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At least 181 records · Page 10Linked to original sources
USGS2016

Bivalve grazing can shape phytoplankton communities

The ability of bivalve filter feeders to limit phytoplankton biomass in shallow waters is well-documented, but the role of bivalves in shaping phytoplankton communities is not. The coupled effect of bivalve grazing at the sediment-water interface and sinking of phytoplankton cells to that bottom filtration zone could influence the relative biomass of sinking (diatoms) and non-sinking phytoplankton. Simulations with a pseudo-2D numerical model showed that benthic filter feeding can interact with sinking to alter diatom:non-diatom ratios. Cases with the smallest proportion of diatom biomass were those with the fastest sinking speeds and strongest bivalve grazing rates. Hydrodynamics modulated the coupled sinking-grazing influence on phytoplankton communities. For example, in simulations with persistent stratification, the non-sinking forms accumulated in the surface layer away from bottom grazers while the sinking forms dropped out of the surface layer toward bottom grazers. Tidal-scale stratification also influenced vertical gradients of the two groups in opposite ways. The model was applied to Suisun Bay, a low-salinity habitat of the San Francisco Bay system that was transformed by the introduction of the exotic clam Potamocorbula amurensis . Simulation results for this Bay were similar to (but more muted than) those for generic habitats, indicating that P. amurensis grazing could have caused a disproportionate loss of diatoms after its introduction. Our model simulations suggest bivalve grazing affects both phytoplankton biomass and community composition in shallow waters. We view these results as hypotheses to be tested with experiments and more complex modeling approaches.

California↗
USGS2021

In vitro effects-based method and water quality screening model for use in pre- and post-distribution treated waters

Recent urban public water supply contamination events emphasize the importance of screening treated drinking water quality after distribution. In vitro bioassays, when run concurrently with analytical chemistry methods, are effective tools to evaluating the efficacy of water treatment processes and water quality. We tested 49 water samples representing the Chicago Department of Water Management service areas for estrogen, (anti)androgen, glucocorticoid receptor-activating contaminants and cytotoxicity. We present a tiered screening approach suitable to samples with anticipated low-level activity and initially tested all extracts for statistically identifiable endocrine activity; performing a secondary dilution-response analysis to determine sample EC 50 and biological equivalency values (BioEq). Estrogenic activity was detected in untreated Lake Michigan intake water samples using mammalian (5/49; median: 0.21 ng E2Eq/L) and yeast cell (5/49; 1.78 ng E2Eq/L) bioassays. A highly sensitive (anti)androgenic activity bioassay was applied for the first time to water quality screening and androgenic activity was detected in untreated intake and treated pre-distribution samples (4/49; 0.93 ng DHTEq/L). No activity was identified above method detection limits in the yeast androgenic, mammalian anti-androgenic, and both glucocorticoid bioassays. Known estrogen receptor agonists were detected using HPLC/MS-MS (estrone: 0.72-1.4 ng/L; 17α-estradiol: 1.3-1.5 ng/L; 17β-estradiol: 1.4 ng/L; equol: 8.8 ng/L), however occurrence did not correlate with estrogenic bioassay results. Many studies have applied bioassays to water quality monitoring using only relatively small samples sets often collected from surface and/or wastewater effluent. However, to realistically adapt these tools to treated water quality monitoring, water quality managers must have the capacity to screen potentially hundreds of samples in short timeframes. Therefore, we provided a tiered screening model that increased sample screening speed, without sacrificing statistical stringency, and detected estrogenic and androgenic activity only in pre-distribution Chicago area samples.

Illinois, Indiana↗
USGS2023

Long short-term memory models to quantify long-term evolution of streamflow discharge and groundwater depth in Alabama

