Environmental flow (E-flow) frameworks advocate holistic, regional-scale,
probabilistic E-flow assessments that consider flow and non-flow drivers of
change in a socio-ecological context as best practice. Regional-scale
ecological risk assessments of multiple stressors to social and ecological
endpoints, which address ecosystem dynamism, have been undertaken
internationally at different spatial scales using the relative-risk model
since the mid-1990s. With the recent incorporation of Bayesian belief
networks into the relative-risk model, a robust regional-scale ecological
risk assessment approach is available that can contribute to achieving the
best practice recommendations of E-flow frameworks. PROBFLO is a holistic
E-flow assessment method that incorporates the relative-risk model and
Bayesian belief networks (BN-RRM) into a transparent probabilistic modelling
tool that addresses uncertainty explicitly. PROBFLO has been developed to
evaluate the socio-ecological consequences of historical, current and future
water resource use scenarios and generate E-flow requirements on regional
spatial scales. The approach has been implemented in two regional-scale case
studies in Africa where its flexibility and functionality has been
demonstrated. In both case studies the evidence-based outcomes facilitated
informed environmental management decision making, with trade-off
considerations in the context of social and ecological aspirations. This
paper presents the PROBFLO approach as applied to the Senqu River catchment
in Lesotho and further developments and application in the Mara River
catchment in Kenya and Tanzania. The 10 BN-RRM procedural steps incorporated
in PROBFLO are demonstrated with examples from both case studies. PROBFLO can
contribute to the adaptive management of water resources and contribute to
the allocation of resources for sustainable use of resources and address
protection requirements.
Introduction
The global use of water resources has altered the wellbeing of aquatic
ecosystems and the benefits that people derive from them (Acreman and Dunbar,
2004; Dudgeon et al., 2006; Growns, 2008; Vörösmarty, 2010; Isaak et
al., 2012; Murray et al., 2012; Grafton et al., 2013; Dudgeon, 2014). Environmental flows (E-flows), according to the Brisbane
Declaration (2007), are defined as the “quantity, timing and quality of water flows required to
sustain freshwater and estuarine ecosystems and the human livelihoods and
wellbeing that depend on these ecosystems”. In an effort to determine
E-flows, the international community has developed a plethora of E-flow
assessment methods, which have been applied on numerous spatial scales in a
wide range of ecosystem types across the globe (Tharme, 2003; Acreman and
Dunbar, 2004; Pahl-Wostl et al., 2013; Poff and Matthews, 2013). These methods
have evolved during three distinct periods according to Poff and
Matthews (2013) including an emergence and synthesis period, consolidation
and expansion period and the current globalization period. During this
globalization period a range of best practice E-flow management and
assessment principles, and associated frameworks to undertake E-flow on
multiple spatial scales in multiple political and/or legislative contexts,
have been developed (Poff et al., 2010; Pahl-Wostl et al., 2013). These
principles promote the use of holistic assessment tools that consider both
social and ecological features of ecosystems on regional spatial scales, are
adaptive and incorporate risk evaluation and address uncertainty (Poff et
al., 2010; Acreman et al., 2014).
Ecological risk assessments have been undertaken internationally at different
spatial scales using the relative-risk model (RRM) established since the
mid-1990s (Hunsaker et al., 1990; Landis and Weigers, 1997, 2007; Wiegers et
al., 1998; Landis, 2004, 2016). The RRM has been applied to evaluate a range
of natural and anthropogenic stressors including water pollution, diseases,
alien species and a range of altered environmental states (Walker et al.,
2001; Moraes et al., 2002; Hayes and Landis, 2004; Colnar and Landis, 2007;
Anderson and Landis, 2012; Ayre and Landis, 2012; Bartolo et al., 2012;
O'Brien et al., 2012; Hines and Landis, 2014; Ayre et al., 2014). This tool
can be used to carry out holistic, probabilistic assessments of the risk to
the availability and conditions of ecosystem service and ecological
endpoints, and facilitate socio-ecological trade-offs. For more information
on the application of the RRM consider Colnar and Landis (2007), Anderson and
Landis (2012) or O'Brien and Wepener (2012). Recent developments to the RRM
incorporate the use of Bayesian networks (BNs) that have been established as
a powerful tool for ecological risk assessment, ecosystem management and
E-flow assessment (Pollino et al., 2007; Hart and Pollino, 2008; Shenton et
al., 2011; Chan et al., 2012; Pang and Sun, 2014; Liu et al., 2016; McDonald
et al., 2016). In 2012, Ayre and Landis combined both approaches and
incorporated BNs into RRMs, which was then formalized into a BN-RRM approach
(Hines and Landis, 2014; Herring et al., 2015; Landis et al., 2016).
Between 2013 and 2016 a BN-RRM-based holistic E-flow assessment approach was developed that adheres to the principles of best E-flow management
practice and can easily be incorporated into regional E-flow frameworks such
as the Ecological Limits of Hydrologic Alteration framework (Poff et al.,
2010). This BN-RRM approach, which we have called PROBFLO, is a transparent and
adaptable, evidence-based probabilistic modelling approach that can also
incorporate expert solicitations and explicitly address uncertainty. PROBFLO
is a scenario-based E-flow assessment tool that allows for the evaluation of
the socio-ecological consequences of altered flows with consideration of the
synergistic effects of non-flow drivers of ecosystem impairment. It adheres
to the regional-scale ecological risk assessment exposure and effects, or
sources of multiple stressors, habitats and ranked ecological impacts
relationship (Wiegers et al., 1998). This paper presents the PROBFLO BN-RRM
approach that was used to establish E-flows for the Senqu River in Lesotho,
and it evaluates the socio-ecological effects of altered flow and non-flow
stressors and developments made in the Mara River in the Kenya and Tanzania case
study.
The upper Senqu River study area with risk regions (RRs) established
for the study including dams associated with Phase I of the Lesotho Highlands
Water Project and the location of the new Polihali Dam planned to be built in
Phase II.
The Mara River basin considered in the study with risk regions (RRs)
and sampling sites.
