Resolution matters: An evaluation of EFlows assessment methods used for hydropower in developing countries

Resolution matters: An evaluation of EFlows assessment methods used for hydropower in developing countries | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Resolution matters: An evaluation of EFlows assessment methods used for hydropower in developing countries Hassan Bukhari, Cate Brown, Karen Esler This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6070177/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sustainable development of river infrastructure requires the use of contemporary Environmental Flows (EFlows) assessment methods that are based on an understanding of river functioning, and which provide information useful for designing potential mitigations and evaluating trade-offs between socio-ecological impacts and economic benefits. Through a systematic search and review, EFlows assessments of 42 hydropower projects in developing countries in Africa and Asia were analysed to understand the factors that explained the resolution of the methods used and whether the resolution used was suitable for the context in which it was applied. In general, reaches downstream of the tailrace were deemed more sensitive to hydropower development than dewatered sections, and in greater need for higher resolution EFlows studies. Despite this, most assessments focused only on the dewatered reaches. Low-resolution hydrological ratio methods were commonly used and did not match the resolution recommended by international good practice, although this is improving with time. Assessment date and the designation of Critical Habitat (a habitat classification based on the threatened status of species in the IUCN Red List) were the only significant drivers of increased resolution of EFlows assessments. However, despite most projects being in the IUCN habitat range of at least one Endangered freshwater species, the environmental studies of only five classified the aquatic area as Critical Habitat. This calls into question the dependence on Critical Habitat as the driving factor in the selection of suitable methods. Moreover, many hydropower specific EFlows assessments were redundant since, on average, 20 additional hydropower projects were planned in the same basin as each of the projects reviewed. In these cases, basin-scale EFlows assessments are needed to provide the requisite knowledge to mitigate impacts. The disconnect between EFlows theory and practice is a cause of concern for the sustainable development and use of river ecosystems. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Developing countries have exploited about 23% of their combined feasible hydropower resources [ 1 ] and, following a brief hiatus in dam construction coinciding with the World Commission on Dams [ 2 ], these countries have experienced an ongoing boom in hydropower development [ 3 – 5 ]. If sited, designed, constructed, or operated without the proper assessment and mitigation of their impacts, these developments are a clear and present threat to riverine ecosystems and the livelihoods that depend on them [ 2 ]. The World Commission on Dams [ 2 ] highlighted the importance of meaningful assessment, and mitigation, of the social and environmental impacts of dams and the importance of Environmental Flows (EFlows; [ 6 , 7 ]). The result was a rapid uptake of methods to assess EFlows in data limited environments, such as those in Africa and parts of Asia [ 8 – 10 ]. Since then, many countries have entrenched the right of rivers and estuaries to a portion of their natural flow regime and protection from pollution [ 11 – 15 ]. Other more recent initiatives, such as the Sustainable Development Goals, specifically Sub-goals 6.5 and 6.6, which target the protection and management of river related ecosystems, are also intrinsically tied to setting and maintaining EFlows [ 16 ]. Presently, a wide variety of EFlows assessment methods are used globally [ 17 , 18 ] and outdated EFlows methods continue to be used despite their many limitations. These obsolete methods (e.g., the 10% rule [ 19 ]) have little or no ecological basis [ 20 ] and do not address the impacts most associated with hydropower development, which tend to be more related to the timing than to volume of flows in the downstream river [ 21 ], the disruption to the supply of sediment from upstream [ 22 , 23 ] and the obstruction of migration routes of fish and other biota [ 24 , 25 ]. They are also unable to consider projected changes in climate and anthropogenic pressures [ 26 ]. More modern EFlows assessment methods address these shortcomings by focussing on the relationships between changes in the volume and timing of the flow of water, sediments and biota and river ecosystem functioning, for example, the DRIFT assessment method [ 27 , 28 ] and the Ecosystems Function Model [ 29 ]. These are interactive models, which makes them more useful when evaluating the location, design, and operation of water-resource developments [ 20 , 30 – 31 ], and in decision-making to balance the use of water for multiple outcomes [ 32 ]. EFlows assessment methods have been reviewed and categorized (e.g., [ 19 , 33 – 35 ]) and the importance of method selection in supporting sustainable development has been recognised by many of the Multilateral Financial Institutions (MFIs; e.g., [ 7 ]); but there are no systematic analyses on the EFlows methods used for major water-resource developments post- World Commission on Dams [ 2 ]. Thus, the objectives of this study were to evaluate the methods used for EFlows assessments conducted for major hydropower developments in developing countries in Africa and Asia, the extent to which they comply with international good practice, and the factors driving the adoption of suitable EFlows methods; so that this may provide insights for policy and planning related to EFlows. This study investigated the EFlows assessment methods used for a selection of new hydropower developments in Africa and Asia over the past two decades against the assessment resolution recommended by international good practice. Two main research questions were addressed regarding the selection of EFlows methods: 1) which explanatory variables predicted whether the EFlows method used matched the recommended resolution, and 2) which explanatory variables explained selection of higher resolution EFlows assessment methods. Fourteen explanatory variables were shortlisted based on hypotheses including links to project stakeholders, project size and design, economic conditions of the host country, date of assessment and aspects that determine the sensitivity of the freshwater ecosystem such as the presence of Critical Habitat (Table 1 ). The focus of the study was on hydropower projects financed by MFIs that have adopted and actively promote the Equator Principles (a set of guidelines for the consideration of social and environmental impacts of large-scale projects) as it was expected that the hydropower projects where they were involved would increasingly meet evolving international good practice related to EFlows assessment [ 36 ]. Table 1 Explanatory variables hypothesized to influence the selection of the EFlows assessment method resolution with reasons for inclusion. Hypothesis or reasoning for inclusion Variables Environmental and social performance can be driven through institutional requirements [ 117 , 118 ] Private sector involvement Environmental consultant experience may determine the quality of the study Involvement of international consulting firm Later assessments may build on previous experience and understanding of river ecosystems and increasing concerns for their sustainable development [ 119 ] EFlows assessment date Larger projects may face more scrutiny or have larger environmental and social assessment budgets Project size in megawatt Project size in US dollars (million) Project size in annual generation (GWh/year) Country economic conditions may impact the level of environmental and social compliance in projects due to varying priorities [ 120 ] Nominal Gross Domestic Product (GDP; billion USD) GDP per capita purchasing power parity (PPP) (USD per person) Access to electricity (% of population) The sensitivity of the river ecosystem, as assessed by good practice guidelines, will influence the methods used for EFlows assessment [ 7 ] Transboundary diversion Trans basin diversion First or most downstream project in a cascade Social dependence on the river Presence of Critical habitat 2. Materials and Methods 2.1 Sample selection The study was limited to projects post 2000, as there was limited consideration of environmental impacts of large dams before then, and included those financed or under consideration for financing by the Asian Development Bank (ADB), the International Finance Corporation (IFC), the International Bank for Reconstruction and Development (IBRD), and the International Development Association (IDA). These were selected because the required documentation is publicly accessible on searchable online databases. A systematic search was conducted across the project databases of ADB (www.adb.org/projects; total records: 10,989) on August 2, 2021, IFC (www.disclosures.ifc.org/; total records: 9,494) on August 22, 2021; and the combined IDA and IBRD database (www.projects.worldbank.org; total records 21,073) on August 25, 2021. The keywords and/or search methods differed depending on the database (Fig. 1). The final screened list resulted in a total of 119 records which yielded a list of 188 hydropower projects (single records occasionally referred to multiple hydropower projects). A set of criteria was then used to develop the sample set for this study. The focus was on medium to large dams, and so small and micro hydropower projects (defined as projects < 30 MW; [37]) were excluded (63 of 188 excluded on this criteria). The study was also limited to new projects, as EFlows assessments are typically not done during privatization or rehabilitation of existing projects (62 of 188 excluded on this criteria). Lastly, in line with the focus on ‘developing countries’ where most of the upcoming hydropower developments are planned, only projects in low or low to middle income countries (as defined by the World Bank) were considered (50 of 188 excluded on this criteria). Three projects that met the above criteria but were dropped from consideration by the MFI because of project delays and conflicts (e.g., disruptions related to the Tajik Civil War) were also excluded from the study. As some projects met multiple exclusion criteria, 126 projects were excluded overall and 47 projects in 16 countries were shortlisted for further evaluation. The environmental assessments for the shortlisted hydropower projects were downloaded from the relevant websites. These comprised 29 Environmental and Social Impact Assessments (ESIAs), one EFlows Assessment, 11 Summary Environmental Impact Assessments (SEIAs), and one Summary Initial Environmental Examination (SIEE). Project design documents and monitoring reports submitted to the UN Clean Development Mechanism were used to augment the information for those projects where only summary assessments were available. In addition, supplementary studies such as biodiversity management plans (e.g., for Karot Hydropower Project) or cumulative impact assessments (e.g., for Patrind Hydropower Project) were accessed as they sometimes included an EFlows component. These documents formed the main data set for this study. EFlows assessment documentation could not be sourced for five of the 47 projects, and so these were excluded from the review. Of the 42 final shortlisted hydropower projects (Fig. 2), 22 were operational, 19 were under-construction and one was in the feasibility stage (i.e., construction contracts had not yet been awarded). The mean estimated capital cost of the hydropower installations was $2.2 million USD per installed megawatt and the median reservoir size was 0.5 square km. Supplementary Table 1 provides the full list of hydropower plants included in the review and Supplementary Table 2 provides summary statistics of their key characteristics. 2.2 EFlows assessment resolution Although, both World Commission on Dams [2] and the Brisbane Declaration [6] lay out best practices for the assessment of EFlows, the guidelines they provide are abstract and not readily operationalized for this study. Opperman et al. [38] sort EFlows methods into three groups of increasing complexity and provide illustrative examples to demonstrate the application of each group; however, their examples are from the United States where data availability and regulatory requirements do not map well onto the conditions present in developing countries [39]. Lastly, The World Bank Group (WBG) Good Practice Handbook on Environmental Flows for Hydropower Projects [7] provides a structured method for selecting the resolution for an EFlows assessment, is specifically tailored for emerging markets, and references requirements of MFI environmental standards such as the IFC Critical Habitat requirements [40]. Therefore, WBG [7] was selected as the benchmark international good practice criteria due to its relevance to this study. The information required for the WBG [7] decision tree (Fig. 3) was compiled for the shortlisted hydropower projects. The requisite information comprises design and operation of the hydropower plant; transboundary and/or trans-basin issues; the type of ecosystem impacted; the extent of social dependence on those ecosystems; the presence of other existing or planned hydropower plants, and whether the host river(s) met the definitions for Critical or Modified Habitat. The methods used to compile and process this information were designed to allow for their uniform application across all shortlisted hydropower projects and are described briefly. The significance of the dewatered reach was evaluated on a case-by-case basis considering the length of the diversion, inflows from other unaffected rivers, quality of river habitat effected, and the social dependence on the reach [7]. The country(ies) in which the hydropower dam, reservoir, powerhouse and tailrace were located was captured and if specific transboundary issues related to water consumption, sediment transport or connectivity were directly mentioned in the environmental reports then these were also noted. From the dam wall, the downstream study area was extended to the outflow of the river into a lake, the sea, or a reservoir and where there were aquatic ecosystems other than the river, e.g., lake, wetland, floodplain and estuary in this study area, this information was captured using Google Earth satellite imagery. Social dependence was evaluated by searching the project environmental reports using the key words ‘fishing’, ‘fisheries’, ‘navigation’, ‘tourism’, ‘festivities’, ‘festival’, ‘ceremony’, ‘ceremonies’, ‘rafting’, ‘hiking’ and ‘recreation’. This was supplemented by a multi-year analysis of Google Earth satellite imagery for the presence of settlements along the banks or within the floodplains of a potentially impacted river (i.e., adjacent to the dewatered reach or just downstream of the tailrace), and if any floodplain agriculture was evident. At minimum, these settlements likely depended on the river ecosystem for water and wastewater removal as well as subsistence, cultural and recreational activities [41]. It was noted if the project was the first constructed in a planned cascade of projects or was the most downstream in an existing cascade (according to the environmental assessment supplemented with Google Earth imagery). Furthermore, to obtain a coarse overview of the full suite of planned hydropower developments, the number of hydropower projects planned for the river basin where each project was located was estimated through web searches. For the MFIs included in the review, Critical Habitat is defined as an area with high biodiversity value including habitat, which is of significant importance to International Union for the Conservation of Nature (IUCN) Red-listed Endangered or Critically Endangered species, endemic, migratory and/or congregatory species [42, 43]. The determinations of Critical and Modified Habitat as stated in project documents were noted. Furthermore, the IUCN Red List that forms the basis of the determination of Critical Habitat was used to cross check the Critical Habitat determinations provided by the environmental assessments. The IUCN Red List includes global assessments for ~ 142,500 species of which ~ 80% have associated spatial data [44] which are routinely used for screening for Critical Habitat in terrestrial and marine environments [45, 46]. The WBG [7] categorization of EFlows assessment methods designates hydrological ratio methods without any local calibration (e.g., the 10% rule [19] and the Tenant Method [47]) as very low-resolution methods. Hydraulic (e.g., wetted perimeter), water quality (e.g., Qual2k [48]) and integrated hydrological (e.g., Indicators of Hydrological Alteration [49]) methods are designated as low-resolution methods. Habitat simulation (e.g., Instream Flow Incremental Methodology [50, 51]), holistic and eco-social methods (e.g., Building Block Methodology, DRIFT, RANA-ICE) can be medium or high resolution depending on the level of detail (e.g., number of components of the ecosystem addressed) and the level of effort in collecting local information. The categories used by WBG [7] align with the EFlows classification suggested by Opperman et al. [38] who provide a three-tier categorization of EFlows assessment methods. They define Level 1 methods as holistic hydrologic desktop approaches such as the Indicators of Hydrological Alteration, which correspond to low-resolution assessment as per WBG [7], and Level 2 methods as holistic expert panel methods, which include the Building Block Methodology and DRIFT, corresponding with medium and high-resolution assessments in WBG [7]. Opperman et al. [38] Level 3 research driven assessments do not fit into any of the categories in WBG [7]. These include a broad range of analytical methods that span over several years and may include experimental releases and monitoring and are typically not used in EFlows assessments for hydropower developments in Asia and Africa. The EFlows assessment methods used for each hydropower project were categorized according to WBG [7] plus two additional categories: studies that did not mention EFlows, and those that stated a downstream release but did not provide a method for its determination. This study evaluated the resolution of the EFlows methods and whether they were suitable for the context in which they were applied i.e., they matched or exceeded the WBG [7] suggested resolution. It did not evaluate whether the methods were implemented correctly, the suitability of the scenarios assessed or the level to which the recommended or adopted EFlows would support the health of the river ecosystems and the livelihoods that depended on them. The possible drivers for increasing EFlows assessment resolution were explored quantitatively by scoring each study on a zero to five scale based on the EFlows resolution used as follows: no EFlows study (score of zero), downstream release stated without discussion of the EFlows method (score of one), very low-resolution study (score of two), low resolution study (score of three), medium resolution study (score of four), and high-resolution study (score of five). This dependent variable was regressed against the possible explanatory variables (Table 1) using multivariable linear regression models which were developed using forward and backward selection. In addition, to understand which variables predicted whether the EFlows assessment matched the resolution recommended by WBG [7] a multi-variable logistic regression [52] was used due to the match/no match binary dependent variable. The match/no match dataset comprised 35 datapoints for the dewatered reach and 42 datapoints downstream of the tailrace outlet. With limited observations there was the risk of overfitting the logistic model, therefore the number of explanatory variables was limited to two [53]. Single variable models were used to test the significance for each independent variable and those with p > 0.20 were removed from the pool. Models made with a combination of the remaining variables with the highest area under the curve (AUC) that contained significant explanatory variables (p < 0.05) were shortlisted for analysis. Data for most of the explanatory variables were obtained from project environmental studies but four economic indicators were obtained from World Bank Open Data (data.worldbank.org; accessed September 15, 2021) for each country for the year of the environmental study viz. : nominal Gross Domestic Product (GDP); GDP per capita purchasing power parity (PPP); and access to electricity. A multicollinearity analysis showed high correlation between the three project size variables (cost, installed capacity, annual generation); of these, annual generation was selected for further use. 3. Results The 42 hydropower projects in the sample were evaluated against each of the eight decision points in the WBG [7] decision tree, from which findings are summarized (see Supplementary Table 3 for detailed results). Although seventeen of the hydropower projects assessed were planned as baseload plants, none of them met the criteria for low impact design and operation due to storage size and length of the diversion. Three of the reviewed hydropower projects were transboundary in nature and eleven had trans-river and/or trans- basin diversions. Seven hydropower projects were considered either the first or most downstream in a cascade and eleven projects had significant social uses of the river ecosystem. Apart from river ecosystems, no other ecosystems were located in the dewatered reaches of any of the hydropower projects studied. For 11 projects, ecosystems other than river occurred