Long short-term memory (LSTM) models have been shown to be efficient for rainfall-runoff modeling, and to a lesser extent, for groundwater depth forecasting. In this study, LSTMs were applied to quantify the spatiotemporal evolution of surface and subsurface hydrographs in Alabama in the Southeastern United States, where water sustainability has not been fully quantified across spatiotemporal scales. First, the surface water LSTM model with extensive dynamic (precipitation and other weather variables) and static (basin characteristics) inputs predicted the main characteristics of streamflow for six years at 19 gauged basins in Alabama. The model tended to underestimate extremely high streamflow but adding drainage density as an input feature slightly improved the predictions of extreme events. Second, to predict the groundwater depth evolution, a groundwater LSTM (GW-LSTM) model was proposed and applied using static inputs capturing the aquifers' hydrogeological properties and dynamic inputs of meteorological information. Three precipitation scenarios were also explored to evaluate the groundwater hydrograph evolution in the next two decades. The GW-LSTM model predicted the general trend of daily groundwater depth fluctuations (at 21 wells distributed across Alabama from 1990 to 2021) including most extremely high groundwater levels, and recovered groundwater depth for locations withheld from model training and validation. This study, therefore, extended the application of LSTMs in quantifying the spatiotemporal evolution of surface water and groundwater, two manifestations of a single integrated resource.

Alabama↗
USGS1995

Removal of algae by the zebra mussel ( Dreissena polymorpha ) population in western Lake Erie: a bioenergetics approach

A bioenergetics model for growth of a zebra mussel ( Dreissena polymorpha ) individual was verified with observations on zebra mussel growth in western Lake Erie. The bioenergetics model was then applied to the zebra mussel population in the western basin of Lake Erie to estimate the removal of phytoplankton by mussels. According to the modeling results, the zebra mussel population consumed 5.0 million tonnes of phytoplankton, while 1.4 million tonnes of phytoplankton was deposited in pseudofeces from the mussels. Thus, a total of 6.4 ± 2.4 million tonnes of phytoplankton was removed from the water column by zebra mussel in western Lake Erie during 1990. Primary production was estimated to be 24.8 million tonnes; therefore, zebra mussel removed the equivalent of 26 ± 10% of the primary production for western Lake Erie.

Canadian Journal of Fisheries and Aquatic Sciences↗
USGS2016

A water balance model to estimate flow through the Old and Middle River corridor

We applied a water balance model to predict tidally averaged (subtidal) flows through the Old River and Middle River corridor in the Sacramento–San Joaquin Delta. We reviewed the dynamics that govern subtidal flows and water levels and adopted a simplified representation. In this water balance approach, we estimated ungaged flows as linear functions of known (or specified) flows. We assumed that subtidal storage within the control volume varies because of fortnightly variation in subtidal water level, Delta inflow, and barometric pressure. The water balance model effectively predicts subtidal flows and approaches the accuracy of a 1–D Delta hydrodynamic model. We explore the potential to improve the approach by representing more complex dynamics and identify possible future improvements.

California↗
USGS2020

Estimation of nonlinear water-quality trends in high-frequency monitoring data

Recent advances in high-frequency water-quality sensors have enabled direct measurements of physical and chemical attributes in rivers and streams nearly continuously. Water-quality trends can be used to identify important watershed-scale changes driven by natural and anthropogenic influences. Statistical methods to estimate trends using high-frequency data are lacking. To address this gap, an evaluation of the generalized additive model (GAM) approach to test for trends in high-frequency data was conducted. Our proposed framework includes methods for handling serial correlation, trend estimation and slope-change detection, and trend interpretation at arithmetic scale for log-transformed variables. Water-temperature and turbidity data, representing two analytes with different temporal patterns, collected from the James River at Cartersville, Virginia, USA, were chosen for this analysis. Results indicated that the model, including flow, season, time covariates, and interaction between flow and season performed well for both analytes. The same model structure was applied to specific conductance data, collected from a small highly urbanized watershed, with satisfactory model performance. The water temperature GAM results indicated that the significant decreasing-then-increasing patterns after 2012 were mainly driven by air temperature changes. The turbidity trend was not significant over time. The specific conductance results showed a consistently upward trend over the last decade due to ever-increasing urbanization in the small watershed. This study suggests that the GAM method has great potential as a useful tool for trend analysis on high-frequency data, and for informing watershed managers of hydro-climatic and human influences on water quality by detecting crucial signal variation over time.

