Research Report

# Towards the Harmonization of Global Environmental Flow Estimates: Comparing the Global Environmental Flow Information System (GEFIS) with Country Data

Nishadi Eriyagama, Mathis Loïc Messager, Chris Dickens, Rebecca Tharme and Retha Stassen
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IWMI Research Report 186

# Towards the Harmonization of Global Environmental Flow Estimates: Comparing the Global Environmental Flow Information System (GEFIS) with Country Data

Nishadi Eriyagama, Mathis Loïc Messager, Chris Dickens, Rebecca Tharme and Retha Stassen
**The authors:**

**Nishadi Eriyagama**, Regional Researcher, International Water Management Institute (IWMI), Colombo, Sri Lanka.

**Mathis Loïc Messager**, PhD student in ecohydrology, Department of Geography, McGill University, Montréal, Quebec, Canada, and RiverLY Research Unit, National Research Institute for Agriculture, Food and Environment (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement) (INRAE), Centre Lyon-Grenoble Auvergne-Rhône-Alpes, Villeurbanne, France.

**Chris Dickens**, Principal Researcher, IWMI, Colombo, Sri Lanka.

**Rebecca Tharme**, Director, Riverfutures, UK, and Adjunct Principal Research Fellow, Australian Rivers Institute, Griffith University, Queensland, Australia.

**Retha Stassen**, consultant, South Africa.

Eriyagama, N.; Messager, M. L.; Dickens, C.; Tharme, R.; Stassen, R. 2024. *Towards the harmonization of global environmental flow estimates: comparing the Global Environmental Flow Information System (GEFIS) with country data.*
Colombo, Sri Lanka: International Water Management Institute (IWMI). 53p. (IWMI Research Report 186).
doi: https://doi.org/10.5337/2024.204

/ environmental flows / estimation / information systems / hydrological data / databases / comparisons / assessment /
Sustainable Development Goals / Goal 6 Clean water and sanitation / freshwater / water resources / water stress / rivers
/ stream flow / runoff / aquatic ecosystems / habitats / ecological factors / catchment areas / land cover / drainage area
/ hydrological modelling / tools / time series analysis / heterogeneity / indicators / indexes /

ISSN 1026-0862
ISBN 978-92-9090-959-0

Copyright © 2024, by IWMI. All rights reserved. IWMI encourages the use of its material provided that the organization is
acknowledged and kept informed in all such instances. The boundaries and names shown and the designations used on
maps do not imply official endorsement or acceptance by IWMI, CGIAR, our partner institutions, or donors.

Please send inquiries and comments to IWMI-Publications@cgiar.org

A free copy of this publication can be downloaded at www.iwmi.org/publications/iwmi-research-reports/
# Acknowledgements

The authors would like to acknowledge the contribution of Chandima Subasinghe (formerly GIS/RS Analyst, International Water Management Institute [IWMI], Colombo, Sri Lanka) in the initial processing of spatial data. Colleagues from the Food and Agriculture Organization of the United Nations (FAO), especially Riccardo Biancalani (Project Coordinator, Integrated Monitoring Initiative for SDG 6) and Michela Marinelli (Environmental Analyst, Integrated Monitoring Initiative for SDG 6), are thanked for their support in sending out requests for e-flow data to several countries. The authors are thankful for the e-flow data received from some of these countries, and gratefully acknowledge the willingness of individual practitioners and institutions to share this knowledge. These data were invaluable for the comparative analyses generated. The authors would also like to thank the two anonymous referees for their constructive comments which helped to enhance the quality of this report.

## Project

This study was undertaken as part of the SDG Support for Food Security and Water Resources project implemented by IWMI. IWMI has been involved in many aspects of the development of the United Nations Sustainable Development Goal (SDG) indicators. The aim of this project was to contribute to the development of SDG Indicator 6.4.2 (level of water stress: freshwater withdrawal as a proportion of available freshwater resources), which includes a component on environmental flows (e-flows).

## Collaborators

IWMI
International Water
Management Institute

International Water Management Institute (IWMI)

McGill

Department of Geography, McGill University, Montréal, Quebec, Canada

INRAE

RiverLY Research Unit, National Research Institute for Agriculture, Food and Environment (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement) (INRAE), Paris, France

<figure><img src="image_1.png" /></figure>

Riverfutures, UK

Griffith
UNIVERSITY
Australian Rivers Institute

Australian Rivers Institute (ARI), Griffith University, Queensland, Australia
Donors

<figure><img src="image_2.png" /></figure>

CGIAR

RESEARCH
PROGRAM ON

Water, Land and
Ecosystems

<figure><img src="image_3.png" /></figure>

CGIAR

INITIATIVE ON

NEXUS Gains

This study was carried out under the CGIAR Research Program on Water, Land and Ecosystems (WLE) and finalized under the CGIAR Initiative on NEXUS Gains, which is grateful for the support of CGIAR Trust Fund contributors (https://www.cgiar.org/funders).

H2O'Lyon Graduate School, Université de Lyon, Villeurbanne, France

<figure><img src="image_4.png" /></figure>

H₂O'Lyon

Université de Lyon

In addition to financial support from the CGIAR Initiative on NEXUS Gains, co-author Mathis Loïc Messager's contribution to this study was supported through funding from the H2O'Lyon Graduate School, Université de Lyon (UdL), Villeurbanne, France (Grant Number: ANR-17-EURE-0018).
# Contents

<table><tr><td>Acronyms and Abbreviations</td><td>vi</td></tr><tr><td>Summary</td><td>vii</td></tr><tr><td>Introduction</td><td>1</td></tr><tr><td>The Need for Globally Available and Harmonized E-flow Data</td><td>1</td></tr><tr><td>GEFIS and its Role as a Global Approach</td><td>2</td></tr><tr><td>Methods</td><td>8</td></tr><tr><td>General Approach</td><td>8</td></tr><tr><td>Data Collection and Database Structure</td><td>8</td></tr><tr><td>Management Class Comparison</td><td>12</td></tr><tr><td>Assessing the Impact of Masking</td><td>12</td></tr><tr><td>Environmental Flow Comparison</td><td>13</td></tr><tr><td>Results</td><td>15</td></tr><tr><td>Overview of the Database of E-flow Assessments</td><td>15</td></tr><tr><td>Hydrological Comparison</td><td>17</td></tr><tr><td>Management Class Comparison</td><td>19</td></tr><tr><td>Assessing the Impact of Masking</td><td>21</td></tr><tr><td>Environmental Flow Comparison</td><td>22</td></tr><tr><td>Discussion</td><td>25</td></tr><tr><td>Conclusions and Future Directions</td><td>27</td></tr><tr><td>References</td><td>28</td></tr><tr><td>Annex 1. Templates for the Collection of E-flow Data on Local Assessments</td><td>31</td></tr><tr><td>Annex 2. Database</td><td>34</td></tr><tr><td>Annex 3. Distribution of Global Climate Zones</td><td>46</td></tr></table>
Acronyms and Abbreviations

<table><tr><td>BBM</td><td>Building Block Method</td></tr><tr><td>CBD</td><td>Convention on Biological Diversity</td></tr><tr><td>DRIFT</td><td>Downstream Response to Imposed Flow Transformation</td></tr><tr><td>DWAF</td><td>Department of Water Affairs and Forestry, South Africa</td></tr><tr><td>EFA</td><td>Environmental Flow Assessment</td></tr><tr><td>ELOHA</td><td>Ecological Limits of Hydrologic Alteration</td></tr><tr><td>EMC</td><td>Ecological Management Class</td></tr><tr><td>FAO</td><td>Food and Agriculture Organization of the United Nations</td></tr><tr><td>FDC</td><td>Flow Duration Curve</td></tr><tr><td>GEFC</td><td>Global Environmental Flow Calculator</td></tr><tr><td>GEFIS</td><td>Global Environmental Flow Information System</td></tr><tr><td>GRDC</td><td>Global Runoff Data Centre</td></tr><tr><td>HFR</td><td>High Flow Requirement</td></tr><tr><td>IBT</td><td>Incident Biodiversity Threat</td></tr><tr><td>IWMI</td><td>International Water Management Institute</td></tr><tr><td>LFR</td><td>Low Flow Requirement</td></tr><tr><td>MAR</td><td>Mean Annual Runoff</td></tr><tr><td>PHABSIM</td><td>Physical Habitat Simulation Model</td></tr><tr><td>SDG</td><td>Sustainable Development Goal</td></tr><tr><td>VNR</td><td>Voluntary National Review</td></tr><tr><td>WBM</td><td>Water Balance Model</td></tr></table>
# Summary

Environmental flows (e-flows) are defined as the quantity, timing and quality of freshwater flows and levels necessary to sustain aquatic ecosystems which, in turn, support human cultures, economies, sustainable livelihoods, and well-being. E-flows have entered the global stage as an important part of sustainable water resources management. They are now recognized as a key component in the estimation of water stress in the United Nations Sustainable Development Goals (SDGs). The source of data used to estimate the e-flow requirement in SDG Indicator 6.4.2 (level of water stress: freshwater withdrawal as a proportion of available freshwater resources) is the Global Environmental Flow Information System (GEFIS), an online tool produced and managed by the International Water Management Institute (IWMI). In addition to the GEFIS estimate, the Food and Agriculture Organization of the United Nations (FAO), as the custodians of the SDG indicator, encourages countries to put forward their locally determined e-flow estimates, especially if it differs from the GEFIS estimate. To date, however, only a few countries have taken up this opportunity.

The aim of this report is to compare e-flows estimated by GEFIS with independent e-flow assessments performed at the local level to gauge the level of agreement between the two sets of estimates. We compared e-flow estimates from GEFIS with local e-flow estimates at 533 river sites. Local e-flow estimates were sourced through formal requests for data, published literature, public governmental reports and research networks. To compare global pixel-based and local estimates, we first aggregated pixel-based GEFIS e-flow estimates upstream of each local assessment site. As expected, the local e-flow assessments—carried out by governmental authorities or academic scientists—were heterogenous in nature due to the variety of methods used, often based on different conceptual approaches. Some methods worked solely with hydrological data, while others incorporated information on ecosystem responses to flow alteration. GEFIS itself relies on methods dominated by hydrology but does include proxies of instream ecosystem condition.

This study reveals that, overall, there is limited agreement between GEFIS estimates and local-level estimates of e-flows (as a percentage of the Mean Annual Runoff [MAR]) determined using other heterogenous methods.

This observed divergence for a given site stems from three combined sources of bias and uncertainty: differences between the streamflow estimated from the global hydrological model and the actual natural flow regime of the watercourse estimated for the local assessment, differences between the present-day ecological conditions inferred by GEFIS and the ecological conditions determined by local assessors, and differences between the method used by GEFIS for estimating e-flow requirements and the e-flow determination method used by the local assessment. In addition to these sources of uncertainty at individual sites, the overall divergence between the two sets of estimates is further amplified by the diversity of methods used in local assessments along with the fundamental differences in scale between global and local estimates. Among these sources of uncertainty, we observed a relatively low degree of disagreement between the hydrological data utilized for GEFIS and local assessments. The observed disagreement results from the use of globally modelled flows in GEFIS as opposed to local assessments which are usually reliant on measured flow or flow estimates from hydrological models that are developed more locally. In comparison, the degree of disagreement is much higher between e-flow estimates (as a percentage of MAR) from GEFIS and local assessments. This heightened disagreement may in part stem from the coarseness of the data used in determining the current ecological status of rivers in GEFIS compared to local assessments, and the purely hydrological character of the method used to estimate e-flows in GEFIS.

This report recommends further investigation using a larger global coverage of data to ascertain whether there is greater/lesser agreement between GEFIS estimates and local-level estimates in different world regions, certain sized catchments and under specific land use conditions. Such an approach may enable the 'calibration' of GEFIS against, for example, more holistic methods which encompass a wider range of ecological data. The paper also illustrates the need for considering an ensemble of global hydrological models and more comprehensive ecological input in global e-flow models. With such improvements, GEFIS has the potential to evolve into a robust e-flow tool better suited to represent on-ground realities and thus enhance the service that it currently provides in global sustainable development.
<figure><img src="image_5.png" /></figure>
# Towards the Harmonization of Global Environmental Flow Estimates: Comparing the Global Environmental Flow Information System (GEFIS) with Country Data

Nishadi Eriyagama, Mathis Loïc Messager, Chris Dickens, Rebecca Tharme and Retha Stassen

## Introduction

Environmental flows (e-flows) are defined as the quantity, timing and quality of freshwater flows and levels necessary to sustain aquatic ecosystems which, in turn, support human cultures, economies, sustainable livelihoods and well-being (Arthington et al. 2018). The field of e-flows evolved in response to the widespread deterioration of aquatic ecosystems and the accompanying loss of freshwater biodiversity because of anthropogenic changes in hydrological regimes, such as the establishment of water infrastructure (e.g., dams and diversions), water abstractions and land use change (Horne et al. 2017). E-flow science aims to ensure that changes in flow regimes are restricted to 'acceptable' levels to protect and restore aquatic biodiversity, ecological integrity and important ecosystem services that support societal development. This necessitates the assessment of e-flows, i.e., the quantification of the flow regime, as described in the definition above, for a river site, reach or basin.

## The Need for Globally Available and Harmonized E-flow Data

With the widespread uptake of e-flow assessment and implementation, it is now understood to be an important part of water resources management; describing the proportion of river flow that needs to be safeguarded. It also provides a quantitative measure against which restoration activities can be assessed. E-flows have already been adopted by the United Nations (UN) 2030 Agenda for Sustainable Development. Indicator 6.4.2 (level of water stress: freshwater withdrawal as a proportion of available freshwater resources) of the Sustainable Development Goals (SDGs) includes a component on e-flows and is recognized as being important for measuring the amount of stress being exerted on water resources, where it defines the amount of water that is not available for allocation to other users. It is also included within the Planetary Boundary approach (Gerten et al. 2013; Steffen et al. 2015) where e-flows provide the boundary for exploitation of water resources from rivers. More recently, it has been recommended as a component indicator of river protection for the Convention on Biological Diversity (CBD) Post-2020 Global Biodiversity Framework (CBD 2021). Since its role as an indicator of the state of river protection is being

increasingly recognized, e-flows are likely to be used in many global reports on the state of ecosystems. However, this global adoption of e-flows is predicated on the existence and availability of harmonized data from around the globe. While this is possible with existing global e-flow models based only on hydrology, the consensus is that these data may not be adequate to globally represent the e-flows of river ecosystems where, ideally, some measure of the ecological response to hydrological alteration should be included. At the local level, there are many detailed methods which work with *in situ* biological data (Poff et al. 2017), but generally it is not possible to upscale these to the global level.

Existing approaches to determine e-flows (Tharme 2003; Poff et al. 2017) are commonly grouped into four basic categories which reflect differences in perspective, models and required data: hydrological, hydraulic rating, habitat simulation and holistic methods. As a result, while one e-flow assessment may be based only on hydrological considerations, another may include a holistic representation of the ecosystem that accounts for ecological and social factors. Furthermore, each approach can be carried out with different levels of intensity and data input which will influence the accuracy of the outputs. The result of this heterogeneity in approaches is that the resulting e-flow estimates are variable; they may represent entirely different aspects of the environmental requirement of the ecosystem. Making comparisons between e-flow estimates originating from different approaches and from different countries is, therefore, fraught with difficulty. The harmonization of e-flow estimates across the world has not yet been conceptualized, but its absence can render the merits of using country derived estimates for calculating a global indicator such as SDG Indicator 6.4.2 to be questionable.

There are many advantages of making use of a global model to represent e-flows for each country, because at least the e-flow determination approach is harmonized across the world. Using a single approach increases the comparability of e-flow estimates across countries, and therefore enables a coherent global assessment of SDG Indicator 6.4.2. The deficiencies associated with this revolve around the data used. Most global modelling efforts are based purely on global hydrological data (e.g., Smakhtin and Eriyagama 2008; Pastor et al. 2014; de
Graaf et al. 2019; Liu et al. 2021) but do not incorporate
ecological data to assess the existing condition of a river,
which is important for setting a target ecological condition
and estimating the corresponding e-flow requirement.
In contrast, the Global Environmental Flow Information
System (GEFIS),<fnref n="1" /> an online tool produced and managed by
the International Water Management Institute (IWMI), has
introduced a desktop-level assessment of the condition
of the river ecosystem for the first time, making use of
a subset of the Incident Biodiversity Threat (IBT) index
of Vörösmarty et al. (2010); this is in addition to global
hydrology. Despite this substantial improvement in
assessing the ecological condition of rivers, GEFIS remains
a hydrological e-flow assessment method, as opposed to
approaches that incorporate ecological information on the
specific flow requirements of freshwater ecosystems.

Objectives

The purpose of this report is to compare e-flow estimates
made using GEFIS with independent site-specific e-flow
estimates that have been determined using multiple
heterogenous methods at the same locations. It is not
possible to evaluate whether a GEFIS-made estimate or a
site-specific estimate is a more accurate representation of
the real e-flow requirement at a site. However, this study
intends to reveal the extent to which GEFIS can be used
to represent the global perspective on e-flows. A single
national e-flow estimate for the SDG Indicator 6.4.2 is
calculated from multiple grid cells in GEFIS. However, site-
specific e-flow estimates in this assessment were derived
using the underlying data in GEFIS as elaborated in the
section Methods.

GEFIS and its Role as a Global
Approach

GEFIS has its origins in the first global assessment of
e-flows by Smakhtin et al. (2004), which presented
global maps of annual e-flow requirement for major river
basins of the world. The study (Smakhtin et al. 2014)

used times series of monthly river discharge (from 1961 to 1990) generated by the WaterGAP2 global hydrology model (Alcamo et al. 2003; Döll et al. 2003) and derived the mean annual e-flow requirement for a 'fair' ecosystem condition for each 0.5° by 0.5° grid cell of the world. The mean annual e-flow requirement was estimated as the sum of a Low Flow Requirement (LFR) and a High Flow Requirement (HFR) (Smakhtin et al. 2004, 309-310). The LFR for a 'fair' condition was the mean monthly discharge with 90% exceedance probability (Q₉₀). The HFR was a flow component varying in magnitude from grid cell to grid cell depending on the ratio between the LFR and the long-term mean annual river discharge of the grid cell. This initial model estimated the total e-flow requirement for a river basin by averaging the requirement for individual grid cells within a basin and was expressed as a percentage of the mean annual river discharge of the basin.

IWMI's Global Environmental Flow Calculator (GEFC)<fnref n="2" /> (Smakhtin and Eriyagama 2008) accelerated the 'operationalizing' of global e-flow assessments by introducing a standalone software tool to determine e-flows for major rivers of the world (Figure 1). While the Smakhtin et al. (2004) assessment estimated e-flows to maintain rivers in only a 'fair' ecosystem condition (but without any ecological basis or data), the GEFC estimated e-flows (at a 0.5° spatial resolution) for six ecosystem conditions, named as Ecological Management Classes (EMCs) A to F (Table 1) following the method put forward by Smakhtin and Anputhas (2006). The set of EMCs were based on those described in the South African classification system for water resources (DWAF 1997). Class A denotes a nearly natural condition while Class E is assumed to be a highly degraded condition. The 'fair' ecosystem condition in the Smakhtin et al. (2004) assessment corresponds to 'Class C' in GEFC. The underlying hydrological data in GEFC are monthly time series of river discharge at 0.5° by 0.5° grid resolution, generated by the University of New Hampshire by combining observed river discharge with those generated by their Water Balance Model (WBM) (Fekete et al. 2002).

<footnote n="1">¹ http://eflows.iwmi.org/ (accessed on December 14, 2021).</footnote>

<footnote n="2">² https://www.iwmi.cgiar.org/resources/data-and-tools/models-and-software/environmental-flow-calculators/ (accessed on December 14, 2021).</footnote>
<figure><img src="image_6.png" /><figcaption>Figure 1. Screenshots showing the user interface of the Global Environmental Flow Calculator (GEFC).</figcaption></figure>
<table><caption>**Table 1.** Description of Ecological Management Classes (EMCs).</caption><thead><tr><td>EMC</td><td>Most likely ecological condition</td><td>Management perspective</td></tr></thead><tbody><tr><td>A (natural)</td><td>Natural rivers with minor modification of in-stream and riparian habitat.</td><td>Protected rivers and basins. Reserves and national parks. No new water projects (dams, diversions) allowed.</td></tr><tr><td>B (largely natural)</td><td>Slightly modified and/or ecologically important rivers with largely intact biodiversity and habitats despite water resources development and/or basin modifications.</td><td>Water supply schemes or irrigation development present and/or allowed.</td></tr><tr><td>C (moderately modified or 'fair' condition)</td><td>The habitats and dynamics of the biota have been disturbed, but basic ecosystem functions are still intact. Some sensitive species are lost and/or reduced in extent. Alien species present.</td><td>Multiple disturbances associated with the need for socioeconomic development, e.g., dams, diversions, habitat modification and reduced water quality.</td></tr><tr><td>D (largely modified)</td><td>Large changes in natural habitat, biota and basic ecosystem functions have occurred. Species richness is lower than expected. Much lowered presence of intolerant species. Alien species prevail.</td><td>Significant and clearly visible disturbances associated with basin and water resources development, including dams, diversions, transfers, habitat modification and water quality degradation.</td></tr><tr><td>E (seriously modified)</td><td>Habitat diversity and availability have declined. A strikingly lower-than-expected species richness. Only tolerant species remain. Alien species have invaded the ecosystem.</td><td>High human population density and extensive water resources exploitation. Generally, this status cannot be acceptable as a management goal. Management interventions are necessary to restore flow pattern and to 'move' a river to a higher management category.</td></tr><tr><td>F (critically modified)</td><td>Modifications have reached a critical level and the ecosystem has been completely modified with almost total loss of natural habitat and biota. In the worst case, the basic ecosystem functions have been destroyed and the changes are irreversible.</td><td>This status is not acceptable from the management perspective. Management interventions are necessary to restore flow pattern and river habitats (if still possible/ feasible) to 'move' the river to a higher management category.</td></tr></tbody></table>

Source: From FAO (2019) based on Smakhtin and Eriyagama (2008).

