1. CUAHSI Webinar, 13 November 2009 Water balance partitioning at the catchment scale: Random Process or Emerging Property? Paul Brooks, Peter Troch, Ciaran Harman and Sally Thompson CUAHSI Webinar 13 November 2009 CUAHSI Webinar, 13 November 2009

2. Motivation: another Horton index… 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)

3. CUAHSI Webinar, 13 November 2009 Horton Index vs. Humidity Index Mean Horton Index Std. Horton Index 53% with Std(H)<0.06 74% with Std(H)<0.07 83% with Std(H)<0.08 93% with Std(H)<0.10 Troch et al., 2009 (HP)

4. CUAHSI Webinar, 13 November 2009 Objective: To address fundamental questions linking Hydrology and Ecology in a data-rich workshop setting Hydrology Where does water go when it rains? What controls that partitioning? Ecosystem Ecology How do we quantify plant available water? How does vegetation respond to changes in precipitation? Can we improve hydrological, ecological, and biogeochemical predictability by introducing a reproducible measure of hydrologic partitioning into existing theory and observations?

5. CUAHSI Webinar, 13 November 2009 Antoine Aubeneau, Ciaran Harman, Bryan Moravec, Andy Neal, Sally Thompson, Hal Voepel, Sheng Ye, Mary Yeager, Stefano Zanardo A Selection of Results from the Summer Institute in Vancouver, BC

6. CUAHSI Webinar, 13 November 2009 What controls the Horton index?

7. CUAHSI Webinar, 13 November 2009 The Horton Index Precip “Fast” runoff “Slow” runoff ET Wetting Annual Evapotranspiration Annual Wetting HI = Proportion of available water vaporized

8. CUAHSI Webinar, 13 November 2009

9. Three approaches explain HI Function Process Pattern HI

10. CUAHSI Webinar, 13 November 2009... all three predict the mean remarkably well ProcessFunction Pattern Uncalibrated Calibrated

11. CUAHSI Webinar, 13 November 2009 HI was predictable based on static or mean catchment properties Pattern HI = f ( ) Humidity index P/EP Mean Topographic Index

12. CUAHSI Webinar, 13 November 2009 Function Functional model predicts mean, variance of HI Wetting potentialFast flow threshold P S U ET W Functional model: → S and U have thresholds → ET and W have upper limit …and using a conceptualization of annual partitioning of precip…

13. CUAHSI Webinar, 13 November 2009 Process... and using a stochastic model based on filtering of storm events. Storage capacity Calibrated storage capacity CalibratedUncalibrated

14. CUAHSI Webinar, 13 November 2009 We gained insight into controls on HI

15. CUAHSI Webinar, 13 November 2009 Regression models suggest that climate and topography are primary controls Pattern Humidity Index Topographic Index Mean: Climate (except in steep, arid regions) CV: topography (humid regions) Mean HI CV HI

16. CUAHSI Webinar, 13 November 2009 Functional model suggests catchment capacity to vaporize and store water are basic controls Ep λs = λu = 0 λs = λu = 0.05 Function Mean: - vaporization potential (~ energy) - catchment “wetability” (to a point) P = 1000mm

17. CUAHSI Webinar, 13 November 2009 Process model also suggests keys are that climate and capacity to store water from storm events Process Mean HI: Humidity Index, storage capacity Variance: only sensitive in arid regions

18. CUAHSI Webinar, 13 November 2009 Prediction of interannual variability opens up questions about other factors Timing of rainfall, vegetation response, landscape change, …? ProcessFunction Pattern

19. CUAHSI Webinar, 13 November 2009 Key unresolved questions: How does variability scale in time? What timescales are important?

20. CUAHSI Webinar, 13 November 2009 Key unresolved questions: What is the role of vegetation in hydrologic partitioning? Are we only able to make predictions because of the co- evolution of vegetation, soils and geomorphology constrained by climate, geology and time? Vegetation paradox: HI predicts vegetation (NDVI): Much of the ET is T No models account for vegetation explicitly!

21. CUAHSI Webinar, 13 November 2009 Variability and Vegetation Learning from Data-Rich Sites

22. CUAHSI Webinar, 13 November 2009 Working Paradigm Classic ecohydrological approach: ET max ~ f(Rn, VPD, LAI,T) ET ~ ET max * f(θ) “Water-limited” paradigm? Plant control of ET?

23. CUAHSI Webinar, 13 November 2009 A Parsimonious Model Penman Monteith Model RnVPDLAIUPT E max E T Interception Model PPT Runoff Drainage Infiltration Multiple Wetting Front Model Root Water Uptake Model

24. CUAHSI Webinar, 13 November 2009 Interannual variability

25. CUAHSI Webinar, 13 November 2009 Sub-daily variability ET (mm/hr)

26. CUAHSI Webinar, 13 November 2009 Seasonal variability ET (mm/hr) Month

27. CUAHSI Webinar, 13 November 2009 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 %

28. CUAHSI Webinar, 13 November 2009 Adding Groundwater Improves Prediction ET (mm/hr) Month

29. CUAHSI Webinar, 13 November 2009 Phenology Changes Seasonality of ET A B C A B C Week Normalized ET, LAI and Rn Howland Forest, Maine

30. CUAHSI Webinar, 13 November 2009 Phenological Effects are Predictable Kendall Grasslands Donaldson Coniferous ForestMorgan Monroe Mixed Forest Poorly correlatedWell correlated ET v Cumulative Growing Degree Days for first 150 Days of the Year Onset of plant growth? Or leaf maturity?

31. CUAHSI Webinar, 13 November 2009 Can Patches Predict Catchments? Humidity Index Horton Index S.O. Catchment M.M. Catchment H.F. Catchment G.C. Catchment Sky Oaks Morg. Monroe Harvard Forest Goodwin Crk.

32. CUAHSI Webinar, 13 November 2009 Conceptual Upscaling Approach Multiple Buckets – different topography, veg, soil etc. PPT, Energy, C ET, Energy, C Deep Drainage, Water Table, Lateral Redistribution Surface redistribution

33. CUAHSI Webinar, 13 November 2009 Ecohydrological catchment classification? Sky Oaks Fort Peck Goodwin Creek Howland Forest Donaldson Kennedy Kendall Austin Cary Metolius Harvard Forest 0.511.50 Morgan Monroe Humidity Index HuI Radiation Phenology GW Access Seasonality

34. CUAHSI Webinar, 13 November 2009 Discussion Points What does all this mean for predicting water cycle dynamics in a changing environment? –Mean behavior of hydrologic partitioning is surprisingly predictable, and –Knowing hydrologic partitioning improves prediction of vegetation response, yet –The inter-annual variability is poorly understood and calls for higher understanding of ecosystem control on water cycle dynamics (do we need to replace the old paradigm?)

35. CUAHSI Webinar, 13 November 2009 Come see us at AGU! Hydrologic Predictions in a Changing Environment Monday 14th Talks: 8:00 – 10:00 am and 10:20am – 12:20pm, 3005 Moscone West Posters: 1:40 – 6:00pm Moscone South