An Integrated Hydrologic Modeling and Data Assimilation Framework

Recent advances in remote sensing tech-nologies have enabled the monitoring and measurement of Earth’s land surface at an unprecedented scale and frequency. Such observations provide a huge volume of valu-

able data about Earth’s land surface conditions, such as vegetation, water, and energy fluxes. These obser-vations must be integrated with state-of-the-art land-surface model (LSM) forecasts using data assimilation tools to generate spatially and temporally continuous estimates of environmental conditions. These analy-ses 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 vari-ety of hydrologically relevant environmental research and operational applications.

The need to develop modeling systems that identify and represent connections between land-surface hydrol-ogy and different components of the Earth system1 pro-vides another key requirement for advancing hydrologi-cal prediction capabilities identified by the land-surface hydrology community. For example, several studies have examined the dependence and sensitivity of atmospheric processes to the land-surface boundary.2-6 These studies

demonstrate the impact of the land surface’s moisture state in modulating the formation of cloud fields and the initialization of convective precipitation events. There-fore, coupled systems that can represent the connections between relevant Earth system components are essential to improving hydrologic prediction capabilities.

Land InformatIon SyStemThe need for an integrated system that enables the

combined use of high-resolution observations and mod-eling tools led to the development of the NASA Land Information System.7,8 LIS itself evolved from two earlier efforts: the North American Land Data Assimilation Sys-tem (NLDAS)9 and the Global Land Data Assimilation System (GLDAS),10 which focused primarily on improv-ing numerical weather prediction skills by improving the characterization of the land surface conditions. How-ever, these systems were employed at the fairly coarse spatial scales of 1/8 and 1/4 degree, respectively. LIS not only integrates the capabilities of these two systems, but also enables modeling and data assimilation at higher spatial and temporal resolutions.

LIS features a high-performance and flexible design, provides an infrastructure for data integration and assimilation, and operates using an ensemble of LSMs for extension over user-specified regional and global

the Land Information System (LIS) is a multiscale hydrologic modeling and data assimilation

framework that integrates the use of satellite and ground-based observational data products

with advanced land-surface modeling tools to aid several application areas, including water

resources management, numerical weather prediction, agricultural management, air quality,

and military mobility assessment.

Sujay Kumar, Christa Peters-Lidard, Yudong Tian, Rolf Reichle, James Geiger, and Charles Alonge, NASA Goddard Space Flight Center

John Eylander, HQ Air Force Weather Agency

Paul Houser, George Mason University and Center for Research in Environment and Water

C o v e r f e a t u r e

52 Computer Published by the IEEE Computer Society 0018-9162/08/$25.00©2008IEEE

December 2008 53

domains. The LIS framework is designed using advanced software engineering principles to facilitate reuse and community sharing of modeling tools, data resources, and assimilation algorithms. It provides generic, model-independent support for high-performance computing, several resource and data management tools, and other functions. The system is designed as an object-oriented framework, with abstractions defined for customization and extension for different applications. These exten-sible interfaces allow the incorporation of new domains, LSMs, land-surface parameters, meteorological inputs, and data assimilation and optimization algorithms. The extensible nature of these interfaces and the system’s component-style specification allow rapid prototyping and development of new applications.

Uniquely suited to exploit advanced environmental moni-toring and Earth system modeling improvements to address computationally challenging hydrological problems, LIS is designed to encapsulate the land- surface component of an Earth sys-tem model. As a result, LIS can be used as a testbed to conduct both uncoupled and coupled land-atmo-sphere studies.

Researchers have developed sev-eral coupled land-atmosphere pro-totypes11 combining LIS with the Weather Research and Forecasting (WRF)12 and the Goddard Cumulus Ensemble (GCE)13 models. The high-resolution modeling capabilities in LIS enable direct use of a vast array of observations, such as those from the next-generation NASA Earth science instruments, and demonstrate their impact at the same high-resolution observational scale. The ability to oper-ate at the atmospheric-boundary layer and cloud-model scales enables representation of important physical and dynamic processes not possible with lower-resolution modeling systems.

