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NASA NTRS2015

Evaluation of a Model-Based Groundwater Drought Indicator in the Conterminous U.S.

Monitoring groundwater drought using land surface models is a valuable alternative given the current lack of systematic in situ measurements at continental and global scales and the low resolution of current remote sensing based groundwater data. However, uncertainties inherent to land surface models may impede drought detection, and thus should be assessed using independent data sources. In this study, we evaluated a groundwater drought index (GWI) derived from monthly groundwater storage output from the Catchment Land Surface Model (CLSM) using a GWI similarly derived from in situ groundwater observations. Groundwater observations were obtained from unconfined or semi-confined aquifers in eight regions of the central and northeastern U.S. Regional average GWI derived from CLSM exhibited strong correlation with that from observation wells, with correlation coefficients between 0.43 and 0.92. GWI from both in situ data and CLSM was generally better correlated with the Standard Precipitation Index (SPI) at 12 and 24 month timescales than at shorter timescales, but it varied depending on climate conditions. The correlation between CLSM derived GWI and SPI generally decreases with increasing depth to the water table, which in turn depends on both bedrock depth (a CLSM parameter) and mean annual precipitation. The persistence of CLSM derived GWI is spatially varied and again shows a strong influence of depth to groundwater. CLSM derived GWI generally persists longer than GWI derived from in situ data, due at least in part to the inability of coarse model inputs to capture high frequency meteorological variability at local scales. The study also showed that groundwater can have a significant impact on soil moisture persistence where the water table is shallow. Soil moisture persistence was estimated to be longer in the eastern U.S. than in the west, in contrast to previous findings that were based on models that did not represent groundwater. Assimilation of terrestrial water storage data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission improved the correlation between CLSM based regional average GWI and that based on in situ data in six of the eight regions. Practical issues regarding the application of GRACE assimilated groundwater storage for drought detection are discussed. An important conclusion of this study is that model parameters that control the depth to the water table, including bedrock depth, strongly influence the evolution and persistence of simulated groundwater and require careful configuration for drought monitoring.

Evaluation↗

NASA NTRS2010

GRAIL - A Microwave Ranging Instrument to Map Out the Lunar Gravity Field

Gravity Recovery and Interior Laboratory, or GRAIL, is a NASA mission to map out the gravity field of the moon to an unprecedented level of detail. The instrument for this mission is based on GRACE (Gravity Recovery and Climate Experiment), an earth-orbiting mission currently mapping out the gravity field of the earth. This paper will describe the similarities and differences between these two instruments with a focus on the microwave ranging measurements used to determine the gravity parameters and the testbed built at Jet Propulsion Laboratory to demonstrate micron level ranging capability. The onboard ultrastable oscillator and RF instruments will be described and noise contributions discussed.

Enzer, Daphna G.↗

NASA NTRS2013

The Gravity Recovery and Interior Laboratory Mission

The Gravity Recovery and Interior Laboratory (GRAIL) mission, launched in September 2011, successfully completed its Primary Science Mission in June 2012 and is currently in Extended Mission operations. Competitively selected under a NASA Announcement of Opportunity in December 2007, GRAIL is a Discovery Program mission subject to a mandatory project cost cap. The purpose of the mission is to precisely map the gravitational field of the Moon to reveal its internal structure from crust to core, determine its thermal evolution, and extend this knowledge to other planets. The mission uses twin spacecraft flying in tandem to provide the gravity map. The GRAIL Flight System, consisting of the spacecraft and payload, was developed based on significant heritage from previous missions such an experimental U.S. Air Force satellite, the Mars Reconnaissance Orbiter (MRO) mission, and the Gravity Recovery and Climate Experiment (GRACE) mission. The Mission Operations System (MOS) was based on high-heritage multimission operations developed by NASA's Jet Propulsion Laboratory and Lockheed Martin. Both the Flight System and MOS were adapted to meet the unique challenges posed by the GRAIL mission design. This paper summarizes the implementation challenges and accomplishments of getting GRAIL ready for launch. It also discusses the in-flight challenges and experiences of operating two spacecraft, and mission results.

