Bibliographic Details
| Title: |
Desktop-based determination of first derivative environmental flow requirements for the Luvuvhu headwater sub-basins in South Africa. |
| Authors: |
Gumbo, Anesu D.1 (AUTHOR) diongumbo@gmail.com, Kapangaziwiri, Evison2 (AUTHOR), Mugari, Ephias3 (AUTHOR), Nethengwe, Nthaduleni S.1 (AUTHOR) |
| Source: |
Hydrological Sciences Journal/Journal des Sciences Hydrologiques. Apr2026, Vol. 71 Issue 5, p934-950. 17p. |
| Subjects: |
Instream flow, Water management, Streamflow, Sensitivity analysis, Riparian areas, Community organization |
| Geographic Terms: |
South Africa |
| Abstract: |
This study assessed environmental flow requirements (EFRs) in the Luvuvhu River headwaters, South Africa, using two automated Department of Water and Sanitation monitored stations, A91AB_Out and A91CD_Out. The Revised Desktop Reserve Model (RDRM), supported by participatory mapping and sensitivity analysis, revealed strong hydrological alteration, particularly at A91AB_Out below Albasini Reservoir, where flows deviate from natural conditions. Required EFRs were 17.21 m3 × 106 (52.7% of MAR) at A91AB_Out and 41.03 m3 × 106 (34.6% of MAR) at A91CD_Out, yet observed flows fell below these thresholds during the dry season. Community insights highlighted the importance of avoiding abstraction, addressing wetland degradation, and improving water governance. Sensitivity tests on baseflow (α = 0.96; β = 0.44), flow thresholds (20th percentile), and hydraulic scaling (0.5) enhanced model robustness. The study recommends seasonally timed reservoir releases to restore sediment transport, channel connectivity, aquatic habitats, and rural water supply through community-led, adaptive flow management in data-scarce catchments. [ABSTRACT FROM AUTHOR] |
|
Copyright of Hydrological Sciences Journal/Journal des Sciences Hydrologiques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |