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NASA NTRS2008

Applications of TRMM-based Multi-Satellite Precipitation Estimation for Global Runoff Simulation: Prototyping a Global Flood Monitoring System

Advances in flood monitoring/forecasting have been constrained by the difficulty in estimating rainfall continuously over space (catchment-, national-, continental-, or even global-scale areas) and flood-relevant time scale. With the recent availability of satellite rainfall estimates at fine time and space resolution, this paper describes a prototype research framework for global flood monitoring by combining real-time satellite observations with a database of global terrestrial characteristics through a hydrologically relevant modeling scheme. Four major components included in the framework are (1) real-time precipitation input from NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA); (2) a central geospatial database to preprocess the land surface characteristics: water divides, slopes, soils, land use, flow directions, flow accumulation, drainage network etc.; (3) a modified distributed hydrological model to convert rainfall to runoff and route the flow through the stream network in order to predict the timing and severity of the flood wave, and (4) an open-access web interface to quickly disseminate flood alerts for potential decision-making. Retrospective simulations for 1998-2006 demonstrate that the Global Flood Monitor (GFM) system performs consistently at both station and catchment levels. The GFM website (experimental version) has been running at near real-time in an effort to offer a cost-effective solution to the ultimate challenge of building natural disaster early warning systems for the data-sparse regions of the world. The interactive GFM website shows close-up maps of the flood risks overlaid on topography/population or integrated with the Google-Earth visualization tool. One additional capability, which extends forecast lead-time by assimilating QPF into the GFM, also will be implemented in the future.

Hong, Yang↗

NASA NTRS2014

A Spatially Coherent Global Soil Moisture Product with Improved Temporal Resolution

Global soil moisture products that are completely independent of any type of ancillary data and solely rely on satellite observations are presented. Additionally, we further develop an existing downscaling technique that enhances the spatial resolution of such products to approximately 11 km. These products are based on internal modules of the Land Parameter Retrieval Model (LPRM), an algorithm that uses the radiative transfer equation to link soil moisture, vegetation optical depth and land surface temperature to observed brightness temperatures. The soil moisture product that is independent of any type of ancillary data uses the internally calculated dielectric constant as a soil moisture proxy. This data product is not influenced by errors associated with coarse-scale global soil property maps or by any other type of forcing (e.g. re-analysis) data and is therefore solely based on satellite microwave observations. The second step builds upon recent developments to increase the spatial resolution of the LPRM retrievals using a smoothing filter downscaling method. With this method we can attain a spatial resolution that can be more useful at the scale of local and regional hydrological studies as well. The steps presented in this paper were applied to observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). The newly derived data sets were validated using ground-based observations from the International Soil Moisture Network (ISMN). The internally calculated dielectric constant product results in significantly more days with valid retrievals than the original soil moisture data products, in particular over arid regions. The dielectric constant product resulted in similar correlations with in situ data as the original soil moisture data product. Together, these findings demonstrate the usefulness of this new dielectric constant product for the hydrological modeling community and climate studies. A case study on the Australian Fitzroy catchment demonstrated that the downscaled data product has a more detailed spatial description of soil moisture, especially during wet and dry conditions with more pronounced dry and wet regions within the catchment. The increased resolution data products could therefore improve runoff predictions and this study demonstrated the potential added value of a transitioning from a spatial resolution of 56 km toward a higher resolution of 11 km. The hydrological implications of these newly developed data records are not only linked to AMSR-E satellite data, but also to the next generation Soil Moisture Active and Passive (SMAP) mission where a 9 km spatial resolution is the target resolution for satellite soil moisture products. The new data products will not replace the current LPRM products, but will be added to the existing array of data products and will become publicly available through our data portals.

