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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Assessing the Impacts of Recent Crop Expansion on Water Quality in the Missouri River Basin Using the Soil and Water Assessment Tool

The Missouri River Basin (MORB) has experienced a resurgence of grassland conversion to crop production, which raised concerns on water quality. We applied the Soil and Water Assessment Tool (SWAT) to address how this conversion would impact water quality. We designed three crop production scenarios representing conversion of grassland to: (a) continuous corn; (b) corn/soybean rotation; and (c) corn/wheat rotation to assess the impact. The SWAT model results showed: (a) the lower MORB produced high total nitrogen (TN) and total phosphorus (TP) load before conversion (baseline) due mainly to high precipitation and high agricultural activity; (b) the greatest percentage increases of TN and TP occurred in the North and South Dakotas, coinciding with the highest amount of grassland conversion to cropland; and (c) grassland conversion to continuous corn resulted in the greatest increase in TN and TP loads, followed by conversion to corn/soybean and then conversion to corn/wheat. Although the greatest percentage increases of TN and TP occurred in the North and South Dakotas, these areas still contributed relatively low TN and TP to total basin loads after conversion. However, watersheds, predominantly in the lower MORB continued to be “hotspots” that contributed the greatest amounts of TN and TP to the total basin loads—driven by a combination of grassland conversion, high precipitation, and loading from pre-existing cropland. At the watershed outlet, the TN and TP loads were increased by 6.4% (13,800 t/yr) and 8.7% (3,400 t/yr), respectively, during the 2008–2016 period for the conversion to continuous corn scenario.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1978

Development and evaluation of a helicopter-borne water-quality monitoring system

A small, helicopter-borne water-quality monitoring package is being developed by the NASA/EPA using a combination of basic in situ water quality sensors and physical sample collector technology. The package is a lightweight system which can be carried and operated by one person as a passenger in a small helicopter typically available by rental at commercial airports. Real-time measurements are made by suspending the water quality monitoring package with a cable from the hovering helicopter. Designed primarily for use in rapidly assessing hazardous material spills in inland and coastal zone water bodies, the system can survey as many as 20 data stations up to 1.5 kilometers apart in 1 hour. The system provides several channels of sensor data and allows for the addition of future sensors. The system will also collect samples from selected sites with sample collection on command. An EPA Spill Response Team member can easily transport, deploy, and operate the water quality monitoring package to determine the distribution, movement, and concentration of the spilled material in the water body.

Wallace, J. W.↗
DOE OSTI2022

Potential of water quality wetlands to mitigate habitat losses from agricultural drainage modernization

Given widespread biodiversity declines, a growing global human population, and demands to improve water quality, there is an immediate need to explore land management solutions that support multiple ecosystem services. Agricultural water quality wetlands designed to provide both water quality benefits and wetland and grassland habitat are an emerging restoration solution that may reverse habitat declines in intensive agricultural areas. Installation of water quality wetlands in the Upper Midwest, USA, when considered alongside the repair and modification of aging agricultural tile drainage infrastructure, is a likely scenario that may mitigate nutrient pollution exported from agricultural systems and improve crop yields. The capacity of water quality wetlands to provide habitat within the wetland pool and the surrounding grassland is not well-studied, particularly with respect to potential habitat changes resulting from drainage infrastructure upgrades. For the current study, we produced spatially explicit models of 37 catchments distributed throughout an important region for agriculture and biodiversity, the Des Moines Lobe of Iowa. Four scenarios were considered - with and without improved drainage and with and without water quality wetlands - to estimate the net potential habitat implications of these scenarios for amphibians, grassland birds, and wild bees. Model results indicate that drainage modification alone will likely result in moderate direct losses of suitable amphibian habitat and large declines in overall habitat quality. However, inclusion of water quality wetlands at the catchment scale may mitigate these amphibian habitat losses while also increasing grassland bird and pollinator habitat. In conclusion, the impacts of water quality wetlands and drainage modernization on waterfowl in the region require additional study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The potential of carbon markets to accelerate green infrastructure based water quality trading

