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NASA NTRS2022

Soil Moisture Estimation in South Asia via Assimilation of SMAP Retrievals

A soil moisture retrieval assimilation framework is implemented across South Asia in an attempt to improve regional soil moisture estimation as well as to provide a consistent regional soil moisture dataset. This study aims to improve the spatiotemporal variability of soil moisture estimates by assimilating Soil Moisture Active Passive (SMAP) near-surface soil moisture retrievals into a land surface model. The Noah-MP (v4.0.1) land surface model is run within the NASA Land Information System software framework to model regional land surface processes. NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA2) and Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals (IMERG) provide the meteorological boundary conditions to the land surface model. Assimilation is carried out using both cumulative distribution function (CDF)-corrected (DA-CDF) and uncorrected SMAP retrievals (DA-NoCDF). CDF matching is applied to correct the statistical moments of the SMAP soil moisture retrieval relative to the land surface model. Comparison of assimilated and model-only soil moisture estimates with publicly available in situ measurements highlights the relative improvement in soil moisture estimates by assimilating SMAP retrievals. Across the Tibetan Plateau, DA-NoCDF reduced the mean bias and RMSE by 8.4 % and 9.4 %, even though assimilation only occurred during less than 10 % of the study period due to frozen (or partially frozen) soil conditions. The best goodness-of-fit statistics were achieved for the IMERG DA-NoCDF soil moisture experiment. The general lack of publicly available in situ measurements across irrigated areas limited a domain-wide direct model validation. However, comparison with regional irrigation patterns suggested correction of biases associated with an unmodeled hydrologic phenomenon (i.e., anthropogenic influence via irrigation) as a result of SMAP soil moisture retrieval assimilation. The greatest sensitivity to assimilation was observed in cropland areas. Improvements in soil moisture potentially translate into improved spatiotemporal patterns of modeled evapotranspiration, although limited influence from soil moisture assimilation was observed on modeled processes within the carbon cycle such as gross primary production. Improvement in fine-scale modeled estimates by assimilating coarse-scale retrievals highlights the potential of this approach for soil moisture estimation over data-scarce regions.

Jawairia Ahmad↗

DOE OSTI2024

Synthesis of historical reservoir operations from 1980 to 2020 for the evaluation of reservoir representation in large-scale hydrologic models

Abstract. All the major river systems in the contiguous United States (CONUS) (and many in the world) are impacted by dams, yet reservoir operations remain difficult to quantify and model due to a lack of data. Reservoir operation data are often inaccessible or distributed across many local operating agencies, making the acquisition and processing of data records quite time-consuming. As a result, large-scale models often rely on simple parameterizations for assumed reservoir operations and have a very limited ability to evaluate how well these approaches match actual historical operations. Here, we use the first national dataset of historical reservoir operations in the CONUS domain, ResOpsUS, to analyze reservoir storage trends and operations in more than 600 major reservoirs across the US. Our results show clear regional differences in reservoir operations. In the eastern US, which is dominated by flood control storage, we see storage peaks in the winter months with sharper decreases in the operational range (i.e., the difference between monthly maximum and minimum storage) in the summer. In the more arid western US where storage is predominantly for irrigation, we find that storage peaks during the spring and summer with increases in the operational range during the summer months. The Lower Colorado region is an outlier because its seasonal storage dynamics more closely mirrored those of flood control basins, yet the region is classified as arid, and most reservoirs have irrigation uses. Consistent with previous studies, we show that average annual reservoir storage has decreased over the past 40 years, although our analyses show a much smaller decrease than previous work. The reservoir operation characterizations presented here can be used directly for development or evaluation of reservoir operations and their derived parameters in large-scale models. We also evaluate how well historical operations match common assumptions that are often applied in large-scale reservoir parameterizations. For example, we find that 100 dams have maximum storage values greater than the reported reservoir capacity from the Global Reservoirs and Dams database (GRanD). Finally, we show that operational ranges have been increasing over time in more arid regions and decreasing in more humid regions, pointing to the need for operating policies which are not solely based on static values.