The method developed by Smakhtin and Anputhas (2006) uses natural (i.e., no human interventions such as abstractions, reservoirs and irrigation) monthly flow time series at a particular location as input to generate six other monthly flow time series. These series differ in magnitude but are similar in pattern to the natural flows. The method first estimates a flow duration curve (FDC) from the natural monthly discharge across the period of record. This FDC is then shifted to the left along the horizontal axis, using a simple rule of thumb, to estimate six synthetic flow duration curves that correspond to the recommended environmental flows for EMCs A to F (Figure 2). The 17 percentage categories on the (horizontal) probability axis are 0.01%, 0.1%, 1%, 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, 99%, 99.9% and 99.99%. These categories are used as steps in this shifting procedure and illustrate the entirety of the flow duration curve. A lateral shift of the FDC by one step (a distance between two adjacent

percentage categories on the horizontal axis) generates the flow duration curve for the next lower EMC. For example, the flow duration curve for EMC A is determined by shifting the 'natural' flow duration curve to the left along the probability axis from its original position; for EMC B, the flow duration curve is derived by shifting the 'natural' flow duration curve two steps to the left. The unit of shift of the FDC (by one percentage category) to reach the next lower EMC has been inferred partially from literature sources and partially through limited 'calibration' against e-flow estimates obtained by more advanced desktop techniques as described in Smakhtin and Eriyagama (2008). Following this lateral shift, the flow duration curves are subsequently converted to time series of environmental flows using a transformation technique developed by Hughes and Smakhtin (1996) and described in detail in Smakhtin and Eriyagama (2008). The underlying principle in this technique is that flows occurring simultaneously in the 'natural' and
‘environmental’ flow time series correspond to similar percentage categories on their respective FDCs. This procedure ensures that, while the annual and monthly flow requirements decrease in magnitude when moving from Class A to Class F, the overall shape of the natural yearly hydrograph—significant in aquatic ecosystem maintenance—is still preserved in each e-flow time series. Although the estimation of e-flow requirements corresponding to all six classes is possible, classes E and F are generally considered unacceptable. The method has been illustrated in detail in Smakhtin and Anputhas (2006) and Smakhtin and Eriyagama (2008). The software facilitates the assessment of e-flow requirements corresponding to the six EMCs for any location (grid cell) on a river by considering the total river discharge draining through that location (grid cell), including the discharge from upstream. The GEFC is available for download and has been used for research, capacity building and water resources planning studies, as per software download statistics and journal papers (e.g., Salik et al. 2016).

The most recent iteration in global e-flow assessment tools is the online Global Environmental Flow Information System (GEFIS) (Figure 3), which is described in Sood et al. (2017). GEFIS uses the same assessment method as the GEFC (Smakhtin and Anputhas 2006), but incorporates some significant differences:

i. The hydrological data underlying GEFIS are monthly time series of natural runoff at a spatial resolution of 0.1" (approximately 10 km at the equator, instead of 0.5") generated by the PCRaster Global Water Balance (PCR-GLOBWB) model Version 2.0 (Wada et al. 2016).

ii. GEFIS incorporates an estimate of the most likely present-day EMC of rivers within each grid cell—a feature not available in GEFC (refer to the section Methods for an explanation of this estimation).

iii. It provides estimates of e-flow requirements for different spatial units including countries, administrative units and river basins by aggregating e-flow requirements for individual grid cells.

iv. GEFIS presents e-flow requirements only for classes A to D since classes E and F are considered as unacceptable ecosystem conditions from a management perspective.

v. In addition to e-flows for classes A to D, GEFIS estimates present-day e-flow requirements, based on the most likely present-day EMC in each cell.

GEFIS has been adopted to provide the global estimation of e-flows that is required for computing SDG Indicator 6.4.2 on water stress (FAO 2019). This water stress indicator offers an estimation of the pressure applied by all economic sectors of a country on its renewable freshwater resources. It is defined as the total freshwater withdrawn (TFWW) by all economic sectors divided by the difference between the total renewable freshwater resources (TRWR) and the environmental flow requirements (EFR), multiplied by 100 (Equation 1). It thus describes how much water is left and available for direct human use in the environment. Both TFWW and TRWR are derived from government statistics or estimates by the Food and Agriculture Organization of the United Nations (FAO).

<figure><img src="image_7.png" /><figcaption>**Figure 2.** Shifting of the natural flow duration curve to generate environmental flow duration curves. Only classes A to D are shown in the figure while classes E and F considered unacceptable are excluded.</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 3. Screenshot of the user interface of the Global Environmental Flow Information System (GEFIS).</figcaption></figure>

Source: http://eflows.iwmi.org

Notes: The grid layer displayed shows the estimated present-day EMC.

$$ Water\ Stress\ (%) = TFWW / (TRWR - EFR) * 100 \quad (1) $$

where: the e-flow data are provided by GEFIS for the present-day EMC within each pixel. EFR in Equation (1) is the aggregate e-flow requirement for all pixels within a given country. Water Stress (%) is calculated as a single national figure.

During the periodic data drive by FAO to publish the SDG Indicator 6.4.2 results, country representatives contribute to the global SDG report by being invited

to endorse the global data generated by GEFIS for that country. Each country receives the e-flow data from FAO and can lodge comments about its accuracy using the template provided by FAO. Optionally, countries that have conducted e-flow assessments independently may report their own values for country-aggregated e-flows in greater detail and in their preferred data format through Voluntary National Reviews (VNRs). This study sets out to compare the e-flow assessments made by GEFIS against local e-flow assessments carried out within countries.

<figure><img src="image_9.png" /><figcaption>Cross section survey in progress, Limpopo River, Southern Africa (photo: James MacKenzie).</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Cross section survey in progress, Limpopo River, Southern Africa (photo: James MacKenzie).</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>A river in Burkina Faso (photo: Chris Dickens).</figcaption></figure>
# Methods

## General Approach

The GEFIS online interface provides global pixel-based estimates of environmental flow requirements at a resolution of 0.1' based on two main data inputs: monthly time series of runoff for natural conditions and the EMC representing the current condition (present-day EMC) of the rivers at that location. The monthly time series of runoff (Wada et al. 2016)—spanning from 1960 to 2010—provides the basis for generating estimates of long-term mean annual runoff (MAR); in million cubic meters per year, $10^6 \text{ m}^3 \text{ yr}^{-1}$) and mean annual e-flow requirements (as a percentage of MAR, or in $10^6 \text{ m}^3 \text{ yr}^{-1}$). The present-day EMC is inferred based on a subset of the indicators making up the IBT index (Vörösmarty et al. 2010). The original IBT index integrated 23 individual stressors to human water security and biodiversity organized under four themes (catchment disturbance, pollution, water resource development and biotic factors). A custom IBT index is computed in GEFIS using only the stressors affected by e-flows (included within the 'water resource development' and 'biotic' themes). EMCs are then determined by reclassifying this custom IBT index from a continuous scale (0 to 1) to the ordinal scale from A to E-F where classes E and F are considered to fall into a single category (Table 1) (Sood et al. 2017) describes this reclassification in detail). E-flows for each 0.1' pixel are computed for classes A-D using these two data sources—monthly time series of runoff and the present-day EMC class—based on the method of Smakhtin and Anputhas (2006) as described above. The pixel-based estimates are aggregated to present results for different spatial units. In case the present-day EMC is found to be E or F for a particular pixel, the e-flow requirement for that pixel is calculated as for Class D, since countries are expected to maintain at least the Class D e-flow requirement.

Three combined sources of bias and uncertainty account for the observed divergence when comparing the e-flow estimate from GEFIS to a local e-flow assessment: (i) differences between the streamflow estimated from the global hydrological model and the actual natural flow regime of the watercourse estimated for the local assessment, (ii) differences between the present-day EMC inferred by GEFIS and the EMC determined by local assessors, and (iii) differences between the e-flow estimation method used by GEFIS and the e-flow determination method used by the local assessment.

In this comparison, the role of each of these sources of bias and uncertainty was analyzed in the following manner:

i. We compared the MAR estimates from GEFIS to two other sources of MAR estimates—the local e-flow assessments and WaterGAP v2.2, a widely used global hydrological model (Müller Schmied et al. 2014).

ii. We compared the present-day EMC predicted by GEFIS against the present-day ecological condition category from the local e-flow assessments. We also analyzed the relationship between the EMC assessment undertaken by local assessors and measures of human impact in the catchment of each local e-flow assessment site (based on data from the RiverATLAS database; Linke et al. 2019).

iii. We compared the e-flow estimate from GEFIS with local e-flow assessments in two ways. First, we compared e-flow estimates as a percentage of MAR (i.e., partly removing bias and uncertainty from errors in the global hydrological model), but based on the present-day EMC estimated by GEFIS. Second, we compared e-flow estimates as a percentage of MAR but based on the EMC that best matches the EMC determined by the local e-flow assessment.

This second comparison intends to remove bias and uncertainty, as much as possible, which can arise both from the hydrological model estimates (by comparing percentages rather than absolute water volumes) as well as from the EMC prediction (by using the locally determined EMC). This approach enabled the assessment of divergence in e-flow estimates between GEFIS and local assessments that mostly stem from differences in the e-flow assessment method (Smakhtin and Anputhas [2006], in the case of GEFIS versus, for instance, a holistic method in the case of the local assessment). This study is a *comparison* between GEFIS estimates of e-flow and the local assessments rather than an evaluation of the accuracy of GEFIS. Considering the diversity of methods across the local e-flow assessments in the database, the local e-flow assessments are not deemed to be 'correct' or necessarily any more accurate than GEFIS estimates.

## Data Collection and Database Structure

A wide range of countries were contacted at the end of July 2021 with a request to provide a summary of in-country e-flow assessment data. The official request for data was sent out by the Land and Water Division of FAO to contacts in each country to supply data within one month of receipt of the request on a template provided (Annex 1). We also asked other known sources with access to data on in-country e-flow assessments. Countries that were contacted by both channels include Armenia, Brazil, Botswana, Austria, China, Costa Rica, France, Germany, Greece, India, Italy, Kenya, Laos, Mexico, Namibia, Peru, Poland, Russia, South Korea, Senegal, Slovenia, South Africa, Switzerland, Tanzania, USA, Uzbekistan and Vietnam. A general request was also sent to the European Union.
In addition, we incorporated e-flow data previously compiled by the International Water Management Institute (IWMI) into a publicly available database (IWMI's Eco-Hydrological Databases-flow database<fnref n="3" />). This database included data from Australia, Canada, China, France, Hungary, India, Lesotho, Mexico, Slovenia, South Africa, Colombia, Spain, Tanzania, Tunisia and Zambia. The data collection effort aimed to ensure the widest possible distribution across ecoregions as well as climatic zones (e.g., tropical, temperate, wet and dry). All data collected in this manner were harmonized by simplifying or lumping e-flow assessment types and ecological classes into generic categories and were compiled into a master database (Annex 2).

The master database contained information for each data point, i.e., the country, river basin, geographic coordinates, type and name of e-flow assessment method, system to determine ecological condition (such as the EMC system), ecological condition in present day (such as the present-day EMC), natural mean annual runoff (natural MAR), e-flow requirement as a volume, e-flow requirement as a percentage of the natural MAR, and further sources of information on the data. The e-flow assessment methods used in producing the data were classified into five categories (Table 2) adapted from those of Tharme (2003) and Poff et al. (2017).

<table><caption>**Table 2.** Normalization of the environmental flow assessment method used into five categories.</caption><thead><tr><td>E-flow method category</td><td>Description</td><td>Examples</td></tr></thead><tbody><tr><td>Hydrological: single indices</td><td>Hydrological index/indices used as e-flow</td><td>Q95, 7-day minimum flow</td></tr><tr><td>Hydrological: time series analysis</td><td>Hydrological time series analysis using ecologically relevant flow metrics</td><td>Indicators of Hydrologic Alteration, South African Desktop Reserve Model</td></tr><tr><td>Hydraulic rating</td><td>Simple relationship(s) between hydraulic variables and discharge as surrogate for habitat factors</td><td>Wetted perimeter, maximum depth</td></tr><tr><td>Habitat simulation</td><td>Model-based analysis of relationships between quantity and suitability of habitat available for target biota under different discharges</td><td>Physical Habitat Simulation Model (PHABSIM)</td></tr><tr><td>Holistic/Ecosystem function</td><td>Assessment for a site or region considering multiple ecosystem components/processes, involving multidisciplinary experts and field data collection and analysis, culminating in a synthesis workshop where an appropriate e-flow regime(s) is negotiated</td><td>Building Block Method (BBM), Downstream Response to Imposed Flow Transformation (DRIFT), PROBFLO, Ecological Limits of Hydrologic Alteration (ELOHA)</td></tr></tbody></table>

Source: Adapted from Tharme 2003 and Poff et al. 2017.

## Spatial Pre-processing

The local e-flow assessment records were spatially pre-processed with two objectives: to ascertain the location of the e-flow assessment site (i.e., its geographic coordinates) and to associate each e-flow assessment site with information from GEFIS and ancillary sources of data. To do so, we first verified whether the coordinates provided for each site corresponded to the correct country and river basin. We subsequently co-registered each site to the global baseline hydrographic dataset HydroRIVERS (Linke et

al. 2019), a widely used representation of the global river network built on the HydroSHEDS hydrographic database (Lehner et al. 2008; Lehner and Grill 2013). Individual river reaches are delineated on the basis of drainage direction and flow accumulation maps derived from elevation data at a pixel resolution of 3 arc-seconds (~90 m at the equator) and subsequently upscaled to 15 arc-seconds (~500 m at the equator). This co-registration of each site with HydroRIVERS enabled us to ensure that the river network position, upstream drainage area and mean annual runoff (MAR) of the location of the site were consistent with

<footnote n="3">³ http://waterdata.iwmi.org/applications/efm/efr_home.php (accessed on December 6, 2021).</footnote>
the attribute information obtained from the local e-flow assessment associated with that site.

The hydrological and e-flow information provided with each record in the local e-flow master database corresponds to the mean annual water volume flowing through a reach or river cross-section from the e-flow site's entire upstream drainage area. By contrast, information in GEFIS is local and pixel-based such that the MAR and e-flow values associated with each pixel correspond to the water volume that is strictly generated from (and flowing out of) that pixel, excluding upstream water inputs. Consequently, to compare information in the master database of local assessments with GEFIS, it was necessary to spatially delineate the upstream drainage area (i.e., catchment) of each site and then aggregate the information from all GEFIS pixels within that area.

We delineated the catchment of individual e-flow sites based on established global flow direction maps at 15 arc-second resolution from HydroSHEDS (Lehner et al. 2008; Lehner and Grill 2013). We then resampled GEFIS data grids for MAR, the custom Incident Biodiversity Threat (IBT) index (on the continuous 0 to 1 scale), the predicted present-day e-flow volume, and the predicted e-flow volume for each EMC (A, B, C and D).

from a resolution of 0.1° (~10 km at the equator) to 15 arc-seconds (~500 m at the equator). This resampling enabled us to weigh the relative contribution of coarse pixels to catchments even if a pixel only partially overlapped with a catchment. For instance, if only 10% of the surface area of a 100 km² pixel fell within a catchment, then it would contribute only a tenth of the MAR estimated by GEFIS for that pixel to the site at the outlet point of that catchment. Similarly, the IBT for that pixel would be weighed by a tenth in computing the weighted average index for the catchment.

Finally, we aggregated GEFIS data for each catchment by (i) summing the MAR and predicted e-flow volume for each EMC, and (ii) averaging the IBT of all resampled pixels within the catchment. The resulting aggregated statistics were used in subsequent comparisons between GEFIS, the local e-flow assessments and other ancillary data. It is worth noting that the sum of MAR across all pixels within the upstream drainage area of a site does not exactly equate to estimating the discharge at the site, since it does not account for in-channel transmission losses through evapotranspiration and infiltration during routing through the river network between the upstream pixels and the site. Figure 4 summarizes the project workflow described above.

<figure><img src="image_12.png" /><figcaption>Cross section for determination of e-flows of the Limpopo River, Southern Africa (photo: Bennie van der Waal).</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Figure 4.** The project workflow.</figcaption></figure>

## Hydrological Comparison

We compared the MAR estimate from GEFIS to two other sources of MAR estimates: (i) local e-flow assessment attribute information, and (ii) estimates from another global hydrological model.

For the estimates from the other global hydrological model, we used modelled long-term (1971–2000) mean natural annual discharge estimates associated with each river reach in HydroRIVERS. These estimates are derived through a geospatial downscaling procedure (Lehner and Grill 2013) based on the 0.5° resolution runoff and discharge layers provided by the global WaterGAP model version 2.2 as of 2014 (Alcamo et al. 2003; Müller Schmied et al. 2014). A validation of the downscaled

discharge estimates was performed against observations at 2,131 Global Runoff Data Centre (GRDC) gauging stations with ≥20 years of streamflow data (1971–2000) by Messager et al. (2021). Including rivers with mean annual discharge ranging from 0.006 to 180,000 m³ s⁻¹, this assessment showed a strong overall correlation (log-log least-square regression, R² = 0.96) and a Symmetric Mean Absolute Percentage Error (sMAPE) (see definition below) of 30% between modelled and observed discharge.

To compare MAR estimates across data sources, we computed a set of standard performance statistics, including the Mean Absolute Error (MAE), the Percent BIAS (%BIAS), and the Symmetric Mean Absolute Percentage Error (sMAPE) as shown in the following equations:
$$MAE = \frac{\sum_{i=1}^{N} |\hat{y}_i - y_i|}{N} \qquad (2)$$

$$\%BIAS = \frac{100}{N} \sum_{i=1}^{N} \frac{\hat{y}_i - y_i}{y_i} \qquad (3)$$

$$sMAPE = \frac{100}{N} \sum_{i=1}^{N} \frac{|\hat{y}_i - y_i|}{(|\hat{y}_i| + |y_i|)/2} \qquad (4)$$

where: $y_i$ is the reference value (e.g., MAR reported by the local assessment) and $\hat{y}_i$ is the comparison value (e.g., MAR estimated by GEFIS).

We also computed the coefficient of determination (R²) of linear least-square regressions between the sources of MAR estimates, with and without outliers (excluded based on studentized residuals larger than three standard deviations).

## Management Class Comparison

The EMC of a site relates to the current or desired condition of a river and is perceived as a scenario of the ecological state of a river (Sood et al. 2017). In GEFIS, the lower the Incident Biodiversity Threat Index, the less modified the river, the higher the EMC (e.g., A versus B), and higher the e-flow prescription for that river. To understand how the EMC inferred by GEFIS relates to the EMC determined by local e-flow assessments, we investigated whether there are significant differences in IBT (corresponding to the present-day EMC in GEFIS) between sites with different locally-determined EMC. We first conducted an ANalysis Of VAriance (ANOVA) test followed by Tukey's Honestly Significant Difference (HSD; Tukey 1949) test to assess, for each pair of EMCs (e.g., A vs. B), whether EMCs determined by local assessments differed in terms of the mean IBT within their catchment. In this analysis, we assumed the EMC provided with local e-flow assessments to correspond to present-day conditions, unless otherwise specified.

To examine the factors determining the EMCs identified by local assessments, we also related the locally determined EMCs to various continuous indicators of anthropogenic stressors on ecosystems associated with each river reach in HydroRIVERS. We obtained these indicators from the RiverATLAS database, version 1.0 (Linke et al. 2019). The database provides hydro-environmental information for all rivers of the world, both within their contributing local reach catchment and across the entire upstream drainage area of every reach. We assessed the differences among sites in different EMCs on the basis of the characteristics of their catchment in terms of area used for crops and pasture (Ramankutty and Foley 1999), urban land covers (Pesaresi et al. 2016), area equipped for irrigation (Siebert et al. 2015) and population density (CIESIN 2016). We also compared the degree of regulation (the percent

ratio between the total reservoir storage volume of all dams on or upstream of the site and the total annual discharge volume available at the site; Lehner et al. 2011) among sites from different EMCs. Although the GEFIS presents results only for classes A to D, the management class comparison nevertheless included Class E rivers to obtain better knowledge of differences between classes.

