2009 Hydrologic Synthesis Reverse Site Visit | Arlington, VA | August 20-21, 2009 Sediment and...

2009 Hydrologic Synthesis Reverse Site Visit | Arlington, VA | August 20-21, 2009

Sediment and Contaminant Dynamics Across Scales

Landscape as Cascading Hydrologic and Biogeochemical Filters

Session 2Nandita Basu (University of Iowa) Suresh Rao (Purdue University)Aaron Packman (Northwestern)

Session 3Marwan Hassan (UBC)

Aaron Packman (Northwestern)


Mgmt.:Chemical

Inputs

Source Release Model

Vadose Zone :Storage, Transport

Retardation,Transformations

Saturated Zone :Transport, Retardation

Transformations

Climate and Veg: Rain, ET

overland flow

subsurface flow

groundwaterflow

Hillslope

Emergent Patterns

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

Water Column

sediment

Reach Scale


Approach: Pattern Based

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 offer a window into landscape processes

… and a starting point for hypotheses


Patterns that Intrigued us…..

Nitrate load-discharge relationships across Mississippi

Sediment load-discharge relationships


Patterns that Intrigued us…..

Nitrate load-discharge relationships across Mississippi

Sediment load-discharge relationships

Why are they linear?Or,

Why are Watersheds Chemostatic?At what scale are they chemostatic?

2009 Hydrologic Synthesis Reverse Site Visit | Arlington, VA | August 20-21, 2009 6

Den

itrifi

catio

n r

ate

cons

tant

(d

-1)

Reach-Scale

REACH SCALEInverse relationship between

denitrification and stream depth

How do reach scale patterns translate to network scale: Spatio-Temporal Averaging

NETWORK SCALESame Inverse Dependence

Donner et al. (2004)Bohlke et al. (2008)


Motivating Questions:

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

2. How do sediments and contaminants get translated through the network?

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


Filtering of solute variability across scales

http://nc.water.usgs.gov/projects/tile_drains/


Hypothesis: Landscapes act as cascading,coupled filters

Observed “patterns” are windows into this 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


Event filtering in the vadose zone


HEIST: A 1-D event-based model of solute loads filtered by the vadose zone


Solute mass in

Sol

ute

mas

s ou

t Increasing depth

Increasing degradation rates

Effects of soil depth: Effects of degradation rate:

Model reveals controls on clustering of events and emergence of extremes


Concentration vs Discharge:

Data analysis across scales


Cum

ulative

oututs over each year

Cum

ulative

outputs over each year

Cumulative precipitation Cumulative discharge

Sulfate

Nitrate

Chloride

Sulfate

Nitrate

Chloride

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

Hubbard Brook WS2


Single tile drain (0.03 km2)Q-C strongly coupled

Watershed (186 km2)Episodically coupled

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


Annual Discharge 106 m3/km2/yr

Annual N

O2 + N

O3 Load (t/km

2/yr)

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


Concentration vs Discharge:

Models to understand controls


MRF model - Conceptual hillslope coupled to network

Storage-dependent CSTR model

Storage

THREW model - Representative

Elementary Watershed

Multi-compartmentflow and BGCprocess model

Multiple models used to test hypotheses about origins of observed patterns


Chemostatic Q – C behavior linked to:

B) Interaction of forcing and filter

timescales

A) Storage – dependentreaction rates

C) Averaging effects of the network

Reaction time

Event input frequencyResidence time


Flow and denitrification in networks


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

REACH SCALEInverse relationship between

denitrification and stream depth

Spatial averagingover network

Temporal averagingover year

Bohlke 2008

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

In-s

trea

m N

Rem

oval

k = 0.2/h

k = 0.06/h

Runoff (mm)


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


Sediment transport: legacy, intermittency and land use


Study Sites

Goodwin Creek, Mississippi Rio Isabena, Spain


Landscape and Network Filtering of Sediment Transport

Rainfall

Land Management

Runoff, Suspended Sediment

Bank Erosion

Deposition and Resuspension

Q(t)

Cu

ml.

Lo

ad

Cuml. Flow


1980 1982 1985 1987 1990 1992 1995 1997-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

De

via

tio

n f

rom

th

e m

ea

n (

m)

Hurst Analysis: Flow

Site 7

Site 9

Site 10

Site 11

Site14

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997-1

0

1

2

3

4

5

6

7

8

9

De

via

tio

n f

rom

th

e m

ea

n (

kg

.m2

)

Hurst Analysis: Sediment Load

Site 7

Site 9

Site 10

Site 11

Site14

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997-600

-400

-200

0

200

400

600

800

1000

1200

1400Hurst Analysis: Precip

P Gage 35P Gage 52P Gage 53P Gage 54P Gage 57

Hillslope Filtering

Precipitation

Flow Sediment MobilizedDev

iati

on

s fr

om

th

e M

ean

(m

m)

Years1982 1997

De

via

tio

ns

fro

m t

he

Me

an

(m

)

Years1982 1997

De

via

tio

ns

fro

m t

he

Me

an

(k

g)

Years1982 1997


1980 1982 1985 1987 1990 1992 1995 1997-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

De

via

tio

n f

rom

th

e m

ea

n (

m)


Site 7

Site 9

Site 10

Site 11

Site14

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997-1

0

1

2

3

4

5

6

7

8

9

De

via

tio

n f

rom

th

e m

ea

n (

kg

.m2

)


