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- Bormann, H., et al.
(författare)
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Comparative discharge prediction from a small artificial catchment without model calibration : Representation of initial hydrological catchment development
- 2011
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Ingår i: Die Bodenkultur. - 0006-5471. ; 62:1-4, s. 23-29
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Tidskriftsartikel (refereegranskat)abstract
- Ten conceptually different models were applied to predict the discharge from the 6 ha artificial Chicken Creek catchment in Lausatia, North-East Germany, which has been created in an open cast mining area. The study consisted of three steps to make a model intercomparison with the objective of a priori prediction of the water balance and the discharge dynamics. In order to test the ability of each model and modeller to predict water flows in an ungauged catchment, only soil texture, topography, vegetation coverage and climate data were provided to the modellers in the first step. Hydrological data on discharge, soil moisture and groundwater levels were withheld. This enabled us to assess the predictive capabilities of the models under sparse data conditions. The predicted components of the water balance varied in a wide range. None of the model simulations came close to the observed water balance for the entire 3-year study period. Discharge was mainly predicted as subsurface flow with little surface runoff. In reality, surface runoff was a major flow component despite the fairly coarse soil texture. In the second step, additional process knowledge was gained during a joint field visit. The occurence of gully erosion and surface crusting was detected and implemented into the models. Consequently, model predictions changed considerably. The previous simulations dominated by subsurface flow changed to surface flow-dominated simulations. Additional data, provided in the third step, mainly confirmed the parameterisations and assisted in a better definition of initial conditions and subsurface storage. The comparison indicates that, in addition to model philosophy, the personal judgement of the modellers was a major source of the differences in the model results. The model parameterisation and choice of initial conditions depended on the modeller's judgement and were therefore a result of the modellers' experience in terms of model types and case studies.
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- Holländer, H. M., et al.
(författare)
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Impact of modellers' decisions on hydrological a priori predictions
- 2014
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Ingår i: Hydrology and Earth System Sciences. - : Copernicus GmbH. - 1027-5606 .- 1607-7938. ; 18:6, s. 2065-2085
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Tidskriftsartikel (refereegranskat)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. Hollander 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.
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