Developing Hydroecological Models to Inform Environmental Flow Standards: a Case Study from England

Focus Article Developing hydroecological models to inform environmental ﬂow standards: a case study from England Megan J. Klaar,1,2∗ Michael J. Dunbar,3 Mark Warren4 and Rob Soley5

The concept of defining environmental flow regimes to balance the provision of water resources for both human and environmental needs has gained wide recognition. As the authority responsible for water resource management within England, the Environment Agency (EA) uses the Environmental Flow Indicator (EFI), which represents an allowable percentage deviation from the natural flow to determine where water may be available for new abstractions. In a simplified form, the EFI has been used as the hydrological supporting component of Water Framework Directive classification, to flag where hydrological alteration may be contributing to failure to achieve good ecological status, and to guide further ecological investigation. As the primary information source for the EFI was expert opinion, the EA aims to improve the evidence base linking flow alteration and ecological response, and to use this evidence to develop improved environmental flow criteria and implementation tools. Such tools will be required to make predictions at locations with no or limited ecological monitoring data. Hence empirical statistical models are required that provide a means to describe observed variation in ecological sensitivity to flow change. Models must also strike a balance between generic and local relationships. Multilevel (mixed effects) regression models provide a rich set of capabilities suitable for this purpose. Three brief examples of the application of these techniques in defining empirical relationships between flow alteration and ecological response are provided. Establishment of testable hydrological–ecological relationships provides the framework for improving data collection, analysis, and ultimately water resources management models. © 2014 Wiley Periodicals, Inc.

How to cite this article: WIREs Water 2014, 1:207–217. doi: 10.1002/wat2.1012

INTRODUCTION ∗ Correspondence to: [email protected] ncreasing demand for water combined with changes 1Institute of Science and the Environment, University of Worcester, in predicted water availability as a result of Worcester, UK I climate trends and variability has sharpened interest 2Evidence Planning Assessment and Reporting, Environment Agency, Solihull, UK in the identification of environmental flow regimes for 3Environment and Business, Water Resources Technical Services, riverine ecosystems. Environmental flows have been Environment Agency, Reading, UK defined as ‘the quantity, timing, and quality of water 4Environment and Business, Water Resources Technical Services, flows required to sustain freshwater and estuarine Environment Agency, Tewkesbury, UK ecosystems and the human livelihood and well-being 5 Environment and Infrastructure UK Ltd, AMEC, Bodmin, UK that depend on these ecosystems’.1 During the 1970s Conflict of interest: The authors have declared no conflicts of interest for this article. and 1980s, research focused on defining primarily