Geology↗

DOE OSTI2020

Nevada National Security Site 2019 Waste Management Monitoring Report Area 3 and Area 5 Radioactive Waste Management Sites

Environmental monitoring data are collected at and around the Area 3 and Area 5 Radioactive Waste Management Sites (RWMSs) within the Nevada National Security Site (NNSS). This report summarizes the 2019 environmental data to provide an overall evaluation of RWMS performance and to support environmental compliance and performance assessment (PA) activities. Some of these data (e.g., radiation exposure, air, and groundwater) are presented in other reports (Mission Support and Test Services, LLC 2019, 2020a, 2020b). Direct radiation monitoring data indicate exposure levels at the Area 3 and Area 5 RWMSs are within the range of background levels measured at the NNSS. Slightly elevated exposure levels outside the Area 3 RWMS are attributed to nearby historical aboveground nuclear weapons tests. Air monitoring data at the Area 3 and Area 5 RWMSs show that tritium concentrations in water vapor and americium and plutonium concentrations in air particles are below Derived Concentration Standards for these radionuclides. Groundwater monitoring data indicate the groundwater in the uppermost aquifer beneath the Area 5 RWMS is not impacted by RWMS operations. Results of groundwater analysis from wells around the Area 5 RWMS are below established investigation levels. Leachate samples collected from the leachate collection systems at the Area 5 mixed low-level waste disposal unit are below established contaminant regulatory limits. During 2019, precipitation at the Area 3 RWMS was 77 percent above average, and precipitation at the Area 5 RWMS was 69 percent above average. Water balance measurements indicate that evapotranspiration from the vegetated weighing lysimeter at the Area 5 RWMS dries the soil and prevents downward percolation of precipitation more effectively than evaporation as measured from the bare-soil weighing lysimeter. Vadose zone monitoring in the Area 3 and Area 5 RWMS soil covers shows no evidence of precipitation percolating through the covers to the waste. Moisture from precipitation did not percolate below 120 centimeters (3.9 feet [ft]) in the vegetated final cover on the U-3ax/bl disposal unit at the Area 3 RWMS during 2019. There was no drainage through 2.4 meters (8 ft) of soil as indicated from the Area 3 drainage lysimeters that received only natural precipitation. At the Area 3 RWMS, which received three times the natural precipitation, 57 percent of the applied precipitation and irrigation drained from the bare-soil drainage lysimeter. All 2019 monitoring data indicate that the Area 3 and Area 5 RWMSs are performing within expectations of the model and parameter assumptions for the facilities’ PAs.

2019↗

NASA NTRS2012

NASA's Water Solutions Using Remote Sensing

NASA Water Resources works within Earth sciences to leverage investments of space-based observation, model results, and development and deployment of enabling technologies, systems, and capabilities into water resources management decision support tools for the sustainable use of water. Earth science satellite observations and modelling products provide a huge volume of valuable data in both near-real-time and extended back nearly 50 years about the Earth's land surface conditions such as land cover type, vegetation type and health, precipitation, snow, soil moisture, and water levels and radiation. Observations of this type combined with models and analysis enable satellite-based assessment of the water cycle. With increasing population pressure and water usage coupled with climate variability and change, water issues are being reported by numerous groups as the most critical environmental problems facing us in the 21st century. Competitive uses and the prevalence of river basins and aquifers that extend across boundaries engender political tensions between communities, stakeholders and countries. The NASA Water Resources Program has the objective to provide NASA products to help deal with these issues with the goal for the sustainable use of water. The Water Resources program organizes its projects under five functional themes: 1) stream-flow and flood forecasting; 2) water consumptive use (includes evapotranspiration) and irrigation; 3) drought; 4) water quality; and 5) climate and water resources. NASA primarily works with national and international groups such as other US government agencies (NOAA, EPA, USGS, USAID) and various other groups to maximize the widest use of the water products. A summary of NASA's water activities linked to helping solve issues for developing countries will be highlighted.