Site 7

Site 9

Site 10

Site 11

Site14

Hillslope Filtering

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997-600

-400

-200

0

200

400

600

800

1000

1200



Precipitation

Flow Sediment Mobilized

Flow ~ unfiltered precipitation1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

-600

-400

-200

0

200

400

600

800

1000

1200



Dev

iati

on

s fr

om

th

e M

ean

(m

m)

Years1982 1997

De

via

tio

ns

fro

m t

he

Me

an

(m

)

Years1982 1997

De

via

tio

ns

fro

m t

he

Me

an

(k

g)

Years1982 1997


Hillslope Filtering

1980 1982 1985 1987 1990 1992 1995 1997-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

De

via

tio

n f

rom

th

e m

ea

n (

m)


Site 7

Site 9

Site 10

Site 11

Site14

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997-1

0

1

2

3

4

5

6

7

8

9

De

via

tio

n f

rom

th

e m

ea

n (

kg

.m2

)


Site 7

Site 9

Site 10

Site 11

Site14

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997-600

-400

-200

0

200

400

600

800

1000

1200



Precipitation


Sediment ~ flow filtered1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

-600

-400

-200

0

200

400

600

800

1000

1200



Dev

iati

on

s fr

om

th

e M

ean

(m

m)

Years1982 1997

1980 1982 1985 1987 1990 1992 1995 1997-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

De

via

tio

n f

rom

th

e m

ea

n (

m)


Site 7

Site 9

Site 10

Site 11

Site14

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997-1

0

1

2

3

4

5

6

7

8

9

De

via

tio

n f

rom

th

e m

ea

n (

kg

.m2

)


Site 7

Site 9

Site 10

Site 11

Site14

De

via

tio

ns

fro

m t

he

Me

an

(m

)

Years1982 1997

De

via

tio

ns

fro

m t

he

Me

an

(k

g)

Years1982 1997


Hillslope Filtering

1980 1982 1985 1987 1990 1992 1995 1997-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

De

via

tio

n f

rom

th

e m

ea

n (

m)


Site 7

Site 9

Site 10

Site 11

Site14

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997-1

0

1

2

3

4

5

6

7

8

9

De

via

tio

n f

rom

th

e m

ea

n (

kg

.m2

)


Site 7

Site 9

Site 10

Site 11

Site14

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997-600

-400

-200

0

200

400

600

800

1000

1200



Precipitation


Sediment ~ flow filtered1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

-600

-400

-200

0

200

400

600

800

1000

1200



Dev

iati

on

s fr

om

th

e M

ean

(m

m)

Years1982 1997

1980 1982 1985 1987 1990 1992 1995 1997-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

De

via

tio

n f

rom

th

e m

ea

n (

m)


Site 7

Site 9

Site 10

Site 11

Site14

1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997-1

0

1

2

3

4

5

6

7

8

9

De

via

tio

n f

rom

th

e m

ea

n (

kg

.m2

)


Site 7

Site 9

Site 10

Site 11

Site14

De

via

tio

ns

fro

m t

he

Me

an

(m

)

Years1982 1997

De

via

tio

ns

fro

m t

he

Me

an

(k

g)

Years1982 1997

Increased Disturbance


EX

CE

ED

EN

CE

P

RO

BA

BIL

ITY

NORMALIZED FLOW AND LOAD

1982 1983 1984 1985

1986 1987 1988 1989

1990 1991 1992 1993

1994 1995 1996 1997

CHANGE IN LANDUSE

FLOW

LOAD

Hillslope Filtering – Land Use


Reach Mass Balance

St. 3Inputs

Bank Erosion

Deposition Mobilization

St. 4

St. 13

St. 14St. 2St. 3

InputsBank Erosion

Deposition Mobilization

St. 4

St. 13

St. 14St. 2


Quantification of Bank Erosion

1996/4/24

1996/12/9

1997/3/4


0 200 400 600 800 1000 1200 14000

1

2

3

4

5

6

Sediment Transport – Waves

Length Down Reach (m)

Sed

imen

t Con

cent

ratio

n in

Bed 1 2 3 4 5 6 7

0

5

10

1 2 3 4 5 6 70

2

4

Concentration

Flow

INPUT


0 200 400 600 800 1000 1200 14000

1

2

3

4

5

6



Sed

imen

t Con

cent

ratio

n in

Bed

1 2 3 4 5 6 70

5

10

1 2 3 4 5 6 70

2

4

Concentration

Flow

INPUT




Sed

imen

t Con

cent

ratio

n in

Bed

0 200 400 600 800 1000 1200 14000

1

2

3

4

5

6

1 2 3 4 5 6 70

5

10

1 2 3 4 5 6 70

2

4

Concentration

Flow

INPUT


Sediment transport behaviour Reproduces features of export patterns

1Data for Isabena River

Cumulative Flow m3/s

Cum

ulat

ive

Load

(g)

0 10

1

Cumulative Flow (m3/day)C

umul

ativ

e Lo

ad

Model Output


Basin-Scale Filtering

Load – relatively homogeneous Load – highlights channel contributions

Land Use Intervention


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)


0 10

1

Cumulative Flow (m3/day)

Cum

ulat

ive

Load

Model Output

Solute mass in

Solu

te m

ass

out

Increasing depth

Order out of Complexity

Vadose Zone

Catchment Scale: Nutrient

Network Scale

Sediment

2009 Hydrologic Synthesis Reverse Site Visit | Arlington, VA | August 20-21, 2009 Sediment and...

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