Volume 1, March/April 2014 © 2014 Wiley Periodicals, Inc. 207 Focus Article wires.wiley.com/water low flow requirements through bottom-up approaches This typically requires the application of hydrologi- based on habitat requirements of target species.2 cal models, although the scope and complexity of In the 1990s, alternative bottom-up frameworks, this application can vary from case to case. A second most notably the building block approach3 emerged, critical step is how to deal with the almost ubiq- focusing on the ecological functions of a range of flow uitous range of aforementioned factors that can act regime components. The natural flow paradigm4 then to confound clear hydroecological relationships. One developed as a more top-down conceptual framework, strategy has been to screen out data subject to elevated with the assumption that deviations from the natural levels of hydromorphological degradation and water flow regime, as a result of anthropogenic flow quality impairment; however, this may often lead to influences (i.e., abstractions and discharges from both insufficient remaining data for the task in hand. The surface and groundwater systems) indicate potentially alternative, probably more satisfactory approach is to detrimental impacts. ensure that data are collected on the potential con- Although the importance of flow as a confounding factors and to use these data in the statistical trolling variable for aquatic ecosystems is relatively modelling approach. well known,4,5 methods of predicting and quantifying As pressure on water resources increases, along biotic responses to altered flow regimes remain elusive. with the need to integrate scientific evidence with This is likely to be due to a combination of factors: socioeconomic management goals, the Environment interactions with other pressures such as habitat mod- Agency (EA) of England is beginning to implement ification, chemical and physico-chemical pollution,5–9 some aspects of ELOHA (outlined in Figure 1). Future lag effects,5 and biological interactions, including with legislative changes to the current water resource nonnative invasive species.5 Given these difficulties in licensing system in England and Wales11,12 are determining the direct effects of altered flow regimes planned, primarily driven by a wish for greater resulting from abstraction (the removal of water from efficiency. Such changes provide an opportunity surface and groundwater sources for anthropogenic to integrate social, economic, and environmental uses including public water supply, agriculture, and scenarios, set against a backdrop of climate change. industry) and other anthropogenically driven alter- Central to the development of future policy is the ations, new methods and techniques which link assessment of current practices, including the level of abstraction with ecological response are required. The protection and evidence that underpins current water strongest inferences would be obtained from designed resource allocation methodologies. artificial flow manipulation experiments, undertaken The rest of this article focuses on describing across multiple catchments. However, the coordi- the current situation in England (and Wales nation and implementation of such an approach is where applicable), before outlining the need for extremely challenging. Hence it is also essential to hydroecological models and providing some examples draw upon existing observational datasets from mon- of their application. itoring programs, both to develop hydroecological relationships and to assess the level of environmental protection provided by current policies. DETERMINATION OF WATER This article focuses on assessing ecological AVAILABILITY AND response along a gradient of hydrological impact, ENVIRONMENTAL FLOW WITHIN using empirical data and statistical models. ELOHA (Ecological Limits of Hydrological Alteration10)has ENGLAND been proposed as a framework to assist water man- Within England, the EA has overall responsibility agers in the development of evidence-based environ- for the management of water resources, including mental flow regimes. It sets out to synthesize and struc- the licensing of surface and groundwater abstractions ture hydroecological evidence, methods, and available and discharges. In the development of the current data in order to develop defensible and empirically water resource management system, previous regional testable regional relationships between flow alter- systems were assessed and those approaches ation and ecological responses.10 Hence a critical representing best practice were drawn together into a step in developing hydroecological relationships is nationally consistent framework. This is underpinned the provision of adequate hydrological data. Many by standards developed in line with EU Water existing studies have focused on historical observed Framework Directive (WFD) requirements for flows flow data only. Central to the ELOHA approach is to support the achievement of Good Ecological an assessment of the nature and degree of hydrologi- Status (GES) objectives.13–15 These standards have cal alteration resulting from anthropogenic influences. been agreed by the UK Technical Advisory

Step 1. Hydrologic foundation Step 2. River classification (for each analysis node) Baseline Hydrologic Geomorphic Hydrographs classification Sub- River type classification CAMS Flow data and Step 3. Flow Alteration (for each analysis node) EA routine modeling process Analysis of Measures of monitoring Developed flow flow hydrographs alteration alteration

Monitoring Step 4. Flow-Ecology Linkages Statistical Flow - Ecology Ecological Flow alteration-Ecological hypotheses (for data (for each response relationships (multilevel) each river type) analysis node) (for each river type) modelling

Social process Acceptable Societal Environmental River Basin Implementation ecological values and flow standards conditions management needs Management Adaptive adjustments Plans