spatial↗

DOE OSTI2020

Annual sea level variations in the Red Sea observed using GNSS

SUMMARY Annual sea level variations in the Red Sea have amplitudes of 15–20 cm as observed using various techniques such as tide gauges, satellite altimetry and recently Gravity Recovery and Climate Experiment (GRACE) satellite data. In this study, we demonstrate that Global Navigation Satellite System (GNSS) observations can also be used to measure the effect of these sea level variations. The extra water mass presses on the seafloor, which causes horizontal and vertical deformations. Using time-series from 10 coastal GNSS stations, we observe annual horizontal and vertical loading displacements with amplitudes of 2–5 mm. When correcting for atmospheric, hydrological and surface water loading and a residual geocentre motion, significant annual signals of approximately 0.5 and 2 mm are still observed for the horizontal and vertical components, respectively. In the northern Red Sea, the observed annual signals and predicted annual sea level loading show good agreement. This confirms that the signal is mostly a result of the variations in water mass and thermal expansion. Furthermore, we conclude that the uncertainties in the hydrological model over Ethiopia and Eritrea influence the loading over the southern Red Sea, which was underestimated in previous studies using GRACE data.

Alothman, A. O.↗

NASA NTRSDate not supplied

On the Prediction of Surface Melt Over the Greenland Ice Sheet at NWP and S2S Timescales

Greenland surface melt “events” – encompassing large swaths of the ice sheet over approximately 4-10 day periods – have occurred with increasing frequency in the 21st Century. Surface processes are significant to the ice sheet’s overall contribution to sea level. In addition, the sudden onset of widespread surface melt produces hazardous conditions for researchers and for communities adjacent to the ice sheet. The mechanisms producing melt events, their associated large-scale forcing, and timescales of interest are highly variable and poorly understood. Here we examine the predictability of melt events in a numerical weather prediction system (NWP) and a subseasonal-to-seasonal system (S2S). The NASA Global Modeling and Assimilation Office forward processing (GMAO-FP) is an NWP system that incorporates an evolving, global hybrid 4D ensemble–variational (4DEnVar) data assimilation system and a state-of-the-art model that is run twice-daily with forecasts out to five and ten days. The GMAO S2S system is a hindcast-anomaly, coupled-model prediction system initialized with an atmospheric analysis, and in situ and satellite ocean data assimilation. Both systems represent ice sheet surface hydrology and snowpack processes. We examine the performance of the systems over the period 2018-2023, a span that includes approximately nine significant events, against analyses and satellite-derived melt extent. In addition to extent, we examine related conditions and their role in the melt forecast, including the onset of both omega and Rex atmospheric blocking patterns, sea surface temperatures, sea ice extent, water vapor transport, precipitation, and surface preconditioning. We further examine the tendency for false-alarm events in each system and assess reasons for the forecast performance. North Atlantic atmospheric blocking patterns leading to surface melt are well forecast by the NWP system out to around eight days, while the S2S system generally underestimates the magnitude of such events at monthly lead times. These systems suggest that while short term forecasts of ice sheet melt and melt events are relatively robust, S2S predictions of surface conditions are highly dependent on the ability of a coupled system to generate the short-term atmospheric conditions associated with surface melt events.

Richard Cullather↗

DOE OSTI2020

Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry

Increasing population and resource-intensive lifestyles are driving enhanced demands for clean water, food, and energy. In parallel, land-use change, climate change, and perturbations—including drought, floods, fires, and early snowmelt—are significantly reshaping interactions within watersheds throughout the world. While watersheds are the Earth's key functional unit for assessing and managing water resources, hydrological processes in watersheds also mediate biogeochemical interactions that support terrestrial life on Earth (Kaushal, Gold, Bernal, & Tank, 2018; National Research Council, 2012). Although society is dependent upon clean water availability, tractable prediction of watershed hydrobiogeochemical behavior, including watershed response to perturbations, remains a challenge. Central to the challenge are complex, multiscale interactions between plants, microorganisms, organic matter, minerals, dissolved constituents, and migrating fluids, which occur within and across bedrock-to-canopy compartments and along extensive lateral gradients of a watershed. Several recent community reports have synthesized formidable challenges associated with watershed science and technology (AGU, 2018; Blöschl et al., 2019). Here, we discuss emerging technologies and collaboration modes that are critical for developing generalizable insights about and predictive understanding of complex watershed hydrobiogeochemical behavior, which are important for underpinning optimized natural resource management.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1993

