Developing SMASH: (Soil Moisture Analysis using a Statistical Heuristic Model) for Mapping soil moisture variability using surface climatological data

Soil moisture is one of the most important climatological / meteorological variables that has a profound impact on various applications ranging from agriculture, environment and hydrological simulations, to climate change simulations. Soil moisture availability partitions the energy balance, and the resulting micro and mesoscale meteorological features. However soil moisture measurements are difficult and often represent a limited area. There is thus a need to determine soil moisture (wetness) at a regional scale using simple techniques and routinely available data. Making use of routinely available relative humidity and air temperature data, a statistical model has been developed to estimate surface soil moisture. A decision - based / heuristic rule based precipitation module and an evapotranspiration module have been linked which allows soil saturation and subsequent dry down. Results of the comparison of the model predicted and observed soil moisture from a research site in North Carolina will be discussed for an entire annual cycle. An algorithm will be presented for the operational mapping of soil moisture in North Carolina using available surface observation networks such as through ASOS , AWOS, and the NC AgNet.