## Assessing the Impact of Masking

Regions with 'negligible streamflow' (Vörösmarty et al. 2010; Sood et al. 2017) were omitted in the production of GEFIS by deploying two separate masks (Figure 5). The first mask, applied by Sood et al. (2017) to the MAR grid and the grids of estimated e-flow for each EMC, excludes all open water bodies and areas with land covers associated with arid and semi-arid climates. It was applied by first aggregating the GlobCover 2009 land cover dataset (Arino et al. 2012) from a resolution of ~300 m to ~10 km and excluding the following land cover categories: 'bare areas', 'water bodies', 'permanent snow and ice', 'closed to open grassland', 'closed to open shrubland' (for North America and South America) and 'sparse vegetation' (for Africa and Australia). This mask results in the exclusion of 34% of global land area (exclusive of Greenland and Antarctica).

In conjunction with the first mask, a second mask which affects the grids of present-day EMC and the estimated present-day e-flow in GEFIS was also applied. This exclusion mask encompasses areas originally excluded in the IBT—all 0.5° pixels (~50 km at the equator) for which the average annual runoff of upstream cells is <10 mm in Vörösmarty et al. (2010). Finally, a large portion of 0.5° pixels along coastal areas have also been excluded. Altogether, 42% of global land area (exclusive of Greenland and Antarctica) is excluded from the grids of present-day EMC and the estimated present-day e-flow.

We evaluated the impact of these exclusion masks on the GEFIS predictions for MAR, present-day EMC, and the corresponding estimate of present-day e-flow by relating the percentage of the catchment that is masked out to the percentage difference in the variable of interest between GEFIS predictions and local e-flow assessments. Greater differences were expected to be observed for sites for which a higher proportion of the catchment is masked out.
<figure><img src="image_14.png" /><figcaption>**Figure 5.** Example maps showing exclusion masks implemented in GEFIS.</figcaption></figure>

Notes: Panel A shows the first mask, which affects grids of Mean Annual Runoff (MAR) and of estimated e-flow by Ecological Management Class (EMC); Panel B shows the second mask, which affects grids of present-day EMC and e-flow based on present-day EMC, overlaid on the first mask; Panel C illustrates the exclusion of many coastal areas such as Panama, by the second mask.

## Environmental Flow Comparison

We compared e-flow estimates between GEFIS and local assessments (i) as a percentage of MAR based on the present-day EMC inferred by GEFIS, and (ii) as a percentage of MAR based on the EMC determined by the local e-flow assessment. Comparisons were performed separately by the method of local e-flow assessment (e.g., hydrological time series analysis

versus holistic). For each comparison, the same set of standard performance statistics were computed as in the comparison between MARs. For sites where e-flow estimates exist for multiple ecological classes but no class is explicitly designated as the present-day conditions, a range of performance statistics were computed. For this analysis, we included only those sites for which less than 70% of the catchment is masked out.
<figure><img src="image_15.png" /><figcaption>Kazinga Channel linking Lakes George and Edward, Uganda (photo: Chris Dickens).</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Danube River in Hungary (photo: Chris Dickens).</figcaption></figure>
# Results

## Overview of the Database of E-flow Assessments

A total of 651 local environmental flow assessments (EFAs) were compiled and formatted into the master database by November 2021. Of these, 611 assessments were associated with 533 unique assessment sites distributed across 27 countries (Figure 6). Some sites were associated with multiple assessments because e-flow requirements are commonly determined for several water use scenarios and/or have been adapted or reassessed over time.

Most local e-flow assessment sites (86%) included in this analysis are either located in South Africa (302 sites) or in the province of Quebec in Canada (154 sites). Consequently, while the sites represent a wide range of environmental conditions, they are only partially

representative of the global river network (Figure 7). Most sites are located on medium-sized rivers (median catchment area = 1,820 km²; median MAR = 10 m³ s⁻¹) with a few located on small rivers and streams draining less than 100 km². On average, the sites drain moderately populated catchments (average population density = 68 people km⁻²) with a range of forest conditions (average and standard deviation of forest extent: 52±34% of catchment area) but limited agricultural activity (average cropland land cover: 13% of catchment area) and urban influence (average urban extent: 3% of catchment area). Sites for which more than half of the catchment lies within protected areas only account for 9% of the dataset, with most sites (65%) draining unprotected catchments (<10% of the protected area). Lastly, over a third of the sites are regulated by upstream reservoirs with total volume being at least 10% of the mean annual runoff.

<figure><img src="image_17.png" /><figcaption>**Figure 6.** Distribution of the local environmental flow assessment sites included in the analysis (n=533).</figcaption></figure>

Notes: The size of the circles is proportional to the number of e-flow assessment (EFA) sites within 1,000 km of the geographic center of the circle.
<figure><img src="image_18.png" /><figcaption>**Figure 7.** Distribution of 12 hydro-environmental variables across the 533 e-flow assessments sites included in the analysis (purple; n=533) and across all reaches of the global river network (blue).

**Notes:** All variables (from RiverATLAS) were averaged across the total drainage area upstream of the reach associated with each local e-flow assessment site. The aridity index is the mean annual precipitation over mean annual potential evapotranspiration. Refer to Figure A3 in Annex 3 for a description of the climate zones (1. Arctic 1, 2. Arctic 2, 3. Extremely cold and wet 1, 4. Extremely cold and wet 2, 5. Cold and wet, 6. Extremely cold and mesic, 7. Cold and mesic, 8. Cool temperate and dry, 9. Cool temperate and xeric, 10. Cool temperate and moist, 11. Warm temperate and mesic, 12. Warm temperate and xeric, 13. Hot and mesic, 14. Hot and dry, 15. Hot and arid, 16. Extremely hot and arid, 17. Extremely hot and xeric, and 18. Extremely hot and moist). No sites were located in climate zones 1-5 and 16.</figcaption></figure>
In most countries with legislation mandating the conservation and/or restoration of e-flows, there is a prevalence of stipulated uses of specific e-flow assessment methods or at least common practices. This explains the overall homogeneity of e-flow assessment methods within countries observed in the database (Figure 8). In total, 305 local e-flow assessments in the database used a holistic method, 156 used single hydrological indices, 98 used hydrological time series analysis and 28 used habitat simulation, while 15 did not specify a method. Most habitat simulations (24 out of 28) correspond to assessments in Poland and nearly all single hydrological index assessments are observed for Quebec, Canada.

## Hydrological Comparison

Comparing the MAR estimates from GEFIS to those

from HydroRIVERS and from local e-flow assessments reveals only moderate predictive performance on the part of GEFIS (Figure 9, panels A and B; Table 3). In both comparisons, GEFIS tends to overestimate MAR for medium to large rivers (MAR > 109 m³ y⁻¹, equivalent to 32 m³ s⁻¹) and underestimate MAR for small rivers and streams (MAR < 108 m³ y⁻¹), indicating a systematic bias. Estimates of MAR by GEFIS account for 82-84% (with and without outliers) of the variance in MAR observed at the local e-flow assessments sites (on a log-log scale, see Table 3). The average percentage error (sMAPE) in the MAR estimates from GEFIS are 20 percentage points higher than the error from HydroRIVERS. We examined the impact of masking on MAR estimates but observed no significant correlation between the proportion of the catchment that was masked out in GEFIS and the percentage error in MAR estimates from GEFIS (using MAR reported by local EFAs as reference; Figure 9, panel D).

<figure><img src="image_19.png" /><figcaption>**Figure 8.** Distribution of environmental flow assessment (EFA) types across countries.</figcaption></figure>

Notes: Multiple EFAs may be associated with a single site.
<figure><img src="image_20.png" /><figcaption>**Figure 9.** Comparisons of Mean Annual Runoff (MAR) estimates from GEFIS, HydroRIVERS and local e-flow assessments (EFA).</figcaption></figure>

Notes: The percent error between the MAR estimate from GEFIS and that of the local EFAs (panel D) is computed as $100 * (MAR_{GEFIS} - MAR_{EFA})/MAR_{EFA}$. MAR estimates from HydroRIVERS are derived through a geospatial downscaling procedure based on the global WaterGAP model version 2.2 (Müller Schmied et al. 2014). Black diagonal lines are identity (1:1) lines.

<table><caption>**Table 3.** Summary statistics for the comparisons of Mean Annual Runoff (MAR) estimates from GEFIS, the global hydrological model HydroRIVERS and local EFAs.</caption><thead><tr><th>Comparison</th><th>sMAPE<br>(%)</th><th>MAE</th><th>%Bias</th><th>R<sup>2</sup></th><th>R<sup>2</sup><br>(without<br>outliers)</th><th>Coefficient<br>p-value</th><th>n</th><th>#<br>Outliers</th></tr></thead><tbody><tr><td>GEFIS - HydroRIVERS</td><td>55</td><td>3,230</td><td>-37</td><td>0.90</td><td>0.90</td><td>&lt; 0.001</td><td>532</td><td>3</td></tr><tr><td>GEFIS - local EFA</td><td>65</td><td>3,691</td><td>-51</td><td>0.82</td><td>0.84</td><td>&lt; 0.001</td><td>497</td><td>4</td></tr><tr><td>HydroRIVERS - local EFA</td><td>45</td><td>759</td><td>-18</td><td>0.90</td><td>0.91</td><td>&lt; 0.001</td><td>497</td><td>1</td></tr></tbody></table>

Notes: Refer to the section Methods for equations of the performance statistics and the criterion used to exclude outliers. Regression statistics are based on log-log linear least square regression analyses.
## Management Class Comparison

The IBT index, used to determine present-day EMCs in GEFIS, marginally captured the ecological classes determined by local e-flow assessments (Figure 10). In only considering sites with a standard EMC determined in the local e-flow assessment, we noted a statistically significant difference in the mean IBT among standard EMCs (ANOVA, *p* = 0.005, df = 8). However, post-hoc tests showed only classes A/B and B to be significantly different from each other in terms of mean IBT (*p* < 0.05). While the lack of differences in IBT associated with sites across EMCs is in part due to the small sample sizes for

several ecological classes (e.g., Class A, D/E and E only had two, one and four sites, respectively), the small range in IBT found across all sites is also a significant factor. Almost all sites in the database had an average IBT in their catchment corresponding to either classes B or C on the scale implemented for GEFIS. For other ecological classes which did not correspond to the standard EMC scale (from A to E; ‘Other’ panel in Figure 10), the small number of sites precluded us from conducting formal tests, but a similarly narrow range of IBT was also observed. Analyzing the IBT value for the specific site locations rather than computing the average IBT across the sites’ catchment yielded similar results (not shown here).

<figure><img src="image_21.png" /><figcaption>**Figure 10.** Distribution of Incident Biodiversity Threat (IBT) index for local environmental flow assessment (EFA) sites in different ecological classes.</figcaption></figure>

Notes: IBT values (x-axis) were computed through spatial averaging across the catchment of each site. Present-day ecological classes (y-axis) are from local EFAs. Background colors reflect the present-day Ecological Management Classes (EMC) that correspond to intervals of IBT in GEFIS. Significant differences in IBT between EMCs are indicated by different letters (ANOVA followed by Tukey's HSD, *p*-value < 0.05). The number of individual sites for each ecological class is in parenthesis to the right of the letters. In the box plots, the main line corresponds to the median, the lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles), whiskers extend from the hinge to the largest value no further than 1.5 times the interquartile range. For descriptions of ecological classes A through E, see Table 1. Classes marked with a dash (⁄) are transitional. ET: Existing Treaty(ies); EWS: Existing Water Structure(s); EWW: Existing Water Withdrawal(s); MinW: Minimum Water Withdrawal; MinD: Minimum Degradation; SimS: Simulation Scenario; MaxW: Maximum Water Withdrawal; RI: Reduced Impacts; SR: Stressed River; None: local EFA system does not include the concept of ecological classes; NA: no ecological information was provided.
For local e-flow assessment sites where a standard EMC is available, GEFIS inferred the same EMC for 38% of the sites. Notably, since no sites determined to be in Class A or D by local assessors were classified as such by GEFIS, the classification accuracy is driven by the predominance of Class B and C sites in the sample, rather than by performance. GEFIS also assigned more sites to Class C (100) than B (77) for sites determined to be in Class B by local assessors.

We assessed how indicators of anthropogenic stress varied among locally determined EMCs (Figure 11).

Indicators related to agricultural stressors, namely the relative extent of cropland, pasture and irrigation across the sites' catchment, do not substantially and consistently vary from less impacted to more impacted classes. However, there is a clear increase from sites deemed as natural (Class A) to seriously modified sites (Class E) in the degree of regulation by upstream reservoirs, population density and extent of urban land cover. For instance, the 31 sites in EMC Class A/B had 4% of their MAR regulated by upstream reservoirs on average, compared to 72% for sites in EMC Class D.

<figure><img src="image_22.png" /><figcaption>**Figure 11.** Distribution of anthropogenic stressors in the catchment of local environmental flow assessment (EFA) sites in different Ecological Management Classes.</figcaption></figure>

Notes: nEFA with standard EMC = 370 (of which 337 are in South Africa). In the box plots, the main line corresponds to the median, the lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles), whiskers extend from the hinge to the largest value no further than 1.5 times the interquartile range.
Assessing the Impact of Masking

The masks implemented in GEFIS excluded land covers
associated with areas of low flow as well as coastal
areas. These exclusion masks resulted in a scarcity of
GEFIS data in the catchments of semi-arid and arid sites
(Figure 12). It was observed that 62 sites (12%) lacked

GEFIS data across more than half of their catchment. The median MAR at these sites (3.15 x 108 m³ y⁻¹; estimated as part of the local e-flow assessments) was only slightly lower than the median MAR at those sites with GEFIS data available across most of their catchment (4.05 x 108 m³ y⁻¹). Only 9 sites lacked GEFIS data across their entire catchment.

<figure><img src="image_23.png" /><figcaption>**Figure 12.** Total extent of masking in the catchment of e-flow assessment sites by country.</figcaption></figure>

Notes: In the box plots, the main line corresponds to the median, the lower and upper hinges correspond to the first and third quartiles (the 25th and 75th percentiles), whiskers extend from the hinge to the largest value no further than 1.5 times the interquartile range. The number of unique sites is written next to each country's name.
## Environmental Flow Comparison

The comparison of e-flow estimates displayed broad agreement but little to no correlation between estimates from GEFIS for present-day EMCs and e-flows determined by local assessors (Figure 13; Table 4). On the one hand, GEFIS estimates are within the same range as local estimates for most sites across local e-flow assessment methods—the mean absolute error (MAE) ranges between 26 and 15 percentage points of MAR (for local assessments using single hydrological indices and holistic assessments, respectively; Table 4). On the other hand, the correlations between GEFIS estimates and local assessments are either weak or non-significant. Indeed, the absolute error translates to a substantial percentage error—GEFIS estimates are on average 50–60% off across e-flow assessment methods. For local assessments in Quebec (Canada) based on single hydrological indices, GEFIS estimates account for 30% of the variance in estimated e-flows, but are substantially higher and thus more conservative than the local estimates (average percentage bias %Bias = 141%).

To control for the uncertainty in e-flow estimates from GEFIS due to the use of different EMCs, another comparison was made between the local e-flow assessments and GEFIS estimates of e-flows for the locally determined EMC. This assessment was constrained to a reduced set of sites where a standard EMC presented by the local assessment was available. As anticipated, matching EMC increased the correspondence between GEFIS estimates and local assessments (Figure 14; Table 5). Although GEFIS still explains only 5-6% of the variance in local e-flow estimates (R² for hydrological time series analysis and holistic assessments, respectively), all other performance metrics improved by matching EMCs.

Based on the present-day EMC predicted by GEFIS, e-flow estimates from GEFIS are, on average, more conservative in terms of total annual e-flow than local assessments (positive percentage bias in Table 4). This conservatism may be explained by the fact that GEFIS classifies most sites as being in Class EMC B (Figure 10). Indeed, when using GEFIS e-flows estimated for the locally determined Ecological Management Class in the comparison, GEFIS e-flow estimates are lower than those from local assessments (negative percentage bias in Table 5).

<figure><img src="image_24.png" /><figcaption>**Figure 13.** Comparisons between e-flows estimated by GEFIS for the present-day Ecological Management Class (EMC) and e-flows estimated by local assessments.</figcaption></figure>

Notes: The panels are divided based on the type of local e-flow assessment method (see Table 2). Black diagonal lines are identity (1:1) lines. Only sites with present-day GEFIS e-flow estimates for at least 30% of their catchment were included.
<table><caption>**Table 4.** Summary statistics for the comparisons between e-flows estimated by GEFIS for the present-day Ecological Management Class (EMC) and e-flows estimated by local assessments (EFAs).</caption><thead><tr><th>Type of local EFA</th><th>sMAPE (%)</th><th>MAE</th><th>%Bias</th><th>R<sup>2</sup></th><th>Coefficient p-value</th><th>n</th></tr></thead><tbody><tr><td>None specified</td><td>73</td><td>18</td><td>102</td><td>-</td><td>-</td><td>10</td></tr><tr><td>Hydrological: single indices</td><td>56</td><td>26</td><td>141</td><td>0.30</td><td>&lt; 0.001</td><td>78</td></tr><tr><td>Hydrological: time series analysis</td><td>61</td><td>17</td><td>304</td><td>0.01</td><td>0.4</td><td>72</td></tr><tr><td>Habitat simulation</td><td>26</td><td>12</td><td>-19</td><td>-</td><td>-</td><td>2</td></tr><tr><td>Holistic</td><td>48-50</td><td>15-16</td><td>70-76</td><td>0.07-0.08</td><td>&lt; 0.001</td><td>246</td></tr></tbody></table>

*Notes*: Refer to the section *Methods* for equations of the performance statistics and Table 2 for a description of the types of local EFAs. Regression statistics are based on least square regression analyses. Only sites with present-day GEFIS e-flow estimates for at least 30% of their catchment were included. The range of performance statistics for holistic EFAs stems from sites where e-flows are estimated for multiple ecological classes without explicit mention of the present-day class.

<figure><img src="image_25.png" /><figcaption>**Figure 14.** Comparisons between e-flows estimated by GEFIS for the locally determined Ecological Management Class (EMC) and e-flows estimated by local assessments (EFAs).</figcaption></figure>

*Notes*: The panels are divided based on the type of local e-flow assessment method (see Table 2). The black diagonal lines are identity (1:1) lines. The blue lines (and grey shading) show linear regression fits for South Africa. These comparisons only include local EFAs for which a standard EMC was provided (i.e., A through E). Only sites with present-day GEFIS e-flow estimates for at least 30% of their catchment were included in the comparisons.
<table><caption>**Table 5.** Summary statistics for the comparisons between e-flows estimated by GEFIS for the locally determined Ecological Management Class (EMC) and e-flows estimated by local assessments (EFAs).</caption><thead><tr><th>Type of local EFA</th><th>sMAPE (%)</th><th>MAE</th><th>%Bias</th><th>R<sup>2</sup></th><th>Coefficient p-value</th><th>N</th></tr></thead><tbody><tr><td>Holistic</td><td>40</td><td>11</td><td>-38</td><td>0.06</td><td>&lt; 0.001</td><td>241</td></tr><tr><td>Hydrological: time series analysis</td><td>51</td><td>13</td><td>-21</td><td>0.05</td><td>0.05</td><td>73</td></tr></tbody></table>

Notes: Refer to the section Methods for equations of the performance statistics and Table 2 for a description of the types of local EFAs. Regression statistics are based on least square regression analyses. Only sites with present-day GEFIS e-flow estimates for at least 30% of their catchment were included in the comparisons.

<figure><img src="image_26.png" /><figcaption>A river in Ghent, Belgium (photo: Chris Dickens).</figcaption></figure>
Discussion

GEFIS is a publicly accessible online platform that
provides countries with estimates of their e-flow
requirements (as a percentage of the MAR) needed to
compute SDG Indicator 6.4.2, along with total water
withdrawals. Until this analysis, however, no comparison
had been undertaken between GEFIS estimates and the
e-flow assessments conducted by countries. This was
compounded by the paucity of available data on local
e-flow assessments to facilitate such a comparison.
This study thus worked with the dual objectives of first
assembling a database of local e-flow assessments and
to then use this database for comparison with GEFIS
estimates of mean annual runoff and e-flow requirements.
The database assembly and evaluation were thus focused
on comparing e-flow estimates in the format required for
the computation of SDG Indicator 6.4.2—the long-term
mean annual water requirement (as a percentage of the
MAR) deemed necessary to sustain aquatic ecosystems.
It should be noted, however, that not merely mean annual
water requirement, but multiple aspects of the flow
regime should be evaluated and protected to sustain
ecosystems, including the frequency, timing, duration and
rate of change of flow events.