Green infrastructure solutions can improve in-stream water quality in lieu of building electricity-consuming gray infrastructure. Permitted under the United States Clean Water Act, these programs allow regulated utilities to trade point-source water quality obligations with non-point source mitigation efforts in the watershed. Carbon financing can provide an incentive for water quality trading. Here we combine data on impaired waters, treatment technologies, and life cycle greenhouse gas emissions in the Contiguous United States, and compare traditional treatment technologies to alternative green infrastructure. We find green infrastructure could save $\$15.6$ billion dollars, 21.2 terawatt-hours of electricity, and 29.8 million tonnes of carbon dioxide equivalent emissions per year while sequestering over 4.2 million tonnes CO2e per year over a 40 year time horizon. Green infrastructure solutions may have the potential to generate $\$679$ million annually in carbon credit revenue (at $\$20$ per credit), which represents a unique opportunity to help accelerate water quality trading.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2023

Perspectives on User Engagement of Satellite Earth Observation for Water Quality Management

The management and governance of our surface waters is core to life and prosperity on our planet. However, monitoring data are not available to many potential users and the disparate nature of water bodies makes consistent monitoring across so many systems difficult. While satellite Earth observation (EO) offers solutions, there are numerous challenges that limit the use of satellite EO for water monitoring. To understand the perceptions of using satellite EO for water quality monitoring, a survey was conducted within academia and the water quality management sector. Study objectives were to assess community understanding of satellite EO water quality data, identify barriers in the adoption of satellite EO data, and analyse trust in satellite EO data. Most (40 %) participants were beginners with little understanding of satellite EO. Participants indicated problems with satellite EO data accessibility (31 %) and interpretability (26 %). Results showed a high level of trust with satellite EO data and higher trust with in-situ EO data. This study highlighted the gap between water science, applied social science, and policy. A transdisciplinary approach to managing water resources is needed to bridge water disciplines and take a key role in areas such as social issues, knowledge brokering, and translation.

water quality↗
NASA NTRS1973

Water quality parameters of harbors of Charlotte Amalie, St. Thomas, Virgin Islands: Acquisition of in situ water data, intercorrelation of selected water parameters, and initial correlation of these in situ biological, chemical and physical data with ERTS-1 bulk CCT MSS band 5 data

The author has identified the following significant results. Remote sensing by the ERTS-1 satellite was compared with selected water quality parameters including pH, salinity, conductivity, dissolved oxygen, water depth, water temperature, turbidity, plankton concentration, current variables, chlorophylla, total carotenoids, and species diversity of the benthic community. Strong correlation between turbidity and MSS-sensed radiance was recorded and less strong correlations between the two plankton pigments and radiance. Turbidity and benthic species diversity were highly correlated furnishing an inferential tie between an easily sensed water quality variable and a sensitive indicator of average water quality conditions.

Coulbourn, W. C.↗
DOE OSTI2020

Modeling Water Quality in Watersheds: From Here to the Next Generation

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2025

Hilo Bay Water Resources: Monitoring Water Quality in Hilo Bay, Hawaii to Support Future Community Planning