Scientific goals of the Cooperative Multiscale Experiment (CME)

Mesoscale Convective Systems (MCS) form the focus of CME. Recent developments in global climate models, the urgent need to improve the representation of the physics of convection, radiation, the boundary layer, and orography, and the surge of interest in coupling hydrologic, chemistry, and atmospheric models of various scales, have emphasized the need for a broad interdisciplinary and multi-scale approach to understanding and predicting MCS's and their interactions with processes at other scales. The role of mesoscale systems in the large-scale atmospheric circulation, the representation of organized convection and other mesoscale flux sources in terms of bulk properties, and the mutually consistent treatment of water vapor, clouds, radiation, and precipitation, are all key scientific issues concerning which CME will seek to increase understanding. The manner in which convective, mesoscale, and larger scale processes interact to produce and organize MCS's, the moisture cycling properties of MCS's, and the use of coupled cloud/mesoscale models to better understand these processes, are also major objectives of CME. Particular emphasis will be placed on the multi-scale role of MCS's in the hydrological cycle and in the production and transport of chemical trace constituents. The scientific goals of the CME consist of the following: understand how the large and small scales of motion influence the location, structure, intensity, and life cycles of MCS's; understand processes and conditions that determine the relative roles of balanced (slow manifold) and unbalanced (fast manifold) circulations in the dynamics of MCS's throughout their life cycles; assess the predictability of MCS's and improve the quantitative forecasting of precipitation and severe weather events; quantify the upscale feedback of MCS's to the large-scale environment and determine interrelationships between MCS occurrence and variations in the large-scale flow and surface forcing; provide a data base for initialization and verification of coupled regional, mesoscale/hydrologic, mesoscale/chemistry, and prototype mesoscale/cloud-resolving models for prediction of severe weather, ceilings, and visibility; provide a data base for initialization and validation of cloud-resolving models, and for assisting in the fabrication, calibration, and testing of cloud and MCS parameterization schemes; and provide a data base for validation of four dimensional data assimilation schemes and algorithms for retrieving cloud and state parameters from remote sensing instrumentation.

Cotton, William↗

DOE OSTI2023

The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment

As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Ecohydrology of wetland plant communities along an estuarine to tidal river gradient

Abiotic filters that interact with wetland plant communities along tidal–fluvial gradients are highly dynamic, and understanding their quantitative thresholds and relationships to interspecific competition is important during an era of sea-level rise and watershed hydrologic change. Yet, landscape-scale studies of major coastal rivers from the river mouth to the head of tide, such as this study, remain rare. Here, we develop a new predictive framework for estuarine–tidal river research and management using a river-specific low-water datum and the wetland inundation indicator SEVg, the growing-season sum exceedance value of hourly surface-water depth. The distribution and variability of the wetland species pool (n = 203) on the 234 river kilometer (rkm) lower Columbia River and estuary floodplain are described for the first time. 4,940 quadrats at 50 marshes were surveyed (2005–2016). Throughout the estuarine–tidal river system, SEVg was well suited to describe the wetland inundation regime and its variability based on the combination of longitudinal river position and elevation. SEVg increased significantly landward. Two primary wetland inundation regimes were identified: the seaward-tidal, usually greatest during the winter months, and landward-fluvial, greatest during the growing season. Nearest the ocean, salinity is the abiotic factor limiting species richness and non-native species. Farther upriver, the daily, seasonal, and interannual variability of the wetting and drying cycle encourage disturbance-tolerant species and non-natives and limit the number of hydrophytes and total vegetative cover. Hence, the average between-year similarity of site-scale areal cover significantly decreased landward. Hierarchical cluster analysis indicated five vegetative groups and five ecohydrologic zones between rkm 0 and 234 were discriminated with 76 significant species–zone associations. All zones had unique indicator species. Species with high indicator values were Carex lyngbyei throughout the estuarine zones, and Eleocharis palustris, Sagittaria latifolia, and the invasive non-native Phalaris arundinacea in the upper estuarine and lower, middle, and upper tidal river zones (IV > 0.90). Competition from C. lyngbyei nearest the ocean and P. arundinacea in the tidal river was associated with reduced species richness when total cover was >65%. This framework of filters informs the design and prediction of future wetland plant communities on coastal river floodplains.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2001

