Enhancement of land surface information and its impact on atmospheric modeling in the Heihe River Basin, Northwest China

[1] With increasing computational resources, atmospheric/environmental models continue to run at finer-grid spacing that can resolve land surface characteristics, such as topography, land use/land cover, and soil texture. This paper assesses the improvement in land surface information data sets and its impact on atmospheric modeling. The study focuses on the Heihe River Basin (HRB) in northwestern China. Fine-scale, remotely sensed, and in situ land surface data in HRB are derived and compared with the global data sets used in most mesoscale models. The incorporation of these fine-scale land surface data, compared to those currently used in MM5, yields substantially improved HRB land surface data sets. HRB local and regional data sets and the global land data set are used in a nonhydrostatic mesoscale model (MM5) to investigate the influences of land surface uncertainty on meteorological modeling in the lower atmosphere. Main results suggest the following: (1) enhanced land data sets have a stronger impact on atmospheric water vapor fields in the lower boundary layer than other meteorological fields. Soil texture data greatly impacts the local precipitation simulation and landuse data improves the air temperature simulation in the lower atmosphere; (2) generally, the average land surface temperature biases are reduced using the enhanced land surface information, but the low bias in zones with higher elevation and high bias in zones with lower elevation still persist; (3) the wet bias over rugged terrain and dry bias in the simulated water vapor in the flat plains are both reduced. Area mean bias of simulated accumulated monthly precipitation is greatly reduced using the enhanced soil data. Convective available potential energy was larger in the HRB mountain regions using the default land data, while it was decreased using the enhanced ones; (4) analysis of the correlation coefficient between simulation bias and the geographic features shows that there are some patterns in the simulation bias distribution. Generally, larger bias still exists in the foothills of the mountains.

[1]  J. Deardorff.  Efficient prediction of ground surface temperature and moisture, with inclusion of a layer of vegetation , 1978 .

[2]  E. Matthews.  Global Vegetation and Land Use: New High-Resolution Data Bases for Climate Studies , 1983 .

[3]  Richard A. Anthes,et al.  Enhancement of Convective Precipitation by Mesoscale Variations in Vegetative Covering in Semiarid Regions , 1984 .

[4]  M. Ek,et al.  The Influence of Atmospheric Stability on Potential Evaporation , 1984 .

[5]  G. Hornberger,et al.  A Statistical Exploration of the Relationships of Soil Moisture Characteristics to the Physical Properties of Soils , 1984 .

[6]  H. Pan,et al.  A two-layer model of soil hydrology , 1984 .

[7]  E. Matthews,et al.  Atlas of Archived Vegetation, Land-use and Seasonal Albedo Data Sets , 1985 .

[8]  A. Dalcher,et al.  A Simple Biosphere Model (SIB) for Use within General Circulation Models , 1986 .

[9]  Ann Henderson-Sellers,et al.  Sensitivity of the biosphere-atmosphere transfer scheme (BATS) to the inclusion of variable soil characteristics , 1987 .

[10]  P. Wetzel,et al.  Concerning the Relationship between Evapotranspiration and Soil Moisture , 1987 .

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

[12]  R. Pielke,et al.  Evaluation of vegetation effects on the generation and modification of mesoscale circulations , 1988 .

[13]  S. Planton,et al.  A Simple Parameterization of Land Surface Processes for Meteorological Models , 1989 .

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

[15]  J. Dudhia.  Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model , 1989 .

[16]  Michel M. Verstraete,et al.  The representation of continental surface processes in atmospheric models , 1990 .

[17]  J. Noilhan,et al.  Sensitivity study and validation of a land surface parameterization using the HAPEX-MOBILHY data set , 1990 .

[18]  J. Dudhia,et al.  Nonhydrostatic, mesobeta-scale, real-data simulations with the Penn State University/National Center for Atmospheric Research mesoscale model , 1992 .

[19]  R. Dickinson,et al.  The Project for Intercomparison of Land Surface Parameterization Schemes (PILPS): Phases 2 and 3 , 1993 .

[20]  Ann Henderson-Sellers,et al.  Biosphere-atmosphere transfer scheme(BATS) version 1e as coupled to the NCAR community climate model , 1993 .

[21]  J. Dudhia.  A Nonhydrostatic Version of the Penn State–NCAR Mesoscale Model: Validation Tests and Simulation of an Atlantic Cyclone and Cold Front , 1993 .

[22]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .

[23]  C. Justice,et al.  Development of vegetation and soil indices for MODIS-EOS , 1994 .

[24]  Jesslyn F. Brown,et al.  Seasonal Land‐Cover Regions of the United States , 1995, Annals of the Association of American Geographers.

[25]  M. Ek,et al.  Impact of soil water property parameterization on atmospheric boundary layer simulation , 1996 .

[26]  C. Justice,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part II: The Generation of Global Fields of Terrestrial Biophysical Parameters from Satellite Data , 1996 .

