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NASA NTRS2019

Snow Depth Variability in the Northern Hemisphere Mountains Observed from Space

Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map the snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4,000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change.

Hans Lievens↗

NASA NTRS1973

Technical consulting services for earth resources program

Evaluations of several sets of test data which show a normal amount of variability in gamma and Dmax on KODAK Aerial Duplicating Film (ESTAR Base) SO-467 are reported. Kodak is now furnishing to engineers at GSFC a full set of test data for each coating of SO-467 film. Glass-enclosed sensitometer step tablets which are being made for the Herrnfeld instrument at GSFC are discussed. These tablets include a gray scale and uniformity check areas as well as tribar resolution patterns imaged in a durable chromium layer. It is recommended that the Sioux Falls EDC procure four new enlarging printers and study the value and economics of a system for printer automation based on edge coding.

Source record↗

NASA NTRS2005

Analysis of Multiple Precipitation Products and Preliminary Assessment of Their Impact on Global Land Data Assimilation System (GLDAS) Land Surface States

Land surface models (LSMs) are computer programs, similar to weather and climate prediction models, which simulate the stocks and fluxes of water (including soil moisture, snow, evaporation, and runoff) and energy (including the temperature of and sensible heat released from the soil) after they arrive on the land surface as precipitation and sunlight. It is not currently possible to measure all of the variables of interest everywhere on Earth with sufficient accuracy and space-time resolution. Hence LSMs have been developed to integrate the available observations with our understanding of the physical processes involved, using powerful computers, in order to map these stocks and fluxes as they change in time. The maps are used to improve weather forecasts, support water resources and agricultural applications, and study the Earth's water cycle and climate variability. NASA's Global Land Data Assimilation System (GLDAS) project facilitates testing of several different LSMs with a variety of input datasets (e.g., precipitation, plant type). Precipitation is arguably the most important input to LSMs. Many precipitation datasets have been produced using satellite and rain gauge observations and weather forecast models. In this study, seven different global precipitation datasets were evaluated over the United States, where dense rain gauge networks contribute to reliable precipitation maps. We then used the seven datasets as inputs to GLDAS simulations, so that we could diagnose their impacts on output stocks and fluxes of water. In terms of totals, the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) had the closest agreement with the US rain gauge dataset for all seasons except winter. The CMAP precipitation was also the most closely correlated in time with the rain gauge data during spring, fall, and winter, while the satellitebased estimates performed best in summer. The GLDAS simulations revealed that modeled soil moisture is highly sensitive to precipitation, with differences in spring and summer as large as 45% depending on the choice of precipitation input.

Gottschalck, Jon↗

NASA NTRS2005

Evaluation of Ten Methods for Initializing a Land Surface Model

Rodell, M.↗

NASA NTRS1989

Focus of attention in an activity-based scheduler

Earlier research in job shop scheduling has demonstrated the advantages of opportunistically combining order-based and resource-based scheduling techniques. An even more flexible approach is investigated where each activity is considered a decision point by itself. Heuristics to opportunistically select the next decision point on which to focus attention (i.e., variable ordering heuristics) and the next decision to be tried at this point (i.e., value ordering heuristics) are described that probabilistically account for both activity precedence and resource requirement interactions. Preliminary experimental results indicate that the variable ordering heuristic greatly increases search efficiency. While least constraining value ordering heuristics have been advocated in the literature, the experimental results suggest that other value ordering heuristics combined with our variable-ordering heuristic can produce much better schedules without significantly increasing search.