A recent enhancement in LIS is the addition of sequen-tial data assimilation capabilities that, for the first time, enable an interoperable system that can employ several sources of hydrologic observations to constrain model predictions and subsequently improve them. These tech-nologies are critical for demonstrating NASA’s unique capabilities in scientific modeling and computational technologies for advancing Earth system studies.

Key featureS In LISLSMs represent soil-water dynamics, plant physiol-

ogy, and small-scale meteorology and hydrology while characterizing the transfer of mass, energy, and momen-tum fluxes across the land-atmosphere interface. By syn-thesizing observational data and land-surface models, land data assimilation systems improve the ability to predict and understand these processes. LIS is a land data assimilation system that integrates the use of sev-

eral community LSMs with observational data sources at various spatial and temporal resolutions. The LSMs in LIS are typically employed in an uncoupled manner (not coupled to an atmospheric model), using observationally based precipitation, radiation, and other meteorological inputs. Key aspects of LIS include the following:

software architecture, which enables development of the large suite of hydrologic modeling tools in LIS;high-performance computing support, which enables application of the system at high spatial and temporal resolutions;land-surface models, which comprise the system’s core; anddata assimilation features, which create a conduit for utilizing observational data to improve model predictions.

LIS software designLIS’s software engineering design7

and its use of high-performance computing and communications technologies14 enable high-resolu-tion ensemble land-surface model-ing. The design of LIS is motivated by the paradigm of problem-solv-ing environments (PSEs),15 where

an integrated system provides the expert tools for com-plex problem-solving domains. LIS is designed to be a PSE for hydrologic-modeling applications by providing model-independent support for generic functionalities along with the required software support for high-per-formance computing to enable a modular and extensible rapid prototyping environment. As described in the liter-ature,7 LIS is designed as an object-oriented framework with the following four primary characteristics:

Modularity. This encompasses generic modeling functionalities—such as those for I/O management, geospatial transforms, time management, and other modeling features—specified as self-consistent components. This eliminates the need for propagating changes as the system evolves and becomes more complex.Reusability. The component-style specification in LIS enables the use of existing features for develop-ing new applications.Extensibility. LIS explicitly defines abstract inter-faces to allow incorporation of new features.Inversion of control. LIS is designed as a skeleton application, with key design features defined at an abstract level.

LIS provides several points of extensibility, called plug-ins, that allow incorporation of new land-surface

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models, meteorological inputs, parameters, grids and domains, data assimilation, and optimization algo-rithms. These features also make LIS the ideal testbed for ensemble studies of land-atmosphere interactions—including the development of techniques for representing land-surface heterogeneities—in the spirit of the Global Energy and Water Cycle Experiment (GEWEX), Project for Intercomparison of Land Surface Parameterization Schemes (PILPS),16 and the GEWEX Global Soil Wet-ness Project.17

In addition to providing an infrastructure that sup-ports land-surface research and applications activities, LIS also adopts other Earth system modeling standards, along with conventions that facilitate using it as the land-surface component of a coupled Earth system model. These standards include the Earth System Modeling Framework (ESMF)18 and Assistance for Land Modeling Activities (ALMA).19 ESMF provides a flexible software infrastructure to foster interoperability, portability, and code reuse in climate, numerical weather prediction, data assimilation, and other Earth science applications. ALMA, originally developed by the GEWEX Land Atmosphere System Study, supports model intercom-parison efforts and has been adopted by the European Programme for Integrated Earth System Modeling.

High-performance computing supportModeling the global land surface as two-dimensional

regions or cells at 1-km horizontal resolution leads to approximately 150 million land points that produce approximately 600 Gbytes of output, several Tbytes of memory, and many months of computational time per single simulation day—almost three orders of magni-

tude higher than the computational requirements of the quarter-degree Global Land Data Assimilation System. LIS, by taking advantage of high-performance comput-ing tools, demonstrated a viable global model simulation at 1-km resolution in near real time. The large I/O and data requirements of the global 1-km configuration were addressed by a high-availability parallel I/O based on the Graphical Analysis and Display System (GrADS) Data System (GDS) servers with dynamic load balancing, dis-tributed data storage, and highly scalable data replica-tion among peer-to-peer technologies. Figure 1 shows a comparison of the normalized timing and simulation throughput for LIS with a global land-surface simulation at 1-km resolution.