Water Vapor Tracers as Diagnostics of the Regional Hydrologic Cycle

Numerous studies suggest that local feedback of surface evaporation on precipitation, or recycling, is a significant source of water for precipitation. Quantitative results on the exact amount of recycling have been difficult to obtain in view of the inherent limitations of diagnostic recycling calculations. The current study describes a calculation of the amount of local and remote geographic sources of surface evaporation for precipitation, based on the implementation of three-dimensional constituent tracers of regional water vapor sources (termed water vapor tracers, WVT) in a general circulation model. The major limitation on the accuracy of the recycling estimates is the veracity of the numerically simulated hydrological cycle, though we note that this approach can also be implemented within the context of a data assimilation system. In the WVT approach, each tracer is associated with an evaporative source region for a prognostic three-dimensional variable that represents a partial amount of the total atmospheric water vapor. The physical processes that act on a WVT are determined in proportion to those that act on the model's prognostic water vapor. In this way, the local and remote sources of water for precipitation can be predicted within the model simulation, and can be validated against the model's prognostic water vapor. As a demonstration of the method, the regional hydrologic cycles for North America and India are evaluated for six summers (June, July and August) of model simulation. More than 50% of the precipitation in the Midwestern United States came from continental regional sources, and the local source was the largest of the regional tracers (14%). The Gulf of Mexico and Atlantic regions contributed 18% of the water for Midwestern precipitation, but further analysis suggests that the greater region of the Tropical Atlantic Ocean may also contribute significantly. In most North American continental regions, the local source of precipitation is correlated with total precipitation. There is a general positive correlation between local evaporation and local precipitation, but it can be weaker because large evaporation can occur when precipitation is inhibited. In India, the local source of precipitation is a small percentage of the precipitation owing to the dominance of the atmospheric transport of oceanic water. The southern Indian Ocean provides a key source of water for both the Indian continent and the Sahelian region.

Bosilovich, Michael G.↗

DOE OSTI2021

The Preservation of Climate‐Driven Landslide Dams in Western Oregon

Abstract Bedrock landsliding, including the formation of landslide dams, is a predominant geomorphic process in steep landscapes. Clarifying the importance of hydrologic and seismic mechanisms for triggering deep‐seated landslides remains an ongoing effort, and formulation of geomorphic metrics that predict dam preservation is crucial for quantifying secondary landslide hazards. Here, we identify >200 landslide‐dammed lakes in western Oregon and utilize dendrochronology and enhanced 14 C dating (“wiggle matching”) of “ghost forests” to establish slope failure timing at 20 sites. Our dated landslide dataset reveals bedrock landsliding has been common since the last Cascadia Subduction Zone earthquake in January 1700 AD. Our study does not reveal landslides that date to 1700 AD. Rather, we observe temporal clustering of at least four landslides in the winter of 1889/1890 AD, coincident with a series of atmospheric rivers that generated one of the largest regionally recorded floods. We use topographic and field analyses to assess the relation between dam preservation and topographic characteristics of the impounded valleys. In contrast to previous studies, we do not observe systematic scaling between dam size and upstream drainage area, though dam stability indices for our sites correspond with “stable” dams elsewhere. Notably, we observe that dams are preferentially preserved at drainage areas of ∼1.5 to 13 km 2 and valley widths of ∼25 to 80 m, which may reflect the reduced downstream influence of debris flows and the accumulation of mature conifer trees upstream from landslide‐dammed lake outlets. We suggest that wood accumulation upstream of landslide dams tempers large stream discharges, thus inhibiting dam incision.

