From real to virtual observatories: From real to virtual observatories: sustaining and improving water and sustaining and improving water and

soil sciencesoil science

Doerthe Tetzlaff

Northern Rivers Institute, Aberdeenwww.abdn.ac.uk/nri

Real observatories at different scales

Catchment scale Hillslope scale Reach scale

Conceptualization – capturing connections at different scales

How can we capture environmental variability (water flow paths, storage, transit times…) in the landscape?

1st order streams

2nd order streams 3rd order streams

zero order streams

Different scales, geographical environments… Different approaches: mapping tool, qualitative conceptual

model, physical model…

Cross-regional observatories – Contextualisation of insights

Girnock (30 km2)

Dee (2000 km2)Allt a’ Mharcaidh

Feshie Aberdeen

North Esk (800 km2)

Towards an integrated process understanding

2. Runoff sources / Mean transit times

3. Towards prediction / Upscaling / Connection

1. Landscape composition / configuration

Soils and hydrological response in a typical upland catchments

“Responsive”

Peat / Peaty GleySaturation overland flow

Shallow sub-surface storm flow

“Freely draining” Humus Iron Podzol

Deep sub-surface storm flow

Groundwater recharge

Soulsby et al. (2006a). J. of Hydrol. 325, 197-221.Soulsby et al. (2006b). Hydrol. Proc. 20, 2461-2465. Tetzlaff et al. (2007a). Hydrol. Proc. 21, 1289-1307.

30 km2 Girnock Burn catchment

Saturation area: Importance for non-linear hydrological response

The Girnock catchment (30 km2)

Hydrology Of Soil Types (HOST) - digital data base

- 29 classes which integrate:• Geology• Topography• Climate• Vegetation

- Infer hydrological pathways from soil properties

http://www.macaulay.ac.uk/host/hostdist.htm

Tetzlaff et al. (2007a) Hydrol. Proc. 21, 1289-1307: Conceptualisation of runoff processes using GIS and tracers in a nested mesoscale catchment.

Conservative tracers for rainfall-runoff dynamics

McGuire and McDonnell, 2006 JoH

Streamwater

01/01/95 01/01/97 01/01/99 01/01/01 01/01/03 Con

cent

ratio

n pr

eci

pita

tion

(mg

l-1)

0

20

40

60

80

100

Con

cent

ratio

n st

ream

wa

ter

(mg

l-1)

0

5

10

15

20

25

30

35

PrecipitationStreamwaterModelled

01/01/88 01/01/90 01/01/92 01/01/94 01/01/96 Con

cent

ratio

n pr

ecip

itatio

n (m

g l-1

)

0

20

40

60

80

100

Con

cent

ratio

n st

ream

wat

er (

mg

l-1)

0

5

10

15

20

25

30

35

PrecipitationStreamwaterModelled

Conservative tracers

Response of catchments: Short TT (rapid connection) / Longer TT (slower connections)

Fast responding catchment Deep-subsurface flow dominated catchment

Tetzlaff et al (2007b) J. of Hydrol. 346, 93-111.

Hrachowitz et al (2009) J. of Hydrol. 367, 237-248.

50%

Cu

m.

de

nsi

ties

fu

nct

ion

50%

Incorporation of non-linearity in catchment response

Dry

Wet

Saturation area extent =

f (API, ET, Soils)

5-30% of catchment area

Birkel et al., 2010, Towards simple dynamic process conceptualization in rainfall runoff models using multi-criteria calibration and tracers in temperate, upland catchments. Hydrological Processes.

Improved representation of system behaviour

Means of constraining models

C. Birkel et al., Water Resources Research, in press: Using time domain and geographic source areas tracers to conceptualize streamflow generation processes in rainfall-runoff models.

Non-linearity + high resolution tracers as objective measures

Some problems with real observatories

• Fragmentation at all levels:• Spatial, temporal gaps in data• Individual and unlinked models• Limited linkages between disciplines, institutes,

agencies, countries (within and outwith UK)

Environmental community does not embrace new information technologies / is poorly equipped to exploit those

Need for consistency in approaches

EVOp Vision

Pilot:

proof of concept project to demonstrate that linking data, models and expert knowledge will provide cost effective answers to wide-ranging environmental issues (initially soil - water system)

The team

• Leadership and Management team– Adrian McDonald (Leeds)– Robert Gurney (Reading)– Bridget Emmett (CEH)

• WP leaders– Phil Haygarth (Lancaster)– Jim Freer (Bristol)– Wouter Buytaert (Imperial)– Gordon Blair (Lancaster) &

Gwyn Rees (CEH)– Doerthe Tetzlaff (Aberdeen)

Full team• Keith Beven (Lancaster)• Gordon Blair (Lancaster)• John Bloomfield (BGS)• Roland Bol (Rothamsted)• Wouter Buytaert (Imperial)• Bridget Emmett (CEH)• Jim Freer (Bristol)• Robert Gurney (Reading)• Phil Haygarth (Lancaster)• Penny Johnes (Reading)• Paul Quinn (Newcastle)• Mark Macklin (Abserystwyth)• Christopher Macleod (Macaulay)• Adrian McDonald (Leeds)• Sim Reaney (Durham)• Gwyn Rees (CEH)• Marc Stutter (Macaulay)• Doerthe Tetzlaff (Aberdeen)

Objectives

(i) To make environmental data more visible / accessible to wide range of potential users and free to use for public;

(ii) To provide tools to facilitate integrated analysis of data, greater access to added knowledge and expert analysis, and visualisation of results;

(iii) To develop new, added-value knowledge from public and private sector data to help tackle environmental challenges.

Soiltrec

Hydronet

Expeer

EU /Global observatories

Sensors

UK observatories

National Capability

Earth Observation Framework

Demonstration test Catchments

Data/portals

Dedicated data centres

GMES

Inspire

Models

Transparency

Environmental issue

FloodingDiffuse pollutionWater resources

EXEMPLARS

Decision support tools

Alternet

Strategy for the Pilot (£2M)Start with mature community which has good stakeholder engagement (i.e. soils and water) and build on various ongoing initiatives:

Environmental issue

Environmental issue

Project structure

• 2 WPs to develop cyber infrastructure: data and modelling tools.

• These will underpin development of 2 exemplars based on soil and water questions framed in local and national context

• Internationalisation

An environmental cloud

A space for:• Exploring data• Linking models• Accessing added knowledge• Visualisation tools

To deliver more:• Efficient, effective and

transparent use of environmental data, models and knowledge

Questions the EVO could help answer

ScientistsHow do we define which model

is better to select from our model ensemble?

What kind of language/tools to use to make models “talk” to

each other.

Water IndustryWhat is the whole impact of

future flooding?How can water security be

assured?How can the industry carbon

impact be reduced?

PublicWill my town run out of

water?What is the state of the

local river? What are the options to protect us from future

flooding?

RegulatorsHow can we reduce monitoring for same

information? Credible apportioning of pollutant load between

industry, water, agriculture, other.

Government Agencies

What models work?What policy works?

How do I do it for less money?

EVO cloud

What is benefit for scientists?

• Currently: many wheels constantly re-invented– Re-implementing models, file import and export routines etc. – Tools and models library will stop this re-invention

• Fewer wheels, more environmental science.

• Models to be tested as hypotheses about system functioning

• Longer term: understanding gained will be used in extending the VO within and outside UK.

Many thanks for your attention!!!

”The more closely we search, the more elusive the edge becomes” (K. Dean Moore)

Check out for papers and posters: www.abdn.ac.uk/nri