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International audience; Calibration and prediction in conceptual rainfall-runoff (CRR) modelling is affected by the sampling and measurement uncertainty in the observed input/output data and by the structural error of the model conceptualisation. The Bayesian Total Error Analysis methodology (BATEA) provides the opportunity to directly and comprehensively address these sources of uncertainty. BATEA is based on Bayesian hierarchical methods and uses explicit error models for input/output data and structural errors. However, previous studies demonstrated that simultaneous inference on forcing (e.g. rainfall) and structural errors requires strong prior knowledge of the error mechanisms (e.g., statistical properties of rainfall errors). This paper investigates methods to derive informative prior knowledge on data (input/output) errors. A geostatistical approach is used to estimate the sampling error made in approximating the areal rainfall by averaging point-values from a raingauge network. The geostatistical model generates an ensemble of rainfall fields conditioned on gauged values (conditional simulation). This ensemble is treated as a distribution of the true areal rainfall over the catchment and used as a prior in the BATEA framework. Prior knowledge on runoff errors is derived from a statistical analysis of the gaugings used to derive the rating curve. A comparative case study is then carried out, by comparing several inference schemes (full BATEA, ignoring input errors, ignoring structural errors, ignoring both input/structural errors). Results show that: (i) the inclusion of prior knowledge on rainfall accuracy allows simultaneous estimation of input and structural errors, whereas in the absence of such information the inference is ill-posed; (ii) accounting for all sources of errors leads to a more reliable estimation of predictive uncertainty. These findings demonstrate the key role played by priors on data (rainfall/runoff) accuracy, since they control the well-posedness of the inference and allow decomposing predictive uncertainty into its contributing sources. This paves the way for a meaningful analysis of structural errors affecting hydrologic models, thus enabling meaningful model comparison and improvement.

Characterising the development of evapotranspiration through time is a difficult task, particularly when utilising remote sensing data, because retrieved information is often spatially dense, but temporally sparse. Techniques to expand these essentially instantaneous measures are not only limited, they are restricted by the general paucity of information describing the spatial distribution and temporal evolution of evaporative patterns. In a novel approach, temporal changes in land surface temperatures, derived from NOAA-AVHRR imagery and a generalised split-window algorithm, are used as a calibration variable in a simple land surface scheme (TOPUP) and combined within the Generalised Likelihood Uncertainty Estimation (GLUE) methodology, to provide estimates of areal evapotranspiration at the pixel scale. Such an approach offers an innovative means of transcending the patch or landscape scale of SVAT type models, to spatially distributed estimates of model output. The resulting spatial and temporal patterns of land surface fluxes and surface resistance are used to more fully understand the hydro-ecological trends observed across a study catchment in eastern Australia. The modelling approach is assessed by comparing predicted cumulative evapotranspiration values with surface fluxes determined from Bowen ratio systems and using auxiliary information such as in-situ soil moisture measurements and depth to groundwater to corroborate observed responses.

International audience; Calibration and prediction in conceptual rainfall-runoff (CRR) modelling is affected by the sampling and measurement uncertainty in the observed input/output data and by the structural error of the model conceptualisation. The Bayesian Total Error Analysis methodology (BATEA) provides the opportunity to directly and comprehensively address these sources of uncertainty. BATEA is based on Bayesian hierarchical methods and uses explicit error models for input/output data and structural errors. Previous studies demonstrated that simultaneous inference on forcing (e.g. rainfall) and structural errors requires strong prior knowledge of the error mechanisms (e.g., statistical properties of rainfall errors).This paper investigates a geostatistical approach to estimate the sampling error made in approximating the areal rainfall by averaging point-values from a raingauge network. The geostatistical model generates an ensemble of rainfall fields conditioned on gauged values. This ensemble is treated as a distribution of the true areal rainfall over the catchment and used as a prior in the BATEA framework. A case study shows that the inclusion of such prior knowledge allows simultaneous estimation of input and structural errors, whereas in the absence of such information the inference is ill-posed.