Assessment of a distributed biosphere hydrological model against streamflow and MODIS land surface temperature in the upper Tone River Basin

summary Land surface temperature (LST) is a key parameter in land–atmosphere interactions. The recently released Moderate Resolution Imaging Spectroradiometers (MODIS) LST Version 5 products have provided good tools to evaluate water and energy budget modelling for river basins. In this study, a distributed biosphere hydrological model (WEB-DHM; so-called water and energy budget-based distributed hydrological model) that couples a biosphere scheme (SiB2) with a geomorphology-based hydrological model (GBHM), is applied to the upper Tone River Basin where flux observations are not available. The model facilitates a better understanding of the water and energy cycles in this region. After being calibrated with discharge data, WEB-DHM is assessed against observed streamflows at four major gauges and MODIS LST. Results show that long-term streamflows including annual largest floods are well reproduced. As well, both daytime and nighttime LSTs simulated by WEB-DHM agree well with MODIS observations for both basin-averaged values and spatial patterns. The validated model is then used to analyze water and energy cycles of the upper Tone River Basin. It was found that from May to October, with relatively large leaf area index (LAI) values, the simulated daily maximum LST is close to soil surface temperature (Tg) since Tg is much greater than canopy temperature (Tc) in their peak values; while the daily minimum LST appears similar to Tc. For other months with relatively small LAI values, the diurnal cycles of LST closely follow Tg.

[1]  W. Cohen,et al.  Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation , 2003 .

[2]  Toshio Koike,et al.  Application of a distributed hydrological model and weather radar observations for flood management in the upper Tone River of Japan , 2004 .

[3]  Tracy E. Twine,et al.  Evaluating a terrestrial ecosystem model with satellite information of greenness , 2008 .

[4]  Fran Li,et al.  Surface temperature and emissivity at various scales: Definition, measurement and related problems , 1995 .

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

[6]  Rachel T. Pinker,et al.  Evaluation of Satellite Estimates of Land Surface Temperature from GOES over the United States , 2009 .

[7]  Laura C. Brown,et al.  Using satellite imagery to validate snow distribution simulated by a hydrological model in large northern basins , 2008 .

[8]  G. Blöschl,et al.  The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models , 2008 .

[9]  楊 大文 Distributed hydrologic model using hillslope discretization based on catchment area function : development and applications , 1998 .

[10]  P. Jones,et al.  Representing Twentieth-Century Space-Time Climate Variability. Part II: Development of 1901-96 Monthly Grids of Terrestrial Surface Climate , 2000 .

[11]  H. Mooney,et al.  Modeling the Exchanges of Energy, Water, and Carbon Between Continents and the Atmosphere , 1997, Science.

[12]  Dawen Yang,et al.  Comparison of different distributed hydrological models for characterization of catchment spatial variability , 2000 .

[13]  David G. Tarboton,et al.  On the extraction of channel networks from digital elevation data , 1991 .

[14]  R. Rigon,et al.  GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets , 2006 .

[15]  T. Jackson,et al.  Development of a distributed biosphere hydrological model and its evaluation with the Southern Great Plains Experiments (SGP97 and SGP99) , 2009 .

[16]  Yann Kerr,et al.  Accurate land surface temperature retrieval from AVHRR data with use of an improved split window algorithm , 1992 .

[17]  Kun Yang,et al.  Improving estimation of hourly, daily, and monthly solar radiation by importing global data sets , 2006 .

[18]  Sujin Lee,et al.  Comparison of land surface temperature (LST) modeled with a spatially-distributed solar radiation model (SRAD) and remote sensing data , 2009, Environ. Model. Softw..

[19]  Giacomo Bertoldi,et al.  Impact of Watershed Geomorphic Characteristics on the Energy and Water Budgets , 2006 .

[20]  Nobuyuki Tamai,et al.  A hybrid model for estimating global solar radiation , 2001 .

[21]  J. Famiglietti,et al.  Multiscale modeling of spatially variable water and energy balance processes , 1994 .

[22]  S. Kanae,et al.  A DISTRIBUTED BIOSPHERE HYDROLOGICAL MODEL (DBHM) FOR LARGE RIVER BASIN , 2006 .

[23]  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 .

[24]  D. Randall,et al.  A Revised Land Surface Parameterization (SiB2) for Atmospheric GCMS. Part I: Model Formulation , 1996 .

[25]  T. Koike,et al.  COMPARISON OF A DISTRIBUTED BIOSPHERE HYDROLOGICAL MODEL WITH GBHM , 2009 .

[26]  Eric F. Wood,et al.  A soil‐vegetation‐atmosphere transfer scheme for modeling spatially variable water and energy balance processes , 1997 .

[27]  M. Wigmosta,et al.  A distributed hydrology-vegetation model for complex terrain , 1994 .

[28]  Donglian Sun,et al.  Estimation of land surface temperature from a Geostationary Operational Environmental Satellite (GOES‐8) , 2003 .

[29]  J. Norman,et al.  Terminology in thermal infrared remote sensing of natural surfaces , 1995 .

[30]  Z. Wan New refinements and validation of the MODIS Land-Surface Temperature/Emissivity products , 2008 .

[31]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[32]  T. M. Crawford,et al.  An Improved Parameterization for Estimating Effective Atmospheric Emissivity for Use in Calculating Daytime Downwelling Longwave Radiation , 1999 .

[33]  John F. O'Callaghan,et al.  The extraction of drainage networks from digital elevation data , 1984, Comput. Vis. Graph. Image Process..

[34]  T. Williams,et al.  OBTAINING SPATIAL AND TEMPORAL VEGETATION DATA FROM LANDSAT MSS AND AVHRR/NOAA SATELLITE IMAGES FOR A HYDROLOGIC MODEL , 1997 .