PD Dr. Tobias HeckmannCath. University of Eichstaett-Ingolstadt

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• Results of first Think Tank MeetingFebruary 2015, Eichstätt

• Linkages between WG4 and other WG• Agenda for WG4• A side note: Network approaches in connectivity research

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USEABLE INDICES of CONNECTIVITY• Different disciplines

• Soil physics• Hydrology• Geomorphology• Remote sensing

• Different foci:• Upscaling• Parameterisation of subgrid-scale processes• Scale dependence of connectivity• Application to

• very flat• very large

study areas

What isconnectivity ?

What is an index ?

What‘s the use ofindices ?

What makes themuseable and useful ?What variables to use

for indices?

Different disciplines, different terminology… ?Lack of diligence and rigourin using terminology !?

WG4 definition:• Connectivity is a state variable of a system,

representing coupling relationships between elementary units within a larger system. It reflects the potential of water/sediment to move through the system

• It results from the (dis-)continuity of runoffand sediment pathways at a given point in time.

• Structural connectivity represents the spatial configuration of state variables,

• Functional connectivity is process-based

WG1 definition (WG1 ThT Vienna):Connectivity is the degree to which a system facilitates the movement of matter and energy through itself. • It is an emergent property of the

system state. • Structural connectivity derives from

the system’s anatomy. • Functional connectivity is inferred

from the system’s process dynamics.

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⇒ Need for universal definition is also the need for a common terminology(c.f. also WG2 ThT)

⇒ Requirement to use terminology that is consistent with physics(because runoff/sediment connectivity occurs in a physical system)

FunctionalconnectivityProcesses

• depending on „spatial unit“, scale• subject to change in time

SPATIOTEMPORAL

INTERACTION

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External forcings, driversPrecipitation etc(magnitude-frequency, event sequence)

Intrinsicstructural propertiesTopography: Slope…Roughness (topographic, vegetation)

Structuralconnectivity

Hydrograph,Sediment production,Sedigraph

possible feedbacks(indices unsuitable !?)

Intrinsicfunctional propertiesMaterial propertiesVegetation properties

Heckmann et al. (2015)Based on a draft byMathieu Javaux

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Connectivity has not been „measured“, or quantified – instead:• structure and structural change• what emerges from connectivity

(Brazier et al. 2015; WG2 ThT)

In the absence of a proper measure of connectivity, we use indices/indicators:• „Measurable variable used as a representation of an associated

(but non-measured or non-measurable) factor or quantity.”(http://www.businessdictionary.com/definition/indicator.html)

• “In science, it is sometimes necessary to study a variable which cannot be measured directly. This can be done by proxy methods, in which a variable which correlates with the variable of interest is measured, and then used to infer the value of the variable of interest.”(https://en.wikipedia.org/wiki/Proxy_(climate))

• Index approach:• Mapping structural properties (DEM, remote sensing data…)• Functional properties often not measured/not measureable:

=> proxies (e.g. catchment size, wetness index, …)=> modelling ?

http://www.businessdictionary.com/definition/indicator.htmlhttps://en.wikipedia.org/wiki/Proxy_(climate))

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• Similarly to WG2 we identified three main spatial scales where connectivityoccurs:

• plot/hillslope• channel reach• catchment

• For each spatial scale, we collected the (most) relevant• external forcing that drives processes• response variables that arise from forcingsproperties interactions

and that could be used for indices• ideal format in which these variables should come• practical (real-world) format in which such variables are customarily available

• Classification of currently available indices, future development:• spatial scale (precisely: support):

• raster cell vs. (sub-)catchment• Integrating upslope contributing area + downslope pathways

• time frame, temporal scale• typical temporal scale of forcing: hour (event) to at most a few decades

(beyond: need to represent process-form-feedbacks that would change the systemthat the index is to assess !)

• static vs. dynamic indices

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plot/hillslope channel reach catchment• Rain kinetic energy + volume

event sequence• Run-in (sediment concentration, volume,

kinetic energy)• Gravity

• Precipitation (sum, intensity) => runoff• Temperature (snow melt, precip. type,

periods with no/frozen water)• Sediment input (e.g. debris flow)• Human impact (e.g. gravel mining,

canals)• Magnitude/frequency of hydrological

events andevent sequence

• Precipitation (sum, intensity, duration)• Temperature (snow melt, precip. type,

periods with no/frozen water, freeze-thaw-cycles)

• Gravity (mass movements)• Magnitude/frequency and sequencing of

events

• Topographic boundary conditions: roughness (at micro-aggregate tolandform scale)

• Soil moisture• Hydraulic conductivity• Particle size distribution + aggregate

stability• …

• Topography (longitudinal: gradient; lateral: width, depth; surface roughness; bedrock sections)