Sadeh, Norman↗

NASA NTRS1975

Advances in automatic extraction of earth resources information from multispectral scanner data

The basis of spectral discrimination was briefly examined indicating sources of variability which tend to obscure the spectral attributes of the classes of interest. Spatial and temporal discrimination bases are also discussed. Automatic processing functions, techniques and methods, and equipment are discussed with emphasis on techniques and equipment required for operational large area surveys with satellite data. Techniques for carrying out major functions of preprocessing for signature extension, feature extraction, discrimination, display, and applications modeling were examined. A multiplicative and additive signature correction technique and a proportion estimation technique are discussed. The development of the multivariate interactive digital analysis system multispectral processor system which represents a breakthrough in cost effective high throughput processing for large area surveys from satellites and aircraft is reviewed. Applications and results are discussed briefly for agricultural crop inventories, environmental monitoring, and resources surveys from ERIM LANDSAT and EREP investigations to indicate the substantial progress achieved to date.

Erickson, J. D.↗

NASA NTRS2017

The Future of Evapotranspiration: Global Requirements for Ecosystem Functioning, Carbon and Climate Feedbacks, Agricultural Management, and Water Resources

The fate of the terrestrial biosphere is highly uncertain given recent and projected changes in climate. This is especially acute for impacts associated with changes in drought frequency and intensity on the distribution and timing of water availability. The development of effective adaptation strategies for these emerging threats to food and water security are compromised by limitations in our understanding of how natural and managed ecosystems are responding to changing hydrological and climatological regimes. This information gap is exacerbated by insufficient monitoring capabilities from local to global scales. Here, we describe how evapotranspiration (ET) represents the key variable in linking ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources, and highlight both the outstanding science and applications questions and the actions, especially from a space-based perspective, necessary to advance them.

Fisher, Joshua B.↗

NASA NTRS2000

Airborne Laser Altimetry Mapping of the Greenland Ice Sheet: Application to Mass Balance Assessment

In 1998 and '99, the Arctic Ice Mapping (AIM) program completed resurveys of lines occupied 5 years earlier revealing elevation changes of the Greenland ice sheet and identifying areas of significant thinning, thickening and balance. In planning these surveys, consideration had to be given to the spatial constraints associated with aircraft operation, the spatial nature of ice sheet behavior, and limited resources, as well as temporal issues, such as seasonal and interannual variability in the context of measurement accuracy. This paper examines the extent to which the sampling and survey strategy is valid for drawing conclusions on the current state of balance of the Greenland ice sheet. The surveys covered the entire ice sheet with an average distance of 21.4 km between each location on the ice sheet and the nearest flight line. For most of the ice sheet, the elevation changes show relatively little spatial variability, and their magnitudes are significantly smaller than the observed elevation change signal. As a result, we conclude that the density of the sampling and the accuracy of the measurements are sufficient to draw meaningful conclusions on the state of balance of the entire ice sheet over the five-year survey period. Outlet glaciers, however, show far more spatial and temporal variability, and each of the major ones is likely to require individual surveys in order to determine its balance.

Abdalati, W.↗

NASA NTRS2011

Flood Inundation Mapping in the Logone Floodplain from Multi Temporal Landsat ETM+Imagery

Yearly flooding in the Logone floodplain makes an impact on agricultural, pastoral, and fishery systems in the Lake Chad Basin. Since the flooding extent and depth are highly variable, flood inundation mapping helps us make better use of water resources and prevent flood hazards in the Logone floodplain. The flood maps are generated from 33 multi temporal Landsat Enhanced Thematic Mapper Plus (ETM+) during three years 2006 to 2008. Flooded area is classified using a short-wave infrared band whereas open water is classified by Iterative Self-organizing Data Analysis (ISODATA) clustering. The maximum flooding extent in the study area increases up to approximately 5.8K km2 in late October 2008. The study also provides strong correlation of the flooding extents with water height variations in both the floodplain and the river based on a second polynomial regression model. The water heights are from ENIVSAT altimetry in the floodplain and gauge measurements in the river. Coefficients of determination between flooding extents and water height variations are greater than 0.91 with 4 to 36 days in phase lag. Floodwater drains back to the river and to the northeast during the recession period in December and January. The study supports understanding of the Logone floodplain dynamics in detail of spatial pattern and size of the flooding extent and assists the flood monitoring and prediction systems in the catchment.