A reasonable throughput of six simulated days per cal-endar day was achieved in this environment, meeting the demands of a real-time system. This particular mode of parallel I/O support was primarily developed to facilitate instances where the I/O and data demands dominate the computational requirements. LIS also supports a more traditional message-passing interface (MPI)–based par-allelization scheme to conduct model simulations with less rigorous data and I/O requirements.

Land-surface modelingLand-surface models provide the LIS framework’s core.

They generate estimates of energy and water states such as snowpack, soil moisture, and temperature and fluxes at the land surface, such as evaporation and runoff. The LSMs are executed on landcover-based tiles employed to simulate the subgrid scale variability according to the mosaic approach20,21 when the grid resolution proves coarser than the supporting land cover data resolution.

The current LIS release, v5.0, includes several commu-nity LSMs: the Noah LSM,22 Community Land Model

(CLM),23 Mosaic,24 Hyssib,25 and Catchment.26

These LSMs require parameters typically based on topography, vegetation, and soils. LIS permits use of several sources of these data. For example, global ter-rain maps of elevation, slope, aspect, and curvature used in LIS are derived from the GTOPO30 global 30-arc-second dataset.27 Static global vegetation datasets at 1-km resolution from the University of Maryland28 and USGS29 are used to specify each LSM’s intrinsic veg-etation-dependent parameters. LIS also supports using different data sources for Leaf Area Index (LAI) and Stem Area Index (SAI) from Advanced Very High Reso-lution Radiometer30 and Moderate Resolution Imaging Spectroradiometer31 instruments. Soil hydraulic proper-ties are specified from either pedotransfer functions or mapped from vertically uniform sand, silt, and clay per-centage datasets from the Food and Agriculture Organi-zation32 or the US’s State Soil Geographic database.33

In uncoupled operation mode, the meteorological boundary conditions for the LSMs are provided from a combination of global or regional model analyses that

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Figure 1. Comparison of the computational performance from a land-surface simulation—using the Community Land Model (CLM) in LIS—for global executions at 1-km spatial resolutions in a multiprocessing environment. Using 192 processors, developers achieved a throughput of approximately six simulated days per calendar day on a custom-built distributed-memory Linux cluster with 192 nodes of AMD XP processors at 1.53 GHz with fast Ethernet connections.

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also include satellite, radar, and gauge-based observa-tions of precipitation, as well as satellite-based observa-tions of radiation. Since the highest resolution of this boundary condition data does not currently exceed 4 km, its use for high-resolution integration requires down-scaling to the desired spatial resolution. LIS provides a combination of topographic, climatalogical, statistical, and dynamic approaches to facilitate downscaled atmo-spheric analyses.34

Hydrologic data assimilationA key recent capability enabled in LIS is the support

for sequential data assimilation. A suite of data assimi-lation tools originally developed at the NASA Global Modeling and Assimilation Office35 has been integrated into LIS,36 providing an infrastructure to ingest various sources of remotely sensed hydrologic observations to produce improved spatially and temporally consistent fields of land-surface states, with reduced associated error. The interoperable nature of LIS’s data assimilation infrastructure allows incorporation of multiple observa-tional sources, land-surface models, and data assimila-tion algorithms.

This design facilitates several cross-comparison studies using many kinds of assimilation algorithms, observation datasets, and land-surface models previ-ously thought infeasible. Figure 2 shows an example in which two different land-surface models are used to conduct surface soil moisture assimilation using an ensemble Kalman filter (EnKF) technique and compar-ing the corresponding improvements in the root zone estimates.