Group (UKTAG16) on the WFD which covers series, or by simulations without artificial flow influ- England, Wales, Scotland, and Northern Ireland, ences from rainfall–runoff models such as LowFlows 29 although individual authorities differ slightly in their Enterprise or runoff, recharge, and groundwater 30 implementation. Flow standards are expressed as flow models. Allowable reductions from this natural deviations from natural flow regime, and vary by flow, deemed to have an acceptable impact upon river type. In developing the UKTAG standards, the river ecosystem, may be licensed for consumptive abstraction.31 The RAM framework incorporates it was recognized that the existing global evidence a variable sensitivity of surface water bodies to base linking water abstraction directly to ecological 17–24 flow change, consisting of simplified UKTAG flow response was weak (with the exception of ), standards, using physical typologies that underlie so that the primary source of information for the reference condition classification models for macroin- standards was expert opinion. Subsequent conversion vertebrate fish, and macrophytes.28,32 Perceived 16,25,26 of expert opinion into practical flow standards varying sensitivity of different biological quality took a risk-based approach whereby it was recognized elements (BQEs) to flow change is summarized as one that some river bodies may fail to achieve the desired of three overall abstraction sensitivity bands (ASBs). ecological status, but that these would be identified by This allows more abstraction in sub-catchments appropriate monitoring.26 considered to be less sensitive to flow change Within England and Wales, the EA and Natural (typically low gradient, clay dominated catchments, Resources Wales (NRW) use Catchment Abstraction termed ‘ASB1’ waterbodies) than those considered to Management Strategies (CAMS) to track, monitor, be more sensitive to flow change (e.g., high gradient and licence water resource availability in water and chalk geology headwaters; ‘ASB3’ waterbodies). bodies. Central to the CAMS process is the Resource The allowable deviation from natural flows used in the RAM framework to determine water Assessment and Management (RAM) framework resource availability is known as the Environmental which determines the water resource availability of Flow Indicator (EFI31). The EFI is drawn below the catchments and WFD waterbodies.27,28 Water avail- long-term natural flow duration curve according to ability is assessed using naturalized flow as a reference the ASB, as show in ‘Step 1’ on the left of Figure 2. condition (i.e., the calculated flow which would be It is used as a risk screening threshold in comparison observed provided no direct anthropogenic influences with scenario flows impacted by abstractions and were operating, sensu ‘baseline conditions’ within the discharges (Step 2 on the right of Figure 2) to indicate ELOHA approach). Natural flows are derived either where abstraction pressure may have an undesirable by removing the impacts of historically measured effect on river habitats and species. As such, it is not abstractions and discharges from gauged flow time implemented as a hard ‘standard’, but rather as a

