An Investigation of Evapotranspiration Models

Compared by Stannard (1993) M.S. Exam Given by Gaj Sivandran, Ph.D., P.E.

Completed by Matt D. Spellacy, M.S. candidate

The Ohio State University

January 5, 2015

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Contents

Prompt ............................................................................................................................................. 2

Background ..................................................................................................................................... 3

Question I ........................................................................................................................................ 6

Question II ...................................................................................................................................... 8

Question III ................................................................................................................................... 11

Question IV ................................................................................................................................... 12

Sources .......................................................................................................................................... 18

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Prompt

Stannard (1993) compares the PenmanMonteith, ShuttleworthWallace, and Modified PriestleyTaylor equations for modeling evapotranspiration in a semiarid rangeland.

(I) Briefly outline the key differences between the three models. Use the equations and diagrams presented in the Stannard (1993) paper to strengthen your argument.

(II) How are the water limitations of an arid system incorporated into each model? (III) Figure 7 shows the performance of each of the models against measured data.

Comment on this lack of agreement after the middle of the day. Why do all these

models fail to capture the late afternoon peak?

(IV) Given your knowledge of microwave remote sensing, how would you use the remote sensing of soil moisture to improve evaporation models?

Note: There is no right or wrong answer to much of this exam. What I am interested is seeing

how you reason your way through the above questions.

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Background

Penman (1948) combined the thermodynamic and aerodynamic aspects of evaporation into a

mathematical equation that provides a simple means to study the surface energy budget and

surface temperature. Evaporation of water from a saturated surface is a thermodynamic process

in which energy is required to change water from liquid to vapor. The latent heat flux is

( ) ( ) ( )

Latent heat flux increases as net available energy (Rn-G) increases and decreases as sensible heat

flux (|H) increases. Evaporation is also an aerodynamic process related to the turbulent transport

of water vapor away from a surface. Latent heat flux increases as evaporative demand, or the

difference in the vapor pressure of air at a given temperature relative to that of saturated air at

that temperature (vapor pressure deficit) increases.

To clarify these points, energy must be absorbed or released to break and form the hydrogen

bonds when evaporation and condensation takes place. Thus evaporation is always accompanied

by a transfer of heat out of a water body, and condensation on the surface by an addition of heat

into the water body, in a process called latent-heat transfer. Water vapor is also transferred

between the surface and the air whenever there is a difference in the vapor pressure between the

surface and the overlying air, and a transfer of latent heat always accompanies the vapor pressure

transfer. A second mode of non-radiant heat transfer occurs in the form of sensible heat (H), that

is, the transfer of heat energy whenever there is a temperature difference between the surface and

the air.

The original derivation of the latent heat flux was developed by Penman for evaporation over

open water. It assumes no heat exchange with the ground, no water-advected energy, and no

change in heat storage, and makes use of one approximation, that the slope of the saturation-

vapor vs. temperature curve at the air temperature can be approximated as

from which

we obtain

. In this approach, evaporation rate is a weighted sun of a rate due to

net radiation and a rate due to mass transfer. Monteith (1965) showed how the Penman equation

can be modified to represent the evapotranspiration rate from a vegetated surface by

incorporating a canopy resistance term. His modified Penman relationship is has come to be

known as the Penman-Monteith equation. The assumptions of no water-advected energy and no

heat-storage effects, which are generally not valued for natural water bodies, are usually

reasonable when considering a vegetated surface.

The fluxes of sensible, latent heat, and CO2 from a leaf can be represented as a diffusion process

analogous to electrical networks. The electrical current between two points on a ducting wire is

equal to the voltage difference divided by the electrical resistance. For an electrical circuit with

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two resistors connected in series, the total resistance is the sum of the individual resistances.

Similarly, the diffusion of materials is related to the concentrations difference divided by a

resistance to diffusion. For sensible heat, this diffusion resistance is defined by the leaf boundary

later resistance. The exchange of water vapor and CO2 between a leaf and the surrounding air

depend on two resistances connected in series: a stomatal resistance from inside the leaf to the

leaf surface and a boundary later resistance from the leaf surface to the air. If stomata are located

on both sides of the leaf, the upper and lower resistances acting in parallel determine the overall

leaf resistance.

