Estimating spatial mean root-zone soil moisture from point-scale observations

Root zone soil moisture is a key variable in many
land surface hydrology models. Often, however, there is
a mismatch in the spatial scales at which models simulate
soil moisture and at which soil moisture is observed. This
complicates model validation. The increased availability of
detailed datasets on space-time variability of root-zone soil
moisture allows for a posteriori analysis of the uncertainties
in the relation between point-scale observations and the
spatial mean. In this paper we analyze three comprehensive
datasets from three different regions. We identify different
strategies to select observation sites. For instance, sites
can be located randomly or according to the rank stability
concept. For each strategy, we present methods to quantify
the uncertainty that is associated with this strategy. In
general there is a large correspondence between the different
datasets with respect to the relative uncertainties for the
different strategies. For all datasets, the uncertainty can be
strongly reduced if some information is available that relates
soil moisture content at that site to the spatial mean. However
this works best if the space-time dynamics of the soil
moisture field are known. Selection of the site closest to the
spatial mean on a single random date only leads to minor reduction
of the uncertainty with respect to the spatial mean
over seasonal timescales. Since soil moisture variability is
the result of a complex interaction between soil, vegetation,
and landscape characteristics, the soil moisture field will be
correlated with some of these characteristics. Using available
information, we show that the correlation with leaf area
index or a wetness coefficient alone is insufficient to predict
if a site is representative for the spatial mean soil moisture
content.