DRIFT has been applied at a basin level and project level for a large number of studies in Africa and several in Asia. Basin-level environmental flow assessments typically evaluate the current baseline flow (water and sediment) conditions with the aim of informing the potential for future development in a catchment or for evaluating the impacts of one or more proposed developments,
Increasingly we are approached by global donor agencies, governments and dam developers to apply DRIFT in river-basin planning exercises, transboundary water planning and monitoring, transboundary conflict resolution and as inputs to dam design, location and operation. DRIFT has been recognised as a good practice EFlows methodology by international lenders such as the World Bank, ADB, IFC; several global conservation NGOs (e.g. IUCN); inter-basin committees (e.g. OKACOM), and Governments (e.g. South Africa, Tanzaniaand Pakistan).
Basin level projects where DRIFT has been used include the Okavango Basin (Angola, Namibia and Botswana), Senqu River (Lesotho/South Africa); Pangani Basin (Tanzania), Luangwa Basin (Zambia), and the Mekong Basin (Lao, Myanmar, Thailand, Vietnam, Cambodia).
DRIFT was developed in a developing country and has been adapted to cater for situations where decision-making must address the livelihood needs of communities, who are often highly dependent on river ecosystems, and where data availability is poor. In such situations DRIFT provides for incorporation of local knowledge and understanding of the ecosystem and its biota (e.g. fish behaviour). The DRIFT method is designed to capture all kinds of knowledge (data, global literature, expert opinion and local wisdom) and use it in a transparent way.
Many developed countries (e.g. USA, Europe) have a different, more prescriptive approach to EFlows assessments and implementation, with different conditions and development imperatives. Here, alternative EFlows methodologies tend to be used, although DRIFT could equally be applied by EFlows practitioners in these countries.
The population of data in DRIFT and the outputs of its subsequent analyses are completely transparent. These are typically done using experienced and local scientists but can also involve project or river managers, which increases the level of understanding and buy-in to the outcomes.
DRIFT is able to integrate different kinds of information, which allows it to
make use of the best available information. For instance:
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