The boundary later resistance governs heat and moisture exchange between the leaf surface and

the air around the leaf. This resistance depends on leaf size and wind speed. The boundary later

resistance represents the resistance to heat and moisture transfer between the leaf surface and

free air above the leaf surface. Wind flowing across a leaf is slowed near the leaf surface and

increases with distance from the surface. Full wind flow occurs only at some distance from the

leaf surface. This transition zone, in which wind speed increases with distance from the surface,

is known as the leaf boundary later. The leaf boundary layer is also a region of temperature and

moisture transition from a typically hot, moist leaf surface to cooler, drier air away from the

surface. The boundary later regulates heat and moisture exchange between a leaf and the air. A

thin boundary later produces a small resistance to heat and moisture transfer. The leaf is closely

coupled to the air and has a temperature similar to that of air. A thick boundary later produces a

large resistance to heat and moisture transfer. Conditions at the leaf surface are decoupled from

the surrounding air and the leaf is several degrees warmer than air.

Stomatal resistance acts in series with boundary later resistance to regulate transpiration.

Transpiration occurs when stomata open to allow a leaf to absorb CO2 during photosynthesis. At

the same time, water diffuses out of the saturated cavities within the foliage to the drier air

surrounding the leaf. The resistance for latent heat exchange, therefore, includes two terms: a

stomatal resistance (rs), which governs the flow of water from inside the leaf to the leaf surface,

and the boundary later resistance, which governs the flow of water from the leaf surface to

surrounding air. The total resistance is the sum of these two resistances. Stomata open and close

in response to a variety of conditions: they open with higher light levels; they close with

temperatures colder or hotter than some optimum; they close as the soil dries; they close if the

surrounding air is too dry; and they vary with atmospheric CO2 concentration. Stomatal

resistance is a measure of how open the pores are.

The principles that determine the temperature, energy balance, and the photosynthetic rate of a

leaf also determine those of plant canopies when integrated over all leaves in the canopy. These

processes are greatly affected by the amount of area, quantified by leaf area index. The vertical

profile of leaf area in the canopy affects the distribution of radiation in the canopy and the

absorption of radiation by leaves. With low leaf area index, plants absorb little solar radiation,

and the overall surface albedo is largely that of soil. The CO2 uptake by a canopy is the

integration of the photosynthetic rates of individual leaves, accounting for variations in light and

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microclimate with depth in the canopy. Similarly, canopy conductance is an aggregate measure

of the conductance of individual leave and the profile of leaf area in the canopy also greatly

affects turbulence within the canopy. The effects of vegetation on surface fluxes can be modeled

by treating the soil-canopy system as an effective bulk surface, analogous to that for a non-

vegetated surface but with radiative exchange averaged over the canopy and resistances adjusted

for the effects of vegetation. This type of formulation is referred to as a big leaf model because

the canopy is treated as a single leaf scaled to represent a canopy. The Penman-Monteith

equation applied to canopies is an example of a big leaf model. Alternatively, the effects of

vegetation on surfaces fluxes can be quantified by separately modeling soil and vegetation, as

done by Shuttleworth and Wallace (1985) and described in more detail later.

Vegetation flux equations in a big leaf model are similar to those of an individual leaf, but with

resistances representative for all the vegetation in the system. In a big leaf model, the effective

height at which sensible heat is exchanged with the atmosphere is within the plant canopy, and

the aerodynamic resistance is adjusted for the roughness length and displacement height of

vegetation. Two resistances acting in series govern latent heat flux. A surface resistance

regulates water vapor exchange from the effective evaporating surface to air in the canopy. An

aerodynamic resistance represents turbulent processes above the canopy in the surface layer of

the atmosphere. Here, the latent heat flux is the combined foliage transpiration and soil

evaporation, and the surface resistance represents the effects of soil water on both these fluxes.

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Question I

The Penman-Monteith equation is an example of a big leaf representation of evapotrans-

piration. For a vegetative surface:

( ) ( )

The first term in the numerator is the energy

term, and the second is the aerodynamic term.

The resistance formulation of the Penman-

Monteith model is shown in Figure 1a of

Standard (1993). Here, is canopy or surface

resistance (stomata resistance to transpiration)

and is the aerodynamic resistance to

sensible heat and water vapor (the resistance

of heat to flow and water to condense and

evaporate). This aerodynamic resistance

governs heat and moisture exchange between

the canopy or soil and the surrounding air.

