Global Change Consulting Consortium, Inc.

Forests – Water, carbon, climate, fire, exotic species, management: What are some of the major issues, and what are the concepts that inform the issues?


    This set of Web pages mixes both Web references (hyperlinks to URLs) and academic references (hyperlinks to book and journal references, which are typically not on the Web). 

    Some links will lead to articles that need permissions.  If you are connected through a provider who has a subscription to the journal or other provider, you will be able to see the article.  If not, you can see the abstract or other entry point, but payment would be required to see the full article.

    Some links lead to other locations in this document.  The location will appear at the very top of the browser window but will not be highlighted.

    The discussion here is not intended to be comprehensive, as this would require a book or set of books.  It highlights a number of current issues.  The GCCC is, or course, willing to address topics beyond those covered here. We have composed a brief list of the kinds of projects that we foresee as productive. 



I. Introduction: forests in a broad context

II. Some of the major issues concerning forests, as currently formulated

III. Some basic phenomena

   A. What environmental and physiological factors, including historic legacies, structure forests?

       i. Density – a measure, and its regulation

      ii. Leaf area – a measure, and its regulation

      iii. Species composition

      iv. Climate change

     v. Fire suppression.

   B. What environmental and plant factors, including historic legacies, set the operating points of forests in water and carbon cycles and in climate?

      i. Leaf transpiration.

     ii. Leaf area development.

     iii. Responses of stomata.

IV. Water in forests

    A. Water balance of individuals and stands

        i. Roots

        ii. Control varies with scale

        iii. Using all the water

        iv. Large scales

    B. Water stress and its stable and unstable forms of occurrence

        i. Stress is inevitable

        ii. Extreme events

        iii. Stress is multi-dimensional

        iv. Stress is distinct from drought

        v. Equilibrating water use

    C. Quantifying stress over large scales

    D. Remote sensing for stress and other measures of vegetation status

        i. Pros and cons

        ii. Electromagnetic signals

        iii. Using multiple signals

        iv. Active remote sensing

        v. Sensing mass

    E. Long-term, integrated measures of stresses and resource use

        i. Growth

        ii. Isotopic composition of tissue

   F. Direct measurement of fluxes of water vapor, CO2, and heat in the air

        i. Eddy covariance

        ii. Limitations

        iii. LIDAR

   G. Ruminations about our abilities to quantify plant, stand, and regional water relations

        i. Inadequate precision in single methods

        ii. What is the solution?

V. Revisiting some specific couplings of forests to climate

   A. Three major effects

   B. Carbon sinks

   C. Carbon sources

   D. Water recycling

   E. Surface water

VI. Forest fires per se

   A. Very brief overview of fire ecologically and in human affairs

   B. Fire regimes

   C. Vegetation adaptation to fire

   D. Conditions for fire ignition and propagation

   E. Monitoring of fuel conditions

   F. Monitoring of extant fires and post-fire effects

   G. Management, before, during, and after fire

   H. Climate change affecting fire regime



I. Introduction: forests in a broad context


Forests cover 4 billion (4,000 million) hectares worldwide, or about 1/3 of the land area.  In the US, they cover about 300 million hectares, also1/3 of the land area.  In the US, about 75% of forests are under private ownership, and 1/3 of the forested area is primary forest.  Western and Rocky Mountain forests comprise approximately half of the forested area. 


Forests are of diverse types in their species composition.  They develop in diverse climates and over diverse soils and topography.   Consequently, issues are diverse and geographically localized.  We shall cover only some of the larger or more controversial issues herein.  


Forests have roles in climate (particularly via energy balance of the land surface), the hydrologic cycle and the attendant availability of water for human use, and the carbon cycle with its links to climate.  They provide ecosystem services beyond their functions in watersheds and as sources of timber, including providing habitat for fauna and flora, flood control, and cultural values.   Forests have roles in atmospheric cycling beyond the major exchanges of oxygen, carbon dioxide, and water, by intercepting pollutants such as ozone and particulate but, conversely, by releasing terpenes that react photochemically, much as anthropogenic emissions that generate smog.  (Note that forests, and all biota, do not exert the major short- or long-term control over atmospheric oxygen; burning all vegetation on earth would not change O2 content of the air by much; rather, it is geochemical control via iron and sulfur cycles, abetted by hundreds of millions of years of geologic burial of carbon made by vegetation or microbes and made resistant to decay, in particular.)


