Summer Synthesis Institute Vancouver, British Columbia June 22 – June 30, 2009 Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment 6/5/20161Benjamin L. Ruddell, Arizona State University

Summer Synthesis Institute Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment BROADER CONTEXT: OBJECTIVES OF THE SYNTHESIS PROJECT  Objective 1. Conduct synthesis activities that will produce transformational outcomes in hydrologic science towards improved predictability of water cycle dynamics in a changing environment  Objective 2. Use the synthesis activities as test cases to evaluate the effectiveness of different modes of synthesis for advancing the field of hydrologic science. 6/5/20162Benjamin L. Ruddell, Arizona State University

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 6/5/20163 Benjamin L. Ruddell, Arizona State University

Summer Synthesis Institute Vancouver, British Columbia June 22 – June 30, 2009 Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment 6/5/20164Benjamin L. Ruddell, Arizona State University

People of Session I Ben Ruddell, Arizona State (session 1 leader) Murugesu Sivapalan, Illinois (summer synthesis leader) Peter Troch & Paul Brooks, Arizona (session 4 leaders) Ciaran Harman, Illinois Bart Rossmann, Illinois Sally Thompson, Duke Gavan McGrath, Western Australia And YOU! Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment 6/5/20165Benjamin L. Ruddell, Arizona State University

Goal of Session I Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment ID the simplest possible hypotheses which can explain observed interannual variability patterns of H and LAI in a broad range of ecosystems and catchments, by testing them using simple models. The emphasis is on understanding, idea-testing, and reproducing observed patterns, not on precision and detail of predictions. We have NINE DAYS so focus is key. Data-based empirical analysis is happening at the University of Arizona, and will be the focus of session 4. “Everything should be made as simple as possible, but not one bit simpler” – Albert Einstein 6/5/20166Benjamin L. Ruddell, Arizona State University

The Horton Index Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment P = Precipitation S = Surface/Fast Runoff W = Soil Wetting E = Plant Evaporation U = Lateral/Subsurface Runoff Q = Total Runoff H = Horton Index (Troch et al.) Budyko & L’vovich argued for competition between W&S or E&U. What is the strategy of the plant ecosystem, and how does is affect the competition of precipitation partitioning? 6/5/20167Benjamin L. Ruddell, Arizona State University

Observed Horton Index Signatures (to be reproduced using a model) Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment 1)Buffering: H-index variability is an order of magnitude lower than that of climate forcing, suggesting that ecosystem is a buffer. 2)Variability: Interannual variability of H-index is highest within catchments where PE/P ~ 1… suggesting the ecosystem actively strategizes to manage water, esp. in marginal climates. 3)Convergence: In all the world’s catchments, H converges to a value of 1 during severe drought conditions. It’s 1 all the time in deserts. 6/5/20168Benjamin L. Ruddell, Arizona State University

Data: MOPEX Catchments Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment 6/5/20169Benjamin L. Ruddell, Arizona State University

Data: MODIS LAI/NPP and USGS Streamflows Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment MODIS remote sensing for MOPEX LAI/Fpar (MOD15A2) – LAI - Leaf Area Index – FPAR - Fraction of Photosynthetically Active Radiation – 1 km; 8 days Gross Primary Productivity (MOD17A2) – GPP - Gross Primary Productivity – PsnNet - Net Photosynthesis – 1 km; 8 days Data spatially aggregated for 431 MOPEX catchments – AVG; STD; CNT Data Source – Data Availability – from 2/2000 to 12/2008 (present) USGS Streamflows for MOPEX Data Availability – from 10/1999 to 12/2008 – 363/88 catchments Computed – Baseflow and Direct runoff – Methods Local minima One parameter filter (Lyne & Hollick, 1979) Recursive filter (Eckhardt; 2005) 6/5/201610Benjamin L. Ruddell, Arizona State University

Baseline: the Null Hypothesis Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment Null Hypothesis: no active plant ecosystem strategy is necessary to explain the signatures of the Horton Index and LAI/NPP. – H0.1: A bare-soil model is sufficient in all times and places. – H0.2: A stomatal-conductance model is sufficient in all times and places. – H1: A model featuring plant carbon allocation strategy is necessary to reproduce observed H-signatures in certain times and places. (there are many variants on H1) 6/5/201611Benjamin L. Ruddell, Arizona State University

The Baseline / H0.2 Model Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment 6/5/201612Benjamin L. Ruddell, Arizona State University

Proposed Working Groups Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment 1)Detailed / Hourly Modeling (Bart Rossmann?) 2)Minimal / Long Term Modeling (Sally Thompson?) 3)Analytical & Stochastic Modeling (???) 4)Nitrogen Limitation Modeling (???) Each group studies and tests a different set of hypotheses Think about which one(s) you would like to join or lead. Please suggest additional hypotheses or groups. 6/5/201613Benjamin L. Ruddell, Arizona State University

Common / Comparable Outcomes from All Working Groups Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment Predict catchment-average H on the growing-season and annual timescales for several MOPEX catchments. Predict LAI/NPP for the same. Predict net carbon profit and % carbon allocation to roots, stems, and leaves. Determine Nitrogen uptake rates required to support the predicted LAI/NPP. Determine water uptake rates required for the same, and compute Water Use Efficiency. Analyze and then model observed variability relationship between H and LAI/NPP. Reproduce observed signature patterns in H (buffering, variability, convergence). MOPEX Catchment ecosystems will be classified (seasonally or monthly) according to (a) precipitation, (b) temperature, (c) phasing of precip and temp, and (d) limiting resource. Catchments where each hypothesis performs well are to be identified. 6/5/201614Benjamin L. Ruddell, Arizona State University

Group 1: Detailed Modeling Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment Detailed process model – Based on the H0.2 model – couples root-zone water fluxes with a two-layer canopy model – Hourly timescale, with some daily data – Link the vegetation allometry and water balance directly through the plant physiology – Account explicitly for the way allocation of carbon to leaves increases leaf area index – Jarvis (1976)-type stomatal resistance model – Cowan and Farqhuar (1977)-type carbon assimilation model – Multiple Wetting Front (Struthers et al 2005) soil moisture model Candidate Hypotheses 1.Simple proportional allocation (based on observed data) 2.More complex proportional allocation that increases allocation to the root zone when roots are large. 3.Allocate to minimize dessication risk given expected climate. 4.Allocate to maximize NPP and/or Net Carbon Profit based on expected climate. 6/5/201615Benjamin L. Ruddell, Arizona State University

Group 2: Minimal Modeling Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment Simplest possible models, using MWF model + simple strategy rules governing carbon allocation. Dimensionless ratios, basic rules. Candidate Hypotheses: 1.Baseline: Allocation based on plant functional type and allometry 2.Allocation Optimality Model (Givnish, 1986 and possible extension) 3.Allocation and optimal canopy model (Schymanski, 2007) 4.Allocation and optimal canopy model (Hilbert 1990) 5.Stomatal Conductance Optimality Model (Cowan, 1986 and similar to Porporato 2001) 6.Stomatal Optimization (invoking stomata) based on avoidance of desiccation (Makkela, 1986) 7.Investors vs. opportunists (MacArthur and Wilson, 1967) 8.Hydrological Statistics (poster by Gavan McGrath at EGU) 6/5/201616Benjamin L. Ruddell, Arizona State University

Group 3: Analytical/Stochastic Modeling Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment The aim of this group is to use this type of approach (Analytical & Stochastic) to understand the role of different allocation strategies on the water balance H and carbon allocation LAI. Candidate Hypotheses 1.Rainfall/ET/soil moisture probability relations (Rodriguez- Iturbe and Porporato 2004, Porporato 2001, Laio 2001, Botter 2007) 2.Nitrate availability (Botter 2008). 3.Optimal rooting depth assuming max carbon profit (Guswa 2008). 6/5/201617Benjamin L. Ruddell, Arizona State University

Group 4: Nitrogen Limitation Modeling Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment Nitrogen is often the limiting resource, and strongly affects carbon allocation – especially in the wetter ecosystems where the H index is not 1 all the time. But we lack N data in the MOPEX catchments. Activities 1.Conceptually Driven Identification of N-limited conditions – Using basic climate, plant type, and soil data, can we predict annual average N concentrations? – Can we predict switching between N and water limitation in a catchment? – Which of our catchments are likely to be N-limited? 2.Data driven Identification of N-limited conditions – What “signature” would we expect N limitation to have in H and LAI results? – What level of N-limitation would cause the errors signatures that group 1, 2, or 3 are seeing? – What data are available to assist in the prediction of N conditions in catchments? 3.Modeling of nitrogen limitation 6/5/201618Benjamin L. Ruddell, Arizona State University

Preparation for Session I Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment Working knowledge of Matlab, with the software available on your own laptop. Read the recommended papers Study the H0.2 baseline model produced by Ciaran Harman and Bart Rossmann Pick one or two working groups to join, or suggest your own topics Leaders needed! Start working on your ideas and preliminary results! Necessary datasets will be provided; ask if you need something specific! Hypotheses to be studied need to be pared-down and focused by group leaders before we can start the session… our current list is too long and insufficiently focused for 10 days! We need to focus on simple, conceptual approaches to modeling- our goal is to identify simple models which meet or beat the accuracy of detailed process models. Get creative! 6/5/201619Benjamin L. Ruddell, Arizona State University

Tangible Goals Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment Think PAPERS… we want to publish two or more papers based on this session’s work. Do work of a conceptual quality such that it can be published as original research. Tie-in with session 4… we need to yield results that are comparable with those of the fourth session. Think NEW IDEAS… this is the time to try your pet theories and hunches in a structured environment, where they may be validated! 6/5/201620Benjamin L. Ruddell, Arizona State University

Example “Crazy Hypothesis”: Vegetation is adapted to variability cycle of its limiting resource Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment LAI as a measurable proxy for NPP in terrestrial ecosystems R is the a limiting resource – Water – Energy (solar) – Carbon Dioxide – Nutrients (N) BUT R varies in time, and the limiting resource switches. Use dimensionless numbers to quantify adaptation of ecosystem to resource variability Try Later 6/5/201621Benjamin L. Ruddell, Arizona State University

Questions? Session I: Quantifying Vegetation Adaptation and Response to Variability in the Environment 6/5/201622Benjamin L. Ruddell, Arizona State University