Technical note: Streamflow seasonality using directional statistics
<p>Hydrological fluxes typically vary across seasons with several existing metrics available to characterize their seasonality. These metrics are beneficial when many catchments across diverse climates and landscapes are studied concurrently. Here, we present directional statistics to characte...
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Copernicus Publications
2025-07-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | https://hess.copernicus.org/articles/29/2851/2025/hess-29-2851-2025.pdf |
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| author | W. R. Berghuijs K. Hale H. Beria H. Beria |
| author_facet | W. R. Berghuijs K. Hale H. Beria H. Beria |
| author_sort | W. R. Berghuijs |
| collection | DOAJ |
| description | <p>Hydrological fluxes typically vary across seasons with several existing metrics available to characterize their seasonality. These metrics are beneficial when many catchments across diverse climates and landscapes are studied concurrently. Here, we present directional statistics to characterize streamflow seasonality, capturing the timing of the streamflow (center of mass timing) and the strength of its seasonal cycle (center of mass concentration). We show that directional statistics are mathematically more robust than several widely used metrics to quantify streamflow seasonality. We extend the application of directional statistics to analyze seasonality in other hydrological fluxes, including precipitation, evapotranspiration, and snowmelt, and we introduce a trend analysis framework for both the timing and strength of seasonal cycles. Using an Alpine catchment (Dischma, Switzerland) as a test bed for this methodology, we identify a shift in the streamflow center of mass to earlier in the year and a weakening of the seasonal cycle. Additionally, we apply directional statistics to streamflow data from 11 118 European catchments, highlighting their utility for large-scale hydrological analyses. The introduced metrics, leveraging directional statistics, can improve our understanding of streamflow seasonality and associated changes and can also be used to study the seasonality of other environmental fluxes within and beyond hydrology.</p> |
| format | Article |
| id | doaj-art-e71e40ea05b34e2f8ae8110435d915c9 |
| institution | Kabale University |
| issn | 1027-5606 1607-7938 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Hydrology and Earth System Sciences |
| spelling | doaj-art-e71e40ea05b34e2f8ae8110435d915c92025-08-20T03:33:28ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382025-07-01292851286210.5194/hess-29-2851-2025Technical note: Streamflow seasonality using directional statisticsW. R. Berghuijs0K. Hale1H. Beria2H. Beria3Department of Earth Sciences, Free University Amsterdam, Amsterdam, the NetherlandsDepartment of Geography, University of British Columbia, Vancouver, CanadaWSL Institute for Snow and Avalanche Research SLF, Davos, SwitzerlandDepartment of Civil, Environmental and Geomatic Engineering, ETH Zurich, Zurich, Switzerland<p>Hydrological fluxes typically vary across seasons with several existing metrics available to characterize their seasonality. These metrics are beneficial when many catchments across diverse climates and landscapes are studied concurrently. Here, we present directional statistics to characterize streamflow seasonality, capturing the timing of the streamflow (center of mass timing) and the strength of its seasonal cycle (center of mass concentration). We show that directional statistics are mathematically more robust than several widely used metrics to quantify streamflow seasonality. We extend the application of directional statistics to analyze seasonality in other hydrological fluxes, including precipitation, evapotranspiration, and snowmelt, and we introduce a trend analysis framework for both the timing and strength of seasonal cycles. Using an Alpine catchment (Dischma, Switzerland) as a test bed for this methodology, we identify a shift in the streamflow center of mass to earlier in the year and a weakening of the seasonal cycle. Additionally, we apply directional statistics to streamflow data from 11 118 European catchments, highlighting their utility for large-scale hydrological analyses. The introduced metrics, leveraging directional statistics, can improve our understanding of streamflow seasonality and associated changes and can also be used to study the seasonality of other environmental fluxes within and beyond hydrology.</p>https://hess.copernicus.org/articles/29/2851/2025/hess-29-2851-2025.pdf |
| spellingShingle | W. R. Berghuijs K. Hale H. Beria H. Beria Technical note: Streamflow seasonality using directional statistics Hydrology and Earth System Sciences |
| title | Technical note: Streamflow seasonality using directional statistics |
| title_full | Technical note: Streamflow seasonality using directional statistics |
| title_fullStr | Technical note: Streamflow seasonality using directional statistics |
| title_full_unstemmed | Technical note: Streamflow seasonality using directional statistics |
| title_short | Technical note: Streamflow seasonality using directional statistics |
| title_sort | technical note streamflow seasonality using directional statistics |
| url | https://hess.copernicus.org/articles/29/2851/2025/hess-29-2851-2025.pdf |
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