The data compilation effort initiated as part of this
project yielded a rich database that illustrates the
global diversity of e-flow assessments in terms of both
geographies and methods. Of the 651 e-flow assessments
across 27 countries compiled and formatted for this
analysis, 207 records were originally part of the erstwhile
IWMI-compiled database, 12 were provided by country
representatives as part of the official data request by
the Land and Water Division of FAO, and the remainder
were contributed by direct contacts of the authors. The
database includes at least 23 different e-flow assessment
methods (the assessment method was not specified for
29% of the records) and 18 different types of ecological
classes (and/or water use scenarios) for water discharges
ranging in size from 0.04 m³ s⁻¹ to 3,303 m³ s⁻¹. To
harmonize the database for analysis, we undertook the
simplification or lumping of e-flow assessment types and
ecological classes into generic categories, necessitating
some degree of subjectivity in the absence of additional
information. This heterogeneity reflects not only the
varied legislative contexts and environmental conditions
across e-flow assessments but also the challenges
associated with establishing a standard of evaluation
at the global scale for SDG Indicator 6.4.2. The data
compilation effort initiated as part of this project is still
ongoing. Therefore, the database will become an evolving
resource for the continued development and evaluation of
global e-flow assessment tools.

The comparison between estimates of MAR, ecological
classes and e-flow requirements from GEFIS and data
from local e-flow assessments highlighted the inherent
limitations involved in the use of global models. Although
GEFIS predictions of MAR were concordant with local

observations, there was limited agreement between
ecological classes and e-flows inferred by GEFIS and local
assessments.

A bias in GEFIS estimates of MAR was found both in the
comparison with local e-flow assessments and with another
global hydrological model. The MAR of small rivers is
underestimated while the MAR of large rivers tends to be
overestimated. This bias is likely to propagate into GEFIS
estimates of e-flow volumes (in 106 m³ yr⁻¹), although it
may not be reflected in e-flows which are presented as
percentages of the MAR. Further investigation is needed
to determine the reason for this bias. To avoid this type of
model-specific bias in future developments, we recommend
implementation of an ensemble approach whereby the
mean (or median) of MAR predictions from multiple
global hydrological models is used in conjunction with
estimates of prediction uncertainties due to inter-model
differences (Sood and Smakhtin 2015; Döll et al. 2016).
This approach has already been implemented for assessing
global e-flows by Hogeboom et al. (2020) and Virkki et
al. (2021). Alternatively, spatially explicit assessments
of model errors may identify which hydrological model
performs best for each region and river type. This may
enable e-flow estimates to be calculated with the most
accurate hydrological predictions for every region and river
type. Nonetheless, hydrological models not only differ in
their regional performance but also in their representation
of individual hydrological processes and the associated
aspects of the flow regime (Beck et al. 2017; Zaherpour et
al. 2018).

One major limitation in this hydrological comparison
stems from the format of MAR estimates in GEFIS. For
local e-flow assessments (and HydroRIVERS), values
of MAR are provided as the total volume of water that
flows past a river cross-section in a given year, i.e., the
discharge at the site summed for a year. By contrast,
GEFIS estimates MAR as the sum of the simulated
contribution of surface runoff and groundwater to river
flow within each cell. Therefore, it excludes certain
processes—evaporation in river channels, transmission
losses, and interactions between river channels and
delta—which may affect river discharge as water flows
(i.e., is routed) downstream (Sood et al. 2017). Although
the runoff estimates from GEFIS within the catchment
of each site were aggregated for this comparison to
approximate the discharge, this lack of routing may
nevertheless account for the overestimation of MAR
by GEFIS for large rivers compared to the observations
from local e-flow assessments and predictions from
HydroRIVERS. This bias is likely to be most pronounced
in semi-arid and arid areas with high rates of
evapotranspiration and river leakage to groundwater (e.g.,
in Nigeria; Nijssen et al. 2001; Mujere et al. 2021). This
lack of routing also contrasts with the IBT index used for
determining present-day EMCs in GEFIS, which accounted
for the downstream impacts of anthropogenic stressors
through routing (Vörösmarty et al. 2010). For carrying
out improved comparisons with local e-flow assessment,
we therefore recommend that future global e-flow
assessment tools incorporate routing processes in their
hydrological module.

The e-flow assessment sites included in the database
spanned a range of ecological classes, yet it was observed
that GEFIS assigned an EMC of B or C to all sites. Even for
those local e-flow assessment sites assigned to classes
B and C by local assessors, GEFIS demonstrated only a
limited predictive performance. This low classification
performance stems from the IBT index used to infer EMC
classes in GEFIS, which does not capture the variability in
anthropogenic stressors found at the local sites. A likely
reason for this lack of congruence is the coarse resolution
of the IBT index grid. Each pixel in the IBT index grid, which
spans ~2,500 km², is likely to encompass a wide range
of environmental conditions and ecological integrity. In
addition, the IBT index grid accounts for the accumulation
and dilution of anthropogenic stressors by water flow
downstream. At this coarse resolution, however, the
routing of stressors mostly reflects the condition of the
largest river within each pixel, likely overestimating the
anthropogenic impact on small and medium watercourses
in headwater regions within the same pixel.

The exclusion masks implemented in GEFIS preclude
e-flow assessment for more than a third of the global
land surface. Several countries dominated by semi-arid
and arid climates (Namibia and Algeria, among others)
are nearly void of hydrological and e-flow information.
This mask was originally implemented by Vörösmarty et
al. (2010) to remove areas with negligible active flow.
Nonetheless, freshwater flow in those areas supports
unique ecosystems with increasingly valued biodiversity
and ecosystem services which are threatened by rapid
degradation (Acuña et al. 2017; Messager et al. 2021).

Moreover, climate change and population growth will
continue to only exacerbate the stress from natural water
scarcity in such regions. It is, therefore, recommended
that future versions of GEFIS include estimates of e-flows
for all climates and land covers through the inclusion of
the full outputs from global hydrological models (without
masking) and by updating the data source used to assess
the present-day EMC with one which includes areas with
low streamflow.

It is observed that the divergence between MAR estimates
of GEFIS and the two other sources of hydrological data
(local e-flow assessments and HydroRIVERS) is less than
the divergence between e-flow estimates of GEFIS and
local e-flow assessments (as a percentage of the MAR).
Therefore, the differences in methods used to determine
the present-day EMC and the e-flow requirement
corresponding to this EMC (in GEFIS as well as in local
e-flow assessments) may account for the enhanced
divergence observed between the two independent e-flow
assessments. To ascertain the validity of this hypothesis,
a future study may benefit from considering a diversity of
methods to estimate the present-day EMC and e-flows at
diverse locations (including at the local e-flow assessment
sites considered in this exercise) and comparing the
results with those obtained from GEFIS at the same
locations. Such a process may enable the 'calibration'
of GEFIS against, for example, more holistic methods
which encompass a wider range of ecological data. A
possible future direction that may be pursued is to revise
the FDC shifts between EMCs (which is currently fixed at
one percentage category on the horizontal [probability]
axis) based on results of the above comparison and other
attributes of selected sites such as the eco-region of the
site's location. In fact, the original publications of the
method—Smakhtin and Anputhas (2006) and Smakhtin
and Eriyagama (2008)—recommend such a revision of the
FDC shifts.

<figure><img src="image_27.png" /></figure>

Total station measurement of cross-sectional dimensions to estimate river flow and stream morphology—the Limpopo River, Southern Africa (photo: Bennie van der Waal).
Conclusions and Future Directions

This study aimed to compare e-flow estimates made
using GEFIS with independent e-flow estimates made at
the same locations using a range of different methods.
The larger goal of this exercise was to chart potential
future directions for the advancement of global e-flow
assessments in general and GEFIS in particular. The study
shows that, at first glance, there is limited agreement
between GEFIS estimates and other independent
estimates of e-flows (as a percentage of the MAR).
However, it is to be noted that this divergence in estimates
does not imply that e-flow estimates made by GEFIS are
always less reliable than those made at the same locations
using other heterogeneous methods. Part of the observed
scatter in the comparison of GEFIS e-flows estimates to
local assessments results from the diversity of methods
implemented in the latter.

While the study found divergence in e-flow estimates
presented as a percentage of the MAR, it did not
investigate whether there is higher agreement in certain

world regions than in others, in certain sized catchments
than in others and in certain types of land uses than in
others. This was in part due to geographical biases in the
dataset of local assessments—most data points were
from South Africa and Canada. Therefore, assessments
delving into finer attributes of the data and involving a
larger global coverage may reveal trends not evident in
the current analysis.

The study illustrates the need for considering an ensemble
of global hydrological models and more comprehensive
ecological input in global e-flow assessments, which
can also advance GEFIS itself. GEFIS is the first attempt
at providing global e-flow data to countries to monitor
and assess their sustainable development targets.
Nevertheless, it has the potential to evolve into an
e-flow tool which is more robust and better calibrated
to represent realities on the ground, thus enhancing the
service that it currently provides in global sustainable
development.

<figure><img src="image_28.png" /><figcaption>Collecting benthic invertebrates from Irrawaddy River, Myanmar (*photo*: Chris Dickens).</figcaption></figure>
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# Annex 1. Templates for the Collection of E-flow Data on Local Assessments.

Tables A1 and A2 show the templates shared with countries to collect data on local e-flow assessments indicating two different levels of requirements: minimum level and detailed level. Alternatively, data providers were also given the option to fill out a spreadsheet.

<figure><img src="image_29.png" /><figcaption>Table A1. Template for minimum data requirement (Level 1).</figcaption></figure>
<figure><img src="image_30.png" /><figcaption>TABLE A2. Template for detailed data requirement (Level 2).</figcaption></figure>
**E-flow hydrology at appropriate scale**

Please provide the *raw data* and/or hydrological statistics for the site, but only if there is no summary report available

- Summary table of e-flow recommendations

- Flow percentile(s) (%flow)

- Flow volume (Mm³/annum)

- Discharge per month/season

- Other (e.g., specific flow events, operating rules, diversion limits)

Please attach or provide a link to the data

**E-flows geomorphological, ecological and sociocultural data.** Please provide the *raw data*, but only if there is no summary report available (e.g., ecological reasons for specific recommended flows)

- No geomorphological, ecological and/or sociocultural reasons documented ☐

<figure><img src="image_31.png" /></figure>

- Geomorphological, ecological and/or sociocultural reasons documented ☐

<figure><img src="image_32.png" /></figure>

Please attach or provide a link to the data

**Additional comments.** Please provide any relevant comment that will enable interpretation of the e-flow data provided against GEFIS
Annex 2. Database.

