Hydrological connectivity on watershed nitrogen transport processes: a review
Abstract Understanding nitrogen transport processes (NTP) is essential for effective watershed nitrogen (N) pollution management, and hydrological connectivity (HC) is an important way for studying these processes. However, the current researches primarily focused on conceptual and structural connec...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-06-01
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| Series: | Applied Water Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s13201-025-02530-1 |
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| Summary: | Abstract Understanding nitrogen transport processes (NTP) is essential for effective watershed nitrogen (N) pollution management, and hydrological connectivity (HC) is an important way for studying these processes. However, the current researches primarily focused on conceptual and structural connectivity, limiting the deeper exploration of NTP. In this review, 136 papers were grouped into three categories: i) influencing factors of HC; ii) influencing factors of NTP and research methods; and iii) the relationship between HC and NTP. The reviewed contributions within each category were 36%, 33%, and 31% papers, respectively. The results showed that rainfall events, land use, and biogeochemical processes were the main factors affecting NTP. HC was mainly influenced by the river networks, human activities, and landscape patterns. The key methods used to study NTP include the stable isotope tracing method, MixSIAR, SWAT, and INCA-N. However, current research on the coupling of HC and NTP is insufficient to study changes in hydrological dynamics, hindering accurate identification of complex changes in NTP. To promote the accurate identification of NTP through the application of HC, we recommend that future research should: i) developing methods for characterizing hydrological functional connectivity (HFC) to enhance the understanding of hydrological changes processes; ii) incorporating HC indicators into NTP models to improve the understanding of NTP; iii) developing a prediction model that combines NTP models with machine learning (ML) to predict future characteristics of NTP changes. Overall, this review helps watershed managers make better decisions about when, where, and how to intervene effectively. |
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| ISSN: | 2190-5487 2190-5495 |