Your search found 2 records
1 Arthington, A. H.; Tickner, D.; McClain, M. E.; Acreman, M. C.; Anderson, E. P.; Babu, S.; Dickens, Chris W. S.; Horne, A. C.; Kaushal, N.; Monk, W. A.; O’Brien, G. C.; Olden, J. D.; Opperman, J. J.; Owusu, Afua G.; Poff, N. L.; Richter, B. D.; Salinas-Rodríguez, S. A.; Shamboko Mbale, B.; Tharme, R. E.; Yarnell, S. M. 2023. Accelerating environmental flow implementation to bend the curve of global freshwater biodiversity loss. Environmental Reviews, 27p. (Online first) [doi: https://doi.org/10.1139/er-2022-0126]
Environmental flows ; Freshwater ; Biodiversity ; Ecosystem services ; Resilience ; Rivers ; Water availability ; Water users ; Stakeholders ; Climate change ; Constraints ; Legislation ; Regulations ; Monitoring ; Funding ; Socioeconomic aspects ; Ecological factors ; Infrastructure ; Human resources ; Capacity development ; Training ; Case studies / USA / Guatemala / Mexico / Canada / UK / South Africa / Zambia / India / China / Australia / Putah Creek Tributary / Usumacinta River / Peace-Athabasca Delta / Savannah River / Roanoke River / Great Brak River Estuary / Olifants River / Luangwa River / Nile River Basin / Ramganga River / Yangtze River / Lower Goulburn River
(Location: IWMI HQ Call no: e-copy only Record No: H052092)
(1.91 MB) (1.91 MB)
Environmental flows (e-flows) aim to mitigate the threat of altered hydrological regimes in river systems and connected waterbodies and are an important component of integrated strategies to address multiple threats to freshwater biodiversity. Expanding and accelerating implementation of e-flows can support river conservation and help to restore the biodiversity and resilience of hydrologically altered and water-stressed rivers and connected freshwater ecosystems. While there have been significant developments in e-flow science, assessment, and societal acceptance, implementation of e-flows within water resource management has been slower than required and geographically uneven. This review explores critical factors that enable successful e-flow implementation and biodiversity outcomes in particular, drawing on 13 case studies and the literature. It presents e-flow implementation as an adaptive management cycle enabled by 10 factors: legislation and governance, financial and human resourcing, stakeholder engagement and co-production of knowledge, collaborative monitoring of ecological and social-economic outcomes, capacity training and research, exploration of trade-offs among water users, removing or retrofitting water infrastructure to facilitate e-flows and connectivity, and adaptation to climate change. Recognising that there may be barriers and limitations to the full and effective enablement of each factor, the authors have identified corresponding options and generalizable recommendations for actions to overcome prominent constraints, drawing on the case studies and wider literature. The urgency of addressing flow-related freshwater biodiversity loss demands collaborative networks to train and empower a new generation of e-flow practitioners equipped with the latest tools and insights to lead adaptive environmental water management globally. Mainstreaming e-flows within conservation planning, integrated water resource management, river restoration strategies, and adaptations to climate change is imperative. The policy drivers and associated funding commitments of the Kunming–Montreal Global Biodiversity Framework offer crucial opportunities to achieve the human benefits contributed by e-flows as nature-based solutions, such as flood risk management, floodplain fisheries restoration, and increased river resilience to climate change.
2 Hughes, D. A.; Read, L.; Jeuland, M.; Kapangaziwiri, E.; Elkurdy, M.; Lambl, D.; Hale, E.; Opperman, J. J.. 2023. Insights from a comparison of two hydrological modelling approaches in the Kwando (Cuando) River and the western tributaries of the Zambezi River Basin. Journal of Hydrology: Regional Studies, 48:101482. [doi: https://doi.org/10.1016/j.ejrh.2023.101482]
Floodplains ; Machine learning ; Models ; Hydrological modelling ; Runoff ; Stream flow ; Water resources ; Evapotranspiration ; Soil moisture ; Uncertainty ; Precipitation ; Water balance / Southern Africa / Kwando River / Zambezi River
(Location: IWMI HQ Call no: e-copy only Record No: H052065)
https://www.sciencedirect.com/science/article/pii/S2214581823001696/pdfft?md5=d11e53c6c8dc2e1c99de355c783c356c&pid=1-s2.0-S2214581823001696-main.pdf
https://vlibrary.iwmi.org/pdf/H052065.pdf
https://vlibrary.iwmi.org/pdf/H052065.pdf
(5.62 MB) (5.62 MB)
Study Region: The Kwando (Cuando) River and the western headwaters of the Zambezi River, which are data-scarce basins of southern Africa. Study Focus: A comparative analysis of the performance of two fundamentally different hydrological modelling approaches (a conceptual model and a theory guided machine learning model) in a data-sparse region. New Hydrological Insights for the Region: The machine learning model (HydroForecast) generally performs better – in terms of statistical fit between simulated and observed flows – than the conceptual model (Pitman). For the Kwando River, the conceptual model explicitly simulates the expected attenuation effects of a large floodplain, while the machine learning model represents this and other processes implicitly. The two models quantify the Kwando sub-basin flow contributions differently, with the conceptual model calibrated manually to align with the available qualitative information that suggests that the majority of the runoff is generated in the upstream sub-basin and then attenuated in the downstream floodplain. Generally, this work offers insight into how the two very different models can simulate historical flows in a large basin when streamflow observations and the forcing rainfall data are limited and of unknown quality, and suggests that a machine learning model better leverages information from multiple training parameters to reproduce the measured streamflows.
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