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Primer on Ecosystem Water
Balances
Lecture 2
Ecohydrology
Water Balance
• Inputs (cross-boundary flows)
– 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
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
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 movement)
Soil (matrix resistance)
• Note that individual plasticity and changes in
composition (i.e., species level variability) affect all of
these at different time scales, and thus create
important feedbacks between the ecosystem and it’s
resistance properties
Figuratively
• Process is 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
Atmospheric
Demand
Boundary layer
Leaf control
Stem control
– The vast majority of water
passes through plant stomata
to the atmosphere even in
wet areas with low canopy
cover (max. evap. ~ 14%)
Root control
Soil resistance
Soil Moisture
Radiation, Wind
-
Vapor
Pressure
Deficit
Rainfall
+
-
+
Boundary,
Leaf, Stem, Soil
Conductance
+
+
Intercepted
Water
-
+
Primary
Production
-
+
Infiltration
+
Soil Moisture
-
Runoff
Key Regulatory
Processes
• Interception
– I = S + a*t
– Interception (I) is canopy
storage plus rain event
evaporation rate * time
• Annual I in forests > crops and
grasses because of seasonal
effects
Zhang et al. (1999)
Key Regulatory Process - ET
ENERGY
AERODYNAMIC
• Penman-Monteith Equation
• Ω is a decoupling coefficient (relative importance of energy vis-à-vis
aerodynamic terms (0-1)
– Forests is usually small and higher in grasslands; vegetation controls this
• 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
ET and Surface Resistance
• Distinct effects of
vegetation on canopy
resistance
ET (indexed to potential) from a dry
canopy as a function of surface resistance
(rs) at constant aerodynamic resistance
– Forests more
sensitive to changes
in rs
– Acquire water from
deeper in the soil
profile, so that effect
can be compensated
Albedo Effects
• Species composition affects the energy budget
of ecosystems
Net-radiative forcing of
boreal forests following fire
is dominated by albedo
effects (Randerson et al
2006)
Vapor Deficit
• Distance between
actual conditions and
saturation line
– Greater distances =
larger evaporative
potential
• Slope of this line (s) is
an important term for
ET prediction
equations
– Usually measured in
mbar/°C
Stomatal Conductance
• Stomatal dynamics
Stomatal Conductance
– Soil moisture and
atmospheric conditions
Decreasing soil
moisture
Saturation Deficit
– Inter-specific differences are
rarely considered but can be
large
• Changes in LAI
– Stand development
• Initially low LAI
• Rapid increase in LAI and sapwood
area = water use (slows in 8-10 years
in conifer forests as canopy closes)
• Incremental decline in LAI : sapwood
means lowered water use over time
Rooting Depth
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
Budyko Curve
Evidence for One Feedback – Forest Cover
Affects Stream Flow
1 kg of dry mass requires 170-340
kg of water transpiration
Jackson et al. (2005)
Moreover – Species Matter
Evidence for Another Feedback – Composition
Effects on Water Balances
• Halophytic salt cedar • Pataki et al. (2005) studied stomatal
invades SW riparian
conductance for both species in response
areas
to increased salinity
• Displaces cottonwoods, de-waters
riparian areas
Pataki et al. (2005)
Adding Processes (and Feedbacks)
• Effects of soil moisture on nutrient
mineralization
• Species differences in stoichiometry of
biomass create a new set of feedbacks that
control productivity
• Water chemistry feedbacks on transpiration
Coupled Equations to Describe PlantWater 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
In Equation Form (yikes)
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
– Tillmans resource limitation hypothesis predicts excess
capacity in a limiting resource will be USED
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
Optimality Criteria #3
• 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 (he presumes
stasis in environmental drivers over deep time, which is
inconsistent with our view of climate dynamics), but as a
prompt to think more deeply about plant-water
relations, it is a huge milestone
permeability
Pore “disconnectedness”
Complex Dynamics
• Emergent behavior from the reciprocal
adjustments between soil moisture and
ecosystem “resistances” in response to
climate (rainfall and VPD)