Between the source and the sensor height, the

latent heat flux is opposed by and ,

whereas the sensible heat flux, which

originates on the surface of the big leaf

(canopy), is opposed by only . This is a big

leaf formulation of latent heat flux in which

the latent heat exchange with the atmosphere

is regulated by two resistance acting in series.

A canopy resistance governs processes within

the plant canopy, and an aerodynamic resistance for water vapor governs turbulent processes

above the canopy.

The big leaf assumption requires that the sources of sensible and latent heat are at the same

height and temperature. This requirement is met by a fully canopy, or a bare surface, but not a

sparse canopy. As with an individual leaf, a canopy has degrees of coupling to the atmosphere

determined by the magnitude of the aerodynamic resistance. Tall forest vegetation has a low

aerodynamic resistance and is closely coupled to the atmosphere. Evapotranspiration approaches

a rate imposed by canopy resistance:

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Short vegetation such as grassland or even bare soil has a large aerodynamic resistance is is

decoupled from the atmosphere. Evapotranspiration approaches an equilibrium rate depend on

available energy with little canopy influence:

( )

The Penman-Monteith approach to treating a vegetation canopy as a big leaf (or bare soil) can

be refined by treating the vegetated and unvegetated portions of a given area separately. Sensible

heat is partitioned inot that from foliage and that from soil, each regulated by different processes.

The leaf boundary later resistance integrated over all leaves in the canopy governs sensible heat

flux from foliage. Turbulent processes within the canopy govern sensible heat flux from the soil.

Latent heat is partitioned into soil evaporation and transpiration. Transpiration is regulated by a

canopy resistance that is an integration of leaf resistance over all the leaves in the canopy. Soil

evaporation is regulated by aerodynamic processes within the plant canopy and by soil moisture.

With reference to the one-later canopy, three sensible heat fluxes can be represented in the soil-

plant-atmosphere system: the flux from vegetation to canopy air, from ground to canopy air, and

from canopy air to atmosphere.

A multi-layer soil-canopy model is described by Shuttleworth and Wallace (1985). Whereas the

Penman-Monteith equation is a single bulk representation of the effective surface for heat and

moisture exchange, the surface can be explicitly represented as comprising ground and

vegetation, a two-component logical extension of the Penman-Monteith expression. A detailed

methodology combining canopy and base-soil evapotranspiration was developed by Shuttleworth

and Wallace (1985). This approach applies the Penman-Monteith equation twice, once to

compute potential transpiration for the vegetated fraction of the region, and again for the ground-

surface evaporation.

Under the Shuttleworth-Wallace model, the latent heat flux is equal to

Where amd are terms that represent the Penman-

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canopy. Between the mean canopy flow and sensor height, the fluxes are opposed by one

resistance.

According the Stannard (1993), the data requirements are rather large for both the Penman-

Monteith and Shuttleworth-Wallace models, and both models are computationally demanding. A

simpler model is presented by the Priestley-Taylor (1972) equation, which estimates potential

evapotranspiration in conditions of minimal advection (heat transfer, ). The equation is a

modified Penman-Monteith equation, whereby the aerodynamic term is about 21% of the energy

term in well-watered conditions. Starting with the energy balance, going through to the Penman-

Monteith equation and ending with the Priestley-Taylor equation:

( )

( ) ( )

( )

Where is a unitless coefficient estimated to equal 1.26 in well-water conditions. From field

studies can be related empirically to soil moisture to extend the Priestley-Taylor equation to

partially dry surfaces. It was reasoned that, as a canopy becomes water-stressed, would

decrease below 1.26 and, from soil moisture data, an empirical estimation of the coefficient

allows for the estimation of evapotranspiration in semiarid environments.

Question II

Largely because of the low rainfall of the site, vegetation is patchy with spacing between plants

ranging from 3 to 10 meters. The large areas of bare soil that exist between plants are often 10-

15K warmer than the air during the day, whereas the leaves of the foliage remain within 1 K of

air temperature. Thus, the hot soil is the primary source of sensible heat, whereas the leaves are

the primary source of water, except immediately following a rainfall. This contrast violates the

big leaf assumption and requires model alteration to accurately characterize the study area. The

patchiness of the canopy also made horizontal placement of the soil heat flux sensors important,

and an attempt was made to place them such that the mean output of all three or four represented

average soil heating conditions at the site by located on in the shade or partial shade and the rest

in the sun.