Chesapeake Bay watershed↗
USGS2011

Use of hydrologic and hydrodynamic modeling for ecosystem restoration

Planning and implementation of unprecedented projects for restoring the greater Everglades ecosystem are underway and the hydrologic and hydrodynamic modeling of restoration alternatives has become essential for success of restoration efforts. In view of the complex nature of the South Florida water resources system, regional-scale (system-wide) hydrologic models have been developed and used extensively for the development of the Comprehensive Everglades Restoration Plan. In addition, numerous subregional-scale hydrologic and hydrodynamic models have been developed and are being used for evaluating project-scale water management plans associated with urban, agricultural, and inland costal ecosystems. The authors provide a comprehensive summary of models of all scales, as well as the next generation models under development to meet the future needs of ecosystem restoration efforts in South Florida. The multiagency efforts to develop and apply models have allowed the agencies to understand the complex hydrologic interactions, quantify appropriate performance measures, and use new technologies in simulation algorithms, software development, and GIS/database techniques to meet the future modeling needs of the ecosystem restoration programs.

Florida↗
USGS2008

An integrated approach to assess broad-scale condition of coastal wetlands - The Gulf of Mexico Coastal Wetlands pilot survey

The Environmental Protection Agency (EPA) and U.S. Geological Survey (USGS) initiated a two-year regional pilot survey in 2007 to develop, test, and validate tools and approaches to assess the condition of northern Gulf of Mexico (GOM) coastal wetlands. Sampling sites were selected from estuarine and palustrine wetland areas with herbaceous, forested, and shrub/scrub habitats delineated by the US Fish and Wildlife Service National Wetlands Inventory Status and Trends (NWI S&T) program and contained within northern GOM coastal watersheds. A multi-level, stepwise, iterative survey approach is being applied to multiple wetland classes at 100 probabilistically-selected coastal wetlands sites. Tier 1 provides information at the landscape scale about habitat inventory, land use, and environmental stressors associated with the watershed in which each wetland site is located. Tier 2, a rapid assessment conducted through a combination of office and field work, is based on best professional judgment and on-site evidence. Tier 3, an intensive site assessment, involves on-site collection of vegetation, water, and sediment samples to establish an integrated understanding of current wetland condition and validate methods and findings from Tiers 1 and 2. The results from this survey, along with other similar regional pilots from the Mid-Atlantic, West Coast, and Great Lakes Regions will contribute to a design and implementation approach for the National Wetlands Condition Assessment to be conducted by EPA's Office of Water in 2011. ?? Springer Science+Business Media B.V. 2008.

Conference Paper↗
USGS2011

Structural and functional effects of herbicides on non-target organisms in aquatic ecosystems with an emphasis on atrazine

Herbicide use has increased dramatically around the world over the past 6 decades (Gianessi and Reigner, 2007). Few herbicides were in use in the 1950s. However, by 2001 approximately 1.14 billion kilograms of herbicides were applied globally for the control of undesireable vegetation in agricultural, silvicultural, lawncare, aquacultural, and irrigation/recreational water management activities (Kiely et al., 2004). Twenty-eight percent of the total mass of herbicides is applied in the United States, with the remaining 72 percent being applied elsewhere around the globe (Kiely et al., 2004). Herbicides represent 36% of global pesticide use, followed by insecticides (25%), fungicides (10%) and other chemical classes (Kiely et al., 2004). Agricultural production accounts for approximately 90% of herbicide use in the U.S. (Kiely et al., 2004). Gianessi and Reigner (2007) indicated that herbicides are routinely used on more than 90% of the area designated for large commercial crops including corn, soybeans, cotton, sugar beets, peanuts, and rice. Increased farm mechanization, technological advancements in production of inexpensive sources of inorganic nitrogen fertilizer (e.g., anhydrous ammonia), and conversion of forest, grassland, and wetland habitats to cropland has led to a tremendous increase in global food production over the past half-century. Herbicides have augmented advances in large-scale agricultural systems and have largely replaced mechanical and hand-weeding control mechanisms (Gianessi and Reigner, 2007). The wide-spread use of herbicides in agriculture has resulted in frequent chemical detections in surface and groundwaters (Gilliom, 2007). The majority of herbicides used are highly water soluble and are therefore prone to runoff from terrestrial environments. In additon, spray drift and atmospheric deposition can contribute to herbicide contamination of aquatic environments. Lastly, selected herbicides are deliberately applied to aquatic environments for controlling nuisance aquatic vegetation. Although aquatic herbicide exposure has been widely documented, these exposures are not necessarily related to adverse non-target ecological effects on natural communities in aquatic environments. This chapter evaluates the potential for effects of herbicides on the structure and function of aquatic envrionments at the population, community, and ecosystem levels of biological organization. In this manuscript I examine several critical aspects of the subject matter area: primary herbicides in use and chemical modes of action; the regulatory process used for registration and risk assessment of herbicides; data regarding non-target risks and the relative sensitivity of aquatic plants, inveretebrates, and fish to herbicides; and emerging areas of science regarding the potential for endocrine-disrupting effects of herbicides on aquatic vertebrates. Much of the focus of this paper is on atrazine due to the extensive database which exists regarding its fate and effects.

