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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Satellite-derived cyanobacteria frequency and magnitude in headwaters & near-dam reservoir surface waters of the Southern U.S.

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016–April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

cyanobacteria↗
NASA NTRS1975

Hydrologic land use classification of the Patuxent River watershed using remotely sensed data

The Patuxent River Watershed is located in central Maryland between Baltimore and Washington, D.C. and is approximately 2330 sq km in area and 175 km long. This region is now at a critical point because of major concerns such as water management and quality, flooding and land use within the watershed. Data from the NASA-directed LANDSAT and Earth Resources Aircraft Programs were used to provide a new dimension in information collection and processing for the management of watersheds. Digital data from LANDSAT-1 were analyzed along with selected IR photography from U-2 flight number 74-060B taken 28 April 1974, which was digitized in three channels. Processing of the data was accomplished using a multispectral analysis system. Land use themes consisting of surface water, wetlands, forest, residential, cropland/pasture, urban, and extractive were developed and delineated through the watershed. Area measurements of watershed themes were obtained and will serve as a calibration input to a deterministic hydrologic model on a sub-watershed. Using the derived residential and urban theme areas from LANDSAT an estimated basin imperviousness was also calculated. Thematic maps were produced at 1:62,500 scale. Floodprone areas were also classified and delineated at a scale of 1:24,000. Comparison with standard floodprone area maps at the same scale have indicated a few areas of discrepancy. Such information can be used for updating or checking floodprone area boundaries as well as monitoring changes in floodplain areas.

Dallam, W. C.↗
NASA NTRS1978

Post LANDSAT D advanced concept evaluation

Preliminary results of a past LANDSAT D advanced concept evaluation study to forecast and identify key technologies required for earth resources satellite systems in the 1985 to 2000 time period are presented. Mission categories discussed include agriculture, range management, forestry, geological resources, land use, water resources, environmental quality, and disaster assessment. Sensor and system concepts are described for a passive L-band radiometer (microsat), a texturometer to measure ground surface texture, and a ferris wheel radar configuration that relies on cable tension for support.

Flatow, F. S.↗
NASA NTRS2023

Seasonal Forecasting Skill for the High Mountain Asia Region in the Goddard Earth Observing System

Seasonal variability of the global hydrologic cycle directly impacts human activities, including hazard assessment and mitigation, agricultural decisions, and water resources management. This is particularly true across the High Mountain Asia (HMA) region, where availability of water resources can change depending on local seasonality of the hydrologic cycle. Forecasting the atmospheric states and surface conditions, including hydrometeorological relevant variables, at subseasonal-to-seasonal (S2S) lead times of weeks-to-months is an area of active research and development. NASA’s 15 Goddard Earth Observing System (GEOS) S2S prediction system has been developed with this research goal in mind. Here, we benchmark the forecast skill of GEOS-S2S (version 2) hydrometeorological forecasts at 1-3 month lead times in the HMA region, including a portion of the Indian Subcontinent, during the retrospective forecast period, 1981-2016. To assess forecast skill, we evaluate 2-m air temperature, total precipitation, fractional snow cover, snow water equivalent, surface soil moisture, and terrestrial water storage forecasts against the Modern-Era Retrospective analysis for Research and 20 Applications, Version 2 (MERRA-2) and independent reanalysis data, satellite observations, and data fusion products. Anomaly correlation is highest when the forecasts are evaluated against MERRA-2 and particularly in variables with long memory in the climate system, likely due to similar initial conditions and model architecture used in GEOS-S2S and MERRA-2. When compared to MERRA-2, results for the 1-month forecast skill range from anomaly correlation of R anom =0.18 for precipitation to R anom =0.62 for soil moisture. Anomaly correlations are consistently lower when forecasts are 25 evaluated against independent observations; results for the 1-month forecast skill range from R anom =0.13 for snow water equivalent to R anom =0.24 for fractional snow cover. We find that, generally, hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology.

