Abstract. Distributed hydrological models are generally overparameterized, resulting in the possibility of multiple parameteriZations from many areas of the parameter space providing acceptable fits to observed data. In this study, TOPMODEL parameterizations are conditioned on discharges, and then further conditioned on estimates of saturated areas derived from ERS-1 synthetic aperture radar (SAR) images combined with the In (a/tan/3) topographic index, and compared to ground truth saturation measurements made in one small subcatchment. The uncertainty associated with the catchment-wide predictions of saturated area is explicitly incorporated into the conditioning through the weighting of estimates within a fuzzy set framework. The predictive uncertainty associated with the parameterizations is then assessed using the generalized likelihood uncertainty estimation (GLUE) methodology. It is shown that despite the uncertainty in the predictions of saturated area the methodology can reject many previously acceptable parameterizations with the consequence of a marked reduction in the acceptable range of a catchment average transmissivity parameter and of improved predictions of some discharge events.
Abstract:As interest shifts to the development of models for predicting runo quality, identi®cation of the source areas for runo becomes increasingly important. Active microwave remote sensing has a unique potential for surveying source areas at the catchment scale. Thresholding of the back-scattering coecient was initially proposed but proved unsatisfactory when applied to the ERS-1 SAR multitemporal images acquired during winter 1992 over the CoeÈ t-Dan catchment, concomitantly with ground observations. Dierence images may, instead, allow the wettest part of the catchment to be identi®ed provided that the two images encompass a marked hydrological event. A saturation plot could not however be obtained for each date; the use of a pair of images may be further limited by the residual speckle (although carefully ®ltered using the multitemporal information) and a slight inaccuracy in the SAR image calibration. It is therefore argued that considering the whole temporal back-scatter pro®le would be, at present, a safer approach to the remote sensing of saturated areas. The back-scatter temporal standard deviation appears, in this light, as a possible good indicator of the local saturation likelihood during the period of study: it is based on the fact that saturation develops on parts of the catchment that are wetter than the others through lateral recharge. Possible applications within the TOPMODEL framework are discussed. #
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