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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Internal Climate Variability Obscures Future Freezing Rain Changes Despite Global Warming Trend

Abstract Although numerous studies have projected changes in freezing rain under future climate conditions, the internal variability of freezing rain remains poorly quantified. Here, we introduce a framework utilizing a novel machine‐learning algorithm to diagnose freezing rain in reanalysis and climate model simulations. By employing multivariate quantile mapping, we decompose the projected freezing rain trend into contributions from changes in temperature, relative humidity, and precipitation, which helps separate the forced response from internal climate variability. Our finding reveals a notable decrease in freezing rain occurrence in most areas. Despite a substantial temperature increase, internal variability overshadows climate forcing across a large portion of the eastern United States until about 2050. This insight has implications for practitioners, suggesting that the observed freezing rain frequency climatology continues to provide a relevant baseline for decision‐making in the near term. However, longer‐term design and adaptation plans should consider the projected changes in these regions.

Zhuang, Haoyu (Richard) [Department of Atmospheric↗
DOE OSTI2021

RASA Field Performance at US International Monitoring Stations

This report attempts to collate, analyze, and summarize RASA performance over time at each of 11 US operated IMS stations. The performance over time was compared to routine maintenance operations. The data, in general, were summarized into a set analyzed almost exclusively in Excel. The data set allows for further probing into RASA performance over time. The data set would permit data to be routinely added for continued analysis. In general, all RASA systems ran within the CTBTO mandated the MDA requirements over their current lifetime of operations. The largest impacts to performance appeared to arise from variabilities of Germanium gamma detectors as they were replaced. It is noted that the air volumes of samples were not provided and had to be assumed to be constant over the period analyzed.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

DER Digital Supply Chain Gap Analysis

Solar photovoltaic (PV) cybersecurity is a growing field of research. As deployments of solar PV have increased, cyber risk has also increased. Utility solar PV installations, however, are not required to comply with the North American Electric Reliability Corporation (NERC) Critical Infrastructure Protection (CIP) plan unless they meet a minimum generation threshold of 75 MW. Individual residential-scale solar PV deployments will not meet that generation threshold and are therefore excluded from the NERC CIP requirements. With most solar installations less than 75 MW, solar PV has been deployed with minimal oversight and highly variable cybersecurity maturity. The resources that comprise the digital supply chain can include software, code, data, and other digital components. But as clean energy technologies advance, cybersecurity threats and vulnerabilities continue to evolve and grow in sophistication. Supply chain cybersecurity represents a critical area for ensuring safe operations as the U.S. moves toward a clean energy future.

cybersecurity↗
DOE OSTI2021

Latent map Gaussian processes for mixed variable metamodeling

Gaussian processes (GPs) are ubiquitously used in sciences and engineering as metamodels. Standard GPs, however, can only handle numerical or quantitative variables. Here we introduce latent map Gaussian processes (LMGPs) that inherit the attractive properties of GPs and are also applicable to mixed data which have both quantitative and qualitative inputs. The core idea behind LMGPs is to learn a continuous, low-dimensional latent space or manifold which encodes all qualitative inputs. To learn this manifold, we first assign a unique prior vector representation to each combination of qualitative inputs. We then use a low-rank linear map to project these priors on a manifold that characterizes the posterior representations. As the posteriors are quantitative, they can be directly used in any standard correlation function such as the Gaussian or Matern. Hence, the optimal map and the corresponding manifold, along with other hyperparameters of the correlation function, can be systematically learned via maximum likelihood estimation. Through a wide range of analytic and real-world examples, we demonstrate the advantages of LMGPs over state-of-the-art methods in terms of accuracy and versatility. In particular, we show that LMGPs can handle variable-length inputs, have an explainable neural network interpretation, and provide insights into how qualitative inputs affect the response or interact with each other. We also employ LMGPs in Bayesian optimization and illustrate that they can discover optimal compound compositions more efficiently than conventional methods that convert compositions to qualitative variables via manual featurization.

