Assessment of environmental flows from complexity to parsimony - Lessons from Lesotho

A. Tegos, W. Schlüter, N. Gibbons, Y. Katselis, and A. Efstratiadis, Assessment of environmental flows from complexity to parsimony - Lessons from Lesotho, Water, 10 (10), 1293, doi:10.3390/w10101293, 2018.

[doc_id=1878]

[English]

Over the last decade, Environmental Flow Assessment (EFA) has focused scientific attention around heavily-modified hydrosystems, such as flow regulated releases downstream of dams. In this light, numerous approaches of varying complexity have been developed, the most holistic of which incorporate hydrological, hydraulic, biological and water quality inputs, as well as socioeconomic issues. Finding the optimal flow releases, informing policy and determining an operational framework are often the main focus. This work exhibits a simplification of the DRIFT framework, and is regarded as the first holistic EFA approach, consisting of three modules, namely hydrological, hydraulic and fish quality. A novel conceptual classification for fish quality is proposed, associating fish fauna requirements with hydraulic characteristics, exported by fish survey analyses. The new methodology was applied and validated successfully at three stream sites in Lesotho, where DRIFT was formerly employed.

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Our works referenced by this work:

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Our works that reference this work:

1.	A. Koskinas, A. Tegos, P. Tsira, P. Dimitriadis, T. Iliopoulou, P. Papanicolaou, D. Koutsoyiannis, and Τ. Williamson, Insights into the Oroville Dam 2017 spillway incident, Geosciences, 9 (37), doi:10.3390/geosciences9010037, 2019.
2.	A. Koskinas, and A. Tegos, StEMORS: A stochastic eco-hydrological model for optimal reservoir sizing, Open Water Journal, 6 (1), 1, 2020.

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2.	Langat, P. K., L. Kumar, and R. Koech, Identification of the most suitable probability distribution models for maximum, minimum, and mean streamflow, Water, 11, 734, doi:10.3390/w11040734, 2019.
3.	Sahoo, B. B., R. Jha, A. Singh, A. and D. Kumar, Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting, Acta Geophysica, 67, 1471-1481, doi:10.1007/s11600-019-00330-1, 2019.
4.	Ding, L., Q. Li, J. Tang, J. Wang, and X. Chen, Linking land use metrics measured in aquatic-terrestrial interfaces to water quality of reservoir-based water sources in Eastern China, Sustainability, 11(18), 4860, doi:10.3390/su11184860, 2019.
5.	Koskinas, A., Stochastics and ecohydrology: A study in optimal reservoir design, Dams and Reservoirs, 30(2), 53-59, doi:10.1680/jdare.20.00009, 2020.
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11.	Nasiri Khiavi, A., R. Mostafazadeh, and F. Ghanbari Talouki, Using game theory algorithm to identify critical watersheds based on environmental flow components and hydrological indicators, Environment, Development and Sustainability, doi:10.1007/s10668-023-04390-8, 2024.

Tagged under: Environment, Hydrological processes