1. Primer on Ecosystem Water Balances
Lecture 2
Ecohydrology

2. Water Balance Inputs (cross-boundary flows) Outputs
Rainfall
Stochastic in interval, intensity and duration
Runin/Groundwater?
Outputs
Evapo-transpiration
Surface runoff
Infiltration
Key internal stores/processes
Soil moisture
Interception
Stomatal regulation
Sap-flow rates
Boundary layer conductance
Capillary wicking 2

3. Water Balance P = ET + R + D + ΔS
P – precipitation
ET – evapotranspiration
Contains interception (I), surface evaporation (E) and plant transpiration (T)
R – runoff
D – recharge to groundwater
ΔS – change in internal storage (soil water)
Quantities on the RHS are functions of each other
ET, R and D are a function of ΔS, and vice versa
Plants mediate all of the relationships

4. Soil-Plant-Atmosphere Continuum
ET through a chain of resistances in series
Boundary layer (canopy architecture)
Leaf resistance (stomatal dynamics)
Xylem resistances (sapwood area, conductivity)
Root resistances (water entry and transport)
Soil (matrix resistance)
Individual plasticity and changes in composition (i.e., species level variability) affect each process at different time scales. Creates important feedbacks between the ecosystem and it’s resistance properties

5. Figuratively
Atmospheric
Demand
Driven by a vapor pressure deficit between the soil and atmosphere and net radiation
Soil evaporation is a minor (~5%) portion of total ecosystem water use
Most water passes through plant stomata even in wet areas with low canopy cover
Evolutionary control on resistances and response to stresses
For example, cavitation of the SPAC in the xylen
Boundary layer
Leaf control
Stem control
Root control
Soil resistance
Soil Moisture

6. The SPAC (soil-plant-atmosphere continuum)
Yw (stem)
 -0.6 MPa
Yw (small
branch)
 -0.8 MPa
Yw (atmosphere)
 -95 MPa
Yw (root)
 -0.5 MPa
Yw(soil)  -0.1 MPa

7. How Does Water Get to the Leaf?
Water is PULLED, not pumped. Water within the whole plant forms a continuous network of liquid columns from the film of water around soil particles to absorbing surfaces of roots to the evaporating surfaces of leaves.
It is hydraulically connected.

8. Leaf, Stem, Soil Conductance
Radiation, Wind + - -
Vapor Pressure
Deficit
Rainfall
Intercepted Water + +
Boundary,
Leaf, Stem, Soil Conductance + + - +
Infiltration
Primary
Production - -
Runoff -
Soil Moisture + 8

9. Vapor Deficit (Dm = es – ea)
Distance between actual conditions and saturation line
Greater distance = larger evaporative potential
Slope of this line (s) is a term in ET prediction equations
Usually measured in mbar/°C

10. Key Regulatory Processes
Interception
I = S + a*t
Interception (I) is canopy storage (S) plus rain event evaporation rate * time
Mean I ~ 20% of P
Strong function of forest structure
Annual I in forests > crops and grasses because of seasonal effects
EF Plot 4
GS- Plot 4
Zhang et al. (1999) 10 10

11. Key Regulatory Process - ET
Penman-Monteith Equation
Ω is a decoupling coefficient (energy vs. aerodynamic terms; 0-1)
Vegetation controls this; higher in forests, lower in grasslands
s is the slope of saturation vapor pressure curve, γ is the psychrometric constant, ε is s/γ, Rn is net radiation, G is ground heat flux, ρ is the density of air, Cp is the specific heat capacity of air, Dm is the vapor pressure deficit, rs is the surface resistance and ra is the aerodynamic resistance
ENERGY
AERODYNAMIC

12. ET and Surface Resistance
ra is the resistance of the air to ET, controlled by wind speed and surface roughness
rs is resistance for vapor flow through the plant or from the bare soil surface
Vegetation effects
Leaf area index (LAI)
Stomatal conductance
Water status (wilting)
ET (indexed to PET) from a dry canopy as a function of surface resistance (rs) at constant aerodynamic resistance (ra)

13. Albedo Effects Species type affects ecosystem energy budget
Net-radiative forcing of boreal forests following fire is dominated by albedo effects (Randerson et al 2006)

14. Stomata – “Ecohydrologic Engineers”
Air openings, mostly on leaf under-side
1% of leaf area, but ~ 60,000 cm-2
Function to acquire CO2 from the air
Open and close diurnally, and in response to soil H2O tension
React to wilting (loss of leaf water)
Guard cells (shape change with turgor pressure)

