Executive Summary
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Overview
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
interaction.
Motivation
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.
Goals
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.
Production
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 evapotranspiration.
Validation
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.
Inter-Comparison
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.