GRAIL↗

NASA NTRS2014

Evaluation of a Model-Based Groundwater Drought Indicator in the Conterminous U.S.

hydrology↗

NASA NTRS2014

Drought Prediction for Socio-Cultural Stability Project

The primary objective of this project is to answer the question: "Can existing, linked infrastructures be used to predict the onset of drought months in advance?" Based on our work, the answer to this question is "yes" with the qualifiers that skill depends on both lead-time and location, and especially with the associated teleconnections (e.g., ENSO, Indian Ocean Dipole) active in a given region season. As part of this work, we successfully developed a prototype drought early warning system based on existing/mature NASA Earth science components including the Goddard Earth Observing System Data Assimilation System Version 5 (GEOS-5) forecasting model, the Land Information System (LIS) land data assimilation software framework, the Catchment Land Surface Model (CLSM), remotely sensed terrestrial water storage from the Gravity Recovery and Climate Experiment (GRACE) and remotely sensed soil moisture products from the Aqua/Advanced Microwave Scanning Radiometer - EOS (AMSR-E). We focused on a single drought year - 2011 - during which major agricultural droughts occurred with devastating impacts in the Texas-Mexico region of North America (TEXMEX) and the Horn of Africa (HOA). Our results demonstrate that GEOS-5 precipitation forecasts show skill globally at 1-month lead, and can show up to 3 months skill regionally in the TEXMEX and HOA areas. Our results also demonstrate that the CLSM soil moisture percentiles are a goof indicator of drought, as compared to the North American Drought Monitor of TEXMEX and a combination of Famine Early Warning Systems Network (FEWS NET) data and Moderate Resolution Imaging Spectrometer (MODIS)'s Normalizing Difference Vegetation Index (NDVI) anomalies over HOA. The data assimilation experiments produced mixed results. GRACE terrestrial water storage (TWS) assimilation was found to significantly improve soil moisture and evapotransportation, as well as drought monitoring via soil moisture percentiles, while AMSR-E soil moisture assimilation produced marginal benefits. We carried out 1-3 month lead-time forecast experiments using GEOS-5 forecasts as input to LIS/CLSM. Based on these forecast experiments, we find that the expected skill in GEOS-5 forecasts from 1-3 months is present in the soil moisture percentiles used to indicate drought. In the case of the HOA drought, the failure of the long rains in April appears in the February 1, March 1 and April 1 initialized forecasts, suggesting that for this case, drought forecasting would have provided some advance warning about the drought conditions observed in 2011. Three key recommendations for follow-up work include: (1) carry out a comprehensive analysis of droughts observed over the entire period of record for GEOS-5 forecasts; (2) continue to analyze the GEOS-5 forecasts in HOA stratifying by anomalies in long and short rains; and (3) continue to include GRACE TWS, Soil Moisture/Ocean Salinity (SMOS) and the upcoming NASA Soil Moisture Active/Passive (SMAP) soil moisture products in a routine activity building on this prototype to further quantify the benefits for drought assessment and prediction.

Peters-Lidard, Christa↗

NASA NTRS2016

Basin-Scale Assessment of the Land Surface Water Budget in the National Centers for Environmental Prediction Operational and Research NLDAS-2 Systems