Toll, David↗

NASA NTRS2015

A Framework for the Cross-Sectoral Integration of Multi-Model Impact Projections: Land Use Decisions Under Climate Impacts Uncertainties

Climate change and its impacts already pose considerable challenges for societies that will further increase with global warming (IPCC, 2014a, b). Uncertainties of the climatic response to greenhouse gas emissions include the potential passing of large-scale tipping points (e.g. Lenton et al., 2008; Levermann et al., 2012; Schellnhuber, 2010) and changes in extreme meteorological events (Field et al., 2012) with complex impacts on societies (Hallegatte et al., 2013). Thus climate change mitigation is considered a necessary societal response for avoiding uncontrollable impacts (Conference of the Parties, 2010). On the other hand, large-scale climate change mitigation itself implies fundamental changes in, for example, the global energy system. The associated challenges come on top of others that derive from equally important ethical imperatives like the fulfilment of increasing food demand that may draw on the same resources. For example, ensuring food security for a growing population may require an expansion of cropland, thereby reducing natural carbon sinks or the area available for bio-energy production. So far, available studies addressing this problem have relied on individual impact models, ignoring uncertainty in crop model and biome model projections. Here, we propose a probabilistic decision framework that allows for an evaluation of agricultural management and mitigation options in a multi-impactmodel setting. Based on simulations generated within the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP), we outline how cross-sectorally consistent multi-model impact simulations could be used to generate the information required for robust decision making. Using an illustrative future land use pattern, we discuss the trade-off between potential gains in crop production and associated losses in natural carbon sinks in the new multiple crop- and biome-model setting. In addition, crop and water model simulations are combined to explore irrigation increases as one possible measure of agricultural intensification that could limit the expansion of cropland required in response to climate change and growing food demand. This example shows that current impact model uncertainties pose an important challenge to long-term mitigation planning and must not be ignored in long-term strategic decision making

farmlands↗

DOE OSTI2023

An innovative approach for atrazine electrochemical oxidation modelling: Process parameter effect, intermediate formation and kinetic constant assessment

Water reuse for irrigation activities is becoming a crucial worldwide challenge due to the depletion of water sources. Anyway, agricultural drainage can potentially contain dangerous contaminants such as metals, pesticides, and herbicides, including atrazine. To address the need for agriculture wastewater purification, we investigated atrazine removal from simulated wastewater by electro-oxidation using platinum-coated titanium electrodes on a lab-scale experimental apparatus. The effects of electrolyte composition and concentration, i.e. ionic strength and applied current density on atrazine removal, were investigated. The results demonstrated that the electrochemical oxidation of the herbicide occurred through two routes, depending on the presence or absence of oxidizing chlorine species. The generation of intermediates during the treatment was monitored and quantified by evaluating the effect of an inert electrolyte (NaClO 4 ) versus an oxidizable chlorine species (NaCl). In both experimental conditions, five intermediates were identified, including desethyl-atrazine (DEA), hydroxyatrazine (ATZ-OH), desisopropyl-atrazine (DIA) and desethyl-desisopropyl-atrazine (DEDIA). A degradation mechanism and a model for describing hydroxyl radicals and active chlorine species contributions at ATZ oxidation were also proposed. Intermediate evolution profiles suggest that ATZ degradation can be considered as a series–parallel reaction system. Finally, the energy requirement assessment for ATZ removal was carried out. The highest ATZ removal (≅98%) was achieved with NaCl = 0.08 M, J = 60 A/m −2 , and E C = 5.83 kWh m −3 . Results highlight that atrazine removal was improved when an active chlorine species (NaCl) was present in the water solution. Moreover, the addition of chlorine species during electro-oxidation is an energy-saving strategy. Collectively, electro-oxidation technique can be efficiently applied to treat polluted water in order to meet the needs of recycling water quality and reduce resource consumption.