The Penman-Monteith, Shuttleworth-Wallace, and Priestley-Taylor models each contains one or

more terms that quantify the surface control on vapor transport upwards. In the Penman-

Monteith model, quantifies stomatal resistance; in the Shuttleworth-Wallace model,

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quantifies canopy resistance to flux of water vapor between stomatal cavities and leaf surfaces,

and quantifies subsurface soil resistance to flux of water vapor between a depth where soil gas

is saturated with water vapor, and soil surface; and in the Priestley-Taylor model, quantifies

stomatal and soil-surface control. All of these variables are incorporated into the models

discussed herein to account for the water limitations of the arid area studied.

The term in the Penman-Monteith model represents the aerodynamic resistance to sensible

heat, and is modeled using equation 17 in Stannard (1993). Due to the wide spacing between

plants at the study site, the wind profile between plants likely extends down to very near the soil

surface (although no data existed at the time of the papers publication to support this), and

therefore the momentum displacement height of vegetation was set to 0. Also, because of the

wide plant spacing, the standard rules of thumb relating the roughness length for sensible heat to

the roughness length of canopy for momentum were not used. Instead, the value of the roughness

length of canopy for sensible heat is estimated from a value of the roughness length of canopy

for momentum and the relation given by equation 18.

Canopy resistance for the Penman-Monteith model is the equivalent sum resistance of the

resistances of all the individual stomates in a canopy, in parallel, and is represenatative of all

of the individual stomates. The fundamental variables controlling are leaf-to-air specific

humidity difference, photosynthetically active radiation, leaf temperature, leaf water potential,

and concentration. Changes in concentration were considered to be small and so

concentration was not included in the model. Humidity, photosynthetic activity, and leaf water

potential all are influenced heavily by water availability and thus needed to be accounted for in

applying the Penman-Monteith model to the arid study area. A nonlinear analysis was used to

develop is given in terms of by equation 20. The reciprocal of , or stomatal conductance,

generally is found to be proportional to the product of two or more functions of the above

variables (humidity, photosynthetic activity, etc.). Each function can vary from virtually zero

(e.g. during well-watered periods, photosynthesis will occur, stomates will be open and there will

be virtually zero stomatal resistance) to some maximum value (often one; e.g. during water-

restricted periods, photosynthesis will not occur, stomates will close, and stomatal resistance will

approach infinity), which is consistent with the observation that any one variable can cause

stomatal closure, resulting in approaching infinity.

The extremely sparse canopy necessitates novel aerodynamic modifications of the Shuttleworth-

Wallace model as well, not used in the original work by Shuttleworth and Wallace (1985). As

discussed for the Penman-Monteith model, it was approximated that the momentum

displacement height of vegetation was about zero and that the wind profile extended down to

very near the soil surface for such a sparse canopy. However, the height of mean canopy flow

was set to one-half of the canopy height, and therefore the aerodynamic resistance to flux of heat

and water vapor between the mean canopy flow and sensor height was calculated by integrating

from one-half the canopy height to the height of the measurement above the soil surface, as

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given by equation 21. Similarly, the aerodynamic resistance to flux of heat and water vapor

between soil surface and mean canopy flow was calculated using an integration from 0 to one-

half the canopy height.

The aerodynamic resistance flux of heat and water vapor between leaf surfaces and mean canopy

flow was calculated as the equivalent resistance of all the leaf boundary laryers in parallel based

on the boundary layer resistance of a leaf, . Because of the extremely small leaf area index, a

semiempircal model for was not used but instead a more fundamental (but approximate)

expression was used, as shown in equation 24. The model selection procedure here was analo-

gous to that for canopy resistance in the Penman-Monteith model.

The occuence of occasional small rains at the study site causes the value of the subsurface soil

resistance to flux of water vapor between a depth where soil gas is saturated with water vapor

and the soil surface, to vary widely. The soil surface, wetted by a rain, was often very dry 1-2

days later. Monteith (1963) presented a simple bucket-type model analogy to address this

between distributed vapor sources in the soil and distributed vapor sources in a full canopy,

which are adequately modeled using a single source height. The basic premise is that the soil

surface resistance at some time ater a rainfall is proportional to the amount of water evaporated

since the rainfall. Because of the sandy soil and the semiarid environment, it is likely that the so

called initial stage of drying lasts only a few hours at the site, during which the soil surface

resistance is about zero.

