functional, mixing exact exchange mitigates the self-interaction error in the meta-generalized-gradient approximation and further softens liquid water toward the experimental direction. For most of the water properties, the SCAN0 functional shows a systematic improvement over the SCAN functional. However, some important discrepancies remain. The H-bond network predicted by the SCAN0 functional is still slightly overstructured compared to the experimental results.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Water under hydrophobic confinement: entropy and diffusion

The properties of liquid water are known to change drastically in confined geometries. A most interesting and intriguing phenomenon is that the diffusion of water is found to be strongly enhanced by the proximity of a hydrophobic confining wall relative to the bulk diffusion. We report a molecular dynamics simulation using a classical water model investigating the water diffusion near a non-interacting smooth confining wall, which is assumed to imitate a hydrophobic surface, revealing a pronounced diffusion enhancement within several water layers adjacent to the wall. We present evidence that the observed diffusion enhancement can be accounted for, with a quantitative accuracy, using the universal scaling law for liquid diffusion that relates the diffusion rate to the excess entropy. These results show that the scaling law, which has so far only been used for the description of the diffusion in simple liquids, can successfully describe the diffusion in water. It is shown that the law can be used for the analysis of water dynamics under nanoscale hydrophobic confinement, which is currently a subject of intense research activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Ionomer Optimization for Hydroxide-Exchange-Membrane Water Electrolyzers Operated with Distilled Water: A Modeling Study

The hydroxide-exchange-membrane water electrolyzer (HEMWE) is a promising means to store intermittent renewable energy in the form of hydrogen chemical energy. The hydroxide-exchange ionomer (HEI) in the gas-evolving electrodes and the hydroxide-exchange membrane (HEM) are key components of HEMWE. In this work, we simulate the cell and examine explicitly the impact of HEI and HEM properties with a focus on improving HEMWE performance when operated with distilled water ( i.e ., no supporting electrolyte). The tradeoff between the ionic conductivity gain and electrochemically active surface area (ECSA) loss is studied. For a constant catalyst loading, distributing more catalyst next to the HEM or making thinner but denser catalyst layer is beneficial for HEMWE performance. The results demonstrate that a higher water diffusion coefficient is desired for HEM to supply reactant water to the cathode. In contrast, a lower water diffusion coefficient is preferred for the cathode HEI to retain the water in the regions with high reaction rates. Overall, the findings provide important insights to optimizing HEI/HEM materials for improved HEMWE performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A Decision-Relevant Factor-Fixing Framework: Application to Uncertainty Analysis of a High-Dimensional Water Quality Model

Factor Fixing (FF) is a common method for reducing the number of model parameters to lower computational cost. FF typically starts with distinguishing the insensitive parameters from the sensitive and pursues uncertainty quantification (UQ) on the resulting reduced-order model, fixing each insensitive parameter at a fixed value. There is a need, however, to expand such a common approach to consider the effects of decision choices in the FF-UQ procedure on metrics of interest. Therefore, to guide the use of FF and increase confidence in the resulting dimension-reduced model, we propose a new adaptive framework consisting of four principles: (a) re-parameterize the model first to reduce obvious non-identifiable parameter combinations, (b) focus on decision relevance especially with respect to errors in quantities of interest (QoI), (c) conduct adaptive evaluation and robustness assessment of errors in the QoI across FF choices as sample size increases, and (d) reconsider whether fixing is warranted. The framework is demonstrated on a spatially-distributed water quality model. The error in estimates of QoI caused by FF can be estimated using a Polynomial Chaos Expansion (PCE) surrogate model. Built with 70 model runs, the surrogate is computationally inexpensive to evaluate and can provide global sensitivity indices for free. For the selected catchment, just two factors may provide an acceptably accurate estimate of model uncertainty in the average annual load of Total Suspended Solids (TSS), suggesting that reducing the uncertainty in these two parameters is a priority for future work before undertaking further formal uncertainty quantification.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Enhancing Drinking Water Quality Modeling: Leveraging Physics Informed Neural Networks for Learning with Imperfect Reaction Models and Partial Data