Study area
The Lesotho Highlands Water Project (LHWP) is a USD multi-billion water
transfer and hydropower project implemented by the governments of Lesotho
and South Africa (LHWP, 1986, 2011). Phase I of the LHWP involved the
application of the Downstream Response to Imposed Flow Transformations
(DRIFT) approach to establish the E-flows associated with the construction of
the Katse and Mohale dams on the Malibamats'o and Senqunyane rivers in
Lesotho respectively (Arthington et al., 2003; King et al., 2003). Phase II
involves the augmentation of the LHWP by construction of the Polihali Dam to
divert water directly from the upper Senqu River to the existing Phase I
infrastructure of the LHWP (Fig. 1). For Phase II, the custodians of the
project, the Lesotho Highlands Development Authority (LHDA), required the
service provider awarded with the E-flow determination project to review and
implement current best E-flow practice. This included the requirements to
implement a probabilistic, regional-scale modelling approach that is
transparent and holistic, addressing socio-ecological components and
endpoints, and one that considers uncertainty explicitly. The PROBFLO
approach has, as a result, been selected for Senqu River in Lesotho as a part
of Phase II of LHWP between the proposed Polihali Dam site
(29.289593∘ S; 28.863890∘ E) and the border of South Africa
(30.413231∘ S; 27.564090∘ E) (LHWP, 2016).
The entire Mara River in Kenya and Tanzania upstream of the mouth into Lake
Victoria (1.518178∘ S; 33.943497∘ E) was considered in this
regional-scale PROBFLO case study (NBI, 2016) (Fig. 2). The Mara River and
its tributaries are an essential source of water for domestic needs,
agriculture, pastoralism and wildlife, including tourism, in Kenya and
Tanzania (Mati et al., 2008; Defersha and Melesse, 2012). Although extensive
research has been undertaken into the management of the terrestrial
ecosystems of the Serengeti National Park and Maasai Mara National Reserve,
there are limited studies on the effects of land use threats to the wellbeing
of the Mara River, with only site-specific evaluations of the environmental
flows of the river (Broten and Said, 1995; Gereta et al., 2002; Onjala, 2002;
Karanja, 2003; Lamprey and Reid, 2004; Hoffman, 2007; Mati et al., 2008;
Atisa, 2009; GLOWS-FIU, 2012; Majule, 2010; Hoffman et al., 2011; Ogutu et
al., 2011; Defersha and Melesse, 2012; Kiambi et al., 2012; Dessu et al.,
2014).
The 10 procedural steps of PROBFLO.
PROBFLO framework for E-flows
The PROBFLO framework is based on 10 procedural RRM steps (Landis,
2004), and it incorporates BN development and evaluation procedures (Marcot et
al., 2006; Ayre and Landis, 2012) into a robust E-flow assessment method
that gives emphasis to adaptive management for holistic E-flow management
(Fig. 3). The PROBFLO approach has been implemented in the Senqu and Mara
River case studies to evaluate the socio-ecological consequences of altered
flows and determine E-flows, which is demonstrated through application of the
following 10 procedural steps.
Step 1: Vision exercise
The importance of having clear water resource management objectives cannot be
overemphasized. Numerous Integrated Water Resource Management strategies,
regional management plans and frameworks, national legislation and
established E-flow assessment tools advocate the establishment of clear goals
or visions to direct the use and protection of water resources (Biswas, 2004;
Mitchell, 2005; Dudgeon et al., 2006; Richter et al., 2006; Poff et al.,
2010; King and Pienaar, 2011; NBI, 2016). Although many vision development
approaches are available, the initial application of PROBFLO involved the
application of the Resource Quality Objectives (RQOs) determination procedure
(DWA, 2011) to describe and document the water quality, water quantity,
habitat and biota objectives for the water resource being evaluated (NBI,
2016; DWA, 2011). The RQO process results in narrative and numerical
descriptions of various ecosystem features required to achieve a balance
between the use and protection of water resources and hence to achieve a
documented vision. As part of the initial development of the RRM approach,
multiple social and ecological endpoints were evaluated in a relative manner.
Social endpoints were limited to the availability and quality of ecosystem
services and ecological endpoints, including the requirements to maintain
selected ecological indicators in an acceptable integrity state or wellbeing.
In addition, for these environmental flow assessment (EFA) case studies,
endpoints associated with socio-ecological impacts of the rivers resulting
directly or indirectly with altered flows were considered. Findings resulted
in relative risks to endpoints that could be compared and used to consider
cost-benefit trade-offs between social and ecological endpoints by adjusting
water resource use and protection scenarios.
The treaties for Phase I and Phase II of the LHWP entered into by the Kingdom
of Lesotho and the Republic of South Africa provided the requirements for the
establishment of a vision for water resource use and protection for the Senqu
River case study (LHWP, 1986, 2011). The Treaty gives emphasis to protection
of the existing quality of the environment and, in particular, requires
maintenance of the wellbeing of persons and communities immediately affected
by the project, including those downstream of the dam. Accordingly, the
vision states that there should be no change to the existing quality of the
downstream environment and that the net effect of the dam should not be
negative to the people living downstream of the dam. For the PROBFLO
assessment, RQOs describing the desired quality and quantity of water,
habitat and biota for the study area were established. The endpoints selected
to represent the social and ecological management objectives for the PROBFLO
assessment were based on the vision represented by the RQOs in this case
study, including the maintenance of the following ecosystem services and
ecological objectives affected by the river: (1) the supply of building sand
from the Senqu River, (2) water for domestic use, (3) recreation/spiritual
use of the river, (4) fish stocks as food for people, (5) edible plants from
the riparian zone as food for people, (6) medicinal plants for people,
(7) floodplain non-woody plants (for grazing), (8) woody plants for fuel and
construction, (9) reeds for construction and (10) fish stocks, (11) aquatic
macro-invertebrate and (12) riparian ecosystem integrity or wellbeing.
The vision for the Mara River case study was based on existing regional Mara
River management objectives (WRMA, 2014). In 2014, a Catchment Management
Strategy (CMS) for the Mara Basin in Kenya was developed to facilitate the
management of the water resources, environment and human behaviour in ways
that achieve equitable, efficient and sustainable use of water for the
benefit of all users (WRMA, 2014). The aims of the Mara River basin as part
of the Strategic Environmental Assessment (EAC, 2003) to maintain “the
people living in harmony with nature while achieving human wellbeing and
sustainable economic development in perpetuity” were also considered. Also
considered were the objectives for the Mara River basin as described by the
Biodiversity, and Strategy Action Plan, which describes “a region rich in
biodiversity which benefits the present and future generations and ecosystem
functions” (GLOWS-FIU, 2012). The Kenyan Water Resources Management
Authority (WRMA) established a high ecological importance, high livelihoods
value and low commercial value vision for the upper Mara River basin
(Nyangores and Amala rivers) and a high ecological importance, moderate
livelihoods value and moderate commercial value vision for the mainstream Mara
River. In this context the endpoints selected for the study included (1) the provision
of water for basic human needs according to the national legislation of
Kenya and Tanzania, (2) the maintenance of the ecological integrity of the riverine
ecosystem (instream and riparian ecosystems), (3) the provision of flows for the
commercial production of crops, (4) the maintenance of existing livestock
industry, (5) the maintenance and viability of the ecotourism industry and
(6) the maintenance of the ecological integrity of the Mara Wetland in the lower
reaches of the basin.