[27]  Michael G. Bosilovich,et al.  Planetary boundary layer and surface layer sensitivity to land surface parameters , 1996 .

[28]  H. Pan,et al.  Nonlocal Boundary Layer Vertical Diffusion in a Medium-Range Forecast Model , 1996 .

[29]  Y. Xue,et al.  Modeling of land surface evaporation by four schemes and comparison with FIFE observations , 1996 .

[30]  K. Mitchell,et al.  Simple water balance model for estimating runoff at different spatial and temporal scales , 1996 .

[31]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[32]  R. Pielke.  “Use of USGS-provided data to improve weather and climate simulations” , 1997 .

[33]  Kiran Alapaty,et al.  UNCERTAINTY IN THE SPECIFICATION OF SURFACE CHARACTERISTICS: A STUDY OF PREDICTION ERRORS IN THE BOUNDARY LAYER , 1997 .

[34]  Roger A. Pielke,et al.  USE OF USGS‐PROVIDED DATA TO IMPROVE WEATHERAND CLIMATE SIMULATIONS , 1997 .

[35]  Roni Avissar,et al.  Representation of heterogeneity effects in Earth system modeling: Experience from land surface modeling , 1997 .

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

[37]  G. Gutman,et al.  The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models , 1998 .

[38]  K. Alapaty,et al.  Uncertainty in the Specification of Surface Characteristics, Part ii: Hierarchy of Interaction-Explicit Statistical Analysis , 1999 .

[39]  Guodong Cheng,et al.  A model for simulating the response of runoff from the mountainous watersheds of inland river basins in the arid area of northwest China to climatic changes , 1999 .

[40]  J. Dudhia,et al.  Coupling an Advanced Land Surface–Hydrology Model with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity , 2001 .

[41]  D. Lüthi,et al.  The Soil-Precipitation Feedback: A Process Study with a Regional Climate Model , 1999 .

[42]  Compton J. Tucker,et al.  Sensitivity of Climate to Changes in NDVI , 2000 .

[43]  C. Tucker,et al.  A Global 9-yr Biophysical Land Surface Dataset from NOAA AVHRR Data , 2000 .

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

[45]  R. Dickinson,et al.  Evaluation of the Utility of Satellite-Based Vegetation Leaf Area Index Data for Climate Simulations , 2001 .

[46]  Keith W. Oleson,et al.  Landscapes as patches of plant functional types: An integrating concept for climate and ecosystem models , 2002 .

[47]  R. Dickinson,et al.  The land surface climatology of the community land model coupled to the NCAR community climate model , 2002 .

[48]  R. Betts,et al.  The influence of land-use change and landscape dynamics on the climate system: relevance to climate-change policy beyond the radiative effect of greenhouse gases , 2002, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[49]  Liu Jiyuan,et al.  The land use and land cover change database and its relative studies in China , 2002 .

[50]  CASES-97: LATE-MORNING WARMING AND MOISTENING OF THE CONVECTIVE BOUNDARY LAYER OVER THE WALNUT RIVER WATERSHED , 2002 .

[51]  Brian D. Wardlow,et al.  A State-Level Comparative Analysis of the GAP and NLCD Land-Cover Data Sets , 2003 .

[52]  G. Cheng,et al.  Simulation of rainfall-runoff and watershed convergence process in the upper reaches of Heihe River Basin, July 2002 , 2004 .

[53]  The Regional climate , 2004 .

[54]  Kevin W. Manning,et al.  A study of convection initiation in a mesoscale model using high-resolution land surface initial conditions , 2004 .

[55]  Fei Chen.  Variability in global land surface energy budgets during 1987–1988 simulated by an off-line land surface model , 2005 .

[56]  G. Meehl,et al.  The Importance of Land-Cover Change in Simulating Future Climates , 2005, Science.

[57]  J. Wickham,et al.  Evaluating the Relative Roles of Ecological Regions and Land-cover Composition for Guiding Establishment of Nutrient Criteria , 2005, Landscape Ecology.

[58]  Fei Chen,et al.  Effect of Land–Atmosphere Interactions on the IHOP 24–25 May 2002 Convection Case , 2006 .

[59]  S. Phillips,et al.  Considering ecological formulations for estimating deposition velocity in air quality models , 2006 .

[60]  R. Pielke,et al.  An overview of regional land-use and land-cover impacts on rainfall , 2007 .

[61]  J. Alfieri,et al.  Influence of Land Cover and Soil Moisture on the Horizontal Distribution of Sensible and Latent Heat Fluxes in Southeast Kansas during IHOP_2002 and CASES-97 , 2007 .

[62]  Souleymane Fall,et al.  A Simple Reclassification Method for Correcting Uncertainty in Land Use/Land Cover Data Sets Used with Land Surface Models , 2007 .