Struble, William T.↗

DOE OSTI2022

Shallow Cumulus Properties as Captured by Adiabatic Fraction in High-Resolution LES Simulations

Abstract Shallow convective clouds are important players in Earth’s energy budget and hydrological cycle, and are abundant in the tropical and subtropical belts. They greatly contribute to the uncertainty in climate predictions due to their unresolved, complex processes that include coupling between the dynamics and microphysics. Analysis of cloud structure can be simplified by considering cloud motions as a combination of moist adiabatic motions like adiabatic updrafts and turbulent motions leading to deviation from adiabaticity. In this work, we study the sizes and occurrence of adiabatic regions in shallow cumulus clouds during their growth and mature stages, and use the adiabatic fraction (AF) as a continuous metric to describe cloud processes and properties from the core to the edge. To do so, we simulate isolated trade wind cumulus clouds of different sizes using the System of Atmospheric Modeling (SAM) model in high resolution (10 m) with the Hebrew University spectral bin microphysics (SBM). The fine features in the clouds’ dynamics and microphysics, including small near-adiabatic volumes and a thin transition zone at the edge of the cloud (∼20–40 m in width), are captured. The AF is shown to be an efficient measure for analyzing cloud properties and key processes determining the droplet-size distribution formation and shape during the cloud evolution. Physical processes governing the properties of droplet size distributions at different cloud regions (e.g., core, edge) are analyzed in relation to AF. Significance Statement 1) This study investigates the evolution of cumulus clouds (Cu) using a 10-m-resolution LES model with spectral bin microphysics. 2) The study improves the understanding of the mutual effects of adiabatic updrafts and lateral entrainment and mixing. 3) The study demonstrates the existence of an adiabatic core in nonprecipitating Cu. 4) Shapes of the droplet size distributions are closely related to the adiabatic fraction values. 5) Utilization of high resolution reveals the existence of physically significant small features in the cloud structure, such as a narrow cloud interface zone and small adiabatic volumes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Uncertainty characterization in a coupled human-natural system: Modeling agricultural adaptation in the Great Lakes Region

The Great Lakes Region's water quality and ecological health are threatened by the export of nutrients from agricultural lands, which causes eutrophication, hypoxia, and destructive algal blooms. The intensification of hydrologic cycles brought about by climate change is expected to exacerbate nutrient loading in the region, and, at the same time, agricultural adaptation to changing conditions is also expected to affect loading through shifting amounts and timing of fertilization. Quantifying these future effects and their interactions necessitates modeling both the human and natural processes as a coupled system, by pairing land use and agricultural management with hydrologic modeling. At the same time, compounding uncertainties arising from the complex interactions in both systems significantly limit our predictive understanding of the region's impacts. This study utilizes the Soil and Water Assessment Tool (SWAT), developed for simulating the impact of various farmer decisions on watershed functions in Western Lake Erie watersheds, and an under-development agent-based model (ABM) for agricultural management decisions. The aim of this study is to use global sensitivity analysis on the coupled ABM and SWAT models to quantify how uncertainty in both models interactively affects nutrient loading. To do so, we will conduct Sobol sensitivity analysis experiments at different levels of coupling assumptions to quantify how various uncertain factors (e.g., soil moisture and crop choice) and their interactions affect our estimates of nutrient loading. The results of this analysis will allow us to quantify how complex interactions and dependencies between both systems amplify the effect of uncertainties. Insights gained from this study will have broader implications for modeling the adaptive co-evolution of human and natural systems under climate change and can inform effective management of nutrient loading in the Great Lakes Region.