The development of the modified Priestley-Taylor model involved evaluating the argument that

for well-watered surfaces, the value of has theoretical significance. For a surface with a highly

variable water supply, models for typically are empirical and relate to soil moisture. Stannard

(1993) found to be independent of soil moisture, which they measured with a moisture probe.

Analysis indicate that at this site, leaf area index is a significant determinate of and because of

the small value of leaf area index at this site, linear regression models for are unrealistic and

therefore a non-linear model was used as displayed by equation 26. In this model for , either a

large leaf area index or a recent rain event are sufficient to drive toward a maximum value of

1.26, which describes a well-watered system. During dry conditions, alpha would be driven

towards a minimum value that reflects the small amount of evaporation that would occur from

the shallow water table alone. For a given site, ranged from intermittent peaks up to ~1.26

immediately following a rainfall event, which would then decay asymptotically back to its pre-

rainfall value as the wetted soil dries out to a minimum value that more strongly reflected an arid

environment.

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Question III

The Penman-Monteith model severe-

ly underestimates the midday values

of for two reasons. First, the

models big leaf assumption does not

hold during dry, sunny periods in the

middle of the day, when a large

fraction of the sensible heat flux

comes from the hot soil. Second,

immediately after a precipitation

event or in the case of snow cover

such is depicted in Figure 7, the

Penman-Monteith model cannot simu-

late the large values of bare soil

evaporation, because it is exclusively

a transpiration model. The function

for canopy resistance (equation 20) is

an unsatisfactory compromise betw-

een these two soil moisture extremes,

and the model does an especially poor

job at predicting midafternoon peak values because it does not take into account the

significant evaporation from bare soil. Couple this with the frequent small afternoon showers

discussed on page 1382, which would contribute to substantially larger values, and it is not

surprising that the model underestimates the late afternoon peak evapotranspiration values to the

extent depicted in Figure 6 and even more so in Figure 7.

The Shuttleworth-Wallace and Priestley-Taylor models, conversely, explicitly account for bare

soil evaporation and thus tend to follow the general trend of measured values even through

afternoon peaks, although they do not follow the short-term (hourly) fluctuations well. After a

snowfall as depicted in Figure 7, however, the models seem to break down after about midday,

and fail to adequately estimate peak values that occur in the early afternoon. In the case of the

Shuttleworth-Wallace model, this can be explained by the fact that it fails to adequately model

the relatively large values of caused by the blanket of snow acting as a frozen free water

surface, i.e. it does not realistically distinguish snow from rain. It is important to make this

distinction because snow remains on the ground surface and thus the subsurface soil resistance to

flux of water vapor between a depth where soil gas is saturated with water vapor and soil surface

is virtually zero in the case of snow cover, whereas the model assumes that this precipitation is

water, which would seep to a depth within the ground where this resistance to evaporation is

significantly greater than is the case for snow at the surface. Hence, while the modeled value of

this ground resistance to evaporation is significantly nonzero because the model calculates the

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subsurface soil resistance u is calculated based on the premise that the soil surface resistance at

some time after a rainfall is proportional to the amount of water that has evaporated since the

rainfall (equation 25), i.e. as time progresses the resistance increases as the soil becomes drier,

because the snow remains on the soil surface the resistance is actually closer to zero, the much

more water is evaporating from the bare surface than the Shuttleworth-Wallace model predicts.

The Priestley-Taylor model is more accurate than the Shuttleworth-Wallace model after a

snowfall because is less sensitive to this premise of increased subsurface ground resistance as

time progresses beyond a rainfall event, but it still under-predicts the amount of water

evaporating from bare soil after a snowfall because the snow is above, rather than beneath, the

soil surface. This relation can be seen for both the Shuttleworth-Wallace and Priestley-Taylor

models in Figures 4a and 5a, respectively, by investigating the open circlespoints representing

times when there was snow on the groundwhich severely underestimate hourly values

relative to those measured. A thorough treatment of sublimation and evaporation from a

snowpack would involve a snowmelt model and a method to account for conditions of partial

snow cover.

Question IV

Much of this sections review of remote sensing of soil moistures potential to improve

evaporation models with specific reference to the importance of soil moisture data has been

adapted from Seneviratne, et al. (2010) and modified to suit this topic.

