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Statistical hydro-ecological models. Mike Dunbar National Hydroecology Technical Advisor [email protected] August 2013 (Statistics for Environmental Evaluation 2004). Structure. (About Me) Statistical modelling using monitoring data

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Statistical hydro ecological models

Statistical hydro-ecological models

Mike Dunbar

National Hydroecology Technical Advisor

[email protected]

August 2013

(Statistics for Environmental Evaluation 2004)


Structure
Structure

  • (About Me)

  • Statistical modelling using monitoring data

  • Hydroecology: river flows and ecological response

  • River ecology and land management stressors


Some history
Some history

  • Mid-1980s

    • Brookes: quantify massive extent of river channelisation in E & W

    • More focus on flows downstream of dams

    • Roll out of national bioassessment methods

  • 1990s

    • Addressing site-specific low flow problems

    • Development of River Habitat Survey

    • Growing interest in river restoration/rehabilitation

    • Development of LIFE metric (see later)

  • 2000s

    • European Water Framework Directive

    • Importance of hydromorphology (made up word) increasingly recognised

    • DRIED-UP project (basis of this talk)



Where it all started for me
Where it all started for me

Extence, Balbi and Chadd (1999)

Dunbar and Clarke, 2002 (2005?)

Centre for Ecology and Hydrology – Mike Dunbar


More context
More context

  • Desperate need to ‘upscale’ our detailed knowledge spatially and temporally for it to be useful for river management

  • It’s generally well known that

    • Physical environment affects river and stream biota

    • Biota have definable niches for physical microhabitat as well as water quality

    • Distribution of biota related to catchment characteristics

    • Multiple pressures are the norm

  • How to upscale: use national datasets

    • Macroinvertebrate biological monitoring

    • River Habitat Survey


Indicator organisms macroinvertebrates
Indicator organisms: Macroinvertebrates

Perla

Caenis

Sigara

Rhyacophlia

Simulium

Sericostoma

Lymnaea

Gerris

Leuctra

Fast velocity water, clean gravel / cobble substrates

Slow / still water and / or silty substrates

Perlodes



Where n = number of different taxa in sample

Groups based on a huge literature survey (which I didn’t do)


Standard sampling method
Standard sampling method

Assesshabitat

3 minute kick/sweep sample

1 minute hand search



Dried up
DRIED-UP

  • Distinguishing the Relative Importance of Environmental Data Underpinning flow Pressure assessment

  • Four R&D phases so far (DU1-4)

  • Mainly funded by Environment Agency, some contribution from NERC/CEH and EU

  • Two papers (DU1&2), ~Three reports

  • Currently undergoing testing in the EA

Centre for Ecology and Hydrology – Mike Dunbar



Analysis
Analysis

  • Data

    • Using subset of Environment Agency historical macroinvertebrate monitoring data

      • Extensively screened for water quality impacts

    • Model historical daily flows where gauges not available

    • Physical habitat quantified by a River Habitat Survey

    • Biotic index LIFE, in the manner of other biotic indices

  • Relate preceding flows to the LIFE score for each sample


Explanatory variables
Explanatory variables

  • Flow magnitudes, statistics of flows preceding samplehttp://www.ceh.ac.uk/data/nrfa/

  • River Habitat Survey

    • Habitat Modification

    • Habitat Quality



Multilevel statistical models
Multilevel statistical models

  • Also called mixed-effects, or hierarchical

  • Extension of linear regression to hierarchically structured data

  • Very common in social sciences, educational, medical statistics

  • Not very common in environmental sciences


Multilevel hierarchical approach
Multilevel / hierarchical approach

Terminology: i sample (level 1), nested within j site (level 2)


Problems with alternative approaches
Problems with alternative approaches

  • Site-by-site

    • You need a surprisingly large amount of biological data to model the LIFE-flow relationship for a site

    • Particularly if you are interested in response to different flow variables

    • So site-specific flow-biology relationships can be highly uncertain (and misleading)

  • If multiple flow variables are “tested”, this uncertainty is even greater than you think

  • Ignore group structure

    • Weak, unrealistic models

    • Unsuitable for prediction

    • Can’t handle multi-level predictors


Common patterns
Common patterns

  • BOTH high (Q10) and low (Q95) flow magnitudes influence LIFE score

  • Autumn samples more sensitive to high flow magnitude

  • Extent of Resectioning decreases LIFE score

  • Extent of Resectioning increases response of LIFE to low flow magnitude

  • Year trend: upwards, varies by site


Dried up 1 2005
DRIED-UP 1: 2005

Data from 11 sites in E.Midlands


Dried up 3 2010
DRIED-UP 3: 2010

Modelled mean response of LIFE score to Q95z for upland and lowland sites as mediated by HMSRS, and response of each individual site. Percentages are of the maximum HMSRS score observed in the dataset. NB model fitted excluding normalised Q10 term.