downstream of the tailrace, however only one of these, the Niger River Floodplain, was likely to be significantly impacted. Two other hydropower projects evaluated in this study, Tanahu and Upper Trishuli 1, would impair EFlows to the Gandak River Floodplain and another two, Nam Theun 2 and Nam Ngiep, would impair EFlows to the Mekong Delta. Each of these were individually assessed and deemed to not have significant impacts on these ecosystems, although it is acknowledged that they contribute to the cumulative impacts of several projects, which are likely to be significant [54–57]. Moreover, in the river basins where the reviewed hydropower projects are under construction, about 400 additional hydropower projects are planned (see Supplementary Table 4). Only in the Kopili River Basin were no other hydropower projects planned, possibly because the basin already contains multiple large dams (e.g., Kopili Dam and Khandong Dam) that are at risk from acidic mine discharge [58]. Five of the environmental studies reviewed assigned Critical Habitat to the host river, i.e., 12% of reviewed projects: Gulpur, Balakot, Kohala, Nachtigal and Batoka Gorge. Notwithstanding this, 27 of the 42 hydropower locations intersected with the spatial extent of one or more Endangered or Critically Endangered freshwater species on the IUCN Red List. In total, there were 55 Endangered and Critically Endangered species whose IUCN spatial extant overlapped with project areas, however, only three of these species ( Glyptothorax kashmirensis, Tor putitora , and Ledermaniella sanagensis) were assessed in the environmental studies to have Critical Habitat in the project area. It should be noted that 26 of the 55 overlapping species were re-classified as Endangered or Critically Endangered only after the completion of project environmental studies (see Supplementary Table 5); for ten species the threatened status was elevated less than three years after completion of project studies, and before the start of construction. In addition, many of the environmental studies relied on IUCN Red List spatial data to identify or confirm the presence of endangered species, sometimes in contradiction of survey data from the host river. For example, the ESIA surveys for the Nam Ngiep Hydropower Project found 195 specimens of the Endangered and migratory Yellow Tail Brook Barb Poropuntius deauratus throughout the study area, but ignored this data as a mischaracterization because the spatial range of the Yellow Tail Brook Barb as suggested by IUCN did not extend to the project’s location [59]. Further misalignment between the survey data and IUCN data was observed in the IUCN spatial extent for the Endangered Golden Mahseer Tor putitora [60]. The IUCN spatial extent omitted occurrences documented through surveys conducted for several of the hydropower projects reviewed (Fig. 4) e.g., Gulpur Hydropower Project on the Poonch River [61], the Vishnugad Pipalkoti Hydropower Project on the Alaknanda River [62] and the Karot Hydropower Project on the Jhelum River [63]; and subsequently published in the scientific literature [64–67]. Subsequently, according to the WBG [7] decision tree for the river section downstream of the outlet, the criteria of a low-resolution assessment were not met by any of the projects, 20 met the criteria for medium resolution and 22 required high-resolution studies. Eight projects had no dewatered reach; For the dewatered sections of the remaining 35 projects, low resolution assessments were recommended for two, medium resolution for 25, and high-resolution studies for eight (Table 2). The applied EFlows assessment resolution matched that suggested by WBG [7] for 23% (8 of the 35) of dewatered reaches and 12% (5 of 42) of reaches downstream of the tailrace. However, over time, there was a move to higher compliance. Five project studies did not mention EFlows and all five of these were conducted prior to 2010. One study [68] provided Terms of Reference for a EFlows study but there was no evidence of this in the available project documentation. Six studies stipulated downstream releases for the dewatered reach with no description of the method used. Nineteen studies used very low-resolution hydrological ratio methods to determine downstream releases. These methods included: 2.5% and 10% of mean annual flows; 10%, 14% and 15% of dry season flows, and; 10% of mean monthly flows. Hydraulic methods were used by two studies and combined hydraulic and pollutant modelling methods were used by another two studies. Seven studies used a holistic/eco-social method, four at a medium resolution, and three at a high resolution. Supplementary Table 6 provides the full list of methods used. The number of hydropower projects that release downstream flow based on EFlows studies, and the resolution of EFlows studies, had increased steadily over the last two decades (Fig. 5). Although the bulk of the EFlows studies (45%) used hydrological ratio methods, in recent years these outdated methods had given way to holistic eco-social methods such as DRIFT (Fig. 5). In the last five years included in this review (2016 to 2020) only 25% of studies used hydrological ratios whereas 50% used holistic eco-social methods. This is favourably compared to the first decade reviewed (2000 and 2010) where 40% of studies used hydrological ratios and not a single study reviewed used holistic eco-social methods. Subsequently, between 2016–2020 more EFlows studies matched the suggested resolution (63% of studies for the dewatered reaches and 38% of studies for the reach downstream of the tailrace outlet) than before 2010 where no EFlows studies matched the suggested resolution (Fig. 6). Despite steady increases in the EFlows resolution with time, there has been no statistically significant change in the volume of water suggested as minimum downstream release (Fig. 7). Furthermore, there was no correlation between the EFlows study resolution and volume of water recommended as a minimum-flow release, neither as a percentage of the dry season flow (correlation coefficient 0.070), nor as a percentage of mean annual flow (correlation coefficient − 0.104), as calculated for 30 studies for which flow data were available. The average downstream release suggested by medium and high-resolution studies was 4% of mean annual flows, slightly lower than the average of 6% for projects assessed by low- and very low-resolution studies between 2011 and 2020. However, the medium and high resolution studies contained additional recommendations that included changes to project operation (e.g., from hydropeaking to baseload operation; [69]), changes to the location of the dam and tailrace outlet (e.g., [61]), and requirements for reducing existing anthropogenic pressures on the river, such as over-fishing and sand-mining [70]. Assessment date and the presence of Critical Habitat were significant factors predicting the use of medium and high EFlows assessments (p 0.05). The regression coefficient for assessment date was 0.12 to 0.196 indicating that over five to eight years the average EFlows assessment resolution increased by one, i.e., from an average resolution of 0.7 (no EFlows method or minimum release downstream of dam only stated) between 2000 and 2005, to an average resolution of 3.3 (low to medium resolution methods) between 2016 to 2020. The regression coefficient of Critical Habitat ranged from 1.57 to 2.38 signifying its strong positive impact on the resolution of the EFlows assessment method used. Critical Habitat was also a significant factor (p < 0.01) in predicting whether the EFlows assessment downstream of the tailrace outlet (Table 4) matched the resolution suggested by WBG [7]. For the reach downstream of the tailrace outlet, the model with only Critical Habitat outperformed most other models (AUC of 0.98) and explained 60% of correct matches and 95% of correct mismatches (Model A; Table 4). In the dewatered reach, however, only assessment date was a highly significant (p < 0.01) explanatory variable. Other variables such as project size and country economic indicators were not statistically significant in predicting whether the EFlows assessment was conducted at a suitable resolution. Table 3 Multivariable linear regression (models A to C) results show that Critical Habitat and Assessment Date were highly significant drivers (p < 0.01) in increasing the resolution of the EFlows study. *, **, *** indicates significance at the 90%, 95%, and 99% level, respectively. Standard error of the regression coefficient is presented in brackets. Mean S.D. Model A Model B Model C Intercept -392*** (85) 1.86*** -240 (71) Private sector involvement 0.310 0.468 0.53 (0.41) International consultant involvement 0.429 0.501 -0.036 (0.45) Annual generation 1,628 3,249 0.00007 (0.00006) Nominal GDP 966 1,428 0.0001 (0.0001) GDP per capita, PPP 4,675 2,628 -0.0002 (0.0001) Access to electricity 74 26 0.013 (0.01) Assessment Date 2011 5 0.196*** (0.042) 0.120*** (0.04) Transboundary issues 0.071 0.261 -0.58 (0.26) Trans-basin diversion 0.262 0.445 0.44 (0.41) First or most downstream 0.167 0.377 -0.59 (0.44) Social Dependence (downstream outlet) 0.190 0.397 -0.28 (0.47) Social Dependence (dewatered reach) 0.095 0.297 0.50 (0.61) Critical Habitat 0.119 0.328 2.38*** (0.56) 1.57*** (0.54) R Square 0.500 0.422 0.487 Adjusted R Square 0.398 0.323 0.461 Significance F 0.001 0.003 0.000 Observations 42 42 42 Table 4 Multivariable logistic regression models developed to explain the match between the suggested and actual EFlows study resolution. Models A through C represent the reach downstream of the tailrace outlet and Models D through G represent the dewatered reach. *, **, *** indicates significance at the 90%, 95%, and 99% level, respectively. Downstream of tailrace outlet (n = 42) Dewatered reach (n = 35) Model A Model B Model C Model D Model E Model F Model G Intercept -2.86*** (0.73) -4.10*** (1.274) -1420* (751) -1.649*** (2.054) -833*** (304) -786** (324) -1064** (433) Private sector involvement 2.413* (1.351) 4.169** (2.115) 2.343* (1.320) Assessment Date 0.703* (0.372) 0.413*** (0.151) 0.390** (0.161) 0.527** (0.215) Critical Habitat 3.268*** (1.167) 3.159** (1.351) 2.054** 1.035 0.451 (1.217) Area under Curve (AUC) 0.98 0.97 0.97 0.96 0.90 0.88 0.93 Correct match prediction 3 (60%) 2 (40%) 2 (40%) 3 (38%) 5 (63%) 5 (63%) 5 (63%) Correct mismatch prediction 35 (95%) 36 (97%) 36 (97%) 26 (93%) 25 (89%) 25 (89%) 25 (89%) p-value 0.004 0.002 0.0003 0.045 0.0004 0.002 0.0002 R-sq (McFadden) 0.273 0.399 0.522 0.105 0.327 0.331 0.439 R-sq (Cox and Snell) 0.181 0.253 0.317 0.106 0.293 0.296 0.372 R-sq (Nagelkerke) 0.349 0.488 0.612 0.162 0.448 0.452 0.569 4. Discussion In general, when evaluated using WBG [ 7 ], the river reaches downstream of the tailrace outlets were deemed more sensitive than the dewatered sections, and thus in more need of medium or high resolution EFlows studies. Despite this, and likely because of the methods used, most of the EFlows assessments provided only a suggestion for a minimum release of water into the dewatered reach, with no consideration of the river downstream of the tailrace outlet, nor of other EFlows-related impacts such as changes in the onset or duration of hydrological seasons, loss of connectivity for sediment and biota, and the extreme inter-day variations in discharge associated with hydropeaking. This was despite the many EFlows methods documented in the scientific literature that can quantify these impacts in a holistic way [ 28 , 50 , 71 ] and the continued calls to use them where applicable [ 7 , 72 ]. The preponderance of the use of basic low-resolution methods also meant that most EFlows studies were at a lower resolution than recommended by international good practice. Surprisingly, there is an abundance of recent literature that puts forward low-resolution methods as viable tools [ 73 , 74 ] and this may partially explain their continued use for decision making related to river development. As discussed previously, these methods have no ecological basis [ 20 ], consider only flows (often only considering minimum release in the dry season) and leave out the other two components (sediments and biota) of EFlows [ 7 , 75 ]. This disconnect between EFlows best practice and application is a severe challenge for sustainable use and conservation of river ecosystems [17. 76]. The only medium/high resolution [ 7 ] or Level 2 [ 38 ] method used in the 42 studies reviewed was the DRIFT eco-social model, although other methods have been applied in other regions (e.g., [ 77 , 78 ]). There are numerous modern mixed methods with outcomes that increase certainty relative to some the low-resolution methods routinely applied for hydropower EFlows assessments, but none of these were featured in the studies evaluated. The chosen methods for EFlows assessments should be commensurate with the design and operation of the hydropower plant and the ecological and social sensitivity of the host river. Selected methods should be able to assess the impacts of hydrological changes, those due to the barriers to movement of sediment and biota, and the impacts of hydropeaking where applicable. To ensure the selection of correct methods, MFIs should mandate application of their own good practice guidelines on conducting EFlows assessments. There was no correlation between the EFlows study resolution and volume of water recommended as a minimum-flow release. Similarly, the literature shows that higher resolution methods can result in both larger and smaller suggested minimum flow releases as compared to lower resolution methods used on the same river [ 79 – 81 ]. This is an important outcome, because it highlights that higher minimum releases are not an inevitable consequence of higher resolution studies. Rather, the higher resolution studies offer greater resolution and more consideration of the complex interactions affecting river ecosystems, and more options for sustainably optimising their use [ 66 , 69 ]. The dominance of Critical Habitat as a trigger for higher resolution assessments may lie in the fact that it is the only factor in the WBG [ 7 ] decision tree that is explicitly codified into the MFI social and environmental safeguards documentation [ 40 , 43 , 82 ]. The designation of Critical Habitat during infrastructure development has contributed to the protection of ecosystems globally [ 83 ]. Other factors, such as transboundary issues and trans basin diversions lack a procedural pathway to motivate for a higher level of applications [ 45 ]. Furthermore, country-related parameters, such as GDP per capita PPP, were found to be insignificant in increasing the resolution of the EFlows assessment method used. Developing countries often require rapid electrification to support their growth and development; and hydropower presents a low carbon and low operational cost alternative [ 84 ]. Gök and Sodhi [ 85 ] found that improvements in governance reduced environmental performance in low-income countries (due to their focus on economic development) and suggested that for these countries a direct shift from economic outcomes to environmental outcomes was required and it would not come due to other factors such as GDP growth in the short term. These factors highlight both the value of MFI environmental and social safeguards in sustainable development and the importance of ensuring that they are up to date with best practice. Although Critical Habitat was a significant driver in the adoption of higher resolution EFlows assessment methods this benefit was not widely realized as only five of the 42 studies designated the affected riverine habitats as Critical Habitat. This was fewer than expected as most projects were located within the IUCN spatial range of Endangered or Critically Endangered freshwater biota. According to Murray et al. [ 86 ] the enhanced environmental performance standards triggered by Critical Habitat may result in higher project costs, lower electricity output, and/or lower peak electricity generation; in some cases, they may lead to the cancellation of the project. There is thus, a disincentive to designate Critical Habitat. Furthermore, as established data pertaining to the extent and distribution of threatened species are limited in the developing world [ 87 ], the responsibility for determining the presence and abundance of trigger species falls on the project environmental studies. To ensure that these have a reasonable chance of recording rare or endangered species, the surveys done in these studies should include (at minimum) multi-season and multi-year surveys using a variety of techniques (e.g., for fish these may comprise electrofishing plus variety of nets such as seine, gill and/or mesh nets) across stretches of oft-inaccessible terrain [ 88 ]. Bennun et al. [ 89 ] and Camaclang et al. [ 90 ], in their assessment of project environmental studies, deemed their data collection and analyses inadequate to this task, although Rees et al. [ 91 ] suggests that improved methods may increase accuracy of such biodiversity analysis. However, there are inherent limitations related to the reliance on the IUCN Red List in the assessment of Critical Habitat that cannot be corrected through improved project-specific ecological surveys. In developing countries, the IUCN Red List required regular updating as a result of the large and increasing anthropogenic pressures on biodiversity and the paucity of data on species abundances and distributions [ 92 – 94 ]. This study found several instances where the IUCN threatened classification of freshwater species changed between the completion of the hydropower project environmental studies and the start of construction. For example, the Ningu Carp Labeo victorianus was found by the ESIA study for the Rusumo Falls Hydropower Project on the Kagera River both upstream and downstream of the proposed project location, but at the time of assessment it was classified as Least Concern and so its presence did not trigger Critical Habitat [ 95 ]. The carp was re-classified as Critically Endangered in 2016 [ 96 ]; in the same year that construction contracts were awarded for the Rusumo Falls project [ 97 ]. Similarly, the Southeast Asian Box Turtle Cuora amboinensis , whose range overlaps with the Lower Kopili Hydropower Project and Upper Cisokan Pumped Storage Scheme, was re-classified from Vulnerable to Endangered in 2020 [ 98 , 99 ]. Both projects are now under construction, and neither was deemed to coincide with Critical Habitat [ 100 , 101 ]. This issue occurred in over a quarter of the reviewed hydropower projects that intersected with the IUCN spatial range of Endangered and Critically Endangered freshwater species; and in no cases did this reclassification seem to trigger more detailed environmental assessments. This is not limited to freshwater ecosystems in the developing world, as Ward et al. [ 102 ], demonstrated that Vulnerable species were left out of consideration during the environmental impact assessment process in Australia, that in many cases became become Endangered with time. While such updates are understandable given the combination of the limited data and large anthropogenic pressures on the aquatic environments in the developing world, this can have serious implications for a hydropower project. If adhered to, projects may require mid-assessment re-evaluation of Critical Habitat designation, with knock-on implications for project location, design, and operating rules. A second limitation pertains to the inaccuracies in the IUCN Red List spatial distribution data of species in the developing world, and is acknowledged by IUCN [ 103 ]. This is equally troublesome as some projects studies rely on these data to verify survey results whereas others exclusively used the IUCN spatial ranges to screen for potential Critical Habitat trigger species (e.g., [ 100 ]). Again because of data deficiencies in developing countries, a tension exists between the results of the project surveys and the IUCN Red List spatial data. This tension could be eased to the benefit of both the developer and IUCN, if there was a requirement for the species distribution data generated by ESIAs to be submitted to IUCN for use in updating the spatial data. Naturally, the acceptance of such data should subject to quality assurance by IUCN, which could improve the methods used in project assessments. Lastly, for the long-term survival of threatened species, the designation of Critical Habitat should be more detailed than area thresholds as currently evaluated, and should include, inter alia , demographic data [ 104 ], habitat availability for each sequential life history [ 105 ], and a consideration of unoccupied habitat [ 106 ]. Improvements in the current arrangements notwithstanding, the idea that river ecosystems can be sustainably developed through site-by-site and project-by-project classification of Critical Habitat based on a rapidly changing assessment of threatened species that inhabit them, requires a radical rethink. This is particularly so given the inconsistent application of good practice and the large number of hydropower project proposed for most basins, such as those in the Himalayas [ 107 , 108 ]. Another option is for a shift in focus from single development assessments to the meaningful inclusion of ecosystem functioning and social reliance data [ 109 ] in basin, and regional, water-resource development planning [ 20 , 110 ]. The idea is to embed medium and high-level biodiversity, social and EFlows assessments in basin scale processes, such as the IFC’s Cumulative Impact Assessment [ 111 ], that are then used to inform the location, design and operation of individual developments, rather than the other way around. The driving need for such a shift is highlighted by the fact that for the reviewed hydropower projects that are still under construction, on average, there are 20 other hydropower projects planned in the basins where they are located. Each one of these additional projects will have an incremental impact to the river hydrology, and connectivity impacts for sediment and biota, all of which may negatively influence the freshwater ecosystems and livelihoods that depend on them. Unfortunately, even if all these projects were assessed at the resolution suggested by good practice, the parallel application of the decision tree and the subsequent parallel assessment of EFlows, will not be able to evaluate the significant cumulative impacts of these developments. Less than 5% of the EFlows literature since 2010 relates to its basin scale application [ 112 ] reinforcing the need for future EFlows research in this direction. Encouragingly, many of the world’s River Basin Organisations have already begun to adopt biodiversity, social and EFlows decision support tools and are applying them at the basin scale [ 10 ]. The current study was limited to projects funded by the ADB, IFC and World Bank, as they had publicly accessible and electronically searchable documentation of their project environmental studies. This limitation can be overcome in future studies that can expand the scope of this research to include other MFIs such as the Asian Infrastructure Investment Bank, African Development Bank, Latin American Development Bank, Islamic Development Bank, as well as various Chinese development banks (e.g., China Development Bank and Export-Import Bank of China) especially as financing by Chinese banks to developing countries now rivals that of the World Bank [ 113 ]. Even though ADB, IFC and World Bank financing is traditionally known to be accompanied by stricter environmental protections [ 114 – 116 ] the current study found that even these MFIs have only recently begun to partially meet good practices related to EFlows. Therefore, the findings of this study are broadly applicable to other MFIs at various stages of implementation of environmental standards. For example, MFIs looking to implement better environmental safeguards in developing countries can opt for adopting holistic ecosystem-based, basin scale approaches to EFlows assessment rather than mimicking the data dependent and single species focus of Critical Habitat led performance standards. There is indication that this is changing with MFI led basin scale cumulative impact assessments and strategic environmental assessments [124. 