Designated as an impaired body of water by both state and federal water quality standards, Hilo Bay, Hawaiʻi is highly susceptible to brown water, a condition where the water becomes murky and is associated with excess levels of bacteria, contaminants, and nutrients. A breakwater in Hilo Bay, which was established to protect Hilo town from tsunamis, interferes with water circulation and prolongs the presence of brown water in the bay. The State of Hawaiʻi issues brown water advisories (BWAs) following flash flood warnings, sewage spills, and other events to indicate a public health concern for those who use Hilo Bay for recreation, cultural purposes, and fishing. Due to the elevated public health risk and ecosystem disturbance that brown water poses to Hilo Bay, we partnered with the Hawaiʻi County Office of Sustainability, Climate, Equity, and Resilience (OSCER) to examine the feasibility of using Earth observations (EO) to monitor water quality in the Hilo Bay region. We leveraged data from Sentinel-2 Multispectral Instrument (MSI), Landsat 8 Operational Land Imager (OLI), Landsat 9 OLI-2, and Aqua and Terra Moderate Resolution Imagine Spectroradiometer (MODIS) instruments to identify and assess spatial and temporal patterns of two main water quality parameters, turbidity and chlorophyll-a, during BWAs. We used the Optical Reef and Coastal Area Assessment (ORCAA) tool in Google Earth Engine to process EO data and generate water quality maps and time series. Our study found that increased turbidity levels can be identified by EO data during BWAs. In addition, our map products indicated the presence of several turbidity plumes along the coast, with the highest concentration of turbidity found within Hilo Bay. While chlorophyll-a levels were relatively flat within our study region during BWAs, we found that regional chlorophyll-a patterns could be derived from MODIS chlorophyll-a data in NASA Worldview. Our study’s multi-sensor approach provided valuable insights for how water quality in the Hilo Bay region can be monitored in the future.

remote sensing↗
NASA NTRS1982

Image processing developments and applications for water quality monitoring and trophic state determination

Remote sensing data analysis of water quality monitoring is evaluated. Data anaysis and image processing techniques are applied to LANDSAT remote sensing data to produce an effective operational tool for lake water quality surveying and monitoring. Digital image processing and analysis techniques were designed, developed, tested, and applied to LANDSAT multispectral scanner (MSS) data and conventional surface acquired data. Utilization of these techniques facilitates the surveying and monitoring of large numbers of lakes in an operational manner. Supervised multispectral classification, when used in conjunction with surface acquired water quality indicators, is used to characterize water body trophic status. Unsupervised multispectral classification, when interpreted by lake scientists familiar with a specific water body, yields classifications of equal validity with supervised methods and in a more cost effective manner. Image data base technology is used to great advantage in characterizing other contributing effects to water quality. These effects include drainage basin configuration, terrain slope, soil, precipitation and land cover characteristics.

Blackwell, R. J.↗
NASA NTRS1979

Water quality monitor

The preprototype water quality monitor (WQM) subsystem was designed based on a breadboard monitor for pH, specific conductance, and total organic carbon (TOC). The breadboard equipment demonstrated the feasibility of continuous on-line analysis of potable water for a spacecraft. The WQM subsystem incorporated these breadboard features and, in addition, measures ammonia and includes a failure detection system. The sample, reagent, and standard solutions are delivered to the WQM sensing manifold where chemical operations and measurements are performed using flow through sensors for conductance, pH, TOC, and NH3. Fault monitoring flow detection is also accomplished in this manifold assembly. The WQM is designed to operate automatically using a hardwired electronic controller. In addition, automatic shutdown is incorporated which is keyed to four flow sensors strategically located within the fluid system.

West, S.↗
DOE OSTI2024

Monitoring water quality in the lower Kansas River using remote sensing

Abstract We demonstrate how to combine remote sensing data from satellite imagery (Sentinel‐2) with in situ water quality gauging (USGS Super Gages and the Gybe hyperspectral radiometer) to create spatially dense maps of water quality parameters (chlorophyll‐a concentration, turbidity, and nitrate plus nitrite concentration) along the lower Kansas River. The water quality maps are created using locally tuned models of the target water quality parameters, and this study describes the steps used to design, calibrate, and validate the empirical correlations. Water quality parameters such as chlorophyll‐a concentration are correlated with well‐studied absorption and scattering features in the visible spectrum (roughly 400–700 nm). Nutrients (such as nitrate plus nitrite concentration) lack strong absorption features in the visible spectrum, and in those cases we describe a novel surrogate data modeling approach that identifies overlapping water parcels between the in situ gauging and the remote sensing imagery. Measurements from the overlapping water parcels yield excellent correlations () for the target water quality parameters for limited windows of time (or limited sections of river reaches). Examples are provided illustrating how the water quality maps can be used to track river inputs from ungauged sources (such as creeks), or reveal the mixing patterns at the confluences.