While both models produce improvements in the root zone from surface soil moisture assimilation, the latency in propagating surface information to a corresponding root zone is smaller in the Catchment LSM because of

its stronger soil water dynamics coupling. The LIS data assimilation framework provides a unique environment for investigating the influence of such model dependen-cies on assimilated products. Given that techniques such as the EnKF rely on maintaining an ensemble of realizations to simulate error characteristics, they also significantly increase the computational requirements. Researchers can use the high-performance computa-tional features in LIS to solve problems that involve such increased computational granularity.6

LIS’S Impact on reSearcH and appLIcatIonS LIS applications can be categorized into four major

areas: satellite mission support, technology develop-ment, scientific research, and end-use applications.

Science teams working on several current and future satellite missions use LIS. These projects include the currently orbiting gravity recovery and climate experi-ment (GRACE), the tropical rainfall measurement mis-sion (TRMM), and the EOS-Aqua advanced microwave scanning radiometer.

Researchers have employed LIS in observation sys-tem simulation experiments to determine the require-ments of future soil moisture and snow missions. The proposed NASA soil moisture active/passive mission plans to use LIS to generate Level 4 data assimilation products by combining the microwave data or Level 3 retrievals with ancillary data. Level 4 product genera-tion typically focuses not only on generating merged measurements and model estimates, but also on improv-ing fields connected to the observations through mod-eled processes. For example, the soil moisture Level 4 product generation system will produce improved surface soil moisture measurements as well as root zone soil moisture and evapotranspiration. LIS provides the necessary infrastructure for generating Level 4

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Figure 2. Domain-averaged root-mean-square errors in root zone soil moisture from the assimilation (EnKF) and open-loop simulations for the (a) Catchment LSM and the (b) Noah LSM assimilation experiments. Units show volumetric soil moisture (m3/m3).

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hydrologic products, providing a suite of land-surface model physics, assimilation techniques, and computa-tional support.

Development of coupled land-atmospheric modeling systems using LIS has enabled new Earth system model-ing technologies. Two such systems have been demon-strated using the Goddard Cumulus Ensemble and the Weather Research and Forecasting models. These tech-nologies have uniquely enhanced our ability to further understand and simulate interactions between clouds, radiation, and the land surface.

Another exciting development is the sensor web framework, designed to enable an interactive moni-toring system.37 LIS will enable on-the-fly, optimal sensor reconfiguration based on the demands of the science and solutions. In addition to providing criti-cal information for sensor web design considerations, this prototype will enable the seamless integration of sensor-configuration and modeling systems.

The unique capabilities that LIS offers have led to its use in several research studies related to global water and energy-cycle modeling. The contributions of LIS integrations supported global intercomparison studies that used several land-surface models in the Global Soil Wetness Project, Phase 2. Other researchers38 used the LIS framework to examine the sensitivity of different parameters with the LSMs in LIS and characterized the dominant relationships of processes such as evapotrans-piration and soil-moisture evolution to the associated parameterizations.

In a separate study, researchers explored the impact of different land-surface physics options on a precipitation forecast compared against a corresponding impact from using different microphysics options in the LIS-WRF-coupled system. Figure 3 shows the impact ensemble

spread in the precipitation forecast from the microphys-ics ensemble and the land-surface ensemble.

Although the domain-averaged spread for the microphys-ics ensemble is higher than that for the land-surface ensem-ble, several locations in the domain show an increased land-surface ensemble spread. This result demonstrates an example of the impact derived from using different land-surface physics in a corresponding precipitation forecast. Researchers39 investigated the impact of land-surface initial conditions on the evolution of sea breeze circulation over portions of northwestern Florida by initializing the WRF system with land-surface initial conditions generated from an LIS integration. They found significant improvements in the timing and evolution of the sea-breeze forecasts with LIS-generated initial land-surface conditions. Other researchers reported similar results40 using LIS-generated initial conditions to improve the predictions of diurnal variations in the cloud fields in the lower troposphere.

