[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.

Abstract. In this paper we develop a conceptual and observational case in which soil water patterns in temperate regions of Australia switch between two preferred states. The wet state is dominated by lateral water movement through both surface and subsurface paths, with catchment terrain leading to organization of wet areas along drainage lines. We denote this as nonlocal control. The dry state is dominated by vertical fluxes, with soil properties and only local terrain (areas of high convergence) influencing spatial patterns.We denote this as local control. The switch is described in terms of the dominance of lateral over vertical water fluxes and vice versa. When evapotranspiration exceeds rainfall, the soil dries to the point where hydraulic conductivity is low and any rainfall that occurs essentially wets up the soil uniformly and is evapotranspired before any significant lateral redistribution takes place. As evapotranspiration decreases and/or rainfall increases, areas of high local convergence become wet, and runoff that is generated moves downslope, rapidly wetting up the drainage lines. In the wet to dry transitional period a rapid increase in potential evapotranspiration (and possibly a decrease in rainfall) causes drying of the soil and "shutting down" of lateral flow. Vertical fluxes dominate and the "dry" pattern is established. Three data sets from two catchments are presented to support the notion of preferred states in soil moisture, and the results of a modeling exercise on catchments from a range of climatic conditions illustrate that the conclusions from the field studies may apply to other areas. The implications for hydrological modeling are discussed in relation to methods for establishing antecedent moisture conditions for event models, for distribution models, and for spatially distributing bulk estimates of catchment soil moisture using indices.

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[1] Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Niño-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric structure for quantifying relationships between variables, including intersite correlations. The Box-Cox transformed multivariate normal distribution has considerable flexibility for modeling a wide range of predictors and predictands. The Bayesian inference formulated allows the use of data that contain nonconcurrent and missing records. The model flexibility and data-handling ability means that the BJP modeling approach is potentially of wide practical application. The paper also presents a number of statistical measures and graphical methods for verification of probabilistic forecasts of continuous variables. Results for streamflows at three river gauges in the Murrumbidgee River catchment in southeast Australia show that the BJP modeling approach has good forecast quality and that the fitted model is consistent with observed data.

[1] This study extends the single parameter precipitation elasticity of streamflow index into a two parameter climate elasticity index, as a function of both precipitation and temperature, in order to assess climatic effects on annual streamflow. Application of the proposed index to two basins indicates that the single parameter precipitation elasticity index may give a general relationship between precipitation and streamflow in some cases, but that it cannot reflect the complicated non-linear relationship among streamflow, precipitation and temperature. For example, for the Spokane River basin the climate elasticity of streamflow index varies from 2.4 to 0.2, for a precipitation increase of 20%, as temperature varies from 1°C lower to 1.8°C higher than the long term mean. Thus a 20% precipitation increase may result in a streamflow increase of 48% if the temperature is 1°C lower but only a 4% increase if the temperature is 1.8°C higher than the long-term mean. The proposed method can be applied to other basins to assess potential climate change effects on annual streamflow. The results of the two case studies can inform planning of long-term basin water management strategies taking into account global change scenarios.