Clustering approaches for analysing similarity in ungagued catchments: input variable selection for hydrological predictions

Date

2018-04-08

Author

Doğulu, Nilay
Batmaz, İnci
Kentel Erdoğan, Elçin

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Catchments are hydrological units that exhibit unique but distinct features that greatly contribute to heterogeneity and complexity of rainfall-runoff processes. While the lure of understanding such diversity has underpinned the focus of many research efforts in hydrology, including predictions in ungauged basins, there is still room for improving our ability to benefit from this diversity in the context of data-driven hydrologic regionalization. An outstanding issue in this line of research concerns enhanced utilization of knowledge on dominant factors affecting catchments’ hydrologic response behaviour under different types of streamflow. Our study addresses this issue by grouping similar catchments across continental USA using the CAMELS dataset (Addor et al., 2017) for the purpose of determining input variables that control streamflow predictability within each group of catchments. To this aim, we explore the performance of different clustering methods in identifying similar catchments based on available topography, soil, geology, vegetation and climate attributes, and then evaluate the set of variables which characterize hydrological attribute of interest (95% flow percentile for low flows, mean daily discharge for medium flows, and 5% flow percentile for high flows) using iterative input variable selection method (Galelli and Castelletti, 2013). We compare three clustering approaches that belong to different family of methods: partitional clustering algorithm (k-means clustering), density-based clustering algorithm, and spectral clustering algorithm. We discuss the results from the perspective of underlying assumptions and capabilities of these methods, and provide insights into effects of clustering method choice in analysing variability of catchment similarity with respect to high, medium and low flows.

URI

https://hdl.handle.net/11511/74828

Conference Name

EGU General Assembly 2018, (8 - 13 Nisan 2018)

Collections

Department of Statistics, Conference / Seminar

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Citation Formats

N. Doğulu, İ. Batmaz, and E. Kentel Erdoğan, “Clustering approaches for analysing similarity in ungagued catchments: input variable selection for hydrological predictions,” presented at the EGU General Assembly 2018, (8 - 13 Nisan 2018), Vienna, Austria, 2018, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/74828.