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Sediment and Contaminant Dynamics Across Scales . Landscape as Cascading Hydrologic and Biogeochemical Filters. Session 2 Nandita Basu (University of Iowa) Suresh Rao (Purdue University) Aaron Packman (Northwestern) Session 3 Marwan Hassan (UBC) Aaron Packman (Northwestern).

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slide1

Sediment and Contaminant Dynamics

Across Scales

Landscape as Cascading Hydrologic and Biogeochemical Filters

Session 2

Nandita Basu (University of Iowa)

Suresh Rao (Purdue University)

Aaron Packman (Northwestern)

Session 3

Marwan Hassan (UBC)

Aaron Packman (Northwestern)

slide2

Conceptual Framework:Hierarchical, Non-linear Filters and Cascading Waves

Reach Scale

Hillslope

Climate and Veg:

Rain, ET

Mgmt.:

Chemical

Inputs

overland

flow

Source Release Model

subsurface

flow

Water

Column

Vadose Zone :

Storage, Transport

Retardation,

Transformations

groundwater

flow

Saturated Zone :

Transport, Retardation

Transformations

sediment

Emergent Patterns

approach pattern based
Approach: Pattern Based

Patterns offer a window into landscape processes

… and a starting point for hypotheses

Hypotheses Testing:

  • WHAT are the “emergent” patterns? – Data
  • HOW are they created? – Models

Hypotheses Generation:

  • WHEN will they cease to exist --- tipping points
    • Data-based (comparative hydrology)
    • Model-based
patterns that intrigued us
Patterns that Intrigued us…..

Why are they linear?

Or,

Why are Watersheds Chemostatic?

At what scale are they chemostatic?

Nitrate load-discharge relationships

across Mississippi

Sediment load-discharge relationships

motivating questions
Motivating Questions:

Can we understand the dominant classes of behavior of landscapes that will pave the way towards catchment biogeochemical classification?

How are sediments and contaminants (dissolved and sediment bound) generated in the hillslope?

How do sediments and contaminants get translated through the network?

hypothesis landscapes act as cascading coupled filters
Hypothesis: Landscapes act as cascading,coupled filters

Observed “patterns” are windows into this filtering

Filtering of variable inputs by landscape structure and biogeochemical processes produces PATTERNS, as water and solutes cascade across spatial and temporal scales

four examples of solute filtering
Four examples of solute filtering

Event filtering in the vadose zone

C vs Q: Data analysis across scales

C vs Q: Models to understand controls

Flow and denitrification in networks

slide11

Model reveals controls on clustering of events and emergence of extremes

Effects of soil depth:

Effects of degradation rate:

Increasing degradation rates

Increasing depth

Solute mass out

Solute mass in

Clustering in time

Increased non-linearity of filter

“Extreme outcomes driven by normal inputs”

Clustering of transported mass

slide12

Concentration vs Discharge:

Data analysis across scales

slide13

Intra-annual filtering of nitrate more complex than less bioactive solutes in experimental watersheds

Sulfate

Sulfate

Nitrate

Nitrate

Chloride

Chloride

Hubbard Brook WS2

Cumulative outputs over each year

Cumulative oututs over each year

Cumulative precipitation

Cumulative discharge

Complex filtering of Nitrate

Simpler, but stronger filtering of less bioactive compounds

slide14

Flow and Nitrate decouple at larger spatial scales, except for specific events, in a data-rich agricultural watershed

Single tile drain (0.03 km2)

Q-C strongly coupled

Watershed (186 km2)

Episodically coupled

Flow vs Nitrate coherence analysis on 10 years of daily data

slide15

Landuse and climate control mean [N], and interannual variability is dampened, at Mississippi watershed scale

Annual NO2 + NO3 Load (t/km2/yr)

Annual Discharge 106 m3/km2/yr

At larger scales, inter-anual variability in concentration is dampened

Average concentration influenced by climate + land-use + ...

slide16

Concentration vs Discharge:

Models to understand controls

slide17

Multiple models used to test hypotheses about origins of observed patterns

MRF model

- Conceptual hillslope coupled to network

THREW model

- Representative

Elementary

Watershed

Storage-dependent CSTR model

Multi-compartment

flow and BGC

process model

Storage

slide18

Chemostatic Q – C behavior linked to:

B) Interaction of

forcing and filter timescales

A) Storage – dependentreaction rates

Reaction time

Residence time

Event input frequency

C) Averaging effects

of the network

slide20

Reach scale dependence on stage shown to produce intriguing patterns when up-scaled in time and space

Simon Donner (UBC)

IBIS-THMB model simulations (65 sq km grid resolution)

Bohlke 2008

k = 0.06/h

In-stream N Removal

Temporal averagingover year

Spatial averaging

over network

Runoff (mm)

REACH SCALE

Inverse relationship between

denitrification and stream depth

k = 0.2/h

order from complexity
Order from complexity
  • Solute filtering behavior most complex at
    • small scales
    • more bioactive solutes
  • Critical control on filtering:
    • Coupling of flow and reaction rates
    • Timescales of forcing, processing
    • Spatial structure of the network
  • Models built around event filtering can reproduce patterns of
    • Episodic leaching
    • Nitrate concentration vs discharge
    • Denitrification across scales
study sites
Study Sites

Rio Isabena, Spain

Goodwin Creek, Mississippi

landscape and network filtering of sediment transport
Landscape and Network Filtering of Sediment Transport

Rainfall

Bank Erosion

Land Management

Runoff,

Suspended Sediment

Deposition and Resuspension

Cuml. Load

Q(t)

Cuml. Flow

hillslope filtering
Hillslope Filtering

Precipitation

Deviations from the Mean (mm)

Flow

Sediment Mobilized

1982

1997

Years

Deviations from the Mean (kg)

Deviations from the Mean (m)

1997

1982

1997

1982

Years

Years

hillslope filtering26
Hillslope Filtering

Precipitation

Deviations from the Mean (mm)

Flow ~ unfiltered precipitation

Flow

Sediment Mobilized

1982

1997

Years

Deviations from the Mean (kg)

Deviations from the Mean (m)

1997

1982

1997

1982

Years

Years

hillslope filtering27
Hillslope Filtering

Precipitation

Deviations from the Mean (mm)

Sediment ~ flow filtered

Flow

Sediment Mobilized

1982

1997

Years

Deviations from the Mean (kg)

Deviations from the Mean (m)

1997

1982

1997

1982

Years

Years

hillslope filtering28
Hillslope Filtering

Precipitation

Deviations from the Mean (mm)

Sediment ~ flow filtered

Flow

Sediment Mobilized

1982

1997

Years

Deviations from the Mean (kg)

Deviations from the Mean (m)

Increased Disturbance

1997

1982

1997

1982

Years

Years

hillslope filtering land use

1982

1983

1984

1985

1986

1987

1988

1989

EXCEEDENCE PROBABILITY

1990

1991

1992

1993

1994

1995

1996

1997

NORMALIZED FLOW AND LOAD

Hillslope Filtering – Land Use

FLOW

LOAD

CHANGE IN LANDUSE

quantification of bank erosion
Quantification of Bank Erosion

1997/3/4

1996/12/9

1996/4/24

sediment transport waves
Sediment Transport – Waves

INPUT

Concentration

Flow

Sediment Concentration in Bed

Length Down Reach (m)

sediment transport waves33
Sediment Transport – Waves

INPUT

Concentration

Flow

Sediment Concentration in Bed

Length Down Reach (m)

sediment transport waves34
Sediment Transport – Waves

INPUT

Concentration

Flow

Sediment Concentration in Bed

Length Down Reach (m)

sediment transport waves35
Sediment Transport – Waves

INPUT

Concentration

Flow

Sediment Concentration in Bed

Length Down Reach (m)

sediment transport behaviour
Sediment transport behaviour
  • Reproduces features of export patterns
basin scale filtering
Basin-Scale Filtering

Land Use Intervention

Load – relatively homogeneous

Load – highlights channel contributions

consequences
Consequences
  • Intact ecosystems  more filtering
  • Network has “memory”
    • Responses vary in space, time
  • Filtering:
    • Nonlinear (e.g. hillslopes)
    • Episodic (e.g. legacy)
    • Stochastic (e.g. bank failure)
slide39

Order out of Complexity

Catchment Scale: Nutrient

Increasing depth

Solute mass out

Vadose Zone

Solute mass in

Network Scale

Sediment

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