The purpose of this study is to evaluate the components of the land surface water budget in the four land surface models (Noah, SAC-Sacramento Soil Moisture Accounting Model, (VIC) Variable Infiltration Capacity Model, and Mosaic) applied in the newly implemented National Centers for Environmental Prediction (NCEP) operational and research versions of the North American Land Data Assimilation System version 2 (NLDAS-2). This work focuses on monthly and annual components of the water budget over 12 National Weather Service (NWS) River Forecast Centers (RFCs). Monthly gridded FLUX Network (FLUXNET) evapotranspiration (ET) from the Max-Planck Institute (MPI) of Germany, U.S. Geological Survey (USGS) total runoff (Q), changes in total water storage (dS/dt, derived as a residual by utilizing MPI ET and USGS Q in the water balance equation), and Gravity Recovery and Climate Experiment (GRACE) observed total water storage anomaly (TWSA) and change (TWSC) are used as reference data sets. Compared to these ET and Q benchmarks, Mosaic and SAC (Noah and VIC) in the operational NLDAS-2 overestimate (underestimate) mean annual reference ET and underestimate (overestimate) mean annual reference Q. The multimodel ensemble mean (MME) is closer to the mean annual reference ET and Q. An anomaly correlation (AC) analysis shows good AC values for simulated monthly mean Q and dS/dt but significantly smaller AC values for simulated ET. Upgraded versions of the models utilized in the research side of NLDAS-2 yield largely improved performance in the simulation of these mean annual and monthly water component diagnostics. These results demonstrate that the three intertwined efforts of improving (1) the scientific understanding of parameterization of land surface processes, (2) the spatial and temporal extent of systematic validation of land surface processes, and (3) the engineering-oriented aspects such as parameter calibration and optimization are key to substantially improving product quality in various land data assimilation systems.

NLDAS-2↗

NASA NTRS2016

Extreme Water Deficit in Brazil Detected from Space

Extreme droughts have caused significant socioeconomic and environmental damage worldwide. In Brazil, ineffective energy development and water management policies have magnified the impacts of recent severe droughts, which include massive agricultural losses, water supply restrictions, and energy rationing. Spaceborne remote sensing data advance our understanding of the spatiotemporal variability of large-scale droughts and enhance the detection and monitoring of extreme water-related events. In this study, data derived from the Gravity Recovery and Climate Experiment (GRACE) mission are used to detect and quantify an extended major drought over eastern Brazil and provide estimates of impacted areas and region-specific water deficits. Two structural breakpoint detection methods were applied to time series of GRACE-based terrestrial water storage anomalies (TWSA), determining when two abrupt changes occurred. One, in particular, defines the beginning of the current drought. Using TWSA, a water loss rate of 26.1 cmyr21 over southeastern Brazil was detected from 2012 to 2015. Based on analysis of Global Land Data Assimilation System(GLDAS) outputs, the extreme drought is mostly related to lower-than-usual precipitation rates, resulting in high soil moisture depletion and lower-than-usual rates of evapotranspiration. A reduction of 2023 of precipitation over an extended period of 3 years is enough to raise serious water scarcity conditions in the country. Correlations between monthly time series of both grid-based TWSA and ground-based water storage measurements at 16 reservoirs located within southeastern Brazil varied from 0.42 to 0.82. Differences are mainly explained by reservoir sizes and proximity to the drought nucleus.

terrestrial↗

NASA NTRS2010

Orbital Gravity Gradiometry Beyond GOCE: Mission Concepts

Significant advances in the technologies needed for space-based cryogenic instruments have been made in the last decade, including cryocoolers, spacecraft architectures and cryogenic amplifiers. These enable considerably more complex instruments to be put into orbit for long-duration missions. One such instrument is the Superconducting Gravity Gradiometer (SGG) developed by Paik, et al. A magnetically levitated version is under consideration for a follow-on mission to GRACE (Gravity Recovery and Climate Experiment) and GOCE (Gravity field and steady-state Ocean Circulation Explorer). With its inherently greater rejection of common mode accelerations and ability to cancel the coupling of angular accelerations into the gradient signal, the SGG can achieve [an accuracy of] 0.01 milli-Eotvos (gravitational gradient of the Earth) divided by the square root of frequency in hertz, with requirements for attitude control that can be met with existing spacecraft. In addition, the use of a cryocooler for cooling the instrument will alleviate the previously severe constraint on mission lifetime imposed by the use of superfluid helium,. enabling mission durations in the 5-10 year range. Studies are underway to determine requirements for orbit (polar versus sun-synchronous), altitude (which affects spacecraft drag), instrument temperature and stability, cryocooler vibration control, and control and readout electronics. These will be used to determine the SGG's sensitivity and ultimate resolution for gravity recovery. This paper will discuss preliminary instrument and spacecraft design, and toplevel mission requirements.