Climate Change↗

NASA NTRS1984

Comparison of Land Cover Information from LANDSAT Multispectral Scanner (MSS) and Airborne Thematic Mapper Simulator (TMS) Data for Hydrologic Applications

Detailed land cover classifications were performed on the Thematic Mapper Simulator (TMS) and MSS data of the Clinton River Basin (acquired on August 19, 1981, and June 28, 1980, respectively) using supervised classification techniques. Differences in interclass separability were compared to select several promising TMS band combinations, selected from the 27 covering the Clinton River Basin. The TMS data produced a more accurate and spatially contiguous classification than MSS for this study site. While the accuracy of the 4-band TM data set was as good as the 7-band, the 3-band TMS data sets were also better than the MSS. These results indicate that both the increased spectral discrimination and spatial resolution contribute to improved classification accuracy. The possibility of reducing the data analysis burden associated with large TM data volumes through effective band selection therefore appears promising. The implications of the improved classification accuracy of TMS data are important for hydrologic and economic modeling. In particular, the higher accuracies for the developed categories (residential and commercial) should improve the predictions of runoff in flood forecasting models and of flood damage for damage calculation models appreciably.

Gervin, J. C.↗

NASA NTRS1992

Biogeochemical factors which regulate the formation and fate of sulfide in wetlands

Coastal wetland areas occupy a small percentage of the terrestrial environment yet are extremely productive regions which support rapid rates of belowground bacterial activity. Wetlands appear to be significant as biogenic sources of gaseous sulfur, carbon, and nitrogen. These gases are important as tracers of man's activities, and they influence atmospheric chemistry. The interactions among wetland biogeochemical processes regulate the anaerobic production of reduced gases and influence the fate of these volatiles. Therefore, spatial and temporal variations in hydrology, salinity, temperature and specification, and growth of vegetation affect the type and magnitude of gas emissions thus hindering predictive estimates of gas flux. Our research is divided into two major components, the first is the biogeochemical characterization of a selected tidal wetland area in terms of factors likely to regulate sulfide flux; the second is a direct measurement of gaseous sulfur flux as related to changes in these biogeochemical conditions. Presently, we are near completion of phase one.

Hines, Mark E.↗

NASA NTRS2003

Lunar and Planetary Science XXXV: Mars Impact Cratering

This document discusses the following topics: High Resolution Digital Elevation Models of Pristine Explosion Craters; Crater Degradation in the Martian Highlands: Morphometric Analysis of the Sinus Sabaeus Region and Simulation Modeling Suggest Fluvial Processes; Analysis of Impact Crater Preservation on Mars Using THEMIS Data; Atmospheric Entry Studies and the Smallest Impact Craters on Mars; Updating the Crater Count Chronology System for Mars; Control of Impact Crater-related Fracture Systems on the Subsurface Hydrology and Ground Collapse; Quantitative Analyses of Terrestrial Crater Deposits: Constraining Formation and Sediment Transport Processes on Mars; and Predicted Effects of Surface Processes on Martian Impact Crater Depth/Diameter Relationships

Stephen J Mackwell↗

NASA NTRS2018

The Global Precipitation Measurement (GPM) Mission's Scientific Achievements and Societal Contributions: Reviewing Four Years of Advanced Rain and Snow Observations

Precipitation represents a life-critical energy and hydrologic exchange between the Earth’s atmosphere and its surface. As such, knowledge of where, when, and how much rain and snow falls is essential for scientific research and societal applications. Building on the 17-year success of the Tropical Rainfall Measurement Mission (TRMM), the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO) is the first U.S. National Aeronautical and Space Administration (NASA) satellite mission specifically designed with sensors to observe the structure and intensities of both rain and falling snow. The GPM-CO has proved to be a worthy successor to TRMM, extending and improving high-quality active and passive microwave observations across all times of day. The GPM-CO launched in early 2014, is a joint mission between NASA and the Japanese Aerospace Exploration Agency (JAXA), with sensors that include the NASA-provided GPM Microwave Imager and the JAXA-provided Dual-frequency Precipitation Radar. These sensors were devised with high accuracy standards enabling them to be used as a reference for inter-calibrating a constellation of partner satellite data. These intercalibrated partner satellite retrievals are used with infrared data to produce merged precipitation estimates at temporal scales of 30 minutes and spatial scales of 0.1° x 0.1°. Precipitation estimates from the GPM-CO and partner constellation satellites, provided in near real time and later reprocessed with all ancillary data, are an indispensable source of precipitation data for operational and scientific users. Advances have been made using GPM data, primarily in improving sensor calibration, retrieval algorithms, and ground validation measurements, and used to further our understanding of the characteristics of liquid and frozen precipitation and the science of water and hydrological cycles for climate/weather forecasting. These advances have extended to societal benefits related to water resources, operational numerical weather prediction, hurricane monitoring, prediction, and disaster response, extremes, and disease.

