Synthesis Integrating Climate-Water-Ecosystem Science

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Synthesis Integrating Climate-Water-Ecosystem Science

Murugesu SivapalanUniversity of Illinois, Urbana-Champaign


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


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


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


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?


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


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


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


Theme #1: Interactions between hydrosphere and biosphere processes

Water balance partitioning at the catchment scalePeter Troch, Ciaran Harman and Sally Thompson


The Horton Index

Precip

“Fast” runoff

“Slow” runoff

ET

Wetting

Annual Evapotranspiration

Annual WettingHI =

Proportion of available water vaporized


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…..


Horton Index vs. Humidity IndexBetween catchments

Troch et al., 2009 (HP)

Between yearsPattern that intrigues…..

Humidity Index =Annual Precipitation

Annual Potential Evaporation



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


... all three predict the mean

ProcessFunctionPattern

Uncalibrated

Calibrated


Predictions of inter-annual variability raise questions about their process controls

Timing of rainfall, vegetation response, landscape change, topography?

ProcessFunctionPattern


FLUXNET sites


Hypothesis

?

?

?


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


Working Paradigm

Classic ecohydrological approach: ETmax ~ f(Rn, VPD, LAI,T)

ET ~ ETmax * f(θ)

“Water-limited” paradigm? Plant control of ET?


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 %


Adding Groundwater Improves PredictionET

(mm

/hr)

Month


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


Theme #2: Interactions of landscape processes

within intensively managed watersheds

Sediment and Contaminant Dynamics Across Scales Nandita Basu, Ciaran Harman, Sally Thompson


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?


Filtering of solute variability across scales:Study Sites

Mississippi Basin

Little Vermilion

Single Tile Drain

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


Hypothesis: Landscapes act as cascading,coupled filters

Observed “patterns” are windows into this filtering


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


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


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


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


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.


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?


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

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Synthesis Integrating Climate-Water-Ecosystem Science