Shirron, Peter J.↗

NASA NTRS2018

A Comparison of Satellite Data-Based Drought Indicators in Detecting the 2012 Drought in the Southeastern US

The drought of 2012 in the North America devastated agricultural crops and pastures, further damaging agriculture and livestock industries and leading to great losses in the economy. The drought maps of the United States Drought Monitor (USDM) and various drought monitoring techniques based on the data collected by the satellites orbiting in space such as the Gravity Recovery and Climate Experiment (GRACE) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are inter-compared during the 2012 drought conditions in the southeastern United States. The results indicated that spatial extent of drought reported by USDM were in general agreement with those reported by the MODIS-based drought maps. GRACE-based drought maps suggested that the southeastern US experienced widespread decline in surface and root-zone soil moisture and groundwater resources. Disagreements among all drought indicators were observed over irrigated areas, especially in Lower Mississippi region where agriculture is mainly irrigated. Besides, we demonstrated that time lag of vegetation response to changes in soil moisture and groundwater partly contributed to these disagreements, as well.

drought indicators↗

NASA NTRS2018

Emerging Trends in Global Freshwater Availability

Freshwater availability is changing worldwide. Here we quantify 34 trends in terrestrial water storage (TWS) observed by the Gravity Recovery and Climate Experiment (GRACE) satellites during 2002-2016 and categorize their drivers as natural interannual variability, unsustainable groundwater consumption, or climate change. Several of these trends had been lacking thorough investigation and attribution, including massive changes in northwestern China and the Okavango delta. Others are consistent with climate model predictions. This observation-based assessment of how the world's water landscape is responding to human impacts and climate variations provides a blueprint for evaluating and predicting emerging threats to water and food security.

Okavango delta↗

NASA NTRS2017

High-Latitude Thermosphere Neutral Density Response to Solar Wind Dynamic Pressure Enhancement

We examine the response of the thermosphere to the impact of solar wind dynamic pressure enhancements using observations and global magneto-hydrodynamics (MHD) simulations by the OpenGGCM model. Combining neutral density observations from the Challenging Minisatellite Payload (CHAMP) and the Gravity Recovery and Climate Experiment (GRACE) satellites with simultaneous Poynting flux measurements from the Defense Meteorological Satellite Program (DMSP) F16, we find that thermospheric density as well as downward Poynting flux intensified shortly after a sudden enhancement of the solar wind dynamic pressure. The intensification manifested mostly on the dayside high-latitude region with peak intensity in the vicinity of the noon and prenoon cusp. OpenGGCM modeling results show that the ionospheric Joule heating increased abruptly in response to the sudden enhancement of the dynamic pressure in the same region as the observed Poynting flux and neutral density enhancements. The modeling results show that the enhanced Joule heating coincides, both in time and location, with the appearance of a pair of high-latitude localized field-aligned currents (FACs) in the cusp region. The FACs intensified and extended azimuthally. Coincidental with the solar wind dynamic pressure enhancement, the y component of the interplanetary magnetic field (IMF) By became strongly positive and, in addition, had some large fluctuations. We explore the separate and combined effects of the dynamic pressure and IMF By perturbations, with specifically designed simulation experiments that isolate the effect of each solar wind parameter. We find that the dynamic pressure enhancement is the primary source for the Joule heating and neutral density enhancements, but the IMF By modulates the level of enhancement.