42 ENGINEERING↗
DOE OSTI2025

The latent variable proximal point algorithm for variational problems with inequality constraints

The latent variable proximal point (LVPP) algorithm is a framework for solving infinite-dimensional variational problems with pointwise inequality constraints. The algorithm is a saddle point reformulation of the Bregman proximal point algorithm. At the continuous level, the two formulations are equivalent, but the saddle point formulation is more amenable to discretization because it introduces a structure-preserving transformation between a latent function space and the feasible set. Working in this latent space is much more convenient for enforcing inequality constraints than the feasible set, as discretizations can employ general linear combinations of suitable basis functions, and nonlinear solvers can involve general additive updates. LVPP yields numerical methods with observed mesh-independence for obstacle problems, contact, fracture, plasticity, and others besides; in many cases, for the first time. The framework also extends to more complex constraints, providing means to enforce convexity in the Monge–Ampère equation and handling quasi-variational inequalities, where the underlying constraint depends implicitly on the unknown solution. Here, in this paper, we describe the LVPP algorithm in a general form and apply it to ten problems from across mathematics.

Inequality constraints↗
DOE OSTI2020

Evaluation of Hybrid FPOG Applications in Regulated and Deregulated Markets Using HERON

Recent changes in the U.S. energy market, such as low natural gas prices and increased electricity production for variable renewable energy (VRE) sources, have led to an economic crisis for existing light-water reactor (LWR) nuclear power plants (NPP). Many owners and operators of LWRs have elected to decommission these plants rather than continue using them as consistent sources of clean baseload power. This has led to exploration of various possibilities to increase the economic viability of these units, including market restructuring to monetize benefits LWRs already provide to the grid through ancillary markets, load following and economic dispatch, and possible integration of secondary systems directly to the NPP for production of additional products through technologies such as hydrogen electrolysis or water desalination. Previous studies have considered the technologies associated with these Integrated Energy Systems (IES) activities, and the analysis of markets for these secondary products. To analyze the economic viability of various system configurations including IES, especially given the uncertainty surrounding load demand, electricity prices, and the availability of VRE resources, the stochastic technoeconomic analysis package HERON (Heuristic Energy Resource Optimization Network) was released earlier this year as an extension of the risk analysis framework RAVEN (Risk Analysis Virtual Environment). HERON focuses foremost on making the complex uncertainty quantification analysis tools approachable for energy systems analysts, also providing general dispatch optimization algorithms for those workflows. HERON continues to be improved and tested as a significant part of the IES viability analyses performed in this work. HERON is not a capacity expansion model. To consider market and grid energy system development in a variety of scenarios, HERON is best used in coupling with modelling tools such as US-REGEN, which sacrifice some of the uncertainty analysis and resolution of HERON's modelling for the ability to efficiently predict the change in the grid energy system's profile due to economic drivers over decades. HERON can then use this information to explore the economic viability of introducing changes to the predicted outcomes, such as the introduction of an IES. In this work, experts at EPRI using US-REGEN provide six projection scenarios for use in HERON stochastic technoeconomic analysis (STEA) in considering the options available for increasing LWR economic viability through introduction of a hydrogen-centric IES using a high-temperature steam electrolysis plant (HTSE), hydrogen storage, and a constant-rate contracted hydrogen consumer. The results obtained are differential in nature; they do not report expected profits for any configuration, but rather report on the possible increase in the NPV of a configuration with respect to a baseline no-IES configuration. Due to the uncertainty captured in the variable net load of the systems, there is likewise uncertainty in the mean values reported. We consider this viability both in terms of a regulated market, where the energy producers and IES are owned and operated by single entity, as well as a deregulated market, where the IES chooses its bid for electricity generation and is then dispatched by the grid system operator. Results indicate that for deregulated markets, the inclusion of the IES is often statistically beneficial. This is especially true in policies that are not favorable towards nuclear, as nuclear is less often dispatched and is forced to deal with frequent idle capacity. In the nominal case as well as the case of carbon tax policies, inclusion of the IES clearly benefited the economic performance of the NPP. In the regulated case, however, there was a trend towards minimizing the IES, likely due to the optimal sizing performed by US-REGEN of the NPP within the system as well as the lack of penalty for idle capacity at the NPP in the regulated market analyses.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7

