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Impact of modellers' decisions on hydrological a priori predictions

Holländer, H.M.; Bormann, H.; Blume, T.; Buytaert, W.; Chirico, G.B.; Exbrayat, J.-F; Gustafsson, David; Hölzel, H.; Krausse, T. and Kraft, P., et al. (2014) In Hydrology and Earth System Sciences 18(6). p.2065-2085
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... (More)
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. (Less)
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publication status
published
subject
keywords
Artificial Catchment, Water, Parameters, Scheme
in
Hydrology and Earth System Sciences
volume
18
issue
6
pages
2065 - 2085
publisher
European Geophysical Society
external identifiers
  • scopus:84901981633
ISSN
1607-7938
DOI
10.5194/hess-18-2065-2014
project
MERGE
language
English
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23c1a84f-8b2c-4677-ad0f-699b5e6ac0b3 (old id 7515418)
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2015-07-08 14:53:08
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@article{23c1a84f-8b2c-4677-ad0f-699b5e6ac0b3,
  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.},
  author       = {Holländer, H.M. and Bormann, H. and Blume, T. and Buytaert, W. and Chirico, G.B. and Exbrayat, J.-F and Gustafsson, David and Hölzel, H. and Krausse, T. and Kraft, P. and Stoll, S. and Blöschl, G. and Flühler, H.},
  issn         = {1607-7938},
  keyword      = {Artificial Catchment,Water,Parameters,Scheme},
  language     = {eng},
  number       = {6},
  pages        = {2065--2085},
  publisher    = {European Geophysical Society},
  series       = {Hydrology and Earth System Sciences},
  title        = {Impact of modellers' decisions on hydrological a priori predictions},
  url          = {http://dx.doi.org/10.5194/hess-18-2065-2014},
  volume       = {18},
  year         = {2014},
}