• Bed material (grain size distribution e.g. percentiles, armoring ratio etc)

• Vegetation and large woody debris(plant/wood density, number of logjams)

• Topographic properties and their spatialdistribution: elevation, slope, curvature, roughness)

• Size and shape of (sub-)catchments• Landcover/vegetation properties and

spatial distribution• Lithology/soil properties and spatial

distribution• Channel network (density, flow length

distribution, artificial drainage); sedimentsources and pathways

• …

• hrDEM• Rainfall-related variables are measured at

meteorological stations: Interpolation necessary (difficult esp. for short term)

• Run-in data available only forexperimental conditions

• Soil moisture retention curves• Hydraulic conductivity: real (soil profile)

vs. effective (hillslope)• …

• Typical runoff for low flow, bankfull, HQ100; Q percentiles often unknown, need to be interpolated/extrapolated ormodelled

• Channel geometry, bedrock, large woodydebris etc from aerial photos and fieldmapping; problems with subaquaticproperties (=> cross profiles)

• Roughness: Grainsize data or hrDEM• Bed material: discrete samples

• Topography from (hr)DEM• Landcover/vegetation, topsoil properties

from remote sensing• Use of models to identify continuous flow

paths and trajectories of massmovements

• „Effective area“ approaches• Aggregation of local/integrated indices

on the catchment scale or calculation ofindices directly on that scale

forc

ing

resp

onse

varia

bles

avai

labi

lity

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plot/hillslope channel reach catchment• Rain kinetic energy + volume

event sequence• Run-in (sediment concentration, volume,

kinetic energy)• Gravity

• Precipitation (sum, intensity) => runoff• Temperature (snow melt, precip. type,

periods with no/frozen water)• Sediment input (e.g. debris flow)• Human impact (e.g. gravel mining,

canals)• Magnitude/frequency of hydrological

events andevent sequence

• Precipitation (sum, intensity, duration)• Temperature (snow melt, precip. type,

periods with no/frozen water, freeze-thaw-cycles)

• Gravity (mass movements)• Magnitude/frequency and sequencing of

events

• Topographic boundary conditions: roughness (at micro-aggregate tolandform scale)

• Soil moisture• Hydraulic conductivity• Particle size distribution + aggregate

stability• …

• Topography (longitudinal: gradient; lateral: width, depth; surface roughness; bedrock sections)

• Bed material (grain size distribution e.g. percentiles, armoring ratio etc)

• Vegetation and large woody debris(plant/wood density, number of logjams)

• Topographic properties and their spatialdistribution: elevation, slope, curvature, roughness)

• Size and shape of (sub-)catchments• Landcover/vegetation properties and

spatial distribution• Lithology/soil properties and spatial

distribution• Channel network (density, flow length

distribution, artificial drainage); sedimentsources and pathways

• …

• hrDEM• Rainfall-related variables are measured at

meteorological stations: Interpolation necessary (difficult esp. for short term)

• Run-in data available only forexperimental conditions

• Soil moisture retention curves• Hydraulic conductivity: real (soil profile)

vs. effective (hillslope)• …

• Typical runoff for low flow, bankfull, HQ100; Q percentiles often unknown, need to be interpolated/extrapolated ormodelled

• Channel geometry, bedrock, large woodydebris etc from aerial photos and fieldmapping; problems with subaquaticproperties (=> cross profiles)

• Roughness: Grainsize data or hrDEM• Bed material: discrete samples

• Topography from (hr)DEM• Landcover/vegetation, topsoil properties

from remote sensing• Use of models to identify continuous flow

paths and trajectories of massmovements

• „Effective area“ approaches• Aggregation of local/integrated indices

on the catchment scale or calculation ofindices directly on that scale

forc

ing

resp

onse

varia

bles

avai

labi

lity

• Hillslope: „Erosion matters, not connectivity“, esp. for managers• Precipitation main driver (type, intensity, volume, event

sequence) => common timeframe: hours to decades (averages)• Sediment connectivity through landslides etc (gravity !)

• DEMs, especially hrDEMs are an important data source• Need to interpolate and/or model forcings

and functional properties• Remote sensing for structural and some functional

properties

• Topography at multiple scales• Material properties• Vegetation / landcover /-use• Human impact on these properties• Catchment scale:

• spatial distribution of these properties (=> „spatial configuration“ of landscape elements)

• importance of attributes along major flowlines (more important thanmean of whole area)

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In a ‘perfect model’ of reality, there would probably be no need for assessing connectivity because connectivity would simply emerge from • the spatial pattern of influencing factors (e.g. “response units”) and• the correctly modelled behaviour of processes on their flow paths across

the landscape

Questions for WG4WG3 interaction:• To what extent do we need models for indices ?• (Where) do models need indices ?