Jung, Hahn Chul↗

NASA NTRS2024

Characterizing the 2019-2021 Drought in La Plata River Basin With GLDAS and SMAP

Study region The La Plata River Basin (LPRB) is an area of interest, as there was a significant drought event from 2019 to 2021. With the LPRB being the second largest river basin in South America, this drought affected all aspects of life for over 100 million people who inhabit this area. Study focus In this study, we utilized satellite and model datasets to examine spatial and temporal variability of hydrological anomalies throughout the basin before and during the drought event. These variable anomalies, such as precipitation and groundwater, are derived from the Global Land Data Assimilation System (GLDAS). Additionally, the Soil Moisture Active Passive (SMAP) observations were used in the spatial and temporal analysis of the drought. The water loss was calculated using the Mann-Kendall test to assess the changes in anomalies for the terrestrial water storage throughout the basin and subbasins. New hydrological insights for the region The main insight from this study is the agreement of the GLDAS anomalies with the SMAP data throughout the drought. The variable anomalies all decreased and showed alignment with the downscaled 1 km SMAP which shows alignment with the trends from in-situ observations. It was also found that the Upper Parana subbasin lost the most water over the drought period. These products improve understanding of the spatial variability within the entire basin and facilitate understanding of droughts and resources to assist in the future management of water resources.

La Plata Basin↗

NASA NTRS1989

Optimal dynamic control of resources in a distributed system

The authors quantitatively formulate the problem of controlling resources in a distributed system so as to optimize a reward function and derive optimal control strategies using Markov decision theory. The control variables treated are quite general; they could be control decisions related to system configuration, repair, diagnostics, files, or data. Two algorithms for resource control in distributed systems are derived for time-invariant and periodic environments, respectively. A detailed example to demonstrate the power and usefulness of the approach is provided.

Shin, Kang G.↗

NASA NTRS2020

Exploring Spatiotemporal Relations Between Soil Moisture, Precipitation, and Streamflow for a Large Set of Watersheds Using Google Earth Engine

An understanding of streamflow variability and its response to changes in climate conditions is essential for water resource planning and management practices that will help to mitigate the impacts of extreme events such as floods and droughts on agriculture and other human activities. This study investigated the relationship between precipitation, soil moisture, and streamflow over a wide range of watersheds across the United States using Google Earth Engine (GEE). The correlation analyses disclosed a strong association between precipitation, soil moisture, and streamflow, however, soil moisture was found to have a higher correlation with the streamflow relative to precipitation. Results indicated different strength of the association depends on the watershed classes and lag times assessments. The perennial watersheds showed higher coherence compared to intermittent watersheds. Previous month precipitation and soil moisture have a stronger influence on the current month streamflow, particularly in the snow-dominated watersheds. Monthly streamflow forecasting models were developed using an autoregressive integrated moving average (ARIMA) and support vector machine (SVM). The results showed that the SVM model generally performed better than the ARIMA model. Overall streamflow forecasting model performance varied considerably among watershed classes, and perennial watersheds tend to exhibit better predictably compared to intermittent watersheds due to lower streamflow variability. The SVM models with precipitation and streamflow inputs performed better than those with streamflow input only. Results indicated that the inclusion of antecedent root-zone soil moisture improved the streamflow forecasting in most of the watersheds, and the largest improvements occurred in the intermittent watersheds. In conclusion, this work demonstrated that knowing the relationship between precipitation, soil moisture, and streamflow in different watershed classes will enhance the understanding of the hydrologic process and can be effectively utilized in improving streamflow forecasting for better satellite-based water resource management strategies.