Volume 1, March/April 2014 © 2014 Wiley Periodicals, Inc. 209 Focus Article wires.wiley.com/water prompt to prioritize more detailed local investigation Resource availability for further licensing of ecological impact, taking account other pressures. depends on comparison of ‘Fully Licensed’ scenario A full flow duration curve comparison of the flows with the EFI at all water bodies or CAMS APs EFI against flows impacted by both ‘Recent Actual’ downstream to the sea. This considers the potential and potential worst case ‘Fully Licensed’ abstraction worst case flow risks assuming all abstractors take and discharge scenarios is carried out at around all of their licensed entitlements. It also provides an 1200 CAMS assessment points (APs) across England indication of the risk of flow deterioration in the and Wales. This assessment is undertaken within a future due to increased uptake within licences, when spreadsheet tool known as the CAMSLedger. CAMS compared with Recent Actual scenario flows. APs are often located at flow gauging stations which An ecological monitoring network, covers the can be used as reference points for ‘Hands-Off Flows’ full WFD river waterbody network for appropriate (HOFs)—constraints applied to surface water abstrac- BQEs, with sampling/survey occurring typically once tions in order to allow more water availability when in every 3 years. At CAMS APs, monitored biological flows are high but to limit their low flow impacts. (primarily macroinvertebrate) and hydrological The detailed EFI flow duration curve defined at these (gauged river flows) data are used to monitor and CAMS APs is built up from a series of HOF threshold ‘ground truth’ environmental conditions. In a risk- steps, above which additional volumes of water based approach, those APs deemed EFI ‘compliant’ become available for licensing (illustrated in Step 1 are monitored less frequently than those which of Figure 2 as the blue ‘TAKE’ boxes in the stacked are described as EFI ‘non-compliant’. As biannual histogram to the left of the flow duration curve). A macroinvertebrate monitoring for CAMS has been ‘minimum residual flow’ is set to protect lowest flows undertaken since 2000, analysis of these data can with the abstractable proportion of natural Qn95 begin to link changes in biological condition with flows ranging from 10% (ASB3) to 20% (ASB1). abstraction and flow pressure to reveal important As flows rise above each higher HOF threshold, generalized ecological responses. By analyzing and additional abstraction TAKE becomes available. assessing the quality of the historical data collected, A broadly equivalent but simpler assessment the EA may also establish a framework for improving of four summary flow conditions (Q30,Q50,Q70, data collection and analysis, and ultimately, develop and Q95) is carried in the Water Resources GIS predictive models to help ongoing water resource at these same 1200 abstraction regulation points management. together with an additional 8000 river and lake water Since the late 1990s, the EA has begun to body outflow points defined for WFD ecological develop ecological metrics designed to respond to classification.13 The integrated screening calculations antecedent flow conditions and hence abstraction also extend downstream to consider abstraction pressure. Extence et al.33 described the macroinver- pressures on the freshwater inflows to ‘transitional tebrate metric LIFE (Lotic-invertebrate Index for waters’ or estuaries. A table of allowable abstraction Flow Evaluation). This used six taxon weightings impacts—as percentages of natural freshwater inflows to aggregate community response in terms of the to estuaries at the four flow conditions—is included balance of abundance of taxa which prefer fast flow- at the bottom left of Figure 2, beneath the tabulated ing clean gravel substrates versus slow flowing or limits assumed for river and lake water body outflows still substrates with deposited fine sediment. As part in the Water Resources GIS. of CAMS, a Hydroecological Validation (HEV) is The outcome of this flow duration curve undertaken at each CAMS AP. This involves the screening process is summarized in simple ‘traffic graphical display of historical river flow data along- light’ colors, as detailed in Step 2 on the right of side historical macroinvertebrate samples summarized Figure 2. Compliance of ‘Recent Actual’ scenario flows as the LIFE, Average Biological Monitoring Work- with the EFI at Q95 (low flows) is used to indicate ing Party (BMWP) Score Per Taxon (ASPT), Ntaxa those water bodies where river flows do or do not (number of BMWP scoring taxa)34 and Proportion of support Good Ecological Status under the WFD. Flow Sediment-sensitive Invertebrates35 (PSI) metrics. Met- ‘non-compliance’ bands are mapped for water body rics are standardized (termed observed/expected, or sub-catchments across England and Wales in Figure 3 O/E) against reference condition using the River Inver- and are used to prioritize more in-depth reviews tebrate Classification Tool (RICT) software which of flow and associated ecological impacts. These implements the River Invertebrate Prediction and impacts are typically associated with abstractions Classification System (RIVPACS) IV model.36 This which predate the licensing system introduced by the standardization is broadly, but not exactly equivalent Water Resources Act (1963). to a WFD Ecological Quality Ratio; the difference

Hands off flows (HOFs) and Flow duration curve example plotted from Q50 Nat Natural flow CAMS resource TAKEs defining environmental to Q100 plotted for Abstraction Sensitivity Environmental availability colour flow indicators for licensing in Band 3 (ASB3, highly sensitive to abstraction) Flow EFI definitions CAMS ledgers Indicator 20 20 20 6 = QN35 RA flow is > EFI supporting low flow detail Net abstraction low flow detail Good Ecological Status 18 18 long term FDCs & discharge 18 long term FDCs 5 = QN50 scenarios new licences may be Natural fl granted depending on 16 16 Higher flow 16 Recent actual local & downstream