Step 2: Mapping and data analyses
The BN-RRM approach that forms the basis of PROBFLO includes the relative
evaluation of multiple sources of stressors to endpoints on a regional scale,
which should be spatially and temporally referenced for regional
comparisons/evaluations in a PROBFLO assessment (Landis, 2004; Landis and
Wiegers, 2007). For this the spatial extent of the study area must be defined
and described, and the locations of potential sources, habitats and impacts
must be identified and spatially referenced. In addition, source-stressor
exposure and habitat/receptor to endpoint pathways/relationships should be
spatially referenced where possible (O'Brien and Wepener, 2012; Landis et
al., 2016). Available data describing the ecosystem need to be reviewed and
spatially referenced and the uncertainties associated with the availability
and quality of data used in the assessment must be documented for evaluation
in Step 7. O'Brien and Wepener (2012) provide an approach to delineate
ecosystem types, the topological features of importance, the catchment and
ecoregion boundaries, the land or water resource use scenarios and the
pathways of stressors' exposure. This approach is used to direct the selection
of risk regions for assessment (Smit et al., 2016). Best practice E-flow
frameworks accentuate the importance of ecosystem type classification as part
of E-flow assessments to improve our understanding of flow–ecosystem
relationships (Poff et al., 2010; Arthington, 2012).
Example of a holistic conceptual model established in a PROBFLO case
study that describes causal risk relationships (arrows) between sources,
stressors, habitats, effects and impacts to endpoints considered in an
assessment.
Step 3: Risk region selection
In this step, combinations of the management objectives, source information
and habitat data are used to establish geographical risk regions that can be
assessed in a relative manner (Landis, 2004; O'Brien and Wepener, 2012). In
the end, the outcomes of the assessment will be available at the spatial
scale established during this step for multiple temporal scenarios associated
with alternative management options. In this regard it is important to
consider the spatial connectivity of multiple variables including flows and
other variables within the study area so that risk regions incorporate
appropriate sources, stressors, habitats and endpoints (Landis, 2004;
O'Brien and Wepener, 2012). The approach can address spatial and temporal
relationships of variables between risk regions, such as the downstream
effect of a source on multiple risk regions, in the context of the
assimilative capacity of the ecosystem or the upstream connectivity
requirements of a migratory fish between risk regions. To demonstrate that
PROBFLO can conform to the regional E-flow assessment frameworks such as
ELOHA (Poff et al., 2010), the selection of RRs should include explicit
hydrological and geomorphological classification. The relative risk outcomes
of the assessment can later be directly related to the system classification
as proposed by ELOHA. With additional E-flow information for a range of
hydrological and geomorphological ecosystem types, the outcomes can be used
to establish regional E-flows.
The selection of risk regions for the Senqu River E-flow assessment was based
on the proposed location of the Polihali Dam and catchment boundaries of the
Senqu River and large tributaries (Malibamatso and Senqunyane rivers) for
this E-flow assessment. Physical access to sampling sites within Lesotho to
conduct biophysical field surveys was extremely difficult and this also
contributed to risk region selection. Four broad risk regions were selected
for the Senqu River PROBFLO study (Fig. 1).
In the Mara River case study, a review of land use and land cover (Mati et
al., 2008; Atisa et al., 2014), hydrology (Mango et al., 2011; McClain et
al., 2014), the vision for the case study, current and future land and water
resource use options and socio-ecological importance (Karanja, 2002; LVBC and
WWF-ESARPO, 2010; Mango et al., 2011; Defersha and Melesse, 2012; GLOWS-FIU,
2012; Dessu et al., 2014; Dutton et al., 2013) was used to select risk
regions. Ten risk regions were selected for the Mara River case study which
conformed to catchment boundaries, ecoregions, land use practices and the
international boundary (Fig. 2).
Step 4: Conceptual model
In this step conceptual models that describe hypothesized relationships
between multiple sources, stressors, habitats and impacts to endpoints
selected for the study are generated (Wiegers et al., 1998) (Fig. 4). This
includes the holistic (consider flow- and non-flow-related variables in
a spatial–temporal context), best practice characterization of flow–ecosystem
and flow–ecosystem service relationships in the context of a regional-scale
E-flows framework (Poff et al., 2010), with relevant non-flow (water quality
and habitat) relationships in the models. Conceptual models should be
constructed by expert stakeholders usually including hydrologists,
geomorphologists, ecologists and ecosystem services, including social and
resource economics scientists. These experts should be familiar with
socio-ecological system processes and be able to describe probable cause and
effect variables and relationships of sources to stressors to multiple
receptors in relation to their impacts on the endpoints, selected for the
study. The conceptual models for the case studies presented addressed
requirements of the ELOHA and the Nile Basin regional-scale E-flow frameworks
to conform to these frameworks (Poff et al., 2010; NBI, 2016). The Nile Basin
regional-scale E-flow framework expands on the ELOHA framework to include an
initial situation assessment, data review and alignment phase and a
governance and Resource Quality Objectives setting phase. The PROBFLO
conceptual model thus conforms to the regional-scale E-flow framework
procedures in (1) the selection of socio-ecological endpoints, to direct the
hydrologic foundations for the study including the selection of hydrological
statistics required; (2) the classification of ecosystem types based on geomorphic,
water quality, quantity and ecoregion considerations; and with these data,
(3) the incorporation of evidence-based flow–ecosystem relationships and
flow–ecosystem service relationships, with relevant non-flow variable
relationships upon which the assessment is based. Initial conceptual model
development considers all relevant sources, stressors, habitat, effects and
impact relationships with spatial and temporal considerations.
Bayesian network models used in the Senqu River case study to
evaluate the risk of flow alterations associated with Phase II of the
Lesotho Highlands Water Project to riparian ecosystem integrity and ecosystem
service endpoints (a) and instream ecological endpoints and
fisheries supply endpoints (b). White nodes represent input exposure
variables, light grey nodes complete the expose leg of the risk assessment,
grey nodes represent effect leg of models and dark grey nodes represent
endpoints. Working Netica™ models are provided in the Supplement.