Book chapter↗
USGS2014

USGS ecosystem research for the next decade: advancing discovery and application in parks and protected areas through collaboration

Ecosystems within parks and protected areas in the United States and throughout the world are being transformed at an unprecedented rate. Changes associated with natural hazards, greenhouse gas emissions, and increasing demands for water, food, land, energy and mineral resources are placing urgency on sound decision making that will help sustain our Nation’s economic and environmental well-being (Millennium Ecosystem Assessment, 2005). In recognition of the importance of science in making these decisions, the U.S. Geological Survey (USGS) in 2007 identified ecosystem science as one of six science directions included in a comprehensive decadal strategy (USGS 2007). The Ecosystems Mission Area was identified as essential for integrating activity within the USGS and as a key to enhanced integration with other Federal and private sector research and management organizations (Myers at al., 2007). This paper focuses on benefits to parks and protected areas from the USGS Ecosystems Mission Area plan that expanded the scope of the original 2007 science strategy, to identify the Bureau’s work in ecosystem science over the next decade (Williams et al., 2013). The plan describes a framework that encompasses both basic and applied science and allows the USGS to continue to contribute meaningfully to conservation and management issues related to the Nation’s parks and ecological resources. This framework relies on maintaining long-standing, collaborative relationships with partners in both conducting science and applying scientific results. Here we summarize the major components of the USGS Ecosystems Science Strategy, articulating the vision, goals and strategic approaches, then outlining some of the proposed actions that will ultimately prove useful to those managing parks and protected areas. We end with a discussion on the future of ecosystem science for the USGS and how it can be used to evaluate ecosystem change and the associated consequences to management of our Nation’s natural resources.

The George Wright Forum↗
USGS1990

A new planktic foraminifer transfer function for estimating pliocene-Holocene paleoceanographic conditions in the North Atlantic

A new planktic foraminifer transfer function (GSF18) related 5 North Atlantic assemblages to winter and summer sea surface temperature. GSF18, based on recombined and simplified core top census data, preserves most environmental information and reproduces modern North Atlantic conditions with approximately the same accuracy as previous transfer functions, but can be more readily applied to faunal samples ranging in age from Pliocene to Holocene. Transfer function GSF18 has been applied to faunal data from Deep Sea Drilling Project Hole 552A to produce a 2.5 m.y. sea-surface temperature (SST) time series. Estimates show several periods between 2.3 and 4.6 Ma during which mean SST's were both several degrees warmer and several degrees cooler than modern conditions. Between 2.9 and 4.0 Ma SST was generally warmer than modern except for a 250 k.y. interval centered at 3.3 Ma. Maximum SST, with respect to modern conditions, occurred after the cool interval near 3.1 Ma when SST was approximately 3.6??C warmer than present conditions. Comparison of SST estimates with stable isotope data suggest that after peak warming at 3.1 Ma, there was an overall surface water cooling with concomitant build up of global ice volume, culminating in Northern Hemisphere glaciation. This event is also indicated by the presence of ice rafted detritus in 552A sediments at about 2.45 Ma. ?? 1990 Elsevier Science Publishers B.V.