Total Licensable impacts. FL flows are > TAKE Resources to Q50 Abs & Dis 14 14 ow reference RA 14 Nat + 10% ASB1 = 50% 4 = QN65 ASB2 = 40% New licences may be ASB3 = 30% 12 12 Fully licensed 12 granted depending on 3 = QN75 EFI for rive Abs, recent local & downstream 10 10 Good Ecological act dis 10 impacts 2 = QN85 FL No new consumptive rs to 8 8 8 licences but flow recovery 1 = QN95 Low flow support probably not needed Daily flow values (MI/day) TAKE* Daily flow values (MI/day) Stat 6 6 6 No new ASB1 = 20% MRF No new us consump. ASB2 = 15% = 75% of Take 4 consump. 4 4 4 licences. ASB3 = 10% QN 99 Take 3 licences. Is paper *i.e. % or QN95 Fixed Take 2 Is actual 2 2 2 licence but subject to HOFs & Take 1 flow Minimum reduction recovery MRF UNC needed? Residual 0 0 0 needed? 50 55 60 65 70 75 80 85 90 95 Flow (MRF) 50 55 60 65 70 75 80 85 90 95 Percent of the time flow exceeded 100 Percent of the time flow exceeded 100 RA flow is lower than EFI EFI abstraction impact Q30 Q50 Q70 Q95 High Med Low V Low limits as % Natural ASB3 24% 20% 15% 10% Q30 Q50 Q70 Q95 Flows – simplified for ASB2 26% 24% 20% 15% WR GIS summary percentile examples of WFD recent actual Q95 WR GIS Rivers, Lakes & ASB1 30% 26% 24% 20% CAMS resource availability colours compliance colour CAMS APs Q95 FL flows would be > Nat + 10% definitions WR GIS estuarine ASB13 35% 31% 29% 25% equivalents FL flows would be > EFI Compliant RA > EFI fresh water inflow EFI ASB12 40% 36% 34% 30% Band 1 RA < EFI limits allow extra ASB11 45% 41% 39% 35% FL flows would be just < EFI FL flows would be < 90% EFI Band 2 RA < (EFI – 25%Nat) impacts beyond ASB1 Non- Band 3 RA < (EFI – 50%Nat) RA flows are < EFI compliant low sensitivity rivers RA flows are < (EFI – 25% Nat) Step 1: Define the Environmental Flow Indicator (EFI) supporting Good Step 2: At each flow condition compare the EFI with predictions of what the Ecological Status – hang it below the natural flow reference condition flow would be for the Recent Actual (RA) Fully Licensed (FL) Scenarios. according to the Abstraction Sensitivity Band. Less impact allowed at low Summarise as WFD low flow compliance bands (RA Q95 only) & CAMS flows. Stepped EFI in ledger based on the assumption of common HOFs. resource availability colours (considering both RA & FL scenarios)

FIGURE 2| Principles of abstraction pressure flow screening against Environmental Flow Indicators (EFI) for Catchment Abstraction Management Strategies (CAMS) resource availability and Water Framework Directive (WFD) recent actual low flow compliance, as calculated in CAMSLedger spreadsheets and the Water Resources GIS. being an O/E ratio for a site in reference condition can plots, there will always be a degree of subjectivity. naturally exceed 1. In HEV, O/E LIFE metric values Furthermore, it becomes difficult to use HEV with through time may be linked graphically to historical short biological data series and is clearly impossible flow, which in turn will be driven both by climate with no data. Hence, generic statistical models, and water use. Providing an acceptable number of calibrated using historical flow and biology data, are spring (March to May) and autumn (September to the logical next step to develop more widely applicable November) macroinvertebrate samples are available assessment tools. Model flexibility is enhanced where (≥9), a lower 10th percentile value for O/E LIFE may a lack of gauged flow data can be substituted with be compared with a guideline value (0.94). At the time modeled hydrological data (both historical flows and of writing, LIFE is not included as a WFD classifica- flow impact compared with natural). tion metric, although it is hoped it can be included within third cycle of WFD River Basin Management Plans. Where this threshold is reached, providing other Quantifying the Links between Changes checks (including other metric plots) are acceptable, in Flow and Ecology using Multilevel this flags potential abstraction impact. The use of a Models: Examples lower 10th percentile explicitly incorporates the eco- By utilizing existing data and multilevel linear logical effects of natural flow variation. Ecological regression modeling, the EA is developing models impacts due to other pressures (e.g., water quality, which can be used to quantify the ecological excess fine sediment, impact of non-native invasive impact of water abstraction. Such models can be species) may be assessed in a semiquantitative manner used to predict ecological response to changes in with reference to the behavior of the four HEV metrics. flow (whether natural or anthropogenic). Following While the EA has made considerable effort to standard principles (see Box 1), predictions, with train and develop staff in the interpretation of HEV appropriate confidence intervals, may be made for