Bayesian network models used in the Mara River case study to
evaluate the risk of water resource use to a range of socio-ecological
endpoints. White nodes represent input exposure variables, light grey nodes
complete the expose leg of the risk assessment, grey nodes represent effect
leg of models and dark grey nodes represent endpoints. Working Netica™
models are provided in the Supplement.
Step 5: Ranking scheme
Ranking schemes are used to represent the state of variables, with unique
measures and units to be comparable as non-dimensional ranks and combined in
BN-RRMs (Landis, 2004; Landis et al., 2016). Four states, designated as zero,
low, moderate and high as traditionally used in RRMs (Colnar and Landis,
2007; O'Brien and Wepener, 2012; Hines and Landis, 2014; Landis et al.,
2016), have been incorporated into the PROBFLO process. The states represent
the range of wellbeing conditions, levels of impacts and management ideals as
follows.
Zero. This describes a pristine state, with no impact/risk, comparable
to the pre-anthropogenic source establishment, baseline or reference state.
Low. This is a largely natural state with low impact/risk, and it describes an ideal range for sustainable
ecosystem use.
Moderate. This state refers to moderate use or a modified state, with moderate
impact/risk, representing the threshold of potential concern or alert range.
High. This describes a significantly altered or impaired state, with unacceptably high
impact/risk.
This ranking scheme selected for PROBFLO represents the full range of
potential risk to the ecosystem and ecosystem services with management
options. Low risk states usually represent management targets with little
impact, and moderate risk states represent partially suitable ecosystem
conditions that usually warrant management/mitigation measures to avoid high
risk conditions. The incorporation of BN modelling into PROBFLO allows the
approach to incorporate the variability between ranks for each model
variable, represented as a percentage for each rank. Indicator flow and
non-flow variables representing the socio-ecological system being evaluated
in a PROBFLO assessment are selected (linked to endpoints – step 1), and
unique measures and units of measurement are converted into, and represented
by, ranks for integration in BN assessments. For the BN assessment, ranks are
assigned scores along a percentage continuum representing the state of the
variables using natural breaks of 0.25 (zero), 0.5 (low), 0.75 (moderate) and
1 (high) in the calculation.
Step 6: Calculate risks
From the general inclusive conceptual models (step 4), with the principle of
requisite simplicity (Stirzaker et al., 2010), smaller social and ecological
endpoint specific models that represent the system being assessed are
unpacked and converted into Bayesian network models (Figs. 5 and 6) for
analyses. These models can be analysed individually or integrated using a
range of BN modelling tools, using nodes representing variables that share
the same indicators and measures. Bayesian networks are probabilistic
modelling networks that graphically represent joint probability distributions
over a set of statistical values (Pollino et al., 2007; Korb and Nicholson,
2010). They include parent or input nodes and child or conditional nodes with
links that represent causal relationships between nodes combined by
Conditional Probability Tables (CPTs) (McCann et al., 2006; Landis et al.,
2016). Conditional Probability Tables describe conditional probabilities
between the occurrence of states in the parent nodes and the resulting
probabilities of states in the child nodes (Landis et al., 2016). The two
PROBFLO case studies presented here made use of the
Netica™ BN software by Norsys Software
(http://www.norsys.com/).
The BNs are initially used to evaluate the risk of anthropogenic/natural
hazards to endpoints per risk region, in relative manner for comparisons, for
multiple temporal periods (high or low flow months and wet or drought phases
etc.) which can also be compared relative to each other. Bayesian networks
also make use of available data and expert solicitations as evidence to
represents risks to current or present scenarios. Present projections of risk
to the endpoints can generally be easily validated using available data,
knowledge of existing relationships between variables and by carrying out
directed field survey campaigns to describe/test risk relationships. Present
risk projections are then calibrated by evaluating benchmark or historical
scenario risk projections using the established models, which can often be
validated with historical data (see data in the Supplement). An example of
how a BN can represent a component of the socio-ecological ecosystem being
evaluated is presented in Figs. 5 and 6. Within Fig. 5 we for example
hypothesize that the ecological integrity of fish in the Senqu can be
selected as an indicator of the Senqu River ecosystem as a suitable
ecological endpoint. In this example the ecological integrity of fish is
hypothesized to be a function of the conditions of the Senqu River
environment for fish, representing the exposure leg of the risk assessment
and the potential for fish to occur within the reach of the river being
considered as an effects component of the study (refer to Table S1 in the
Supplement). The conditions of the Senqu River environment itself are
hypothesized in this study to be a function of the potential of communities
to be a “disturbance to wildlife”. This describes the instream habitat conditions and migration
access for fish as source-stressor relationships with local communities,
multiple barriers and other source/stressors that affect instream habitat
wellbeing, selected indicators representing ecosystem components with associated measures in the study. Water
resource use scenarios were used to describe the state of source/stressor and
effect nodes as inputs into the model. These variables were integrated using
CPTs to represent other system variables, which ultimately result in risk
described to endpoints (refer to Table S2). These models are then used to
determine E-flow requirements according to acceptable trade-off of risk to
endpoints selected for the study, and the consequences of alternative water
resource use, management and/or climatic condition scenarios.
To determine E-flow requirements in PROBFLO, trade-offs of acceptable risk to
social and ecological endpoints are initially established for each risk
region by stakeholders. This usually takes place within a legislative context
where regional legislation/policies/agreements are addressed, such as
requirements to maintain ecosystem sustainability, which affects trade-off
developments. These trade-offs of acceptable risk are represented in the BNs
as forced endpoint risk distributions or profiles. These profiles usually
range between low and moderate risk, with usually no high risk probabilities.
In relation to the definitions of the ranks used in PROBFLO, trade-offs of
acceptable risk for E-flow determination should only dominate the
“moderate” risk range when there is certainty that the E-flow requirements
can be provided, such as in the case of E-flow releases from a dam. In case
studies where there is high uncertainty associated with the ability to
provide E-flow requirements, such as the management of multiple water
resource users to cumulatively maintain E-flows, then a buffer should be
provided according to the definition of ranks and the “low” risk range
should be selected. After the selection of trade-offs of acceptable risk are
established, the calibrated BNs are forced to generate the state (rank
distributions) of input flow variables used in the assessments. These
flow-related variable state requirements that are spatially and temporally
referenced are provided to a hydrologist or geomorphologist, for example, to
describe the E-flow requirements which can be presented in various formats,
such as daily or monthly water (usually m3 s-1) and sediment
(usually kg m-3 s-1) discharge duration percentiles. During
E-flow determination procedures, the state of non-flow variable nodes, which
contribute to the risk to endpoints, associated with flow variables can
either be maintained in their current state, and described as such or amended
with available water resource use information. This can include the increased
requirement of water for basic human needs, for increases in growths of human
populations depending on the resource, for example. Following the
establishment of E-flows, the socio-ecological consequences of altered flows,
associated with alternative water resource management options or climate
change variability, for example, can be evaluated in a relative manner by
generating and evaluating a range of future scenarios in PROBFLO.