Marine Micropaleontology↗
USGS1988

Hydraulic conductivity of a sandy soil at low water content after compaction by various methods

To investigate the degree to which compaction of a sandy soil influences its unsaturated hydraulic conductivity K , samples of Oakley sand (now in the Delhi series; mixed, thermic, Typic Xeropsamments) were packed to various densities and K was measured by the steady-state centrifuge method. The air-dry, machine packing was followed by centrifugal compression with the soil wet to about one-third saturation. Variations in (i) the impact frequency and (ii) the impact force during packing, and (iii) the amount of centrifugal force applied after packing, produced a range of porosity from 0.333 to 0.380. With volumetric water content θ between 0.06 and 0.12, K values were between 7 × 10 −11 and 2 × 10 −8 m/s. Comparisons of K at a single θ value for samples differing in porosity by about 3% showed as much as fivefold variation for samples prepared by different packing procedures, while there generally was negligible variation (within experimental error of 8%) where the porosity difference resulted from a difference in centrifugal force. Analysis involving capillary-theory models suggests that the differences in K can be related to differences in pore-space geometry inferred from water retention curves measured for the various samples.

Soil Science Society of America Journal↗
USGS2014

A GIS-based vulnerability assessment of brine contamination to aquatic resources from oil and gas development in eastern Sheridan County, Montana

Water (brine) co-produced with oil in the Williston Basin is some of the most saline in the nation. The Prairie Pothole Region (PPR), characterized by glacial sediments and numerous wetlands, covers the northern and eastern portion of the Williston Basin. Sheridan County, Montana, lies within the PPR and has a documented history of brine contamination. Surface water and shallow groundwater in the PPR are saline and sulfate dominated while the deeper brines are much more saline and chloride dominated. A Contamination Index (CI), defined as the ratio of chloride concentration to specific conductance in a water sample, was developed by the Montana Bureau of Mines and Geology to delineate the magnitude of brine contamination in Sheridan County. Values > 0.035 indicate contamination. Recently, the U.S. Geological Survey completed a county level geographic information system (GIS)-based vulnerability assessment of brine contamination to aquatic resources in the PPR of the Williston Basin based on the age and density of oil wells, number of wetlands, and stream length per county. To validate and better define this assessment, a similar approach was applied in eastern Sheridan County at a greater level of detail (the 2.59 km 2 Public Land Survey System section grid) and included surficial geology. Vulnerability assessment scores were calculated for the 780 modeled sections and these scores were divided into ten equal interval bins representing similar probabilities of contamination. Two surface water and two groundwater samples were collected from the section with the greatest acreage of Federal land in each bin. Nineteen of the forty water samples, and at least one water sample from seven of the ten selected sections, had CI values indicating contamination. Additionally, CI values generally increased with increasing vulnerability assessment score, with a stronger correlation for groundwater samples (R 2 = 0.78) than surface water samples (R 2 = 0.53).

Montana↗
USGS2010

Establishing a Multi-scale Stream Gaging Network in the Whitewater River Basin, Kansas, USA

Investigating the routing of streamflow through a large drainage basin requires the determination of discharge at numerous locations in the channel network. Establishing a dense network of stream gages using conventional methods is both cost-prohibitive and functionally impractical for many research projects. We employ herein a previously tested, fluid-mechanically based model for generating rating curves to establish a stream gaging network in the Whitewater River basin in south-central Kansas. The model was developed for the type of channels typically found in this watershed, meaning that it is designed to handle deep, narrow geomorphically stable channels with irregular planforms, and can model overbank flow over a vegetated floodplain. We applied the model to ten previously ungaged stream reaches in the basin, ranging from third- to sixth-order channels. At each site, detailed field measurements of the channel and floodplain morphology, bed and bank roughness, and vegetation characteristics were used to quantify the roughness for a range of flow stages, from low flow to overbank flooding. Rating curves that relate stage to discharge were developed for all ten sites. Both fieldwork and modeling were completed in less than 2 years during an anomalously dry period in the region, which underscores an advantage of using theoretically based (as opposed to empirically based) discharge estimation techniques. ?? 2010 Springer Science+Business Media B.V.