WFD Recent Actual Q95 compliance colour definitions Compliant RA > EFI Band 1 RA < EFI Band 2 RA < (EFI – 25%Nat) Non- Band 3 RA < (EFI – 50%Nat) compliant

Some features of this map are based on digital spatial data licensed from the Centre for Hydrology and Ecology © CEH

Creation date 1 April 2013

FIGURE 3| Recent Actual scenario low ﬂow compliance with the EFI for Water Framework Directive (WFD) waterbodies and Catchment Abstraction Management Strategies (CAMS) assessment points across England and Wales (from the Water Resources GIS, April 2013 version). individual sites in the model, and a generic prediction made for sites not in the model. Data from ‘new’ convenient to interpret environmental variation locations not originally used in model development (for example, spatial and temporal variation), can be assimilated in order to make a prediction for in a hierarchical manner. Multilevel modeling that location, regardless of the available length of has mainly been developed with a focus on ecological time series. social and medical sciences where natural hierarchies occur, e.g., observations on pupils within classes within schools, and observations on patients within hospitals. It allows multilevel MULTILEVEL MODELING IN ECOLOGICAL explanatory variables to be used within a model AND ENVIRONMENTAL STUDIES (or group of models) by allowing residual Multilevel regression models are increasingly variance at the varying levels (termed ‘random being used by ecologists.37 The terms mixed effects’38) to be modeled separately, allowing effects, random coefﬁcient, and hierarchical differences in response among groups (e.g., sites are often used as alternative descriptors of or waterbodies) to be taken in to account. such models: while there are subtle differences Multilevel models are also able to predict even in meaning, at a high level the techniques for groups with patchy data availability and are equivalent. The way we sample the record length by ‘borrowing’ model strength environment is naturally hierarchical, and it is from other sites/time periods (Dunbar, 2013,