Senqu River risk calculation
In the Lesotho case study the nine social endpoints and three ecological
endpoints were used to represent the socio-ecological endpoints of interest
in the study. The 12 BN models established for the study included cause and
effect linkages used to estimate risk (refer to BN models provided in the Supplement).
These BNs were used to evaluate the risk of multiple
sources and stressors with flow-related stressors for base winter (low),
summer (high) and drought flows. Where appropriate, CPTs of the BNs for
endpoints were adjusted between RRs to represent the subtle changes in
ecosystem process dynamics down the length of the Senqu River (refer to
Tables S1 and S2).
Evidence used includes the historical understanding of flow–ecosystem and
flow–ecosystem service relationships established during Phase I of the LHWP
and data derived from a series of biophysical surveys of the study area
(refer to Tables S1 and S2). The surveys included hydrology, hydraulic, water
quality and geomorphology components grouped as ecosystem driver components,
and fish, macro-invertebrates and riparian vegetation grouped as ecosystem
responder components. Hydrological data used in the case study included
recently updated basin-wide Integrated Water Resources Management Plan
outcomes (ORASECOM, 2014). This database was updated in the study with latest
available rainfall records, obtained from the Lesotho Meteorological
Services, and regional evaporation information. Observed monthly flow data
with actual discharge measurements determined in the study were used to
calibrate the monthly WRSM2000 model against rainfall to obtain catchment-specific rainfall-runoff parameters (Pitman et al., 2006). A range of
hydrological statistics were used in the RRM-BN model. Findings were used to
identify a range of indicators to represent hypothesized causal relationships
of the socio-ecological system being evaluated and to identify measures for
indicators, with units of measurement and node rank thresholds and
relationships between variables in the form of CPTs (refer to Table S2).
Netica™ was used to carry out the assessment
(Ayre et al., 2014, for example). The tool is versatile and incorporates a
range of features used to optimize the assessment. This includes equation
features to weight the relative importance of parent variables and generate
initial CPTs that were easily refined and applied to the daughter nodes for
the assessment. The tool includes case file generation options, which allow
the BNs to be linked to Microsoft® Excel,
where data can be rapidly analysed and used to populate BNs for the analyses.
Risk outcome distributions were also linked to Excel, where scenarios and
social and ecological endpoints could be integrated using Monte Carlo
randomization approaches that are part of the Oracle Crystal Ball software
(Landis, 2004). After establishing BN models for each RR, input
parameters were changed using RR-specific data for a range of scenarios
including the following.
Scenario 1 represents the present-day scenario based on present state
hydrology and associated source to endpoint variable state relationships
that represent observable conditions. This scenario is based on existing data
and additional data collected during the field surveys.
Scenario 2 represents a pre-anthropogenic water resource development
scenario, considered to represent “natural” hydrology, which was modelled
using historical and modelled hydrology and rainfall data, and hypothesized
state distributions for non-flow variables. This scenario was selected to
calibrate the PROBFLO model for the study.
Scenario 3 includes the presence of the new proposed Polihali Dam with full
modelled interbasin transfer (IBT) supply. Only large floods overtopping the
dam have been considered to be available downstream of the dam, with the
existing E-Flows from the downstream lateral tributaries bringing water from
Katse and Mohale dams available in RR3 and 4. Non-flow source/stressor
catchment conditions were based on the present-day scenario.
Scenario 4 is based on scenario 3 but includes E-Flow releases established
as 36 % of the natural mean annual runoff (MAR) from the Polihali Dam,
with suitable freshet and flood flows. The range of percentages of the MAR
considered in these scenarios were selected by stakeholders to evaluate
different levels of water resource use for the development.
Scenario 5 is based on scenario 3, but with only 25 % of the natural MAR available
to contribute towards E-flows, with all floods retained in the Polihali Dam
for transfer into the IBT.
Scenario 6 is based on scenario 5 with one additional 40 m3 s-1
freshet (small spring flood) released from the dam in addition to the
25 % of the natural MAR to contribute towards E-flows.
Scenario 7 is based on scenario 3 with only 18 % of the natural MAR available
to contribute towards E-flows with one single 40 m3 s-1 freshet
(small spring flood).
Scenario 8 is based on scenario 6 but with additional stress imposed by
further reduction of available flows to 12 % of the natural MAR, released
for maintenance but including the single 40 m3 s-1 freshet (small
spring flood).
Senqu River case study mean relative risk scores (with SD)
considered in the assessment including relative risk to: riparian
vegetation (a), macro-invertebrates (b) and
fish (c) wellbeing endpoints for the four risk regions per scenario
(SC).
Senqu River mean relative risk scores (with SD) for wood for
fuel (a), marginal vegetation for livestock grazing (b) and
fish for food (c) social endpoints for the four risk regions per
scenario (SC) considered in the study.
Probability profiles generated during a PROBFLO assessment, overlaid
onto risk rank range (Zero, Low, Moderate and High) to describe the
relative risk of the multiple sources and stressors, including altered flows,
associated with alternative management scenarios considered in the Lesotho
case study to the fish wellbeing endpoint.
Probability profiles generated during a PROBFLO assessment, overlaid
onto risk rank range (Zero, Low, Moderate and High) to describe the
relative risk of the multiple sources and stressors, including altered flows,
associated with alternative management scenarios considered in the Lesotho
case study to all of the endpoints integrated in the assessment.
