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USGS2025

Using visualization science to inform the design of environmental decision-support tools—A case study of the U.S. Geological Survey Waterwatch

Environmental decision-support tools are increasingly being used to serve both expert and non-expert audiences. Many existing tools are primarily expert-focused, and redesigning them can be challenging because experts and non-experts interact with tools differently, existing users may be resistant to changes, and there is little guidance on how to prioritize redesign efforts and demonstrate their efficacy. In this report, we present a case study of a user-centered redesign of an established environmental decision-support tool—the U.S. Geological Survey WaterWatch. WaterWatch supports flood, drought, and other water resource management decisions through the display of water levels at gages across the United States. Using a participatory process, we identified a functional change (replacing the existing rainbow colormap), created an alternative design, and tested the alternative’s usability through two general public surveys. The results showed that replacing the rainbow colormap with a more intuitive diverging colormap improves usability, regardless of the audience’s subjective preference for the rainbow color scheme. In addition, we demonstrated the importance of using legends to improve the audience’s understanding of the map symbols. This study demonstrates how user-centered design approaches can be used to inform the design of high-profile products and tools.

Scientific Investigations Report↗
USGS2024

Prioritizing US Geological Survey science on salinization and salinity in candidate and selected priority river basins

The US Geological Survey (USGS) is selecting and prioritizing basins, known as Integrated Water Science basins, for monitoring and intensive study. Previous efforts to aid in this selection process include a scientifically defensible and quantitative assessment of basins facing human-caused water resource challenges (Van Metre et al. in Environmental Monitoring and Assessment , 192(7), 458 2020). In the present work, we explore this ranking process based on water quality considerations, specifically salinity and salinization. We selected top candidate basins to study salinity and salinization issues in 18 hydrologic regions that include 163 candidate basins. Our prioritization is based on quantitative assessment of sources of salinity, drivers of change, and receptors that must respond to those sources and drivers. Source terms represented in the prioritization include geology, depth to brackish groundwater, stream conductivity, chloride in precipitation, urban and agricultural land use, application of road salt as a deicer, and irrigation. Drivers represented in prioritization include changes in chemical weathering as a result of changes in rainwater chemistry. Receptors include measures of water stress, measurements of stream ecological health, and socioeconomic factors. In addition, we present research activities for the USGS on salinity and salinization that can be pursued in these basins including assessment of sources, pathways, and loadings; predicting and understanding changes in sources, peaks, and trends; understanding the components of salinity and mobilization of contaminants; understanding the relationship between salinization and changing ecosystems; and developing knowledge on the causes and distribution of groundwater salinity, brackish water resources, and challenges related to desalination.

Enviornmental Monitoring and Assessment↗
USGS2025

Total uncertainty quantification in inverse solutions with deep learning surrogate models

We propose an approximate Bayesian method for quantifying the total uncertainty in inverse partial differential equation (PDE) solutions obtained with machine learning surrogate models, including operator learning models. The proposed method accounts for uncertainty in the observations, PDE, and surrogate models. First, we use the surrogate model to formulate a minimization problem in the reduced space for the maximum a posteriori (MAP) inverse solution. Then, we randomize the MAP objective function and obtain samples of the posterior distribution by minimizing different realizations of the objective function. We test the proposed framework by comparing it with the iterative ensemble smoother and deep ensembling methods for a nonlinear diffusion equation with an unknown space-dependent diffusion coefficient. Among other applications, this equation describes the flow of groundwater in an unconfined aquifer. Depending on the training dataset and ensemble sizes, the proposed method provides similar or more descriptive posteriors of the parameters and states than the iterative ensemble smoother method. Deep ensembling underestimates uncertainty and provides less-informative posteriors than the other two methods. Our results show that, despite inherent uncertainty, surrogate models can be used for parameter and state estimation as an alternative to the inverse methods relying on (more accurate) numerical PDE solvers.