Abstract. Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. Consequently, the role of enhanced horizontal resolution in improved process representation in all components of the climate system continues to be of great interest. Recent simulations suggest the possibility of significant changes in both large-scale aspects of the ocean and atmospheric circulations and in the regional responses to climate change, as well as improvements in representations of small-scale processes and extremes, when resolution is enhanced. The first phase of the High-Resolution Model Intercomparison Project (HighResMIP1) was successful at producing a baseline multi-model assessment of global simulations with model grid spacings of 25–50 km in the atmosphere and 10–25 km in the ocean, a significant increase when compared to models with standard resolutions on the order of 1° that are typically used as part of the Coupled Model Intercomparison Project (CMIP) experiments. In addition to over 250 peer-reviewed manuscripts using the published HighResMIP1 datasets, the results were widely cited in the Intergovernmental Panel on Climate Change report and were the basis of a variety of derived datasets, including tracked cyclones (both tropical and extratropical), river discharge, storm surge, and impact studies. There were also suggestions from the few ocean eddy-rich coupled simulations that aspects of climate variability and change might be significantly influenced by improved process representation in such models. The compromises that HighResMIP1 made should now be revisited, given the recent major advances in modelling and computing resources. Aspects that will be reconsidered include experimental design and simulation length, complexity, and resolution. In addition, larger ensemble sizes and a wider range of future scenarios would enhance the applicability of HighResMIP. Therefore, we propose the High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) to improve and extend the previous work, to address new science questions, and to further advance our understanding of the role of horizontal resolution (and hence process representation) in state-of-the-art climate simulations. With further increases in high-performance computing resources and modelling advances, along with the ability to take full advantage of these computational resources, an enhanced investigation of the drivers and consequences of variability and change in both large- and synoptic-scale weather and climate is now possible. With the arrival of global cloud-resolving models (currently run for relatively short timescales), there is also an opportunity to improve links between such models and more traditional CMIP models, with HighResMIP providing a bridge to link understanding between these domains. HighResMIP also aims to link to other CMIP projects and international efforts such as the World Climate Research Program lighthouse activities and various digital twin initiatives. It also has the potential to be used as training and validation data for the fast-evolving machine learning climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Shortened Duration of Global Warming Slowdowns with Elevated Greenhouse Gas Emissions

Continuous emissions of anthropogenic greenhouse gases (GHGs) and aerosols in the last 160 years have resulted in an increasing trend of global mean surface temperatures (GMSTs). Due to interactions with natural variability, rates of the combined anthropogenically and naturally induced warming trends are characterized by significant slowdowns and speedups on decadal timescales. In this work, by analyzing observed and model-simulated data, we investigate how the duration of these episodes will change with different strengths of GHG and aerosol forcing. We found that the duration of warming slowdowns can be more than 30 yr with a slower rate of anthropogenic emissions but would shorten to about 5 yr with a higher one. This duration reduction depends on both the magnitude of the climate response to anthropogenic forcing and the strength of the internal variability. Moreover, the warming slowdowns can still occur even towards the end of this century under high emissions scenarios but with significantly shortened duration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Scale interactions and anisotropy in Rayleigh–Taylor turbulence

We study energy scale transfer in Rayleigh–Taylor (RT) flows by coarse graining in physical space without Fourier transforms, allowing scale analysis along the vertical direction. Two processes are responsible for kinetic energy flux across scales: baropycnal work Λ, due to large-scale pressure gradients acting on small scales of density and velocity; and deformation work Π, due to multiscale velocity. Our coarse-graining analysis shows how these fluxes exhibit self-similar evolution that is quadratic-in-time, similar to the RT mixing layer. We find that Λ is a conduit for potential energy, transferring energy non-locally from the largest scales to smaller scales in the inertial range where Π takes over. In three dimensions, Π continues a persistent cascade to smaller scales, whereas in two dimensions Π rechannels the energy back to larger scales despite the lack of vorticity conservation in two-dimensional (2-D) variable density flows. This gives rise to a positive feedback loop in 2-D RT (absent in three dimensions) in which mixing layer growth and the associated potential energy release are enhanced relative to 3-D RT, explaining the oft-observed larger α values in 2-D simulations. Despite higher bulk kinetic energy levels in two dimensions, small inertial scales are weaker than in three dimensions. Moreover, the net upscale cascade in two dimensions tends to isotropize the large-scale flow, in stark contrast to three dimensions. Furthermore, our findings indicate the absence of net upscale energy transfer in three-dimensional RT as is often claimed; growth of large-scale bubbles and spikes is not due to ‘mergers’ but solely due to baropycnal work Λ.