Chemical kinetics models, typically formulated as systems of ordinary or partial differential equations, are valuable tools for simulating drinking water quality. However, these models often face inaccuracies due to discrepancies between the laboratory and the real-world conditions, as well as limitations in experimental analytical methods, hindering the accurate representation of the true underlying chemical mechanisms. In this study, we propose a Physics Informed Neural Network (PINN), using the eXtreme Theory of Functional Connections, to improve the prediction of chemical concentrations over time. The PINN method accounts for imperfect chemical models and incorporates partial data to improve predictions. Focusing on reactions describing water disinfection residual and disinfectant byproduct formation, which are crucial for public health and regulatory compliance, we demonstrate that the PINN model is able to accurately predict the concentrations of chemical species across various pH values. Notably, the model extends its accuracy to predict concentrations of chemical species not originally included in its training data. The developed method can be extended to a variety of chemical systems, offering a wide array of potential applications.

13 HYDRO ENERGY↗
DOE OSTI2022

Spatial heterogeneity effects on land surface modeling of water and energy partitioning

Abstract. Understanding the influence of land surface heterogeneity on surface water and energy fluxes is crucial for modeling earth system variability and change. This study investigates the effects of four dominant heterogeneity sources on land surface modeling, including atmospheric forcing (ATM), soil properties (SOIL), land use and land cover (LULC), and topography (TOPO). Our analysis focused on their impacts on the partitioning of precipitation (P) into evapotranspiration (ET) and runoff (R), partitioning of net radiation into sensible heat and latent heat, and corresponding water and energy fluxes. An initial set of 16 experiments were performed over the continental US (CONUS) using the E3SM land model (ELMv1) with different combinations of heterogeneous and homogeneous datasets. The Sobol' total and first-order sensitivity indices were utilized to quantify the relative importance of the four heterogeneity sources. Sobol' total sensitivity index measures the total heterogeneity effects induced by a given heterogeneity source, consisting of the contribution from its own heterogeneity (i.e., the first-order index) and its interactions with other heterogeneity sources. ATM and LULC are the most dominant heterogeneity sources in determining spatial variability of water and energy partitioning, mainly contributed by their own heterogeneity and slightly contributed by their interactions with other heterogeneity sources. Their heterogeneity effects are complementary, both spatially and temporally. The overall impacts of SOIL and TOPO are negligible, except TOPO dominates the spatial variability of R/P across the transitional climate zone between the arid western and humid eastern CONUS. Accounting for more heterogeneity sources improves the simulated spatial variability of water and energy fluxes when compared with ERA5-Land reanalysis dataset. An additional set of 13 experiments identified the most critical components within each heterogeneity source, which are precipitation, temperature, and longwave radiation for ATM, soil texture, and soil color for SOIL and maximum fractional saturated area parameter for TOPO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

H2OI95: A Stand-Alone Fortran Code for Evaluating the IAPWS-95 Equation-of-State Model for Water (Rev. 1)