Nazmus Sazib↗

NASA NTRS1993

Passive Microwave Remote Sensing for Land Resources

Spaceborne microwave radiometry is an important technique for obtaining global estimates of parameters important to Earth's hydrologic cycle, land resources, environmental monitoring, and climate. They key physical processes involved in these applications are the land-atmosphere exchanges of heat and moisture, and their status and variability on different time and space scales.

microwave radiometry hydrology land resources↗

NASA NTRS2020

Interannual Variability in the Thermal Habitat of Alexandrium catenella in the Bay of Fundy and the Implications of Climate Change

Globally, harmful algal blooms (HABs) are an increasing problem. In the Gulf of Maine and Bay of Fundy, blooms of the toxic dinoflagellate Alexandrium catenella are annually recurrent phenomena. As this region is one of the most rapidly warming areas of the global ocean, an improved understanding of the mechanisms driving the initiation of local A. catenella blooms, their interannual variability and the implications of future climate change is critical to local monitoring strategies and marine resources management. A 27-year (1988–2014) time series of weekly A. catenella cell counts from the Bay of Fundy and concurrent satellite-measured sea surface temperature, freshwater discharge from the St. John River and wind-driven turbulence are compared to assess their relationship to variability in bloom phenology metrics. The mean thermal habitat associated with early detection of A. catenella is 6.5 ± 1.6°C, whereas that of bloom initiation averages 9.2 ± 1.5°C. Both thermal habitats for A. catenella are trending earlier over the study period. Bloom initiations that precede the arrival of the thermal habitat mean (occur in colder water) are associated with higher spring freshwater discharge and are generally weaker blooms. Increased spring freshwater discharge is also associated with earlier bloom initiation and earlier maximum concentration dates. No significant relationship was observed with the strength of wind-driven mixing. Removal of the mean thermal seasonal cycle shows that surface temperature anomalies have a strong negative relationship to the bloom phenology metrics and arrival of thermal habitat: warmer years are linked to earlier arrival of thermal habitats (∼12 days °C–1) and earlier detection and bloom initiation dates (∼33 days °C–1). Using these relationships and present trends in Bay of Fundy surface temperature warming over the period 1982–2019, we project the arrival dates of bloom thermal habitat and bloom phenology metrics out to the middle of this century. Based on current rates of sea surface temperature change, bloom phenology metrics (e.g., bloom initiation, early detection), can be expected to shift 1–2 months earlier in the season by mid-century. Such changes in the phenology of A. catenella blooms will need to be incorporated into both monitoring strategies and forecasting models for the region.

Andre F. Bucci↗

NASA NTRS1980

Spatial variability of surface temperature as related to cropping practice with implications for irrigation management

Crop stress measured using thermal infrared emission is evaluated with the stress-degree-day (SDD) concept. Throughout the season, the accumulation of SDD during the reproductive stage of growth is inversely related to yield. This relationship is shown for durum wheat, hard red winter wheat, barley, grain sorghum and soybeans. It is noted that SDD can be used to schedule irrigations for maximizing yields and for applying remotely sensed data to management of water resources. An airborne flight with a thermal-IR scanner was used to examine the variability in temperature that may exist from one field to another and to determine realistic within-field temperature variations. It was found that the airborne and the ground-based data agreed very well and that there was less variability in the fields that were completely covered with crops than those of bare soil.

Hatfield, J. L.↗

NASA NTRS1989

Specification of parameters for development of a spatial database for drought monitoring and famine early warning in the African Sahel

