[1] Mean annual evapotranspiration from a catchment is determined largely by precipitation and potential evapotranspiration; characteristics of the catchment (e.g., soil, topography, etc.) play only a secondary role. It has been shown that the ratio of mean annual potential evapotranspiration to precipitation (referred as the index of dryness) can be used to estimate mean annual evapotranspiration by using one additional parameter. This study evaluates the effects of climatic and catchment characteristics on the partitioning of mean annual precipitation into evapotranspiration using a rational function approach, which was developed based on phenomenological considerations. Over 470 catchments worldwide with long-term records of precipitation, potential evapotranspiration, and runoff were considered, and results show that model estimates of mean annual evapotranspiration agree well with observed evapotranspiration taken as the difference between precipitation and runoff. The mean absolute error between modeled and observed evapotranspiration was 54 mm, and the model was able to explain 89% of the variance with a slope of 1.00 through the origin. This indicates that the index of dryness is the most significant variable in determining mean annual evapotranspiration. Results also suggest that forested catchments tend to show higher evapotranspiration than grassed catchments and their evapotranspiration ratio (evapotranspiration divided by precipitation) is most sensitive to changes in catchment characteristics for regions with the index of dryness around 1.0. Additionally, a stepwise regression analysis was performed for over 270 Australian catchments where detailed information of vegetation cover, precipitation characteristics, catchment slopes, and plant available water capacity was available. It is shown that apart from the index of dryness, average storm depth, plant available water capacity, and storm arrival rate are also significant.

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Understanding afforestation impacts on streamflow is important for water resources management. This study presents a predictive method for determining afforestation impacts on streamflow using data from four Australian experimental catchments with considerable forest cover change. Monthly values of rainfall, potential evapotranspiration, and streamflow are available for these catchments, as well as other data including plant available water capacity, minimum and maximum elevations, and index of valley bottom flatness. The proposed method is based on a dynamic water balance model (DWBM) with parameter values estimated from climate and catchment characteristics using projection pursuit regression (PPR). To predict the impacts of afforestation on monthly streamflow, the DWBM model was calibrated for pretreatment conditions and afforestation-induced changes in the model parameters were determined by adjusting the calibrated model parameter values with PPR predicted model parameter values under pretreatment and posttreatment conditions. Predicted monthly streamflow agreed well with measured streamflow for the period following afforestation. The success of the method indicates that the DWBM model appropriately represents the key catchment processes and characteristics. On the basis of the assessment of model parameter changes, increased storage capacity and evapotranspiration efficiency are the key factors responsible for the reduced monthly streamflowobserved. The degree of change in the model parameters due to afforestation is also influenced by other characteristics of the catchments and the local climatic conditions. This study demonstrated the strength of the DWBM model and its ability to predict afforestation impacts on monthly streamflow when combined with the PPR for estimating model parameters.