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| 2. |
- 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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| 3. |
- Exbrayat, J. -F, et al.
(författare)
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Ensemble modelling of nitrogen fluxes : Data fusion for a Swedish meso-scale catchment
- 2010
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Ingår i: Hydrology and Earth System Sciences. - : Copernicus GmbH. - 1607-7938 .- 1027-5606. ; 14:12, s. 2383-2397
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Tidskriftsartikel (refereegranskat)abstract
- Model predictions of biogeochemical fluxes at the landscape scale are highly uncertain, both with respect to stochastic (parameter) and structural uncertainty. In this study 5 different models (LASCAM, LASCAM-S, a selfdeveloped tool, SWAT and HBV-N-D) designed to simulate hydrological fluxes as well as mobilisation and transport of one or several nitrogen species were applied to the mesoscale River Fyris catchment in mid-eastern Sweden. Hydrological calibration against 5 years of recorded daily discharge at two stations gave highly variable results with Nash-Sutcliffe Efficiency (NSE) ranging between 0.48 and 0.83. Using the calibrated hydrological parameter sets, the parameter uncertainty linked to the nitrogen parameters was explored in order to cover the range of possible predictions of exported loads for 3 nitrogen species: nitrate (NO3), ammonium (NH4) and total nitrogen (Tot-N). For each model and each nitrogen species, predictions were ranked in two different ways according to the performance indicated by two different goodness-of-fit measures: the coefficient of determination R2 and the root mean square error RMSE. A total of 2160 deterministic Single Model Ensembles (SME) was generated using an increasing number of members (from the 2 best to the 10 best single predictions). Finally the best SME for each model, nitrogen species and discharge station were selected and merged into 330 different Multi-Model Ensembles (MME). The evolution of changes in R2 and RMSE was used as a performance descriptor of the ensemble procedure. In each studied case, numerous ensemble merging schemes were identified which outperformed any of their members. Improvement rates were generally higher when worse members were introduced. The highest improvements were achieved for the nitrogen SMEs compiled with multiple linear regression models with R2 selected members, which resulted in the RMSE decreasing by up to 90%. © Author(s) 2010.
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| 4. |
- Exbrayat, J. F., et al.
(författare)
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Multi-model data fusion as a tool for PUB : example in a Swedish mesoscale catchment
- 2011
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Ingår i: Advances in Geosciences. - : Copernicus Publications. - 1680-7340 .- 1680-7359. ; 29, s. 43-50
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Tidskriftsartikel (refereegranskat)abstract
- Post-processing the output of different rainfall-runoff models allows one to pool strengths of each model to produce more reliable predictions. As a new approach in the frame of the "Prediction in Ungauged Basins" initiative, this study investigates the geographical transferability of different parameter sets and data-fusion methods which were applied to 5 different rainfall-runoff models for a low-land catchment in Central Sweden. After usual calibration, we adopted a proxy-basin validation approach between two similar but non-nested sub-catchments in order to simulate ungauged conditions. Many model combinations outperformed the best single model predictions with improvements of efficiencies from 0.70 for the best single model predictions to 0.77 for the best ensemble predictions. However no "best" data-fusion method could be determined as similar performances were obtained with different merging schemes. In general, poorer model performance, i.e. lower efficiency, was less likely to occur for ensembles which included more individual models.
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| 5. |
- Hollaender, H. M., et al.
(författare)
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Comparative predictions of discharge from an artificial catchment (Chicken Creek) using sparse data
- 2009
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Ingår i: Hydrology and Earth System Sciences. - : Copernicus GmbH. - 1027-5606 .- 1607-7938. ; 13:11, s. 2069-2094
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Tidskriftsartikel (refereegranskat)abstract
- Ten conceptually different models in predicting discharge from the artificial Chicken Creek catchment in North-East Germany were used for this study. Soil texture and topography data were given to the modellers, but discharge data was withheld. We compare the predictions with the measurements from the 6 ha catchment and discuss the conceptualization and parameterization of the models. The predictions vary in a wide range, e.g. with the predicted actual evapotranspiration ranging from 88 to 579 mm/y and the discharge from 19 to 346 mm/y. The predicted components of the hydrological cycle deviated systematically from the observations, which were not known to the modellers. Discharge was mainly predicted as subsurface discharge with little direct runoff. In reality, surface runoff was a major flow component despite the fairly coarse soil texture. The actual evapotranspiration (AET) and the ratio between actual and potential ET was systematically overestimated by nine of the ten models. None of the model simulations came even close to the observed water balance for the entire 3-year study period. The comparison indicates that the personal judgement of the modellers was a major source of the differences between the model results. The most important parameters to be presumed were the soil parameters and the initial soil-water content while plant parameterization had, in this particular case of sparse vegetation, only a minor influence on the results.
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| 6. |
- 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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