appropriate indicators of deviation from the natural unpublished data). These properties make flow and their relationship with WFD BQEs. multilevel modeling particularly applicable for use in ecological and environmental flow studies. Quantifying the Links between River Flows Downstream of Impounded Lakes and Juvenile Atlantic Salmon (Salmo salar) Assessment of Environmental Flow Generalized linear mixed-effects models (GLMMs) Non-Compliance and Ecological Response were used to model the response of juvenile (0+) As noted above, non-compliance with the EFI is used Atlantic salmon abundance to antecedent flows down- to trigger ecological investigations to gather evidence stream of impounded lakes where water abstraction for any genuine ecological failure caused by that is directly from the lake. Counts of juvenile salmon non-compliance. To date, the degree and occurrence were obtained from routine EA electric fishing surveys of non-compliance in relation to actual ecological between 1999 and 2011 at 39 sites across four rivers condition have yet to be considered. Key questions in the North West of England. The rivers are all requiring attention are: downstream of impounded lakes used for public water supply and their flow regimes are largely 1. Do the WFD BQEs (i.e., monitored fish, dictated by various abstraction licence conditions macroinvertebrate and macrophytes, summa- which ensure that downstream river flows are rized as WFD ecological quality ratios) relate provided for other water users and to avoid the drying to EFI compliance? of rivers. The conditions have no specific ecological target, and so are not considered ‘environmental 2. Is the EFI aligned with generally accepted eco- flows’ in the context of ELOHA or other ecological logically relevant flow elements (i.e., compo- flow standards. Droughts in the in the mid-1990s nents of the natural flow paradigm4)? led to the use of drought orders and permits by the water company in order to ensure security of water In order to address these questions and supply to the public. This resulted in extreme low determine the ecological significance of non- flows and raised concerns over the risk to salmon compliance with the EFI (as determined using the table populations from future drought order/permit use. in Figure 2), a multilevel linear regression modeling Increased fish surveys were then implemented to gain approach was used to describe macroinvertebrate a better understanding of the ecological responses of community (O/E LIFE score, fitted using maximum juvenile (0+) Atlantic salmon to flow variability. likelihood, followed by a data dredge using MuMIN; Data from a flow gauge on each river covering MultiModel Inference39) response through time to the survey period were obtained, and two time-varying non-compliance events, summarized as their duration, summary flow statistics, Q95 and Q5, were calculated magnitude, frequency, and rate of change. A dataset from the gauged daily mean flows for the 121-day of 24 paired biological–hydrological sites from the period from April 1 to July 31 in each year. This period Anglian region of the EA was used. was chosen apriorito represent the flow regime con- Results from a set of 11 top candidate models ditions experienced by 0+ Atlantic salmon after emer- (ranked using difference in Akaike’s Information gence from river gravels in late winter/early spring Criterion <4) show that calendar year was the through to the late summer/autumn fish survey season. strongest predictor (positive partial relationship) of The Q95 and Q5 flow statistics represent antecedent O/E LIFE score, followed by the total number of summer low flow and summer high flow respectively. days of EFI non-compliant flows (negative partial Along with calendar year of each survey, these flows relationship) over an antecedent 6-month period prior were used to predict late summer/autumn counts of to sampling. The number of non-compliance events 0+ Atlantic salmon with a negative binomial GLMM and the duration of these events over this period using a log link between the response variable and the showed negative partial relationships with O/E LIFE, predictor function containing explanatory variables. but these were notably weaker. Results show that the abundance of 0+ Atlantic Initial results confirm that in it’s current form, salmon has significantly increased through time the EFI may be used as an indicator of where low flow between 1999 and 2011. In addition, summer low pressure may occur, but it cannot be used to indicate flow had a significant positive linear association with ecological condition or response to non-compliance abundance (Figure 4). Model residuals were checked events through time. Further targeted analysis and as part of model validation and provided no evidence statistical models should be used to develop the most of nonlinearity for this dataset. These results suggest

(a) (b) 150 200 250 150 200 250 0+ Count 0+ Count 50 100 50 100 0 0

−1 0 12 −6 −4 −2 0 24 Summer Q95 Ye a r

FIGURE 4| Model outputs of the response of juvenile Atlantic salmon to flow downstream of impounded lakes. (a) Model fit using normalised Summer Q95 as a predictor (Year held constant). (b) Model fit using Year as a predictor (Summer Q95 held constant). Year on the x-axis is centered. The shaded area represents the mean model fit and 95% confidence intervals. Open circles are raw data points. that despite temporal trends, summer (April to July) it avoids the problems inherent in developing low flows may play an important role in determining site-specific biology-flow models with inadequate the abundance of 0+ Atlantic salmon in late summer biological time series or range of flow conditions. and early autumn and could positively influence first The models predict the metric LIFE; however, the summer survival. The model indicates that for this methods are generic and could be set up for use with dataset (sites downstream of impounded lakes in NW any ecological response metric.. Metric predictions England) there was some generality in the relationship may also be compared with reference values from between 0+ salmon abundance and summer low the RICT model: in this case both ‘observed’ and flow. Further testing of this relationship using data ‘expected’ are model predictions. from other regions is required. The models provide an encapsulation of the generic understanding that both flow and physical Quantifying the Variation in Relationships habitat degradation influence the macroinvertebrate between Antecedent Flow community. These pressures are not simply additive: and Macroinvertebrate LIFE Metrics more degraded channels on average have lower overall Since 2006, a series of hydroecological models LIFE and a steeper response of LIFE to antecedent low (given the informal internal acronym DRIED- flow (Figure 5). Furthermore, there is a suggestion UP; Determining the Relative Importance of that although macroinvertebrates in lowland rivers Environmental Data Underpinning flow Pressure are more sensitive to low flow and extent of channel assessment) have been developed for the EA.40–42 resectioning separately, together these may have a The focus of this work has been to develop statistical greater negative effect in upland rivers. models which describe: These results have been validated across multiple datasets. The central message is that macroinvertebrates in channels with more natural 1. the generic response of the macroinvertebrate morphologies are probably more resilient to in-year community to antecedent flow; low flow events. This suggests that within river basin 2. how individual sites vary in their response plans, water resources interventions such as reducing around the generic response; and abstraction levels need to be viewed in the context of 3. whether any variation in response is related to other measures, particularly river restoration. simple indicators of physical habitat, primarily the extent of channel resectioning and livestock poaching (trampling of banks and bed) CONCLUSION A lack of hydroecological evidence linking historical This combined generic/specific focus is only flow and flow alteration to ecological response has possible using a multilevel modeling framework: required environmental flow criteria to be based

