Statistical hydro-ecological models

1. Statistical hydro-ecological models Mike Dunbar National Hydroecology Technical Advisor mike.dunbar@environment-agency.gov.uk August 2013 (Statistics for Environmental Evaluation 2004)

2. Structure • (About Me) • Statistical modelling using monitoring data • Hydroecology: river flows and ecological response • River ecology and land management stressors

6. 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)

7. EU Water Framework Directive

8. Where it all started for me Extence, Balbi and Chadd (1999) Dunbar and Clarke, 2002 (2005?) Centre for Ecology and Hydrology – Mike Dunbar

9. 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

10. 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

11. Centre for Ecology and Hydrology – Mike Dunbar

12. Where n = number of different taxa in sample Groups based on a huge literature survey (which I didn’t do)

13. Standard sampling method Assesshabitat 3 minute kick/sweep sample 1 minute hand search

14. Sample processing

15. 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

18. Original data

19. 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

20. Explanatory variables • Flow magnitudes, statistics of flows preceding samplehttp://www.ceh.ac.uk/data/nrfa/ • River Habitat Survey • Habitat Modification • Habitat Quality

22. Examples of sites

25. 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

26. Multilevel / hierarchical approach Terminology: i sample (level 1), nested within j site (level 2)

27. 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

28. 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

29. DRIED-UP 1: 2005 Data from 11 sites in E.Midlands

30. 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.

31. 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

32. 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..

33. 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

34. More information?

35. Taking the modelling forward DRUWID – DRIED-UP with Incremental Drought

36. 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

37. E.g. River Misbourne Photo: Misbourne River Action

38. DRUWID concept • 6 years in the making... • How to capture more of the complexity of the flow regime? • How to describe impact of drought

39. Solutions • Mixed effects approach • More flow variables AND • Flow-flow interactions

40. 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...

41. Hydrological complexity: existing approaches:

42. 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

44. 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

45. Chilterns DRUWID: Summary of variables

46. Interactions are v.v. important

47. Further illustration of one flow:flow interaction

48. Prediction

49. 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..

50. DRUWID shows that • Can expand number of antecedent flow descriptors without model selection / over-fitting problems • Can use interaction effects neatly