Electro-chemical oxidation↗

DOE OSTI2026

Farm Practice Typologies as a Strategy for Management-Relevant Land Use and Land Cover Mapping in the Great Lakes Region (Version 1) [Dataset]

Dataset overview and development This dataset provides spatially explicit agricultural land-use and land-management typologies developed for the Great Lakes Region (GLR) at the farm-parcel level. The typologies were designed to characterize not only the land-use and land-cover (LULC) associated with individual agricultural farm parcels, but also the land-management practices (LMPs), including irrigation, tile drainage, and conservation easements, occurring within those parcels and how these characteristics change through time. The dataset contains four related typology products: Annual integrated typology – describes the combined LULC and land-management characteristics for each farm parcel for individual years. LULC transition typology – describes the temporal pattern of LULC change for each farm parcel across the study period (2008-2023). LMP trend typology – describes the temporal pattern in the occurrence of LMPs for each farm parcel across the study period. Multi-year integrated typology – combines the LULC transition typology and LMP trend typology to provide an integrated characterization of long-term land-use and management patterns. Purpose of the dataset The purpose of these products is to provide a management-relevant integrated and consistent framework for evaluating the spatial and temporal organization of agricultural landscapes across the GLR. The resulting typologies can: support landscape-scale environmental and land-use analysis; provide spatial information relevant to land-management strategies, conservation planning, policy development, and program evaluation; characterize spatial patterns of agricultural land use and management; examine changes in agricultural landscapes through time; and identify persistent, transitional, and changing agricultural systems. Please refer to the README file provided in Files for more details.

Agriculture↗

DOE OSTI2023

Enhancing the representation of water management in global hydrological models

Abstract. This study enhances an existing global hydrological model (GHM), Xanthos, by adding a new water management module that distinguishes between the operational characteristics of irrigation, hydropower, and flood control reservoirs. We remapped reservoirs in the Global Reservoir and Dam (GRanD) database to the 0.5∘ spatial resolution in Xanthos so that a single lumped reservoir exists per grid cell, which yielded 3790 large reservoirs. We implemented unique operation rules for each reservoir type, based on their primary purposes. In particular, hydropower reservoirs have been treated as flood control reservoirs in previous GHM studies, while here, we determined the operation rules for hydropower reservoirs via optimization that maximizes long-term hydropower production. We conducted global simulations using the enhanced Xanthos and validated monthly streamflow for 91 large river basins, where high-quality observed streamflow data were available. A total of 1878 (296 hydropower, 486 irrigation, and 1096 flood control and others) out of the 3790 reservoirs are located in the 91 basins and are part of our reported results. The Kling–Gupta efficiency (KGE) value (after adding the new water management) is ≥ 0.5 and ≥ 0.0 in 39 and 81 basins, respectively. After adding the new water management module, model performance improved for 75 out of 91 basins and worsened for only 7. To measure the relative difference between explicitly representing hydropower reservoirs and representing hydropower reservoirs as flood control reservoirs (as is commonly done in other GHMs), we use the normalized root mean square error (NRMSE) and the coefficient of determination (R2). Out of the 296 hydropower reservoirs, the NRMSE is > 0.25 (i.e., considering 0.25 to represent a moderate difference) for over 44 % of the 296 reservoirs when comparing both the simulated reservoir releases and storage time series between the two simulations. We suggest that correctly representing hydropower reservoirs in GHMs could have important implications for our understanding and management of freshwater resource challenges at regional-to-global scales. This enhanced global water management modeling framework will allow the analysis of future global reservoir development and management from a coupled human–earth system perspective.