magneto-hydrodynamics↗

NASA NTRS2017

Upper Blue Nile Basin Water Budget from a Multi-Model Perspective

Improved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. The intercomparison and evaluation of multiple land surface models (LSMs) associated with different meteorological forcing and precipitation datasets can offer a moderate range of water budget variable estimates. In this context, two LSMs, Noah version 3.3 (Noah3.3) and Catchment LSM version Fortuna 2.5 (CLSMF2.5) coupled with the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme are used to produce hydrological estimates over the region. The two LSMs were forced with different combinations of two reanalysis-based meteorological datasets from the Modern-Era Retrospective analysis for Research and Applications datasets (i.e., MERRA-Land and MERRA-2) and three observation-based precipitation datasets, generating a total of 16 experiments. Modeled evapotranspiration (ET), streamflow, and terrestrial water storage estimates were evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) ET, in-situ streamflow observations, and NASA Gravity Recovery and Climate Experiment (GRACE) products, respectively. Results show that CLSMF2.5 provided better representation of the water budget variables than Noah3.3 in terms of Nash-Sutcliffe coefficient when considering all meteorological forcing datasets and precipitation datasets. The model experiments forced with observation-based products, the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), outperform those run with MERRA-Land and MERRA-2 precipitation. The results presented in this paper would suggest that the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System incorporate CLSMF2.5 and HyMAP routing scheme to better represent the water balance in this region.

Rainfall↗

NASA NTRS2018

Data Assimilation of Terrestrial Water Storage to Adjust Precipitation Fluxes

The Gravity Recovery and Climate Experiment (GRACE) mission has provided unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. TWS is defined as the sum of groundwater, soil moisture, snow, surface water, ice and biomass water. Data assimilation of GRACE TWS observations has been shown to improve simulation of groundwater, streamflow, and snow water equivalent, and has also proven useful for drought monitoring and identifying human impacts on the water cycle. From a modeling perspective, the TWS components are defined as "prognostic hydrological states". Existing GRACE data assimilation schemes update these prognostic states directly. In this work, we propose an alternate approach in which precipitation fluxes are adjusted in order to achieve the desired change in the hydrological prognostic states. Limitations of such an approach include the assumption that all errors in TWS originate from errors in precipitation. Nonetheless, benefits comprise (1) the water balance is maintained, as opposed to having to add increments to the water budget components, (2) the model automatically determines how to distribute the updates among the TWS prognostic states, and (3) it is not necessary to know the exact time of the observation TWS, because the TWS change timing is determined by the precipitation forcing.

Girotto, Manuela↗

NASA NTRS2018

A Comparative Study of Available Water in the Major River Basins of the World

Numerous large river basins of the world have few and irregular observations of the components of the terrestrial hydrological cycle with the exception of stream gauges at a few locations and at the outlet along with sparsely distributed rain gauges. Using observations from satellite sensors and output from global land surface models, it is possible to study these under-observed river basins. With populations greater than a billion people, some of these rivers (e.g., the Ganga-Brahmaputra, the Yangtze, the Nile and the Mekong) are the economic engines of the countries they transect, yet thorough assessment of their flow dynamics and variability in regard to water resource management is still lacking. In this paper, we use soil moisture (0-2m) and surface runoff from the NASA Global Land Data Assimilation System (GLDAS), evapotranspiration, and Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and rainfall from the Tropical Rainfall Measuring Mission (TRMM) and total water storage anomaly from the Gravity Recovery and Climate Experiment (GRACE) to examine variability of individual water balance components. To this end, understanding the inter-annual and intra-seasonal variability and the spatial variability of the water balance components in the major river basins of the world will help to plan for improved management of water resources for the future.

GLDAS↗

NASA NTRS2018

The Sensitivity of US Wildfire Occurrence to Pre-Season Soil Moisture Conditions Across Ecosystems

It is well accepted that drought and low moisture conditions are linked with increased wildfire occurrence. However, quantifying the sensitivity of wildfire to surface moisture state has been challenging due to a lack of soil moisture observations at an appropriate spatial scale. Here we apply model simulations of surface soil moisture that numerically assimilate observations from NASA’s Gravity Recovery and Climate Experiment (GRACE) mission, combined in a predictive algorithm with the US Forest Service’s Fire-Occurrence Database. We estimate a relationship between historic surface moisture and wildfire occurrence to produce annual probable wildfire occurrence and burned area at 0.25-degree resolution for the contiguous United States by land-cover classification. Cross-validation indicates increased frequency of smaller fires when the months preceding fire season are wet, while larger fires are more frequent when soils are dry. This demonstrates that assimilated GRACE data holds information that could aid national-scale fire potential assessments for early decision-support.