GEOS↗
NASA NTRSDate not supplied

Evaluating Rock Pool Hydroperiod Fluctuation using Climate Variables to Inform Habitat Monitoring and Protection in the Western Sonoran Desert

As warming and drying trends continue to impact the greater American Southwest, effective ecosystem management increasingly relies upon understanding the relationships between climate and water resources. Ephemeral freshwater rock pools, known as tinajas, have great ecological and cultural importance as some of the only sources of surface water in the western Sonoran Desert (WSD). Tinaja flooding and drying cycles, known as hydroperiods, vary based on meteorologic and climatologic conditions; however, a lack of extensive research relating climatic impacts to tinajas puts these critical ecosystems further at risk. Tinajas throughout the WSD are monitored by the National Park Service (NPS) using resource intensive strategies including in situ trail camera observation and direct measurement. To aid NPS monitoring efforts, this research used remotely sensed climate data to analyze spatiotemporal climate trends and relationships between climate variables and tinaja hydroperiods in the WSD between 1979–2022. Using Aqua and Terra Moderate Resolution Imaging Spectroradiometers (MODIS), University of Idaho Gridded Surface Meteorological Dataset (gridMET), and OpenET data, the project analyzed land surface temperature, evapotranspiration, precipitation, wind velocity, and solar radiation. The team generated climate anomaly time series and climate normal maps for the WSD, identifying statistically significant spatial and temporal trends. These data were then compared to daily, qualitative in situ hydroperiod observations taken between 2019–2022. This work will be used by the NPS to inform the monitoring and protection of tinajas in the WSD. Findings contribute to a limited body of research concerning climate and tinajas, which are often overlooked despite their disproportionate ecological importance.

Annie Britton↗
NASA NTRS2015

Benchmark Data Set for Wheat Growth Models: Field Experiments and AgMIP Multi-Model Simulations.

The data set includes a current representative management treatment from detailed, quality-tested sentinel field experiments with wheat from four contrasting environments including Australia, The Netherlands, India and Argentina. Measurements include local daily climate data (solar radiation, maximum and minimum temperature, precipitation, surface wind, dew point temperature, relative humidity, and vapor pressure), soil characteristics, frequent growth, nitrogen in crop and soil, crop and soil water and yield components. Simulations include results from 27 wheat models and a sensitivity analysis with 26 models and 30 years (1981-2010) for each location, for elevated atmospheric CO2 and temperature changes, a heat stress sensitivity analysis at anthesis, and a sensitivity analysis with soil and crop management variations and a Global Climate Model end-century scenario.

climate change impact↗
NASA NTRS2005

Geospatial Analysis and Remote Sensing from Airplanes and Satellites for Cultural Resources Management

Cultural resource management consists of research to identify, evaluate, document and assess cultural resources, planning to assist in decision-making, and stewardship to implement the preservation, protection and interpretation of these decisions and plans. One technique that may be useful in cultural resource management archaeology is remote sensing. It is the acquisition of data and derivative information about objects or materials (targets) located on the Earth's surface or in its atmosphere by using sensor mounted on platforms located at a distance from the targets to make measurements on interactions between the targets and electromagnetic radiation. Included in this definition are systems that acquire imagery by photographic methods and digital multispectral sensors. Data collected by digital multispectral sensors on aircraft and satellite platforms play a prominent role in many earth science applications, including land cover mapping, geology, soil science, agriculture, forestry, water resource management, urban and regional planning, and environmental assessments. Inherent in the analysis of remotely sensed data is the use of computer-based image processing techniques. Geographical information systems (GIS), designed for collecting, managing, and analyzing spatial information, are also useful in the analysis of remotely sensed data. A GIS can be used to integrate diverse types of spatially referenced digital data, including remotely sensed and map data. In archaeology, these tools have been used in various ways to aid in cultural resource projects. For example, they have been used to predict the presence of archaeological resources using modern environmental indicators. Remote sensing techniques have also been used to directly detect the presence of unknown sites based on the impact of past occupation on the Earth's surface. Additionally, remote sensing has been used as a mapping tool aimed at delineating the boundaries of a site or mapping previously unknown features. All of these applications are pertinent to the goals of site discovery and assessment in cultural resource management.

Giardino, Marco J.↗
NASA NTRS2021

Highland Lakes Water Resources: Using NASA Earth Observations to Improve Detection Systems for Harmful Algal Events in the Highland Lakes in Central Texas