Influence of HMSRS on response Influence of HMSRS on response (upland sites) (lowland sites) 8.5 8.5 8.0 8.0

7.5 33% 7.5 66%

7.0 100% 7.0 0% 33% Life score (family) Life score (family) Life 66% 6.5 6.5

100% 6.0 6.0 −2 −110 2 −22−110 Normalised antecdent low flows (Q95) Normalised antecdent low flows (Q95)

FIGURE 5| Modeled mean response of Lotic-invertebrate Index for Flow Evaluation (LIFE) score to antecedent low flow for upland and lowland sites as mediated by proportion of the channel that is resectioned [Habitat Modification Score Re-sectioning (HMSRS); 0–100%]. largely on expert opinion. In England and Wales, such flow compliance. Predictions may then be used criteria have been implemented as the EFI, which is not to estimate the relative benefits and potential considered a fixed standard. Although this process has risks of flow and habitat management through been used successfully for a number of years within stakeholder engagement.46,47 England and Wales, increasing pressure on water 43 44,45 resources, forecasted changes to flow regimes, Appropriately applied, statistical models are able and plans to reform the current licensing strategy are to account for multiple correlated flow variables48,49 motivating the development of empirical statistical as well as confounding and interaction effects (e.g., models describing relationships between antecedent water quality and flow50). Ultimately good models flow, flow alteration, and ecological response, in the should reduce the need for decision making based presence of potentially confounding factors. on site-specific ecological monitoring and help target The examples above illustrate how historical future monitoring to reduce the most significant biological and hydrological data from routine sources of uncertainty. Additionally, new metrics monitoring programs can be used to provide which describe the response of all relevant WFD quantitative evidence of ecological responses to river BQEs should be incorporated into similar statistical flow. These models have several potential applications models.51 for the assessment of the ecological impacts of water Only by beginning to assess the efficacy of resource development (in the context of licensing current water resource management practices, and and restoring sustainable abstraction), and natural the creation of predictive tools which are able flow variation (including impacts of drought). These to take hierarchical data and complex, interacting include: variables into account can we begin to build robust environmental flow standards. Establishment 1. Providing generic models of links between WFD of testable hydrological–ecological relationships using BQEs and antecedent flow when no or limited modeling techniques, combined with validation and biological data are available. testing of alternative biotic metrics are urgently 2. Prediction of biological responses to changes in required to help form the basis of future environmental flow, habitat modification, and environmental flow criteria.

ACKNOWLEDGMENTS The views expressed in this paper reﬂect those of the authors and are not necessarily indicative of the position held by the Environment Agency. The authors would like to thank numerous colleagues in the Environment Agency who have contributed to the tools and thinking presented in this article. We acknowledge and thank the

Volume 1, March/April 2014 © 2014 Wiley Periodicals, Inc. 215 Focus Article wires.wiley.com/water support provided by a NERC Policy Placement Fellowship (RRAL15820) and the University of Birmingham and University of Worcester to MJK which facilitated this research.

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