Applications↗

NASA NTRS2020

Kenya Food Security & Agriculture II: Utilizing NASA Earth Observations to Enhance Drought Warning Systems and Develop Capacity to Use the RHEAS Model in Kenya

Twenty-three counties in Kenya experience frequent drought, which damages agricultural productivity and threatens the health and well being of millions. NASA DEVELOP partnered with NASA SERVIR, the Regional Centre for Mapping of Resources for Development (RCMRD) and Kenya’s National Drought Management Authority (NDMA) to enhance drought-detection capacity using NASA Earth observations. Currently, the NDMA publishes monthly Early Warning Bulletins with drought conditions for each arid or semi-arid county in Kenya. These bulletins utilize the Vegetation Condition Index (VCI) from the Moderate Resolution ImagingSpectroradiometer (MODIS) as well as a variety of biophysical, social and economic drought indicators, but do not allow for advanced forecasting. To improve drought-monitoring capabilities, the team created a Combined Drought Indicator (CDI) using the Regional Hydrologic Extremes Assessment System (RHEAS) model and data from Aqua and Terra MODIS and the National Centers for Environmental Prediction. The CDI combined precipitation anomalies, soil moisture anomalies, evaporative stress, and VCI according to weights determined by principal component analysis. To evaluate the performance of the CDI across Kenya, the team compiled a dataset of drought events from historical reports and the NDMA’s records. Results suggested that the CDI detected drought earlier thanVCI alone. However, more validation is needed to ensure that the CDI accurately and consistently detects drought earlier than current warning systems. In order to better understand the behavior of individual indices and the potential for earlier drought detection, the team also conducted a time-lag analysis. The team found that VCI responds to drought, on average, one month later than most other indices included in the CDI, suggesting that incorporating additional indices could improve early drought warning systems in Kenya.

Food Security & Agriculture↗

DOE OSTI2023

Prediction of Long-Term Geochemical Change in Bentonite Based on the Interpretative THMC Model of the FEBEX In Situ Test

Since nuclear energy is crucial in the decarbonization of the energy supply, one hurdle to remove is the handling of high-level radioactive waste (HLW). Disposal of HLW in a deep geological repository has long been deemed a viable permanent option. In the design of a deep geological repository, compacted bentonite is the most commonly proposed buffer material. Predicting the long-term chemical evolution in bentonite, which is important for the safety assessment of a repository, has been challenging because of the complex coupled processes. Models for large-scale tests and predictions based on such models have been some of the best practices for such purposes. An 18-year-long in situ test with two dismantling events provided a unique set of chemical data that allowed for studying chemical changes in bentonite. In this paper, we first developed coupled thermal, hydrological, mechanical, and chemical (THMC) models to interpret the geochemical data collected in the in situ test and then extended the THMC model to 200 years to make long-term prediction of the geochemical evolution of bentonite. The interpretive coupled THMC model shows that the geochemical profiles were strongly affected by THM processes such as evaporation/condensation, porosity change caused by swelling, permeability change, and the shape of concentration profiles for major cations were largely controlled by transport processes, but concentration levels were regulated by chemical reactions, and the profiles of some species such as pH, bicarbonate, and sulfate were dominated by these reactions. The long-term THMC model showed that heating prolongs the time that bentonite becomes fully saturated in the area close to the heater/canister; however, once the bentonite becomes fully saturated, high concentrations of ions in bentonite near the heater, which was observed in the field test, will disappear; illitization continues for 50 years but will not proceed further.

Zheng, Liange↗