Soil moisture plays an important part in both budgets through its impact on latent heat flux E.

Soil moisture plays a key role both for the water and energy cycles (through its impact on the

energy partitioning at the surface). In addition, it is also linked to several biogeochemical cycles

(e.g. carbon and nitrogen cycles) through the coupling between plants' transpiration and

photosynthesis. Thus, modelling approaches representing evapotranspiration are dependent on

soil moisture. Furthermore, in the investigation of soil moistureclimate interactions,

evapotranspiration is a key variable for three reasons: 1) In the terrestrial water balance, soil

moisture and evapotranspiration are the two main unknowns, and thus measurements of one of

these two quantities can allow to derive estimates in the other (with rainfall and runoff

measurements); 2) Concomitant evapotranspiration and soil moisture measurements are crucial

to derive soil moistureevapotranspiration relationships; 3) In some instances, inferences

regarding soil moisture control on evapotranspiration can be derived from evapotranspiration

measurements alone (e.g. during dry-downs).

An important impact of soil moistur1e on near-surface climate is related to changes in air

temperature. Whenever soil moisture limits the total energy used by latent heat flux, more energy

is available for sensible heating, inducing an increase of near-surface air temperature. Soil

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moisturetemperature interactions can significantly impact near-surface climate, and are relevant

for the occurrence of extreme hot temperatures and heat waves

The investigation of soil moistureprecipitation coupling has been the subject of much research

for several decades. The mechanistic studies investigating this coupling highlight that, in many

instances, the key for understanding soil moisture precipitation interactions lies more in the

impact of soil moisture anomalies on boundary-layer stability and precipitation formation than in

the absolute moisture input resulting from modified evapotranspiration. For instance, the

additional precipitated water falling over wet soils may originate from oceanic sources, but the

triggering of precipitation may itself be the result of enhanced instability induced by the wet soil

conditions (or in some cases dry soil conditions). Moreover, also non-local feedbacks can be

important, i.e. advection of evaporated moisture for neighboring land regions. In addition, land

surface heterogeneity may play a role for the generation of mesoscale features and resulting

precipitation formation.

There currently exist large discrepancies between modelling studies, and it there appears

increasingly necessary to investigate soil moistureprecipitation relationships with observational

data. However, the scarcity of the soil moisture measurements, combined with the low temporal

resolution of most available long-term datasets, is a critical issue. Moreover, because of the

direct impact of precipitation on soil moisture, links of causality between soil moisture and

precipitation are difficult to establish.

For the investigation of soil moistureclimate interactions in general, a major impediment is the

scarcity of soil moisture observations. Ground observations are cost-intensive and require

important efforts to be put in place. As a consequence, only few ground-based soil moisture

measurement networks are available. Remote sensing measurements, which provide indirect

estimates of soil moisture (e.g. microwave remote sensing and GRACE satellite mission),

represent some alternatives to intensive ground measurements, however they have several

limitations. There are at present no comprehensive and global datasets of observed soil moisture,

since ground observations are very limited in space, remote sensing estimates have limitations

regarding temporal and/or spatial coverage, accuracy and their exact link with root-zone soil

moisture, and atmosphericterrestrial water-balance estimates are available only at coarse scale

and in river basins with observed runoff (with issues regarding long-term trends).

Several techniques are available to measure soil moisture in situ. The only technique measuring

soil water content per se, however, is the gravimetric technique, and there are several issues with

its implementation. The most important is that the measurement method is destructive, and can

thus not be reproduced. Moreover, significant manpower is required for the retrieval of the

samples and the lab measurements. For these reasons, the temporal resolution of long-term

measurement networks using this technique is usually coarse, typically of the order of 12 weeks

at best. There exist several indirect methods for in-situ measurements of soil moisture. Two of

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the most common techniques are either based on time domain reflectometry (TDR) or soil

capacitance measurements

The TDR sensors, which operate at higher frequencies, are significantly more accurate than

capacitance sensors. However, the latter are of much lower cost, which can allow a higher

number of instruments and thus much denser networks. Given the strong spatial heterogeneity of

soil moisture properties, it can appear attractive for some applications to choose slightly less

accurate but cheaper sensors in order to increase the spatial sampling. But their low accuracy for

wet conditions remains a significant issue for the interpretation of the data. As is the case for

TDR and capacitance sensors, microwave remote sensing techniques make use of the fact that

the soil dielectric constant increases with increasing water content. Both active and passive

microwave remote sensing are commonly used to retrieve soil moisture information. The main

limitations associated with microwave remote sensing are that only surface soil moisture can be

retrieved (a few centimeters at most), while it is root-zone soil moisture that is relevant for most

climate applications.

