Global Soil Wetness Project

Project Description

The Global Soil Wetness Project (GSWP) is an ongoing modeling activity
of the International Satellite Land-Surface Climatology Project
(ISLSCP), a contributing project of the Global Energy and Water Cycle
Experiment (GEWEX). The GSWP is charged with producing a 2-year global
data set of soil moisture, temperature, runoff, and surface fluxes by
integrating one-way uncoupled land surface process models (LSPs) using
externally specified surface forcings and standardized soil and
vegetation distributions, namely, the ISLSCP Initiative I CD-ROM
data. Approximately one dozen participating LSP groups in five nations
have taken the common ISLSCP forcing data to execute their
state-of-the-art models over the 1987-1988 period to generate global
data sets.

Results of the pilot phase suggest that the GSWP framework is very
useful and valuable for assessing and developing land surface models
on a global scale with relatively little computational expense, and to
investigate questions of land surface hydrology and land-atmosphere


The motivation for GSWP stems from the paradox that soil wetness is an
important component of the global energy and water balance, but it is
unknown over most of the globe. Soil wetness is the reservoir for the
land surface hydrologic cycle, it is a boundary condition for
atmosphere, it controls the partitioning of land surface heat fluxes,
affects the status of overlying vegetation, and modulates the thermal
properties of the soil. Knowledge of the state of soil moisture is
essential for climate predictability on seasonal-annual time
scales. However, soil moisture is difficult to measure in situ, remote
sensing techniques are only partially effective, and few long-term
climatologies of any kind exist.


The goals of GSWP are fourfold. The project will produce
state-of-the-art global data sets of soil moisture, surface fluxes,
and related hydrologic quantities. It is a means of testing and
developing large-scale validation techniques over land. It serves as a
large-scale validation and quality check of the ISLSCP Initiative I
data sets. GSWP is also a global comparison of a number of LSPs, and
includes a series of sensitivity studies of specific parameterizations
which should aid future model development.


The GSWP consists of three components: the Production Group, the
Validation Group, and the Inter-Comparison Center. The Production
Group consists of land surface modelers who conduct offline
integrations of land surface models over a global 1 degree grid for
1987-1988 using prescribed atmospheric forcing based on observations,
remote sensing and analyses. Each member of the production group
produces global time-mean and instantaneous fields of surface energy
and water balance terms three times per month using his/her LSP. These
data are produced in a standard format and sent to the
Inter-Comparison Center. In addition, each model is used to perform
specific sensitivity studies. The sensitivity experiments are intended
to evaluate the impact of uncertainties in model parameters and
forcing fields on simulation of the surface water and energy balances.

A number of different sensitivity studies were conducted by members of
the Production team. Perhaps the most significant general conclusion
that can be drawn from the studies is that sub-grid scale variability
in infiltration, whether due to heterogeneity in soil properties or
the distribution of rainfall within a grid box, has a significant
impact on the simulation of runoff. Variations in vegetation
properties, the vertical structure of the soil, and radiation seem to
have less of an impact on simulations. These results suggest that some
sort of accounting for sub-grid heterogeneity, whether through an
explicit modeling of small tiles or a statistical approach, is
necessary to properly partition surface water between runoff and


There is also a Validation Group which assembles data sets and
coordinates studies to validate the global products, either directly
(by comparison to field studies or soil moisture measuring networks)
or indirectly (e.g. use of modeled runoff to drive river routing
schemes for comparison to streamflow data). The soil wetness data
produced are being tested within a general circulation model (GCM) to
evaluate their quality and their impact on seasonal to interannual
climate simulations. The Winand Staring Center has volunteered to lead
the validation process.

The validation effort allows some other important conclusions to be
drawn about the quality of the GSWP results. The use of the soil
moisture product as a specified boundary condition improves the
forecast ability of a climate model. This is most likely as a result
of mitigating the effects of poor rainfall simulations on the surface
water balance of the climate model. Secondly, comparison with
observations in more detail still point to significant problems in the
way the LSPs deal with soil moisture, or more generally, land surface
hydrology. Yet, it is clear that the quality of the land surface model
simulations are critically dependant on the quality of the land
surface data (soils, vegetation, terrain, radiative parameters) and
the meteorological forcing data.


An Inter-Comparison Center (ICC) has been established at the Center
for Climate System Research, University of Tokyo for evaluating and
comparing data from the different models. Comparison among the model
results is used to assess the uncertainty in estimates of surface
components of the moisture and energy balances at large scales, and as
a quality check on the model products themselves. The ICC is also the
community re-distribution point for the data produced in GSWP.

The inter-comparison effort has shown that there is a large spread
among the participating LSPs in terms of their partitioning of surface
energy between latent and sensible heat flux, and of water between
runoff and evapotranspiration. Most of the LSPs underestimated
basin-scale runoff, possible due to the GSWP specification of the
treatment of convective precipitation. Nonetheless, validation of the
consensus runoff against streamflow data show that the LSPs as a group
perform quite well where sufficient gauge-based precipitation forcing
data were available, and performed poorly where gauges are sparse.

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