H2OI95 is a stand-alone Fortran code for evaluating the IAPWS-95 equation-of-state model for the thermodynamic properties of water. It also evaluates the corresponding thermochemical properties of water consistent with the CODATA recommendations. The IAPWS-95 model is based on a model equation for the dimensionless Helmholtz energy for which the primary variables are the inverse reduced temperature (τ = T cr /T ) and reduced density (δ = ρ/ρ cr ). Here T is the absolute temperature (K), ρ is density (kg/m³), and the subscript “ cr ” refers to the critical point of water (647.096 K and 22.064 MPa pressure in this model, for which ρ cr is 322 kg/m³). The code solves four basic types of problems, distinguished by the specified inputs: 1. Temperature (K) and density (ρ kg/m³) or reduced density (δ) 2. Temperature (K) and pressure (MPa). 3. Temperature (K) on the saturation (liquid-vapor equilibrium) curve 4. Pressure (MPa) on the saturation curve. Each type of problem is run using a corresponding input (text) file. All but the first type of problem require iteration. For example, to solve for desired temperature and pressure, the reduced density must be adjusted to give the desired pressure. Iteration is accomplished using the Newton-Raphson method, though the secant method is also used in solving the fourth type of problem. For the last three types of problems, H2OI95 has been used to conduct numerical studies of convergence and the problem of multiple numerical solutions, some of which are not physically valid. Obtaining valid results depends mainly on appropriate choice of starting values for the density. The default values used in H2OI95 appear to consistently lead to generally desired results. With modification (not addressed here), H2OI95 can be used to support SUPCRT92 and similar codes that compute chemical thermodynamic properties of species and reactions over a wide range of temperature and pressure (273.16-1273K and 0-1000 MPa). Version 1.1 of this software calculates an extended set of water properties, including some derivable solely from IAPWS-95, such as the isobaric thermal expansivity and the isothermal compressibility, and still other properties based on supplementary models sanctioned by IAPWS, including the dynamic viscosity, thermal conductivity, surface tension, and static dielectric constant, and various other properties (notably Debye-Hückel parameters) derivable from the dielectric constant and its partial derivatives with respect to temperature and pressure. A coding error in Version 1.0 was identified while testing the implementation of the viscosity model. This error is described in Appendix C. It mainly affects calculation of “higher order” properties such as compressibility and heat capacity near the critical point.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Hydrological analysis in watersheds with a variable-resolution global climate model (VR-CESM)

Traditionally, watershed-scale hydrology is simulated by distributed hydrological models with offline meteorological forcing data, or by regional regional climate models that link atmospheric and land hydrology interactions. Global climate models (GCMs) are rarely used to study watershed-scale hydrology due to the relatively coarse grid resolution, computationally expensive downscaling, and simplified physical processes. Recently, however, watershed-scale hydrology analysis has become possible in GCMs due to the development of variable-resolution GCMs that dynamically couple the hydrological processes between atmosphere and land systems at fine resolutions in selected regions and coarse resolution elsewhere. In this study, we used the variable-resolution Community Earth System Model (VR-CESM) with refined-resolution (14 km) in the western U.S. and eastern China to evaluate watershed-scale hydrology. Furthermore, we compared the historical VR-CESM outputs with gauge measurements and other hydrological models (e.g., National Water Model in the U.S.) and calibrated the subsurface runoff capacities in four mountainous watersheds. An RCP8.5 projection from 2007 to 2050 is used to estimate the impact of changing precipitation and snow climatology on watershed hydrology. We also analyzed the long-term runoff variability and the possibility of extreme runoff events as simulated by the VR-CESM. Although calibration is not possible in larger-scale watersheds, VR-CESM simulates the long-term annual variability of watersheds and provides insights on climate change impacts on hydrology. We conclude that refined-resolution VR-CESM can be used for watershed-scale hydrology analysis to understand water resources and water balance, in addition to traditional watershed-scale hydrological models. It enables hydrological analysis at multiple watersheds in one simulation and can help understand the two-way dynamics between land surface hydrology and atmospheric processes, and is especially practical for projecting climate change impacts. However, it is challenging to apply VR-CESM for hydrologic analysis in regulated watersheds as human factors (e.g., pumping, irrigation, water diversion) have not been fully addressed in VR-CESM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Utilizing waste heat in wastewater treatment plants for water desalination: Modeling and Multi-Objective optimization of a Multi-Effect desalination system using Decision Tree Regression and Pelican optimization algorithm