One particular issue of most datasets is their spatial resolution and coverage. Ground

observations (e.g. TDR) are generally point measurements, confined within networks of limited

extent, with necessarily scattered coverage. Satellite remote sensing observations (e.g.

microwave remote sensing), on the other hand, are generally global, but of low resolution and

often of poor quality on part of the globe depending on the measurement characteristics and

limitations of the retrieval algorithms. The scale discrepancy between available observa-

tional/estimation methods is often an issue for the comparison and systematic analysis of existing

validation datasets. Several studies have investigated approaches to characterize the spatial

variability and mean regional behavior of point-scale soil moisture observations. In order to be

able to characterize soil moistureclimate relationships at the regional scale, dense ground

observational networks would be ideally needed, as variability in point-scale observations is

large.

While soil moisture fields exhibit a certain rank stability, establishing relations between point-

scale and regional soil moisture is problematic and these relations are also impacted by climate

variability. Similarly, the strength of coupling between evapotranspiration and soil moisture may

strongly vary at the local scale. Despite these issues, spatial scales of soil moisture variability

may overall be divided in two categories: the small-scale variability (less than 20 km), which is

impacted by small-scale variations in surface and subsurface characteristics (soil characteristics,

soil in homogeneities, small-scale variations in land cover), and the regional-scale variability

(50 400 km), impacted by meteorological/climatological forcing (weather systems, precipi-

tation, and radiation). Thus, even with a moderately dense ground-measurement network, it

should be possible to at least capture the relationships controlling the regional-scale variability of

soil moisture. Similarly, analyses from point-scale observations in Illinois have shown that,

though absolute soil moisture content can be highly variable between neighboring sites, the

dynamic evolution of soil moisture (seasonal soil moisture changes) are often closely related. It

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is thus the dynamics of soil moisture at the regional scale that should be compared between

validation datasets based on different sensors or estimation approaches.

In sum, an important limitation for improving our understanding of soil moistureclimate

interactions is the lack of comprehensive soil moisture and evapotranspiration observational

datasets. While new datasets are becoming available, in particular satellite soil moisture datasets,

new measurements and water balance estimates are often confined to a limited part of the globe,

and have distinct temporal and spatial coverage and resolution. Moreover, even where

observational data are available, it is not always straightforward to use them in an effective way

to infer landclimate interactions, due to methodological issues.

The first challenge for research in this area is to continue to develop ground-based monitoring

networks for soil moisture and evapotranspiration. Indirect estimates based on satellite data,

diagnostic approaches, and/or modelling approaches need to be evaluated with ground

observations, and their performance may depend on the climate regime and land cover

characteristics. Thus, more extended ground observation data will be essential for improving our

understanding of soil moistureclimate interactions, especially in regions where data coverage is

still poor.

The application of RFID technology to in-situ radiometry measurement of soil moisture may

provide the opportunity to close much of the data gap in global climate modeling with respect to

soil-climate interactions. RFID chips can be arranged in dense networks over large areas,

allowing for high-resolution and high-spatial coverage. In theory, they could be used to measure

soil moisture by a reader attached to an unmanned aerial aircraft that uses active microwave

emissions in the L-band to detect changes in phase and strength of the signal reflected from the

RFID chips. Measurements could be arranged for the small aircrafts at frequent time intervals,

allowing for collection of high-temporal datasets at significant convenience relative to other in-

situ measurement technologies. The use of RFID chips also offers the advantage of minimal

perturbation of the soil, and because the chips cost only a few cents apiece, there exists a huge

applicability of the technology to large-scale data collection is.

Another important benefit of using microwave remote sensing to detect soil moisture is the

extent of information possible with a multi-wavelength system. There is evidence that such data

could be used to expand the basic soil surface soil moisture estimate to include an estimation of

the soil moisture profile to a specified depth. There are four basic approaches to accomplish this,

as outlined by Jackson (1993): statistical extrapolation of the surface observation; integration of

surface observations in a profile water budget model; inversion of radiative transfer methods;

and a parametric profile model. Finally, the use of microwaves to detect changes in soil moisture

is limited to the distance to which microwaves can travel through a soil medium, reflect and

travel back through the medium, and be read by a sensing device.

