The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system (original) (raw)
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An Integrated Hydrologic Modeling and Data Assimilation Framework
Computer, 2000
R ecent advances in remote sensing technologies have enabled the monitoring and measurement of Earth's land surface at an unprecedented scale and frequency. Such observations provide a huge volume of valuable data about Earth's land surface conditions, such as vegetation, water, and energy fluxes. These observations must be integrated with state-of-the-art landsurface model (LSM) forecasts using data assimilation tools to generate spatially and temporally continuous estimates of environmental conditions. These analyses will subsequently provide decision makers with the resources to address socially relevant issues such as water resources, agricultural management, hazard mitigation, and mobility assessment. Thus, integrating observation and modeling systems is critical for a variety of hydrologically relevant environmental research and operational applications.
Journal of Geophysical Research, 2004
This paper compares and evaluates streamflow and water balance results from four different Land Surface Models (LSMs) which participated in the multi-institutional North American Land Data Assimilation System (NLDAS). These LSMs have been run for the retrospective period 10/01/1996 to 09/30/1999 forced by atmospheric observations from the Eta Data Assimilation System (EDAS) analysis and ETA model output, measured precipitation and downward solar radiation. We have evaluated these simulations using measured daily streamflow data within 9 large major basins within the US and with 1145 smaller basins from 23 km 2 to 10,000 km 2 distributed over the NLDAS domain from the U.S. Geological Survey (USGS). Model runoff was routed with a distributed and a lumped optimized linear routing model. The diagnosis of the model results shows that the LSMs have a wide spread in their partitioning of precipitation into evapotranspiration and runoff. The modeled mean annual runoff shows large regional differences by a factor of up to four between models. The corresponding difference in mean annual evapotranspiration is about a factor of two. Runoff timing for the LSMs is influenced by snow melt timing with differences in the streamflow peaks of up to four months. While the modeled mean annual runoff shows large regional differences among the models and between the models and observations, it is under-estimated in areas with significant snowfall by all models. The monthly water budget shows that in the summer the model differences in runoff are as large as are soil water storage changes.
Journal of Geophysical Research, 2012
1] Results are presented from the second phase of the multiinstitution North American Land Data Assimilation System (NLDAS-2) research partnership. In NLDAS, the Noah, Variable Infiltration Capacity, Sacramento Soil Moisture Accounting, and Mosaic land surface models (LSMs) are executed over the conterminous U.S. (CONUS) in realtime and retrospective modes. These runs support the drought analysis, monitoring and forecasting activities of the National Integrated Drought Information System, as well as efforts to monitor large-scale floods. NLDAS-2 builds upon the framework of the first phase of NLDAS (NLDAS-1) by increasing the accuracy and consistency of the surface forcing data, upgrading the land surface model code and parameters, and extending the study from a 3-year (1997-1999) to a 30-year (1979-2008) time window. As the first of two parts, this paper details the configuration of NLDAS-2, describes the upgrades to the forcing, parameters, and code of the four LSMs, and explores overall model-to-model comparisons of land surface water and energy flux and state variables over the CONUS. Focusing on model output rather than on observations, this study seeks to highlight the similarities and differences between models, and to assess changes in output from that seen in NLDAS-1. The second part of the two-part article focuses on the validation of model-simulated streamflow and evaporation against observations. The results depict a higher level of agreement among the four models over much of the CONUS than was found in the first phase of NLDAS. This is due, in part, to recent improvements in the parameters, code, and forcing of the NLDAS-2 LSMs that were initiated following NLDAS-1. However, large inter-model differences still exist in the northeast, Lake Superior, and western mountainous regions of the CONUS, which are associated with cold season processes. In addition, variations in the representation of sub-surface hydrology in the four LSMs lead to large differences in modeled evaporation and subsurface runoff. These issues are important targets for future research by the land surface modeling community. Finally, improvement from NLDAS-1 to NLDAS-2 is summarized by comparing the streamflow measured from U.S. Geological Survey stream gauges with that simulated by four NLDAS models over 961 small basins.
