Details
| Original language | English |
|---|---|
| Pages (from-to) | 3145-3161 |
| Number of pages | 17 |
| Journal | Hydrological processes |
| Volume | 29 |
| Issue number | 14 |
| Early online date | 16 Jan 2015 |
| Publication status | Published - 1 Jul 2015 |
Abstract
The application of stationary parameters in conceptual hydrological models, even under changing boundary conditions, is a common yet unproven practice. This study investigates the impact of non-stationary model parameters on model performance for different flow indices and time scales. Therefore, a Self-Organizing Map based optimization approach, which links non-stationary model parameters with climate indices, is presented and tested on seven meso-scale catchments in northern Germany. The algorithm automatically groups sub-periods with similar climate characteristics and allocates them to similar model parameter sets. The climate indices used for the classification of sub-periods are based on (a) yearly means and (b) a moving average over the previous 61days. Classification b supports the estimation of continuous non-stationary parameters. The results show that (i) non-stationary model parameters can improve the performance of hydrological models with an acceptable growth in parameter uncertainty; (ii) some model parameters are highly correlated to some climate indices; (iii) the model performance improves more for monthly means than yearly means; and (iv) in general low to medium flows improve more than high flows. It was further shown how the gained knowledge can be used to identify insufficiencies in the model structure.
Keywords
- Hydrological modelling, Non-stationary parameter, Self-Organizing Map
ASJC Scopus subject areas
- Environmental Science(all)
- Water Science and Technology
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In: Hydrological processes, Vol. 29, No. 14, 01.07.2015, p. 3145-3161.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Non-stationary hydrological model parameters
T2 - A framework based on SOM-B
AU - Wallner, Markus
AU - Haberlandt, Uwe
N1 - Publisher Copyright: © 2015 John Wiley & Sons, Ltd.
PY - 2015/7/1
Y1 - 2015/7/1
N2 - The application of stationary parameters in conceptual hydrological models, even under changing boundary conditions, is a common yet unproven practice. This study investigates the impact of non-stationary model parameters on model performance for different flow indices and time scales. Therefore, a Self-Organizing Map based optimization approach, which links non-stationary model parameters with climate indices, is presented and tested on seven meso-scale catchments in northern Germany. The algorithm automatically groups sub-periods with similar climate characteristics and allocates them to similar model parameter sets. The climate indices used for the classification of sub-periods are based on (a) yearly means and (b) a moving average over the previous 61days. Classification b supports the estimation of continuous non-stationary parameters. The results show that (i) non-stationary model parameters can improve the performance of hydrological models with an acceptable growth in parameter uncertainty; (ii) some model parameters are highly correlated to some climate indices; (iii) the model performance improves more for monthly means than yearly means; and (iv) in general low to medium flows improve more than high flows. It was further shown how the gained knowledge can be used to identify insufficiencies in the model structure.
AB - The application of stationary parameters in conceptual hydrological models, even under changing boundary conditions, is a common yet unproven practice. This study investigates the impact of non-stationary model parameters on model performance for different flow indices and time scales. Therefore, a Self-Organizing Map based optimization approach, which links non-stationary model parameters with climate indices, is presented and tested on seven meso-scale catchments in northern Germany. The algorithm automatically groups sub-periods with similar climate characteristics and allocates them to similar model parameter sets. The climate indices used for the classification of sub-periods are based on (a) yearly means and (b) a moving average over the previous 61days. Classification b supports the estimation of continuous non-stationary parameters. The results show that (i) non-stationary model parameters can improve the performance of hydrological models with an acceptable growth in parameter uncertainty; (ii) some model parameters are highly correlated to some climate indices; (iii) the model performance improves more for monthly means than yearly means; and (iv) in general low to medium flows improve more than high flows. It was further shown how the gained knowledge can be used to identify insufficiencies in the model structure.
KW - Hydrological modelling
KW - Non-stationary parameter
KW - Self-Organizing Map
UR - http://www.scopus.com/inward/record.url?scp=84947867933&partnerID=8YFLogxK
U2 - 10.1002/hyp.10430
DO - 10.1002/hyp.10430
M3 - Article
AN - SCOPUS:84947867933
VL - 29
SP - 3145
EP - 3161
JO - Hydrological processes
JF - Hydrological processes
SN - 0885-6087
IS - 14
ER -