42 ENGINEERING↗
DOE OSTI2025

Intensifying Renewable Energy Droughts in the Western U.S. Amid Evolving Infrastructure and Climate

If renewable energy resources continue to become a larger part of the generation mix in the United States (U.S.), so does the potential impact of prolonged periods of low wind and solar generation, known as variable renewable energy (VRE) droughts. In such a future, naturally occurring VRE droughts need to be evaluated for their potential impact on grid reliability. This study is the first of its kind to examine the impacts of compound VRE energy droughts in the Western U.S. across a range of potential future climate and infrastructure scenarios. We find that compound VRE drought severity may increase significantly in the future, primarily due to the dramatic increase in wind and solar generation needed in some future infrastructure scenarios. We find that in our future climate scenario, the variability of energy drought severity increases, which has implications for sizing energy storage necessary for mitigating drought events. We also examine the spatial patterns of compound VRE drought events that effect multiple regions of the grid simultaneously. These co-occurring events have distinct spatial patterns depending on the season. We observed overall fewer connected events in the future with the combined effect of potential climate and infrastructure changes, although in the fall we observe a climate-induced shift toward events which impact more regions simultaneously.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

The influence of laser power modulation on melt pool dynamics in laser powder bed fusion

While the majority of laser powder bed fusion (LPBF) metal additive manufacturing uses a continuous wave (CW) laser heat source, some commercial applications of LPBF additive manufacturing instead involve the modulation of the laser power on tens-of-microsecond timescales as an adjustable process variable. This article reports the use of in situ, high speed x-ray and optical imaging to probe melt pool fluid flow, defect formation, and nearby powder motion during LPBF with both modulated and CW laser heat sources. We observe melt pool dynamics unique to modulated laser melting even at very high duty cycles that are related to fluctuations in vapor depression depth, complex pore formation mechanisms, and changes to denudation physics when compared to CW melting. These behaviors are present in Ti–6Al–4V, 316L stainless steel, and AL1100 alloys but vary slightly as a function of material, indicating a substantial dependence on the viscosity and surface tension of the liquid metal. While high duty cycles produce weld tracks of comparable quality to CW melting, lower duty cycles introduce substantial defect concentrations. At intermediate duty cycles, careful control of modulation parameters can repeatably and precisely yield one pore per laser pulse, suggesting a method for intentionally inserting engineered porosity at specific sites during an LPBF build.

3D printing↗
DOE OSTI2022

Residential Solar Adoption Timelines and Impacts from the COVID-19 Pandemic

In this study we evaluate PII and other PV adoption timelines from 2017-2021. We use project-level data collected by the National Renewable Energy Laboratory (NREL) for the Solar Time-Based Residential Analytics and Cycle Time Estimator (SolarTRACE). Additionally, we conducted a survey of 171 AHJs about their experiences, challenges, and process changes during the first 18 months of the COVID-19 pandemic. The survey findings were supplemented with follow up interviews with 5 AHJs from 4 states. We find that the pandemic moderately increased the duration and variability of pre-install timelines (contract signing to install), particularly in the permit review phase (permit submit to approval). In contrast, post-install timelines (install to final utility interconnection) continued to decline during the pandemic. The net result is that overall project timelines (contract signing to final interconnection) continued to decline during the pandemic. Our findings suggest that AHJs and installers faced challenges throughout the pandemic but ongoing improvements in PII processes - particularly post-install processes - more than offset these challenges. Furthermore, the pandemic may have catalyzed or accelerated a widespread adoption of online/electronic permitting, among other process efficiency improvements.