125] but progress is slow and, given the perilous state of the world’s rivers, should be accelerated. In addition to updating regulations and safeguarding documentation, the successful implementation of EFlows assessments requires suitable and sufficient stakeholder capacity, knowledge and engagement [ 126 ]. All tiers of stakeholders should be involved in EFlows related outreach and training including the community, commercial water users, scientists and engineers, water management agencies, and regional and national leaders [ 127 ], as the engagement and capacity building of stakeholders improves the implementation of EFlows [ 128 ]. On realising this as an enabling factor, there are several avenues where training and knowledge exchange around EFlows is increasing. For example, the Nile Basin Initiative regularly hosts free online classes related to EFlows on its e-learning platform [ 129 ] and Bukhari et al. [ 130 ] have recently published a comprehensive library of ecosystem indicators and driver-response relationships for rivers and estuaries in southern Africa by collating over a decade of experience in ecosystem based EFlows assessments. The open access publishing of data and information related to EFlows facilitates knowledge exchange, promotes ecosystem-based EFlows assessments and accelerate the entry of young EFlows professionals and researchers into the field. 5. Conclusions Although hydropower projects, and other water-resource developments, have significant social and economic benefits their potential for large and long-term negative impacts on freshwater ecosystems, with devastating knock-on impacts to the communities that depend on them has been widely acknowledged. This has been recognised by MFIs, many of whom have introduced guidelines and procedures to ensure that the assessments of impacts adhere to good practice, which includes the application of appropriate level EFlows assessment. This study, through a systematic search and review, found low adherence to international good practice guidelines in terms of the use of EFlows assessments methods. Low resolution methods continue to be used which do not provide adequate information to stakeholders to guide decision making related to ecological, social, and economic trade-offs related to hydropower development. This disconnect between theory and practice is a cause of concern for the sustainable development and use of river ecosystems. Only assessment date and presence of Critical Habitat were found to be significant explanatory variables in predicting whether the resolution used matched that recommended by good practice. However, the dependence on the classification of Critical Habitat, for which several shortcomings were described in the study, is of concern. Further, with a few exceptions, assessments were focused on single hydropower projects, many of which are situated in river basins where numerous other hydropower projects and water-resource developments are planned. Although the use of suitable EFlows assessment methods is a first and urgent step to mitigate development impact, it may not be sufficient and instead basin-level EFlows assessments that consider existing and planned infrastructure are required. Such basin wide assessments should assess impacts on water quality and the flow of water, sediments, and biota; for the dewatered reaches and the river, and other aquatic ecosystems, downstream of the tailrace for as far downstream as the influence of each hydropower plant extends. Declarations Data Availability The data used to support the findings of this study are included in the article and supplementary materials. Conflicts of Interest The authors declare that they have no conflicts of interest. Supplementary Materials Supplementary materials as a set of tables provide results at the individual hydropower level which were deemed too detailed for the main manuscript. References International Finance Corporation (IFC). (2009). Directions in hydropower: scaling up for development. Washington D.C.: World Bank Group World Commission on Dams. (2000). Dams and development. A new framework for decision-making. The report of the World Commission on Dams. London: Earthscan. Goodland, R. (2010). Viewpoint–The World Bank versus the world commission on dams. Water alternatives , 3 (2), 384–398. Zarfl, C., Lumsdon, A.E., Berlekamp, J., Tydecks, L. & Tockner, K. (2015). A global boom in hydropower dam construction. Aquatic Sciences , 77 (1), 161–170. Moran, E.F., Lopez, M.C., Moore, N., Müller, N. & Hyndman, D.W. (2018). Sustainable hydropower in the 21st century. Proceedings of the National Academy of Sciences , 115 (47), 11891–11898. Brisbane Declaration, (2007). The Brisbane Declaration: environmental flows are essential for freshwater ecosystem health and human well-being. 10th International River Symposium . Brisbane: International River Foundation. World Bank Group (WBG). (2018). Good Practice Handbook: Environmental Flows for Hydropower Projects. Guidance for the Private Sector in Emerging Markets . Prepared under the direction of Environment, Social and Governance Department of the International Finance Corporation, World Bank Group. Washington D.C.: World Bank Group King J., Tharme R.E., & De Villiers M.S. (2000). Environmental flow assessments for rivers: manual for the Building Block Methodology. Pretoria: Water Research Commission. King J. & Brown C. (2010). Integrated Flow Assessments: concepts and method development in Africa and South-east Asia. Freshwater Biology , 55 (1), 127–146. Bukhari, H. & Brown, C. (2022). A comparative review of decision support tools routinely used by selected transboundary River Basin Organizations. African Journal of Aquatic Science, 47 (3), 318–337. Australian Capital Territory Parliamentary Counsel. (1999). Environmental Flow Guidelines. China State Department. (2006). Dispatch Act of Water Quantity from the Yellow River. Department of Water Affairs and Forestry. (1998). National Water Act No. 36 . 1998. South Africa European Union. (2000). The EU Water Framework Directive . Brussels: European Commission. Himachal Pradesh Energy Development Agency. (2006). Hydro Power Policy 2006 . India: Himachal Pradesh Government. Brown, C., Campher, D. & King, J. (2020). Status and trends in EFlows in southern Africa. Natural Resources Forum, 44 (1), 66–88. Theodoropoulos, C., Georgalas, S., Mamassis, N., Stamou, A., Rutschmann, P. & Skoulikidis, N. (2018). Comparing environmental flow scenarios from hydrological methods, legislation guidelines, and hydrodynamic habitat models downstream of the Marathon Dam (Attica, Greece). Ecohydrology, 11 (8), e2019. Katopodis, C. (2020). A perspective on e-flows at hydroelectric projects in Canada. Hydrobiologia , 849, 461–484. Tharme, R. (2003). A global perspective on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. River Research and Applications , 19 (5-6), 397–441. King J. & Brown C. (2018a). Environmental Flow Assessments Are Not Realizing Their Potential as an Aid to Basin Planning. Frontiers in Environmental Science , 6, 113. Magilligan, F.J. & Nislow, K.H. (2005). Changes in hydrologic regime by dams. Geomorphology , 71 (1-2), 61–78. Csiki, S. & Rhoads, B.L. (2010). Hydraulic and geomorphological effects of run-of-river dams. Progress in Physical Geography , 34 (6), 755–780. Gupta, H., Kao, S.J. & Dai, M. (2012). The role of mega dams in reducing sediment fluxes: A case study of large Asian rivers. Journal of Hydrology , 464, 447–458. Liermann, C.R., Nilsson, C., Robertson, J. & Ng, R.Y. (2012). Implications of dam obstruction for global freshwater fish diversity. BioScience , 62 (6), 539–548. Barbarossa, V., Schmitt, R.J., Huijbregts, M.A., Zarfl, C., King, H. & Schipper, A.M. (2020). Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide. Proceedings of the National Academy of Sciences , 117 (7), 3648–3655. Capon, S.J., Leigh, C., Hadwen, W.L., George, A., McMahon, J.M., Linke, S., Reis, V., Gould, L. & Arthington, A.H. (2018). Transforming environmental water management to adapt to a changing climate. Frontiers in Environmental Science , 6, 80. Brown, C., Joubert A., Beuster H., & Greyling A. (2013). DRIFT: DSS software development for integrated flow assessments. WRC Report No. 873/1/13. Pretoria: Water Research Commission. Joubert, A.R., Brown, C., King, J.M., Beuster, H., & Greyling, A. (2022). DRIFT: Incorporating an eco-social system network and time-series approach into environmental flow assessments. African journal of Aquatic Sciences , 47 (3), 338– 352. Shafroth, P.B., Wilcox, A.C., Lytle, D.A., Hickey, J.T., Andersen, D.C., Beauchamp, V.B., Hautzinger, A., McMullen, L.E. & Warner, A. (2010). Ecosystem effects of environmental flows: modelling and experimental floods in a dryland river. Freshwater Biology , 55 (1), 68–85. King J. and Brown C. (2018b). Environmental Flows, minimum flows and the mystery of ten percent. In: Gough, P. & Royte, J. eds. Sea to Source. Protection and restoration of fish migration in rivers worldwide . The Netherlands: World Fish Migration Foundation, 107–111. Null, S.E., Farshid, A., Goodrum, G., Gray, C.A., Lohani, S., Morrisett, C.N., Prudencio, L. & Sor, R. (2020). A meta-analysis of environmental tradeoffs of hydropower dams in the Sekong, Sesan, and Srepok (3S) rivers of the lower Mekong basin. Water , 13 (1), 63. Overton, I., Smith, D., Dalton, J., Barchiesi, S., Acreman, M., Stromberg, J., & Kirby, J.M. (2014). Implementing environmental flows in integrated water resources management and the ecosystem approach. Hydrological Sciences Journal , 59, 860–877. Poff, N.L. & Matthews, J.H. (2013). Environmental flows in the Anthropocence: past progress and future prospects. Current Opinion in Environmental Sustainability , 5(6), 667–675. Horne, A., et al., ed., 2017. Water for the environment: From policy and science to implementation and management. Cambridge: Academic Press. Poff, N.L., Tharme, R.E. & Arthington, A.H. (2017). Evolution of environmental flows assessment science, principles, and methodologies. In : Water for the Environment. Cambridge: Academic Press, 203–236. Wright, C. (2012). Global banks, the environment, and human rights: The impact of the Equator Principles on lending policies and practices. Global Environmental Politics, 12, 56–77. National Renewable Energy Laboratory. (2001). Small hydropower systems. National Renewable Energy Laboratory, Department of Energy. Report No DOE/GO-102001-1173. Merrifield VA: Energy Efficiency and Renewable Energy Clearinghouse Opperman, J.J., Kendy, E., Tharme, R.E., Warner, A.T., Barrios, E. & Richter, B.D. (2018). A three-level framework for assessing and implementing environmental flows. Frontiers in Environmental Science , 6 , 76. Wang, C., Ghadimi, P., Lim, M.K. & Tseng, M.L. (2019). A literature review of sustainable consumption and production: A comparative analysis in developed and developing economies. Journal of Cleaner Production , 206, 741 – 754. IFC. (2012). Performance Standards on Environmental and Social Sustainability. Washington D.C.: World Bank Group Everard, M. & Moggridge, H.L. (2012). Rediscovering the value of urban rivers. Urban Ecosystems , 15 (2), 293–314. IUCN. (2020). The IUCN Red List of threatened species v. 2019-3 . Gland: IUCN. Available from: www.iucnredlist.org [Accessed 14 December 2021]. IFC. (2012). Performance Standard 6: Biodiversity Conservation and Sustainable Management of Living Natural Resources. Washington D.C.: World Bank Group IUCN. (2021). Spatial Data Download [online]. The IUCN Red List of Threatened Species. Available from: https://www.iucnredlist.org/resources/spatial-data-download [Accessed on 14 December 2021]. Brauneder, K.M., Montes, C., Blyth, S., Bennun, L., Butchart, S.H., Hoffmann, M., Burgess, N.D., Cuttelod, A., Jones, M.I., Kapos, V. & Pilgrim, J. (2018). Global screening for Critical Habitat in the terrestrial realm. PloS one , 13 (3), p.e0193102. Martin, C.S., Tolley, M.J., Farmer, E., Mcowen, C.J., Geffert, J.L., Scharlemann, J.P.W., Thomas, H.L., van Bochove, J.H., Stanwell-Smith, D., Hutton, J.M. & Lascelles, B. (2015). A global map to aid the identification and screening of critical habitat for marine industries. Marine Policy , 53, 45–53. Tenant, D.L. (1976). Instream flow regimens for fish, wildlife, recreation and related environmental resources. Fisheries, 1, 6–10. Chapra, S.C., Pelletier, G.J., & Tao, H. (2008). QUAL2K: a modelling framework for simulating river and stream water quality, version 2.11: documentation and user manual. Civil and Environmental Engineering Department. Tufts University, Medford, MA. Greco, M., Arbia, F. & Giampietro, R. (2021). Definition of the Ecological Flow (EFlow) using the Indicators of Hydrological Alteration and IARI as an operative tool for water management. Environments , 8 (8), 77. Stalnaker, C., Lamb, B.L., Henriksen, J., Bovee, K. & Bartholow, J. (1995). The Instream Flow Incremental Methodology: A primer for IFIM . Biological Report 29. Washington, D.C.: National Biological Service, US Department of the Interior. Hedden-Nicely, D.R. (2020). The Contemporary Methodology for Quantifying Reserved Instream Flow Water Rights to Support Aquatic Habitat. Environmental Law , 50 (1), 257–286. Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society: Series B (Methodological) , 20 (2), 215–232. Choueiry, G. (2022). Which Variables Should You Include in a Regression Model? [online] Available from: https://quantifyinghealth.com/variables-to-include-in-regression/ [Accessed 30 May 2022]. Kondolf, G.M., Rubin, Z.K. & Minear, J.T. (2014). Dams on the Mekong: Cumulative sediment starvation. Water Resources Research , 50, 5158–5169. Kondolf, G.M., Schmitt, R.J.P., Carling, P.A., Goichot, M., Keskinen, M., Arias, M.E., Bizzi, S., Castelletti, A., Cochrane, T.A., Darby, S.E. & Kummu, M. (2022). Save the Mekong Delta from drowning. Science , 376 (6593), 583–585. Mekong River Commission. (2017). The Council Study: study on the sustainable management and development of the Mekong River, including impacts of mainstream hydropower projects. Biological Resource Assessment Final Technical Report Series. Volume 1: Specialists’ Report. Vientiane: Mekong River Commission. Duc, N.A., Nguyen, L.T., Thai, T.H., Khan, A., Rautenstrauch, K. & Schmidt, C. (2020). Assessing cumulative impacts of the proposed Lower Mekong Basin hydropower cascade on the Mekong River floodplains and Delta–Overview of integrated modeling methods and results. Journal of Hydrology , 581, 122511. Guha, N. and Ghosh, S. (2019) Negligence narrative unfolds as Kopili Hydropower Plant Pipeline Burst Kills Four [online]. Mongabay-India. Available at: https://india.mongabay.com/2019/10/negligence-narrative-unfolds-as-kopili-hydropower-plant-pipeline-burst-kills-four/ [Accessed 16 July 2023]. Nam Ngiep Power Company. (2014). Draft Environmental Impact Assessment Nam Ngiep 1 Hydropower Project . Prepared by Nam Ngiep Power Company Ltd. with assistance from ERM-Siam Co., Ltd. and Environmental Research Institute, Chulalongkorn University for the Asian Development Bank. Bournemouth University. (2018). Tor putitora . The IUCN Red List of Threatened Species. Version 2022-2 Mira Power Limited. (2014). Environmental and Social Impact Assessment Gulpur Hydropower Project. Islamabad: Mira Power Limited. Consulting Engineering Services. (2009). Consolidated Environmental Assessment Volume 1, Environmental Studies for Vishnugad Pipalkoti Hydro Electric Project. Prepared for THDC India Limited. Karot Power Company. (2015). 720 MW Karot Hydropower Project Updated Environmental and Social Impact Assessment. Islamabad: Mott Mcdonald Nautiyal, P., Mishra, A.S., Singh, K.R. & Singh, U. (2013). Longitudinal distribution of the fish fauna in the river Ganga from Gangotri to Kanpur. Journal of Applied and Natural Science , 5 (1) 63–68. Annandale, D. & Hagler Bailly Pakistan. (2014). Strategic Environmental Assessment of Hydropower Development in Azad Jammu and Kashmir. Islamabad: IUCN Pakistan. Brown, C., Zakaria, V., Joubert, A., Rafique, M., Murad, J., King, J., Hughes, J., Cardinale, P. & Alonzo, L. (2019). Achieving an environmentally sustainable outcome for the Gulpur hydropower project in the Poonch River Mahaseer National Park, Pakistan. Sustainable Water Resources Management , 5 (2), 611–628. Yadav, P., Kumar, A., Hussain, S.A. & Gupta, S.K. (2020). Evaluation of the effect of longitudinal connectivity in population genetic structure of endangered golden mahseer, Tor putitora (Cyprinidae), in Himalayan rivers: Implications for its conservation. PloS one , 15 (6), 0234377. SWECO. (2007). Song Bung 4 Hydropower Project Phase II . Manila: Asian Development Bank. TA 4625-VIE Karot Power Company. (2016). Karot Biodiversity Management Plan. Islamabad: Hagler Baily Pakistan Pakhtunkhwa Energy Development Organization. (2019a). Environmental Impact and Social Impact Assessment for Gabral Kalam Hydropower Project . Pakistan: Pakhtunkhwa Energy Development Organization King, J. & Louw, D. (1998). Instream flow assessments for regulated rivers in South Africa using the Building Block Methodology. Aquatic Ecosystem Health & Management , 1 (2), 109–124. Tickner, D., Kaushal, N., Speed, R. & Tharme, R. (2020). Implementing environmental flows: Lessons for policy and practice. Frontiers in Environmental Science , 8, 106. Ramesh, V.S. & Thampi, S.G. (2023). Application of hydrologic and hydraulic methods to calculate the environmental flow requirements of the Chaliyar river. Environmental Earth Sciences , 82 (7), 166. Suwal, N., Kuriqi, A., Huang, X., Delgado, J., Młyński, D. & Walega, A. (2020). Environmental flows assessment in Nepal: the case of Kaligandaki River. Sustainability , 12 (21), 8766. Arthington, A.H., Bhaduri, A., Bunn, S.E., Jackson, S.E., Tharme, R.E., Tickner, D., Young, B., Acreman, M., Baker, N., Capon, S. & Horne, A.C. (2018). The Brisbane declaration and global action agenda on environmental flows (2018). Frontiers in Environmental Science , 6, 45. Sharma, U. & Dutta, V. (2020). Establishing environmental flows for intermittent tropical rivers: Why hydrological methods are not adequate?. International journal of environmental science and technology, 17(5), pp.2949-2966. Chaves, A., Pacheco, A., Krasovskaia, I. & Gottschalck, L. (2010). RANA-ICE, A Methodology to Estimate Compensatory Runoff in Costa Rica. Hydrocomplexity: New Tools for Solving Wicked Water Problems Kovacs Colloquium , 2–3 July 2010 Paris. Multiconsult. (2018). EFlows Assessment Report – Final Version, December 21, 2018. Hydraulic Study and EFlows Assessment for Mpatamanga. Abdi, R. & Yasi, M. (2015). Evaluation of environmental flow requirements using eco-hydrologic–hydraulic methods in perennial rivers. Water Science and Technology , 72(3), 354-363. Hao, C.F., He, L.M., Niu, C.W. & Jia, Y.W. (2016) August. A review of environmental flow assessment: methodologies and application in the Qianhe River. In IOP Conference Series: Earth and Environmental Science, 39 (1), 012067. IOP Publishing. Nikghalb, S., Shokoohi, A., Singh, V.P. & Yu, R. (2016). Ecological regime versus minimum environmental flow: comparison of results for a river in a semi Mediterranean region. Water resources management , 30, 4969 – 4984. Asian Development Bank (ADB). (2009). Safeguard Policy Statement . Manila: Asian Development Bank Narain, D., Maron, M., Teo, H.C., Hussey, K. & Lechner, A.M. (2020). Best-practice biodiversity safeguards for Belt and Road Initiative’s financiers. Nature Sustainability , 3(8), 650-657. Markkanen, S. & Braeckman, J.P. (2020). Financing sustainable hydropower: an introduction to concepts and terminology. Int J Hydropower Dams , 27 (5). Gök, A. & Sodhi, N. (2021). The environmental impact of governance: a system-generalized method of moments analysis. Environmental Science and Pollution Research , 28 (25), 32995–33008. Murray, C.C., Wong, J., Singh, G.G., Mach, M., Lerner, J., Ranieri, B., Peterson St-Laurent, G., Guimaraes, A. & Chan, K. (2018). The insignificance of thresholds in environmental impact assessment: An illustrative case study in Canada. Environmental Management , 61 (6), 1062–1071. Hochkirch A. (2021). A strategy for the next decade to address data deficiency in neglected biodiversity. Conservation Biology , 35 (2), 502–509. Gullison, T., Hardner, J., Anstee, S. & Meyer, M. (2015). Good practices for the collection of biodiversity baseline data. Prepared for the Multilateral Financing Institutions Biodiversity Working Group & Cross-Sector Biodiversity Initiative. Bennun, L., Pollard, E., Starkey, M. & Temple, H. (2016). Critical Habitat Determination for Linear Infrastructure: Approaches to Delineating Discrete Management Units under IFC Performance Standard 6. In SPE International Conference and Exhibition on Health, Safety, Security, Environment, and Social Responsibility . OnePetro. Camaclang, A.E., Maron, M., Martin, T.G. & Possingham, H.P. (2015). Current practices in the identification of critical habitat for threatened species. Conservation Biology , 29 (2), 482–492. Rees, H.C., Maddison, B.C., Middleditch, D.J., Patmore, J.R. & Gough, K.C. (2014). The detection of aquatic animal species using environmental DNA–a review of eDNA as a survey tool in ecology. Journal of Applied Ecology , 51 (5), 1450–1459. Ratajczak, Z., Carpenter, S.R., Ives, A.R., Kucharik, C.J., Ramiadantsoa, T., Stegner, M.A., Williams, J.W., Zhang, J. & Turner, M.G. (2018). Abrupt change in ecological systems: inference and diagnosis. Trends in Ecology & Evolution , 33 (7), 513–526. Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. (2019). Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Bonn: IPBES Secretariat. Reis, R.E., Albert, J.S., Di Dario, F., Mincarone, M.M., Petry, P. & Rocha, L.A. (2016). Fish biodiversity and conservation in South America. Journal of fish biology , 89 (1), 12–47. Nile Equatorial Lakes Subsidiary Action Program. (2013). Rusumo Falls Hydroelectric Project Dam & Powerplant Component Environmental and Social Impact Assessment . Prepared by Artelia Eau & Environment on behalf of the Nile Equatorial Lakes Subsidiary Action Program. FishBase team RMCA & Geelhand, D. (2016). Labeo victorianus [online]. The IUCN Red List of Threatened Species. Available from: https://www.iucnredlist.org/species/60318/47182908 [Accessed 16 December 2021]. Power Technology. 2021. Regional Rusumo Hydroelectric Project, Rwanda [online]. Verdict Media Limited. Available from: https://www.power-technology.com/projects/regional-rusumo-hydroelectric-project-rwanda/ [Accessed 17 December 2021]. Asian Turtle Trade Working Group. (2000). Cuora amboinensis (errata version published in 2016) [online]. The IUCN Red List of Threatened Species. Available from: https://dx.doi.org/10.2305/IUCN.UK.2000.RLTS.T5958A11953035.en [Accessed 16 December 2021]. Cota, M., Hoang, H., Horne, B.D., Kusrini, M.D., McCormack, T., Platt, K., Schoppe, S. & Shepherd, C. (2020). Cuora amboinensis [online]. The IUCN Red List of Threatened Species. Available from: https://dx.doi.org/10.2305/IUCN.UK.2020-2.RLTS.T5958A3078812.en [Accessed 16 December 2021]. Assam Power Generation Corporation Limited. (2018). Environmental Impact Assessment, Assam Power Sector Investment Program – Tranche 3, 120 MW Lower Kopili Hydroelectric Project . Guwahati: Assam Power Generation Corporation Limited, Government of Assam Perusahaan Listrik Negara. 2020. Environmental and Social Impact Assessment Upper Cisokan Pumped Storage Hydropower Project 1040 MW. Indonesia: Perusahaan Listrik Negara Ward, M.S., Simmonds, J.S., Reside, A.E., Watson, J.E., Rhodes, J.R., Possingham, H.P., Trezise, J., Fletcher, R., File, L. and Taylor, M., 2019. Lots of loss with little scrutiny: The attrition of habitat critical for threatened species in Australia. Conservation Science and Practice, 1(11), p.e117. IUCN. (2021b). The IUCN Red List 2017–2020 Report [online]. The IUCN Red List of Threatened Species. Available from: https://www.iucnredlist.org [Accessed 9 September 2022]. Van der Hoek, Y., Zuckerberg, B. & Manne, L.L. (2015). Application of habitat thresholds in conservation: Considerations, limitations, and future directions. Global Ecology and Conservation , 3, 736–743. Rosenfeld, J.S. & Hatfield, T. (2006). Information needs for assessing critical habitat of freshwater fish. Canadian Journal of Fisheries and Aquatic Sciences , 63 (3), 683–698. Camaclang, A.E. (2016). Identifying critical habitat for threatened species: concepts and challenges. Thesis (PhD). The University of Queensland. Hussain, A., Sarangi, G.K., Pandit, A., Ishaq, S., Mamnun, N., Ahmad, B. & Jamil, M.K. (2019). Hydropower development in the Hindu Kush Himalayan region: Issues, policies and opportunities. Renewable and Sustainable Energy Reviews , 107, 446–461. Khera, D.V. & Singh, M. (2020). New Trends in the Development of Hydropower Projects in India. In Hydropower in the New Millennium. ). Boca Raton: CRC Press, 29-36. Tseliou, F. & Tselepides, A. (2020). The importance of the ecosystem approach in the management of the marine environment. Euro-Mediterranean Journal for Environmental Integration . 5 (2), 1–5. Opperman, J.J., Carvallo, J.P., Kelman, R., Schmitt, R.J., Almeida, R., Chapin, E., Flecker, A., Goichot, M., Grill, G., Harou, J.J. & Hartmann, J. (2023). Balancing renewable energy and river resources by moving from individual assessments of hydropower projects to energy system planning. Frontiers in Environmental Science , 10 . IFC. (2013). Good Practice Handbook on Cumulative Impact Assessment and Management: Guidance for the Private Sector in Emerging Markets. Washington DC: World Bank Group Gebreegziabher, G.A., Degefa, S., Furi, W. & Legesse, G. (2023). Evolution and concept of environmental flows (e-flows): meta-analysis. Water Supply , 23 (6), 2466 – 2490. Ray, R. (2023). Small Is Beautiful: A New Era for Chinese Overseas Development Finance? GCI Policy Brief 017, Boston: Boston University Global Development Policy Center. Chen, Y. and Landry, D. (2018). Capturing the rains: Comparing Chinese and World Bank hydropower projects in Cameroon and pathways for South-South and North South technology transfer. Energy Policy , 115, 561-571. Gerlak, A.K., Saguier, M., Mills-Novoa, M., Fearnside, P.M. and Albrecht, T.R. (2020). Dams, Chinese investments, and EIAs: A race to the bottom in South America? Ambio , 49, 156-164. Ghimire, H.R., Phuyal, S & Singh, N.R. (2021). Environmental compliance of hydropower projects in Nepal. Environmental Challenges , 5, 100307 Stafford, S.L. (2012). Private Policing of Environmental Performance: Does it Further Public Goals. Boston College Environmental Affairs Law Review , 39, 73. Barta, J. & Éri, V. (2017). Environmental attitudes of banks and financial institutions. In: Sustainable Banking. Oxfordshire: Routledge, 120–132. Mitkidis, K. & Valkanou, T.N. (2020). Climate Change Litigation: Trends, Policy Implications and the Way Forward. Transnational Environmental Law , 9, 11–16. Grossman, G.M. & Krueger A.B. (1995). Economic growth and the environment. The quarterly journal of economics , 110 (2), 353–377. Haddaway, N., Macura, B., Whaley, P. & Pullin, A. (2017). ROSES flow diagram for systematic reviews. Version 1.0. doi: 10.6084/m9.figshare.5897389 Shafi, N., Ayub, J., Ashraf, N. & Mian, A. (2016). Genetic Diversity in Different Populations of Mahseer (Tor putitora) in Pakistan: A RAPD Based Study. International Journal of Agriculture & Biology , 18(6). Ramsar. (2020). Beas Conservation Reserve [online]. Ramsar Sites Information Service. Available from: https://rsis.ramsar.org/ris/2408 [Accessed 3 March 2022]. IFC. 2020. Cumulative Impact Assessment and Management: Hydropower Development in the Trishuli River Basin, Nepal. International Finance Corporation, Washington, D.C., USA. IFC. 2021. Strategy for Sustainable Hydropower Development in the Jhelum Poonch River Basin Pakistan. International Finance Corporation, Washington, D.C., USA. Harwood, A., Johnson, S., Richter, B., Locke, A., Yu, X., and Tickner, D. (2017). Listen to the River: Lessons from a Global Review of Environmental Flow Success Stories. Woking: WWF-UK. O'Keeffe, J.H., (2018). A perspective on training methods aimed at building local capacity for the assessment and implementation of environmental flows in rivers. Frontiers in Environmental Science , 6, 125. WWF-Zambia (2016). Proceedings of the E flows Technical Resource Group Introductory Workshop. Hosted by Water Resources Management Authority with support from WWF. Nile Basin Initiative (2024) NBI eLearning Courses. Available at https://elearn.nilebasin.org/. Accessed 10 June 2024 Bukhari H, Brown C, Van Niekerk L, Van Deventer H, Joubert A, Taljaard S, Goso L, Reinecke K, Hansen C, Smith-Adao L and co-authors (2024) Eco-Social Assessments of Aquatic Ecosystems, Volume 1: Library of Generic Relationships for SADC Rivers and Estuaries. Africa's Living Rivers Programme (WRC Report No. TT 934/1/23). Water Research Commission, Pretoria. Table 2 Table 2 is available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files Table2.docx IJERSupplementaryPreprint.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6070177","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":418456737,"identity":"aca82d55-6131-461c-a4f2-9caa72613b9d","order_by":0,"name":"Hassan Bukhari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYBACAwhlAyLYSNKSBsTMUC2EdEK1HCZBizn78ccffu44b88vkX/swcc9dxL75zcf+/iDwU6Ogf/wA2xaLHtyzCR7z9xOnDkjmd1wxrNniTOOsSXP5mFINmZgOGaA1WEHctgYeNtuJxjcSGaT5jlwOLfhGI8xMwPDgcQGxgbsWs4/f/zxb9s5e7CWP0At84/xf2b8AdLCzP4Bq5YbCQbSvG0HGDeAtDAAtWw4xsPMwAPSwsaD3ZYbb8ykZduSE2f2PDaT7DlwuH7jsTRjZh6DZGM2Hp4C7A5Lf/zxbZudPT974jOJHwcOG8sdPvyY8UeFnRw///ENWEMZBzAgOjGMglEwCkbBKMACACmxYRvLHC9MAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-2850-7925","institution":"Stellenbosch University","correspondingAuthor":true,"prefix":"","firstName":"Hassan","middleName":"","lastName":"Bukhari","suffix":""},{"id":418456738,"identity":"b54ced12-51b2-400c-a7ad-2993ea78a7e3","order_by":1,"name":"Cate Brown","email":"","orcid":"https://orcid.org/0000-0001-6632-6992","institution":"University of the Western Cape","correspondingAuthor":false,"prefix":"","firstName":"Cate","middleName":"","lastName":"Brown","suffix":""},{"id":418456739,"identity":"387ef2fc-a3da-4e7f-b21a-5fce32a1f31e","order_by":2,"name":"Karen Esler","email":"","orcid":"https://orcid.org/0000-0001-6510-727X","institution":"Stellenbosch University","correspondingAuthor":false,"prefix":"","firstName":"Karen","middleName":"","lastName":"Esler","suffix":""}],"badges":[],"createdAt":"2025-02-20 08:49:18","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6070177/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6070177/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77763285,"identity":"aefbe4f4-7c70-4b18-93a2-18b0bf6f7119","added_by":"auto","created_at":"2025-03-05 09:48:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78422,"visible":true,"origin":"","legend":"\u003cp\u003eRoses flow diagram [121] of the systematic review that was conducted for this study.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6070177/v1/ca35233d6cc8e4f9b69f95fb.png"},{"id":77765191,"identity":"907ee23c-9b9b-4311-8ae1-96970fee9386","added_by":"auto","created_at":"2025-03-05 09:56:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230347,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of the dam wall of the 42 hydropower projects shortlisted for the study. Six projects were located in Africa: Burundi (1), Cameroon (1), Niger (1), Rwanda (1), Uganda (1), Zambia (1); two projects were located in the Caucasus both in Georgia; twenty nine projects were located in South Asia: Bhutan (1), China (7), India (10), Nepal (3), Pakistan (8); and five in Southeast Asia: Indonesia (1), Lao PDR (2), and Vietnam (2).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6070177/v1/041a3f6178c90704c7b49fce.png"},{"id":77763284,"identity":"d214f959-3c4d-417a-bdec-538c08a99178","added_by":"auto","created_at":"2025-03-05 09:48:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70524,"visible":true,"origin":"","legend":"\u003cp\u003eDecision Tree for determining resolution for EFlows assessments. The dashed line is to assess the resolution for the dewatered river between the dam and the tailrace outlet, and the solid line is for the river reach downstream of the tailrace outlet. Figure adapted from WBG [7].\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6070177/v1/c93ead34abff9caa65ada785.png"},{"id":77765189,"identity":"63cf4a4d-3c2e-4951-a336-f9cd7adc29da","added_by":"auto","created_at":"2025-03-05 09:56:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":199924,"visible":true,"origin":"","legend":"\u003cp\u003eThe IUCN Red List extant of occupancy for the Golden Mahaseer \u003cem\u003eTor putitora\u003c/em\u003e excluded occurrences documented in hydropower project environmental studies: A) the Poonch River Mahaseer National Park where the Gulpur dam is located [122] and B) the Alaknanda River where the Vishnugad Pipalkoti dam is located [64, 67]. C) The Beas River Conservation Reserve is also documented habitat of \u003cem\u003eTor putitora\u003c/em\u003e [123].\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6070177/v1/1c276e86b20c79eacde5f36f.png"},{"id":77763282,"identity":"5b1d7b87-8adf-44e4-845f-dff94da5e1c5","added_by":"auto","created_at":"2025-03-05 09:48:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":235895,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in the use of EFlows assessments methods (left) and EFlows assessment resolution (right).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6070177/v1/ca82a062cc593ac842bec8f9.png"},{"id":77765190,"identity":"ac8cbef5-e741-4b9a-ba09-cb69367f4f4b","added_by":"auto","created_at":"2025-03-05 09:56:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":178271,"visible":true,"origin":"","legend":"\u003cp\u003eNo EFlows assessments reviewed matched the resolution suggested by good practice prior to 2010, however, there is an increasing trend in the use of suitable methods for both the dewatered reach (left) and the reach downstream of the tailrace outlet (right).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6070177/v1/654ad518ac33d9ee63b7893c.png"},{"id":77765193,"identity":"fe4d5e57-7eac-4a26-b54e-71396e5bcbc6","added_by":"auto","created_at":"2025-03-05 09:56:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":211788,"visible":true,"origin":"","legend":"\u003cp\u003eThe proposed downstream release of water by EFlows studies show a slight (and statistically insignificant) positive trend with time. Data presented is for 30 hydropower projects for which hydrological data was available.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6070177/v1/452a570cfad82576b9de7bb2.png"},{"id":77767791,"identity":"5acba4e9-9458-417f-88d3-b95f37fad4c5","added_by":"auto","created_at":"2025-03-05 10:20:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1897976,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6070177/v1/8b61f87b-d7fb-46a7-a7b5-d870b928889c.pdf"},{"id":77765203,"identity":"eb2f365a-fcf0-4377-b2a7-11002db3bc84","added_by":"auto","created_at":"2025-03-05 09:56:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":39926,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6070177/v1/f3599d76f140304cce13c984.docx"},{"id":77765195,"identity":"7eee2615-28d2-49a7-a0b8-307028b81080","added_by":"auto","created_at":"2025-03-05 09:56:47","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":91081,"visible":true,"origin":"","legend":"","description":"","filename":"IJERSupplementaryPreprint.docx","url":"https://assets-eu.researchsquare.com/files/rs-6070177/v1/b73e63fdaf3800c9b4236c42.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eResolution matters: An evaluation of EFlows assessment methods used for hydropower in developing countries\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDeveloping countries have exploited about 23% of their combined feasible hydropower resources [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and, following a brief hiatus in dam construction coinciding with the World Commission on Dams [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], these countries have experienced an ongoing boom in hydropower development [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. If sited, designed, constructed, or operated without the proper assessment and mitigation of their impacts, these developments are a clear and present threat to riverine ecosystems and the livelihoods that depend on them [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe World Commission on Dams [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] highlighted the importance of meaningful assessment, and mitigation, of the social and environmental impacts of dams and the importance of Environmental Flows (EFlows; [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]). The result was a rapid uptake of methods to assess EFlows in data limited environments, such as those in Africa and parts of Asia [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Since then, many countries have entrenched the right of rivers and estuaries to a portion of their natural flow regime and protection from pollution [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Other more recent initiatives, such as the Sustainable Development Goals, specifically Sub-goals 6.5 and 6.6, which target the protection and management of river related ecosystems, are also intrinsically tied to setting and maintaining EFlows [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePresently, a wide variety of EFlows assessment methods are used globally [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and outdated EFlows methods continue to be used despite their many limitations. These obsolete methods (e.g., the 10% rule [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]) have little or no ecological basis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and do not address the impacts most associated with hydropower development, which tend to be more related to the timing than to volume of flows in the downstream river [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the disruption to the supply of sediment from upstream [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and the obstruction of migration routes of fish and other biota [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. They are also unable to consider projected changes in climate and anthropogenic pressures [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. More modern EFlows assessment methods address these shortcomings by focussing on the relationships between changes in the volume and timing of the flow of water, sediments and biota and river ecosystem functioning, for example, the DRIFT assessment method [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] and the Ecosystems Function Model [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These are interactive models, which makes them more useful when evaluating the location, design, and operation of water-resource developments [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and in decision-making to balance the use of water for multiple outcomes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEFlows assessment methods have been reviewed and categorized (e.g., [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]) and the importance of method selection in supporting sustainable development has been recognised by many of the Multilateral Financial Institutions (MFIs; e.g., [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]); but there are no systematic analyses on the EFlows methods used for major water-resource developments post- World Commission on Dams [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Thus, the objectives of this study were to evaluate the methods used for EFlows assessments conducted for major hydropower developments in developing countries in Africa and Asia, the extent to which they comply with international good practice, and the factors driving the adoption of suitable EFlows methods; so that this may provide insights for policy and planning related to EFlows.\u003c/p\u003e \u003cp\u003eThis study investigated the EFlows assessment methods used for a selection of new hydropower developments in Africa and Asia over the past two decades against the assessment resolution recommended by international good practice. Two main research questions were addressed regarding the selection of EFlows methods: 1) which explanatory variables predicted whether the EFlows method used matched the recommended resolution, and 2) which explanatory variables explained selection of higher resolution EFlows assessment methods. Fourteen explanatory variables were shortlisted based on hypotheses including links to project stakeholders, project size and design, economic conditions of the host country, date of assessment and aspects that determine the sensitivity of the freshwater ecosystem such as the presence of Critical Habitat (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The focus of the study was on hydropower projects financed by MFIs that have adopted and actively promote the Equator Principles (a set of guidelines for the consideration of social and environmental impacts of large-scale projects) as it was expected that the hydropower projects where they were involved would increasingly meet evolving international good practice related to EFlows assessment [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExplanatory variables hypothesized to influence the selection of the EFlows assessment method resolution with reasons for inclusion.