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Abstract:Regionalization approaches to daily streamflow prediction are investigated for 13 catchments in the Coweeta Hydrologic Laboratory using a conceptual rainfall-runoff model of low complexity (six parameters). Model parameters are considered to represent the dynamic response characteristics (DRCs) of a catchment. It is demonstrated that all catchments within the region cannot be assumed to have a similar hydrological behaviour, and thence a regionalization approach considering differences in physical catchment descriptors (PCDs) is required. Such a regionalization approach can be regarded as a top-down method, in the sense that factors controlling parameter variability are identified first within the entire region under study, and then such information is exploited to predict runoff in a smaller sub-region. Regionalization results reveal that consideration of interrelations between dependent variables, which here are the parameters of the rainfall-runoff model, can improve performance of regression as a regionalization method. Breaking the parameter correlation structure inherent in the model, and exploiting merely relationships between model parameters and PCDs (no matter how weakly related they are), can result in a significant decrease in regionalization performance. Also, high significance of regression between values of PCDs and DRCs does not guarantee a set of parameters with a good predictive power. When there is a reason to believe that, in the sense of hydrological behaviour, a gauged catchment resembles the ungauged catchment, then it may be worthwhile to adopt the entire set of calibrated parameters from the gauged catchment instead of deriving quantitative relationships between catchment descriptors and model parameters.

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Ditch networks in drained peatland forests are maintained regularly to prevent water table rise and subsequent decrease in tree growth. The growing tree stand itself affects the level of water table through evapotranspiration, the magnitude of which is closely related to the living stand volume. In this study, regression analysis was applied to quantify the relationship between the late summer water table depth (DWT) and tree stand volume, mean monthly summertime precipitation (Ps), drainage network condition, and latitude. The analysis was based on several large data sets from southern to northern Finland, including concurrent measurements of stand volume and summer water table depth. The identified model demonstrated a nonlinear effect of stand volume on DWT, a linear effect of Ps on DWT, and an interactive effect of both stand volume and Ps. Latitude and ditch depth showed only marginal influence on DWT. A separate analysis indicated that an increase of 10 m3·ha–1 in stand volume corresponded with a drop of 1 cm in water table level during the growing season. In a subsample of the data, high bulk density peat showed deeper DWT than peat with low bulk density at the same stand volume.

Thermal infrared sensing of evapotranspiration (E) through surface energy balance (SEB) models is challenging due to uncertainties in determining the aerodynamic conductance (g A ) and due to inequalities between radiometric (T R ) and aerodynamic temperatures (T 0 ). We evaluated a novel analytical model, the Surface Temperature Initiated Closure (STIC1.2), that physically integrates T R observations into a combined Penman-Monteith Shuttleworth-Wallace (PM-SW) framework for directly estimating E, and overcoming the uncertainties associated with T 0 and g A determination. An evaluation of STIC1.2 against high temporal frequency SEB flux measurements across an aridity gradient in Australia revealed a systematic error of 10-52% in E from mesic to arid ecosystem, and low systematic error in sensible heat fluxes (H) (12-25%) in all ecosystems. Uncertainty in T R versus moisture availability relationship, stationarity assumption in surface emissivity, and SEB closure corrections in E were predominantly responsible for systematic E errors in arid and semi-arid ecosystems. A discrete correlation (r) of the model errors with observed soil moisture variance (r 5 0.33-0.43), evaporative index (r 5 0.77-0.90), and climatological dryness (r 5 0.60-0.77) explained a strong association between ecohydrological extremes and T R in determining the error structure of STIC1.2 predicted fluxes. Being independent of any leaf-scale biophysical parameterization, the model might be an important value addition in working group (WG2) of the Australian Energy and Water Exchange (OzEWEX) research initiative which focuses on observations to evaluate and compare biophysical models of energy and water cycle components.Plain Language Summary Evapotranspiration modeling and mapping in arid and semi-arid ecosystems are uncertain due to empirical approximation of surface and atmospheric conductances. Here we demonstrate the performance of a fully analytical model which is independent of any leaf-scale empirical parameterization of the conductances and can be potentially used for continental scale mapping of ecosystem water use as well as water stress using thermal remote sensing satellite data.

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