14 SOLAR ENERGY↗
DOE OSTI2020

Kinetics Modeling for Design of Continuous Enzymatic Hydrolysis

Enzymatic hydrolysis of cellulose to monomeric sugars continues to be a limiting step in cost-effectively producing sugar and fermentation-based biofuels from biomass. In particular, the high cost of enzymes coupled with the long time-scale of reaction pose challenges to economic viability. Performing enzymatic hydrolysis in a continuous mode with enzyme recycle may provide a path towards substantially reduced costs for sugar production, but design and analysis are complicated by a lack of suitable kinetics models. Computationally attainable models, such as fractal-based models, require knowledge of the reaction-history of the biomass, and are thus only suitable for describing the batch reactions from which they are derived. Fundamental models, while potentially more generalizable, are often too computationally intensive to use in process or reactor modeling. In this work, a phenomenological rate model is proposed based on a two-phase substrate representation. Good agreement is seen between batch and continuous enzymatic hydrolysis (CEH) experiment data, which validates the model and enables us to solve for reactor design parameters, such as CEH reactor size and stream flow rates, based on process variables like yield. This model is integrated with techno-economic analysis software to explore economic sensitivities. Important design optimizations and tradeoffs are identified and quantified, including the relative cost imposed by rate slowdown from sugar inhibition versus the cost to remove and concentrate sugars at a lower concentration. It also identifies, high-leverage avenues for further exploration, such as increasing the maximum feasible solids concentration, and sustaining high membrane flux and reliability.

09 BIOMASS FUELS↗
DOE OSTI2020

California Time-of-Use (TOU) Transition: Effects on Distributed Wind and Solar Economic Potential

Time-of-use (TOU) retail energy rates price electricity differently by the time of day, communicating to consumers the costs of supplying electricity diurnally. This study evaluates the impact of Decision D.15.07-001, which mandates a transition in 2020 to default TOU rates for residential ratepayers in California, on the economic viability of behind-the-meter distributed wind and solar systems. Previous studies have demonstrated that increasing wind and solar generation will materially affect the diurnal cost of wholesale electricity. As retail and wholesale prices become more aligned, this could affect the inter-technology economic viability of distributed generation. We assess economic viability through 'economic potential,' or the amount of generation capacity that exceeds a rate of return of 5.4%. In addition, we identify sensitivities of economic potential to capital costs and other techno-economic factors as well as the specific counties and sectors with substantial economic potential. We find that TOU implementation is projected to moderately increase economic potential of distributed wind and marginally decrease that of distributed solar. Over the long-term as variable renewable energy penetration increases in California, particularly utility-scale solar, net peak load and thus peak prices will shift later in the evening. To the extent that retail rates continue to evolve with wholesale prices, we find the changes could positively affect the value of wind generation more than that of solar generation. Over the scenarios considered, model results indicate realistic pathways to reaching a robust distributed wind market in California though solar economic potential was found to be substantially larger than that of wind.

17 WIND ENERGY↗
DOE OSTI2023

Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells

Abstract Simultaneously optimizing the processing parameters of functional thin films remains a challenge. The design and utilization of a fully automated platform called SPINBOT is presented for the engineering of solution‐processed functional thin films. The SPINBOT is capable of performing experiments with high sampling variability through the unsupervised processing of hundreds of substrates with exceptional experimental control. Through the iterative optimization process enabled by the Bayesian optimization (BO) algorithm, the SPINBOT explores an intricate parameter space, continuously improving the quality and reproducibility of the produced thin films. This machine learning (ML)‐guided reliable SPINBOT platform enables the acceleration of the optimization process of perovskite solar cells via a simple photoluminescence characterization of films. As a result, this study arrives at an optimal film that, when processed into a solar cell in an ambient atmosphere, immediately yields a champion power conversion efficiency (PCE) of 21.6% with satisfactory performance reproducibility. The unsealed devices retain 90% of their initial efficiency after 1100 h of continuous operation at 60–65 °C under metal‐halide lamps. It is anticipated that the integration of robotic platforms with the intelligent algorithm will facilitate the widespread adoption of effective autonomous experimentation to address the evolving needs and constraints within the materials science research community.

