2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Synthesis Integrating Climate-Water-Ecosystem Science
Murugesu SivapalanUniversity of Illinois, Urbana-Champaign
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Water Cycle Dynamics in a Changing EnvironmentAdvancing Hydrologic Science through Synthesis
To organize and employ synthesis activities to produce transformational outcomes that will be utilized to improve the predictability of water cycle dynamics in a changing Earth environment.
Objective: 1
To use the synthesis activities as test cases to evaluate the effectiveness of different modes of synthesis for advancing the field of hydrologic science
Objective: 2
Principal Investigators: Murugesu Sivapalan, Praveen Kumar, Bruce Rhoads, Don Wuebbles
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Water Crisis: A crisis in water management
Water management in the context of fast increasing demand (e.g., population increase), degradation of an already poorly distributed resource base, in the presence of considerable uncertainty (e.g., due to climate change), and subject to significant social and economic bottlenecks
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Water Crisis: Solution Strategies
Social: population control, lifestyle changes, recycling, policy development
Technology: agricultural (irrigation, rainwater harvesting, plant breeding), recycling
Economics: pay for ecosystem services, fair price for water, enhancement of trade
Science: predict water cycle dynamics under global change amid uncertainty, predict resource availability and hazards, and help value and protect the environment from further degradation
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Matter and Forces Mechanistic perspective
Patterns and processes Evolutionary perspective
Could there be a broadening of our perspectives?
Could there be a synthesis?
What do we observe? How do we analyze? How do we predict?
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Open/Dissipative System Paradigm Natural systems don’t exist – they evolve.
The evolution is driven by the exogenous variability imposed on them by weather, climate and anthropogenic factors, and endogenous variability generated by the subsystems as a result of the adaptive process.
The variability allows the system to explore a variety of states to find an optimal one for its sustenance during the evolutionary process.
The response of the system, which we are most concerned with, evolves along with the evolution of the system itself giving rise to combinatorial or co-operative effects new functional (emergent) patterns arise from the systematic alterations of historically discrete configurations of functional relationships
Praveen Kumar
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Working Hypothesis: Learning from Patterns
• Patterns help us to reduce the complexity through reduced dimensionality, and thus help to improve predictions
• Patterns (both observed and so far unobserved) are emergent properties arising out of complex interactions and feedbacks between a multitude of processes.
• Study of patterns (how to describe them, why they emerge, their impact on the overall response) yields new insights and lead to increased understanding.
• Study of observed patterns (why they emerge) may give insights into unobservable or as yet unobserved patterns, and help to make improved predictions
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Summary of Approach• Data based: recognize/extract patterns from data• Patterns needing a multitude of perspectives from
different disciplines to explain or interpret• Interpretation of patterns using parsimonious models: a
top-down approach• What are the minimum processes needed to describe strong physical, chemical
and biological coupling over a wide range of spatial and temporal scales?• How do complex highly heterogeneous physical, chemical and biological systems
respond to changes in forcing behavior and system structure?
• Comparative hydrology: develop generalizable insights through comparisons and classification
• Summer Synthesis Institute in Vancouver, June-July 2009
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Theme #1: Interactions between hydrosphere and biosphere processes
Water balance partitioning at the catchment scalePeter Troch, Ciaran Harman and Sally Thompson
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
The Horton Index
Precip
“Fast” runoff
“Slow” runoff
ET
Wetting
Annual Evapotranspiration
Annual WettingHI =
Proportion of available water vaporized
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Horton, 1933 (AGU)
H constantVW
V : Growing-season vaporization (E+T)W : Growing-season wetting (P-S)
“The natural vegetation of a region tends to develop to such an extent that it can utilize the largest possible proportion of the available soil moisture supplied by infiltration” (Horton, 1933, p.455)
Pattern that intrigues…..
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Horton Index vs. Humidity IndexBetween catchments
Troch et al., 2009 (HP)
Between yearsPattern that intrigues…..
Humidity Index =Annual Precipitation
Annual Potential Evaporation
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Three approaches explain HI
FunctionProcessPattern
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Pred
icte
d HI
_50
0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1HI_50
HI
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
... all three predict the mean
ProcessFunctionPattern
Uncalibrated
Calibrated
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Predictions of inter-annual variability raise questions about their process controls
Timing of rainfall, vegetation response, landscape change, topography?
ProcessFunctionPattern
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
FLUXNET sites
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Hypothesis
?
?
?