Water Resources Management↗
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↗
USGS2008

A linked hydrodynamic and water quality model for the Salton Sea

A linked hydrodynamic and water quality model was developed and applied to the Salton Sea. The hydrodynamic component is based on the one-dimensional numerical model, DLM. The water quality model is based on a new conceptual model for nutrient cycling in the Sea, and simulates temperature, total suspended sediment concentration, nutrient concentrations, including PO4-3, NO3-1 and NH4+1, DO concentration and chlorophyll a concentration as functions of depth and time. Existing water temperature data from 1997 were used to verify that the model could accurately represent the onset and breakup of thermal stratification. 1999 is the only year with a near-complete dataset for water quality variables for the Salton Sea. The linked hydrodynamic and water quality model was run for 1999, and by adjustment of rate coefficients and other water quality parameters, a good match with the data was obtained. In this article, the model is fully described and the model results for reductions in external phosphorus load on chlorophyll a distribution are presented. ?? 2008 Springer Science+Business Media B.V.

Hydrobiologia↗
USGS2017

Holistic assessment of occurrence and fate of metolachlor within environmental compartments of agricultural watersheds

Background : Metolachlor [(RS)-2-Chloro- N -(2-ethyl-6-methyl-phenyl)- N -(1-methoxypropan-2-yl)acetamide] and two degradates (metolachlor ethane-sulfonic acid and metolachlor oxanilic acid) are commonly observed in surface and groundwater. The behavior and fate of these compounds were examined over a 12-year period in seven agricultural watersheds in the United States. They were quantified in air, rain, streams, overland flow, groundwater, soil water, subsurface drain water, and water at the stream/groundwater interface. The compounds were frequently detected in surface and groundwater associated with agricultural areas. A mass budget approach, based on all available data from the study and literature, was used to determine a percentage-wise generalized distribution and fate of applied parent metolachlor in typical agricultural environments. Results : In these watersheds, about 90% of applied metolachlor was taken up by plants or degraded, 10% volatilized, and 0.3% returned as rainfall. One percent was transported to surface water, while an equal amount infiltrated into the unsaturated zone soil water. < 0.02% reached the groundwater. Subsurface flow paths resulted in greater degradation of metolachlor because degradation reactions had more time to proceed. Conclusions : An understanding of the residence times of water in the different environmental compartments, and the important processes affecting metolachlor as it is transported along flowpaths among the environmental compartments allows for a degree of predictability of metolachlor's fate. Degradates with long half-lives can be used (in a limited capacity) as tracers of metolachlor, because of their persistence and widespread occurrence in the environment.

Science of the Total Environment↗
USGS2024

Novel quantitative methods to enable multispectral identification of high-purity water ice exposures on Mars using High Resolution Imaging Science Experiment (HiRISE) images

Reliable detection and characterization of water ice on the Martian surface is pivotal to not only understand its present and past climate, but to also provide valuable information on in-situ resource availability and distribution for future human exploration missions. Ice-rich features are currently identified with visible/near-IR (VNIR), thermal IR and radar data. However, their coarse spatial scale sometimes limits confident characterization of small (i.e., meter-scale) icy exposures resulting from recent activity like new impacts. Water ice bearing materials possess weaker spectral characteristics at wavelengths shorter than ∼1030 nm that may be resolved by VNIR imaging instruments like the High Resolution Imaging Science Experiment (HiRISE) and the Colour and Stereo Surface Imaging System (CaSSIS). Our study assesses the spectral capability of HiRISE colour observations to help distinguish high purity water ice exposures from ice-poor materials. We report detailed methodologies for reliable colour characterization of icy surface using unfiltered HiRISE images. We present the first quantitative approach to uniquely characterize high-purity ice-rich materials through spectral shape and spectral parameterization methods at high spatial resolution (∼50 cm/pixel). We also present three spectral parameters to aid detection of pure water ice features, while also providing statistical constraints to enable a quantitative interpretation scheme. Our methods are observed to work well in characterizing and separating ice-rich features uniquely from ice-poor and ferrous materials. However, we do observe that these methods have a lower grain size detection limit of ∼250–300 μm, and may not be able to uniquely separate frosts from ground ice exposures. We also apply these methods to better constrain the composition of bright materials exposed by recent impacts identified in previous surveys, where substantial evidence for ice-bearing materials was previously unavailable. Overall, our work proposes HiRISE colour-based methods as a novel approach for high-resolution multispectral characterization of ice-rich features on the Martian surface, which is of particular value since the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has ceased operations.

Icarus↗