In this assessment, risk was calculated for 12 endpoints, for three temporal
periods and for eight scenarios, thus representing 312 BN models that were
relatively comparable. The results include the mean relative risk rank scores
with associated standard deviation for each endpoint including the
maintenance of riparian vegetation, macro-invertebrates and fish wellbeing as ecological
endpoints and the maintenance of wood for fuel, marginal vegetation for livestock
grazing and fish for food as social endpoints (Figs. 7 and 8). These initial
relative mean risk scores allow for the comparison of alternative spatial and
temporal socio-ecological risk projections to the endpoints used in the
assessment. Initial risk to ecological endpoints compared between the natural
(SC2) and present (SC1) scenarios demonstrates that the number of sources and
stressors with associated risk to endpoints has increased in the study area,
particularly in RR2 to RR4. These changes can largely be attributed to the
consequences of Phase I of the LHWP (Fig. 7). These findings include the
synergistic effect of non-flow stressors (such as water quality and habitat
condition) to the wellbeing of the Senqu River ecosystem in the study area.
Effects of the altered hydrology between natural and present-day scenarios to
the social endpoints were less obvious (Fig. 8). Spatial trends in the risk
results associated with SC3 to SC8 generally include elevated risk to RR1,
directly downstream of the proposed dam in particular. These results
demonstrate that the impact on socio-ecological endpoints considered will be
highest directly below the dam. Thereafter scenarios that exclude floods and
freshets (SC3 and SC5) resulted in excessive risk, demonstrating the
importance of flood and freshet flows to the socio-ecological endpoints.
Outcomes for scenarios 6 to 7 for riparian vegetation and invertebrates
include consistent increases in risk spatially from the proposed new dam
towards the lower reaches of the study area, which is ascribed to
accumulative effects of the existing Phase I dams on the lateral tributaries.
The relative risk to the fish community endpoint includes an opposite trend
where a reduction in risk from RR1 to RR4 was observed for all scenarios.
These results are indicative of the increased relative resilience of the
resident and seasonal migratory fish communities to flow alterations in the
Senqu River associated with dam developments, due to the increasing size of
the river and associated increases in habitat diversity towards the lower
reaches of the study area. In addition, reductions in river connectivity
(barrier formation) associated with existing impacts from Phase I and the
synergistic new stressors associated with Phase II of the LHWP were also shown
to contribute to the increase in risk from the lower reaches of the study
area in RR4 for fish migrating upstream to RR1. Interestingly the outcomes
included improvements to the condition of, or reductions in risk to the
wellbeing of social endpoints for scenarios 6 to 8. This included potential
improvements in the availability of and/or condition of wood for fuel in RR2
and grazing for livestock for RR1 and RR4 in particular. These projections
allow for trade-off considerations where the wellbeing of some social
endpoints will decrease in some parts of the study area and increase in
others. From our current understanding of the socio-ecological system
represented in this study, the results evidence some additional sustainable
development opportunities that may be available for stakeholders to consider
(Fig. 8). These opportunities may improve the viability of the Phase 2
development and contribute to the establishment of a suitable balance between
the use and protection of the ecosystem being evaluated. These relative risk
projections to the multiple socio-ecological endpoints considered are based
on flow alterations associated with development scenarios, in the context of
the exacerbating of non-flow variable determinants on regional scales.
The cumulative risk of all ecological and social endpoints for each RR, for
each temporal period, per scenario, were evaluated using Monte Carlo
simulations (5000 trials, Oracle Crystal Ball software, Oregon) (Ayre et al.,
2014). The outcomes included relative risk projections, displayed as relative
profiles to single endpoints from multiple RRs, and multiple social and
ecological endpoints or all endpoints per RR in the study for comparisons and
evaluation. These profiles were generated for multiple scenarios to evaluate
the potential social and ecological consequences of alternative water
resource development scenarios. This is demonstrated by considering the
cumulative risk projections to the fish wellbeing endpoint, which
demonstrates that relative to the “natural” hydrology scenario
(Scenario 2), in which there is a 83 % probability that risk to the fish endpoint occurred
in a zero to low risk range, for the present scenario (Scenario 1), Phase II
with the dam and no E-flows scenario (Scenario 3) and Scenario 7 (Phase II
with the dam, 18 % release of natural MAR and 40 m3 s-1
freshets) all range between the moderate and high risk range (Fig. 9). The
risk outcomes of all future management options suggest that objectives of the
stakeholders to maintain the existing wellbeing of the ecosystem could not be
achieved because flow scenarios considered would not satisfactorily mitigate the
effects of fish migration barriers, which themselves reduce ecosystem
wellbeing. To address this, an additional, amended scenario (Scenario 7) was
then modelled, which included successful mitigation measures for the existing
man-made barriers in the Senqu River as amendments. The outcomes included a
reduction in risk in the low to moderate risk ranges, demonstrating that
scenarios that promote moderate to high use of the water resources, with
barrier mitigation measures (such as construction of fishways) could result
in the achievement of the fish wellbeing endpoints in the study. This
approach established for this case study allows for the relative comparison
of the integrated social and/or ecological consequences of altered flows in
the context of non-flow variables for each scenario for each endpoint used to
represent the use and protection management objectives of the study as shown
in Fig. 10. In Fig. 10 the integrated risk probability profiles to all
endpoints for each RR are shown, which compares Scenario 2 (reference scenario) to the
high use Scenario 3. These results include elevated risk probabilities for
RR1 (84 % moderate and 15 % high rank range) and RR2 (81 %
moderate), while existing E-flows from Phase I dams reduce the risk posed for
this scenario in RR3 and RR4. The relative risk results to endpoints and
integrated risk profiles were presented to stakeholders who used these
outcomes to select E-flows and associated water resource use mitigation
measures (such as barrier mitigation measures) to be implemented for Phase II
of the LHWP. In this case study E-flows were established by initially
evaluating the individual flow tolerance exceedance thresholds for numerous
social and ecological indicators used in the BNs. This information resulted
in the development of a hydrological scenario matching the shape of the
natural hydrology that was evaluated in the BNs. Thereafter the BNs
themselves were used to generate the hydrological statistic variable
conditions required to achieve a suitable risk profile for each social and
ecological endpoint that represents the E-flows for the study in the context
of the vision for RRs in the study. With this information, a range of
alterative water resource use scenarios were generated and considered to
explore risk trade-offs between social and ecological endpoints.
Mara River risk calculation
In the Mara River case study, the relative risk of stressors and the E-flows
were established according to the four social and two ecological endpoints
considered in the assessment. The Mara River case study (refer to Table S1)
was based on existing data from historical surveys (Mati et al., 2008;
McCartney, 2010; Majule, 2010; LVBC and WWF-ESARPO, 2010; Mango et al., 2011;
Kanga et al., 2011; Defersha and Melesse, 2012; Defersha et al., 2012;
GLOWS-FIU, 2012; Dutton et al., 2013; Atisa et al., 2014; Gichana et al.,
2014; Kilonzo et al., 2014; McClain et al., 2014) and a single site visit to
refine the CPTs (NBI, 2016). During this survey seven sites were selected to
represent the variability of all of the RRs in the study area. After
establishing BN models for each RR (refer to BN models provided in the Supplement),
input parameters were changed using RR-specific data for
two scenarios, including the present condition and alternately the E-flow
requirement to achieve the basic human needs and ecological wellbeing of the
Mara River known as the Ecological Reserve (United Republic of Tanzania,
2009; Government of Kenya, 2002).
