Optimal design of surface networks for observation of soil moisture

By analyzing in situ soil moisture data, we show that soil moisture variabi lity consists of two components, one of which is related to large-scale atm ospheric forcing, and the other related to small-scale land surface variabi lity and hydrologic processes. We use empirically estimated spatial autocor relation functions for Illinois to estimate errors of spatial averaging of soil moisture observations, using the method of statistically optimal avera ging of meteorological fields. The estimated dependence of the root-mean-sq uare errors of averaging all the soil moisture station network density can be used to analyze existing observational networks and for designing new on es. For the application of providing information on a regular grid for nume rical models of weather and climate, we show that the new, relatively high density networks of soil moisture observations in Oklahoma, may not provide estimates with very much more accuracy than the relatively low density cur rently operational network in Illinois. This prediction must be tested when we receive sufficiently long time series of observations from Oklahoma.