Datasets Developed by the Hydrological Sciences Lab

NEWS Water and Energy Cycle Climatology

NASA’s Energy and Water Cycle Study (NEWS) program supported an assessment of the state of the global water and energy cycles at the start of the millennium, based on data from the most advanced space and ground based measurement systems and output from observation-integrating models (Rodell et al., 2015; L’Ecuyer et al., 2015).  This was a critical step towards the NEWS objective of evaluating water and energy cycle consequences of climate change.  The study team, which included 24 scientists from 14 institutions, used modern observation-integrating products and associated error-analyses to develop a climatology of water and energy fluxes for each continental/oceanic region and the global scale.  The assessment serves as a baseline for studies of climate related water and energy cycle changes and as a benchmark for evaluating climate prediction models.  

http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings 

Groundwater and Soil Moisture Conditions from GRACE Data Assimilation

We have developed wetness/drought indicator maps for shallow groundwater and surface and root zone soil moisture.  The maps integrate data from multiple ground and space based observing systems, including NASA's Gravity Recovery and Climate Experiment (GRACE) satellite mission.  The GRACE satellite pair detects small changes in the Earth's gravity field caused by the redistribution of water on and beneath the land surface.  The data are integrated within a sophisticated numerical model of land surface hydrological and energetic processes.  Using a long-term meteorological dataset, we have generated a continuous record of soil moisture and groundwater that stretches back to 1948.  The maps are meant to depict wetness conditions and drought associated with climatic variability, as opposed to depletion of aquifers due to groundwater withdrawals that exceed recharge.  They are updated weekly, incorporated into the U.S. Drought Monitor, and distributed through the National Drought Mitigation Center's data portal.

http://drought.unl.edu/MonitoringTools/NASAGRACEDataAssimilation.aspx

Landslide Hazard Assessment

Landslide Monitoring and Forecast Website

Rainfall-triggered landslides affect nearly every state in the U.S. and every country in the world, causing significant economic damage and resulting in thousands of fatalities each year. Characterizing and modeling these hazards over large scales is challenging due to the fairly small areas over which they typically occur. A new website has been developed to provide a regional and global perspective on rainfall-triggered landslides. The website houses the Global Landslide Catalog (GLC), which was developed at NASA GSFC with the goal of identifying rainfall-triggered landslide events around the world, regardless of size, impacts or location. The GLC considers all types of mass movements triggered by rainfall, which have been reported in the media, disaster databases, scientific reports, or other sources. The GLC has been compiled since 2007 and the website provides this information in a searchable and exportable format. There is also a hazard event editor that is designed to enable the community to contribute to global flood and landslide event reports using an online portal and crowd sourcing environment. Anyone is invited to participate in this collaborative framework.

The website also provides visualizations of daily precipitation information from the TRMM and GPM missions. Lastly, there is a prototype Landslide Hazard Assessment model for Situational Awareness (LHASA) that provides near real-time landslide hazard nowcasts at a regional scale. Currently this system provides information over Central America and Hispaniola.

Website: http://ojo-streamer.herokuapp.com/

FLDAS: Famine Early Warning Systems Network

FLDAS is the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (http://ldas.gsfc.nasa.gov/FLDAS/). The FLDAS is a custom instance of the NASA Land Information System (LIS; https://lis.gsfc.nasa.gov/) that has been adapted to work with domains, data streams, and monitoring and forecast requirements associated with food security assessment in data-sparse, developing country settings. Adopting LIS allows FEWS NET to leverage existing land surface models and generate ensembles of soil moisture, ET, and other variables based on multiple meteorological inputs or land surface models. The goal of the FLDAS project is to achieve more effective use of limited available hydroclimatic observations and is designed to be adopted for routine use for FEWS NET decision support. The FLDAS includes a crop water balance model used operationally by FEWS NET (GeoWRSI: Verdin and Klaver, 2002; Senay and Verdin, 2003), Africa specific daily rainfall from NOAA Climate Prediction Center (RFE2; Xie and Arkin, 1997) and the CHIRPS, a quasi-global rainfall dataset designed for seasonal drought monitoring and trend analysis (Funk et al., 2014). Additional features include a temporal disaggregation scheme so that daily rainfall inputs can be used in both energy and water balance calculations, an irrigation module, and global irrigation and crop maps. State-of-the-practice land data assimilation methods are available in LIS, and will be explored in an associated forecasting project.

http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings

GLDAS: Global Land Data Assimilation System Data

The goal of the Global Land Data Assimilation System (GLDAS; http://ldas.gsfc.nasa.gov) is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes (Rodell et al., 2004a).  The software, which has been streamlined and parallelized by the Land Information System (LIS) sister project, drives multiple, offline (not coupled to the atmosphere) land surface models, integrates a huge quantity of observation based data, executes globally at high resolutions (2.5° to 1 km), and is capable of producing results in near-real time.  A vegetation-based tiling approach is used to simulate sub-grid scale variability, with a 1 km global vegetation dataset as its basis.  Soil and elevation parameters are based on high resolution global datasets.  Observation-based precipitation and downward radiation products and the best available analyses from atmospheric data assimilation systems are employed to force the models.  Intercomparison and validation of these products is being performed with the aim of identifying an optimal forcing scheme.  Data assimilation techniques for incorporating satellite based hydrological products, including snow cover and water equivalent, soil moisture, surface temperature, and leaf area index, are now being implemented as part of a follow-on project funded by the NASA Energy and Water Cycle Study (NEWS) Initiative.  The high-quality, global land surface fields provided by GLDAS support several current and proposed weather and climate prediction, water resources applications, and water cycle investigations.  The project has resulted in a massive archive of modeled and observed, global, surface meteorological data, parameter maps, and output which includes 1° and 0.25° resolution 1979-present simulations of the Noah, CLM, and Mosaic land surface models.

http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings

NLDAS: North American Land Data Assimilation System Data

The goal of NLDAS is to construct quality-controlled, and spatially and temporally consistent, land-surface model (LSM) datasets from the best available observations and model output to support modeling activities. Specifically, this system is intended to reduce the errors in the stores of soil moisture and energy which are often present in numerical weather prediction models, and which degrade the accuracy of forecasts. NLDAS is currently running in near real-time on a 1/8th-degree grid resolution over central North America; retrospective NLDAS datasets and simulations also extend back to January 1979. NLDAS constructs a forcing dataset from gauge-based observed precipitation data (temporally disaggregated using Stage II radar data), bias-correcting shortwave radiation, and surface meteorology reanalyses to drive several different LSMs to produce model outputs of surface fluxes, soil moisture, and snow cover. NLDAS is a collaboration project among several groups: NOAA/NCEP's Environmental Modeling Center (EMC), NASA's Goddard Space Flight Center (GSFC), Princeton University, the NWS Office of Hydrological Development (OHD), the University of Washington, and NCEP's Climate Prediction Center (CPC). NLDAS is a core project with support from NOAA's Climate Prediction Program for the Americas (CPPA). Data from the project can be accessed from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) as well as from the NCEP/EMC NLDAS website. Further information about NLDAS can be found at the previous locations as well as at the GSFC NLDAS website. 

http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings