Evaluation of Global Soil Wetness Project Soil Moisture Simulations

Abstract

To produce a global soil moisture "data set," ten different land surface models were forced with meteorological observations for a two-year period for the Global Soil Wetness Project (GSWP). We compare observed plant-available soil moisture in the top 1-m soil layer to the same quantity generated by the models. Our soil moisture observations are from grasslands and agricultural regions in Russia, Illinois (USA), China, and Mongolia.None of the models does a good job of producing the actual soil moisture value for any of the regions. Thus, GSWP has not yet demonstrated the ability to produce global soil wetness data sets. Once the bias is removed, the models do a fairly good job of reproducing the seasonal cycle of soil moisture for the various areas. The model biases are different in different locations, so correcting them with a simple adjustment of the mean will not produce correct results. Better specification of parameters, or better representation of physical and biological processes, is still needed to improve these models.Future GSWP experiments should be conducted for a longer time period. They should emphasize catchment scale validation and higher time resolution of model output. Increased soil moisture observations, possibly incorporating satellite measurements, also would greatly improve a second project.

Retrieving snow mass from GRACE terrestrial water storage change with a land surface model

“…[5] Various model intercomparison projects [e.g., Entin et al, 1999;Guo and Dirmeyer, 2006] have indicated that LSMs are much better at simulating a ''soil moisture'' anomaly (i.e., deviation from the mean) than simulating the absolute value of soil moisture. However, changes in DS bg can be affected by snowmelt, permeability of frozen soil, runoff parameterization, and vegetation dynamics in Arctic regions.…”

A Long-term Numerical Study of the Potential Predictability of Seasonal Mean Fields of Water Resource Variables using MRI/JMA-AGCM

“…The JMA-SiB model seasonal changes in soil moisture at some in-situ observation points were reproduced qualitatively, but not quantitatively, as well as the other models participating in the Global Soil Wetness Project (GSWP) (Entin et al 1999). The JMA-SiB model is consistently close to the median of the GSWP models in the data series (Rodell and Famiglietti 1999).…”

The potential for satellite-based monitoring of groundwater storage changes using GRACE: the High Plains aquifer, Central US

“…Eliminating the ®rst of these extremes from the calculation reduces the coef®cient of variation to 0.42, and eliminating both reduces it to 0.20. According to Entin et al (1999), the magnitude of the differences among the GSWP models is typically similar to or larger than the magnitude of the differences between observations and any one model. Therefore, using an uncertainty coef®cient of 0.30, which is in the range of the coef®cients of variation computed earlier, is reasonable.…”