Impact of modellers' decisions on hydrological a priori predictions
- 1Chair of Hydrology and Water Resources Management, Brandenburg University of Technology, Cottbus, Germany
- 2Department of Civil Engineering, University of Manitoba, Winnipeg, Canada
- 3Department of Civil Engineering, University of Siegen, Siegen, Germany
- 4GFZ German Research Centre for Geosciences, Potsdam, Germany
- 5Department of Civil and Environmental Engineering and Grantham Institute for Climate Change, Imperial College London, London, UK
- 6Department of Agricultural Engineering, University di Naples Federico II, Naples, Italy
- 7Institute for Landscape Ecology and Resources Management, University of Giessen, Giessen, Germany
- 8Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, New South Wales, Australia
- 9School of GeoSciences and National Centre for Earth Observation, University of Edinburgh, Edinburgh, UK
- 10Department of Land and Water Resources Engineering, Royal Institute of Technology KTH, Stockholm, Sweden
- 11Institute of Hydrology and Meteorology, University of Technology Dresden, Dresden, Germany
- 12Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
- 13Institute of Hydraulic Engineering and Water Resources Management, TU Vienna, Vienna, Austria
- 14Department of Environmental System Sciences, ETH Zurich, Zurich, Switzerland
Abstract. 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.
In this qualitative analysis of a statistically small number of predictions we learned (i) that soft information such as the modeller's system understanding is as important as the model itself (hard information), (ii) that the sequence of modelling steps matters (field visit, interactions between differently experienced experts, choice of model, selection of available data, and methods for parameter guessing), and (iii) that added process understanding can be as efficient as adding data for improving parameters needed to satisfy model requirements.