13 HYDRO ENERGY↗

DOE OSTI2021

Multiple Indicators of Extreme Changes in Snow-Dominated Streamflow Regimes, Yakima River Basin Region, USA

Snow plays a major role in the hydrological cycle. Variations in snow duration and timing can have a negative impact on water resources. Excluding predicted changes in snowmelt rates and amounts could result in deleterious infrastructure, military mission, and asset impacts at military bases across the US. A change in snowpack can also lead to water shortages, which in turn can affect the availability of irrigation water. We performed trend analyses of air temperature, snow water equivalent (SWE) at 22 SNOTEL stations, and streamflow extremes for selected rivers in the snow-dependent and heavily irrigated Yakima River Basin (YRB) located in the Pacific Northwest US. There was a clear trend of increasing air temperature in this study area over a 30 year period (water years 1991–2020). All stations indicated an increase in average air temperatures for December (0.97 °C/decade) and January (1.12 °C/decade). There was also an upward trend at most stations in February (0.28 °C/decade). In December–February, the average air temperatures were 0.82 °C/decade. From these trends, we estimate that, by 2060, the average air temperatures for December–February at most (82%) stations will be above freezing. Furthermore, analysis of SWE from selected SNOTEL stations indicated a decreasing trend in historical SWE, and a shift to an earlier peak SWE was also assumed to be occurring due of the shorter snow duration. Decreasing trends in snow duration, rain-on-snow, and snowmelt runoff also resulted from snow modeling simulations of the YRB and the nearby area. We also observed a shift in the timing of snowmelt-driven peak streamflow, as well as a statistically significant increase in winter maximum streamflow and decrease in summer maximum and minimum streamflow trends by 2099. From the streamflow trends and complementary GEV analysis, we show that the YRB basin is a system in transition with earlier peak flows, lower snow-driven maximum streamflow, and higher rainfall-driven summer streamflow. This study highlights the importance of looking at changes in snow across multiple indicators to develop future infrastructure and planning tools to better adapt and mitigate changes in extreme events.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Hydrological analysis in watersheds with a variable-resolution global climate model (VR-CESM)

Traditionally, watershed-scale hydrology is simulated by distributed hydrological models with offline meteorological forcing data, or by regional regional climate models that link atmospheric and land hydrology interactions. Global climate models (GCMs) are rarely used to study watershed-scale hydrology due to the relatively coarse grid resolution, computationally expensive downscaling, and simplified physical processes. Recently, however, watershed-scale hydrology analysis has become possible in GCMs due to the development of variable-resolution GCMs that dynamically couple the hydrological processes between atmosphere and land systems at fine resolutions in selected regions and coarse resolution elsewhere. In this study, we used the variable-resolution Community Earth System Model (VR-CESM) with refined-resolution (14 km) in the western U.S. and eastern China to evaluate watershed-scale hydrology. Furthermore, we compared the historical VR-CESM outputs with gauge measurements and other hydrological models (e.g., National Water Model in the U.S.) and calibrated the subsurface runoff capacities in four mountainous watersheds. An RCP8.5 projection from 2007 to 2050 is used to estimate the impact of changing precipitation and snow climatology on watershed hydrology. We also analyzed the long-term runoff variability and the possibility of extreme runoff events as simulated by the VR-CESM. Although calibration is not possible in larger-scale watersheds, VR-CESM simulates the long-term annual variability of watersheds and provides insights on climate change impacts on hydrology. We conclude that refined-resolution VR-CESM can be used for watershed-scale hydrology analysis to understand water resources and water balance, in addition to traditional watershed-scale hydrological models. It enables hydrological analysis at multiple watersheds in one simulation and can help understand the two-way dynamics between land surface hydrology and atmospheric processes, and is especially practical for projecting climate change impacts. However, it is challenging to apply VR-CESM for hydrologic analysis in regulated watersheds as human factors (e.g., pumping, irrigation, water diversion) have not been fully addressed in VR-CESM.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Openet: Applications of Satellite-Based Evapotranspiration Data for Water Resources Management in the Western United States

