2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Synthesis Integrating Climate- Water-Ecosystem Science Murugesu Sivapalan University of Illinois, Urbana-Champaign

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Water Cycle Dynamics in a Changing Environment Advancing 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 scale Peter 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 Wetting HI = Proportion of available water vaporized

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Horton, 1933 (AGU) 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 Index Between catchments Troch et al., 2009 (HP) Between years Pattern that intrigues….. Humidity Index = Annual Precipitation Annual Potential Evaporation

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

Three approaches explain HI Function Process Pattern HI

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA... all three predict the mean ProcessFunction Pattern 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? ProcessFunction Pattern

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 RnVPDLAIUPT E max 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: ET max ~ f(Rn, VPD, LAI,T) ET ~ ET max * f(θ) “Water-limited” paradigm? Plant control of ET?

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Soil Moisture Drydown v ET Kendall Sky Oaks ET increases as soil moisture declines! ET Soil Moisture ET correlates to soil moisture Days ET (mm/hr) or θ %

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Adding Groundwater Improves Prediction ET (mm/hr) Month

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Phenology Changes Seasonality of ET A B C A B C Week Normalized ET, LAI 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

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 Filtering of variable inputs by landscape structure and biogeochemical processes produces PATTERNS, as water and solutes cascade across spatial and temporal scales 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: Clustering of transported mass Effects of degradation rate: Clustering in time Increased non-linearity of filter “Extreme outcomes driven by normal inputs” 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-compartment flow and BGC process 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 – dependent reaction 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 SCALE Inverse relationship between denitrification and stream depth WATERSHED SCALE Inverse relationship between Annual flow and in-stream removal Spatial averaging over network Temporal averaging over year Bohlke 2008 Reach scale dependence on stage shown to produce catchment-scale inter-annual variability in N delivery In-stream N Removal Runoff (mm)

2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 33 k = 0.06/h k = 0.2/h Reach scale parameterization Watershed scale behavior 33 k ~ Q 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