Journal of Computational Physics↗
USGS2026

Estimating the importance of floating surface material to the total phosphorus transport in Silver Creek, Wisconsin using Particle Image Velocimetry

Various techniques are used to estimate nutrient delivery in streams that combine flow and water-quality data. However, the transport of surface floating material is difficult to measure, and is therefore typically neglected when stream sampling and in the estimated nutrient delivery. Here, we describe an approach to estimate the amount of material (duckweed ( Lemna genus), filamentous algae, and other macrophyte fragments) and associated nutrients (in this case, phosphorus, P) transported on the surface of Silver Creek, Wisconsin, to determine if this material is an important transport mechanism and if historical P loads were underestimated. This approach includes estimating the transport of surface material using 10 s videos collected every 15 min from a downward-looking camera installed beneath a bridge. The average velocity of the surface material was first determined using Large-Scale Particle Image Velocimetry (LSPIV), which uses short videos to analyze surface particle movement. The amount of surface material in each video was then computed using computer-vision techniques. The P load associated with the transported surface material was then estimated by combining surface velocities, coverage of floating material, and laboratory-measured P content. Surface material transported ~9–11% of the total summer P load and ~4–7% of the annual load in Silver Creek.

Wisconsin↗
USGS2026

Deep groundwater total dissolved solids mapping in the Dakota Group, Williston Basin, USA

Growing concern about the quantity of available freshwater around the world has led to interest in surveying groundwater total dissolved solids (TDS) below water well depths. Deep TDS has not been systematically mapped, and there is much to learn about the distribution and controls on deeper groundwater. In sedimentary basins across the United States, groundwater resources often overlie hydrocarbon resources, providing an opportunity to use borehole geophysical data collected for hydrocarbons to characterize groundwater and pore space resources. This study adapts a recently developed subsurface geostatistical and geophysical modeling approach to continuously map groundwater TDS, porosity, and temperature in the Dakota Group of the Williston Basin—an undercharacterized regional aquifer system overlying deeper hydrocarbon reservoirs. Groundwater TDS in the Dakota Group ranges from approximately 4800 to 26,900 mg/L. TDS patterns are stratified with higher TDS in the lower and upper Dakota Group, and relatively lower TDS in the middle Dakota Group. The lower TDS in the middle zone may represent a preferential regional flow path for lower-TDS meteoric recharge from the west. The alternating pattern of TDS may also be evidence of higher-TDS inflows into the Dakota Group from underlying and potentially from overlying aquifers. Porosity is lower near the center of the Williston Basin and tends to be higher to the east, which may be related to grain size distributions. The new regional TDS and porosity modeling serves as a quantitative reference for water users and provides supporting evidence for hypotheses on Dakota Group recharge.

Montana, North Dakota, South Dakota↗
USGS2026

Towards a refined definition of “tidal influence” for the coastal wetland sciences

The astronomical tide relates to lunar (and to a lesser extent solar) influences on rising and falling water bodies, and is a key control of many coastal processes. However, tides manifest along coastal margins in different ways to muddle application beyond Sir Isaac Newton’s first descriptions. Here, we argue that tide, or specifically “tidal,” has not been adequately defined for the coastal wetland sciences to facilitate contemporary interdisciplinary distinction for rapidly changing coastlines and future climate adaptation planning. We provide a brief history of the tidal concept, and we describe tides in a diversity of uncommonly recognized marginal coastal environments. Common astronomical tide manifestations are described through hydrographic examples, then expanded to include those tidal environments exposed additionally to fluvial, meteorological (wind), or groundwater influences. Our expanded definition of “tidal” is intended to be a forward-looking construct as environments will inevitably change with sea-level rise, water extraction, and anthropogenic flow alteration. Our definition of wetland tidal influence, which we broaden, includes those “wetlands affected by astronomic water level fluctuations within coastally restricted environments causing frequent or infrequent surface inundation or groundwater tidal variation that affects the biogeochemistry of the soil (e.g., ion exchange capacity, oxygen state, mineralization processes)”. Astronomic water level fluctuations are often amplified or dampened by non-astronomical events, such as wind. Expanding the definition of what is considered tidal by the scientific community is critical for improved understanding of coastal wetland function and the suite of ecosystem services they provide people and society, including climate mitigation opportunity, management, and adaptation.