Advancing water security in overallocated river basins globally requires consistent and reproducible information on consumptive use of water that can anchor the development of data-driven solutions to the challenge of balancing water supply and demand. OpenET is a fully automated system for field-scale (30 m), satellite-based mapping of evapotranspiration (ET) at daily, monthly and annual timesteps. OpenET currently provides spatially contiguous data throughout the 23 westernmost states in the continental US, and includes both current information as well as multi-year timeseries of ET. The OpenET consortium has implemented an ensemble of satellite-based ET models (ALEXI/DisALEXI, eeMETRIC, PT-JPL, geeSEBAL, SIMS and SSEBop) on Google Earth Engine, which provides a shared computing platform for collaboration on processing of data from Landsat and other satellites, land cover and meteorological inputs, leading to increased consistency and accuracy across the ensemble of models. Earth Engine also facilitates hosting and distribution of data via open data collections and an application programming interface. We provide updates on the OpenET framework, open data services and data access tools, approach to geographic expansion, recent accuracy assessments, and describe how a user-driven design approach has facilitated successful applications of OpenET data for a wide range of water resource management activities. Applications to date include: use of ET data to improve quantification of ET and consumptive use in Oregon, Utah and the Upper Colorado River Basin; streamlining of water use reporting requirements in the California Delta; support for calculation of water budgets for the implementation of the Sustainable Groundwater Management Act in California; and integration into decision support tools for irrigation management. The use cases demonstrate how satellite-derived ET data that are easily accessed and seen as broadly accepted can accelerate adoption of innovative water management practices at scale, and support advances in the sustainability of water supplies. Uptake and use of data by the OpenET science community has also led to advances in our understanding of the impacts of landcover change, irrigation intensification and wildfire events on hydrology and the water security.

Applications↗

NASA NTRS1998

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model: Overview - Part 1

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Crosson, William L.↗

DOE OSTI2025

Changes in the Regional Water Cycle and Their Impact on Societies

ABSTRACT Changes in “blue water”, which is the total supply of fresh water available for human extraction over land, are quite closely related to changes in runoff or equivalently precipitation minus evaporation, . This article examines how climate change‐driven recent past and future changes in the regional water cycle relate to blue water availability and changes in human blue water demand. Although at the largest scales theoretical and numerical model predictions are in broad agreement with observations, at continental scales and below models predict large ranges of possible future and runoff especially at the scale of individual river catchments and for shorter timescale subseasonal floods and droughts. Nevertheless, it is expected that the occurrence and severity of floods will increase and that of droughts may increase, possibly compounded by human‐driven non‐climatic changes such as changes in land use, dam water impoundment, irrigation and extraction of groundwater. Contemporary assessments predict that increases in 21st century human water extraction in many highly‐populated regions are unlikely to be sustainable given projections of future . To reduce uncertainty in future predictions, there is an urgent need to improve modeling of atmospheric, land surface and human processes and how these components are coupled. This should be supported by maintaining the observing network and expanding it to improve measurements of land surface, oceanic and atmospheric variables. This includes the development of satellite observations stable over multiple decades and suitable for building reanalysis datasets appropriate for model evaluation.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2014

Large Plant Growth Chambers: Flying Soon on a Space Station near You!

The International Space Station (ISS) now has platforms for conducting research on horticultural plant species, and those capabilities continue to grow. The Veggie vegetable production system will be deployed to the ISS in Spring of 2014 to act as an applied research platform with goals of studying food production in space, providing the crew with a source of fresh food, allowing behavioral health and plant microbiology experimentation, and being a source of recreation and enjoyment for the crew. Veggie was conceived, designed, and constructed by Orbital Technologies Corporation (ORBITEC, Madison, WI). Veggie is the largest plant growth chamber that NASA has flown to date, and is capable of growing a wide array of horticultural crops. It was designed for low energy usage, low launch mass and stowage volume, and minimal crew time requirements. The Veggie flight hardware consists of a light cap containing red (630 nanometers), blue, (455 nanometers) and green (530 nanometers) light emitting diodes. Interfacing with the light cap is an extendable bellows baseplate secured to the light cap via magnetic closures and stabilized with extensible flexible arms. The baseplate contains vents allowing air from the ISS cabin to be pulled through the plant growth area by a fan in the light cap. The baseplate holds a Veggie root mat reservoir that will supply water to plant pillows attached via elastic cords. Plant pillows are packages of growth media and seeds that will be sent to ISS dry and installed and hydrated on orbit. Pillows can be constructed in various sizes for different plant types. Watering will be via passive wicking from the root mat to the pillows. Science procedures will include photography or videography, plant thinning, pollination, harvesting, microbial sampling, water sampling, etcetera. Veggie is one of the ISS flight options currently available for research investigations on plants. The Plant Habitat (PH) is being designed and constructed through a NASA-ORBITEC collaboration, and is scheduled to fly on ISS around 2016. This large plant chamber will control light quality, level, and timing, temperature, CO2, relative humidity, and irrigation, while scrubbing ethylene. Additional monitoring capabilities include leaf temperature sensing and root zone moisture and oxygen sensing. The PH light cap will have red (630 nanometers), blue (450 nanometers), green (525 nanometers), far red (730 nanometers) and broad spectrum white light emitting diodes. There will be several internal cameras to monitor and record plant growth and operations.