15. Stomatal Conductance
Rate of CO2 (H2O) exchange with air (mmol m-2 s-1) 15

16. Specific Variation Conductance properties vary by species
Feedbacks between water use and succession
Comparative climate change vulnerability

17. Rooting Depth
Forest Soil

18. Rooting Depth Effects
Surface
2 months later

19. Hydraulic Redistribution
Roots equilibrate soil moisture (even when stomata are closed)
Cohesion-tension theory, where tension is exerted by potential gradients, and water forms a continuous “ribbon” because of cohesion forces
Water transport from well watered locations to dry locations
Local spatial variation in irrigation
Deep water access via tap-roots (“hydraulic lifting”)
Facilitation effects (deep-rooted plants supplying shallow moisture)
Richards and Caldwell (1987)

20. Soil Moisture Dynamics: Consumption and Hydraulic Redistribution
Field Capacity
Wilting Point

21. Watching ET Occur
Across depths, integrate to quantify Total Soil Moisture (TSM)
Assess 24-hr change in TSM, due to: ET
Hydraulic lift
𝐸𝑇= 𝑇𝑆𝑀 𝑡 − 𝑇𝑆𝑀 𝑡 ∗𝑠

22. A Simple Catchment Water Balance
Consider the net effects of the various water balance components (esp. ET)
At long time scales (e.g., > 1 year) and large spatial scales (so G is ~ 0): P = R + ET
The Budyko Curve
Divides the world into “water limited” and “energy limited” systems
Dry conditions: when Eo:P → ∞, ET:P → 1 and R:P → 0
Wet conditions: when Eo:P → 0 ET → Eo

23. Budyko Curve

24. Evidence for One Feedback – Forest Cover Affects Stream Flow
H2O
1 : 300
Jackson et al. (2005) 24

25. Moreover – Species Matter

26. Evidence for Another Feedback – Composition Effects on Water Balances
Halophytic salt cedar invades SW riparian areas
Displaces cotton-woods, de-waters riparian areas
Pataki et al. (2005) studied stomatal conductance for both species in response to increased salinity
Pataki et al. (2005) 26

27. Adding Processes (and Feedbacks)
Organic matter affects soil moisture dynamics
Vegetation affects soil depth (erosion rates)
Soil moisture affects nutrient mineralization (esp. N)
Inter- and intra-specific interactions (facilitation, inhibition) 27

28. Coupled Equations to Describe Plant-Water Relations in a Forest
Peter Eagleson (1978a-g)
14 parameter model links rain to production via soil moisture
Posits three “optimality criteria” at different scales

29. In Equation Form (yikes)

30. Eagleson’s Optimality Hypothesis #1
Vegetation canopy density will equilibrate with climate and soil parameters to minimize water stress (= maximize soil moisture)
Idea of an equilibrium is reasonable
“Growth-stress” trade-off
Stress not explicitly included in the model
Evidence is contrary to maximizing soil moisture
Communities self-organize to maximize productivity subject to risks of overusing water between storms
Tillman’s resource limitation hypothesis predicts excess capacity in a limiting resource will be USED

31. Optimality Criteria #2
Over successional time, plant interactions with repeated drought will yield a community with an optimal transpiration efficiency (again maximizing soil moisture, because that is how a plant community buffers drought stress)
Actually impossible (or nonsense at least)
A community that uses less water will replace a community that uses more (contradicts all of successional dynamics)
The equilibrium occurs at “zero photosynthesis” because that is the state at which transpiration loss is minimized.
While the central prediction is probably in error, the basic idea of some non-obvious equilibrium emerging from the negotiation between climate, plants and soils is an idea that others have built on

32. Optimality Criteria #3
permeability
Plant-soil co-evolution occurs in response to slow moving optimality
Changes in soil permeability and percolation attributes
Assumes no change in species transpiration efficiencies
First inkling that, embedded in the collective control of plant communities on abiotic state variables has evolutionary implications
Selection based on group criteria
Constraints of efficiency
Unlikely to hold in Eagleson’s formulation (presumes stasis in environmental drivers over deep time, which is inconsistent with climate dynamics), but as a prompt to think more deeply about plant-water relations, it is a huge milestone
Pore “disconnectedness”

33. Simplifying Complex Dynamics
Emergent behavior from reciprocal adjustments between soil moisture and ecosystem “resistances” (water use, biomass growth) in response to climate (rainfall)
Read Porporato et al. (2004)

34. Next Time…
Arid land ecohydrology