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• We compare connectivity indices from different disciplines• Hydrology• Geomorphology• Ecology

• Criteria of comparison• structural vs. functional connectivity• object scale (=support)• within-object information only vs. integrated index• lateral vs. longitudinal connectivity (for reaches)• static vs. dynamic• basis: forcing, structural properties, functional properties, production functions• computational complexity (max: need to run a model)• data requirement

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Preliminary conclusions:• Most if not all indices are of little or intermediate computational complexity• Data is readily and increasingly available (hrDEMs, remote sensing data)• Indices are mostly

• lacking representation of forcing• static• Not process-based

• Hydrological/sediment connectivity indices are difficult to comparewith ecological indices:

• directed vs. undirected connectivity• no proper external forcing in ecology

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The final aspect leads back to the origin:WHAT do we need indices for ?

„we actually do not have a clear picture of the existing knowledge and perceived relevance of connectivity issues of stakeholders involved in real-world problems related to connectivity” (WG5 ThT 2015)

Questions:• Are stakeholders aware of the connectivity concept (do they use it, for example,

“under another name” ?) and does it appear relevant for them ?• “soil erosion” or “proper sediment connectivity” ?• Connectivity as explanatory concept that links on-site process to off-site effects ?

• What are the requirements of catchment managers and other stakeholders towards indices, and (how) can we meet them ?

• Maps ? => Spatial distribution of „high“ and „low“ connectivity ? • Values ? => Quantitative assessment linked to i.e. specific sediment yield ?

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http://connecteur.info/resources/literature/

Username: contact@connecteur.infoPassword: ConnecteurES1306

http://connecteur.info/resources/literature/mailto:contact@connecteur.info

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• Write review paper on existing indices• Second ThinkTank meeting in 2016 (Tallinn)• Training school (joint proposal with WG2) in July 2016

Vinschgau, South Tyrol, Italy• Strengthening linkages with other WG, above all

WG1, WG3

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Chorley&Kennedy (1971), Summerfield (1991)

Rana & Wood (2000)

Fryirs (2013)

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Here: coarse sediment cascades in high-mountain basins

node

node

edge

Nodes represent spatial entities• (sub-)catchments• landforms• other terrain units• raster cells of a DTM

Edges represent couplingof spatial entities• binary (coupled, not coupled)• rate of sediment transfer• transition probability• attribute: geomorphic process• …

rockface

talus cone

debris cone

channelnetwork

• Geomorph. Mapping• (Semi-)automated

DTM segmentation• predictive mapping

rock

fall

debr

isflo

w

• Geomorph. Mapping• DTM analysis• numerical models

A path is the succession of directededges leading from one node toanother… here: a sediment cascade

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Edges: Numerical models forrockfall, debris flows,slope wash and fluvialerosion pathways

Connectivity: Functional connectivity(spatial entities coupledby geomorph. processes)

Nodes: Raster cells of a DTM(not restricted to discrete landforms)

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Nodes: Landforms fromgeomorphological map(Götz, Buckel 2012)

Edges: Coupled landformsidentified in the field &on geomorphological map

Connectivity: Functional connectivity(spatial entities coupledby geomorph. processes)

LegendStoragelandform

alluvial fan

alluvial plain

bedrock

complex valley fill deposit

debris cone

glacier

lake

mass movement

mire

moraine

regolith

rock fall deposits

rock glacier (active)

rock glacier (inactive)

talus cone

talus sheet

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Nodes: Landforms fromgeomorphological map(Götz, Buckel 2012)

Edges: Coupled landformsidentified in the field &on geomorphological map

Connectivity: Functional connectivity(spatial entities coupledby geomorph. processes)

Source node

Link node

Sink node

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Structural graph:…if all areas were coupled by surface runoff

along flowpaths according to DEM…if all sensors were ON (there is flux)…edge weight=% of discrete area

draining into downslope sensor…forms a „benchmark“ for

(relative) connectivity assessment

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For each point in time, we can analyse• Properties of specific nodes

(e.g. their strength)• …of specific edges

(e.g. their centrality)• …of the whole network

(e.g. its density, number of components etc)

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Node property:Average node strength(total flux into/from node)during measuring period

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Rainfall intensity [mm/h]

graph density(% struct.graph)

graph diameter(length of longest geodesic path)

Observations: • Different response for similar events• Many measures of connectivity

are highly correlated => all describe the same

-> time

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• Connectivity is an important system property• It describes

• how system compartments are linked by sediment flux• how sediment pathways form sediment cascades to reach

storage landforms and the catchment outlet• Networks can be used to model the linkage of

locations/landforms/catchments by sediment transfer• Nodes: sediment sources and sinks (