Several application projects are also using LIS in the areas of water resource management, weather prediction, air quality control, and homeland security. LIS’s ability to operate at different spatial and temporal scales makes it an ideal platform for applications related to managing high-impact events such as floods and droughts. Our interagency users are applying LIS as a decision-support tool in the following ways:

to improve global estimates of soil moisture, soil temperature, and precipitation data, and to improve the fine-scale-modeling capabilities of the US Air Force Weather Agency and the NOAA Operational Hydrologic Remote Sensing Center;to provide better guidance on water resource man-agement and planning (with the US Bureau of Rec-lamation, Mississippi Research Consortium);

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Figure 3. Comparing the influence of different microphysics options (right) and different land-surface physics options (left) on a precipitation forecast from a 36-hour forecast for 13 June 2002. In general, the influence of microphysics is higher in most parts of the domain. The land surface also has a significant influence in certain locations of the domain such as the southeastern regions.

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to supply energy and soil moisture inputs to improve weather and climate forecasting (with NOAA’s National Centers for Environmental Prediction and Air Force Weather Agency); andto refine agricultural management, flood and drought prediction, military mobility, and traffic-ability (with the US Army Corps of Engineers and US Air Force Weather Agency).

LIS is currently being deployed as the operational land-surface modeling and data assimilation system at the US Air Force Weather Agency. Finally, NASA’s Energy and Water Cycle Study (NEWS) program employs LIS extensively to understand the nature of land-atmosphere coupling and help better predict weather and climate.

A s a comprehensive land-surface modeling and data assimilation system developed to aid in predict-ing the global terrestrial water and energy cycle,

LIS provides a numerical environment that integrates new hydrologic modeling, observations, and monitoring capabilities. The high-performance computing support in LIS enables the application of its land-surface models and interacting components at diurnal to centennial tim-escales and over fine regional- to global-spatial scales.

By enabling data assimilation and supporting coupled land-atmosphere systems, LIS provides a system suitable for addressing the challenges the hydrologic community has identified.1,41 In addition to serving as a problem-solving environment, LIS can also be used as a decision-support tool to aid in application areas such as managing natural resources. Researchers can use LIS to perform what-if studies to investigate the impact of changes in environmental conditions on water and energy cycling. Thus, the LIS framework offers a suite of modeling and analysis tools that supports a broad range of hydrologic research, technology development, and applications.

Several enhancements to the system are currently in process. A growing suite of land-surface models continues to be added, providing an unprecedented suite of model physics. Researchers are using the interoperable features in LIS to develop several coupled modeling systems.

Another key modeling capability being planned is the generic support for optimization algorithms, which can be used for calibrating model parameters and generat-ing optimal performance of data assimilation settings, among others. This capability would also evolve LIS into a simultaneous optimization and data assimilation sys-tem,42 enabling better characterization of uncertainty in model states, parameters, and observations. The data assimilation structure currently facilitates the addition of several sequential assimilation algorithms.

Functional extensions to support variational and smoothing approaches are also being planned, while exploring several linkages to other relevant modeling

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groups. For example, the Joint Center for Satellite Data Assimilation’s Community Radiative Transfer Model (CRTM) is being coupled with LIS, providing a multi-band land-surface emission modeling component to enable future assimilation of radiance-based estimates and producing improved retrievals of precipitation, radiation, and other meteorological analyses over land regions.

The suite of land-surface models in LIS, currently being enhanced by incorporating terrestrial carbon dynam-ics models, will enable the characterization of carbon exchanges in addition to global water and energy fluxes. Further, hydrologic models that include representations of lateral subsurface flow and routing processes will also be incorporated. These enhancements will enable LIS to incorporate an increased suite of hydrologic observa-tions, improve its ability to faithfully depict global water and energy fluxes, determine the extent to which their spatiotemporal variations are consistent with each other, and complement water-storage variations. ■

acknowledgmentsWe acknowledge the support from NASA’s Earth Sci-

ence Technology Office, Computational Technologies project, which originally funded LIS development. We also acknowledge the additional support of the NASA ESTO Advanced Information Systems Technologies pro-gram, the US Army Corps of Engineers and US Depart-ment of Agriculture, and more recent support from our partners at the US Air Force Weather Agency, Joint Cen-ter for Satellite Data Assimilation, the NASA Energy and Water Cycle Program, the NASA Earth Observing Sys-tem program, and an internal investment from NASA Goddard Space Flight Center. In addition, the LIS team appreciates the support and assistance of the GLDAS and NLDAS teams.