Beginning in 2019, harmful algal events in Austin, Texas, caused canine deaths in the Lady Bird Lake and Lake Travis reservoirs. These reservoirs are part of the larger Highland Lakes chain, managed by the Lower Colorado River Authority (LCRA) and the City of Austin Department of Watershed Protection (CoA DWP), which fulfill municipal, commercial, and agricultural water demands. Given the recent increase in favorable algal event conditions in central Texas, the LCRA and CoA DWP partnered with NASA DEVELOP to improve algal event early-warning systems through the application of remote sensing and machine learning. An Earth observation-based algal monitoring system will assist the responsible agencies in predicting algal conditions and communicating hazards to the public. The NASA DEVELOP team utilized Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data to produce products including chlorophyll-a concentrations, cyanobacteria detections, turbidity, and water surface temperature. Chlorophyll-a concentrations were retrieved with a pre-trained machine learning model (mixture density network) and spectral indices, while the other products were derived from spectral indices. In situ field data were used to validate and quantify uncertainties for each product. The validations show strong correlations for chlorophyll-a and water surface temperature. Time series analyses of chlorophyll-a concentrations show peaks in the severe drought years (2015 and 2016). This project's resulting products enable monitoring of environmental proxies relevant to algal event presence in the Highland Lakes chain and will ultimately support water management, decision making, and risk communication.

Kaitlynn Hietpas↗
NASA NTRS1999

The Potential for Predicting Precipitation on Seasonal-to-Interannual Timescales

The ability to predict precipitation several months in advance would have a significant impact on water resource management. This talk provides an overview of a project aimed at developing this prediction capability. NASA's Seasonal-to-Interannual Prediction Project (NSIPP) will generate seasonal-to-interannual sea surface temperature predictions through detailed ocean circulation modeling and will then translate these SST forecasts into forecasts of continental precipitation through the application of an atmospheric general circulation model and a "SVAT"-type land surface model. As part of the process, ocean variables (e.g., height) and land variables (e.g., soil moisture) will be updated regularly via data assimilation. The overview will include a discussion of the variability inherent in such a modeling system and will provide some quantitative estimates of the absolute upper limits of seasonal-to-interannual precipitation predictability.

Koster, R. D.↗
NASA NTRS2007

Soil Moisture Retrieval During a Corn Growth Cycle using L-band (1.6 GHz) Radar Observations

New opportunities for large-scale soil moisture monitoring will emerge with the launch of two low frequency (L-band 1.4 GHz) radiometers: the Aquarius mission in 2009 and the Soil Moisture and Ocean Salinity (SMOS) mission in 2008. Soil moisture is an important land surface variable affecting water and heat exchanges between atmosphere, land surface and deeper ground water reservoirs. The data products from these sensors provide valuable information in a range of climate and hydrologic applications (e.g., nume~cal weather prediction, drought monitoring, flood forecasting, water resources management, etc.). This paper describes a unique data set that was collected during a field campaign at OPE^ (Optimizing Production Inputs for Economic and Environmental Enhancements) site in Beltsville, Maryland throughout the eompj2ete corn growing in 2002. This investigation describes a simple methodology to correct active microwave observations for vegetation effects, which could potentially be implemented in a global soil moisture monitoring algorithm. The methodology has been applied to radar observation collected during the entire corn growth season and validation against ground measurements showed that the top 5-cm soil moisture can be retrieved with an accuracy up to 0.033 [cu cm/cu cm] depending on the sensing configuration.

Joseph, Alicia T.↗
NASA NTRS2019

The NASA Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Mission: an Emerging Era of Global, Hyperspectral Earth System Remote Sensing

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission represents NASA's next investment in satellite ocean color and the study of Earth's ocean-atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth's changing climate. PACE objectives include extending systematic cloud, aerosol, and ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles and ecosystem responses to a changing climate, and improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will also provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument - the Ocean Color Instrument (OCI) - is a hyperspectral imaging radiometer that spans the ultraviolet to shortwave infrared, with a ground sample distance of 1-kilometer at nadir. This includes continuous collection of spectra from 340 to 890 nanometers in 5-nanometer steps. The PACE payload is complemented by two multi-angle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022-to-early 2023, the PACE observatory will enable significant advances in the study of Earth's biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean-atmosphere system. We present a brief overview of the PACE mission, followed by a detailed discussion of the capabilities and design concept of OCI.

Passive Remote Sensing↗
NASA NTRS2019

The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission: An Emerging Era of Global, Hyperspectral Earth System Remote Sensing

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission represents NASA’s next investment in satellite ocean color and the study of Earth’s ocean-atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth's changing climate. PACE objectives include extending systematic cloud, aerosol, ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles and ecosystem responses to a changing climate, as well as improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will also provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument – the Ocean Color Instrument (OCI) – is a hyperspectral imaging radiometer that spans the ultraviolet to shortwave infrared, with a ground sample distance of 1-km at nadir. This includes continuous collection of spectra from 340 nm to 890 nm in 5 nm steps. The PACE payload is complemented by two multi-angle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022-to-early 2023, the PACE observatory will enable significant advances in the study of Earth’s biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean-atmosphere system. We present a brief overview of the PACE mission, followed by a discussion of the capabilities and design concept of OCI.