This paper examines the feasibility of using waste heat from wastewater treatment plants (WWTPs) for water desalination. A model was developed to utilize waste heat from the gensets at As Samra WWTP in Jordan, using real data and TRNSYS® software to calculate available waste heat. The desalination process was then modeled with ASPEN PLUS® software, focusing on multi-effect desalination (MED). Both series and parallel configurations for the MED system were compared. The study investigated the effects of system feeding flow rate, feeding pressure, and heat input on productivity, performance ratio, and recovery ratio. The study also introduces a novel optimization technique combining machine learning and modern optimization algorithms to maximize system productivity and performance. Initially, a decision tree regression (DTR) model is developed to establish relationships between key independent variables (flow rate, feed pressure, and heat input) and dependent variables (productivity, performance ratio, and recovery ratio). The Pelican Optimization Algorithm (POA) is then used to identify the optimal values of the independent variables for maximum productivity and performance. The results show that using a series configuration yields a system productivity of 3984.2 kg/hr, a performance ratio of 3.78, and a recovery ratio of 0.991 at a feed flow rate of 4000 kg/hr, feed pressure of 3 bars, and heat input of 719 kW. Optimal productivity (4421 kg/hr), performance ratio (3.81), and recovery ratio (0.851) are achieved at a feed flow rate of 5166 kg/hr, feed pressure of 3.2 bars, and heat input of 794 kW. In conclusion, the techno-economic assessment indicates a levelized cost of water of 1.63 USD/m 3 for parallel configurations and 1.65 USD/m 3 for series configurations, with a payback period of less than two years.

42 ENGINEERING↗
DOE OSTI2022

Insights into Sorption and Molecular Transport of Aqueous Glucose into Zeolite Nanopores

Liquid-phase heterogeneous catalysis using zeolites is important for biomass conversion to fuels and chemicals. There is a substantial body of work on gas-phase sorption in zeolites with different topologies; however, studies investigating the diffusion of complex molecules in liquid medium into zeolitic nanopores are scarce. Here, we present a molecular dynamics study to understand the sorption and diffusion of aqueous β-d-glucose into β-zeolite silicate at T = 395 K and P = 1 bar. Through 2-μs-long molecular dynamics trajectories, we reveal the role of the solvent, the kinetics of the pore filling, and the effect of the water model on these properties. We find that the glucose and water loading is a function of the initial glucose concentration. Although the glucose concentration increases monotonically with the initial glucose concentration, the water loading exhibits a nonmonotonic behavior. At the highest initial concentration (~20 wt %), we find that the equilibrium loading of glucose is approximately five molecules per unit cell and displays a weak dependence on the water model. Glucose molecules follow a single-file diffusion in the nanopores due to confinement. Here, the dynamics of glucose and water molecules slows significantly at the interface. The average residence time for glucose molecules is an order of magnitude larger than that in the bulk solution, while it is about twice as large for the water molecules. Our simulations reveal critical molecular details of the glucose molecule’s local environment inside the zeolite pore relevant to catalytic conversion of biomass to valuable chemicals.

09 BIOMASS FUELS↗
DOE OSTI2022

Homogeneous ice nucleation in an ab initio machine-learning model of water

Molecular simulations have provided valuable insight into the microscopic mechanisms underlying homogeneous ice nucleation. While empirical models have been used extensively to study this phenomenon, simulations based on first-principles calculations have so far proven prohibitively expensive. Here, we circumvent this difficulty by using an efficient machine-learning model trained on density-functional theory energies and forces. We compute nucleation rates at atmospheric pressure, over a broad range of supercoolings, using the seeding technique and systems of up to hundreds of thousands of atoms simulated with ab initio accuracy. The key quantity provided by the seeding technique is the size of the critical cluster (i.e., a size such that the cluster has equal probabilities of growing or melting at the given supersaturation), which is used together with the equations of classical nucleation theory to compute nucleation rates. We find that nucleation rates for our model at moderate supercoolings are in good agreement with experimental measurements within the error of our calculation. We also study the impact of properties such as the thermodynamic driving force, interfacial free energy, and stacking disorder on the calculated rates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