An Integrated Hydrologic Modeling and Data Assimilation Framework

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.

[1]  Andrea N. Hahmann,et al.  A proposal for a general interface between land surface schemes and general circulation models , 1998 .

[2]  Alexander Khain,et al.  Microphysics, Radiation and Surface Processes in the Goddard Cumulus Ensemble (GCE) Model , 2003 .

[3]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[4]  T. Meyers,et al.  Sensitivity of Land Surface Simulations to Model Physics, Land Characteristics, and Forcings, at Four CEOP Sites , 2007 .

[5]  Wade T. Crow,et al.  Recent Advances in Land Data Assimilation at the NASA Global Modeling and Assimilation Office , 2009 .

[6]  Pedro Viterbo,et al.  Land-surface, boundary layer, and cloud-field coupling over the southwestern Amazon in ERA-40 , 2005 .

[7]  Thomas J. Jackson,et al.  Estimating soil water‐holding capacities by linking the Food and Agriculture Organization Soil map of the world with global pedon databases and continuous pedotransfer functions , 2000 .

[8]  David Mocko,et al.  Refinements to SSiB with an Emphasis on Snow Physics: Evaluation and Validation Using GSWP and Valdai Data , 2001 .

[9]  Minghua Zhang,et al.  Evaluating Clouds in Long-Term Cloud-Resolving Model Simulations with Observational Data , 2007 .

[10]  Wade T. Crow,et al.  A land surface data assimilation framework using the land information system : Description and applications , 2008 .

[11]  Jordan G. Powers,et al.  A Description of the Advanced Research WRF Version 2 , 2005 .

[12]  S. Running,et al.  Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data , 2002 .

[13]  Sujay V. Kumar,et al.  An integrated high-resolution hydrometeorological modeling testbed using LIS and WRF , 2008, Environ. Model. Softw..

[14]  R. Pielke.  Influence of the spatial distribution of vegetation and soils on the prediction of cumulus Convective rainfall , 2001 .

[15]  G. Gayno,et al.  Implementation of Noah land-surface model advances in the NCEP operational mesoscale Eta model , 2003 .

[16]  Roger A. Pielke,et al.  The impact of plant stomatal control on mesoscale atmospheric circulations , 1991 .

[17]  Ann Henderson-Sellers,et al.  Predicting land‐surface climates‐better skill or moving targets? , 2003 .

[18]  Jeffrey P. Walker,et al.  New technologies require advances in hydrologic data assimilation , 2003 .

[19]  James R. Anderson,et al.  A land use and land cover classification system for use with remote sensor data , 1976 .

[20]  Cecelia DeLuca,et al.  The architecture of the Earth System Modeling Framework , 2003, Computing in Science & Engineering.

[21]  E. Gallopoulos,et al.  Computer as thinker/doer: problem-solving environments for computational science , 1994, IEEE Computational Science and Engineering.

[22]  Dean B. Gesch,et al.  New land surface digital elevation model covers the Earth , 1999 .

[23]  J. D. Tarpley,et al.  The multi‐institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system , 2004 .

[24]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[25]  J. D. Tarpley,et al.  Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model , 2003 .

[26]  Fei Chen,et al.  The impact of land-surface wetness heterogeneity on mesoscale heat fluxes , 1994 .

[27]  Sujay V. Kumar,et al.  Land information system: An interoperable framework for high resolution land surface modeling , 2006, Environ. Model. Softw..

[28]  C. Diks,et al.  Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation , 2005 .

[29]  Douglas A. Miller,et al.  A Conterminous United States Multilayer Soil Characteristics Dataset for Regional Climate and Hydrology Modeling , 1998 .

[30]  Praveen Kumar,et al.  A catchment‐based approach to modeling land surface processes in a general circulation model: 1. Model structure , 2000 .

[31]  H. Pan,et al.  Interaction between soil hydrology and boundary-layer development , 1987 .

[32]  Sujay V. Kumar,et al.  High-performance Earth system modeling with NASA/GSFC’s Land Information System , 2007, Innovations in Systems and Software Engineering.

[33]  R. Dickinson,et al.  The Common Land Model , 2003 .

[34]  R. Koster,et al.  Modeling the land surface boundary in climate models as a composite of independent vegetation stands , 1992 .

[35]  Roger A. Pielke,et al.  A parameterization of heterogeneous land surfaces for atmospheric numerical models and its impact on regional meteorology , 1989 .

[36]  Yudong Tian,et al.  Integrating Sensor Webs with Modeling and Data-assimilation Applications: An SOA Implementation , 2008, 2008 IEEE Aerospace Conference.

[37]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[38]  Sujay V. Kumar,et al.  High-performance land surface modeling with a Linux cluster , 2008, Comput. Geosci..

[39]  David C. Goodrich,et al.  Spatial interpolation of precipitation in a dense gauge network for monsoon storm events in the southwestern United States , 2008 .