Parameters were described for spatial database to facilitate drought monitoring and famine early warning in the African Sahel. The proposed system, referred to as the African Drought and Famine Information System (ADFIS) is ultimately recommended for implementation with the NASA/FEMA Spatial Analysis and Modeling System (SAMS), a GIS/Dymanic Modeling software package, currently under development. SAMS is derived from FEMA'S Integration Emergency Management Information System (IEMIS) and the Pacific Northwest Laborotory's/Engineering Topographic Laboratory's Airland Battlefield Environment (ALBE) GIS. SAMS is primarily intended for disaster planning and resource management applications with the developing countries. Sources of data for the system would include the Developing Economics Branch of the U.S. Dept. of Agriculture, the World Bank, Tulane University School of Public Health and Tropical Medicine's Famine Early Warning Systems (FEWS) Project, the USAID's Foreign Disaster Assistance Section, the World Resources Institute, the World Meterological Institute, the USGS, the UNFAO, UNICEF, and the United Nations Disaster Relief Organization (UNDRO). Satellite imagery would include decadal AVHRR imagery and Normalized Difference Vegetation Index (NDVI) values from 1981 to the present for the African continent and selected Landsat scenes for the Sudan pilot study. The system is initially conceived for the MicroVAX 2/GPX, running VMS. To facilitate comparative analysis, a global time-series database (1950 to 1987) is included for a basic set of 125 socio-economic variables per country per year. A more detailed database for the Sahelian countries includes soil type, water resources, agricultural production, agricultural import and export, food aid, and consumption. A pilot dataset for the Sudan with over 2,500 variables from the World Bank's ANDREX system, also includes epidemiological data on incidence of kwashiorkor, marasmus, other nutritional deficiencies, and synergistically-related infectious diseases.

Rochon, Gilbert L.↗

NASA NTRS2022

Groundwater Variability Across India, Under Contrasting Human and Natural Conditions

Characterizing local to regional scale water cycles and water resources will be crucial for achieving the United Nations' water-related Sustainable Developmental Goals. However, quantification and understanding of groundwater extraction across scales have been hampered by inadequate water usage reporting and limited information on irrigation practices. Here we analyze observations from ∼15,000 groundwater monitoring wells and the Gravity Recovery and Climate Experiment satellites together with irrigation, agricultural, and meteorological datasets to show how drought-induced coupling between natural and anthropogenic groundwater storage variations has caused sustainability challenges in India, the world's biggest consumer of groundwater for irrigation. Notably, the mechanisms and consequences of such coupling differ significantly depending on aquifer types. In Andhra Pradesh's hard rock aquifer, groundwater declines have been limited, despite the nearly constant water scarcity that its farmers face. Moreover, its free farm power policy involves an annual irrigation energy consumption of 26 billion kWh that costs US$ 2.5 billion, possibly unparalleled compared to any other part of the world of similar size (0.27 million km2). In West Bengal's highly permeable alluvial aquifer, the water table is declining rapidly (15 cm/yr) due to a policy that encourages irrigation. Situated between these two states, Odisha's aquifer shows substantial resilience to drought, owing to the state's relatively natural landscape and forest restoration policy. The findings of this study provide new insights to understand the divergent aspects of groundwater irrigation in north versus south India, which can enable development of adaptation and mitigation strategies to avert the looming water crisis.

Dileep K Panda↗

NASA NTRS1993

Investigating pyrolysis/incineration as a method of resource recovery from solid waste

Pyrolysis/incineration (P/I) is a physicochemical method for the generation of recoverable resources from solid waste materials such as inedible plant biomass (IPB), paper, plastics, cardboard, etc. P/I permits the collection of numerous gases with a minimal amount of solid residue. Pyrolysis, also known as starved air incineration, is usually conducted at relatively high temperatures (greater than 500 deg C) in the absence of oxygen. Incineration is conducted at lower temperatures in the presence of oxygen. The primary purpose of this study was to design, construct, and test a model P/I. The system design includes safety requirements for temperature and pressure. The objectives of this study were: (1) to design and construct a P/I system for incorporation with the Hybrid Regenerative Water Recovery System; (2) to initiate testing of the P/I system; (3) to collect and analyze P/I system data; (4) to consider test variables; and (5) to determine the feasibility of P/I as an effective method of resource recovery. A P/I system for the recovery of reuseable resources from solid waste materials was designed, constructed, and tested. Since a large amount of inedible plant biomass (IPB) will be generated in a space-based habitat on the lunar surface and Mars, IPB was the primary waste material tested in the system. Analysis of the effluent gases was performed to determine which gases could be used in a life support system.

Robertson, Bobby J.↗