Wetlands↗
USGS2025

Cascading land surface hazards as a nexus in the Earth system

Earth’s surface is sculpted by numerous processes that move sediment, ranging from gradual and benign to abrupt and catastrophic. Although infrequent, high-magnitude sediment mobilization events can be hazardous to people and infrastructure, leaving topographic imprints on the landscape and remarkable narratives in the historical record. Hazardous events such as fires, storms, and earthquakes accelerate erosion and sediment transport, increasing landscape sensitivity to subsequent perturbations, thus forming a cascading hazard. Although the redistribution of sediment across Earth’s landscape can result in higher risks to vulnerable populations, cascading processes are commonly unaccounted for in hazard assessments. Cascading hazards can occur almost immediately after triggering events, such as coseismic landslides, or over months, years, or even decades after an initial perturbation, such as debris flows after wildfires or flooding in channels alluviated by volcanic debris. Sediment cascades span Earth’s surface, from mountaintops to river valleys, where erosion, deposition, and aggradation can lead to a myriad of hazardous processes, including decreased river conveyance capacity, which increases the likelihood of downstream flooding. An improved understanding of the magnitude, frequency, and persistence of cascading hazards is critical given the rapid changes in the frequency and severity of storms, fires, sea-level change, and cryospheric melting, as well as the expansion of high-population-density urban footprints in regions susceptible to solid Earth hazards. Understanding the full consequences and underlying physics of Earth’s cascading land surface hazards can help minimize future human and economic losses.

California↗
USGS2026

Ten new insights in climate science 2025

Interdisciplinary understanding is vital for delivering sound climate policy advice. However, navigating the ever-growing and increasingly diverse scholarly literature on climate change is challenging for any individual researcher. This annual synthesis highlights and explains recent advances across a variety of fields of climate change research. This year, the 10 insights focus on: (1) the record-warmth of 2023/2024 and the elevated Earth energy imbalance; (2) acceleration of ocean warming and intensifying marine heatwaves; (3) northern land carbon sinks under strain; (4) reinforcing feedback between biodiversity loss and climate change; (5) accelerated depletion of groundwater; (6) global dengue incidence; (7) global income losses and labour productivity declines; (8) strategic scaling of CDR; (9) integrity challenges in carbon credit markets and emerging responses; and (10) effective policy mixes for emissions reductions. The insights have been written to be accessible to researchers from different fields, serving as entry-points to specific topics, as well as providing an overview of the evolving landscape of climate change research. In the final section, the insights are used to develop overarching policy-relevant messages. This paper provides the basis for a science-policy report that was shared with all Party delegations ahead of COP30 in Belém, Brazil.

Global Sustainability↗
USGS2025

Scientific opportunities in the National Landscape Conservation System

The National Landscape Conservation System consists of unique and beautiful places across America’s landscapes where identified resources and values are protected and science is highlighted. The mission of the National Landscape Conservation System (NLCS), which is managed by the Bureau of Land Management and is often referred to as the agency’s National Conservation Lands, is to conserve, protect, and restore nationally significant landscapes for their cultural, ecological, and scientific values. This clear inclusion of science in the NLCS mission sets the stage for individual units to serve as places of learning, teaching, discovery, and innovation. Science is an integral part of managing the National Conservation Lands, and science conducted within and across the more than 900 units that make up the NLCS can inform and influence conservation and public land management well beyond its boundaries. Here, we highlight seven core aspects of National Conservation Lands that present valuable science opportunities: (1) the scientific values for which individual units are designated; (2) the many other resources, objects, and values within units; (3) the value of units as “control” sites for understanding the effects of activities such as mineral extraction that commonly occur elsewhere on multiple-use public lands but are often prohibited within National Conservation Lands; (4) the value of units for studying the effects of activities such as recreation that regularly occur and may be intensified on National Conservation Lands; (5) the high visibility of units, which draws strong interest and engagement from scientists, partners, and the public; (6) the functioning of the units as a network managed for a common purpose, which provides an opportunity to explore cross-cutting science questions across widely varying contexts and geographies; and (7) the opportunities units provide to promote and apply Indigenous Knowledge to scientific research to manage natural and cultural resources. Because of all of these characteristics, National Conservation Lands can serve as hubs for basic and applied science that can inform management of all public lands and resources into the future. We highlight these science opportunities through examples from existing units and suggest two actions that could help further science activities and impact on National Conservation Lands.

