Water balance partitioning at the catchment scale: Random Process or Emerging Property?

1. 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… 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) Horton, 1933 (AGU)

3. HortonIndex vs. Humidity Index Std. Horton Index Mean 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. 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. A Selection of Results from the Summer Institute in Vancouver, BC Antoine Aubeneau, Ciaran Harman, Bryan Moravec, Andy Neal, Sally Thompson, Hal Voepel, Sheng Ye, Mary Yeager, Stefano Zanardo

6. What controls the Horton index?

7. The Horton Index Proportion of available water vaporized Precip “Fast” runoff ET Wetting “Slow” runoff Annual Evapotranspiration HI = Annual Wetting

9. Three approaches explain HI Pattern Process Function HI

10. ... all three predict the mean remarkably well Uncalibrated Calibrated Pattern Function Process

11. HI was predictable based on static or mean catchment properties Pattern Humidity index P/EP HI = f ( ) Mean Topographic Index <Log (a / tan β)>

12. …and using a conceptualization of annual partitioning of precip… Function P S ET W U • Functional model predicts mean, variance of HI • Functional model: • → S and U have thresholds • → ET and W have upper limit Fast flow threshold Wetting potential

13. ... and using a stochastic model based on filtering of storm events. Process Uncalibrated Calibrated Storage capacity Calibrated storage capacity

14. We gained insight into controls on HI

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

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

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

18. Prediction of interannual variability opens up questions about other factors Pattern Function Process Timing of rainfall, vegetation response, landscape change, …?

19. Key unresolved questions: • How does variability scale in time? • What timescales are important?

20. 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. Variability and Vegetation Learning from Data-Rich Sites

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

23. Rn VPD LAI U P T A Parsimonious Model Penman Monteith Model PPT Interception Model E Emax Infiltration Runoff Multiple Wetting Front Model T Root Water Uptake Model Drainage

24. Interannual variability

25. Sub-daily variability ET (mm/hr)

26. Seasonal variability Month ET (mm/hr)

27. Soil Moisture Drydown v ET ET correlates to soil moisture Kendall ET (mm/hr) or θ % Days ET increases as soil moisture declines! Sky Oaks ET Soil Moisture ET (mm/hr) or θ % Days

28. Adding Groundwater Improves Prediction Month ET (mm/hr)

29. Phenology Changes Seasonality of ET Howland Forest, Maine C B Normalized ET, LAI and Rn C B A A Week

30. Phenological Effects are Predictable ET v Cumulative Growing Degree Days for first 150 Days of the Year Kendall Grasslands Donaldson Coniferous Forest Morgan Monroe Mixed Forest Poorly correlated Onset of plant growth? Or leaf maturity? Well correlated

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

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

33. Ecohydrological catchment classification? Donaldson Sky Oaks Harvard Forest Kennedy Morgan Monroe Austin Cary HuI Radiation Phenology GW Access Seasonality Metolius Howland Forest Fort Peck Goodwin Creek Kendall 0 0.5 1 1.5 Humidity Index

34. 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. 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