Variable spatio-temporal source contributions during storm hydrographs revealed by composite fingerprinting
Study Region: The Kasilian watershed with an area of 67.22 km2 is located in the eastern part of Mazandaran province. Study Focus: This study investigated the spatio-temporal variations in sediment sources during rainfall events. For this purpose, source samples were collected from various land uses, including rangeland, natural forest, hand-planted forest, and agriculture, as well as from the river bed. Suspended sediment sampling was conducted at 60-minute intervals during three rainfall events at two monitoring stations: MS1 (mid-watershed) and MS2 (watershed outlet). The concentration of 59 geochemical elements in source and sediment samples was measured, and composite fingerprints were selected using statistical tests in the FingerPro package in R software. New Hydrological Insights for the Region: The study found that the contributions of rangelands, natural forests, hand-planted forests, and the river bed at MS1 were 46 %, 24 %, 19 %, and 11 %, respectively, while at MS2, the contributions were 72 %, 7 %, 4 %, 14 %, and 3 % for agricultural lands. Additionally, intra-event variations showed that at MS1, rangelands contributed the most at the hydrograph peak, whereas during the rising and falling limbs, both rangelands and natural forests were dominant. At MS2, rangelands had the highest contribution throughout all hydrograph phases. These findings provide valuable information for managers in developing management programs.
| Item Type | Article |
|---|---|
| Open Access | Gold |
| Additional information | Acknowledgements The current research was undertaken as the doctoral dissertation of Fatemeh Akbari Emamzadeh with the financial support of Tarbiat Modares University, Iran. The contribution by ALC was funded by the UK Research and Innovation–Biotechnology and Biological Sciences Research Council (UKRI-BBSRC) via grant award BB/X010961/1 (Resilient Farming Futures) – specifically work package 2 - BBS/E/RH/230004B; Detecting agroecosystem ‘resilience’ using novel data science methods |
| Keywords | Flood hydrographs, Geochemical tracers, Sediment tracing, Soil erosion |
| Project | Resilient Farming Futures, Resilient Farming Futures (WP2): Detecting agroecosystem ‘resilience’ using novel data science methods, Tarbiat Modares University, Iran |
| Date Deposited | 05 Dec 2025 10:44 |
| Last Modified | 19 Dec 2025 14:58 |
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