scholarly journals Validation of the NOAH-OSU land surface model using surface flux measurements in Oklahoma

2002 ◽  
Vol 107 (D20) ◽  
Author(s):  
V. Sridhar
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Flux ◽  
Surface Model ◽  
Flux Measurements

scholarly journals Calibration of a distributed hydrology and land surface model using energy flux measurements

2016 ◽  
Vol 217 ◽  
pp. 74-88 ◽  
Author(s):  
Morten A.D. Larsen ◽  
Jens C. Refsgaard ◽  
Karsten H. Jensen ◽  
Michael B. Butts ◽  
Simon Stisen ◽  
...  
Keyword(s):  
Energy Flux ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Flux Measurements

Evaluation of anomalies in GLDAS-1996 dataset

2013 ◽  
Vol 67 (8) ◽  
pp. 1718-1727 ◽  
Author(s):  
Xinyao Zhou ◽  
Yongqiang Zhang ◽  
Yonghui Yang ◽  
Yanmin Yang ◽  
Shumin Han
Keyword(s):  
Land Surface ◽  
Daily Precipitation ◽  
Land Surface Model ◽  
Surface Flux ◽  
Shortwave Radiation ◽  
Surface Model ◽  
Precipitation Data ◽  
Daily Precipitation Data ◽  
Downward Longwave Radiation ◽  
The Impact

Global Land Data Assimilation System (GLDAS) data are widely used for land-surface flux simulations. Therefore, the simulation accuracy using GLDAS dataset is largely contingent upon the accuracy of the GLDAS dataset. It is found that GLDAS land-surface model simulated runoff exhibits strong anomalies for 1996. These anomalies are investigated by evaluating four GLDAS meteorological forcing data (precipitation, air temperature, downward shortwave radiation and downward longwave radiation) in six large basins across the world (Danube, Mississippi, Yangtze, Congo, Amazon and Murray-Darling basins). Precipitation data from the Global Precipitation Climatology Centre (GPCC) are also compared with GLDAS forcing precipitation data. Large errors and lack of monthly variability in GLDAS-1996 precipitation data are the main sources for the anomalies in the simulated runoff. The impact of the precipitation data on simulated runoff for 1996 is investigated with the Community Atmosphere Biosphere Land Exchange (CABLE) land-surface model in the Yangtze basin, for which area high-quality local precipitation data are obtained from the China Meteorological Administration (CMA). The CABLE model is driven by GLDAS daily precipitation data and CMA daily precipitation, respectively. The simulated daily and monthly runoffs obtained from CMA data are noticeably better than those obtained from GLDAS data, suggesting that GLDAS-1996 precipitation data are not so reliable for land-surface flux simulations.

L'apport de la télédétection spatiale à la modélisation des surfaces continentales

2020 ◽  
pp. 052
Author(s):  
Jean-Christophe Calvet ◽  
Jean-Louis Champeaux
Keyword(s):  
Satellite Data ◽  
Land Surface ◽  
Land Surface Model ◽  
Static Model ◽  
Geographic Information ◽  
Surface Model ◽  
Model Parameters

Cet article présente les différentes étapes des développements réalisés au CNRM des années 1990 à nos jours pour spatialiser à diverses échelles les simulations du modèle Isba des surfaces terrestres. Une attention particulière est portée sur l'intégration, dans le modèle, de données satellitaires permettant de caractériser la végétation. Deux façons complémentaires d'introduire de l'information géographique dans Isba sont présentées : cartographie de paramètres statiques et intégration au fil de l'eau dans le modèle de variables observables depuis l'espace. This paper presents successive steps in developments made at CNRM from the 1990s to the present-day in order to spatialize the simulations of the Isba land surface model at various scales. The focus is on the integration in the model of satellite data informative about vegetation. Two complementary ways to integrate geographic information in Isba are presented: mapping of static model parameters and sequential assimilation of variables observable from space.

Sign in / Sign up

Export Citation Format

Share Document