14 SOLAR ENERGY↗
DOE OSTI2026

Structural Conservation of the A 1 Binding Site in Photosystem I across Cyanobacteria and Green Algae

Time-resolved step-scan Fourier transform infrared (FTIR) difference spectroscopy was used to obtain (A 1 − − A 1 ) FTIR difference spectra from photosystem I (PSI) samples isolated from eight phylogenetically diverse cyanobacterial strains and one green alga, totaling 13 PSI preparations. These included samples from cells grown under farred light and PSI in monomeric, dimeric, trimeric, and tetrameric states. Spectral profiles were shown to be independent of oligomeric state. Remarkably, all (A 1 − − A 1 ) FTIR difference spectra exhibited high similarity, underscoring the robustness of the technique and indicating minimal experimental variability. This congruence reveals a highly conserved environment for the phylloquinone cofactor at the A 1 binding site across diverse taxa. Conserved bands associated with the A 0 pigment further suggest structural continuity from A 0 to A 1 . To leverage this consistency, we constructed a composite (A 1 − − A 1 ) FTIR difference spectrum by averaging all 13 spectra. This composite spectrum provides enhanced resolution, enabling unambiguous identification of previously unresolved bands. The fact that a highly resolved composite spectrum can be obtained by averaging demonstrates the similarity in the spectra from the different types of samples. Band assignments were refined using prior studies, yielding an improved spectral framework for future investigations of PSI electron transfer cofactors.

Charge transfer↗
DOE OSTI2025

Streamflow Response to Glacier Mass Loss Varies With Basin Precipitation Across Alaska

Abstract Diminishing glaciers affect streamflow, and given the extent of glaciers in Alaska and adjacent Canada, continued glacier mass loss is likely to have profound effects on ecosystems sensitive to runoff. The effects of glacier mass loss on streamflow are likely to vary across the wide ranges of basin size, glacier cover, and precipitation in this region. In this study, we use U.S. Geological Survey (USGS) streamflow data with satellite‐based glacier volume change estimates to quantify how glacier mass loss subsidized streamflow over the 2000–2019 period for 116 glacierized basins. We examine interannual variability in that subsidy at three USGS‐monitored glaciers to explore the ability of the subsidy to buffer streamflow derived solely from precipitation. We found the relative importance of percent glacier cover on streamflow magnitude increases in drier basins. In the driest basins, glaciers produced 40 times greater percent glacier mass loss subsidies to streamflow for the percent glacier cover compared to the wettest basins. While the subsidy from glacier mass loss buffers interannual variability in streamflow to varying degrees, it can also increase streamflow variability. Smaller amounts of percent glacier cover are needed to produce summer‐melt‐dominated seasonal flow regimes in drier basins than in wetter basins. Decreasing glacier cover will eventually decrease summer streamflow, increasing spring streamflow in drier basins, and attenuating seasonality with increasing spring and autumnal streamflow in wetter basins. Quantifying the downstream effects of continued glacier mass loss without the computational expense of a hydrological model is broadly applicable in this changing climate.

Curran, Janet H. [U.S. Geological Survey Alaska Sc↗
DOE OSTI2025

Climatic and Socioeconomic Drivers of Water Use and Their Spatio‐Temporal Patterns for Small and Mid‐Sized Cities in the Contiguous United States

This study explores the drivers of urban water use and their spatial-temporal patterns in 142 small and mid-sized cities across the Contiguous United States (CONUS) by analyzing the data directly collected from these cities and using advanced machine learning techniques. We identify five distinguished clusters across CONUS, each showing unique trends of the impact of drivers on water use. We find that socioeconomic factors significantly influence water use in eastern and southwestern cities, while climatic variables such as precipitation and temperature range dominate in central and northwestern regions. Temporal analysis reveals the impacts of major socioeconomic and climatic disruptions on urban water use in the period 2011–2021, including the COVID lockdown, the rapid growth of data centers, and the drought of 2012. In addition, our analysis suggests that economic growth in small and mid-sized US cities continues to be accompanied by rising water use, contrasting with the opposite trend observed in large cities in prior studies. This implies that as smaller cities develop, their water use may increase above current levels until incomes reach a higher threshold, highlighting the need to improve water use efficiency. This study also presents useful insights for developing effective water demand management strategies in response to climatic variability and socioeconomic growth in small and mid-sized cities.

54 ENVIRONMENTAL SCIENCES↗