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.
All Science Journal Classification (ASJC) codes
- Water Science and Technology
- Land surface models
- Soil moisture