TY - JOUR
T1 - Evaluation of multi-model simulated soil moisture in NLDAS-2
AU - Xia, Youlong
AU - Sheffield, Justin
AU - Ek, Michael B.
AU - Dong, Jiarui
AU - Chaney, Nathaniel
AU - Wei, Helin
AU - Meng, Jesse
AU - Wood, Eric F.
N1 - Funding Information:
This work by NCEP/EMC was supported by the Climate Program Project of the Americas (CPPA) of NOAA Climate Program Office (CPO) as the core project for the EMC (Y. Xia, H. Wei, J. Meng) and the Office of Hydrological Development (J. Dong). We thank the NOAA Office of Global Program and NASA Land Surface Hydrology Program for their purchase of the Oklahoma Mesonet soil moisture data for NLDAS investigators, Illinois State Water Survey for providing soil moisture observations in Illinois State, Rolf Reichle who provided us quality-controlled SCAN daily soil moisture data, Drs. Yan Luo, and Yihua Wu from EMC, Dr. Konstantine Georgakakos from Hydrologic Research Center, and two anonymous reviewers whose comments and suggestions greatly improved the quality of this paper. The authors also thank Ruolan Xu from Princeton University in assisting for VIC model simulations.
PY - 2014/5/6
Y1 - 2014/5/6
N2 - The North American Land Data Assimilation System (NLDAS) phase 2 (NLDAS-2) has generated 31-years (1979-2008) of water and energy products from four state-of-the-art land surface models (Noah, Mosaic, SAC, VIC). The soil moisture data from these models have been used for operational drought monitoring activities, but so far have not yet been comprehensively evaluated. In this study, three available in situ soil moisture observation data sets in the United States were used to evaluate the model-simulated soil moisture for different time scales varying from daily to annual. First, we used the observed multiple layer monthly and annual mean soil moisture from the Illinois Climate Network to evaluate 20-years (January 1985-December 2004) of model-simulated soil moisture in terms of skill and analysis of error statistics. Second, we utilized 6-years (1 January 1997-31 December 2002) of daily soil moisture observed from 72 sites over the Oklahoma Mesonet network to assess daily and monthly simulation skill and errors for 3 model soil layers (0-10. cm, 10-40. cm, 40-100. cm). Third, we extended the daily assessment to sites over the continental United States using 8-years (1 January 2002-31 December 2009) of observations for 121 sites from the Soil Climate Analysis Network (SCAN). Overall, all models are able to capture wet and dry events and show high skill (in most cases, anomaly correlation is larger than 0.7), but display large biases when compared to in situ observations. These errors may come from model errors (i.e., model structure error, model parameter error), forcing data errors, and in situ soil moisture measurement errors. For example, all models simulate less soil moisture due to lack of modeled irrigation and ground water processes in Illinois, Oklahoma, and the other Midwest states.
AB - The North American Land Data Assimilation System (NLDAS) phase 2 (NLDAS-2) has generated 31-years (1979-2008) of water and energy products from four state-of-the-art land surface models (Noah, Mosaic, SAC, VIC). The soil moisture data from these models have been used for operational drought monitoring activities, but so far have not yet been comprehensively evaluated. In this study, three available in situ soil moisture observation data sets in the United States were used to evaluate the model-simulated soil moisture for different time scales varying from daily to annual. First, we used the observed multiple layer monthly and annual mean soil moisture from the Illinois Climate Network to evaluate 20-years (January 1985-December 2004) of model-simulated soil moisture in terms of skill and analysis of error statistics. Second, we utilized 6-years (1 January 1997-31 December 2002) of daily soil moisture observed from 72 sites over the Oklahoma Mesonet network to assess daily and monthly simulation skill and errors for 3 model soil layers (0-10. cm, 10-40. cm, 40-100. cm). Third, we extended the daily assessment to sites over the continental United States using 8-years (1 January 2002-31 December 2009) of observations for 121 sites from the Soil Climate Analysis Network (SCAN). Overall, all models are able to capture wet and dry events and show high skill (in most cases, anomaly correlation is larger than 0.7), but display large biases when compared to in situ observations. These errors may come from model errors (i.e., model structure error, model parameter error), forcing data errors, and in situ soil moisture measurement errors. For example, all models simulate less soil moisture due to lack of modeled irrigation and ground water processes in Illinois, Oklahoma, and the other Midwest states.
KW - Land surface models
KW - NLDAS-2
KW - Soil moisture
KW - U.S.
KW - Validation
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U2 - 10.1016/j.jhydrol.2014.02.027
DO - 10.1016/j.jhydrol.2014.02.027
M3 - Article
AN - SCOPUS:84896108711
SN - 0022-1694
VL - 512
SP - 107
EP - 125
JO - Journal of Hydrology
JF - Journal of Hydrology
ER -