Borrowing strength
Borrowing strength

  • In DRIED-UP, each site in the model “borrows strength” from the dataset as a whole

  • Or.. The DU dataset makes site-specific relationships more robust

  • This is very handy for prediction


Prediction
Prediction

  • In ecology at least, too much focus on model selection as the end point

  • Actually we should take more time making predictions...

  • Plug in flow (norm seasonal Q95 and Q10) + habitat

  • No new biol data

  • New biol data (borrowing strength)

    • Example later..


Conclusions
Conclusions

  • Modelling approach accounts for the spatial-temporal structure in the data

  • Common effect of both high and low flows for both upland and lowland sites

  • Physical habitat can influence both overall LIFE and its response to flow

    • Consistent signature from resectioning across upland and lowland

  • Effect of high flows greater on autumn samples (ie summer flows)

  • There are implications for water resource management, river rehabilitation, climate change mitigation



Taking the modelling forward

Taking the modelling forward

DRUWID – DRIED-UP with Incremental Drought


Chilterns nw of london
Chilterns NW of London

  • Major aquifer: large number of water supply boreholes

  • Abstraction impacts on river flows

  • New housing development

  • “Chalk Streams”: high conservation value and public interest

  • Strong climatic control, overlaid with anthropogenic influence


E g river misbourne
E.g. River Misbourne

Photo: Misbourne River Action


Druwid concept
DRUWID concept

  • 6 years in the making...

  • How to capture more of the complexity of the flow regime?

  • How to describe impact of drought


Solutions
Solutions

  • Mixed effects approach

  • More flow variables AND

  • Flow-flow interactions


Multi model inference
Multi-model inference

  • Rank alternate competing hypotheses

  • Often no single model “best”

  • Stepwise etc approaches all flawed

  • Totally avoids issues of “significance”, in-out

    • Information Theoretic approach

  • Further details:

    • Burnham and Anderson (2002): model selection and multi-model inference...

    • Anderson (2010): model-based inference in the life sciences...



Druwid application to chilterns
DRUWID application to Chilterns

  • 42 sites in 9 catchments

  • Still using gauged flows, but also indicator as to whether site was dry the summer before sampling

  • Chose lags up to 2 years as reasonable compromise

  • Separate models for spring and autumn


Reasonable compromise this formula
Reasonable compromise- this formula:

20 fixed parameters (intercept and 19 slopes) plus 6 variances and 6 covariances

And catchment ID only varies overall LIFE, not any of the flow response slopes






Druwid is work in progress
DRUWID is work in progress

  • Was funded by CEH, but development used EA Chilterns data

  • Methodology can be applied elsewhere

  • Relatively quick to set up, just need the data..


Druwid shows that
DRUWID shows that

  • Can expand number of antecedent flow descriptors without model selection / over-fitting problems

  • Can use interaction effects neatly


Chilterns druwid shows that
Chilterns DRUWID shows that

  • See lag effects in ecological response to past flow conditions, over at least two years

  • Sequencing is important

  • Drying pattern matters

  • Resectioning important again... Also livestock poaching


Habitat still matters
Habitat still matters...

Photo: Misbourne River Action


Dried up and druwid summary
DRIED-UP and DRUWID summary

  • Both totally reliant on multilevel / mixed effects approach

  • DRIED-UP = a “national” model

    • derive robust site-specific relationships where relatively short series of monitoring data are available

    • WFD, RSA, drought, ?licensing?

    • influential in building our understanding that ecological response is a consequence of interacting multiple stressors

  • DRUWID extends the DRIED-UP concept to consider impacts of drought

    • DRUWID is a framework rather than a specific model

    • It’s more complex so works best using relatively compact regional datasets


Stats learnings
Stats learnings

  • Power of mixed-effects / multilevel approach

  • Need good understanding of multiple linear regression

  • No single go-to book

  • Look outside environmental sciences: social, medical



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