A monthly, two-soil-layer statistical-dynamical water balance model for hydroecologically focused climate impact assessments

An operational version of a statistical-dynamical water balance model is developed which is applicable to hydroecologically focused regional-scale climate impact assessments. Major improvements to the model include implementation at a monthly scale, addition of snow and frozen soil, division of vadose-zone soil into two layers, and a more realistic representation of vegetation. The latter is achieved by coupling the water balance model to the Shuttleworth-Wallace evapotranspiration model. The coupled model is applied to the central United States over a half-degree grid using vegetation, soil and climate data from the Vegetation Ecosystem Modeling and Analysis Project. After detailed review of the literature, careful estimation of the parameters of the evapotranspiration, soil-hydraulic and stochastic-precipitation sub-models is performed. An excellent match of modeled mean annual runoff to contours of streamflow is achieved with only minimal calibration of two evapotranspiration parameters. Model validity is further established through comparison of results for the mean and interannual variability of the water balance with observations of leaf area index (LAI), vegetation productivity, and soil moisture, as well as streamflow-based estimates of surface runoff and groundwater recharge.; The partitioning of evapotranspiration in the model is highly dependent on vegetation density in the form of LAI. The spatial and interannual variation in LAI is captured in the model through application of the hypothesis that, in any year in which water is significantly limiting, vegetation will draw soil moisture down in the latter half of the growing season approximately to the point at which the vegetation just begins to experience water stress. This "LAI-maximization" hypothesis is supported through the analysis of observed soil moisture, soil-moisture retention data and water-stress studies in the plant physiology literature. Analysis of the sensitivity of model-maximized LAI to soil texture shows that the model is able to reproduce the inverse texture effect, which consists of the observation that natural vegetation in dry climates tends to be most productive in sandy soils.