<table><thead><tr><th>No.</th><th>Country</th><th>River</th><th>Latitude</th><th>Longitude</th><th>E-flow as a percentage of natural/naturalized Mean Annual Runoff (%)</th></tr></thead><tbody><tr><td>1</td><td>Australia</td><td>Balonne</td><td>-28.78</td><td>147.93</td><td>43%</td></tr><tr><td>2</td><td>Australia</td><td>Balonne</td><td>-28.78</td><td>147.93</td><td>58%</td></tr><tr><td>3</td><td>Australia</td><td>Balonne</td><td>-28.78</td><td>147.93</td><td>77%</td></tr><tr><td>4</td><td>Australia</td><td>Balonne</td><td>-28.78</td><td>147.93</td><td>53%</td></tr><tr><td>5</td><td>Australia</td><td>Derwent</td><td>-42.69</td><td>146.91</td><td>51%</td></tr><tr><td>6</td><td>Australia</td><td>Glenelg</td><td>-37.92</td><td>141.27</td><td>18%</td></tr><tr><td>7</td><td>Australia</td><td>Glenelg</td><td>-37.37</td><td>141.21</td><td>30%</td></tr><tr><td>8</td><td>Australia</td><td>Glenelg</td><td>-37.17</td><td>141.59</td><td>23%</td></tr><tr><td>9</td><td>Australia</td><td>Lachlan</td><td>-34.35</td><td>143.95</td><td>1.54%</td></tr><tr><td>10</td><td>Australia</td><td>Lachlan</td><td>-34.35</td><td>143.95</td><td>2.31%</td></tr><tr><td>11</td><td>Australia</td><td>Macalister</td><td>-30.5</td><td>135.6</td><td>2.4%</td></tr><tr><td>12</td><td>Australia</td><td>Ord</td><td>-15.8</td><td>128.68</td><td>36%</td></tr><tr><td>13</td><td>Australia</td><td>Shoalhaven</td><td>-34.87</td><td>150.73</td><td>5%</td></tr><tr><td>14</td><td>Australia</td><td>Shoalhaven</td><td>-34.87</td><td>150.73</td><td>9%</td></tr><tr><td>15</td><td>Australia</td><td>Shoalhaven</td><td>-34.87</td><td>150.73</td><td>3%</td></tr><tr><td>16</td><td>Australia</td><td>Wimmera</td><td>-36.13</td><td>141.95</td><td>19%</td></tr><tr><td>17</td><td>Australia</td><td>Wimmera</td><td>-36.13</td><td>141.95</td><td>2%</td></tr><tr><td>18</td><td>Canada</td><td>West Salmon</td><td>48.2</td><td>-56.25</td><td>30%</td></tr><tr><td>19</td><td>China</td><td>Haihe</td><td>38.95</td><td>117.72</td><td>11.11%</td></tr><tr><td>20</td><td>China</td><td>Haihe</td><td>38.95</td><td>117.72</td><td>15.03%</td></tr><tr><td>21</td><td>China</td><td>Haihe</td><td>38.95</td><td>117.72</td><td>8.51%</td></tr><tr><td>22</td><td>China</td><td>Luanhe</td><td>27.06</td><td>102.21</td><td>28.51%</td></tr><tr><td>23</td><td>China</td><td>Luanhe</td><td>27.06</td><td>102.21</td><td>11.44%</td></tr><tr><td>24</td><td>China</td><td>Luanhe</td><td>27.06</td><td>102.21</td><td>16.96%</td></tr><tr><td>25</td><td>China</td><td>Tarim</td><td>41.06-39.40</td><td>86.62-88.5</td><td>47-61%</td></tr><tr><td>26</td><td>China</td><td>Zhangweixin</td><td>38.23</td><td>117.82</td><td>29.62%</td></tr><tr><td>27</td><td>China</td><td>Zhangweixin</td><td>38.23</td><td>117.82</td><td>53.08%</td></tr><tr><td>28</td><td>China</td><td>Zhangweixin</td><td>38.23</td><td>117.82</td><td>41.21%</td></tr><tr><td>29</td><td>Colombia</td><td>Río Palacé (Cauca)</td><td></td><td></td><td>92%</td></tr><tr><td>30</td><td>France</td><td>Durance</td><td>43.92</td><td>4.73</td><td></td></tr><tr><td>31</td><td>France</td><td>Rhone</td><td>45.75</td><td>4.85</td><td>28%</td></tr><tr><td>32</td><td>Hungary</td><td>Hungary Danube</td><td>48</td><td>17.2</td><td>18-24%</td></tr><tr><td>33</td><td>India</td><td>Satluj</td><td>31.16</td><td>77.33</td><td>64.5%</td></tr><tr><td>34</td><td>Lesotho</td><td>Malibamasto</td><td>-28.47</td><td>29.33W</td><td>33%</td></tr><tr><td>35</td><td>Lesotho</td><td>Malibamasto</td><td>-28.47</td><td>29.33W</td><td>66%</td></tr><tr><td>36</td><td>Lesotho</td><td>Malibamasto</td><td>-28.47</td><td>29.33W</td><td>19%</td></tr><tr><td>37</td><td>Lesotho</td><td>Malibamasto</td><td>-28.47</td><td>29.33W</td><td>4%</td></tr><tr><td>38</td><td>Lesotho</td><td>Matsoku</td><td>-28.62</td><td>29.38W</td><td>15%</td></tr><tr><td>39</td><td>Lesotho</td><td>Senqunyane</td><td>-28.62</td><td>29.38W</td><td>10%</td></tr><tr><td>40</td><td>Mexico</td><td>Colorado</td><td>32.5</td><td>114.75W</td><td>0.71%</td></tr><tr><td>41</td><td>Senegal</td><td>Senegal</td><td>13.19</td><td>10.43W</td><td>60%</td></tr><tr><td>42</td><td>Slovenia</td><td>Rizana</td><td>45.55</td><td>13.75</td><td>4%</td></tr><tr><td>43</td><td>South Africa</td><td>Berg</td><td>-33.906</td><td>19.057</td><td>39%</td></tr><tr><td>44</td><td>South Africa</td><td>Berg</td><td>-33.906</td><td>19.057</td><td>33.6%</td></tr><tr><td>45</td><td>South Africa</td><td>Berg</td><td>-33.426</td><td>18.971</td><td>32.6%</td></tr><tr><td>46</td><td>South Africa</td><td>Bivane</td><td>-27.531</td><td>31.077</td><td>29.1%</td></tr><tr><td>47</td><td>South Africa</td><td>Bivane</td><td>-27.461</td><td>31.275</td><td>37.4%</td></tr><tr><td>48</td><td>South Africa</td><td>Bivane</td><td>-27.461</td><td>31.275</td><td>29.2%</td></tr><tr><td>49</td><td>South Africa</td><td>Bosemans</td><td>-33.335</td><td>26.078</td><td>30.6%</td></tr><tr><td>50</td><td>South Africa</td><td>Breede</td><td></td><td></td><td>33%</td></tr><tr><td>51</td><td>South Africa</td><td>Breede</td><td></td><td></td><td>27%</td></tr><tr><td>52</td><td>South Africa</td><td>Breede</td><td></td><td></td><td>29%</td></tr><tr><td>53</td><td>South Africa</td><td>Breede</td><td>-34.39</td><td>20.83</td><td>39%</td></tr><tr><td>54</td><td>South Africa</td><td>Breede</td><td>-34.39</td><td>20.83</td><td>49%</td></tr></tbody></table>
<table><thead><tr><th>No.</th><th>Country</th><th>River</th><th>Latitude</th><th>Longitude</th><th>E-flow as a percentage of natural/naturalized Mean Annual Runoff (%)</th></tr></thead><tbody><tr><td>55</td><td>South Africa</td><td>Breede</td><td>-33.72</td><td>19.158</td><td>49%</td></tr><tr><td>56</td><td>South Africa</td><td>Breede</td><td>-33.78</td><td>19.672</td><td>40%</td></tr><tr><td>57</td><td>South Africa</td><td>Breede</td><td>-34.179</td><td>20.506</td><td>35.6%</td></tr><tr><td>58</td><td>South Africa</td><td>Breede</td><td>-34.076</td><td>19.634</td><td>42%</td></tr><tr><td>59</td><td>South Africa</td><td>Breede</td><td>-34.002</td><td>19.502</td><td>71%</td></tr><tr><td>60</td><td>South Africa</td><td>Buffalo</td><td>-32.784</td><td>27.379</td><td>37.1%</td></tr><tr><td>61</td><td>South Africa</td><td>Buffalo</td><td>-32.969</td><td>27.527</td><td>26.1%</td></tr><tr><td>62</td><td>South Africa</td><td>Buffalo</td><td>-33.004</td><td>27.821</td><td>19.3%</td></tr><tr><td>63</td><td>South Africa</td><td>Crocodile</td><td>-25.425</td><td>30.789</td><td>40.8%</td></tr><tr><td>64</td><td>South Africa</td><td>Elands</td><td>-25.631</td><td>30.325</td><td>35.2%</td></tr><tr><td>65</td><td>South Africa</td><td>Elands</td><td>-25.631</td><td>30.325</td><td>48.8%</td></tr><tr><td>66</td><td>South Africa</td><td>Elands</td><td>-25.631</td><td>30.325</td><td>33.1%</td></tr><tr><td>67</td><td>South Africa</td><td>Elands</td><td>-25.566</td><td>30.668</td><td>28.4%</td></tr><tr><td>68</td><td>South Africa</td><td>Elands</td><td>-25.566</td><td>30.668</td><td>43.8%</td></tr><tr><td>69</td><td>South Africa</td><td>Gatberg</td><td>-31.294</td><td>28.169</td><td>39.7%</td></tr><tr><td>70</td><td>South Africa</td><td>Gatberg</td><td>-31.285</td><td>28.213</td><td>35.7%</td></tr><tr><td>71</td><td>South Africa</td><td>Gqunube</td><td>-32.798</td><td>27.863</td><td>18.8%</td></tr><tr><td>72</td><td>South Africa</td><td>Koekedou/Dwars</td><td>-33.371</td><td>19.29</td><td>24.6%</td></tr><tr><td>73</td><td>South Africa</td><td>Koekedou/Dwars</td><td>-33.364</td><td>19.302</td><td>23.6%</td></tr><tr><td>74</td><td>South Africa</td><td>Koekedou/Dwars</td><td>-33.38</td><td>19.321</td><td>22.7%</td></tr><tr><td>75</td><td>South Africa</td><td>Komati</td><td>-26.103</td><td>31.41</td><td>54.5%</td></tr><tr><td>76</td><td>South Africa</td><td>Komati</td><td>-25.684</td><td>31.778</td><td>27%</td></tr><tr><td>77</td><td>South Africa</td><td>Kromme</td><td>-33.931</td><td>24.261</td><td>21.8%</td></tr><tr><td>78</td><td>South Africa</td><td>Kromme</td><td>-33.931</td><td>24.261</td><td>29.5%</td></tr><tr><td>79</td><td>South Africa</td><td>Kromme</td><td>-34.013</td><td>24.498</td><td>8.1%</td></tr><tr><td>80</td><td>South Africa</td><td>Kromme</td><td>-34.092</td><td>24.743</td><td>17.2%</td></tr><tr><td>81</td><td>South Africa</td><td>Kromme</td><td>-33.998</td><td>24.7</td><td>23.5%</td></tr><tr><td>82</td><td>South Africa</td><td>Kromme</td><td>-34.008</td><td>24.847</td><td>3.3%</td></tr><tr><td>83</td><td>South Africa</td><td>Kromme</td><td>-34.008</td><td>24.847</td><td>15.4%</td></tr><tr><td>84</td><td>South Africa</td><td>Kromme</td><td>-34.008</td><td>24.847</td><td>24.2%</td></tr><tr><td>85</td><td>South Africa</td><td>Kubusi</td><td>-32.591</td><td>27.456</td><td>14.6%</td></tr><tr><td>86</td><td>South Africa</td><td>Kubusi</td><td>-32.564</td><td>27.687</td><td>23%</td></tr><tr><td>87</td><td>South Africa</td><td>Kubusi</td><td>-32.52</td><td>27.736</td><td>22.5%</td></tr><tr><td>88</td><td>South Africa</td><td>Letaba</td><td>-23.885</td><td>30.366</td><td>42.3%</td></tr><tr><td>89</td><td>South Africa</td><td>Letaba</td><td>-23.639</td><td>30.729</td><td>25.4%</td></tr><tr><td>90</td><td>South Africa</td><td>Letaba</td><td>-23.643</td><td>31.069</td><td>0.7%</td></tr><tr><td>91</td><td>South Africa</td><td>Luvuvhu</td><td>-22.836</td><td>30.758</td><td>13.1%</td></tr><tr><td>92</td><td>South Africa</td><td>Luvuvhu</td><td>-22.429</td><td>31.198</td><td>13.7%</td></tr><tr><td>93</td><td>South Africa</td><td>Matlabas</td><td>-24.121</td><td>27.449</td><td>27.1%</td></tr><tr><td>94</td><td>South Africa</td><td>Mhlatuze</td><td>-28.741</td><td>31.612</td><td>28.1%</td></tr><tr><td>95</td><td>South Africa</td><td>Mhlatuze</td><td>-28.842</td><td>31.878</td><td>25.8%</td></tr><tr><td>96</td><td>South Africa</td><td>Mkomazi</td><td>-29.74</td><td>29.913</td><td>30.7%</td></tr><tr><td>97</td><td>South Africa</td><td>Mkomazi</td><td>-29.923</td><td>30.086</td><td>25.5%</td></tr><tr><td>98</td><td>South Africa</td><td>Mkomazi</td><td>-30.01</td><td>30.251</td><td>33.6%</td></tr><tr><td>99</td><td>South Africa</td><td>Mkomazi</td><td>-30.129</td><td>30.673</td><td>33.5%</td></tr><tr><td>100</td><td>South Africa</td><td>Mogalakwena</td><td>-23.704</td><td>28.602</td><td>12.1%</td></tr><tr><td>101</td><td>South Africa</td><td>Mogalakwena</td><td>-23.502</td><td>28.662</td><td>8.6%</td></tr><tr><td>102</td><td>South Africa</td><td>Mogalakwena</td><td>-23.058</td><td>28.686</td><td>11%</td></tr><tr><td>103</td><td>South Africa</td><td>Molenaars</td><td>-33.73</td><td>19.26</td><td>42%</td></tr><tr><td>104</td><td>South Africa</td><td>Molenaars</td><td>-33.73</td><td>19.26</td><td>53%</td></tr><tr><td>105</td><td>South Africa</td><td>Molenaars</td><td>-33.73</td><td>19.26</td><td>38%</td></tr><tr><td>106</td><td>South Africa</td><td>Mooi</td><td>-29.1</td><td>30.175</td><td>45.2%</td></tr><tr><td>107</td><td>South Africa</td><td>Mooi</td><td>-29.05</td><td>30.304</td><td>40%</td></tr><tr><td>108</td><td>South Africa</td><td>Mtata</td><td>-31.554</td><td>29.259</td><td>36.8%</td></tr><tr><td>109</td><td>South Africa</td><td>Mtata</td><td>-32.001</td><td>28.854</td><td>48.3%</td></tr><tr><td>110</td><td>South Africa</td><td>Mtata</td><td>-31.78</td><td>28.896</td><td>15.2%</td></tr></tbody></table>
<table><thead><tr><th>No.</th><th>Country</th><th>River</th><th>Latitude</th><th>Longitude</th><th>E-flow as a percentage of natural/naturalized Mean Annual Runoff (%)</th></tr></thead><tbody><tr><td>111</td><td>South Africa</td><td>Mtata</td><td>-31.925</td><td>29.136</td><td>16%</td></tr><tr><td>112</td><td>South Africa</td><td>Mvoti</td><td>-29.151</td><td>30.687</td><td>23.7%</td></tr><tr><td>113</td><td>South Africa</td><td>Mvoti</td><td>-29.239</td><td>30.986</td><td>16.2%</td></tr><tr><td>114</td><td>South Africa</td><td>Mvoti</td><td>-29.245</td><td>31.031</td><td>25.1%</td></tr><tr><td>115</td><td>South Africa</td><td>Olifants</td><td>-32.45</td><td>18.98</td><td>56%</td></tr><tr><td>116</td><td>South Africa</td><td>Olifants</td><td>-25.729</td><td>29.285</td><td>18.6%</td></tr><tr><td>117</td><td>South Africa</td><td>Olifants</td><td>-24.468</td><td>30.416</td><td>12.1%</td></tr><tr><td>118</td><td>South Africa</td><td>Olifants</td><td>-24.329</td><td>30.736</td><td>8.5%</td></tr><tr><td>119</td><td>South Africa</td><td>Olifants</td><td>-24.423</td><td>30.825</td><td>34.5%</td></tr><tr><td>120</td><td>South Africa</td><td>Olifants</td><td>-24.093</td><td>31.033</td><td>23.5%</td></tr><tr><td>121</td><td>South Africa</td><td>Olifants</td><td>-24</td><td>30.679</td><td>31.2%</td></tr><tr><td>122</td><td>South Africa</td><td>Olifants</td><td>-24.022</td><td>31.143</td><td>24.8%</td></tr><tr><td>123</td><td>South Africa</td><td>Olifants</td><td>-24.052</td><td>31.231</td><td>21.6%</td></tr><tr><td>124</td><td>South Africa</td><td>Olifants</td><td>-25.489</td><td>29.244</td><td>22.5%</td></tr><tr><td>125</td><td>South Africa</td><td>Olifants</td><td>-25.489</td><td>29.244</td><td>19.2%</td></tr><tr><td>126</td><td>South Africa</td><td>Olifants</td><td>-25.673</td><td>29.342</td><td>27%</td></tr><tr><td>127</td><td>South Africa</td><td>Olifants</td><td>-25.673</td><td>29.342</td><td>19.7%</td></tr><tr><td>128</td><td>South Africa</td><td>Olifants</td><td>-25.621</td><td>29.001</td><td>29.9%</td></tr><tr><td>129</td><td>South Africa</td><td>Olifants</td><td>-25.311</td><td>29.426</td><td>19%</td></tr><tr><td>130</td><td>South Africa</td><td>Olifants</td><td>-25.311</td><td>29.426</td><td>24.6%</td></tr><tr><td>131</td><td>South Africa</td><td>Olifants</td><td>-25.11</td><td>28.949</td><td>16.9%</td></tr><tr><td>132</td><td>South Africa</td><td>Olifants</td><td>-24.56</td><td>29.537</td><td>12.6%</td></tr><tr><td>133</td><td>South Africa</td><td>Olifants</td><td>-24.235</td><td>30.073</td><td>15.2%</td></tr><tr><td>134</td><td>South Africa</td><td>Olifants</td><td>-24.864</td><td>30.044</td><td>14.5%</td></tr><tr><td>135</td><td>South Africa</td><td>OlifDor</td><td>-32.177</td><td>18.887</td><td>33.8%</td></tr><tr><td>136</td><td>South Africa</td><td>OlifDor</td><td>-31.878</td><td>18.643</td><td>36.4%</td></tr><tr><td>137</td><td>South Africa</td><td>OlifDor</td><td>-31.58</td><td>18.356</td><td>16%</td></tr><tr><td>138</td><td>South Africa</td><td>Palmiet</td><td>-34.097</td><td>19.052</td><td>56.2%</td></tr><tr><td>139</td><td>South Africa</td><td>Palmiet</td><td>-34.291</td><td>18.946</td><td>41.1%</td></tr><tr><td>140</td><td>South Africa</td><td>Palmiet</td><td>-34.332</td><td>18.989</td><td>46.6%</td></tr><tr><td>141</td><td>South Africa</td><td>Pienaars</td><td>-25.652</td><td>28.348</td><td>49.9%</td></tr><tr><td>142</td><td>South Africa</td><td>Pienaars</td><td>-25.129</td><td>27.915</td><td>26.1%</td></tr><tr><td>143</td><td>South Africa</td><td>Pienaars</td><td>-25.642</td><td>28.344</td><td>54.5%</td></tr><tr><td>144</td><td>South Africa</td><td>Sabie</td><td>-24.993</td><td>31.115</td><td>40.2%</td></tr><tr><td>145</td><td>South Africa</td><td>Sabie</td><td>-25.018</td><td>31.251</td><td>29.2%</td></tr><tr><td>146</td><td>South Africa</td><td>Sabie</td><td>-24.981</td><td>31.306</td><td>50.1%</td></tr><tr><td>147</td><td>South Africa</td><td>Sabie</td><td>-24.959</td><td>31.559</td><td>33.1%</td></tr><tr><td>148</td><td>South Africa</td><td>Sabie</td><td>-25.049</td><td>31.812</td><td>45.2%</td></tr><tr><td>149</td><td>South Africa</td><td>Sabie</td><td>-24.72</td><td>31.23</td><td>47.2%</td></tr><tr><td>150</td><td>South Africa</td><td>Sabie</td><td>-24.796</td><td>31.539</td><td>46.1%</td></tr><tr><td>151</td><td>South Africa</td><td>Sabie</td><td>-24.967</td><td>31.623</td><td>46.7%</td></tr><tr><td>152</td><td>South Africa</td><td>Swartkops</td><td>-33.723</td><td>25.317</td><td>33.6%</td></tr><tr><td>153</td><td>South Africa</td><td>Swartkops</td><td>-33.803</td><td>25.27</td><td>17.3%</td></tr><tr><td>154</td><td>South Africa</td><td>Swartkops</td><td>-33.867</td><td>25.468</td><td>18.8%</td></tr><tr><td>155</td><td>South Africa</td><td>Swartkops</td><td>-33.851</td><td>25.353</td><td>16%</td></tr><tr><td>156</td><td>South Africa</td><td>Swartkops</td><td>-33.82</td><td>25.57</td><td>16%</td></tr><tr><td>157</td><td>South Africa</td><td>Thukela</td><td>-28.724</td><td>29.369</td><td>29.9%</td></tr><tr><td>158</td><td>South Africa</td><td>Thukela</td><td>-28.724</td><td>29.369</td><td>17.3%</td></tr><tr><td>159</td><td>South Africa</td><td>Thukela</td><td>-29.2</td><td>30.029</td><td>18.6%</td></tr><tr><td>160</td><td>South Africa</td><td>Thukela</td><td>-29.2</td><td>30.029</td><td>29.9%</td></tr><tr><td>161</td><td>South Africa</td><td>Thukela</td><td>-29.2</td><td>30.029</td><td>43.6%</td></tr><tr><td>162</td><td>South Africa</td><td>Thukela</td><td>-29.117</td><td>30.135</td><td>23.5%</td></tr><tr><td>163</td><td>South Africa</td><td>Thukela</td><td>-29.117</td><td>30.135</td><td>36.2%</td></tr><tr><td>164</td><td>South Africa</td><td>Thukela</td><td>-29.117</td><td>30.135</td><td>42.1%</td></tr><tr><td>165</td><td>South Africa</td><td>Thukela</td><td>-28.902</td><td>30.439</td><td>23%</td></tr><tr><td>166</td><td>South Africa</td><td>Thukela</td><td>-28.902</td><td>30.439</td><td>35.4%</td></tr></tbody></table>
<table><thead><tr><th>No.</th><th>Country</th><th>River</th><th>Latitude</th><th>Longitude</th><th>E-flow as a percentage of natural/naturalized Mean Annual Runoff (%)</th></tr></thead><tbody><tr><td>167</td><td>South Africa</td><td>Thukela</td><td>-28.902</td><td>30.439</td><td>41%</td></tr><tr><td>168</td><td>South Africa</td><td>Thukela</td><td>-28.154</td><td>30.477</td><td>22.2%</td></tr><tr><td>169</td><td>South Africa</td><td>Thukela</td><td>-28.154</td><td>30.477</td><td>16%</td></tr><tr><td>170</td><td>South Africa</td><td>Thukela</td><td>-28.438</td><td>30.595</td><td>18.1%</td></tr><tr><td>171</td><td>South Africa</td><td>Thukela</td><td>-28.438</td><td>30.595</td><td>18.1%</td></tr><tr><td>172</td><td>South Africa</td><td>Thukela</td><td>-28.785</td><td>30.912</td><td>18.3%</td></tr><tr><td>173</td><td>South Africa</td><td>Thukela</td><td>-28.785</td><td>30.912</td><td>25.5%</td></tr><tr><td>174</td><td>South Africa</td><td>Thukela</td><td>-29.148</td><td>31.332</td><td>36.1%</td></tr><tr><td>175</td><td>South Africa</td><td>Thukela</td><td>-29.148</td><td>31.332</td><td>28.4%</td></tr><tr><td>176</td><td>South Africa</td><td>Thukela</td><td>-28.717</td><td>29.621</td><td>18.1%</td></tr><tr><td>177</td><td>South Africa</td><td>Thukela</td><td>-28.717</td><td>29.621</td><td>38.5%</td></tr><tr><td>178</td><td>South Africa</td><td>Thukela</td><td>-28.717</td><td>29.621</td><td>27.3%</td></tr><tr><td>179</td><td>South Africa</td><td>Thukela</td><td>-28.781</td><td>29.616</td><td>24.7%</td></tr><tr><td>180</td><td>South Africa</td><td>Thukela</td><td>-28.781</td><td>29.616</td><td>18.6%</td></tr><tr><td>181</td><td>South Africa</td><td>Thukela</td><td>-28.781</td><td>29.616</td><td>42.8%</td></tr><tr><td>182</td><td>South Africa</td><td>Thukela</td><td>-28.746</td><td>30.145</td><td>22%</td></tr><tr><td>183</td><td>South Africa</td><td>Thukela</td><td>-28.746</td><td>30.145</td><td>31.7%</td></tr><tr><td>184</td><td>South Africa</td><td>Thukela</td><td>-28.746</td><td>30.145</td><td>28.4%</td></tr><tr><td>185</td><td>South Africa</td><td>Thukela</td><td>-28.897</td><td>30.036</td><td>45.4%</td></tr><tr><td>186</td><td>South Africa</td><td>Thukela</td><td>-28.897</td><td>30.036</td><td>32.7%</td></tr><tr><td>187</td><td>South Africa</td><td>Thukela</td><td>-28.801</td><td>30.167</td><td>32.1%</td></tr><tr><td>188</td><td>South Africa</td><td>Thukela</td><td>-28.801</td><td>30.167</td><td>44.7%</td></tr><tr><td>189</td><td>South Africa</td><td>Thukela</td><td>-28.458</td><td>30.054</td><td>25.8%</td></tr><tr><td>190</td><td>South Africa</td><td>Thukela</td><td>-28.458</td><td>30.054</td><td>36.8%</td></tr><tr><td>191</td><td>South Africa</td><td>Thukela</td><td>-28.637</td><td>30.203</td><td>28.7%</td></tr><tr><td>192</td><td>South Africa</td><td>Thukela</td><td>-28.637</td><td>30.203</td><td>14.4%</td></tr><tr><td>193</td><td>South Africa</td><td>Thukela</td><td>-28.769</td><td>30.515</td><td>20.3%</td></tr><tr><td>194</td><td>South Africa</td><td>Thukela</td><td>-28.769</td><td>30.515</td><td>27.8%</td></tr><tr><td>195</td><td>South Africa</td><td>Vaal</td><td>-29.047</td><td>23.836</td><td>14.8%</td></tr><tr><td>196</td><td>South Africa</td><td>Vaal</td><td>-29.047</td><td>23.836</td><td>14.8%</td></tr><tr><td>197</td><td>South Africa</td><td>Vaal</td><td>-29.047</td><td>23.836</td><td>14.8%</td></tr><tr><td>198</td><td>South Africa</td><td>Waterval</td><td>-26.646</td><td>29.019</td><td>21.9%</td></tr><tr><td>199</td><td>South Africa</td><td>Waterval</td><td>-26.646</td><td>29.019</td><td>15.8%</td></tr><tr><td>200</td><td>South Africa</td><td>Waterval</td><td>-26.885</td><td>28.884</td><td>20.1%</td></tr><tr><td>201</td><td>South Africa</td><td>Waterval</td><td>-26.885</td><td>28.884</td><td>14.3%</td></tr><tr><td>202</td><td>Spain</td><td>Ebro/Tortosa upstream</td><td>31.11-42.81</td><td>0.78 to -7</td><td>18-23%</td></tr><tr><td></td><td></td><td>Ebro delta</td><td></td><td></td><td></td></tr><tr><td>203</td><td>Tanzania</td><td>Great Ruaha/Msembe Ferry</td><td>-7.5</td><td>35</td><td>22%</td></tr><tr><td>204</td><td>Tunisia</td><td>Ichekuel/World Heritage site</td><td>9.67</td><td>37.17</td><td>69%</td></tr><tr><td>205</td><td>Tunisia</td><td>Ichekuel/World Heritage site</td><td>9.67</td><td>37.17</td><td>26%</td></tr><tr><td>206</td><td>Zambia</td><td>Kafue/Itetzhi-Tezhi dam</td><td>-26</td><td>-15.75</td><td>18%</td></tr><tr><td>207</td><td>Zambia</td><td>Zambezi/Delta Marromeu Complex</td><td></td><td></td><td></td></tr><tr><td>208</td><td>South Africa</td><td>Buffalo</td><td>-27.6221</td><td>29.9617</td><td>23.44%</td></tr><tr><td>209</td><td>South Africa</td><td>Horn</td><td>-27.888</td><td>29.921</td><td>33.65%</td></tr><tr><td>210</td><td>South Africa</td><td>Ncandu</td><td>-27.8017</td><td>29.884</td><td>29.36%</td></tr><tr><td>211</td><td>South Africa</td><td>Ngagane</td><td>-27.819</td><td>29.987</td><td>19.44%</td></tr><tr><td>212</td><td>South Africa</td><td>Buffalo</td><td>-28.0107</td><td>30.3931</td><td>18.15%</td></tr><tr><td>213</td><td>South Africa</td><td>Buffalo</td><td>-28.153</td><td>30.476</td><td>17.36%</td></tr><tr><td>214</td><td>South Africa</td><td>Buffalo</td><td>-28.437</td><td>30.595</td><td>23.24%</td></tr><tr><td>215</td><td>South Africa</td><td>Sundays</td><td>-28.3479</td><td>29.9682</td><td>31.48%</td></tr><tr><td>216</td><td>South Africa</td><td>Sundays</td><td>-28.458</td><td>30.053</td><td>19.71%</td></tr><tr><td>217</td><td>South Africa</td><td>Sundays</td><td>-28.636</td><td>30.204</td><td>19.55%</td></tr><tr><td>218</td><td>South Africa</td><td>Nsonge/Hlatikulu</td><td>-29.2377</td><td>29.7853</td><td>28.99%</td></tr><tr><td>219</td><td>South Africa</td><td>Mooi</td><td>-29.21</td><td>30.002</td><td>18.34%</td></tr><tr><td>220</td><td>South Africa</td><td>Mooi</td><td>-29.116</td><td>30.135</td><td>20.57%</td></tr></tbody></table>