Parks Stewardship Forum↗
USGS2026

Evaluating machine learning approaches to identify and predict oil and gas produced water lithium concentrations

Recently, the demand for battery-grade lithium has substantially increased, largely due to electrification of the transportation sector. The search for new lithium sources has turned to produced waters (frequently brines), a large-volume wastewater by-product of oil and gas extraction. Geochemical analysis indicates the presence of varying concentrations of lithium from produced water samples collected across the United States and represented in the U.S. Geological Survey’s National Produced Water Geochemical Database, as well as mixtures of Marcellus Shale produced water included in the Pennsylvania Department of Environmental Protection’s Oil and Gas Well Waste Reports. We first examined whether the geochemical signature of the lithium-bearing produced waters is sufficiently distinct so that machine learning (ML) can be used to correctly classify samples to the formation of origin. The produced water sample data used to assess classification accuracy were from the Marcellus Shale, Utica Shale and Point Pleasant Formation (Utica), and Smackover Formation oil and gas wells. Further, we evaluated the potential for ML to accurately classify Marcellus Shale produced water spatially (i.e., northeast versus southwest Pennsylvania). We then investigated whether ML algorithms applied to a suite of geochemical concentration data (i.e. Ba, Br, Cl, K, Mg, Sr) may be used to predict the lithium concentration of an unknown sample. Finally, we applied an estimated economic lithium grade cutoff of 150 milligrams per liter (mg/l) and assessed the utility of ML to predict whether a produced water sample would fall above or below the grade cutoff based on the suite of geochemical parameters. Four machine learning algorithms—Random Forest (RF), Gradient Boosting Trees (GBT), Extreme Boosting (XGBoost), and Deep Neural Networks (DNN) were assessed. This study successfully demonstrates that all four machine learning methods can precisely and accurately estimate lithium concentrations and geologic formation classification. The products of this study contribute to the growing body of knowledge aimed at expanding the lithium resource base within the United States.

Alabama, Arkansas, Florida, Georgia, Louisiana, Mi↗
USGS2026

Determination of baseline water-quality conditions using continuous specific conductance and discrete measurements of total dissolved solids and selected major ions, Arkansas River Basin, Colorado

From 2022 through 2024, the U.S. Geological Survey (USGS) in cooperation with the Regional Resources Planning Group (Southeastern Colorado Water Conservancy District, Colorado Springs Utilities, Pueblo Water, Lower Arkansas Valley Water Conservancy District, Aurora Water, and Upper Arkansas Water Conservancy District) established a basinwide water-quality monitoring network in the Arkansas River Basin in Colorado. The study leveraged ongoing continuous measurements of streamflow and specific conductance (SC) at 13 sites along the main stem Arkansas River and Fountain Creek from the USGS 07108600 Arkansas River at Granite, Colo., (Ark Granite) site to the USGS 07137500 Arkansas River near Coolidge, Kansas, (Ark Coolidge) site. Baseline water-quality conditions were established using data from October 1989 through September 2022 to help identify changes in water quality that could result from changes in land and water use. Total dissolved solids (TDS) concentration was the main indicator of general water quality in this study because it can be accurately estimated from SC and is sensitive to changes in streamflow, water operations, and source contributions. Median TDS concentrations in Arkansas River samples (October 1989 through December 2023) ranged from 75 milligrams per liter (mg/L) at Ark Granite near the headwaters to 3,660 mg/L at Ark Coolidge near the Colorado–Kansas State line. Distinct seasonal differences in median streamflow and TDS were apparent during the winter and summer months. Seasonal Kendall tests showed no significant trends in monthly mean streamflow (October 1995 through September 2022) at Arkansas River sites upstream from Pueblo Reservoir. Upward trends in TDS concentrations and loads occurred only at Ark Granite. Downward trends in streamflow, TDS concentrations, and TDS loads occurred at Arkansas River sites from USGS 07109500 Arkansas River near Avondale, Colo., to Ark Coolidge. Downward trends in streamflow and SC (surrogate for TDS) were more prevalent during winter base flow months and may indicate changes in groundwater quantity, and possibly groundwater quality, potentially affecting the river water-quality condition.

Colorado↗
USGS2024

U.S. Geological Survey Earthquake Hazards Program decadal science strategy, 2024–33