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Models of landscapes as nonlinear filters Penman Monteith
Model
Rn VPD LAI U P T
Emax
E
T
Interception Model
PPT
Runoff
Drainage
Infiltration
Multiple Wetting Front Model Root Water Uptake Model
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Working Paradigm
Classic ecohydrological approach: ETmax ~ f(Rn, VPD, LAI,T)
ET ~ ETmax * f(θ)
“Water-limited” paradigm? Plant control of ET?
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Soil Moisture Drydown v ET
0 50 100 150 200 250 300 350 4000
0.5
1
1.5
2
2.5
3
ETSo il Mo is ture
800 900 1000 1100 1200 1300 1400-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Kendall
Sky OaksET increases as soil moisture declines! ET
Soil Moisture
ET correlates to soil moisture
Days
Days
ET (m
m/h
r) o
r θ %
ET (m
m/h
r) o
r θ %
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Adding Groundwater Improves PredictionET
(mm
/hr)
Month
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Phenology Changes Seasonality of ET
10 20 30 40 500
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
17
DOY
Nor
mal
ized
ET
, LA
I, R
n
L A I
ET
R n
0 50 100 1500
0.02
0.04
0.06
0.08
0.1
0.127
Radiation
ET
0 50 100 1500
0.02
0.04
0.06
0.08
0.1
0.129
Radiation
ET
0 1000
0.04
0.08
0.1213
Radiation
ET
A
B
C
A
BC
Week
Nor
mal
ized
ET,
LA
I and
Rn
Howland Forest, Maine
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Theme #2: Interactions of landscape processes
within intensively managed watersheds
Sediment and Contaminant Dynamics Across Scales Nandita Basu, Ciaran Harman, Sally Thompson
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Patterns that intrigue…..
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?
And why?
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Filtering of solute variability across scales:Study Sites
Mississippi Basin
Little Vermilion
Single Tile Drain
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Hypothesis: Landscapes act as cascading,coupled filters
Observed “patterns” are windows into this filtering
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
HEIST: A 1-D event-based model of solute loads filtered by the vadose zone
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Solute mass in
Solute mass out
Increasing depth
Increasing degradation rates
Effects of soil depth: Effects of degradation rate:
Model reveals controls on clustering of events and emergence of extremes
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
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
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
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
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Simon Donner (UBC)IBIS-THMB model simulations (65 sq km grid resolution)
REACH SCALEInverse relationship between
denitrification and stream depth
WATERSHED SCALEInverse relationship between
Annual flow and in-stream removalSpatial averagingover network
Temporal averagingover year
Bohlke 2008
Reach scale dependence on stage shown to produce catchment-scale inter-annual variability in N delivery
In-s
tream
N R
emov
al
Runoff (mm)
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 33
k = 0.06/h
k = 0.2/h
Reach scaleparameterization
Watershed scalebehavior
33
k ~ Q-0.35
Emergent scaling?
“Hydraulic BGC”
Reach-scale process emerge at larger scales despite time-space averaging
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Synthesis process• Summer institute adopted as the mode of synthesis this year;
this turned out to be a success• Short time frame (~6 weeks), team effort (students, mentors),
preparation before and follow-up after• Grassroots level activities and discussions led to the themes
adopted for the summer institute• Freedom to experiment with ideas, yet targeted towards
realizable goals, with a “trail blazing” aspect• Selection of mentors and students brought together was
predicated on the need to represent diverse disciplines and a broad range of technical skills.
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Conclusions• Transformative outcomes:
– Models of the Horton Index and Links between Horton Index and vegetation response, topography
– New watershed modeling framework of watersheds as a hierarchical and nonlinear physical (hydrological and geomorphological) and biogeochemical filters
• Order out of complexity: Landscapes, comprising of vegetated hillslopes and converging, fractal stream networks, act as nonlinear hierarchical dynamic filters to modulate the random, stochastic input signals (rainfall, chemical inputs, etc.) to produce consistent and persistent emergent spatio-temporal patterns (Courtesy: Suresh Rao)
• Possibly the evolutionary outcome of integrated climatic, biogeochemical, geomorphological, ecological, pedological feedbacks: what is their ecosystem function?
2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA
Additional Remarks (Suresh Rao)
• Synthesis: “By combining ideas from hydrology, biogeochemistry, geomorphology etc. to build new, parsimonious models that recreated the patterns observed across scales and under varying forcing conditions.” In this sense “synthesis did occur”
• Consilience: “the concurrence and convergence of induction drawn from synthesis of different datasets and model simulations.” What happened was more than synthesis, what emerged was element of consilience, a synthesis of synthesis, leading to unity of knowledge