Transcript Hydrologic Synthesis Project

Summer Synthesis Institute
Overview of Synthesis Project
Synthesis Project Descriptions
Summer Institute Logistics
Vancouver, British Columbia
June 22 – August 5
Water Cycle Dynamics in a Changing
Environment:
Advancing Hydrologic Science
through Synthesis
Murugesu Sivapalan
Praveen Kumar, Bruce Rhoads, Don Wuebbles
University of Illinois
Urbana, Illinois
Session 2
Contaminant Dynamics across Scales:
Temporal and Spatial Patterns
Suresh Rao
Purdue University
Aaron Packman
Northwestern University
Nandita Basu
University of Iowa
Conceptual
Model Controls
Cascading
Landscape
(non-linear
filter)
Climate
(rainfall, ET)
Streamflow
Biogeochemis
try (nonlinear filter)
Aquatic
Habitat and
Biodiversity
Contaminant
Loads
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
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
mean annual Q
Inter-annual
Slope = RC
?
mean annual P
Intra-annual
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
flow
Catchment mean residence time estimated
from hydrograph recession curve analysis
Exceedance
Probability
Able to describe pdfs of streamflows across
several catchments in US
6
Example 1: Emergent Pattern:
LAPU and Load Duration Curve (LDC)
Chemical Export
Inter-annual
Slope = LAPU
?
Chemical Input
load
Intra-annual
Exceedance
Probability
LAPU: Load as a Percent Used (analogous to
RC)
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
7
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 approaches
5. 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 exceedance
probability
exceedance
probability
normalized
load
1
?
normalized
flow
1
1
What
does
the
DDC
depend
on?
Biogeochemical Filter:
Dual Duration Curve (DDC)
1
A – nitrate
B – atrazine
1994 - A
1994 - B
1995 -A
normalized load
0.8
Why is nitrate
so different
from atrazine?
1995 - B
1996 - A
1996 - B
0.6
1997 - A
How can we
classify
chemicals or
watersheds
based on such
signatures?
1997 - B
0.4
0.2
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
normalized flow
0.8
0.9
1
Mean Annual Patterns: Flow
vs. Load
y = 0.0087x
R² = 0.8826
500
Atrazine load (kg/d)
Nitrate load (kg/d)
600
400
300
200
100
0
0
20000
40000
Flow (m3/d)
60000
80000
0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
y = 2E-06x
R² = 0.1505
0
20000
40000
60000
Flow (m3/d)
Intra-annual patterns observed in DDC
persists in the mean annual behavior…
80000
Network Models: Spatial Patterns
1
no Q dependence
0.8
Q^0.2
0.6
Q^0.4
0.4
0.2
0
0
200
400
600
800
area (km2)
area (km2)
10
100
1000
1
LAPU
kg/km2
ke (per day)
Nitrogen Yield
0.9
y = 1.22x-0.09
R² = 0.97
0.8
0.7
0.6
Q^0.4
0.5
Q^0
0.4
Q^0.2
0.3
y = 1.52x-0.16
R² = 0.97
y = 2.31x-0.29
R² = 0.94
13
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 Signatures
Humid: 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 required
1. 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….
Intra-annual Variability
Inter-annual Variability
Q
Exceedance
Probability
Exceedance
Probability
Body burden
Smallest scale
physiologic response
Time
Time
Budyko
Exceedance
Probability
Watershed Classification
Guide Management Decisions
Prediction in Un-gauged Basins
Ep/P
area
LAPU
LAPU
C
load
Time
E/Q
RC
flow
1
area
Ep/P