Tufillaro, Nicholas↗
NASA NTRSDate not supplied

NASA’s Prototype Spectral Water Inversion Processor and Emulator (SWIPE): Towards Global Coastal and Inland Water Quality and Algal Biodiversity Monitoring

Degradation of Earth’s inland water resources due to anthropogenic perturbations and climate anomalies at both local and global scales continues to place human health at substantial risk. There is now a growing necessity to develop pragmatic approaches that allow timely and effective extrapolation of local processes, to spatially resolved global products, and to promote operational and sustainable resource policy management. This presentation will provide updates on NASA’s prototype open-source aquatic modeling platform, Spectral Water Inversion Processor and Emulator (SWIPE), which is a comprehensive, multi-faceted modeling platform for both forward and inverse modeling of diverse aquatic ecosystems from the benthos to top-of-atmosphere (TOA). SWIPE provides a cohesive application which leverages recent advancements in particle modeling, Big Data analytics, and machine learning to develop a high-fidelity synthetic training ground for sensitivity studies and algorithm development for multispectral or upcoming hyperspectral missions. Some of the prominent features of SWIPE to be discussed include: 1. Advanced hyperspectral modeling of globally diverse algal and non-algal particles using a novel two-layer coated sphere scattering model and radiative transfer modeling, 2. Massive, highly detailed synthetic spectral libraries of Analysis-Ready-Data (ARD) which include spectral libraries of particle microphysics, water biogeophysical and optical properties, as well as surface and TOA reflectances at 1 nm resolution, 3. An ensemble of pre-built analytic, machine learning, and deep learning inversion algorithms for various water quality and biodiversity related retrieval parameters and uncertainty quantification, 4. Sensor-agnostic water quality inversion at wide ranging spatial and spectral resolutions including a codebase for seamless application in the Google Earth Engine and NASA Earth Exchange (NEX) for planetary scale analysis. SWIPE will be a fully open-source platform based in python with comprehensive documentation, tutorials, and options for distributed computing on high performance computing clusters or on single, local machines. Further, we will discuss how we envision SWIPE contributing towards a global analysis of coastal and inland water quality dynamics.

top-of-atmosphere (TOA)↗
DOE OSTI2025

Postfire Biogeochemical Processes: Implications to Source Water Quality in Fire-Influenced Watersheds

Forested watersheds are instrumental in providing purified and reliable water to millions of people worldwide. The changing climate has increased the frequency and severity of global fire events. Forested watersheds and their ecosystem functions are greatly disrupted during fire activity. Postfire concerns in forested watersheds include unpredictable and potentially simultaneous alterations in source water quality and hydro-biogeochemical processes. Here, the degree of fire severity can complexly modify water quality through the production of fire-transformed constituents on the burned forest floor (i.e., nutrients, metal(loid)s, dissolved organic matter, and the formation of disinfection byproducts). Correspondingly, fire severity and postfire rainfall patterns can refine hydro-biogeochemical processes that influence the transport of the fire-transformed constituents (i.e., vegetation function, soil structure, hydrological pathways, and microbial communities). Postfire alterations to water quality and hydro-biogeochemical processes introduce further complexity with varying temporal influence, which ranges from months to decades. As postfire water quality and watershed response research progresses, it is essential to homogenize interdisciplinary expertise to bridge knowledge gaps between fields ranging from forest ecology, hydrology, microbiology, and geochemistry. A multidisciplinary approach in wildfire research will facilitate a comprehensive perception of the diverse water quality risks associated with fire activity and mitigate fire concerns on a global level.

