Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.1017/eds.2023.17 |
Licence  |
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| Title (Primary) | Clustering of causal graphs to explore drivers of river discharge |
| Author | Günther, W.; Miersch, P.
; Ninad, U.; Runge, J. |
| Source Titel | Environmental Data Science |
| Year | 2023 |
| Department | CHS |
| Volume | 2 |
| Page From | e25 |
| Language | englisch |
| Topic | T5 Future Landscapes |
| Supplements | Supplement 1 Supplement 2 |
| Keywords | catchment hydrology; causal effect estimation; causal inference; clustering |
| Abstract | This work aims to classify catchments through the lens of causal inference and cluster analysis. In particular, it uses causal effects (CEs) of meteorological variables on river discharge while only relying on easily obtainable observational data. The proposed method combines time series causal discovery with CE estimation to develop features for a subsequent clustering step. Several ways to customize and adapt the features to the problem at hand are discussed. In an application example, the method is evaluated on 358 European river catchments. The found clusters are analyzed using the causal mechanisms that drive them and their environmental attributes. |
| Günther, W., Miersch, P., Ninad, U., Runge, J. (2023): Clustering of causal graphs to explore drivers of river discharge Environ. Data Sci. 2 , e25 10.1017/eds.2023.17 |
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