In this case study relative risk results were used to generate E-flow
requirements that would not pose excessive risk to the wellbeing of
ecological endpoints and social endpoints as described by the RQOs. The
assessment hypothesizes that sufficient flows currently exist to maintain the
endpoints in an acceptable condition. In addition, in the context of the
precautionary principle, additional flows can be allocated before risk to the
endpoints exceeds acceptable, sustainable thresholds. Results further
demonstrate that sustainable water allocations would reduce risk to selected
social endpoints selected in the study and meet the desired balance between
the use and protection of the resource (Fig. 11). The approach highlighted
the probable effect of non-flow-related stressors that are affecting the
ecological wellbeing of Mara River, including water physiochemical impacts
and habitat alteration stressors associated with urban and rural communities,
livestock grazing and watering and the effect of the recent exponential
increase in local Hippopotamus amphibius populations in the
tributaries of the Mara River in particular that are affecting water quality
in the system (Kanga et al., 2011; Subalusky et al., 2015). These results
were used to demonstrate the relative risk of sources or water resource
activities that affect flows relative to other sources at the risk of ecosystem wellbeing (Fig. 11). The approach successfully demonstrated how the
BN-RRM approach in PROBFLO can be used to generate acceptable risk profiles
for endpoints to evaluate the socio-ecological consequences of altered flows,
and it is also demonstrated how these models can be used to determine E-flows and associated
information for water resource use.
Relative spatial risk of the multiple sources and stressors
associated with current (a) and planned (b) water resource
use to risk regions (RRs) selected for the Mara River basin case study.
Step 7: Uncertainty evaluation
Best ecological risk assessment practice requires the explicit evaluation of
uncertainty, or confidence assessment, (O'Brien and Wepener, 2012; Landis,
2004), which has been incorporated into the PROBFLO approach. Any and all
aspects of uncertainty associated with the entire BN-RRM process, including
objectives and endpoint selection for the assessment, availability and use of
evidence, expert solicitations and model uncertainty, for example, must be
addressed. In an effort to reduce uncertainty, the BN-RRM approach adopted by
PROBFLO inherently considers uncertainty associated with cause and effect
relationships and the use of real data with expert solicitations (Uusitalo,
2006; Landis et al., 2016). The additional incorporation of entropy reduction
analysis in relative risk calculations using Monte Carlo simulations also
contributes to uncertainty reduction in PROBFLO. Additional analyses of the
sensitivity of the BN-RRM should be addressed within the uncertainty
evaluation section (Pollino et al., 2007; Hines and Landis, 2014), where the
relative influence of input nodes on the endpoints can be evaluated as part
of the PROBFLO assessment. The results of the uncertainty assessment are used
to provide context to the stakeholders of a PROBFLO assessment and contribute
to the decision making process in E-flow assessment studies.
For all of the BNs created in the PROBFLO assessments of the Senqu and Mara
River case studies, the sensitivity of the input variables was evaluated in
Netica™ using the “Sensitivity to Findings”
tool (Marcot, 2012). This approach allows for the relative contribution of
each variable to be evaluated. These assessments are used to evaluate model
structure and interpret risk result outcomes with the stakeholders of the
assessment (Marcot, 2012; Landis et al., 2016). This test demonstrates to
both PROBFLO operators and stakeholders where models and associated
assessments are sensitive to input data. Evidence to justify these sensitive
determinants are imperative to a robust assessment. Additional sources of
uncertainty include the comparative availability of evidence and expert
knowledge pertaining to the socio-ecological systems considered in the
assessments. The Senqu River case study addressed the second phase of a water
resource use development that already has two substantial flow-altering
developments with more than 15 years of pre- and post-development E-flow
assessment (using holistic EFA methods; Arthington et al., 2003) monitoring
and evaluations. Additional field surveys of the study area were carried out
to generate additional information and to test existing hypotheses for the
assessment. The Mara River case study was based largely on available
historical information and existing EFA results for parts of the study area
(McClain and Kashaigili, 2013; Dessu et al., 2014).
Step 8: Hypotheses establishment
In the hypotheses establishment step of PROBFLO, suitable hypotheses for
field and laboratory experiments are established to test flow–ecosystem and
flow–ecosystem service relationships (Landis, 2004; O'Brien and Wepener,
2012). In PROBFLO the fundamental adaptive management approach to improving
our understanding of socio-ecological risk relationships, while revisiting
outcomes and re-evaluating approaches, is formalized in the hypotheses
establishment and testing phase. This process is based on a similar process
in the RRM approach, established to reduce uncertainties and to confirm the
risk rankings in risk assessments (Landis, 2004). In PROBFLO these adaptive
management principles acknowledge that socio-ecological systems are dynamic
and that our limited understanding of these processes necessitates the
incorporation of many assumptions. In many case studies, uncertainties
associated with the outcomes need to be mitigated before they can be used to
inform decision making. To reduce uncertainty, assumptions can be tested
rigorously and early. The adaptive management processes should
(1) be informed by iterative learning about the flow–ecosystem and
flow–ecosystem service relationships, (2) consider and respond to earlier
management successes and failures and (3) increase present-day
socio-ecological system resilience that can improve the ability of E-flows
management to respond to the threats of increasing resource use (Lee, 2004).
In the Senqu River case study, many hypotheses associated with the
flow–ecosystem and flow–ecosystem service relationships, largely
established on data associated with Phase I of the LHWP, were established and
tested during the field surveys. These hypotheses included the following.
(1) Woody vegetation communities, sustainably harvested by local communities
for fuel, respond to reduced average flows by increasing in abundance due to
reduced flow variability, reduced stream power and through the colonization
of new lower marginal zones. (2) Migratory cyprinid fish respond to
ecological cue flows, which include increased discharges associated with
reduced salinity, and which initiate fish migration. (3) Grazing for
livestock of local communities depends on freshet flows lifting water onto
the river banks and floodplains to stimulate vegetation growth. Data were
collected from the study area to address these hypotheses and improve the
understanding of the flow–ecosystem and flow–ecosystem service
relationships considered in the study. In the Mara River case study available
flow–ecosystem and flow–ecosystem service information was used in the
PROBFLO assessment. A range of hypotheses associated with our understanding
of the relationships were generated to refine and improve E-flow assessments
of the study area.