Executive Summary Earthquakes represent one of our Nation’s most significant and costly natural hazards, with estimated annual loses from earthquakes close to $15 billion in 2023. Over the past two centuries, 37 U.S. States have experienced an earthquake exceeding a magnitude of 5, and 50 percent of States have a significant potential for future damaging shaking; these statistics speak to the need for nationwide interest and investment in earthquake hazard characterization and risk reduction. Authorized under the Earthquake Hazards Reduction Authorization Act, the U.S. Geological Survey (USGS) Earthquake Hazards Program (EHP) provides the scientific information, situational awareness, and knowledge necessary to reduce deaths, injuries, and economic losses from earthquakes and earthquake-induced tsunamis, landslides, and soil liquefaction. The EHP supports activities in three focused topical areas: (1) earthquake monitoring, (2) hazard assessment, and (3) applied research, using the results of each—and the coordination among them—to further support risk translation and communication in regions at risk nationwide. For earthquake monitoring, the Advanced National Seismic System (ANSS), a cooperative effort of USGS networks, university partner regional seismic networks, and real-time geodetic networks, collects and analyzes data on earthquakes; issues timely, reliable notifications of their occurrence and impacts; and provides data for earthquake research, hazard, and risk assessment as a foundation for building an earthquake-resilient Nation. The USGS-operated ShakeAlert Earthquake Early Warning system is a recent addition to EHP’s ANSS infrastructure. In the realm of earthquake hazard assessment, the EHP contributes to earthquake risk mitigation strategies by developing the National Seismic Hazard Model and maps, and other related products, that describe the likelihood and potential effects of earthquakes nationwide, especially in the urban areas of highest risk. The EHP also conducts research on the causes, characteristics, and effects of earthquakes and prioritizes work that directly increases the accuracy and precision of earthquake hazards assessments, earthquake forecasts, and earthquake monitoring and situational-awareness products and that supports the Nation’s earthquake mitigation practices. Bridging the EHP’s efforts across research, hazard assessments, and earthquake monitoring is a broad and comprehensive collection of earthquake information products, including the National Seismic Hazard Model, ShakeAlert, and other products describing impact, such as ShakeMap and PAGER (Prompt Assessment of Global Earthquakes for Response), which have been developed and integrated into EHP’s real-time monitoring systems. EHP funds external partners to carry out many important collaborative activities through an active external grants program—one of the largest in the USGS—and through cooperative agreements with other partners such as the university-operated regional seismic networks, funded as part of the ANSS. To continue its support of earthquake hazard characterization and risk reduction, the EHP aims to strengthen its foundational products and practices while positioning itself to respond to the evolving needs of the Nation and follow best practices of the scientific community. This document describes a strategy for the program to ensure it can meet these demands. The foundational priorities outlined in this strategy represent those activities that remain critical to the core functionality of the program and those that can be supported under current fiscal year 2024-level appropriations. Priorities described as aspirational are important for future growth, and to maintain the program’s position as a leading global resource in earthquake science, but would require increases in appropriated funding to be fully realized. Across the program’s portfolio of activities, several major themes have been identified as the most critical activities to advance EHP science over the coming decade. Together, these activities provide the framework necessary to integrate critical hazard characterization and risk reduction activities across the program. They provide the structure for research to advance the understanding of where, when, and why earthquakes occur and how we can use improved knowledge to drive short-term and actionable forecasts of seismic activity. They expand the usefulness of critical earthquake products and advance the sophistication of those products to keep pace with the rapidly evolving needs of an ever-expanding user base while maintaining the position of the USGS as a global leader in earthquake science. Focus on system-level science. Establish an automated earthquake-processing pipeline. Enhance the accuracy and reliability of the ShakeAlert Earthquake Early Warning system and plan for extension to other regions. Implement time-dependent earthquake forecasting. Develop physically realistic models. Expand computational capacity. This science strategy is organized into three primary sections. The first section provides an overview of the EHP and its budget, governance, and program council. Readers familiar with the program may wish to focus on the second section, which describes the core of the science strategy, including priorities across each of the EHP’s major program activities in monitoring, hazard assessment, and targeted research. The third section outlines science priorities that cut across program activities, including those involving collaborations external to the EHP.

American Samoa, Guam, Northern Marianas Islands, U↗
USGS2025

VIPER site analysis

We needed to evaluate available orbital data of NASA’s Volatiles Investigating Polar Exploration Rover (VIPER) mission area in order to derive a variety of maps to help the science team identify scientifically interesting places for the rover to visit and to provide scientific context for our mission. Some of these maps also fulfilled engineering and mission design needs to enable safe and efficient landing and roving. We incorporated data from the Lunar Reconnaissance Orbiter Camera, the Lunar Orbital Laser Altimeter, the Mini-RF instrument, the Chandrayaan-2 Orbital High Resolution Camera, the Korean Pathfinder Lunar Orbiter’s Shadowcam, the Kaguya Spectral Profiler and Multiband Imager, and the Chandrayaan-1 Moon Mineralogy Mapper. We used a variety of techniques to build these maps, including stereogrammetry, shape-from-shading, ice stability depth and surface temperature calculations, and the horizon method for solar illumination and direct-to-Earth communications maps. Altogether, these maps allowed us to survey for boulders, evaluate features in permanently shadowed regions that VIPER might explore, provide mineralogic context for what VIPER’s instruments may learn, estimate the ages and radar properties of craters in the VIPER mission area, and evaluate the potential for gravity traverses with the rover. These data and techniques provided a rich set of information from which both the VIPER science team and engineering teams were able to draw in order to plan a safe landing and to plan a VIPER surface mission that will be both scientifically valuable and robust from an operational perspective.