hyperspectral radiometer↗
NASA NTRS2019

The Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Mission: Status, Science, Advances

The PACE mission represents NASA’s next investment in ocean color, cloud, and aerosol data records to enable continued and advanced insight into oceanographic and atmospheric responses to Earth’s changing climate. The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission represents NASA’s next investment in satellite ocean color and the study of Earth’s ocean-atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth's changing climate. PACE objectives include extending systematic cloud, aerosol, and ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles, food web processes, and ecosystem responses to a changing climate, and improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and air and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument is a spectrometer that spans the ultraviolet to shortwave infrared, with a ground sample distance of 1-km at nadir. This payload is complemented by two multi-angle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022-to-early 2023, the PACE observatory will enable significant advances in the study of Earth’s biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean-atmosphere-land system. Here, we present an overview of the PACE mission, including its developmental history, science objectives, instrument payload, observatory characteristics, and data products.

P Jeremy Werdell↗
NASA NTRS2011

Multi-Scale Hydrometeorological Modeling, Land Data Assimilation and Parameter Estimation with the Land Information System

The Land Information System (LIS; http://lis.gsfc.nasa.gov) is a flexible land surface modeling framework that has been developed with the goal of integrating satellite-and ground-based observational data products and advanced land surface modeling techniques to produce optimal fields of land surface states and fluxes. As such, LIS represents a step towards the next generation land component of an integrated Earth system model. In recognition of LIS object-oriented software design, use and impact in the land surface and hydrometeorological modeling community, the LIS software was selected as a co-winner of NASA?s 2005 Software of the Year award.LIS facilitates the integration of observations from Earth-observing systems and predictions and forecasts from Earth System and Earth science models into the decision-making processes of partnering agency and national organizations. Due to its flexible software design, LIS can serve both as a Problem Solving Environment (PSE) for hydrologic research to enable accurate global water and energy cycle predictions, and as a Decision Support System (DSS) to generate useful information for application areas including disaster management, water resources management, agricultural management, numerical weather prediction, air quality and military mobility assessment. LIS has e volved from two earlier efforts -- North American Land Data Assimilation System (NLDAS) and Global Land Data Assimilation System (GLDAS) that focused primarily on improving numerical weather prediction skills by improving the characterization of the land surface conditions. Both of GLDAS and NLDAS now use specific configurations of the LIS software in their current implementations.In addition, LIS was recently transitioned into operations at the US Air Force Weather Agency (AFWA) to ultimately replace their Agricultural Meteorology (AGRMET) system, and is also used routinely by NOAA's National Centers for Environmental Prediction (NCEP)/Environmental Modeling Center (EMC) for their land data assimilation systems to support weather and climate modeling. LIS not only consolidates the capabilities of these two systems, but also enables a much larger variety of configurations with respect to horizontal spatial resolution, input datasets and choice of land surface model through "plugins". LIS has been coupled to the Weather Research and Forecasting (WRF) model to support studies of land-atmosphere coupling be enabling ensembles of land surface states to be tested against multiple representations of the atmospheric boundary layer. LIS has also been demonstrated for parameter estimation, who showed that the use of sequential remotely sensed soil moisture products can be used to derive soil hydraulic and texture properties given a sufficient dynamic range in the soil moisture retrievals and accurate precipitation inputs.LIS has also recently been demonstrated for multi-model data assimilation using an Ensemble Kalman Filter for sequential assimilation of soil moisture, snow, and temperature.Ongoing work has demonstrated the value of bias correction as part of the filter, and also that of joint calibration and assimilation.Examples and case studies demonstrating the capabilities and impacts of LIS for hydrometeorological modeling, assimilation and parameter estimation will be presented as advancements towards the next generation of integrated observation and modeling systems

Peters-Lidard, Christa D.↗
NASA NTRS2004

Electrochemical, H2O2-Boosted Catalytic Oxidation System

An improved water-sterilizing aqueous-phase catalytic oxidation system (APCOS) is based partly on the electrochemical generation of hydrogen peroxide (H2O2). This H2O2-boosted system offers significant improvements over prior dissolved-oxygen water-sterilizing systems in the way in which it increases oxidation capabilities, supplies H2O2 when needed, reduces the total organic carbon (TOC) content of treated water to a low level, consumes less energy than prior systems do, reduces the risk of contamination, and costs less to operate. This system was developed as a variant of part of an improved waste-management subsystem of the life-support system of a spacecraft. Going beyond its original intended purpose, it offers the advantage of being able to produce H2O2 on demand for surface sterilization and/or decontamination: this is a major advantage inasmuch as the benign byproducts of this H2O2 system, unlike those of systems that utilize other chemical sterilants, place no additional burden of containment control on other spacecraft air- or water-reclamation systems.