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42. J.A. Vrugt et al., “Improved Treatment of Uncertainty in Hydrologic Modeling: Combining the Strengths of Global Optimization and Data Assimilation,” Water Resources Res., vol. 41, 2005, W01017; doi:10.1029/2004WR003059, 2005.

Sujay Kumar is a senior scientific engineer at the Science Applications International Corp. His research interests include land-surface modeling and data assimilation, study of land-atmosphere interactions, and high-perfor-mance computing and heuristic optimization. Kumar received a PhD in computer-aided engineering from North Carolina State University. Contact him at sujay.v.kumar@nasa.gov.

Christa Peters-Lidard is physical scientist and head of the Hydrological Sciences Branch at the NASA Goddard Space Flight Center. Her research interests include land-atmosphere interactions, soil moisture measurement and modeling, and the application of high-performance com-puting and communications technologies in Earth system modeling. Peters-Lidard received a PhD in civil engineer-ing and operations research from Princeton University. Contact her at christa.peters@nasa.gov.

Yudong Tian is an assistant research scientist at the God-dard Earth Sciences Technology Center at the University of Maryland, Baltimore County. His research interests include climate dynamics, land-surface modeling, remote sensing of precipitation, and sensor webs. Tian received a PhD in atmospheric sciences from UCLA. Contact him at yudong.tian@nasa.gov.

Rolf Reichle is a research physical scientist at the NASA Global Modeling and Assimilation Office. His research

interests include land data assimilation, satellite remote sensing, and applications to land-atmosphere interactions and short-term climate forecasting. Reichle received a PhD in environmental engineering from the Massachu-setts Institute of Technology. Contact him at rolf.reichle@nasa.gov.

James Geiger is a computer scientist at the NASA God-dard Space Flight Center. His research interests include land-surface modeling and high-performance comput-ing applications. Geiger received a BS in mathematics from Old Dominion University. Contact him at james.v.geiger@nasa.gov.

Charles Alonge is a visiting scientist at the NASA God-dard Space Flight Center. His research interests include land-surface studies and mesoscale meteorology. Alonge received an MS in atmospheric sciences from the Univer-sity of Albany.

John Eylander is the chief of the Specialized Modeling Team at the Air Force Weather Agency. His research inter-ests include hydrometeorology, mesoscale meteorology, numerical weather prediction, data assimilation, cloud physics, and remote sensing. Eylander received an MS in atmospheric science from the South Dakota School of Mines and Technology. Contact him at john.eylander@us.af.mil.

Paul Houser is an associate professor at the George Mason University. His research interests include land-surface remote sensing, hydrologic modeling and data assimila-tion, and global water cycle studies. Houser received a PhD in hydrology and water resources from the University of Arizona. Contact him at prhouser@gmail.com.

Rapid Accreditation Growth in Computing Spawns a Need for Additional Program EvaluatorsCSAB—representingtheACM,theIEEEComputerSociety,andtheAIS—andtheABETleadsocietyforcomputerscience,infor-mationsystems,andinformationtechnologyandacooperatingsocietyforsoftwareengineering,isacceptingapplicationsfromvolunteersfromtheacademic,private,andpublicsectorstoparticipateintheaccreditationprocessforprogramsatthebaccalaureatelevelintheacademicdisciplinesoverwhichithasleadsocietyresponsibilities.PleaseaccesstheCSABwebsiteatwww.csab.orgortheABETwebsiteatwww.abet.orgformoreinformationandtocompleteanapplication,orcallorwritetheExecutiveDirectorofCSAB,PatrickM.LaMalva:2039751117;lamalva@csab.org.