16

Some caveats to the technology include the following:

The application of radiometry to soil moisture requires the direct measurement of brightness

temperature, thus the absolute temperature of the soil is also needed. Dividing the former by the

latter gives the emissivity at a specified wavelength or frequency, which is proportional to the

inverse of the permittivity of the soil, which itself is about equal to the soils dielectric constant.

The dielectric constant will vary based on water content, thus allowing us to measure the soils

rise in moisture as the result of a precipitation event. Within the soil, the dielectric properties of a

water molecule are governed by the strength of forces acing on the molecule. The net strength of

these forces decreases exponentially with distance of a water molecule from the soil surface.

Hence, the dielectric constant of a water molecule should increase with distance from a particle

surface as electrostatic forces diminish.

Because soil water content is determined by the specific surface area and pore size distribution of

a soil, knowledge of on soil texture and mineralogy is essential to measuring soil moisture. The

water initially infiltrated into the soil has a relatively low matric potential, but as this water

redistributes itself within the soil medium under the influences of osmotic potential gradients as

limited by the hydraulic conductivity of the soil and described by Darcys law, the net matric

potential of the water increases until an equilibrium condition is attained. Hysteresis can affect

this process in addition to a soils hydraulic conductivity, which is also dependent on soil texture.

For instance, Sandy soils typically have a rather large hydraulic conductivity and relatively small

hysteresis potential, whereas the opposite is true for clays, especially at high moisture contents

(Dobson and Ulaby, 1981).

A soils dielectric properties also vary based on the soils salinity, the wavelength used by the

radiometry backscatter reader, the angle of incidence and polarization of those microwaves, as

well as the surface roughness of the soil. All of these variables must be accounted for and taken

into consideration when taking measurements (e.g. maintain a constant incidence angle for

measurement before and after storm event), and when calculating the soils change in water

content. All variables being equal except for soil water content, however, should allow for

accurate measurement of soil water content, with a liquid water measurement accuracy of at least

2% by volume within the soil and at least 0.7% for overlying snow (Denoth, 1997).

The choice of wavelength used to collect data is crucial, as the depth of soil later that contributes

to the measured brightness temperature increases with wavelength and result in less interference

from vegetation, however radio frequency interference increases at wavelengths beyond about 21

cm (Jackson, 1993). Hence, while the best wavelengths to use are the longest in the microwave

spectrum, radio frequency interference limits the specific regions that can be used to anything

beyond the L-band, and thus it is suggested that the experiment use a reader that emits

wavelengths between 10 and 21 cm (1.4 and 3.0 GHz). At this frequency range, however,

salinity will impact measurements so caution is advised for measurements in the spring when soil

salinity is higher as the result of winter de-icing salt surface runoff from roadways. Finally,

17

horizontal polarization is preferred because the sensitivity to soil moisture is higher throughout

all look angles for both bare and vegetated soils.

A unique advantage of microwave sensing is that at long wavelengths, such as the range

suggested here, vegetation has an attenuating effect on the background emission rather than the

masking effect observed at shorter wavelengths. The primary determinants of this attenuation are

the water content of the vegetation and structure of the canopy. To avoid complications in

estimated these parameters, it is suggested that the experimental sensors be deployed either in an

area of bare soil where vegetative attenuation can be avoid altogether (in addition to the

aforementioned transpiration from said vegetation), or grassland where vegetation water content

is relatively homogeneous and easy to quantify.

Finally, emissivity will vary between about 0 for dry soils and unity for saturated soils. The soils

surface roughness characteristics need to be determined too, because measuring soil moisture

using radiometry requires equation-specific data on surface roughness (specifically the

parameters height and Polarizing mixing due to surface roughness).Wang and Choudhury (1981)

describe a method where these parameters can be calculated using data from as little as two

measurements.

18

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Dingman, S. L. Physical Hydrology. 2nd ed. Upper Saddle River, N.J.: Prentice Hall, 2002.

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Njoku, Eni G., and Dara Entekhabi. "Passive microwave remote sensing of soil moisture."

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