Summer Synthesis Institute Vancouver, British Columbia June 22 – August 5 Overview of Synthesis...
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Transcript of Summer Synthesis Institute Vancouver, British Columbia June 22 – August 5 Overview of Synthesis...
Summer Synthesis Institute
Vancouver, British ColumbiaJune 22 – August 5
Overview of Synthesis ProjectSynthesis Project Descriptions
Summer Institute Logistics
Water Cycle Dynamics in a Changing Environment:
Advancing Hydrologic Science through Synthesis
Murugesu SivapalanPraveen Kumar, Bruce Rhoads, Don Wuebbles
University of IllinoisUrbana, Illinois
Session 2
Suresh RaoPurdue University
Nandita BasuUniversity of Iowa
Contaminant Dynamics across Scales: Temporal and Spatial Patterns
Aaron PackmanNorthwestern University
Non linear filters create emergent patterns/signatures across scales
Signatures integrate ecosystem structure and function
Relationship of water flow and water quality to stream ecosystems
Examining signatures using data analysis and models
Conceptual Model
Climate(rainfall, ET)
Landscape (non-linear
filter)
Streamflow
Biogeochemistry (non-
linear filter)
Contaminant Loads
Aquatic Habitat and Biodiversity
Cascading Controls
Overall Hypothesis
Despite process complexity at the local scale, non-linear interactions in the cascade of filters and buffers generate emergent spatio-temporal patterns or signatures that can be expressed as simple functions of the hydrologic and biogeochemical drivers of the system.
5
Emergent Patterns: Runoff Coefficient (RC) and Flow Duration Curve
6Exceedance Probability
flow
Budyko Curve describes the mean annual streamflow across the climatic gradient
Botter et al. (2009) showed that FDC can be predicted as a simple analytical function of λ/k
- λ (runoff frequency) - k (catchment mean residence time)
Runoff frequency can be expressed in terms of underlying soil vegetation and rainfall properties
Catchment mean residence time estimated from hydrograph recession curve analysis
Able to describe pdfs of streamflows across several catchments in US
mean annual P
mea
n an
nual
Q
?
Inter-annual
Intra-annual
Slope = RC
7
Example 1: Emergent Pattern: LAPU and Load Duration Curve (LDC)
Exceedance Probability
load
1. Formulate Hypotheses
LDC is a function of
FDC since water carries the chemical
Chemical Properties (sorption, degradation, etc.)
Chemical input functions (atmospheric deposition vs. fertilizer application)
Landscape Biogeochemical Filter
Chemical Input
Chem
ical
Exp
ort
?
LAPU: Load as a Percent Used (analogous to RC)
Slope = LAPU
Inter-annual
Intra-annual
Emergent Pattern: Load Duration Curve (LDC)
2. Run Model to explore dominant controls on LDC
Two available transient hillslope-network coupled models
- Model A (Reggiani et al.) Sheng Ye and Hongyi Li- Model B (Rinaldo et al.) Stefano Zanardo
3. Analyze data to explore dominant controls on LDC
4. Develop simple analytical approaches5. Response to change
Hydrologic and Biogeochemical Filters
Two Functions of Filters:
1. Decrease in mass - Hydrologic Filter: runoff coefficient- Biogeochemical Filter: load as a percent
used
2. Alteration of the distribution: - relationship between flow distribution curve and rainfall duration curve (Hydrologic Filter)- relationship between load distribution curve and flow duration curve (Biogeochemical
Filter)
Example 2: Biogeochemical Filter:Dual Duration Curve (DDC)
1
11
1
normalized flow
norm
aliz
ed
load
exceedanceprobability
exce
edan
cepr
obab
ility
?
What does the DDC depend on?
0
0.2
0.4
0.6
0.8
1
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
norm
aliz
ed lo
ad
normalized flow
1994 - A
1994 - B
1995 -A
1995 - B
1996 - A
1996 - B
1997 - A
1997 - B
Biogeochemical Filter:Dual Duration Curve (DDC)
A – nitrateB – atrazine
Why is nitrate so different from atrazine?
How can we classify chemicals or watersheds based on such signatures?
y = 0.0087xR² = 0.8826
0
100
200
300
400
500
600
0 20000 40000 60000 80000
Nit
rate
load
(kg/
d)
Flow (m3/d)
Mean Annual Patterns: Flow vs. Load
Intra-annual patterns observed in DDC persists in the mean annual behavior…
y = 2E-06xR² = 0.1505
00.020.040.060.08
0.10.120.140.160.18
0.2
0 20000 40000 60000 80000
Atra
zine
load
(kg/
d)
Flow (m3/d)
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Network Models: Spatial Patterns
Nitrogen Yieldkg/km2
y = 1.22x-0.09
R² = 0.97
y = 2.31x-0.29
R² = 0.94
y = 1.52x-0.16
R² = 0.97
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
10 100 1000
LA
PU
area (km2)
Q^0.4
Q^0
Q^0.2
0
0.2
0.4
0.6
0.8
1
0 200 400 600 800
ke (p
er d
ay)
area (km2)
no Q dependence
Q^0.2
Q^0.4
Objectives/Tasks
(1) Identify relevant hydrologic, biogeochemical and ecological signatures
(2) Understand the functioning of the hydrologic and biogeochemical filters that modify the forcing functions (rainfall and chemical application)
- Formulate hypotheses- Run model- Analyze Data
(3) Develop simple analytical approaches to predict the signatures as a function of the key parameters of the filters and forcings
(4) Identify how land use or climate change would alter the attributes of the filters, and thus change the signatures.
Data based SignaturesHumid: Little Vermilion Watershed in Illinois:
tile-drained agricultural watershed, approximately 480 km2 Arid: Avon River Basin in Western Australia:
agricultural watershed of size 120,000 km2
We are searching for other catchments with water quality data --- suggest your favorite catchment
Chemicals of interest: Dissolved (Nitrate, pesticides etc)
Key preparation work required1. Read the papers and familiarize yourself with the primary
assumptions in the two models
2. Question the assumptions and think what they would mean in terms of the observed signatures
3. Start thinking about the signatures and filters --- other interesting signatures or questions that you may want to explore
4. Read the questions/hypotheses in the framework and think about additional ones that you want to explore.
5. Contact me if you have or know of contrasting watersheds with water quality data
More thinking than doing….
Exceedance Probability
flow
Exceedance Probability
load
Intra-annual Variability Inter-annual Variability
1
Time
Q
Time
C
area
RC
area
LAPU
Ep/P
E/Q
Budyko
Time
Smal
lest
scal
e ph
ysio
logic
resp
onse
Exceedance Probability
Body
bur
den
Ep/P
LAPU
Watershed ClassificationGuide Management DecisionsPrediction in Un-gauged Basins