<table><thead><tr><th>No.</th><th>Country</th><th>River</th><th>Latitude</th><th>Longitude</th><th>E-flow as a percentage of natural/naturalized Mean Annual Runoff (%)</th></tr></thead><tbody><tr><td>221</td><td>South Africa</td><td>Mnyamvubu</td><td>-29.161</td><td>30.2884</td><td>19.94%</td></tr><tr><td>222</td><td>South Africa</td><td>Mooi</td><td>-28.9193</td><td>30.4189</td><td>29.82%</td></tr><tr><td>223</td><td>South Africa</td><td>Bushmans</td><td>-28.897</td><td>30.035</td><td>29.04%</td></tr><tr><td>224</td><td>South Africa</td><td>Bushmans</td><td>-28.8483</td><td>30.1496</td><td>40.62%</td></tr><tr><td>225</td><td>South Africa</td><td>Bushmans</td><td>-28.801</td><td>30.167</td><td>30.47%</td></tr><tr><td>226</td><td>South Africa</td><td>Thukela</td><td>-28.722</td><td>29.376</td><td>7.04%</td></tr><tr><td>227</td><td>South Africa</td><td>Thukela</td><td>-28.717</td><td>29.621</td><td>17.67%</td></tr><tr><td>228</td><td>South Africa</td><td>Little Thukela</td><td>-28.383</td><td>29.616</td><td>24.71%</td></tr><tr><td>229</td><td>South Africa</td><td>Klip</td><td>-28.3952</td><td>29.7197</td><td>22.15%</td></tr><tr><td>230</td><td>South Africa</td><td>Thukela</td><td>-28.747</td><td>30.145</td><td>25.09%</td></tr><tr><td>231</td><td>South Africa</td><td>Thukela</td><td>-28.769</td><td>30.515</td><td>20.26%</td></tr><tr><td>232</td><td>South Africa</td><td>Thukela</td><td>-28.785</td><td>30.911</td><td>21.98%</td></tr><tr><td>233</td><td>South Africa</td><td>Thukela</td><td>-29.1603</td><td>31.3373</td><td>37.83%</td></tr><tr><td>234</td><td>South Africa</td><td>Thukela</td><td>-29.1677</td><td>31.4037</td><td>37.79%</td></tr><tr><td>235</td><td>South Africa</td><td>Upper Elands</td><td>-25.303074</td><td>28.46311</td><td>20.87%</td></tr><tr><td>236</td><td>South Africa</td><td>Lower Wilge</td><td>-25.619625</td><td>28.999047</td><td>36.28%</td></tr><tr><td>237</td><td>South Africa</td><td>Wilge River</td><td>-25.843984</td><td>28.871978</td><td>15.11%</td></tr><tr><td>238</td><td>South Africa</td><td>Olifants</td><td>-25.759183</td><td>29.309564</td><td>17.8%</td></tr><tr><td>239</td><td>South Africa</td><td>Klein Olifants</td><td>-25.748872</td><td>29.458649</td><td>27.47%</td></tr><tr><td>240</td><td>South Africa</td><td>Olifants</td><td>-25.496324</td><td>29.254597</td><td>29.83%</td></tr><tr><td>241</td><td>South Africa</td><td>Kranspoortspruit</td><td>-25.437714</td><td>29.475619</td><td>30.26%</td></tr><tr><td>242</td><td>South Africa</td><td>Selons</td><td>-25.379969</td><td>29.435557</td><td>21.86%</td></tr><tr><td>243</td><td>South Africa</td><td>Klein Olifants</td><td>-25.6736</td><td>29.342</td><td>19.8%</td></tr><tr><td>244</td><td>South Africa</td><td>Olifants</td><td>-25.304</td><td>29.422</td><td>12.51%</td></tr><tr><td>245</td><td>South Africa</td><td>Lower Elands</td><td>-25.116</td><td>28.9565</td><td>10.48%</td></tr><tr><td>246</td><td>South Africa</td><td>Spookspruit</td><td>-25.8605</td><td>29.4029</td><td>30.12%</td></tr><tr><td>247</td><td>South Africa</td><td>Olifants</td><td>-24.5289</td><td>29.5464</td><td>9.89%</td></tr><tr><td>248</td><td>South Africa</td><td>Olifants</td><td>-24.239917</td><td>30.082457</td><td>15.19%</td></tr><tr><td>249</td><td>South Africa</td><td>Olifants</td><td>-24.307563</td><td>30.785695</td><td>12.81%</td></tr><tr><td>250</td><td>South Africa</td><td>Lower Spekboom</td><td>-24.694155</td><td>30.361267</td><td>23.16%</td></tr><tr><td>251</td><td>South Africa</td><td>Steelpoort</td><td>-24.775</td><td>30.165</td><td>23.33%</td></tr><tr><td>252</td><td>South Africa</td><td>Dwars</td><td>-24.8358</td><td>30.08345</td><td>31.24%</td></tr><tr><td>253</td><td>South Africa</td><td>Steelpoort</td><td>-24.4965</td><td>30.399</td><td>12.69%</td></tr><tr><td>254</td><td>South Africa</td><td>Ohrigstad</td><td>-24.5473</td><td>30.73807</td><td>17.41%</td></tr><tr><td>255</td><td>South Africa</td><td>Upper Blyde</td><td>-24.734412</td><td>30.778321</td><td>46.08%</td></tr><tr><td>256</td><td>South Africa</td><td>Lower Blyde</td><td>-24.407481</td><td>30.827404</td><td>31.14%</td></tr><tr><td>257</td><td>South Africa</td><td>Lower Ga-Selati</td><td>-24.0225</td><td>31.146667</td><td>19.45%</td></tr><tr><td>258</td><td>South Africa</td><td>Upper Ga-Selati</td><td>-24.0012</td><td>30.6823</td><td>27.53%</td></tr><tr><td>259</td><td>South Africa</td><td>Olifants</td><td>-24.12843</td><td>31.01457</td><td>22.37%</td></tr><tr><td>260</td><td>South Africa</td><td>Olifants</td><td>-24.049426</td><td>31.731751</td><td>21.06%</td></tr><tr><td>261</td><td>South Africa</td><td>Letaba</td><td>-23.8268</td><td>31.59061</td><td>17.34%</td></tr><tr><td>262</td><td>South Africa</td><td>Letsitele</td><td>-23.893155</td><td>30.357356</td><td>17.59%</td></tr><tr><td>263</td><td>South Africa</td><td>Great Letaba</td><td>-23.915</td><td>30.05228</td><td>24.76%</td></tr><tr><td>264</td><td>South Africa</td><td>Broederstroom</td><td>-23.80068</td><td>29.97741</td><td>49.22%</td></tr><tr><td>265</td><td>South Africa</td><td>Shingwedzi</td><td>-23.1849</td><td>31.52508</td><td>22.5%</td></tr><tr><td>266</td><td>South Africa</td><td>Vaal</td><td>-26.8728</td><td>29.61384</td><td>39.41%</td></tr><tr><td>267</td><td>South Africa</td><td>Vaal</td><td>-26.9211</td><td>29.27929</td><td>13.61%</td></tr><tr><td>268</td><td>South Africa</td><td>Waterval</td><td>-26.64608</td><td>29.01857</td><td>3.5%</td></tr><tr><td>269</td><td>South Africa</td><td>Waterval</td><td>-26.88543</td><td>28.88357</td><td>6.4%</td></tr><tr><td>270</td><td>South Africa</td><td>Vaal</td><td>-26.99087</td><td>28.72971</td><td>14.3%</td></tr><tr><td>271</td><td>South Africa</td><td>Vaal</td><td>-26.84262</td><td>28.1123</td><td>21.55%</td></tr><tr><td>272</td><td>South Africa</td><td>Vaal</td><td>-26.93243</td><td>27.01367</td><td>34.1%</td></tr><tr><td>273</td><td>South Africa</td><td>Klip</td><td>-27.36166</td><td>29.48503</td><td>26.54%</td></tr><tr><td>274</td><td>South Africa</td><td>Wilge</td><td>-28.20185</td><td>29.55827</td><td>45.88%</td></tr><tr><td>275</td><td>South Africa</td><td>Wilge</td><td>-27.80017</td><td>28.76778</td><td>11.77%</td></tr><tr><td>276</td><td>South Africa</td><td>Suikerbosrand</td><td>-26.6467</td><td>28.38197</td><td>41.89%</td></tr></tbody></table>
<table><thead><tr><th>No.</th><th>Country</th><th>River</th><th>Latitude</th><th>Longitude</th><th>E-flow as a percentage of natural/naturalized Mean Annual Runoff (%)</th></tr></thead><tbody><tr><td>277</td><td>South Africa</td><td>Suikerbosrand</td><td>-26.68137</td><td>28.16798</td><td>34.39%</td></tr><tr><td>278</td><td>South Africa</td><td>Blesbokspruit</td><td>-26.47892</td><td>28.42488</td><td>18.14%</td></tr><tr><td>279</td><td>South Africa</td><td>Klein Vaal</td><td>-26.91275</td><td>30.17497</td><td>24.71%</td></tr><tr><td>280</td><td>South Africa</td><td>Mooi River</td><td>-26.25867</td><td>27.15973</td><td>19.05%</td></tr><tr><td>281</td><td>South Africa</td><td>Vaal</td><td>-26.93615</td><td>26.85025</td><td>28.28%</td></tr><tr><td>282</td><td>South Africa</td><td>Vaal</td><td>-27.10413</td><td>26.52185</td><td>35.8%</td></tr><tr><td>283</td><td>South Africa</td><td>Vals</td><td>-27.48685</td><td>26.8132</td><td>17.05%</td></tr><tr><td>284</td><td>South Africa</td><td>Vet</td><td>-27.93482</td><td>26.12569</td><td>18.2%</td></tr><tr><td>285</td><td>South Africa</td><td>Klein Vet</td><td>-28.564708</td><td>26.943946</td><td>19.54%</td></tr><tr><td>286</td><td>South Africa</td><td>Sand</td><td>-28.1131994</td><td>26.9080556</td><td>23.82%</td></tr><tr><td>287</td><td>South Africa</td><td>Sand</td><td>-28.1228333</td><td>26.5855555</td><td>23.49%</td></tr><tr><td>288</td><td>South Africa</td><td>Schoonspruit</td><td>-26.31172</td><td>26.31172</td><td>35.8%</td></tr><tr><td>289</td><td>South Africa</td><td>Schoonspruit</td><td>-26.675</td><td>26.586108</td><td>30.9%</td></tr><tr><td>290</td><td>South Africa</td><td>Schoonspruit</td><td>-26.93333</td><td>26.66528</td><td>31.2%</td></tr><tr><td>291</td><td>South Africa</td><td>Vaal</td><td>-27.65541</td><td>25.59564</td><td>13.02%</td></tr><tr><td>292</td><td>South Africa</td><td>Harts</td><td>-28.37694</td><td>24.30305</td><td>85.95%</td></tr><tr><td>293</td><td>South Africa</td><td>Vaal</td><td>-28.70758</td><td>24.07578</td><td>21.87%</td></tr><tr><td>294</td><td>South Africa</td><td>Crocodile</td><td>-25.8004</td><td>27.896</td><td>24.07%</td></tr><tr><td>295</td><td>South Africa</td><td>Jukskei</td><td>-25.9539</td><td>27.9621</td><td>29.19%</td></tr><tr><td>296</td><td>South Africa</td><td>Crocodile</td><td>-25.7168</td><td>27.8431</td><td>25.02%</td></tr><tr><td>297</td><td>South Africa</td><td>Pienaars</td><td>-25.4155</td><td>28.312</td><td>20.98%</td></tr><tr><td>298</td><td>South Africa</td><td>Pienaars</td><td>-25.12657</td><td>27.80457</td><td>11.82%</td></tr><tr><td>299</td><td>South Africa</td><td>Hex</td><td>-25.5214</td><td>27.3749</td><td>14.96%</td></tr><tr><td>300</td><td>South Africa</td><td>Crocodile</td><td>-24.88661</td><td>27.51743</td><td>9.14%</td></tr><tr><td>301</td><td>South Africa</td><td>Crocodile</td><td>-24.64476</td><td>27.32569</td><td>14.22%</td></tr><tr><td>302</td><td>South Africa</td><td>Magalies</td><td>-25.72655</td><td>27.56581</td><td>45.58%</td></tr><tr><td>303</td><td>South Africa</td><td>Elands</td><td>-25.80739</td><td>26.72044</td><td>30.48%</td></tr><tr><td>304</td><td>South Africa</td><td>Sterkstroom</td><td>-28.2224</td><td>27.47848</td><td>28.41%</td></tr><tr><td>305</td><td>South Africa</td><td>Buffelspruit</td><td>-24.8304</td><td>28.2224</td><td>35.85%</td></tr><tr><td>306</td><td>South Africa</td><td>Elands</td><td>-25.48108</td><td>26.69039</td><td>21.9%</td></tr><tr><td>307</td><td>South Africa</td><td>Waterkloofspruit</td><td>-25.48108</td><td>26.69039</td><td>28.27%</td></tr><tr><td>308</td><td>South Africa</td><td>Magalies</td><td>-25.8969</td><td>27.5982</td><td>21.18%</td></tr><tr><td>309</td><td>South Africa</td><td>Rietvlei</td><td>-26.01885</td><td>28.30442</td><td>27.83%</td></tr><tr><td>310</td><td>South Africa</td><td>Kaaloog-se-Loop</td><td>-25.777</td><td>26.433</td><td>76.32%</td></tr><tr><td>311</td><td>South Africa</td><td>Groot Marico</td><td>-25.669</td><td>26.435</td><td>50.26%</td></tr><tr><td>312</td><td>South Africa</td><td>Groot Marico</td><td>-25.461</td><td>26.392</td><td>23.62%</td></tr><tr><td>313</td><td>South Africa</td><td>Groot Marico</td><td>-24.706</td><td>26.424</td><td>7.96%</td></tr><tr><td>314</td><td>South Africa</td><td>Klein Marico</td><td>-25.516</td><td>26.159</td><td>4.67%</td></tr><tr><td>315</td><td>South Africa</td><td>Polkadraaispruit</td><td>-25.64697</td><td>26.48928</td><td>31.87%</td></tr><tr><td>316</td><td>South Africa</td><td>Mokolo</td><td>-24.28937</td><td>28.0924</td><td>22.6%</td></tr><tr><td>317</td><td>South Africa</td><td>Mokolo</td><td>-24.17828</td><td>27.97768</td><td>17.6%</td></tr><tr><td>318</td><td>South Africa</td><td>Mokolo</td><td>-24.06496</td><td>27.78716</td><td>19.8%</td></tr><tr><td>319</td><td>South Africa</td><td>Sterkstroom</td><td>-24.30554</td><td>27.89699</td><td>28.41%</td></tr><tr><td>320</td><td>South Africa</td><td>Mokolo</td><td>-23.968</td><td>27.72689</td><td>12.5%</td></tr><tr><td>321</td><td>South Africa</td><td>Mokolo</td><td>-23.7712</td><td>27.75525</td><td>16.5%</td></tr><tr><td>322</td><td>South Africa</td><td>MatlabasZynKloof</td><td>-24.41203</td><td>27.60324</td><td>57.07%</td></tr><tr><td>323</td><td>South Africa</td><td>Komati</td><td>-23.91769</td><td>30.05083</td><td>27.5%</td></tr><tr><td>324</td><td>South Africa</td><td>Gladdespruit</td><td>-23.25081</td><td>30.49572</td><td>26.9%</td></tr><tr><td>325</td><td>South Africa</td><td>Komati</td><td>-23.88806</td><td>30.36125</td><td>18.3%</td></tr><tr><td>326</td><td>South Africa</td><td>Teespruit</td><td>-23.75264</td><td>31.40731</td><td>35.3%</td></tr><tr><td>327</td><td>South Africa</td><td>Komati</td><td>-23.67753</td><td>31.09864</td><td>17.2%</td></tr><tr><td>328</td><td>South Africa</td><td>Lomati</td><td>-23.64939</td><td>30.66064</td><td>17.3%</td></tr><tr><td>329</td><td>South Africa</td><td>Crocodile</td><td>-25.29.647</td><td>30.08.656</td><td>30.3%</td></tr><tr><td>330</td><td>South Africa</td><td>Crocodile</td><td>-25.24.555</td><td>30.18.955</td><td>35.63%</td></tr><tr><td>331</td><td>South Africa</td><td>Crocodile</td><td>-25.27.127</td><td>30.40.865</td><td>48.8%</td></tr><tr><td>332</td><td>South Africa</td><td>Elands</td><td>-25.631</td><td>30.32625</td><td>48.82%</td></tr></tbody></table>
<table><thead><tr><th>No.</th><th>Country</th><th>River</th><th>Latitude</th><th>Longitude</th><th>E-flow as a percentage of natural/naturalized Mean Annual Runoff (%)</th></tr></thead><tbody><tr><td>333</td><td>South Africa</td><td>Elands</td><td>-25.567972</td><td>30.666694</td><td>45.02%</td></tr><tr><td>334</td><td>South Africa</td><td>Crocodile</td><td>-25 30.146</td><td>31 10.919</td><td>31.74%</td></tr><tr><td>335</td><td>South Africa</td><td>Kaap</td><td>-25 38.968</td><td>31 14.572</td><td>21.84%</td></tr><tr><td>336</td><td>South Africa</td><td>Crocodile</td><td>-25 28.972</td><td>31 30.464</td><td>22.2%</td></tr><tr><td>337</td><td>South Africa</td><td>Crocodile</td><td>-25 23.430</td><td>31 58.467</td><td>12.53%</td></tr><tr><td>338</td><td>South Africa</td><td>Sabie</td><td>-25 04.424</td><td>30 50.924</td><td>40.31%</td></tr><tr><td>339</td><td>South Africa</td><td>Mac Mac</td><td>-25 00.800</td><td>31 00.243</td><td>45.31%</td></tr><tr><td>340</td><td>South Africa</td><td>Sabie</td><td>-25 01.675</td><td>31 03.099</td><td>28.2%</td></tr><tr><td>341</td><td>South Africa</td><td>Marite</td><td>-25 01.077</td><td>31 07.997</td><td>28.57%</td></tr><tr><td>342</td><td>South Africa</td><td>Sabie</td><td>-24 59.256</td><td>31 17.572</td><td>37.94%</td></tr><tr><td>343</td><td>South Africa</td><td>Thulandziteka (Sand)</td><td>-24 40.829</td><td>31 05.188</td><td>32.67%</td></tr><tr><td>344</td><td>South Africa</td><td>Mutlumuvi</td><td>-24 45.352</td><td>31 07.923</td><td>28.46%</td></tr><tr><td>345</td><td>South Africa</td><td>Sand</td><td>-24 58.045</td><td>31 37.641</td><td>25.46%</td></tr><tr><td>346</td><td>South Africa</td><td>Heinesspruit</td><td>-29.13054</td><td>30.640024</td><td>27.9%</td></tr><tr><td>347</td><td>South Africa</td><td>Mvoti</td><td>-29.26398</td><td>31.03513</td><td>24.7%</td></tr><tr><td>348</td><td>South Africa</td><td>Mngeni</td><td>-29.46184</td><td>30.29832</td><td>20%</td></tr><tr><td>349</td><td>South Africa</td><td>Mngeni</td><td>-29.64521</td><td>30.74556</td><td>25.8%</td></tr><tr><td>350</td><td>South Africa</td><td>Mkomazi</td><td>-29.921</td><td>30.08448</td><td>35%</td></tr><tr><td>351</td><td>South Africa</td><td>Mkomazi</td><td>-29.921</td><td>30.08448</td><td>35.4%</td></tr><tr><td>352</td><td>South Africa</td><td>Mkomazi</td><td>-30.132</td><td>30.66245</td><td>30.7%</td></tr><tr><td>353</td><td>South Africa</td><td>Mngeni</td><td>-29.5125</td><td>30.09417</td><td>26.2%</td></tr><tr><td>354</td><td>South Africa</td><td>Karkloof</td><td>-29.4401</td><td>30.30328</td><td>43.5%</td></tr><tr><td>355</td><td>South Africa</td><td>Lovu</td><td>-30.09997</td><td>30.73603</td><td>37.9%</td></tr><tr><td>356</td><td>South Africa</td><td>Mtanvuna</td><td>-30.85608</td><td>30.07268</td><td>41.2%</td></tr><tr><td>357</td><td>South Africa</td><td>Tsitsa</td><td>-31.148</td><td>28.674</td><td>31%</td></tr><tr><td>358</td><td>South Africa</td><td>Thina</td><td>-31.072</td><td>28.913</td><td>30.1%</td></tr><tr><td>359</td><td>South Africa</td><td>Kinira</td><td>-30.758</td><td>28.994</td><td>33.3%</td></tr><tr><td>360</td><td>South Africa</td><td>Mzimvubu</td><td>-31.39636</td><td>29.29671</td><td>23.8%</td></tr><tr><td>361</td><td>South Africa</td><td>Groot Brak</td><td>-33° 58.621'</td><td>22° 11.510'</td><td>30%</td></tr><tr><td>362</td><td>South Africa</td><td>Malgas</td><td>-33° 56.251'</td><td>22° 25.278'</td><td>32%</td></tr><tr><td>363</td><td>South Africa</td><td>Kaaimans</td><td>-33° 58.263'</td><td>22° 32.864'</td><td>49.7%</td></tr><tr><td>364</td><td>South Africa</td><td>Goukamma/Homtini</td><td>-33° 56.845'</td><td>22° 55.160'</td><td>47%</td></tr><tr><td>365</td><td>South Africa</td><td>Diep</td><td>-33° 54' 48.9"</td><td>22° 42'29'</td><td>26.9%</td></tr><tr><td>366</td><td>South Africa</td><td>Karatara</td><td>-33° 52' 56.5"</td><td>22° 50' 18.7"</td><td>36.4%</td></tr><tr><td>367</td><td>South Africa</td><td>Knysna</td><td>-33° 53' 27.8"</td><td>23° 01' 57.1"</td><td>33%</td></tr><tr><td>368</td><td>South Africa</td><td>Gouna</td><td>-33° 59' 27.3"</td><td>23° 02' 29.2"</td><td>46.5%</td></tr><tr><td>369</td><td>South Africa</td><td>Assegaaai</td><td>-27 344.28"</td><td>30 59'19.68"</td><td>33.32%</td></tr><tr><td>370</td><td>South Africa</td><td>Pongola</td><td>-27 21'50.88"</td><td>30 58'10.62"</td><td>51.33%</td></tr><tr><td>371</td><td>South Africa</td><td>Mkuze</td><td>-27 35'31.56"</td><td>32 13'4.80"</td><td>44.53%</td></tr><tr><td>372</td><td>South Africa</td><td>Black Mfolozi</td><td>-27 56'20.04"</td><td>31 12'37.08"</td><td>30.1%</td></tr><tr><td>373</td><td>South Africa</td><td>Black Mfolozi</td><td>-28 0'50.04"</td><td>31 19'27.48"</td><td>30.11%</td></tr><tr><td>374</td><td>South Africa</td><td>White Mfolozi</td><td>-28 13'53.24"</td><td>31 11'17.97"</td><td>50.27%</td></tr><tr><td>375</td><td>South Africa</td><td>Nseleni</td><td>-28 38'2.76"</td><td>31 55'51.24"</td><td>46.15%</td></tr><tr><td>376</td><td>South Africa</td><td>Matigulu</td><td>-29 1'12.36"</td><td>31 28'13.44"</td><td>43.18%</td></tr><tr><td>377</td><td>South Africa</td><td>Orange</td><td>-29.0055</td><td>22.16225</td><td>15.2%</td></tr><tr><td>378</td><td>South Africa</td><td>Orange</td><td>-28.4287</td><td>19.9983</td><td>19.2%</td></tr><tr><td>379</td><td>South Africa</td><td>Orange</td><td>-28.7553</td><td>17.71696</td><td>12.2%</td></tr><tr><td>380</td><td>South Africa</td><td>Caledon</td><td>-28.6508</td><td>28.3875</td><td>26%</td></tr><tr><td>381</td><td>South Africa</td><td>Caledon</td><td>-30.4523</td><td>26.27088</td><td>20.1%</td></tr><tr><td>382</td><td>South Africa</td><td>Kraai</td><td>-30.8306</td><td>26.92056</td><td>18.1%</td></tr><tr><td>383</td><td>South Africa</td><td>Touws</td><td>-33.72707</td><td>21.16507</td><td>28.2%</td></tr><tr><td>384</td><td>South Africa</td><td>Gamka</td><td>-33.36472</td><td>21.63051</td><td>25%</td></tr><tr><td>385</td><td>South Africa</td><td>Buffels</td><td>-33.38452</td><td>20.94169</td><td>28%</td></tr><tr><td>386</td><td>South Africa</td><td>Gouritz</td><td>-33.90982</td><td>21.65233</td><td>23.8%</td></tr><tr><td>387</td><td>South Africa</td><td>Keurbooms</td><td>-33.88955</td><td>23.24392</td><td>46.7%</td></tr><tr><td>388</td><td>South Africa</td><td>Duiwenhoks</td><td>-34.25167</td><td>20.99194</td><td>27.1%</td></tr></tbody></table>
<table><thead><tr><th>No.</th><th>Country</th><th>River</th><th>Latitude</th><th>Longitude</th><th>E-flow as a percentage of natural/naturalized Mean Annual Runoff (%)</th></tr></thead><tbody><tr><td>389</td><td>South Africa</td><td>Goukou</td><td>-34.09324</td><td>21.293</td><td>21%</td></tr><tr><td>390</td><td>South Africa</td><td>Doring</td><td>-33.79137</td><td>20.92699</td><td>22.8%</td></tr><tr><td>391</td><td>South Africa</td><td>Olifants</td><td>-33.43813</td><td>23.20587</td><td>26.1%</td></tr><tr><td>392</td><td>South Africa</td><td>Kammanassie</td><td>-33.73286</td><td>22.6974</td><td>21%</td></tr><tr><td>393</td><td>Lesotho</td><td>Senqu</td><td>-29.35442</td><td>28.80408</td><td>42.29%</td></tr><tr><td>394</td><td>Lesotho</td><td>Senqu</td><td>-29.5518</td><td>28.7466</td><td>38.92%</td></tr><tr><td>395</td><td>Lesotho</td><td>Senqu</td><td>-30.0657</td><td>28.4091</td><td>21.32%</td></tr><tr><td>396</td><td>Lesotho</td><td>Senqu</td><td>-30.3653</td><td>27.5737</td><td>29.33%</td></tr><tr><td>397</td><td>Mali</td><td>Niandan</td><td></td><td></td><td>39.32%</td></tr><tr><td>398</td><td>Mali</td><td>Sankarani</td><td></td><td></td><td>34.43%</td></tr><tr><td>399</td><td>Mali</td><td>Niger</td><td></td><td></td><td>58.12%</td></tr><tr><td>400</td><td>Mali</td><td>Bani</td><td></td><td></td><td>40.93%</td></tr><tr><td>401</td><td>Mali</td><td>Niger</td><td></td><td></td><td>63.35%</td></tr><tr><td>402</td><td>Mali</td><td>Niger</td><td></td><td></td><td>58.16%</td></tr><tr><td>403</td><td>Zambia</td><td>Kalungwishi</td><td></td><td></td><td>22.76%</td></tr><tr><td>404</td><td>Zambia</td><td>Kalungwishi</td><td></td><td></td><td>23.58%</td></tr><tr><td>405</td><td>Botswana</td><td>Limpopo</td><td>-23.944697</td><td>26.930778</td><td></td></tr><tr><td>406</td><td>South Africa</td><td>Limpopo</td><td>-22.18419</td><td>29.40524</td><td></td></tr><tr><td>407</td><td>Zimbabwe</td><td>Mwanedzi</td><td>-22.0639</td><td>31.42312</td><td></td></tr><tr><td>408</td><td>Mozambique</td><td>Limpopo</td><td>-22.4596</td><td>31.503</td><td></td></tr><tr><td>409</td><td>Mozambique</td><td>Limpopo</td><td>-23.47173</td><td>32.44381</td><td></td></tr><tr><td>410</td><td>South Africa</td><td>Shingwedzi</td><td>-23.144094</td><td>31.472816</td><td></td></tr><tr><td>411</td><td>Mozambique</td><td>Limpopo</td><td>-24.50018</td><td>33.01039</td><td></td></tr><tr><td>412</td><td>Mozambique</td><td>Changane</td><td>-24.11416</td><td>33.78387</td><td></td></tr><tr><td>413</td><td>Tanzania</td><td>Kagera</td><td>-1.24943</td><td>31.420205</td><td>57.7%</td></tr><tr><td>414</td><td>Kenya</td><td>Victoria Nile</td><td>0.515718</td><td>33.12336</td><td>53.5%</td></tr><tr><td>415</td><td>South Sudan</td><td>Bahr el Jebel</td><td>4.885574</td><td>31.646235</td><td>52%</td></tr><tr><td>416</td><td>South Sudan</td><td>Baro River</td><td>8.247126</td><td>34.576519</td><td>49.6%</td></tr><tr><td>417</td><td>South Sudan</td><td>Sobat</td><td>9.335111</td><td>31.588712</td><td>46.2%</td></tr><tr><td>418</td><td>Sudan</td><td>White Nile</td><td>9.538513</td><td>31.643643</td><td>64.3%</td></tr><tr><td>419</td><td>Sudan</td><td>Blue Nile</td><td>11.859816</td><td>34.375262</td><td>41.9%</td></tr><tr><td>420</td><td>Sudan</td><td>Atbara</td><td>14.364169</td><td>35.855135</td><td>30.5%</td></tr><tr><td>421</td><td>Sudan</td><td>Nile</td><td>19.183147</td><td>30.489857</td><td>55%</td></tr><tr><td>422</td><td>Kenya</td><td>Mara</td><td></td><td></td><td>42.75%</td></tr><tr><td>423</td><td>Kenya</td><td>Mara</td><td></td><td></td><td>43.86%</td></tr><tr><td>424</td><td>Kenya</td><td>Talek</td><td></td><td></td><td>42.7%</td></tr><tr><td>425</td><td>Kenya</td><td>Nyangores</td><td></td><td></td><td>38.64%</td></tr><tr><td>426</td><td>Kenya</td><td>Amala</td><td></td><td></td><td>49.71%</td></tr><tr><td>427</td><td>Brazil</td><td></td><td></td><td></td><td>50%</td></tr><tr><td>428</td><td>Brazil</td><td></td><td></td><td></td><td>50%</td></tr><tr><td>429</td><td>Brazil</td><td></td><td></td><td></td><td>50%</td></tr><tr><td>430</td><td>Brazil</td><td></td><td></td><td></td><td>50%</td></tr><tr><td>431</td><td>Brazil</td><td></td><td></td><td></td><td>50%</td></tr><tr><td>432</td><td>Brazil</td><td></td><td></td><td></td><td>50%</td></tr><tr><td>433</td><td>Brazil</td><td></td><td></td><td></td><td>50%</td></tr><tr><td>434</td><td>Brazil</td><td></td><td></td><td></td><td>50%</td></tr><tr><td>435</td><td>Brazil</td><td></td><td></td><td></td><td>50%</td></tr><tr><td>436</td><td>Brazil</td><td></td><td></td><td></td><td>50%</td></tr><tr><td>437</td><td>Brazil</td><td></td><td></td><td></td><td>50%</td></tr><tr><td>438</td><td>Brazil</td><td></td><td></td><td></td><td>50%</td></tr><tr><td>439</td><td>India</td><td>Reach from Gangotri to Rishikesh</td><td>30°04'29.9"</td><td>78°30'09.9"</td><td>72% in a normal year and 44% in a drought year</td></tr><tr><td>440</td><td>India</td><td>Reach from Narora to Farrukhabad</td><td>27°55'59.8"</td><td>78°51'42.5"</td><td>45% in a normal year and 18% in a drought year</td></tr><tr><td>441</td><td>India</td><td>Reach from Kannauj to Kanpur</td><td>26°36'51.9"</td><td>80°16'28.6"</td><td>47% in a normal year and 14% in a drought year</td></tr></tbody></table>
