1. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Water balance partitioning at the catchment scale: Hydrosphere-biosphere interactions Peter Troch, Ciaran Harman and Sally Thompson 2009 Hydrologic Synthesis Reverse Site Visit August 20-21 2009 Arlington, VA 2009 Hydrologic Synthesis Reverse Site Visit – Arlington, VA

2. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 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. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 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. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Climate VegetationGeologyTopography The Horton Index Ecosystem Productivity Catchment Biogeochemistry

5. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA What controls the Horton index?

6. 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

7. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA

8. Three approaches explain HI Function Process Pattern HI

9. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA... all three predict the mean remarkably well ProcessFunction Pattern Uncalibrated Calibrated

10. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA HI was predictable based on static or mean catchment properties Pattern HI = f ( ) Humidity index P/EP Mean Topographic Index

11. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 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…

12. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Process... and using a stochastic model based on filtering of storm events. Storage capacity Calibrated storage capacity CalibratedUncalibrated

13. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA We gained insight into controls on HI

14. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 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

15. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 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

16. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 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

17. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Prediction of interannual variability opens up questions about other factors Timing of rainfall, vegetation response, landscape change, …? ProcessFunction Pattern

18. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Key unresolved questions: How does variability scale in time? What timescales are important?

19. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 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!

20. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Variability and Vegetation Learning from Data-Rich Sites

21. 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?

22. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 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

23. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Interannual variability

24. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Sub-daily variability ET (mm/hr)

25. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Seasonal variability ET (mm/hr) Month

26. 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 %

27. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Adding Groundwater Improves Prediction ET (mm/hr) Month

28. 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

29. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 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?

30. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 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.

31. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA Conceptual Upscaling Approach Multiple Buckets – different topography, veg, soil etc. PPT, Energy, C ET, Energy, C Deep Drainage, Water Table, Lateral Redistribution Surface redistribution

32. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 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

33. 2009 Hydrologic Synthesis Reverse Site Visit – Arlington VA 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?)