Lecture 2

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)