Disinfection Byproducts↗
NASA NTRS1981

Use of ocean color scanner data in water quality mapping

Remotely sensed data, in combination with in situ data, are used in assessing water quality parameters within the San Francisco Bay-Delta. The parameters include suspended solids, chlorophyll, and turbidity. Regression models are developed between each of the water quality parameter measurements and the Ocean Color Scanner (OCS) data. The models are then extended to the entire study area for mapping water quality parameters. The results include a series of color-coded maps, each pertaining to one of the water quality parameters, and the statistical analysis of the OCS data and regression models. It is found that concurrently collected OCS data and surface truth measurements are highly useful in mapping the selected water quality parameters and locating areas having relatively high biological activity. In addition, it is found to be virtually impossible, at least within this test site, to locate such areas on U-2 color and color-infrared photography.

Khorram, S.↗
DOE OSTI2021

Comparison of Deterministic and Statistical Models for Water Quality Compliance Forecasting in the San Joaquin River Basin, California

Model selection for water quality forecasting depends on many factors including analyst expertise and cost, stakeholder involvement and expected performance. Water quality forecasting in arid river basins is especially challenging given the importance of protecting beneficial uses in these environments and the livelihood of agricultural communities. In the agriculture-dominated San Joaquin River Basin of California, real-time salinity management (RTSM) is a state-sanctioned program that helps to maximize allowable salt export while protecting existing basin beneficial uses of water supply. The RTSM strategy supplants the federal total maximum daily load (TMDL) approach that could impose fines associated with exceedances of monthly and annual salt load allocations of up to $1 million per year based on average year hydrology and salt load export limits. The essential components of the current program include the establishment of telemetered sensor networks, a web-based information system for sharing data, a basin-scale salt load assimilative capacity forecasting model and institutional entities tasked with performing weekly forecasts of river salt assimilative capacity and scheduling west-side drainage export of salt loads. Web-based information portals have been developed to share model input data and salt assimilative capacity forecasts together with increasing stakeholder awareness and involvement in water quality resource management activities in the river basin. Two modeling approaches have been developed simultaneously. The first relies on a statistical analysis of the relationship between flow and salt concentration at three compliance monitoring sites and the use of these regression relationships for forecasting. The second salt load forecasting approach is a customized application of the Watershed Analysis Risk Management Framework (WARMF), a watershed water quality simulation model that has been configured to estimate daily river salt assimilative capacity and to provide decision support for real-time salinity management at the watershed level. Analysis of the results from both model-based forecasting approaches over a period of five years shows that the regression-based forecasting model, run daily Monday to Friday each week, provided marginally better performance. However, the regression-based forecasting model assumes the same general relationship between flow and salinity which breaks down during extreme weather events such as droughts when water allocation cutbacks among stakeholders are not evenly distributed across the basin. A recent test case shows the utility of both models in dealing with an exceedance event at one compliance monitoring site recently introduced in 2020.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

The Validity Chlorophyll-a Estimation by Sun Induced Fluorescence in Estuarine Waters: An Analysis of Long-term (2003-2011) Water Quality Data from Tampa Bay, Florida (USA)