Space Biology↗

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 NTRS2014

Soil Moisture Data Assimilation in the NASA Land Information System for Local Modeling Applications and Improved Situational Awareness

As part of the NASA Soil Moisture Active Passive (SMAP) Early Adopter (EA) program, the NASA Shortterm Prediction Research and Transition (SPoRT) Center has implemented a data assimilation (DA) routine into the NASA Land Information System (LIS) for soil moisture retrievals from the European Space Agency's Soil Moisture Ocean Salinity (SMOS) satellite. The SMAP EA program promotes application‐driven research to provide a fundamental understanding of how SMAP data products will be used to improve decision‐making at operational agencies. SPoRT has partnered with select NOAA/NWS Weather Forecast Offices (WFOs) that use output from a real‐time regional configuration of LIS, without soil moisture DA, to initialize local numerical weather prediction (NWP) models and enhance situational awareness. Improvements to local NWP with the current LIS have been demonstrated; however, a better representation of the land surface through assimilation of SMOS (and eventually SMAP) retrievals is expected to lead to further model improvement, particularly during warm‐season months. SPoRT will collaborate with select WFOs to assess the impact of soil moisture DA on operational forecast situations. Assimilation of the legacy SMOS instrument data provides an opportunity to develop expertise in preparation for using SMAP data products shortly after the scheduled launch on 5 November 2014. SMOS contains a passive L‐band radiometer that is used to retrieve surface soil moisture at 35‐km resolution with an accuracy of 0.04 cu cm cm (exp -3). SMAP will feature a comparable passive L‐band instrument in conjunction with a 3‐km resolution active radar component of slightly degraded accuracy. A combined radar‐radiometer product will offer unprecedented global coverage of soil moisture at high spatial resolution (9 km) for hydrometeorological applications, balancing the resolution and accuracy of the active and passive instruments, respectively. The LIS software framework manages land surface model (LSM) simulations and includes an Ensemble Kalman Filter for conducting land surface DA. SPoRT has added a module to read, quality‐control and bias‐correct swaths of Level II SMOS soil moisture retrievals prior to assimilation within LIS. The impact of SMOS DA is being tested using the Noah LSM. Experiments are being conducted to examine the impacts of SMOS soil moisture DA on the resulting LISNoah fields and subsequent NWP simulations using the Weather Research and Forecasting (WRF) model initialized with LIS‐Noah output. LIS‐Noah soil moisture will be validated against in situ observations from Texas A&M's North American Soil Moisture Database to reveal the impact and possible improvement in soil moisture trends through DA. WRF model NWP case studies will test the impacts of DA on the simulated near‐surface and boundary‐layer environments, and precipitation during both quiescent and disturbed weather scenarios. Emphasis will be placed on cases with large analysis increments, especially due to contributions from regional irrigation patterns that are not represented by precipitation input in the baseline LIS‐Noah run. This poster presentation will describe the soil moisture DA methodology and highlight LIS‐Noah and WRF simulation results with and without assimilation.

Case, Jonathan L.↗