The Planetary Science Journal↗
USGS2025

Overcoming the data limitations in landslide susceptibility modelling

Data-driven models widely used for assessing landslide susceptibility are severely limited by the landslide and environmental data needed to create them. They rely on inventories of past landslide locations, which are difficult to collect and often nonrepresentative. Furthermore, susceptibility maps are most needed in regions without the means to assemble an inventory. To overcome these challenges, we develop a method for assessing shallow landslide susceptibility based on a probabilistic morphometric analysis of the landscape’s topography, rather than the characteristics of landslides. The model assumes that hillslopes with higher relief and gradient compared to the surrounding landscape are more prone to landslides. We demonstrate the superior performance of this approach over contrasting data-driven models across the northwestern United States. As our morphometric model only requires elevation data, it overcomes the major limitations of data-driven models and facilitates the creation of effective susceptibility models in areas where it was previously unfeasible.

Oregon, Washington↗
USGS2026

Remote compositional analyses of space-weathered lunar maria

Visible-to-shortwave infrared (VSWIR) reflectance spectroscopy has revolutionized our understanding of planetary surface compositions. However, space-weathering processes on airless bodies complicate quantitative compositional analyses. Here, we present a framework to isolate the signatures of space weathering in VSWIR spectra of lunar maria by leveraging radiative transfer modeling under the assumptions that (i) a space-weathered target can be expressed as a mixture of fresh and fully space-weathered components and (ii) remaining signatures can be modeled by including agglutinates as an end-member component. We first validate this approach against laboratory spectra of space-weathered Apollo mare soils of known mineral compositions using a probabilistic Markov Chain Monte Carlo implementation of the Hapke radiative transfer model. Second, we illustrate how this approach can be applied to orbital Moon Mineralogy Mapper data. The proposed space-weathering correction workflow for lunar maria could be expanded to other lunar lithologies and applied to existing and future data sets.

Planetary Science Journal↗
USGS2024

Aftershock forecasting

Aftershocks can compound the impacts of a major earthquake, disrupting recovery efforts and potentially further damaging weakened buildings and infrastructure. Forecasts of the probability of aftershocks can therefore aid decision-making during earthquake response and recovery. Several countries issue authoritative aftershock forecasts. Most aftershock forecasts are based on simple statistical models that were first developed in the 1980s and remain the best available models. We review these statistical models, and the wide-ranging research to advance aftershock forecasting through better statistical, physical, and machine learning methods. Physics-based forecasts based on mainshock stress changes can sometimes match the statistical models in testing, but don’t yet outperform them. Physical models are also hampered by unsolved problems such as the mechanics of dynamic triggering and the influence of background conditions. Initial work on machine learning forecasts shows promise, and new machine learning earthquake catalogs provide an opportunity to advance all types of aftershock forecasts.

Annual Review of Earth and Planetary Sciences↗
USGS2025

Prospectivity modeling of the NASA VIPER landing site at Mons Mouton near the Lunar South Pole

We use a high-resolution digital elevation model and a numerical thermal model to produce a variety of inputs for a water-ice prospectivity model for the Volatiles Investigating Polar Exploration Rover (VIPER) landing site. These input data are maps of topography, surface slope, surface aspect, surface curvature, maximum temperature, depth to ice stability, permanently shadowed regions (PSRs), distance to PSRs, and PSR density. This model predicts where water ice is most likely within the top meter of regolith, assuming plausible relationships between ice concentration and the various inputs. The model is designed to be adjusted in near-real time as data are collected during the VIPER mission. As such, it is a tool for both analyzing data from the mission as well as planning operations. Since the current model, at this point, relies only on orbital remote sensing, the final version will also be a tool to extrapolate the VIPER mission results across the lunar poles.

Planetary Science Journal↗