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis or reasoning for inclusion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental and social performance can be driven through institutional requirements [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrivate sector involvement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental consultant experience may determine the quality of the study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInvolvement of international consulting firm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLater assessments may build on previous experience and understanding of river ecosystems and increasing concerns for their sustainable development [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEFlows assessment date\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLarger projects may face more scrutiny or have larger environmental and social assessment budgets\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProject size in megawatt\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProject size in US dollars (million)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProject size in annual generation (GWh/year)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCountry economic conditions may impact the level of environmental and social compliance in projects due to varying priorities [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNominal Gross Domestic Product (GDP; billion USD)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP per capita purchasing power parity (PPP) (USD per person)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccess to electricity (% of population)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eThe sensitivity of the river ecosystem, as assessed by good practice guidelines, will influence the methods used for EFlows assessment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransboundary diversion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrans basin diversion\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFirst or most downstream project in a cascade\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial dependence on the river\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresence of Critical habitat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Sample selection\u003c/h2\u003e\n \u003cp\u003eThe study was limited to projects post 2000, as there was limited consideration of environmental impacts of large dams before then, and included those financed or under consideration for financing by the Asian Development Bank (ADB), the International Finance Corporation (IFC), the International Bank for Reconstruction and Development (IBRD), and the International Development Association (IDA). These were selected because the required documentation is publicly accessible on searchable online databases.\u003c/p\u003e\n \u003cp\u003eA systematic search was conducted across the project databases of ADB (www.adb.org/projects; total records: 10,989) on August 2, 2021, IFC (www.disclosures.ifc.org/; total records: 9,494) on August 22, 2021; and the combined IDA and IBRD database (www.projects.worldbank.org; total records 21,073) on August 25, 2021. The keywords and/or search methods differed depending on the database (Fig.\u0026nbsp;1). The final screened list resulted in a total of 119 records which yielded a list of 188 hydropower projects (single records occasionally referred to multiple hydropower projects). A set of criteria was then used to develop the sample set for this study. The focus was on medium to large dams, and so small and micro hydropower projects (defined as projects \u0026lt; 30 MW; [37]) were excluded (63 of 188 excluded on this criteria). The study was also limited to new projects, as EFlows assessments are typically not done during privatization or rehabilitation of existing projects (62 of 188 excluded on this criteria). Lastly, in line with the focus on ‘developing countries’ where most of the upcoming hydropower developments are planned, only projects in low or low to middle income countries (as defined by the World Bank) were considered (50 of 188 excluded on this criteria). Three projects that met the above criteria but were dropped from consideration by the MFI because of project delays and conflicts (e.g., disruptions related to the Tajik Civil War) were also excluded from the study. As some projects met multiple exclusion criteria, 126 projects were excluded overall and 47 projects in 16 countries were shortlisted for further evaluation.\u003c/p\u003e\n \u003cp\u003eThe environmental assessments for the shortlisted hydropower projects were downloaded from the relevant websites. These comprised 29 Environmental and Social Impact Assessments (ESIAs), one EFlows Assessment, 11 Summary Environmental Impact Assessments (SEIAs), and one Summary Initial Environmental Examination (SIEE). Project design documents and monitoring reports submitted to the UN Clean Development Mechanism were used to augment the information for those projects where only summary assessments were available. In addition, supplementary studies such as biodiversity management plans (e.g., for Karot Hydropower Project) or cumulative impact assessments (e.g., for Patrind Hydropower Project) were accessed as they sometimes included an EFlows component. These documents formed the main data set for this study. EFlows assessment documentation could not be sourced for five of the 47 projects, and so these were excluded from the review. Of the 42 final shortlisted hydropower projects (Fig.\u0026nbsp;2), 22 were operational, 19 were under-construction and one was in the feasibility stage (i.e., construction contracts had not yet been awarded). The mean estimated capital cost of the hydropower installations was $2.2\u0026nbsp;million USD per installed megawatt and the median reservoir size was 0.5 square km. Supplementary Table\u0026nbsp;1 provides the full list of hydropower plants included in the review and Supplementary Table\u0026nbsp;2 provides summary statistics of their key characteristics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 EFlows assessment resolution\u003c/h2\u003e\n \u003cp\u003eAlthough, both World Commission on Dams [2] and the Brisbane Declaration [6] lay out best practices for the assessment of EFlows, the guidelines they provide are abstract and not readily operationalized for this study. Opperman et al. [38] sort EFlows methods into three groups of increasing complexity and provide illustrative examples to demonstrate the application of each group; however, their examples are from the United States where data availability and regulatory requirements do not map well onto the conditions present in developing countries [39]. Lastly, The World Bank Group (WBG) Good Practice Handbook on Environmental Flows for Hydropower Projects [7] provides a structured method for selecting the resolution for an EFlows assessment, is specifically tailored for emerging markets, and references requirements of MFI environmental standards such as the IFC Critical Habitat requirements [40]. Therefore, WBG [7] was selected as the benchmark international good practice criteria due to its relevance to this study.\u003c/p\u003e\n \u003cp\u003eThe information required for the WBG [7] decision tree (Fig.\u0026nbsp;3) was compiled for the shortlisted hydropower projects. The requisite information comprises design and operation of the hydropower plant; transboundary and/or trans-basin issues; the type of ecosystem impacted; the extent of social dependence on those ecosystems; the presence of other existing or planned hydropower plants, and whether the host river(s) met the definitions for Critical or Modified Habitat. The methods used to compile and process this information were designed to allow for their uniform application across all shortlisted hydropower projects and are described briefly.\u003c/p\u003e\n \u003cp\u003eThe significance of the dewatered reach was evaluated on a case-by-case basis considering the length of the diversion, inflows from other unaffected rivers, quality of river habitat effected, and the social dependence on the reach [7]. The country(ies) in which the hydropower dam, reservoir, powerhouse and tailrace were located was captured and if specific transboundary issues related to water consumption, sediment transport or connectivity were directly mentioned in the environmental reports then these were also noted. From the dam wall, the downstream study area was extended to the outflow of the river into a lake, the sea, or a reservoir and where there were aquatic ecosystems other than the river, e.g., lake, wetland, floodplain and estuary in this study area, this information was captured using Google Earth satellite imagery. Social dependence was evaluated by searching the project environmental reports using the key words ‘fishing’, ‘fisheries’, ‘navigation’, ‘tourism’, ‘festivities’, ‘festival’, ‘ceremony’, ‘ceremonies’, ‘rafting’, ‘hiking’ and ‘recreation’. This was supplemented by a multi-year analysis of Google Earth satellite imagery for the presence of settlements along the banks or within the floodplains of a potentially impacted river (i.e., adjacent to the dewatered reach or just downstream of the tailrace), and if any floodplain agriculture was evident. At minimum, these settlements likely depended on the river ecosystem for water and wastewater removal as well as subsistence, cultural and recreational activities [41]. It was noted if the project was the first constructed in a planned cascade of projects or was the most downstream in an existing cascade (according to the environmental assessment supplemented with Google Earth imagery). Furthermore, to obtain a coarse overview of the full suite of planned hydropower developments, the number of hydropower projects planned for the river basin where each project was located was estimated through web searches. For the MFIs included in the review, Critical Habitat is defined as an area with high biodiversity value including habitat, which is of significant importance to International Union for the Conservation of Nature (IUCN) Red-listed Endangered or Critically Endangered species, endemic, migratory and/or congregatory species [42, 43]. The determinations of Critical and Modified Habitat as stated in project documents were noted. Furthermore, the IUCN Red List that forms the basis of the determination of Critical Habitat was used to cross check the Critical Habitat determinations provided by the environmental assessments. The IUCN Red List includes global assessments for ~ 142,500 species of which ~ 80% have associated spatial data [44] which are routinely used for screening for Critical Habitat in terrestrial and marine environments [45, 46].\u003c/p\u003e\n \u003cp\u003eThe WBG [7] categorization of EFlows assessment methods designates hydrological ratio methods without any local calibration (e.g., the 10% rule [19] and the Tenant Method [47]) as very low-resolution methods. Hydraulic (e.g., wetted perimeter), water quality (e.g., Qual2k [48]) and integrated hydrological (e.g., Indicators of Hydrological Alteration [49]) methods are designated as low-resolution methods. Habitat simulation (e.g., Instream Flow Incremental Methodology [50, 51]), holistic and eco-social methods (e.g., Building Block Methodology, DRIFT, RANA-ICE) can be medium or high resolution depending on the level of detail (e.g., number of components of the ecosystem addressed) and the level of effort in collecting local information. The categories used by WBG [7] align with the EFlows classification suggested by Opperman \u003cem\u003eet al.\u003c/em\u003e [38] who provide a three-tier categorization of EFlows assessment methods. They define Level 1 methods as holistic hydrologic desktop approaches such as the Indicators of Hydrological Alteration, which correspond to low-resolution assessment as per WBG [7], and Level 2 methods as holistic expert panel methods, which include the Building Block Methodology and DRIFT, corresponding with medium and high-resolution assessments in WBG [7]. Opperman \u003cem\u003eet al.\u003c/em\u003e [38] Level 3 research driven assessments do not fit into any of the categories in WBG [7]. These include a broad range of analytical methods that span over several years and may include experimental releases and monitoring and are typically not used in EFlows assessments for hydropower developments in Asia and Africa.\u003c/p\u003e\n \u003cp\u003eThe EFlows assessment methods used for each hydropower project were categorized according to WBG [7] plus two additional categories: studies that did not mention EFlows, and those that stated a downstream release but did not provide a method for its determination. This study evaluated the resolution of the EFlows methods and whether they were suitable for the context in which they were applied i.e., they matched or exceeded the WBG [7] suggested resolution. It did not evaluate whether the methods were implemented correctly, the suitability of the scenarios assessed or the level to which the recommended or adopted EFlows would support the health of the river ecosystems and the livelihoods that depended on them.\u003c/p\u003e\n \u003cp\u003eThe possible drivers for increasing EFlows assessment resolution were explored quantitatively by scoring each study on a zero to five scale based on the EFlows resolution used as follows: no EFlows study (score of zero), downstream release stated without discussion of the EFlows method (score of one), very low-resolution study (score of two), low resolution study (score of three), medium resolution study (score of four), and high-resolution study (score of five). This dependent variable was regressed against the possible explanatory variables (Table\u0026nbsp;1) using multivariable linear regression models which were developed using forward and backward selection.\u003c/p\u003e\n \u003cp\u003eIn addition, to understand which variables predicted whether the EFlows assessment matched the resolution recommended by WBG [7] a multi-variable logistic regression [52] was used due to the match/no match binary dependent variable. The match/no match dataset comprised 35 datapoints for the dewatered reach and 42 datapoints downstream of the tailrace outlet. With limited observations there was the risk of overfitting the logistic model, therefore the number of explanatory variables was limited to two [53]. Single variable models were used to test the significance for each independent variable and those with p \u0026gt; 0.20 were removed from the pool. Models made with a combination of the remaining variables with the highest area under the curve (AUC) that contained significant explanatory variables (p \u0026lt; 0.05) were shortlisted for analysis.\u003c/p\u003e\n \u003cp\u003eData for most of the explanatory variables were obtained from project environmental studies but four economic indicators were obtained from World Bank Open Data (data.worldbank.org; accessed September 15, 2021) for each country for the year of the environmental study \u003cem\u003eviz.\u003c/em\u003e: nominal Gross Domestic Product (GDP); GDP per capita purchasing power parity (PPP); and access to electricity. A multicollinearity analysis showed high correlation between the three project size variables (cost, installed capacity, annual generation); of these, annual generation was selected for further use.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe 42 hydropower projects in the sample were evaluated against each of the eight decision points in the WBG [7] decision tree, from which findings are summarized (see Supplementary Table\u0026nbsp;3 for detailed results). Although seventeen of the hydropower projects assessed were planned as baseload plants, none of them met the criteria for low impact design and operation due to storage size and length of the diversion. Three of the reviewed hydropower projects were transboundary in nature and eleven had trans-river and/or trans- basin diversions. Seven hydropower projects were considered either the first or most downstream in a cascade and eleven projects had significant social uses of the river ecosystem. Apart from river ecosystems, no other ecosystems were located in the dewatered reaches of any of the hydropower projects studied. For 11 projects, ecosystems other than river occurred downstream of the tailrace, however only one of these, the Niger River Floodplain, was likely to be significantly impacted. Two other hydropower projects evaluated in this study, Tanahu and Upper Trishuli 1, would impair EFlows to the Gandak River Floodplain and another two, Nam Theun 2 and Nam Ngiep, would impair EFlows to the Mekong Delta. Each of these were individually assessed and deemed to not have significant impacts on these ecosystems, although it is acknowledged that they contribute to the cumulative impacts of several projects, which are likely to be significant [54–57]. Moreover, in the river basins where the reviewed hydropower projects are under construction, about 400 additional hydropower projects are planned (see Supplementary Table\u0026nbsp;4). Only in the Kopili River Basin were no other hydropower projects planned, possibly because the basin already contains multiple large dams (e.g., Kopili Dam and Khandong Dam) that are at risk from acidic mine discharge [58].\u003c/p\u003e\n\u003cp\u003eFive of the environmental studies reviewed assigned Critical Habitat to the host river, i.e., 12% of reviewed projects: Gulpur, Balakot, Kohala, Nachtigal and Batoka Gorge. Notwithstanding this, 27 of the 42 hydropower locations intersected with the spatial extent of one or more Endangered or Critically Endangered freshwater species on the IUCN Red List. In total, there were 55 Endangered and Critically Endangered species whose IUCN spatial extant overlapped with project areas, however, only three of these species (\u003cem\u003eGlyptothorax kashmirensis, Tor putitora\u003c/em\u003e, and \u003cem\u003eLedermaniella sanagensis)\u003c/em\u003e were assessed in the environmental studies to have Critical Habitat in the project area. It should be noted that 26 of the 55 overlapping species were re-classified as Endangered or Critically Endangered only after the completion of project environmental studies (see Supplementary Table 5); for ten species the threatened status was elevated less than three years after completion of project studies, and before the start of construction. In addition, many of the environmental studies relied on IUCN Red List spatial data to identify or confirm the presence of endangered species, sometimes in contradiction of survey data from the host river. For example, the ESIA surveys for the Nam Ngiep Hydropower Project found 195 specimens of the Endangered and migratory Yellow Tail Brook Barb \u003cem\u003ePoropuntius deauratus\u003c/em\u003e throughout the study area, but ignored this data as a mischaracterization because the spatial range of the Yellow Tail Brook Barb as suggested by IUCN did not extend to the project’s location [59]. Further misalignment between the survey data and IUCN data was observed in the IUCN spatial extent for the Endangered Golden Mahseer \u003cem\u003eTor putitora\u003c/em\u003e [60]. The IUCN spatial extent omitted occurrences documented through surveys conducted for several of the hydropower projects reviewed (Fig. 4) e.g., Gulpur Hydropower Project on the Poonch River [61], the Vishnugad Pipalkoti Hydropower Project on the Alaknanda River [62] and the Karot Hydropower Project on the Jhelum River [63]; and subsequently published in the scientific literature [64–67].\u003c/p\u003e\n\u003cp\u003eSubsequently, according to the WBG [7] decision tree for the river section downstream of the outlet, the criteria of a low-resolution assessment were not met by any of the projects, 20 met the criteria for medium resolution and 22 required high-resolution studies. Eight projects had no dewatered reach; For the dewatered sections of the remaining 35 projects, low resolution assessments were recommended for two, medium resolution for 25, and high-resolution studies for eight (Table\u0026nbsp;2).\u003c/p\u003e\n\u003cp\u003eThe applied EFlows assessment resolution matched that suggested by WBG [7] for 23% (8 of the 35) of dewatered reaches and 12% (5 of 42) of reaches downstream of the tailrace. However, over time, there was a move to higher compliance. Five project studies did not mention EFlows and all five of these were conducted prior to 2010. One study [68] provided Terms of Reference for a EFlows study but there was no evidence of this in the available project documentation. Six studies stipulated downstream releases for the dewatered reach with no description of the method used. Nineteen studies used very low-resolution hydrological ratio methods to determine downstream releases. These methods included: 2.5% and 10% of mean annual flows; 10%, 14% and 15% of dry season flows, and; 10% of mean monthly flows. Hydraulic methods were used by two studies and combined hydraulic and pollutant modelling methods were used by another two studies. Seven studies used a holistic/eco-social method, four at a medium resolution, and three at a high resolution. Supplementary Table\u0026nbsp;6 provides the full list of methods used.\u003c/p\u003e\n\u003cp\u003eThe number of hydropower projects that release downstream flow based on EFlows studies, and the resolution of EFlows studies, had increased steadily over the last two decades (Fig. 5). Although the bulk of the EFlows studies (45%) used hydrological ratio methods, in recent years these outdated methods had given way to holistic eco-social methods such as DRIFT (Fig. 5). In the last five years included in this review (2016 to 2020) only 25% of studies used hydrological ratios whereas 50% used holistic eco-social methods. This is favourably compared to the first decade reviewed (2000 and 2010) where 40% of studies used hydrological ratios and not a single study reviewed used holistic eco-social methods. Subsequently, between 2016–2020 more EFlows studies matched the suggested resolution (63% of studies for the dewatered reaches and 38% of studies for the reach downstream of the tailrace outlet) than before 2010 where no EFlows studies matched the suggested resolution (Fig.