In practice, the catchment hydrologist is often confronted with the task of predicting discharge without having the needed records for calibration. Here, we report the discharge predictions of 10 modellers - using the model of their choice - for the man-made Chicken Creek catchment (6 ha, northeast Germany, Gerwin et al., 2009b) and we analyse how well they improved their prediction in three steps based on adding information prior to each following step. The modellers predicted the catchment's hydrological response in its initial phase without having access to the observed records. They used conceptually different physically based models and their modelling experience differed largely. Hence, they encountered two problems: (i) to simulate discharge for an ungauged catchment and (ii) using models that were developed for catchments, which are not in a state of landscape transformation. The prediction exercise was organized in three steps: (1) for the first prediction the modellers received a basic data set describing the catchment to a degree somewhat more complete than usually available for a priori predictions of ungauged catchments; they did not obtain information on stream flow, soil moisture, nor groundwater response and had therefore to guess the initial conditions; (2) before the second prediction they inspected the catchment on-site and discussed their first prediction attempt; (3) for their third prediction they were offered additional data by charging them pro forma with the costs for obtaining this additional information.
Holländer et al. (2009) discussed the range of predictions obtained in step (1). Here, we detail the modeller's assumptions and decisions in accounting for the various processes. We document the prediction progress as well as the learning process resulting from the availability of added information. For the second and third steps, the progress in prediction quality is evaluated in relation to individual modelling experience and costs of added information.
Department of Hydrology and Water Resources Management, Brandenburg University of Technology, Cottbus, Germany; Department of Civil Engineering, University of Manitoba, Winnipeg, Canada; Department of Civil Engineering, University of Siegen, Siegen, Germany; GFZ German Research Centre for Geosciences, Potsdam, Germany; Department of Civil and Environmental Engineering, Grantham Institute for Climate Change, Imperial College London, London, United Kingdom; Department of Agricultural Engineering, University di Naples Federico II, Naples, Italy; Institute for Landscape Ecology and Resources Management, University of Giessen, Giessen, Germany; Climate Change Research Centre, ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, NSW, Australia; School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh, United Kingdom; Department of Land and Water Resources Engineering, Royal Institute of Technology KTH, Stockholm, Sweden; Institute of Hydrology and Meteorology, University of Technology Dresden, Dresden, Germany; Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland; Institute of Hydraulic Engineering and Water Resources Management, TU Vienna, Vienna, Austria; Department of Environmental System Sciences, ETH Zurich, Zurich, Switzerland
Recommended Citation:
Holländer H,M,, Bormann H,et al. Impact of modellers' decisions on hydrological a priori predictions[J]. Hydrology and Earth System Sciences,2014-01-01,18(6)