<table><thead><tr><th>No.</th><th>Country</th><th>River</th><th>Latitude</th><th>Longitude</th><th>E-flow as a percentage of natural/naturalized Mean Annual Runoff (%)</th></tr></thead><tbody><tr><td>442</td><td>Nepal</td><td>NA</td><td>Latitude and longitude not available</td><td></td><td>56%</td></tr><tr><td>443</td><td>Nepal</td><td>NA</td><td>Latitude and longitude not available</td><td></td><td>56%</td></tr><tr><td>444</td><td>Nepal</td><td>NA</td><td>Latitude and longitude not available</td><td></td><td>56%</td></tr><tr><td>445</td><td>Costa Rica</td><td>Reventazón</td><td>10°05' 10.08"</td><td>-83°33' 18.72"</td><td>23.33%</td></tr><tr><td>446</td><td>Costa Rica</td><td>Savegre</td><td>9°27' 2.76"</td><td>-83°57' 59.05"</td><td>12.06%</td></tr><tr><td>447</td><td>Costa Rica</td><td>Río General y Térraba</td><td>9°5' 27.77"</td><td>-83°16' 32.0"</td><td>7.9%</td></tr><tr><td>448</td><td>Canada</td><td>Saskatchewan</td><td>53.7069°</td><td>-103.2986°</td><td></td></tr><tr><td>449</td><td>Canada</td><td>Grande Rivière</td><td>48.4777778</td><td>-64.52861111</td><td>29.45%</td></tr><tr><td>450</td><td>Canada</td><td>Bonaventure</td><td>48.18722222</td><td>-65.55916667</td><td>35.36%</td></tr><tr><td>451</td><td>Canada</td><td>Petite Cascapédia</td><td>48.23222222</td><td>-65.73305556</td><td>38.74%</td></tr><tr><td>452</td><td>Canada</td><td>Cascapédia</td><td>48.62916667</td><td>-66.16611111</td><td>46.20%</td></tr><tr><td>453</td><td>Canada</td><td>Nouvelle</td><td>48.15722222</td><td>-66.34861111</td><td>35.97%</td></tr><tr><td>454</td><td>Canada</td><td>Matapédia</td><td>48.49166667</td><td>-67.44888889</td><td>34.09%</td></tr><tr><td>455</td><td>Canada</td><td>Matapédia</td><td>48.10777778</td><td>-67.13027778</td><td></td></tr><tr><td>456</td><td>Canada</td><td>Saint-Jean</td><td>48.76916667</td><td>-64.51583333</td><td></td></tr><tr><td>457</td><td>Canada</td><td>York</td><td>48.83416667</td><td>-64.62805556</td><td>33.17%</td></tr><tr><td>458</td><td>Canada</td><td>York</td><td>48.80694444</td><td>-64.91666667</td><td>38.36%</td></tr><tr><td>459</td><td>Canada</td><td>Au Renard</td><td>48.98222222</td><td>-64.42694444</td><td></td></tr><tr><td>460</td><td>Canada</td><td>Dartmouth</td><td>48.97777778</td><td>-64.69972222</td><td>21.86%</td></tr><tr><td>461</td><td>Canada</td><td>Madeleine</td><td>49.20277778</td><td>-65.29472222</td><td>44.47%</td></tr><tr><td>462</td><td>Canada</td><td>Sainte-Anne</td><td>49.04361111</td><td>-66.47583333</td><td>39.29%</td></tr><tr><td>463</td><td>Canada</td><td>Cap-Chat</td><td>49.05555556</td><td>-66.66916667</td><td>36.98%</td></tr><tr><td>464</td><td>Canada</td><td>Matane</td><td>48.77361111</td><td>-67.54027778</td><td></td></tr><tr><td>465</td><td>Canada</td><td>Blanche</td><td>48.76694444</td><td>-67.66611111</td><td></td></tr><tr><td>466</td><td>Canada</td><td>Neigette</td><td>48.51777778</td><td>-68.15972222</td><td>17.58%</td></tr><tr><td>467</td><td>Canada</td><td>Rimouski</td><td>48.41277778</td><td>-68.555</td><td></td></tr><tr><td>468</td><td>Canada</td><td>Des Trois Pistoles</td><td>48.08916667</td><td>-69.19527778</td><td></td></tr><tr><td>469</td><td>Canada</td><td>Du Loup</td><td>47.61194444</td><td>-69.64472222</td><td>18.68%</td></tr><tr><td>470</td><td>Canada</td><td>Ouelle</td><td>47.38111111</td><td>-69.95388889</td><td>15.28%</td></tr><tr><td>471</td><td>Canada</td><td>Boyer Sud</td><td>46.7075</td><td>-70.96055556</td><td>12.54%</td></tr><tr><td>472</td><td>Canada</td><td>Boyer</td><td>46.81583333</td><td>-70.90055556</td><td></td></tr><tr><td>473</td><td>Canada</td><td>Du Sud</td><td>46.82</td><td>-70.75611111</td><td></td></tr><tr><td>474</td><td>Canada</td><td>Etchemin</td><td>46.69138889</td><td>-71.06805556</td><td>26.89%</td></tr><tr><td>475</td><td>Canada</td><td>Beaurivage</td><td>46.65694444</td><td>-71.28888889</td><td>18.47%</td></tr><tr><td>476</td><td>Canada</td><td>Chaudière</td><td>46.58694444</td><td>-71.21361111</td><td></td></tr><tr><td>477</td><td>Canada</td><td>Chaudière</td><td>45.69166667</td><td>-70.78527778</td><td></td></tr><tr><td>478</td><td>Canada</td><td>Famine</td><td>46.16694444</td><td>-70.63916667</td><td>17.71%</td></tr><tr><td>479</td><td>Canada</td><td>Chaudière</td><td>46.20111111</td><td>-70.74444444</td><td></td></tr><tr><td>480</td><td>Canada</td><td>Chaudière</td><td>46.09638889</td><td>-70.65444444</td><td></td></tr><tr><td>481</td><td>Canada</td><td>Bras d'Henri</td><td>46.54027778</td><td>-71.34</td><td>15.69%</td></tr><tr><td>482</td><td>Canada</td><td>Petite du Chêne</td><td>46.50055556</td><td>-72.10833333</td><td>14.90%</td></tr><tr><td>483</td><td>Canada</td><td>Bécancour</td><td>46.30611111</td><td>-71.45055556</td><td>25.09%</td></tr><tr><td>484</td><td>Canada</td><td>Bullard</td><td>46.17555556</td><td>-71.45722222</td><td></td></tr><tr><td>485</td><td>Canada</td><td>Bécancour</td><td>46.04527778</td><td>-71.44722222</td><td></td></tr><tr><td>486</td><td>Canada</td><td>Bécancour</td><td>46.19472222</td><td>-72.28333333</td><td>22.27%</td></tr><tr><td>487</td><td>Canada</td><td>Nicolet Sud-Ouest</td><td>45.79166667</td><td>-71.96805556</td><td></td></tr><tr><td>488</td><td>Canada</td><td>Nicolet</td><td>46.06027778</td><td>-72.31305556</td><td>20.18%</td></tr><tr><td>489</td><td>Canada</td><td>Coaticook</td><td>45.28444444</td><td>-71.90083333</td><td></td></tr><tr><td>490</td><td>Canada</td><td>Eaton</td><td>45.46805556</td><td>-71.655</td><td>21.14%</td></tr><tr><td>491</td><td>Canada</td><td>Saint-Germain</td><td>45.87666667</td><td>-72.51027778</td><td></td></tr><tr><td>492</td><td>Canada</td><td>Au Saumon</td><td>45.58</td><td>-71.385</td><td>20.94%</td></tr><tr><td>493</td><td>Canada</td><td>Noire</td><td>45.49972222</td><td>-72.90583333</td><td></td></tr><tr><td>494</td><td>Canada</td><td>Yamaska Sud-Est</td><td>45.20611111</td><td>-72.7475</td><td></td></tr><tr><td>495</td><td>Canada</td><td>David</td><td>45.95416667</td><td>-72.85972222</td><td>5.65%</td></tr><tr><td>496</td><td>Canada</td><td>Yamaska Nord</td><td>45.38388889</td><td>-72.50111111</td><td></td></tr><tr><td>497</td><td>Canada</td><td>Yamaska Nord</td><td>45.35027778</td><td>-72.5152778</td><td></td></tr></tbody></table>
<table><thead><tr><th>No.</th><th>Country</th><th>River</th><th>Latitude</th><th>Longitude</th><th>E-flow as a percentage of natural/naturalized Mean Annual Runoff (%)</th></tr></thead><tbody><tr><td>498</td><td>Canada</td><td>Yamaska</td><td>45.6288889</td><td>-72.93916667</td><td></td></tr><tr><td>499</td><td>Canada</td><td>Noire</td><td>45.6186111</td><td>-72.60416667</td><td></td></tr><tr><td>500</td><td>Canada</td><td>Des Hurons</td><td>45.49055556</td><td>-73.18583333</td><td>7.41%</td></tr><tr><td>501</td><td>Canada</td><td>Aux Brochets</td><td>45.12277778</td><td>-72.99638889</td><td>12.26%</td></tr><tr><td>502</td><td>Canada</td><td>L'Acadie</td><td>45.39027778</td><td>-73.37083333</td><td>6.10%</td></tr><tr><td>503</td><td>Canada</td><td>Châteauguay</td><td>45.33027778</td><td>-73.76222222</td><td></td></tr><tr><td>504</td><td>Canada</td><td>Des Anglais</td><td>45.15805556</td><td>-73.82472222</td><td>8.86%</td></tr><tr><td>505</td><td>Canada</td><td>Doncaster</td><td>46.09638889</td><td>-74.12111111</td><td>33.66%</td></tr><tr><td>506</td><td>Canada</td><td>Rouge</td><td>46.35388889</td><td>-74.77916667</td><td></td></tr><tr><td>507</td><td>Canada</td><td>Rouge</td><td>45.73833333</td><td>-74.68916667</td><td></td></tr><tr><td>508</td><td>Canada</td><td>Saint-Louis</td><td>46.37388889</td><td>-74.50472222</td><td></td></tr><tr><td>509</td><td>Canada</td><td>De la Petite Nation</td><td>45.79083333</td><td>-75.08944444</td><td>23.06%</td></tr><tr><td>510</td><td>Canada</td><td>Suffolk</td><td>45.88388889</td><td>-75.02166667</td><td></td></tr><tr><td>511</td><td>Canada</td><td>Désert</td><td>46.47361111</td><td>-76.04277778</td><td></td></tr><tr><td>512</td><td>Canada</td><td>Picanoc</td><td>46.07638889</td><td>-76.07305556</td><td>37.70%</td></tr><tr><td>513</td><td>Canada</td><td>Gatineau</td><td>47.08333333</td><td>-75.75361111</td><td>48.39%</td></tr><tr><td>514</td><td>Canada</td><td>Coulonge</td><td>45.87361111</td><td>-76.68416667</td><td></td></tr><tr><td>515</td><td>Canada</td><td>Dumoine</td><td>46.34638889</td><td>-77.81555556</td><td>52.51%</td></tr><tr><td>516</td><td>Canada</td><td>Dumoine</td><td>46.82222222</td><td>-77.86805556</td><td>68.63%</td></tr><tr><td>517</td><td>Canada</td><td>Maganasipi</td><td>46.33138889</td><td>-78.34722222</td><td>29.66%</td></tr><tr><td>518</td><td>Canada</td><td>Kinojévis</td><td>48.36583333</td><td>-78.85444444</td><td>46.44%</td></tr><tr><td>519</td><td>Canada</td><td>Du Chêne</td><td>45.56027778</td><td>-73.9775</td><td></td></tr><tr><td>520</td><td>Canada</td><td>Matawin</td><td>46.67722222</td><td>-73.915</td><td>39.77%</td></tr><tr><td>521</td><td>Canada</td><td>Croche</td><td>47.76861111</td><td>-72.735</td><td>47.76%</td></tr><tr><td>522</td><td>Canada</td><td>Vermillon</td><td>47.67694444</td><td>-73.04083333</td><td></td></tr><tr><td>523</td><td>Canada</td><td>Batiscan</td><td>46.58555556</td><td>-72.40472222</td><td>47.21%</td></tr><tr><td>524</td><td>Canada</td><td>Sainte-Anne</td><td>46.8525</td><td>-71.87472222</td><td></td></tr><tr><td>525</td><td>Canada</td><td>Bras du Nord</td><td>46.97666667</td><td>-71.8475</td><td>50.49%</td></tr><tr><td>526</td><td>Canada</td><td>Portneuf</td><td>46.70916667</td><td>-71.87416667</td><td>36.86%</td></tr><tr><td>527</td><td>Canada</td><td>Aux Pommes</td><td>46.69638889</td><td>-71.68777778</td><td>35.07%</td></tr><tr><td>528</td><td>Canada</td><td>Décharge du lac Clair</td><td>46.96638889</td><td>-71.66416667</td><td></td></tr><tr><td>529</td><td>Canada</td><td>Montmorency</td><td>46.89583333</td><td>-71.15222222</td><td></td></tr><tr><td>530</td><td>Canada</td><td>des Eaux Volées</td><td>47.27166667</td><td>-71.16222222</td><td>73.86%</td></tr><tr><td>531</td><td>Canada</td><td>des Eaux Volées</td><td>47.27055556</td><td>-71.13722222</td><td>64.82%</td></tr><tr><td>532</td><td>Canada</td><td>des Aulnaies</td><td>47.29</td><td>-71.16194444</td><td>60.26%</td></tr><tr><td>533</td><td>Canada</td><td>Noire</td><td>47.32666667</td><td>-71.10222222</td><td></td></tr><tr><td>534</td><td>Canada</td><td>Du Gouffre</td><td>47.4475</td><td>-70.51</td><td>45.25%</td></tr><tr><td>535</td><td>Canada</td><td>Malbaie</td><td>47.69416667</td><td>-70.21777778</td><td></td></tr><tr><td>536</td><td>Canada</td><td>Ouareau</td><td>46.03083333</td><td>-73.705</td><td></td></tr><tr><td>537</td><td>Canada</td><td>L'Assomption</td><td>46.01305556</td><td>-73.42944444</td><td></td></tr><tr><td>538</td><td>Canada</td><td>Noire</td><td>46.34055556</td><td>-73.65416667</td><td></td></tr><tr><td>539</td><td>Canada</td><td>Beauport</td><td>45.89333333</td><td>-73.86555556</td><td></td></tr><tr><td>540</td><td>Canada</td><td>De L'Achigan</td><td>45.84611111</td><td>-73.4925</td><td>15.37%</td></tr><tr><td>541</td><td>Canada</td><td>Maskinongé</td><td>46.30111111</td><td>-73.09611111</td><td></td></tr><tr><td>542</td><td>Canada</td><td>Mastigouche</td><td>46.44166667</td><td>-73.46194444</td><td></td></tr><tr><td>543</td><td>Canada</td><td>Du Loup</td><td>46.60055556</td><td>-73.18611111</td><td></td></tr><tr><td>544</td><td>Canada</td><td>Petit Saguenay</td><td>48.18611111</td><td>-70.05</td><td>33.15%</td></tr><tr><td>545</td><td>Canada</td><td>Petit Saguenay</td><td>48.09166667</td><td>-70.02972222</td><td></td></tr><tr><td>546</td><td>Canada</td><td>Ha! Ha!</td><td>48.27472222</td><td>-70.86722222</td><td></td></tr><tr><td>547</td><td>Canada</td><td>Aux Écorces</td><td>48.18277778</td><td>-71.64472222</td><td>67.92%</td></tr><tr><td>548</td><td>Canada</td><td>Pikauba</td><td>47.94194444</td><td>-71.38222222</td><td>63.85%</td></tr><tr><td>549</td><td>Canada</td><td>Cyriac</td><td>48.23583333</td><td>-71.28861111</td><td>47.43%</td></tr><tr><td>550</td><td>Canada</td><td>Belle Rivière</td><td>48.41194444</td><td>-71.70361111</td><td></td></tr><tr><td>551</td><td>Canada</td><td>Métabetchouane</td><td>48.37555556</td><td>-71.99666667</td><td>56.06%</td></tr><tr><td>552</td><td>Canada</td><td>Petite Péribonca</td><td>48.81472222</td><td>-72.04638889</td><td>53.90%</td></tr><tr><td>553</td><td>Canada</td><td>Ashuapmushuan</td><td>48.68555556</td><td>-72.48777778</td><td>75.31%</td></tr></tbody></table>
<table><thead><tr><th>No.</th><th>Country</th><th>River</th><th>Latitude</th><th>Longitude</th><th>E-flow as a percentage of natural/naturalized Mean Annual Runoff (%)</th></tr></thead><tbody><tr><td>554</td><td>Canada</td><td>Ashuapmushuan</td><td>49.27861111</td><td>-73.35555556</td><td>82.14%</td></tr><tr><td>555</td><td>Canada</td><td>Ashuapmushuan</td><td>49.25666667</td><td>-73.70777778</td><td>83.38%</td></tr><tr><td>556</td><td>Canada</td><td>Aux Saumons</td><td>48.68388889</td><td>-72.51277778</td><td>35.75%</td></tr><tr><td>557</td><td>Canada</td><td>Mistassini</td><td>48.88861111</td><td>-72.2725</td><td>73.11%</td></tr><tr><td>558</td><td>Canada</td><td>Mistassibi</td><td>48.89972222</td><td>-72.21083333</td><td>88.69%</td></tr><tr><td>559</td><td>Canada</td><td>Valin</td><td>48.48805556</td><td>-70.97222222</td><td>65.62%</td></tr><tr><td>560</td><td>Canada</td><td>Sainte-Marguerite Nord-Est</td><td>48.26805556</td><td>-69.90833333</td><td>53.56%</td></tr><tr><td>561</td><td>Canada</td><td>Des Escoumins</td><td>48.37138889</td><td>-69.47361111</td><td></td></tr><tr><td>562</td><td>Canada</td><td>Portneuf</td><td>48.64833333</td><td>-69.18194444</td><td></td></tr><tr><td>563</td><td>Canada</td><td>Godbout</td><td>49.33083333</td><td>-67.65472222</td><td>46.66%</td></tr><tr><td>564</td><td>Canada</td><td>Moisie</td><td>50.3525</td><td>-66.18666667</td><td>91.58%</td></tr><tr><td>565</td><td>Canada</td><td>Aux Pékans</td><td>52.18888889</td><td>-66.89055556</td><td></td></tr><tr><td>566</td><td>Canada</td><td>Au Tonnerre</td><td>50.2825</td><td>-64.78194444</td><td>42.31%</td></tr><tr><td>567</td><td>Canada</td><td>Magpie</td><td>50.68555556</td><td>-64.57861111</td><td>82.66%</td></tr><tr><td>568</td><td>Canada</td><td>Aguanish</td><td>50.2475</td><td>-62.11666667</td><td></td></tr><tr><td>569</td><td>Canada</td><td>Natashquan</td><td>51.14166667</td><td>-61.61027778</td><td></td></tr><tr><td>570</td><td>Canada</td><td>Natashquan</td><td>50.4275</td><td>-61.71222222</td><td>79.10%</td></tr><tr><td>571</td><td>Canada</td><td>Étamaniou</td><td>50.38361111</td><td>-59.98972222</td><td></td></tr><tr><td>572</td><td>Canada</td><td>Du Petit Mécatina</td><td>51.84361111</td><td>-60.12361111</td><td></td></tr><tr><td>573</td><td>Canada</td><td>Du Petit Mécatina</td><td>50.68083333</td><td>-59.60194444</td><td></td></tr><tr><td>574</td><td>Canada</td><td>Saint-Augustin</td><td>51.6125</td><td>-58.70138889</td><td></td></tr><tr><td>575</td><td>Canada</td><td>Coxipi</td><td>51.50222222</td><td>-58.395</td><td></td></tr><tr><td>576</td><td>Canada</td><td>Saint-Paul</td><td>51.77083333</td><td>-57.60111111</td><td>62.80%</td></tr><tr><td>577</td><td>Canada</td><td>Harricana</td><td>48.59777778</td><td>-78.11027778</td><td>68.10%</td></tr><tr><td>578</td><td>Canada</td><td>Turgeon</td><td>49.98527778</td><td>-79.095</td><td>43.80%</td></tr><tr><td>579</td><td>Canada</td><td>Nottaway</td><td>50.13444444</td><td>-77.42083333</td><td>100.43%</td></tr><tr><td>580</td><td>Canada</td><td>Waswanipi</td><td>49.69666667</td><td>-75.98472222</td><td></td></tr><tr><td>581</td><td>Canada</td><td>Bell</td><td>49.75472222</td><td>-77.61694444</td><td>71.58%</td></tr><tr><td>582</td><td>Canada</td><td>Waswanipi</td><td>49.8575</td><td>-77.18722222</td><td>97.00%</td></tr><tr><td>583</td><td>Canada</td><td>Broadback</td><td>50.74583333</td><td>-76.38722222</td><td>96.06%</td></tr><tr><td>584</td><td>Canada</td><td>De Rupert</td><td>51.44861111</td><td>-76.86611111</td><td>113.28%</td></tr><tr><td>585</td><td>Canada</td><td>Témiscamie</td><td>51.08333333</td><td>-72.87722222</td><td>98.33%</td></tr><tr><td>586</td><td>Canada</td><td>Rupert</td><td>51.04194444</td><td>-73.80972222</td><td>129.27%</td></tr><tr><td>587</td><td>Canada</td><td>Pontax</td><td>51.53361111</td><td>-78.09666667</td><td>71.69%</td></tr><tr><td>588</td><td>Canada</td><td>Eastmain</td><td>52.17111111</td><td>-74.59166667</td><td></td></tr><tr><td>589</td><td>Canada</td><td>De Pontois</td><td>53.1675</td><td>-74.4725</td><td></td></tr><tr><td>590</td><td>Canada</td><td>Anistuwach</td><td>54.41527778</td><td>-78.80305556</td><td></td></tr><tr><td>591</td><td>Canada</td><td>Grande rivière de la Baleine</td><td>55.23777778</td><td>-76.98472222</td><td></td></tr><tr><td>592</td><td>Canada</td><td>Denys</td><td>55.00861111</td><td>-77.06361111</td><td></td></tr><tr><td>593</td><td>Canada</td><td>Du Nord</td><td>56.53472222</td><td>-76.21388889</td><td></td></tr><tr><td>594</td><td>Canada</td><td>Nastapoca</td><td>56.86138889</td><td>-76.20805556</td><td></td></tr><tr><td>595</td><td>Canada</td><td>Aux Mélèzes</td><td>57.25861111</td><td>-71.07916667</td><td></td></tr><tr><td>596</td><td>Canada</td><td>Aux Mélèzes</td><td>57.67972222</td><td>-69.61722222</td><td></td></tr><tr><td>597</td><td>Canada</td><td>Swampy Bay</td><td>56.64277778</td><td>-68.56305556</td><td></td></tr><tr><td>598</td><td>Canada</td><td>FALSE</td><td>57.67111111</td><td>-68.26916667</td><td></td></tr><tr><td>599</td><td>Canada</td><td>À la Baleine</td><td>57.88861111</td><td>-67.6</td><td></td></tr><tr><td>600</td><td>Canada</td><td>George</td><td>56.78305556</td><td>-64.86805556</td><td></td></tr><tr><td>601</td><td>Canada</td><td>Dauphine</td><td>46.96666667</td><td>-70.85583333</td><td></td></tr><tr><td>602</td><td>Canada</td><td>À l'Huile</td><td>49.80138889</td><td>-63.57222222</td><td></td></tr><tr><td>603</td><td>Greece</td><td>Acheloos Uper part (mountainous part)</td><td>39.479443</td><td>21.32651</td><td></td></tr><tr><td>604</td><td>Poland</td><td>fish biological type 1</td><td></td><td></td><td>200%</td></tr><tr><td>605</td><td>Poland</td><td>fish biological type 2</td><td></td><td></td><td>125%</td></tr><tr><td>606</td><td>Poland</td><td>fish biological type 3</td><td></td><td></td><td>155%</td></tr></tbody></table>
<table><thead><tr><th>No.</th><th>Country</th><th>River</th><th>Latitude</th><th>Longitude</th><th>E-flow as a percentage of natural/naturalized Mean Annual Runoff (%)</th></tr></thead><tbody><tr><td>607</td><td>Poland</td><td>fish biological type 4</td><td></td><td></td><td>103%</td></tr><tr><td>608</td><td>Poland</td><td>fish biological type 5</td><td></td><td></td><td>121%</td></tr><tr><td>609</td><td>Poland</td><td>fish biological type 6</td><td></td><td></td><td>108%</td></tr><tr><td>610</td><td>Poland</td><td>fish biological type 1</td><td></td><td></td><td>129%</td></tr><tr><td>611</td><td>Poland</td><td>fish biological type 2</td><td></td><td></td><td>93%</td></tr><tr><td>612</td><td>Poland</td><td>fish biological type 3</td><td></td><td></td><td>113%</td></tr><tr><td>613</td><td>Poland</td><td>fish biological type 4</td><td></td><td></td><td>74%</td></tr><tr><td>614</td><td>Poland</td><td>fish biological type 5</td><td></td><td></td><td>111%</td></tr><tr><td>615</td><td>Poland</td><td>fish biological type 6</td><td></td><td></td><td>88%</td></tr><tr><td>616</td><td>Poland</td><td>fish biological type 1</td><td></td><td></td><td>165%</td></tr><tr><td>617</td><td>Poland</td><td>fish biological type 2</td><td></td><td></td><td>111%</td></tr><tr><td>618</td><td>Poland</td><td>fish biological type 3</td><td></td><td></td><td>188%</td></tr><tr><td>619</td><td>Poland</td><td>fish biological type 4</td><td></td><td></td><td>157%</td></tr><tr><td>620</td><td>Poland</td><td>fish biological type 5</td><td></td><td></td><td>178%</td></tr><tr><td>621</td><td>Poland</td><td>fish biological type 6</td><td></td><td></td><td>164%</td></tr><tr><td>622</td><td>Poland</td><td>fish biological type 1</td><td></td><td></td><td>126%</td></tr><tr><td>623</td><td>Poland</td><td>fish biological type 2</td><td></td><td></td><td>160%</td></tr><tr><td>624</td><td>Poland</td><td>fish biological type 3</td><td></td><td></td><td>141%</td></tr><tr><td>625</td><td>Poland</td><td>fish biological type 4</td><td></td><td></td><td>153%</td></tr><tr><td>626</td><td>Poland</td><td>fish biological type 5</td><td></td><td></td><td>134%</td></tr><tr><td>627</td><td>Poland</td><td>fish biological type 6</td><td></td><td></td><td>142%</td></tr><tr><td>628</td><td>Poland</td><td>Skawa</td><td>49.636497</td><td>19.830306</td><td>194%</td></tr><tr><td>629</td><td>Poland</td><td>Kamienna</td><td>50.822537</td><td>15.439504</td><td>118%</td></tr><tr><td>630</td><td>Poland</td><td>Mienia</td><td>52.145127</td><td>21.273621</td><td>127%</td></tr><tr><td>631</td><td>Poland</td><td>Sąpólna</td><td>53.660012</td><td>15.168414</td><td>183%</td></tr><tr><td>632</td><td>Poland</td><td>Swider</td><td>52.13903</td><td>21.330356</td><td>136%</td></tr><tr><td>633</td><td>Poland</td><td>Drawa</td><td>53.217643</td><td>15.7639</td><td>144%</td></tr><tr><td>634</td><td>Poland</td><td>Skawa</td><td>49.636497</td><td>19.830306</td><td>93%</td></tr><tr><td>635</td><td>Poland</td><td>Kamienna</td><td>50.822537</td><td>15.439504</td><td>132%</td></tr><tr><td>636</td><td>Poland</td><td>Mienia</td><td>52.145127</td><td>21.273621</td><td>99%</td></tr><tr><td>637</td><td>Poland</td><td>Sąpólna</td><td>53.660012</td><td>15.168414</td><td>126%</td></tr><tr><td>638</td><td>Poland</td><td>Swider</td><td>52.13903</td><td>21.330356</td><td>97%</td></tr><tr><td>639</td><td>Poland</td><td>Drawa</td><td>53.217643</td><td>15.7639</td><td>99%</td></tr><tr><td>640</td><td>Poland</td><td>Skawa</td><td>49.636497</td><td>19.830306</td><td>116%</td></tr><tr><td>641</td><td>Poland</td><td>Kamienna</td><td>50.822537</td><td>15.439504</td><td>132%</td></tr><tr><td>642</td><td>Poland</td><td>Mienia</td><td>52.145127</td><td>21.273621</td><td>280%</td></tr><tr><td>643</td><td>Poland</td><td>Sąpólna</td><td>53.660012</td><td>15.168414</td><td>201%</td></tr><tr><td>644</td><td>Poland</td><td>Swider</td><td>52.13903</td><td>21.330356</td><td>186%</td></tr><tr><td>645</td><td>Poland</td><td>Drawa</td><td>53.217643</td><td>15.7639</td><td>120%</td></tr><tr><td>646</td><td>Poland</td><td>Skawa</td><td>49.636497</td><td>19.830306</td><td>150%</td></tr><tr><td>647</td><td>Poland</td><td>Kamienna</td><td>50.822537</td><td>15.439504</td><td>111%</td></tr><tr><td>648</td><td>Poland</td><td>Mienia</td><td>52.145127</td><td>21.273621</td><td>373%</td></tr><tr><td>649</td><td>Poland</td><td>Sąpólna</td><td>53.660012</td><td>15.168414</td><td>251%</td></tr><tr><td>650</td><td>Poland</td><td>Swider</td><td>52.13903</td><td>21.330356</td><td>186%</td></tr><tr><td>651</td><td>Poland</td><td>Drawa</td><td>53.217643</td><td>15.7639</td><td>158%</td></tr></tbody></table>
Annex 3. Distribution of Global Climate Zones.