\u0026nbsp;6).\u003c/p\u003e\n\u003cdiv\u003eDespite steady increases in the EFlows resolution with time, there has been no statistically significant change in the volume of water suggested as minimum downstream release (Fig. 7). Furthermore, there was no correlation between the EFlows study resolution and volume of water recommended as a minimum-flow release, neither as a percentage of the dry season flow (correlation coefficient 0.070), nor as a percentage of mean annual flow (correlation coefficient − 0.104), as calculated for 30 studies for which flow data were available. The average downstream release suggested by medium and high-resolution studies was 4% of mean annual flows, slightly lower than the average of 6% for projects assessed by low- and very low-resolution studies between 2011 and 2020. However, the medium and high resolution studies contained additional recommendations that included changes to project operation (e.g., from hydropeaking to baseload operation; [69]), changes to the location of the dam and tailrace outlet (e.g., [61]), and requirements for reducing existing anthropogenic pressures on the river, such as over-fishing and sand-mining [70].\u003c/div\u003e\n\u003cp\u003eAssessment date and the presence of Critical Habitat were significant factors predicting the use of medium and high EFlows assessments (p \u0026lt; 0.01; Table 3). None of the other variables, such as stakeholder-related factors or economic indicators, were significant (p \u0026gt; 0.05). The regression coefficient for assessment date was 0.12 to 0.196 indicating that over five to eight years the average EFlows assessment resolution increased by one, i.e., from an average resolution of 0.7 (no EFlows method or minimum release downstream of dam only stated) between 2000 and 2005, to an average resolution of 3.3 (low to medium resolution methods) between 2016 to 2020. The regression coefficient of Critical Habitat ranged from 1.57 to 2.38 signifying its strong positive impact on the resolution of the EFlows assessment method used. Critical Habitat was also a significant factor (p \u0026lt; 0.01) in predicting whether the EFlows assessment downstream of the tailrace outlet (Table 4) matched the resolution suggested by WBG [7]. For the reach downstream of the tailrace outlet, the model with only Critical Habitat outperformed most other models (AUC of 0.98) and explained 60% of correct matches and 95% of correct mismatches (Model A; Table 4). In the dewatered reach, however, only assessment date was a highly significant (p \u0026lt; 0.01) explanatory variable. Other variables such as project size and country economic indicators were not statistically significant in predicting whether the EFlows assessment was conducted at a suitable resolution.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMultivariable linear regression (models A to C) results show that Critical Habitat and Assessment Date were highly significant drivers (p \u0026lt; 0.01) in increasing the resolution of the EFlows study. *, **, *** indicates significance at the 90%, 95%, and 99% level, respectively. Standard error of the regression coefficient is presented in brackets.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.D.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel A\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel B\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel C\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-392***\u003c/p\u003e\n \u003cp\u003e(85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.86***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-240\u003c/p\u003e\n \u003cp\u003e(71)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate sector involvement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003cp\u003e(0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternational consultant involvement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003cp\u003e(0.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual generation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00007\u003c/p\u003e\n \u003cp\u003e(0.00006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNominal GDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003cp\u003e(0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGDP per capita, PPP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0002\u003c/p\u003e\n \u003cp\u003e(0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAccess to electricity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssessment Date\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.196***\u003c/p\u003e\n \u003cp\u003e(0.042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.120***\u003c/p\u003e\n \u003cp\u003e(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransboundary issues\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003cp\u003e(0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTrans-basin diversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003cp\u003e(0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFirst or most downstream\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003cp\u003e(0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial Dependence (downstream outlet)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003cp\u003e(0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial Dependence (dewatered reach)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e(0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCritical Habitat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.38***\u003c/p\u003e\n \u003cp\u003e(0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57***\u003c/p\u003e\n \u003cp\u003e(0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjusted R Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSignificance F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMultivariable logistic regression models developed to explain the match between the suggested and actual EFlows study resolution. Models A through C represent the reach downstream of the tailrace outlet and Models D through G represent the dewatered reach. *, **, *** indicates significance at the 90%, 95%, and 99% level, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eDownstream of tailrace outlet (n = 42)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eDewatered reach (n = 35)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel A\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel B\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel C\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel D\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel E\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel F\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel G\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.86***\u003c/p\u003e\n \u003cp\u003e(0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.10***\u003c/p\u003e\n \u003cp\u003e(1.274)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1420*\u003c/p\u003e\n \u003cp\u003e(751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.649***\u003c/p\u003e\n \u003cp\u003e(2.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-833***\u003c/p\u003e\n \u003cp\u003e(304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-786**\u003c/p\u003e\n \u003cp\u003e(324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1064**\u003c/p\u003e\n \u003cp\u003e(433)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate sector involvement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.413*\u003c/p\u003e\n \u003cp\u003e(1.351)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.169**\u003c/p\u003e\n \u003cp\u003e(2.115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.343*\u003c/p\u003e\n \u003cp\u003e(1.320)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAssessment Date\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.703*\u003c/p\u003e\n \u003cp\u003e(0.372)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.413***\u003c/p\u003e\n \u003cp\u003e(0.151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.390**\u003c/p\u003e\n \u003cp\u003e(0.161)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.527**\u003c/p\u003e\n \u003cp\u003e(0.215)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCritical Habitat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.268***\u003c/p\u003e\n \u003cp\u003e(1.167)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.159**\u003c/p\u003e\n \u003cp\u003e(1.351)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.054**\u003c/p\u003e\n \u003cp\u003e1.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003cp\u003e(1.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea under Curve (AUC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorrect match prediction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (60%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (38%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorrect mismatch prediction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (97%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-sq (McFadden)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-sq (Cox and Snell)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-sq (Nagelkerke)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn general, when evaluated using WBG [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], the river reaches downstream of the tailrace outlets were deemed more sensitive than the dewatered sections, and thus in more need of medium or high resolution EFlows studies. Despite this, and likely because of the methods used, most of the EFlows assessments provided only a suggestion for a minimum release of water into the dewatered reach, with no consideration of the river downstream of the tailrace outlet, nor of other EFlows-related impacts such as changes in the onset or duration of hydrological seasons, loss of connectivity for sediment and biota, and the extreme inter-day variations in discharge associated with hydropeaking. This was despite the many EFlows methods documented in the scientific literature that can quantify these impacts in a holistic way [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] and the continued calls to use them where applicable [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. The preponderance of the use of basic low-resolution methods also meant that most EFlows studies were at a lower resolution than recommended by international good practice. Surprisingly, there is an abundance of recent literature that puts forward low-resolution methods as viable tools [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] and this may partially explain their continued use for decision making related to river development. As discussed previously, these methods have no ecological basis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], consider only flows (often only considering minimum release in the dry season) and leave out the other two components (sediments and biota) of EFlows [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. This disconnect between EFlows best practice and application is a severe challenge for sustainable use and conservation of river ecosystems [17. 76]. The only medium/high resolution [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] or Level 2 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] method used in the 42 studies reviewed was the DRIFT eco-social model, although other methods have been applied in other regions (e.g., [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]). There are numerous modern mixed methods with outcomes that increase certainty relative to some the low-resolution methods routinely applied for hydropower EFlows assessments, but none of these were featured in the studies evaluated. The chosen methods for EFlows assessments should be commensurate with the design and operation of the hydropower plant and the ecological and social sensitivity of the host river. Selected methods should be able to assess the impacts of hydrological changes, those due to the barriers to movement of sediment and biota, and the impacts of hydropeaking where applicable. To ensure the selection of correct methods, MFIs should mandate application of their own good practice guidelines on conducting EFlows assessments.\u003c/p\u003e \u003cp\u003eThere was no correlation between the EFlows study resolution and volume of water recommended as a minimum-flow release. Similarly, the literature shows that higher resolution methods can result in both larger and smaller suggested minimum flow releases as compared to lower resolution methods used on the same river [\u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. This is an important outcome, because it highlights that higher minimum releases are not an inevitable consequence of higher resolution studies. Rather, the higher resolution studies offer greater resolution and more consideration of the complex interactions affecting river ecosystems, and more options for sustainably optimising their use [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe dominance of Critical Habitat as a trigger for higher resolution assessments may lie in the fact that it is the only factor in the WBG [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] decision tree that is explicitly codified into the MFI social and environmental safeguards documentation [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. The designation of Critical Habitat during infrastructure development has contributed to the protection of ecosystems globally [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Other factors, such as transboundary issues and trans basin diversions lack a procedural pathway to motivate for a higher level of applications [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Furthermore, country-related parameters, such as GDP per capita PPP, were found to be insignificant in increasing the resolution of the EFlows assessment method used. Developing countries often require rapid electrification to support their growth and development; and hydropower presents a low carbon and low operational cost alternative [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. G\u0026ouml;k and Sodhi [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e] found that improvements in governance reduced environmental performance in low-income countries (due to their focus on economic development) and suggested that for these countries a direct shift from economic outcomes to environmental outcomes was required and it would not come due to other factors such as GDP growth in the short term. These factors highlight both the value of MFI environmental and social safeguards in sustainable development and the importance of ensuring that they are up to date with best practice.\u003c/p\u003e \u003cp\u003eAlthough Critical Habitat was a significant driver in the adoption of higher resolution EFlows assessment methods this benefit was not widely realized as only five of the 42 studies designated the affected riverine habitats as Critical Habitat. This was fewer than expected as most projects were located within the IUCN spatial range of Endangered or Critically Endangered freshwater biota. According to Murray \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e] the enhanced environmental performance standards triggered by Critical Habitat may result in higher project costs, lower electricity output, and/or lower peak electricity generation; in some cases, they may lead to the cancellation of the project. There is thus, a disincentive to designate Critical Habitat. Furthermore, as established data pertaining to the extent and distribution of threatened species are limited in the developing world [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e], the responsibility for determining the presence and abundance of trigger species falls on the project environmental studies. To ensure that these have a reasonable chance of recording rare or endangered species, the surveys done in these studies should include (at minimum) multi-season and multi-year surveys using a variety of techniques (e.g., for fish these may comprise electrofishing plus variety of nets such as seine, gill and/or mesh nets) across stretches of oft-inaccessible terrain [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Bennun \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e] and Camaclang \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], in their assessment of project environmental studies, deemed their data collection and analyses inadequate to this task, although Rees \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e] suggests that improved methods may increase accuracy of such biodiversity analysis. However, there are inherent limitations related to the reliance on the IUCN Red List in the assessment of Critical Habitat that cannot be corrected through improved project-specific ecological surveys. In developing countries, the IUCN Red List required regular updating as a result of the large and increasing anthropogenic pressures on biodiversity and the paucity of data on species abundances and distributions [\u003cspan additionalcitationids=\"CR93\" citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. This study found several instances where the IUCN threatened classification of freshwater species changed between the completion of the hydropower project environmental studies and the start of construction. For example, the Ningu Carp \u003cem\u003eLabeo victorianus\u003c/em\u003e was found by the ESIA study for the Rusumo Falls Hydropower Project on the Kagera River both upstream and downstream of the proposed project location, but at the time of assessment it was classified as Least Concern and so its presence did not trigger Critical Habitat [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. The carp was re-classified as Critically Endangered in 2016 [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]; in the same year that construction contracts were awarded for the Rusumo Falls project [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Similarly, the Southeast Asian Box Turtle \u003cem\u003eCuora amboinensis\u003c/em\u003e, whose range overlaps with the Lower Kopili Hydropower Project and Upper Cisokan Pumped Storage Scheme, was re-classified from Vulnerable to Endangered in 2020 [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. Both projects are now under construction, and neither was deemed to coincide with Critical Habitat [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. This issue occurred in over a quarter of the reviewed hydropower projects that intersected with the IUCN spatial range of Endangered and Critically Endangered freshwater species; and in no cases did this reclassification seem to trigger more detailed environmental assessments. This is not limited to freshwater ecosystems in the developing world, as Ward \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e], demonstrated that Vulnerable species were left out of consideration during the environmental impact assessment process in Australia, that in many cases became become Endangered with time. While such updates are understandable given the combination of the limited data and large anthropogenic pressures on the aquatic environments in the developing world, this can have serious implications for a hydropower project. If adhered to, projects may require mid-assessment re-evaluation of Critical Habitat designation, with knock-on implications for project location, design, and operating rules. A second limitation pertains to the inaccuracies in the IUCN Red List spatial distribution data of species in the developing world, and is acknowledged by IUCN [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. This is equally troublesome as some projects studies rely on these data to verify survey results whereas others exclusively used the IUCN spatial ranges to screen for potential Critical Habitat trigger species (e.g., [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]). Again because of data deficiencies in developing countries, a tension exists between the results of the project surveys and the IUCN Red List spatial data. This tension could be eased to the benefit of both the developer and IUCN, if there was a requirement for the species distribution data generated by ESIAs to be submitted to IUCN for use in updating the spatial data. Naturally, the acceptance of such data should subject to quality assurance by IUCN, which could improve the methods used in project assessments. Lastly, for the long-term survival of threatened species, the designation of Critical Habitat should be more detailed than area thresholds as currently evaluated, and should include, \u003cem\u003einter alia\u003c/em\u003e, demographic data [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], habitat availability for each sequential life history [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e], and a consideration of unoccupied habitat [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImprovements in the current arrangements notwithstanding, the idea that river ecosystems can be sustainably developed through site-by-site and project-by-project classification of Critical Habitat based on a rapidly changing assessment of threatened species that inhabit them, requires a radical rethink. This is particularly so given the inconsistent application of good practice and the large number of hydropower project proposed for most basins, such as those in the Himalayas [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e]. Another option is for a shift in focus from single development assessments to the meaningful inclusion of ecosystem