Fire-Occurrence database↗

NASA NTRS2019

Long-Term, Non-Anthropogenic Groundwater Storage Changes Simulated by Three Global-Scale Hydrological Models

This study examined long-term, natural (i.e., excluding anthropogenic impacts) variability of groundwater storage worldwide. Groundwater storage changes were estimated by forcing three global-scale hydrological models with three 50+ year meteorological datasets. Evaluation using in situ groundwater observations from the U.S. and terrestrial water storage derived from the Gravity Recovery and Climate Experiment (GRACE) satellites showed that these models reasonably represented inter-annual variability of water storage, as indicated by correlations greater than 0.5 in most regions. Empirical orthogonal function analysis revealed influences of the El Niño Southern Oscillation (ENSO) on global groundwater storage. Simulated groundwater storage, including its global average, exhibited trends generally consistent with that of precipitation. Global total (natural) groundwater storage decreased over the past 5-7 decades with modeled rates ranging from 0.01 to 2.18 mm year-1. This large range can be attributed in part to groundwater’s low frequency (inter-decadal) variability, which complicates identification of real long-term trends even within a 50+ year time series. Results indicate that non-anthropogenic variability in groundwater storage is substantial, making knowledge of it fundamental to quantifying direct human impacts on groundwater storage.

Groundwater storage↗

NASA NTRS2019

Terrestrial Water Storage

Terrestrial water storage can be defined as the summation of all water on the land surface and in the subsurface. It includes surface soil moisture, root zone soil moisture, groundwater, snow,ice, water stored in the vegetation, river and lake water. Terrestrial water storage (TWS) changes have been observed by the Gravity Recovery and Climate Experiment (GRACE) mission since 2002. GRACE has provided an unprecedented view of the terrestrial water storage variations at large scales. Extremes in water storage often are associated with droughts and flooding events because they are driven by the surplus or deficit of water. Few hydrologic observing networks yield sufficient data for comprehensive monitoring of changes in the total amount of water stored in a region. GRACE observations have helped to fill this gap. This book chapter is divided into 4 sections.Section 1 provides an overview of the GRACE mission. Section 2 reviews the terrestrial water storage solutions that are available. Section 3 describes regional to global applications of GRACE for monitoring extremes in the terrestrial water storage, including droughts and flooding events. Conclusions and future directions are reported in Section 4.

Terrestrial Water Storage↗

NASA NTRS2019

Radar Altimetry as a Proxy for Determining Terrestrial Water Storage Variability in Tropical Basins

The Gravity Recovery and Climate Experiment (GRACE) mission has provided us with unforeseen information on terrestrial water-storage (TWS) variability, contributing to our understanding of global hydrological processes, including hydrological extreme events and anthropogenic impacts on water storage. Attempts to decompose GRACE-based TWS signals into its different water storage layers, i.e., surface water storage (SWS), soil moisture, groundwater and snow, have shown that SWS is a principal component, particularly in the tropics, where major rivers flow over arid regions at high latitudes. Here, we demonstrate that water levels, measured with radar altimeters at a limited number of locations, can be used to reconstruct gridded GRACE-based TWS signals in the Amazon basin, at spatial resolutions ranging from 0.5 to 3°, with mean absolute errors (MAE) as low as 2.5 cm and correlations as high as 0.98. We show that, at 3° spatial resolution, spatially-distributed TWS time series can be precisely reconstructed with as few as 41 water-level time series located within the basin. The proposed approach is competitive when compared to existing TWS estimates derived from physically based and computationally expensive methods. Also, a validation experiment indicates that TWS estimates can be extrapolated to periods beyond that of the model regression with low errors. The approach is robust, based on regression models and interpolation techniques, and offers a new possibility to reproduce spatially and temporally distributed TWS that could be used to fill inter-mission gaps and to extend GRACE-based TWS time series beyond its timespan.

Terrestrial water storage↗