Step 9: Test hypotheses
The two PROBFLO case studies included the design of long-term monitoring
programmes to test the accuracy of risk projections and improve the
understanding of the flow–ecosystem and flow–ecosystem service
relationships. In the Senqu River case study a data management system (DMS)
with automated data evaluation components was established. In the Mara River
case study a range of hypotheses were established and used to design a
monitoring plan and associated research programme to confirm the
flow–ecosystem and flow–ecosystem service relationships considered in the
study.
Step 10: Communicate outcomes
Regional-scale ecological risk assessments of water resources are carried out
on behalf of stakeholders of the use and/or protection of those resources.
Stakeholders need information generated with robust, best scientific practice
methodologies in a transparent, clear and concise format, to evaluate the
socio-ecological consequences of water resource use options. The PROBFLO
approach highlights the importance of communicating the outcomes of
assessments in the context of the uncertainty identified in an assessment
(Hayes and Landis, 2004). A variety of techniques and tools are available to
assist in the communication of the E-flow outcomes and associated
socio-ecological consequences of altered flows and careful attention must be
paid to ensure that the relevant stakeholders of any case study are presented
with information that can easily be understood (O'Brien and Wepener, 2012).
In the Senqu River case study, the LHDA with South African and Lesotho
governmental delegates participated in a project outcomes workshop in 2014.
During this workshop the PROBFLO approach adopted for the study, results and
outcomes were discussed. Risk results of sources and stressors to social and
ecological endpoints were compared in a relative manner, facilitating water
resource use and protection trade-off considerations for the LHWP Phase II.
In the Mara River case study the PROBFLO assessment successfully formed a
part of the Nile E-flows framework development (NBI, 2016) and the ongoing
Mau Mara Serengeti (MaMaSe) Sustainable Water Initiative
(http://mamase.org). The application of the PROBFLO and associated
uncertainty assessment was used to establish a monitoring plan that should be
implemented with water resource use scenarios selected from the case studies.
These plans were designed to validate the model by testing the response of
the receiving environment to observed ecosystem driver conditions, associated
with implemented scenarios, and to improve the understanding of the causal
relationships hypothesized in the original assessment with real
data.
Conclusion
The regional-scale ecological risk assessment approach was established in
1997 in response to the need to apply ecological risk assessments that consider multiple
sources, stressors and receptors in the context of spatial and temporal
ecosystem dynamics, on multiple spatial scales (Landis and Wiegers, 1997,
2007). The approach, which includes the RRM, has been widely implemented,
reviewed and proven to be a robust probabilistic modelling tool to contribute
to the sustainable management of ecological resources (Landis and Wiegers,
2007). Recent developments in E-flow frameworks (Poff et al., 2010; NBI,
2016) now also call for holistic, regional-scale, probabilistic E-flow
assessments that consider flow and non-flow drivers of change in
socio-ecological contexts. We have established a regional-scale ecological
risk assessment method, incorporating the BN-RRM approach, to evaluate the
socio-ecological consequences of altered flows and establish E-flows called
PROBFLO. PROBFLO is a robust method for E-flow assessments that meets current
standards of best scientific practice and can make a positive contribution to
the sustainable management of water resources. The approach provides true
transparency and adaptability options for holistic E-flow management. PROBFLO
has already been successfully implemented in two major case studies where its
flexibility and functionality has been demonstrated. In both case studies the
evidence-based outcomes facilitated informed environmental management
decision making, in the context of social and ecological aspirations. From
these outcomes, stakeholders have, in addition, been able to consider
sustainable social and ecological trade-offs in an attempt to balance the use
and protection of water resources. The PROBFLO outcomes used to direct the
sustainable use of water resources in the case studies are probabilistic and
need to be validated with monitoring data during implementation phases.
PROBFLO is an adaptable tool that allows for the rapid incorporation of new
information, which will inform adaptive management and reduce uncertainty
associated with the accuracy of the projections. In the case studies,
stakeholders were presented with evidence-based probabilistic projections of
PROBFLO and used the risk projections to consider water resource use
trade-off options. Both of these case studies are being used by stakeholders
to make water resource use decisions that are currently being undertaken.
PROBFLO is a holistic, evidence-based probability modelling E-flow assessment
tool that is transparent and adaptable, and it is suitable for application on
multiple spatial scales. PROBFLO has the potential to contribute to the
sustainable management of water resources for the benefit of social and
ecological components of these systems.
Code availability
The code used in this paper is available in the
Supplement.
Information about the Supplement
The Supplement includes justification tables for the exposure and effects
input nodes, and the conditional probability justification tables for the
daughter nodes use in the Bayesian Network models in the study. In addition
two Netica™ working Bayesian Network models
used in the risk assessment for the Mara River and Senqu River case study is
provided.
The supplement related to this article is available online at: https://doi.org/10.5194/hess-22-957-2018-supplement.
Author contributions
The BN-RRM approach established for use in E-flow assessments was
co-developed by GCO'B, CD and VW. The approach was
implemented by this team including LQ, KF, JM, MG and RS. The paper was written by
GCO'B and CD and edited by WGL, VW, EH and RS.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The PROBFLO E-flow assessment approach was established through the Institute
of Natural Resources NPC (Pietermaritzburg, South Africa) as part of the
LHWP Phase II study funded by LHDA. We acknowledge the contributions made to
the initial Senqu River case study by the extended team of ecological and
social scientists and engineers who worked on the project. Contributions
were also made through the NBI Guidance Document on Environmental Flows
study, prepared by HYDROC GmbH in collaboration with Mau Mara Serengeti
Sustainable Water Initiative study on behalf of the Nile Basin Initiative
and Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ). In
particular, the contributions made by Michael McClain and John Conallin are
acknowledged. The authors also acknowledge the advice provided by fellow
E-flow assessment scientists that have contributed to the development of
PROBFLO including Jacqueline King, Cate Brown, Rebecca Tharme and Jay O'Keeffe.
Finally, we appreciate the comments on the manuscript provided
during the discussion document phase of the HESS publication process and the
highly valuable comments and suggestions provided by the reviewers of the
manuscript.
Edited by: Stacey Archfield
Reviewed by: two anonymous referees
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