functioning and social reliance data [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e] in basin, and regional, water-resource development planning [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. The idea is to embed medium and high-level biodiversity, social and EFlows assessments in basin scale processes, such as the IFC\u0026rsquo;s Cumulative Impact Assessment [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e], that are then used to inform the location, design and operation of individual developments, rather than the other way around. The driving need for such a shift is highlighted by the fact that for the reviewed hydropower projects that are still under construction, on average, there are 20 other hydropower projects planned in the basins where they are located. Each one of these additional projects will have an incremental impact to the river hydrology, and connectivity impacts for sediment and biota, all of which may negatively influence the freshwater ecosystems and livelihoods that depend on them. Unfortunately, even if all these projects were assessed at the resolution suggested by good practice, the parallel application of the decision tree and the subsequent parallel assessment of EFlows, will not be able to evaluate the significant cumulative impacts of these developments. Less than 5% of the EFlows literature since 2010 relates to its basin scale application [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e] reinforcing the need for future EFlows research in this direction. Encouragingly, many of the world\u0026rsquo;s River Basin Organisations have already begun to adopt biodiversity, social and EFlows decision support tools and are applying them at the basin scale [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current study was limited to projects funded by the ADB, IFC and World Bank, as they had publicly accessible and electronically searchable documentation of their project environmental studies. This limitation can be overcome in future studies that can expand the scope of this research to include other MFIs such as the Asian Infrastructure Investment Bank, African Development Bank, Latin American Development Bank, Islamic Development Bank, as well as various Chinese development banks (e.g., China Development Bank and Export-Import Bank of China) especially as financing by Chinese banks to developing countries now rivals that of the World Bank [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e]. Even though ADB, IFC and World Bank financing is traditionally known to be accompanied by stricter environmental protections [\u003cspan additionalcitationids=\"CR115\" citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e] the current study found that even these MFIs have only recently begun to partially meet good practices related to EFlows. Therefore, the findings of this study are broadly applicable to other MFIs at various stages of implementation of environmental standards. For example, MFIs looking to implement better environmental safeguards in developing countries can opt for adopting holistic ecosystem-based, basin scale approaches to EFlows assessment rather than mimicking the data dependent and single species focus of Critical Habitat led performance standards. There is indication that this is changing with MFI led basin scale cumulative impact assessments and strategic environmental assessments [124. 125] but progress is slow and, given the perilous state of the world\u0026rsquo;s rivers, should be accelerated.\u003c/p\u003e \u003cp\u003eIn addition to updating regulations and safeguarding documentation, the successful implementation of EFlows assessments requires suitable and sufficient stakeholder capacity, knowledge and engagement [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e]. All tiers of stakeholders should be involved in EFlows related outreach and training including the community, commercial water users, scientists and engineers, water management agencies, and regional and national leaders [\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e], as the engagement and capacity building of stakeholders improves the implementation of EFlows [\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e]. On realising this as an enabling factor, there are several avenues where training and knowledge exchange around EFlows is increasing. For example, the Nile Basin Initiative regularly hosts free online classes related to EFlows on its e-learning platform [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e] and Bukhari et al. [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e] have recently published a comprehensive library of ecosystem indicators and driver-response relationships for rivers and estuaries in southern Africa by collating over a decade of experience in ecosystem based EFlows assessments. The open access publishing of data and information related to EFlows facilitates knowledge exchange, promotes ecosystem-based EFlows assessments and accelerate the entry of young EFlows professionals and researchers into the field.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eAlthough hydropower projects, and other water-resource developments, have significant social and economic benefits their potential for large and long-term negative impacts on freshwater ecosystems, with devastating knock-on impacts to the communities that depend on them has been widely acknowledged. This has been recognised by MFIs, many of whom have introduced guidelines and procedures to ensure that the assessments of impacts adhere to good practice, which includes the application of appropriate level EFlows assessment. This study, through a systematic search and review, found low adherence to international good practice guidelines in terms of the use of EFlows assessments methods. Low resolution methods continue to be used which do not provide adequate information to stakeholders to guide decision making related to ecological, social, and economic trade-offs related to hydropower development. This disconnect between theory and practice is a cause of concern for the sustainable development and use of river ecosystems. Only assessment date and presence of Critical Habitat were found to be significant explanatory variables in predicting whether the resolution used matched that recommended by good practice. However, the dependence on the classification of Critical Habitat, for which several shortcomings were described in the study, is of concern. Further, with a few exceptions, assessments were focused on single hydropower projects, many of which are situated in river basins where numerous other hydropower projects and water-resource developments are planned. Although the use of suitable EFlows assessment methods is a first and urgent step to mitigate development impact, it may not be sufficient and instead basin-level EFlows assessments that consider existing and planned infrastructure are required. Such basin wide assessments should assess impacts on water quality and the flow of water, sediments, and biota; for the dewatered reaches and the river, and other aquatic ecosystems, downstream of the tailrace for as far downstream as the influence of each hydropower plant extends.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eThe data used to support the findings of this study are included in the article and supplementary materials.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eSupplementary Materials\u003c/p\u003e\n\u003cp\u003eSupplementary materials as a set of tables provide results at the individual hydropower level which were deemed too detailed for the main manuscript. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eInternational Finance Corporation (IFC). (2009). Directions in hydropower: scaling up for development. Washington D.C.: World Bank Group\u003c/li\u003e\n\u003cli\u003eWorld Commission on Dams. (2000). \u003cem\u003eDams and development. A new framework for decision-making.\u003c/em\u003e The report of the World Commission on Dams. London: Earthscan.\u003c/li\u003e\n\u003cli\u003eGoodland, R. (2010). Viewpoint\u0026ndash;The World Bank versus the world commission on dams. \u003cem\u003eWater alternatives\u003c/em\u003e, 3 (2), 384\u0026ndash;398.\u003c/li\u003e\n\u003cli\u003eZarfl, C., Lumsdon, A.E., Berlekamp, J., Tydecks, L. \u0026amp; Tockner, K. (2015). A global boom in hydropower dam construction. \u003cem\u003eAquatic Sciences\u003c/em\u003e, 77 (1), 161\u0026ndash;170.\u003c/li\u003e\n\u003cli\u003eMoran, E.F., Lopez, M.C., Moore, N., M\u0026uuml;ller, N. \u0026amp; Hyndman, D.W. (2018). Sustainable hydropower in the 21st century. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, 115 (47), 11891\u0026ndash;11898.\u003c/li\u003e\n\u003cli\u003eBrisbane Declaration, (2007). The Brisbane Declaration: environmental flows are essential for freshwater ecosystem health and human well-being. \u003cem\u003e10th International River Symposium\u003c/em\u003e. Brisbane: International River Foundation.\u003c/li\u003e\n\u003cli\u003eWorld Bank Group (WBG). (2018). \u003cem\u003eGood Practice Handbook: Environmental Flows for Hydropower Projects. Guidance for the Private Sector in Emerging Markets\u003c/em\u003e. Prepared under the direction of Environment, Social and Governance Department of the International Finance Corporation, World Bank Group. Washington D.C.: World Bank Group\u003c/li\u003e\n\u003cli\u003eKing J., Tharme R.E., \u0026amp; De Villiers M.S. (2000). Environmental flow assessments for rivers: manual for the Building Block Methodology. Pretoria: Water Research Commission.\u003c/li\u003e\n\u003cli\u003eKing J. \u0026amp; Brown C. (2010). Integrated Flow Assessments: concepts and method development in Africa and South-east Asia. \u003cem\u003eFreshwater Biology\u003c/em\u003e, 55 (1), 127\u0026ndash;146.\u003c/li\u003e\n\u003cli\u003eBukhari, H. \u0026amp; Brown, C. (2022). A comparative review of decision support tools routinely used by selected transboundary River Basin Organizations. African Journal of Aquatic Science, 47 (3), 318\u0026ndash;337.\u003c/li\u003e\n\u003cli\u003eAustralian Capital Territory Parliamentary Counsel. (1999). \u003cem\u003eEnvironmental Flow Guidelines.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eChina State Department. (2006). Dispatch Act of Water Quantity from the Yellow River.\u003c/li\u003e\n\u003cli\u003eDepartment of Water Affairs and Forestry. (1998). \u003cem\u003eNational Water Act No. 36\u003c/em\u003e. 1998. South Africa\u003c/li\u003e\n\u003cli\u003eEuropean Union. (2000). \u003cem\u003eThe EU Water Framework Directive\u003c/em\u003e. Brussels: European Commission.\u003c/li\u003e\n\u003cli\u003eHimachal Pradesh Energy Development Agency. (2006).\u003cem\u003e Hydro Power Policy 2006\u003c/em\u003e. India: Himachal Pradesh Government. \u003c/li\u003e\n\u003cli\u003eBrown, C., Campher, D. \u0026amp; King, J. (2020). Status and trends in EFlows in southern Africa. \u003cem\u003eNatural Resources Forum,\u003c/em\u003e 44 (1), 66\u0026ndash;88. \u003c/li\u003e\n\u003cli\u003eTheodoropoulos, C., Georgalas, S., Mamassis, N., Stamou, A., Rutschmann, P. \u0026amp; Skoulikidis, N. (2018). Comparing environmental flow scenarios from hydrological methods, legislation guidelines, and hydrodynamic habitat models downstream of the Marathon Dam (Attica, Greece). Ecohydrology, 11 (8), e2019.\u003c/li\u003e\n\u003cli\u003eKatopodis, C. (2020). A perspective on e-flows at hydroelectric projects in Canada. \u003cem\u003eHydrobiologia\u003c/em\u003e, 849, 461\u0026ndash;484.\u003c/li\u003e\n\u003cli\u003eTharme, R. (2003). A global perspective on environmental flow assessment: emerging trends in the development and application of environmental flow methodologies for rivers. \u003cem\u003eRiver Research and Applications\u003c/em\u003e, 19 (5-6), 397\u0026ndash;441.\u003c/li\u003e\n\u003cli\u003eKing J. \u0026amp; Brown C. (2018a). Environmental Flow Assessments Are Not Realizing Their Potential as an Aid to Basin Planning. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, 6, 113.\u003c/li\u003e\n\u003cli\u003eMagilligan, F.J. \u0026amp; Nislow, K.H. (2005). Changes in hydrologic regime by dams. \u003cem\u003eGeomorphology\u003c/em\u003e, 71 (1-2), 61\u0026ndash;78.\u003c/li\u003e\n\u003cli\u003eCsiki, S. \u0026amp; Rhoads, B.L. (2010). Hydraulic and geomorphological effects of run-of-river dams. \u003cem\u003eProgress in Physical Geography\u003c/em\u003e, 34 (6), 755\u0026ndash;780.\u003c/li\u003e\n\u003cli\u003eGupta, H., Kao, S.J. \u0026amp; Dai, M. (2012). The role of mega dams in reducing sediment fluxes: A case study of large Asian rivers. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, 464, 447\u0026ndash;458.\u003c/li\u003e\n\u003cli\u003eLiermann, C.R., Nilsson, C., Robertson, J. \u0026amp; Ng, R.Y. (2012). Implications of dam obstruction for global freshwater fish diversity. \u003cem\u003eBioScience\u003c/em\u003e, \u003cem\u003e62 \u003c/em\u003e(6), 539\u0026ndash;548.\u003c/li\u003e\n\u003cli\u003eBarbarossa, V., Schmitt, R.J., Huijbregts, M.A., Zarfl, C., King, H. \u0026amp; Schipper, A.M. (2020). Impacts of current and future large dams on the geographic range connectivity of freshwater fish worldwide. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e117 \u003c/em\u003e(7), 3648\u0026ndash;3655.\u003c/li\u003e\n\u003cli\u003eCapon, S.J., Leigh, C., Hadwen, W.L., George, A., McMahon, J.M., Linke, S., Reis, V., Gould, L. \u0026amp; Arthington, A.H. (2018). Transforming environmental water management to adapt to a changing climate. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, 6, 80.\u003c/li\u003e\n\u003cli\u003eBrown, C., Joubert A., Beuster H., \u0026amp; Greyling A. (2013). DRIFT: DSS software development for integrated flow assessments. WRC Report No. 873/1/13. Pretoria: Water Research Commission. \u003c/li\u003e\n\u003cli\u003eJoubert, A.R., Brown, C., King, J.M., Beuster, H., \u0026amp; Greyling, A. (2022). DRIFT: Incorporating an eco-social system network and time-series approach into environmental flow assessments. \u003cem\u003eAfrican journal of Aquatic Sciences\u003c/em\u003e, 47 (3), 338\u0026ndash; 352.\u003c/li\u003e\n\u003cli\u003eShafroth, P.B., Wilcox, A.C., Lytle, D.A., Hickey, J.T., Andersen, D.C., Beauchamp, V.B., Hautzinger, A., McMullen, L.E. \u0026amp; Warner, A. (2010). Ecosystem effects of environmental flows: modelling and experimental floods in a dryland river. \u003cem\u003eFreshwater Biology\u003c/em\u003e, 55 (1), 68\u0026ndash;85.\u003c/li\u003e\n\u003cli\u003eKing J. and Brown C. (2018b). Environmental Flows, minimum flows and the mystery of ten percent. \u003cem\u003eIn:\u003c/em\u003e Gough, P. \u0026amp; Royte, J. eds. \u003cem\u003eSea to Source. Protection and restoration of fish migration in rivers worldwide\u003c/em\u003e. The Netherlands: World Fish Migration Foundation, 107\u0026ndash;111.\u003c/li\u003e\n\u003cli\u003eNull, S.E., Farshid, A., Goodrum, G., Gray, C.A., Lohani, S., Morrisett, C.N., Prudencio, L. \u0026amp; Sor, R. (2020). A meta-analysis of environmental tradeoffs of hydropower dams in the Sekong, Sesan, and Srepok (3S) rivers of the lower Mekong basin. \u003cem\u003eWater\u003c/em\u003e, 13 (1), 63.\u003c/li\u003e\n\u003cli\u003eOverton, I., Smith, D., Dalton, J., Barchiesi, S., Acreman, M., Stromberg, J., \u0026amp; Kirby, J.M. (2014). Implementing environmental flows in integrated water resources management and the ecosystem approach. \u003cem\u003eHydrological Sciences Journal\u003c/em\u003e, 59, 860\u0026ndash;877.\u003c/li\u003e\n\u003cli\u003ePoff, N.L. \u0026amp; Matthews, J.H. (2013). Environmental flows in the Anthropocence: past progress and future prospects. \u003cem\u003eCurrent Opinion in Environmental Sustainability\u003c/em\u003e, 5(6), 667\u0026ndash;675.\u003c/li\u003e\n\u003cli\u003eHorne, A., et al., ed., 2017. Water for the environment: From policy and science to implementation and management. Cambridge: Academic Press.\u003c/li\u003e\n\u003cli\u003ePoff, N.L., Tharme, R.E. \u0026amp; Arthington, A.H. (2017). Evolution of environmental flows assessment science, principles, and methodologies. \u003cem\u003eIn\u003c/em\u003e: \u003cem\u003eWater for the Environment.\u003c/em\u003e Cambridge: Academic Press, 203\u0026ndash;236.\u003c/li\u003e\n\u003cli\u003eWright, C. (2012). Global banks, the environment, and human rights: The impact of the Equator Principles on lending policies and practices. \u003cem\u003eGlobal Environmental Politics,\u003c/em\u003e 12, 56\u0026ndash;77.\u003c/li\u003e\n\u003cli\u003eNational Renewable Energy Laboratory. (2001). Small hydropower systems. National Renewable Energy Laboratory, Department of Energy. Report No DOE/GO-102001-1173. Merrifield VA: Energy Efficiency and Renewable Energy Clearinghouse\u003c/li\u003e\n\u003cli\u003eOpperman, J.J., Kendy, E., Tharme, R.E., Warner, A.T., Barrios, E. \u0026amp; Richter, B.D. (2018). A three-level framework for assessing and implementing environmental flows. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e, 76.\u003c/li\u003e\n\u003cli\u003eWang, C., Ghadimi, P., Lim, M.K. \u0026amp; Tseng, M.L. (2019). A literature review of sustainable consumption and production: A comparative analysis in developed and developing economies. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e, 206, 741 \u0026ndash; 754.\u003c/li\u003e\n\u003cli\u003eIFC. (2012). Performance Standards on Environmental and Social Sustainability. Washington D.C.: World Bank Group\u003c/li\u003e\n\u003cli\u003eEverard, M. \u0026amp; Moggridge, H.L. (2012). Rediscovering the value of urban rivers. \u003cem\u003eUrban Ecosystems\u003c/em\u003e, 15 (2), 293\u0026ndash;314.\u003c/li\u003e\n\u003cli\u003eIUCN. (2020). \u003cem\u003eThe IUCN Red List of threatened species v. 2019-3\u003c/em\u003e. Gland: IUCN. Available from: www.iucnredlist.org [Accessed 14 December 2021].\u003c/li\u003e\n\u003cli\u003eIFC. (2012). Performance Standard 6: Biodiversity Conservation and Sustainable Management of Living Natural Resources. Washington D.C.: World Bank Group\u003c/li\u003e\n\u003cli\u003eIUCN. (2021). \u003cem\u003eSpatial Data Download \u003c/em\u003e[online]. The IUCN Red List of Threatened Species. Available from: https://www.iucnredlist.org/resources/spatial-data-download [Accessed on 14 December 2021].\u003c/li\u003e\n\u003cli\u003eBrauneder, K.M., Montes, C., Blyth, S., Bennun, L., Butchart, S.H., Hoffmann, M., Burgess, N.D., Cuttelod, A., Jones, M.I., Kapos, V. \u0026amp; Pilgrim, J. (2018). Global screening for Critical Habitat in the terrestrial realm. \u003cem\u003ePloS one\u003c/em\u003e, \u003cem\u003e13 \u003c/em\u003e(3), p.e0193102.\u003c/li\u003e\n\u003cli\u003eMartin, C.S., Tolley, M.J., Farmer, E., Mcowen, C.J., Geffert, J.L., Scharlemann, J.P.W., Thomas, H.L., van Bochove, J.H., Stanwell-Smith, D., Hutton, J.M. \u0026amp; Lascelles, B. (2015). A global map to aid the identification and screening of critical habitat for marine industries. \u003cem\u003eMarine Policy\u003c/em\u003e, 53, 45\u0026ndash;53.\u003c/li\u003e\n\u003cli\u003eTenant, D.L. (1976). Instream flow regimens for fish, wildlife, recreation and related environmental resources. \u003cem\u003eFisheries, \u003c/em\u003e1, 6\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eChapra, S.C., Pelletier, G.J., \u0026amp; Tao, H. (2008). QUAL2K: a modelling framework for simulating river and stream water quality, version 2.11: documentation and user manual. Civil and Environmental Engineering Department. Tufts University, Medford, MA.\u003c/li\u003e\n\u003cli\u003eGreco, M., Arbia, F. \u0026amp; Giampietro, R. (2021). Definition of the Ecological Flow (EFlow) using the Indicators of Hydrological Alteration and IARI as an operative tool for water management. \u003cem\u003eEnvironments\u003c/em\u003e, 8 (8), 77. \u003c/li\u003e\n\u003cli\u003eStalnaker, C., Lamb, B.L., Henriksen, J., Bovee, K. \u0026amp; Bartholow, J. (1995). \u003cem\u003eThe Instream Flow Incremental Methodology: A primer for IFIM\u003c/em\u003e. Biological Report 29. Washington, D.C.: National Biological Service, US Department of the Interior.\u003c/li\u003e\n\u003cli\u003eHedden-Nicely, D.R. (2020). The Contemporary Methodology for Quantifying Reserved Instream Flow Water Rights to Support Aquatic Habitat. \u003cem\u003eEnvironmental Law\u003c/em\u003e, 50 (1), 257\u0026ndash;286.\u003c/li\u003e\n\u003cli\u003eCox, D. R. (1958). The regression analysis of binary sequences. \u003cem\u003eJournal of the Royal Statistical Society: Series B (Methodological)\u003c/em\u003e, 20 (2), 215\u0026ndash;232.\u003c/li\u003e\n\u003cli\u003eChoueiry, G. (2022). \u003cem\u003eWhich Variables Should You Include in a Regression Model?\u003c/em\u003e [online] Available from: https://quantifyinghealth.com/variables-to-include-in-regression/ [Accessed 30 May 2022].\u003c/li\u003e\n\u003cli\u003eKondolf, G.M., Rubin, Z.K. \u0026amp; Minear, J.T. (2014). Dams on the Mekong: Cumulative sediment starvation. \u003cem\u003eWater Resources Research\u003c/em\u003e, 50, 5158\u0026ndash;5169.\u003c/li\u003e\n\u003cli\u003eKondolf, G.M., Schmitt, R.J.P., Carling, P.A., Goichot, M., Keskinen, M., Arias, M.E., Bizzi, S., Castelletti, A., Cochrane, T.A., Darby, S.E. \u0026amp; Kummu, M. (2022). Save the Mekong Delta from drowning. \u003cem\u003eScience\u003c/em\u003e, 376 (6593), 583\u0026ndash;585.\u003c/li\u003e\n\u003cli\u003eMekong River Commission. (2017). The Council Study: study on the sustainable management and development of the Mekong River, including impacts of mainstream hydropower projects. Biological Resource Assessment Final Technical Report Series. Volume 1: Specialists\u0026rsquo; Report. Vientiane: Mekong River Commission.\u003c/li\u003e\n\u003cli\u003eDuc, N.A., Nguyen, L.T., Thai, T.H., Khan, A., Rautenstrauch, K. \u0026amp; Schmidt, C. (2020). Assessing cumulative impacts of the proposed Lower Mekong Basin hydropower cascade on the Mekong River floodplains and Delta\u0026ndash;Overview of integrated modeling methods and results. \u003cem\u003eJournal of Hydrology\u003c/em\u003e, 581, 122511.