Satellite observation of phytoplankton concentration or chlorophyll-a is an important characteristic, critically integral to monitoring coastal water quality. However, the optical properties of estuarine and coastal waters are highly variable and complex and pose a great challenge for accurate analysis. Constituents such as suspended solids and dissolved organic matter and the overlapping and uncorrelated absorptions in the blue region of the spectrum renders the blue-green ratio algorithms for estimating chlorophyll-a inaccurate. Measurement of sun-induced chlorophyll fluorescence, on the other hand, which utilizes the near infrared portion of the electromagnetic spectrum, may provide a better estimate of phytoplankton concentrations. While modelling and laboratory studies have illustrated both the utility and limitations of satellite baseline algorithms based on the sun induced chlorophyll fluorescence signal, few have examined the empirical validity of these algorithms using a comprehensive long term in situ data set. In an unprecedented analysis of a long term (2003-2011) in situ monitoring data from Tampa Bay, Florida (USA), we assess the validity of the FLH product from the Moderate Resolution Imaging Spectrometer (MODIS) against chlorophyll ]a and a suite of water quality parameters taken in a variety of conditions throughout a large optically complex estuarine system. A systematic analysis of sampling sites throughout the bay is undertaken to understand how the relationship between FLH and in situ chlorophyll-a responds to varying conditions within the estuary including water depth, distance from shore and structures and eight water quality parameters. From the 39 station for which data was derived, 22 stations showed significant correlations when the FLH product was matched with in situ chlorophyll-alpha data. The correlations (r2) for individual stations within Tampa Bay ranged between 0.67 (n=28, pless than 0.01) and-0.457 (n=12, p=.016), indicating that for some areas within the Bay, FLH can be a good predictor of chlorophyll-alpha concentration and hence a useful tool for the analysis of water quality. Overall, the results show a 106% increase in the validity of chlorophyll -a concentration estimates using FLH over the standard the blue-green OC3M algorithm. This analysis also illustrates that the correlations between FLH and in situ chlorophyll -a measurements increases with increasing water depth and distance of the monitoring sites from both the shore and structures. However, due to confounding factors related to the complexity of the estuarine system, a linear improvement in the FLH to chlorophyll ]a relationship was not clearly noted with increasing depth and distance from shore alone. Correlations of FLH with turbidity, nutrients (total nitrogen and total phosphorous) biological oxygen demand, salinity, sea surface temperature correlated positively with FLH concentrations, while dissolved oxygen and pH showed negative correlations. Principle component analyses are employed to further describe the relationships between the multivariate water quality parameters and the FLH product. The majority of sites with higher and very significant correlations (pless than 0.01) also showed high correlation values for nutrients, turbidity and biological oxygen demand. These sites were on average in greater than seven meters of water and over five kilometers from shore. A thorough understanding of the relationship between the MODIS FLH product and in situ water quality parameters will enhance our understanding of the accuracy MODIS fs global FLH algorithm and assist in optimizing its calibration for use in monitoring the quality of estuarine and coastal waters worldwide.

Moreno-Madrinan, Max Jacobo↗
DOE OSTI2022

Sensor-Equipped Unmanned Surface Vehicle for High-Resolution Mapping of Water Quality in Low- to Mid-Order Streams

Longitudinal profiling of water quality via the deployment of sensors from watercraft has advanced the understanding of spatial patterns in large rivers and lakes; however, a similar approach in low- to mid-order streams is lacking. To fill this gap, we developed an unmanned surface vehicle (USV)-water quality measurement platform (the “AquaBOT”). The components of the AquaBOT included a nitrate sensor, multiparameter sonde (temperature, conductivity, turbidity, dissolved oxygen, chlorophyll), quantum sensor, and global positioning system (GPS) mounted to a small pontoon-style USV. The AquaBOT was tested in four streams and rivers in Iowa and Tennessee. All measured water quality parameters varied longitudinally, and greater ranges were generally observed along the low-order, agriculturally influenced streams in Iowa. Nitrate, in particular, was spatially heterogeneous. For example, during one run in early June, concentrations ranged from 10.5 to 12.5 mg N L –1 along a 2.3 km reach and hotspots were observed directly downstream of some tile drains. The spatial resolution of AquaBOT data collected in June was 10× higher than grab sampling data, and measurements were collected in less time and at a comparable cost. Here, the AquaBOT can complement existing measurement approaches and will lead to advancements in understanding the processes driving water quality along the stream-to-river continuum.

47 OTHER INSTRUMENTATION↗
NASA NTRS1978

Relationship of land use to water quality in the Chesapeake Bay region

Both the proportions of the various land use categories present on each watershed and the specific management practices in use in each category affect the quality of runoff waters, and the water quality of the Bay. Several permanent and portable stations on various Maryland Rivers collect volume-integrated water samples. All samples are analyzed for a series of nutrient, particulate, bacterial, herbicide, and heavy metal parameters. Each basin is mapped with respect to land use by the analysis of low-elevation aerial photos. Analyses are verified and adjusted by ground truth surveys. Data are processed and stored in the Smithsonian Institution data bank. Land use categories being investigated include forests/old fields, pastureland, row crops, residential areas, upland swamps, and tidal marshes.

Correll, D. L.↗