<figure><img src="image_33.png" /><figcaption>Figure A3. Distribution of global climate zones used in Figure 6.</figcaption></figure>

Source: Data provided by Global Environmental Stratification ([GEoS]; Metzger et al. 2013).
IWMI Research Report Series

<figure><img src="image_34.png" /></figure>

**186** Towards the Harmonization of Global Environmental Flow Estimates: Comparing the Global Environmental Flow Information System (GEFIS) with Country Data
https://doi.org/10.5337/2024.204

<figure><img src="image_35.png" /></figure>

**185** Institutional Gender Mainstreaming in Small-Scale Irrigation: Lessons from Ethiopia
https://doi.org/10.5337/2023.218

<figure><img src="image_36.png" /></figure>

**184** Land Cover Changes in the Upper Great Ruaha (Tanzania) and the Upper Awash (Ethiopia) River Basins and their Potential Implications for Groundwater Resources
https://doi.org/10.5337/2023.212

<figure><img src="image_37.png" /></figure>

**183** Living Customary Water Tenure in Rights-based Water Management in Sub-Saharan Africa
https://doi.org/10.5337/2022.214

<figure><img src="image_38.png" /></figure>

**182** Legal Recognition of Customary Water Tenure in Sub-Saharan Africa: Unpacking the Land-Water Nexus
https://doi.org/10.5337/2022.215

<figure><img src="image_39.png" /></figure>

**181** Analysis of Water Reuse Potential for Irrigation in Lebanon
https://doi.org/10.5337/2022.211
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