\u003c/li\u003e\n\u003cli\u003eGuha, N. and Ghosh, S. (2019) \u003cem\u003eNegligence narrative unfolds as Kopili Hydropower Plant Pipeline Burst Kills Four\u003c/em\u003e [online]. Mongabay-India. Available at: https://india.mongabay.com/2019/10/negligence-narrative-unfolds-as-kopili-hydropower-plant-pipeline-burst-kills-four/ [Accessed 16 July 2023]. \u003c/li\u003e\n\u003cli\u003eNam Ngiep Power Company. (2014). \u003cem\u003eDraft Environmental Impact Assessment Nam Ngiep 1 Hydropower Project\u003c/em\u003e. Prepared by Nam Ngiep Power Company Ltd. with assistance from ERM-Siam Co., Ltd. and Environmental Research Institute, Chulalongkorn University for the Asian Development Bank.\u003c/li\u003e\n\u003cli\u003eBournemouth University. (2018). \u003cem\u003eTor putitora\u003c/em\u003e. The IUCN Red List of Threatened Species. Version 2022-2\u003c/li\u003e\n\u003cli\u003eMira Power Limited. (2014). Environmental and Social Impact Assessment Gulpur Hydropower Project. Islamabad: Mira Power Limited.\u003c/li\u003e\n\u003cli\u003eConsulting Engineering Services. (2009). Consolidated Environmental Assessment Volume 1, Environmental Studies for Vishnugad Pipalkoti Hydro Electric Project. Prepared for THDC India Limited.\u003c/li\u003e\n\u003cli\u003eKarot Power Company. (2015). 720 MW Karot Hydropower Project Updated Environmental and Social Impact Assessment. Islamabad: Mott Mcdonald\u003c/li\u003e\n\u003cli\u003eNautiyal, P., Mishra, A.S., Singh, K.R. \u0026amp; Singh, U. (2013). Longitudinal distribution of the fish fauna in the river Ganga from Gangotri to Kanpur. \u003cem\u003eJournal of Applied and Natural Science\u003c/em\u003e, 5 (1) 63\u0026ndash;68.\u003c/li\u003e\n\u003cli\u003eAnnandale, D. \u0026amp; Hagler Bailly Pakistan. (2014). Strategic Environmental Assessment of Hydropower Development in Azad Jammu and Kashmir. Islamabad: IUCN Pakistan. \u003c/li\u003e\n\u003cli\u003eBrown, C., Zakaria, V., Joubert, A., Rafique, M., Murad, J., King, J., Hughes, J., Cardinale, P. \u0026amp; Alonzo, L. (2019). Achieving an environmentally sustainable outcome for the Gulpur hydropower project in the Poonch River Mahaseer National Park, Pakistan. \u003cem\u003eSustainable Water Resources Management\u003c/em\u003e, 5 (2), 611\u0026ndash;628.\u003c/li\u003e\n\u003cli\u003eYadav, P., Kumar, A., Hussain, S.A. \u0026amp; Gupta, S.K. (2020). Evaluation of the effect of longitudinal connectivity in population genetic structure of endangered golden mahseer, \u003cem\u003eTor putitora\u003c/em\u003e (Cyprinidae), in Himalayan rivers: Implications for its conservation. \u003cem\u003ePloS one\u003c/em\u003e, 15 (6), 0234377.\u003c/li\u003e\n\u003cli\u003eSWECO. (2007). \u003cem\u003eSong Bung 4 Hydropower Project Phase II\u003c/em\u003e. Manila: Asian Development Bank. TA 4625-VIE\u003c/li\u003e\n\u003cli\u003eKarot Power Company. (2016). \u003cem\u003eKarot Biodiversity Management Plan. \u003c/em\u003eIslamabad: Hagler Baily Pakistan\u003c/li\u003e\n\u003cli\u003ePakhtunkhwa Energy Development Organization. (2019a). \u003cem\u003eEnvironmental Impact and Social Impact Assessment for Gabral Kalam Hydropower Project\u003c/em\u003e. Pakistan: Pakhtunkhwa Energy Development Organization\u003c/li\u003e\n\u003cli\u003eKing, J. \u0026amp; Louw, D. (1998). Instream flow assessments for regulated rivers in South Africa using the Building Block Methodology. \u003cem\u003eAquatic Ecosystem Health \u0026amp; Management\u003c/em\u003e, 1 (2), 109\u0026ndash;124.\u003c/li\u003e\n\u003cli\u003eTickner, D., Kaushal, N., Speed, R. \u0026amp; Tharme, R. (2020). Implementing environmental flows: Lessons for policy and practice. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, 8, 106.\u003c/li\u003e\n\u003cli\u003eRamesh, V.S. \u0026amp; Thampi, S.G. (2023). Application of hydrologic and hydraulic methods to calculate the environmental flow requirements of the Chaliyar river. \u003cem\u003eEnvironmental Earth Sciences\u003c/em\u003e, 82 (7), 166.\u003c/li\u003e\n\u003cli\u003eSuwal, N., Kuriqi, A., Huang, X., Delgado, J., Młyński, D. \u0026amp; Walega, A. (2020). Environmental flows assessment in Nepal: the case of Kaligandaki River. \u003cem\u003eSustainability\u003c/em\u003e, 12 (21), 8766.\u003c/li\u003e\n\u003cli\u003eArthington, A.H., Bhaduri, A., Bunn, S.E., Jackson, S.E., Tharme, R.E., Tickner, D., Young, B., Acreman, M., Baker, N., Capon, S. \u0026amp; Horne, A.C. (2018). The Brisbane declaration and global action agenda on environmental flows (2018). \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, 6, 45. \u003c/li\u003e\n\u003cli\u003eSharma, U. \u0026amp; Dutta, V. (2020). Establishing environmental flows for intermittent tropical rivers: Why hydrological methods are not adequate?. International journal of environmental science and technology, 17(5), pp.2949-2966.\u003c/li\u003e\n\u003cli\u003eChaves, A., Pacheco, A., Krasovskaia, I. \u0026amp; Gottschalck, L. (2010). RANA-ICE, A Methodology to Estimate Compensatory Runoff in Costa Rica. \u003cem\u003eHydrocomplexity: New Tools for Solving Wicked Water Problems\u003c/em\u003e \u003cem\u003eKovacs Colloquium\u003c/em\u003e, 2\u0026ndash;3 July 2010 Paris.\u003c/li\u003e\n\u003cli\u003eMulticonsult. (2018). EFlows Assessment Report \u0026ndash; Final Version, December 21, 2018. Hydraulic Study and EFlows Assessment for Mpatamanga.\u003c/li\u003e\n\u003cli\u003eAbdi, R. \u0026amp; Yasi, M. (2015). Evaluation of environmental flow requirements using eco-hydrologic\u0026ndash;hydraulic methods in perennial rivers. \u003cem\u003eWater Science and Technology\u003c/em\u003e, 72(3), 354-363.\u003c/li\u003e\n\u003cli\u003eHao, C.F., He, L.M., Niu, C.W. \u0026amp; Jia, Y.W. (2016) August. A review of environmental flow assessment: methodologies and application in the Qianhe River. In \u003cem\u003eIOP Conference Series: Earth and Environmental Science,\u003c/em\u003e 39 (1), 012067. IOP Publishing.\u003c/li\u003e\n\u003cli\u003eNikghalb, S., Shokoohi, A., Singh, V.P. \u0026amp; Yu, R. (2016). Ecological regime versus minimum environmental flow: comparison of results for a river in a semi Mediterranean region. \u003cem\u003eWater resources management\u003c/em\u003e, 30, 4969 \u0026ndash; 4984.\u003c/li\u003e\n\u003cli\u003eAsian Development Bank (ADB). (2009). \u003cem\u003eSafeguard Policy Statement\u003c/em\u003e. Manila: Asian Development Bank\u003c/li\u003e\n\u003cli\u003eNarain, D., Maron, M., Teo, H.C., Hussey, K. \u0026amp; Lechner, A.M. (2020). Best-practice biodiversity safeguards for Belt and Road Initiative\u0026rsquo;s financiers. \u003cem\u003eNature Sustainability\u003c/em\u003e, 3(8), 650-657.\u003c/li\u003e\n\u003cli\u003eMarkkanen, S. \u0026amp; Braeckman, J.P. (2020). Financing sustainable hydropower: an introduction to concepts and terminology. \u003cem\u003eInt J Hydropower Dams\u003c/em\u003e, 27 (5).\u003c/li\u003e\n\u003cli\u003eG\u0026ouml;k, A. \u0026amp; Sodhi, N. (2021). The environmental impact of governance: a system-generalized method of moments analysis. \u003cem\u003eEnvironmental Science and Pollution Research\u003c/em\u003e, 28 (25), 32995\u0026ndash;33008.\u003c/li\u003e\n\u003cli\u003eMurray, C.C., Wong, J., Singh, G.G., Mach, M., Lerner, J., Ranieri, B., Peterson St-Laurent, G., Guimaraes, A. \u0026amp; Chan, K. (2018). The insignificance of thresholds in environmental impact assessment: An illustrative case study in Canada. \u003cem\u003eEnvironmental Management\u003c/em\u003e, 61 (6), 1062\u0026ndash;1071.\u003c/li\u003e\n\u003cli\u003eHochkirch A. (2021). A strategy for the next decade to address data deficiency in neglected biodiversity. \u003cem\u003eConservation Biology\u003c/em\u003e, 35 (2), 502\u0026ndash;509.\u003c/li\u003e\n\u003cli\u003eGullison, T., Hardner, J., Anstee, S. \u0026amp; Meyer, M. (2015). Good practices for the collection of biodiversity baseline data. Prepared for the Multilateral Financing Institutions Biodiversity Working Group \u0026amp; Cross-Sector Biodiversity Initiative.\u003c/li\u003e\n\u003cli\u003eBennun, L., Pollard, E., Starkey, M. \u0026amp; Temple, H. (2016). Critical Habitat Determination for Linear Infrastructure: Approaches to Delineating Discrete Management Units under IFC Performance Standard 6. \u003cem\u003eIn\u003c/em\u003e \u003cem\u003eSPE International Conference and Exhibition on Health, Safety, Security, Environment, and Social Responsibility\u003c/em\u003e. OnePetro.\u003c/li\u003e\n\u003cli\u003eCamaclang, A.E., Maron, M., Martin, T.G. \u0026amp; Possingham, H.P. (2015). Current practices in the identification of critical habitat for threatened species. \u003cem\u003eConservation Biology\u003c/em\u003e, \u003cem\u003e29 \u003c/em\u003e(2), 482\u0026ndash;492.\u003c/li\u003e\n\u003cli\u003eRees, H.C., Maddison, B.C., Middleditch, D.J., Patmore, J.R. \u0026amp; Gough, K.C. (2014). The detection of aquatic animal species using environmental DNA\u0026ndash;a review of eDNA as a survey tool in ecology. \u003cem\u003eJournal of Applied Ecology\u003c/em\u003e, 51 (5), 1450\u0026ndash;1459.\u003c/li\u003e\n\u003cli\u003eRatajczak, Z., Carpenter, S.R., Ives, A.R., Kucharik, C.J., Ramiadantsoa, T., Stegner, M.A., Williams, J.W., Zhang, J. \u0026amp; Turner, M.G. (2018). Abrupt change in ecological systems: inference and diagnosis. \u003cem\u003eTrends in Ecology \u0026amp; Evolution\u003c/em\u003e, 33 (7), 513\u0026ndash;526.\u003c/li\u003e\n\u003cli\u003eIntergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. (2019). Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Bonn: IPBES Secretariat.\u003c/li\u003e\n\u003cli\u003eReis, R.E., Albert, J.S., Di Dario, F., Mincarone, M.M., Petry, P. \u0026amp; Rocha, L.A. (2016). Fish biodiversity and conservation in South America. \u003cem\u003eJournal of fish biology\u003c/em\u003e, 89 (1), 12\u0026ndash;47.\u003c/li\u003e\n\u003cli\u003eNile Equatorial Lakes Subsidiary Action Program. (2013). \u003cem\u003eRusumo Falls Hydroelectric Project Dam \u0026amp; Powerplant Component Environmental and Social Impact Assessment\u003c/em\u003e. Prepared by Artelia Eau \u0026amp; Environment on behalf of the Nile Equatorial Lakes Subsidiary Action Program.\u003c/li\u003e\n\u003cli\u003eFishBase team RMCA \u0026amp; Geelhand, D. (2016). \u003cem\u003eLabeo victorianus \u003c/em\u003e[online]. The IUCN Red List of Threatened Species. Available from: https://www.iucnredlist.org/species/60318/47182908 [Accessed 16 December 2021].\u003c/li\u003e\n\u003cli\u003ePower Technology. 2021. \u003cem\u003eRegional Rusumo Hydroelectric Project, Rwanda\u003c/em\u003e [online]. Verdict Media Limited. Available from: https://www.power-technology.com/projects/regional-rusumo-hydroelectric-project-rwanda/ [Accessed 17 December 2021].\u003c/li\u003e\n\u003cli\u003eAsian Turtle Trade Working Group. (2000). \u003cem\u003eCuora amboinensis\u003c/em\u003e \u003cem\u003e(errata version published in 2016) \u003c/em\u003e[online]. The IUCN Red List of Threatened Species. Available from: https://dx.doi.org/10.2305/IUCN.UK.2000.RLTS.T5958A11953035.en [Accessed 16 December 2021].\u003c/li\u003e\n\u003cli\u003eCota, M., Hoang, H., Horne, B.D., Kusrini, M.D., McCormack, T., Platt, K., Schoppe, S. \u0026amp; Shepherd, C. (2020). \u003cem\u003eCuora amboinensis \u003c/em\u003e[online]. The IUCN Red List of Threatened Species. Available from: https://dx.doi.org/10.2305/IUCN.UK.2020-2.RLTS.T5958A3078812.en [Accessed 16 December 2021].\u003c/li\u003e\n\u003cli\u003eAssam Power Generation Corporation Limited. (2018). \u003cem\u003eEnvironmental Impact Assessment, Assam Power Sector Investment Program \u0026ndash; Tranche 3, 120 MW Lower Kopili Hydroelectric Project\u003c/em\u003e. Guwahati: Assam Power Generation Corporation Limited, Government of Assam\u003c/li\u003e\n\u003cli\u003ePerusahaan Listrik Negara. 2020. Environmental and Social Impact Assessment Upper Cisokan Pumped Storage Hydropower Project 1040 MW. Indonesia: Perusahaan Listrik Negara\u003c/li\u003e\n\u003cli\u003eWard, M.S., Simmonds, J.S., Reside, A.E., Watson, J.E., Rhodes, J.R., Possingham, H.P., Trezise, J., Fletcher, R., File, L. and Taylor, M., 2019. Lots of loss with little scrutiny: The attrition of habitat critical for threatened species in Australia. Conservation Science and Practice, 1(11), p.e117.\u003c/li\u003e\n\u003cli\u003eIUCN. (2021b). \u003cem\u003eThe IUCN Red List 2017\u0026ndash;2020 Report\u003c/em\u003e [online]. The IUCN Red List of Threatened Species. Available from: https://www.iucnredlist.org [Accessed 9 September 2022].\u003c/li\u003e\n\u003cli\u003eVan der Hoek, Y., Zuckerberg, B. \u0026amp; Manne, L.L. (2015). Application of habitat thresholds in conservation: Considerations, limitations, and future directions. \u003cem\u003eGlobal Ecology and Conservation\u003c/em\u003e, 3, 736\u0026ndash;743.\u003c/li\u003e\n\u003cli\u003eRosenfeld, J.S. \u0026amp; Hatfield, T. (2006). Information needs for assessing critical habitat of freshwater fish. \u003cem\u003eCanadian Journal of Fisheries and Aquatic Sciences\u003c/em\u003e, 63 (3), 683\u0026ndash;698.\u003c/li\u003e\n\u003cli\u003eCamaclang, A.E. (2016). Identifying critical habitat for threatened species: concepts and challenges. Thesis (PhD). The University of Queensland.\u003c/li\u003e\n\u003cli\u003eHussain, A., Sarangi, G.K., Pandit, A., Ishaq, S., Mamnun, N., Ahmad, B. \u0026amp; Jamil, M.K. (2019). Hydropower development in the Hindu Kush Himalayan region: Issues, policies and opportunities. \u003cem\u003eRenewable and Sustainable Energy Reviews\u003c/em\u003e, 107, 446\u0026ndash;461.\u003c/li\u003e\n\u003cli\u003eKhera, D.V. \u0026amp; Singh, M. (2020). New Trends in the Development of Hydropower Projects in India. \u003cem\u003eIn Hydropower in the New Millennium.\u003c/em\u003e). Boca Raton: CRC Press, 29-36.\u003c/li\u003e\n\u003cli\u003eTseliou, F. \u0026amp; Tselepides, A. (2020). The importance of the ecosystem approach in the management of the marine environment. \u003cem\u003eEuro-Mediterranean Journal for Environmental Integration\u003c/em\u003e. 5 (2), 1\u0026ndash;5.\u003c/li\u003e\n\u003cli\u003eOpperman, J.J., Carvallo, J.P., Kelman, R., Schmitt, R.J., Almeida, R., Chapin, E., Flecker, A., Goichot, M., Grill, G., Harou, J.J. \u0026amp; Hartmann, J. (2023). Balancing renewable energy and river resources by moving from individual assessments of hydropower projects to energy system planning. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eIFC. (2013). Good Practice Handbook on Cumulative Impact Assessment and Management: Guidance for the Private Sector in Emerging Markets. Washington DC: World Bank Group \u003c/li\u003e\n\u003cli\u003eGebreegziabher, G.A., Degefa, S., Furi, W. \u0026amp; Legesse, G. (2023). Evolution and concept of environmental flows (e-flows): meta-analysis. \u003cem\u003eWater Supply\u003c/em\u003e, 23 (6), 2466 \u0026ndash; 2490.\u003c/li\u003e\n\u003cli\u003eRay, R. (2023). Small Is Beautiful: A New Era for Chinese Overseas Development Finance? GCI Policy Brief 017, Boston: Boston University Global Development Policy Center. \u003c/li\u003e\n\u003cli\u003eChen, Y. and Landry, D. (2018). Capturing the rains: Comparing Chinese and World Bank hydropower projects in Cameroon and pathways for South-South and North South technology transfer. \u003cem\u003eEnergy Policy\u003c/em\u003e, 115, 561-571.\u003c/li\u003e\n\u003cli\u003eGerlak, A.K., Saguier, M., Mills-Novoa, M., Fearnside, P.M. and Albrecht, T.R. (2020). Dams, Chinese investments, and EIAs: A race to the bottom in South America? \u003cem\u003eAmbio\u003c/em\u003e, 49, 156-164.\u003c/li\u003e\n\u003cli\u003eGhimire, H.R., Phuyal, S \u0026amp; Singh, N.R. (2021). Environmental compliance of hydropower projects in Nepal. \u003cem\u003eEnvironmental Challenges\u003c/em\u003e, 5, 100307\u003c/li\u003e\n\u003cli\u003eStafford, S.L. (2012). Private Policing of Environmental Performance: Does it Further Public Goals. \u003cem\u003eBoston College Environmental Affairs Law Review\u003c/em\u003e, 39, 73.\u003c/li\u003e\n\u003cli\u003eBarta, J. \u0026amp; \u0026Eacute;ri, V. (2017). Environmental attitudes of banks and financial institutions. \u003cem\u003eIn:\u003c/em\u003e \u003cem\u003eSustainable\u003c/em\u003e \u003cem\u003eBanking.\u003c/em\u003e Oxfordshire: Routledge, 120\u0026ndash;132. \u003c/li\u003e\n\u003cli\u003eMitkidis, K. \u0026amp; Valkanou, T.N. (2020). Climate Change Litigation: Trends, Policy Implications and the Way Forward. \u003cem\u003eTransnational Environmental Law\u003c/em\u003e, 9, 11\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eGrossman, G.M. \u0026amp; Krueger A.B. (1995). Economic growth and the environment. \u003cem\u003eThe quarterly journal of economics\u003c/em\u003e, 110 (2), 353\u0026ndash;377.\u003c/li\u003e\n\u003cli\u003eHaddaway, N., Macura, B., Whaley, P. \u0026amp; Pullin, A. (2017). ROSES flow diagram for systematic reviews. Version 1.0. doi: 10.6084/m9.figshare.5897389\u003c/li\u003e\n\u003cli\u003eShafi, N., Ayub, J., Ashraf, N. \u0026amp; Mian, A. (2016). Genetic Diversity in Different Populations of Mahseer (Tor putitora) in Pakistan: A RAPD Based Study. \u003cem\u003eInternational Journal of Agriculture \u0026amp; Biology\u003c/em\u003e, 18(6).\u003c/li\u003e\n\u003cli\u003eRamsar. (2020). \u003cem\u003eBeas Conservation Reserve\u003c/em\u003e [online]. Ramsar Sites Information Service. Available from: https://rsis.ramsar.org/ris/2408 [Accessed 3 March 2022].\u003c/li\u003e\n\u003cli\u003eIFC. 2020. Cumulative Impact Assessment and Management: Hydropower Development in the Trishuli River Basin, Nepal. International Finance Corporation, Washington, D.C., USA.\u003c/li\u003e\n\u003cli\u003eIFC. 2021. Strategy for Sustainable Hydropower Development in the Jhelum Poonch River Basin Pakistan. International Finance Corporation, Washington, D.C., USA.\u003c/li\u003e\n\u003cli\u003eHarwood, A., Johnson, S., Richter, B., Locke, A., Yu, X., and Tickner, D. (2017). Listen to the River: Lessons from a Global Review of Environmental Flow Success Stories. Woking: WWF-UK.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;Keeffe, J.H., (2018). A perspective on training methods aimed at building local capacity for the assessment and implementation of environmental flows in rivers. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e, 6, 125.\u003c/li\u003e\n\u003cli\u003eWWF-Zambia (2016). Proceedings of the E flows Technical Resource Group Introductory Workshop. Hosted by Water Resources Management Authority with support from WWF.\u003c/li\u003e\n\u003cli\u003eNile Basin Initiative (2024) NBI eLearning Courses. Available at https://elearn.nilebasin.org/. Accessed 10 June 2024\u003c/li\u003e\n\u003cli\u003eBukhari H, Brown C, Van Niekerk L, Van Deventer H, Joubert A, Taljaard S, Goso L, Reinecke K, Hansen C, Smith-Adao L and co-authors (2024) Eco-Social Assessments of Aquatic Ecosystems, Volume 1: Library of Generic Relationships for SADC Rivers and Estuaries. Africa\u0026apos;s Living Rivers Programme (WRC Report No. TT 934/1/23). Water Research Commission, Pretoria.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Stellenbosch University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6070177/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6070177/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSustainable development of river infrastructure requires the use of contemporary Environmental Flows (EFlows) assessment methods that are based on an understanding of river functioning, and which provide information useful for designing potential mitigations and evaluating trade-offs between socio-ecological impacts and economic benefits. Through a systematic search and review, EFlows assessments of 42 hydropower projects in developing countries in Africa and Asia were analysed to understand the factors that explained the resolution of the methods used and whether the resolution used was suitable for the context in which it was applied. In general, reaches downstream of the tailrace were deemed more sensitive to hydropower development than dewatered sections, and in greater need for higher resolution EFlows studies. Despite this, most assessments focused only on the dewatered reaches. Low-resolution hydrological ratio methods were commonly used and did not match the resolution recommended by international good practice, although this is improving with time. Assessment date and the designation of Critical Habitat (a habitat classification based on the threatened status of species in the IUCN Red List) were the only significant drivers of increased resolution of EFlows assessments. However, despite most projects being in the IUCN habitat range of at least one Endangered freshwater species, the environmental studies of only five classified the aquatic area as Critical Habitat. This calls into question the dependence on Critical Habitat as the driving factor in the selection of suitable methods. Moreover, many hydropower specific EFlows assessments were redundant since, on average, 20 additional hydropower projects were planned in the same basin as each of the projects reviewed. In these cases, basin-scale EFlows assessments are needed to provide the requisite knowledge to mitigate impacts. The disconnect between EFlows theory and practice is a cause of concern for the sustainable development and use of river ecosystems.\u003c/p\u003e","manuscriptTitle":"Resolution matters: An evaluation of EFlows assessment methods used for hydropower in developing countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-05 09:48:41","doi":"10.21203/rs.3.rs-6070177/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3deba89a-4bee-4f2c-bea5-c1f7a1ed5b47","owner":[],"postedDate":"March 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-05T09:48:42+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-05 09:48:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6070177","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6070177","identity":"rs-6070177","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}