The Global Land Data Assimilation System
Bulletin of the American Meteorological Society, 2004
The Global Land Data Assimilation System (GLDAS) is generating a series of land surface state (e.g., soil moisture and surface temperature) and flux (e.g., evaporation and sensible heat flux) products simulated by four land surface models (CLM, Mosaic, Noah and VIC). These products are now accessible at the Hydrology Data and Information Services Center (HDISC), a component of the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Current data holdings include a set of 1.0 degree resolution data products from the four models, covering 1979 to the present; and a 0.25 degree data product from the Noah model, covering 2000 to the present. The products are in Gridded Binary (GRIB) format and can be accessed through a number of interfaces. Users can search the products through keywords and perform on-the-fly spatial and parameter subsetting and format conversion of selected data. More advanced visualization, access and analysis capabilities will be available in the future. The long term GLDAS data are used to develop climatology of water cycle components and to explore the teleconnections of droughts and pluvial.
Catchment-scale hydrological modeling and data assimilation
Advances in Water Resources, 2003
The study of the response of a river basin to atmospheric forcing is of critical importance to applied hydrologists and water resource managers and remains a major research challenge. Continued progress in our scientific understanding of hydrological processes at the catchment-scale relies on making the best possible use of advanced simulation models and the large amounts of environmental data that are increasingly being made available. Processes at the interface between the land surface and the atmosphere, for instance, determine the partitioning of rainfall into infiltration and runoff and the redistribution of water between the surface, soil, underlying aquifers, and streams. Understanding and predicting these exchanges is important to agriculture (irrigation planning and vegetation and crop growth), climate studies (weather prediction and global change), natural hazards prevention and mitigation (floods, droughts, erosion, landslides), and water quality management (point and nonpoint source pollutants in catchment and stream waters).
Preface Catchment-scale hydrological modeling and data assimilation
The study of the response of a river basin to atmospheric forcing is of critical importance to applied hydrologists and water resource managers and remains a major research challenge. Continued progress in our scientific understanding of hydrological processes at the catchment-scale relies on making the best possible use of advanced simulation models and the large amounts of environmental data that are increasingly being made available. Processes at the interface between the land surface and the atmosphere, for instance, determine the partitioning of rainfall into infiltration and runoff and the redistribution of water between the surface, soil, underlying aquifers, and streams. Understanding and predicting these exchanges is important to agriculture (irrigation planning and vegetation and crop growth), climate studies (weather prediction and global change), natural hazards prevention and mitigation (floods, droughts, erosion, landslides), and water quality management (point and nonpoint source pollutants in catchment and stream waters).
Water Resources Research, 2010
1] Advanced land surface models (LSMs) offer detailed estimates of distributed hydrological fluxes and storages. These estimates are extremely valuable for studies of climate and water resources, but they are difficult to verify as field measurements of soil moisture, evapotranspiration, and surface and subsurface runoff are sparse in most regions. In contrast, river discharge is a hydrologic flux that is recorded regularly and with good accuracy for many of the world's major rivers. These measurements of discharge spatially integrate all upstream hydrological processes. As such, they can be used to evaluate distributed LSMs, but only if the simulated runoff is properly routed through the river basins. In this study, a rapid, computationally efficient source-to-sink (STS) routing scheme is presented that generates estimates of river discharge at gauge locations based on gridded runoff output. We applied the scheme as a postprocessor to archived output of the Global Land Data Assimilation System (GLDAS). GLDAS integrates satellite and ground-based data within multiple offline LSMs to produce fields of land surface states and fluxes. The application of the STS routing scheme allows for evaluation of GLDAS products in regions that lack distributed in situ hydrological measurements. We found that the four LSMs included in GLDAS yield very different estimates of river discharge and that there are distinct geographic patterns in the accuracy of each model as evaluated against gauged discharge. The choice of atmospheric forcing data set also had a significant influence on the accuracy of simulated discharge.