Akse, James R.↗
NASA NTRS2000

A Spacebased Ocean Surface Exchange Data Analysis System

Emerging technologies have provided unprecedented opportunities to transform information into knowledge and disseminate them in a much faster, cheaper, and userfriendly mode. We have set up a system to produce and disseminate high level (gridded) ocean surface wind data from the NASA Scatterometer and European Remote Sensing missions. The data system is being expanded to produce real-time gridded ocean surface winds from an improved sensor SeaWinds on the Quikscat Mission. The wind field will be combined with hydrologic parameters from the Tropical Rain Measuring Mission to monitor evolving weather systems and natural hazard in real time. It will form the basis for spacebased Ocean Surface Exchange Data Analysis System (SOSEDAS) which will include the production of ocean surface momentum, heat, and water fluxes needed for interdisciplinary studies of ocean-atmosphere interaction. Various commercial or non-commercial software tools have been compared and selected in terms of their ability in database management, remote data accessing, graphical interface, data quality, storage needs and transfer speed, etc. Issues regarding system security and user authentication, distributed data archiving and accessing, strategy to compress large-volume geophysical and satellite data/image. and increasing transferring speed are being addressed. A simple and easy way to access information and derive knowledge from spacebased data of multiple missions is being provided. The evolving 'knowledge system' will provide relevant infrastructure to address Earth System Science, make inroads in educating an informed populace, and illuminate decision and policy making.

Tang, Wenqing↗
NASA NTRS2000

Rainfall Across the Globe: Precipitation. The Role of Landmass in Monsoon Development. The Relationship Between Precipitation and Sea Surface Temperature on Decadal Time Scales

The numerical simulation of precipitation helps scientists understand the complex mechanisms that determine how and why rainfall is distributed across the globe. Simulation aids in the development of forecastin,g efforts that inform policies regarding the management of water resources. Precipitation modeling also provides short-term warnings, for emergencies such as flash floods and mudslides. Just as precipitation modeling can warn of an impending abundance of rainfall, it can help anticipate the absence of rainfall in drought. What constitutes a drought? A meteorological drought simply means that an area is getting a significantly lower amount of rain than usual over a prolonged period of time and an agricultural drought is based on the level of soil moisture.

Chao, Winston↗
NASA NTRS2026

Spacecraft Water Impurity Monitor, a System for Water Quality Analysis on Exploration Missions Beyond Low Earth Orbit

Exploration missions beyond low-earth-orbit (LEO) will require advanced instrumentation to monitor water quality. Traveling beyond LEO means the transfer of water samples to an Earth-based laboratory for detailed analysis is not feasible. Detailed analysis of water composition during exploration is still necessary, because having the capability to determine the specific organic chemical causing a change in total organic carbon (TOC) or the specific metal or ionic species causing a change in conductivity has the potential to inform the crew health and system management decisions. On a new vehicle such as a lunar or Mars surface habitat or a Mars transit vehicle, finding “new” impurities not seen on ISS should be expected. The key is to identify the impurity so the correct action can be taken. On ISS we can measure TOC, conductivity, and other physical properties but do not have the capability for detailed analysis using vehicle instrumentation. This is acceptable because ISS can send samples down to Earth for further analysis and obtain the detailed composition. The Spacecraft Water Impurity Monitor (SWIM) will provide detailed water quality analysis for missions in which sample down mass is not available. SWIM is a system comprising organic and inorganic analysis modules. For organic chemicals, a gas chromatograph mass spectrometer (GCMS) detects and identifies organic impurities. For inorganic species, a capillary electrophoresis capacitively coupled contactless conductivity detection (CE-C4D) system as well as ion specific electrodes detect and identify metal ions and other inorganic salts / acids. The SWIM technology demonstration project has completed a System Requirements Review (SRR) to finalize detection requirements and is currently working to refine vehicle interface requirements for a future technology demonstration. The project also has begun preliminary flight design activities for the core analyzers in the instrument suite.

Water Monitoring↗