Forests are notably subject to deforestation, which is not the topic here, that being functioning forests.  Forests are also subject to: fire, both wildfire and of human origin (land clearance, controlled burns, arson); insect attacks, which may be of enormous scale; diseases, whose spread is often linked back to insects, as in Dutch elm disease, but also to simple abiotic transport by wind and traffic; invasion by exotic species, often in the understory but sometimes displacing the forest overstory; climatic changes, which have been dramatic over the millenia and which are now proceeding at a great rate; and a range of management practices (harvesting, replanting, thinning, fire suppression, controlled burns, spraying for insect control) and their collateral actions (e.g., soil erosion). 


II. Some of the major issues concerning forests, as currently formulated


Issues vary widely by region.  We may introduce the topic of issues – that is, of management issues, primarily – by using a few examples.  A number of them will be discussed in more detail later.


In the Western US, forest fires and massive insect damage are of great concern recently. 

Many forests with a regime of frequent surface fires have been managed with fire suppression, leading to a buildup of intermediate-height trees as ladder fuels; some natural events of massive establishment after rainy seasons have also contributed.  A new regime of large fires has ensued.  Firefighting of fires on all land-cover classes has generated large budget deficits for these states.  What management responses are appropriate, timely, and cost-effective – prescribed burns? Thinning?  In what spatial pattern to reduce fire spread?  Should the urban-wildland interface that takes much of the firefighting resources be afforded less protection?


On another front, drought and high temperatures occurred in unprecedented combination across western North America in 2000-2003.  Reduced tree defenses and an extra generation time for bark beetles in a longer warm season led to the greatest tree death ever recorded, and a massive pulse of carbon release back to the atmosphere.  Is human-induced (anthropogenic) climate warming contributory?  How do we assign causation, or, at least, increased risk of such events?  If such increased risk occurs, it adds considerable weight to the demand for control of greenhouse gases. 


Another issue is yield of surface water from forested watersheds.  Has stand condition from fire suppression or soil condition from logging compromised water yields in some forest types?  Is climate change altering water yields?  What are the controls, in both weather or climate (meteorology) and plant physiology, over water use?  How do they vary with spatial scale and time, both as cycles and as trends?


For several hundred million years, forests have played major roles in the carbon and water cycles and in climate. An imposing issue is climate change itself – how forests affect climate, and the converse.  Does afforestation or reforestation sequester a large and stable amount of carbon as CO2 from the atmosphere, reducing its impact as a greenhouse gas?  Is such forest expansion sustainable?  How will the extra transpiration of water with more leaf area affect water yields?  Does reforestation at high latitudes actually increase global warming?  A recent climate modelling study concludes that dark forests absorbing sunlight so much more strongly that unforested land that this warming cancels or exceeds the cooling from forest uptake of CO2 as a greenhouse gas. Will climate change – and the rise in CO2 itself - induce major changes in the biogeographic distribution of tree species?  Are such changes predictable?  Are there strategies of social adaptation to such changes?


Returning to the phenomenon of drought and water stress: Droughts are meteorological phenomena, episodic decreases in precipitation from some local norm.  Water stress is a physiological phenomenon that is specific to given plant species or genotypes and their location on the landscape (soil depth, slope, fertility…).  Both are inevitable.  Their extremes are, nonetheless, dramatic and may be very disturbing to human endeavors, such as in the case of the massive tree loss in North America just noted.  What are the natural regimes of water stress, so that we may recognize them?  What are the extremes, how do they increase with time scale (e.g, the 100-year drought or the 100-year stress, and the 500-year, and…)?  In our analysis of extreme events, we find that they are composed of meteorological sequences, not point events, and that they are highly organism-specific.  We need a deep understanding of meteorology, plant physiology, and ecology to answer several pressing questions.  How is the spectrum of drought, or of stress, changing with climate change? What are the consequences of droughts, small and large, for stand persistence, species composition, and ecosystem services?


Introduced species have wrought changes, from local to continental and small to catastrophic, in forests.  Introductions have included competing plants (e.g., Myrica faya trees in Hawaii, which reduces soil stability on slopes; cheatgrasses prone to fires that kill tree seedlings, among other things), insect pests (Asian longhorned beetle; gypsy moths in North American forests), vertebrate pests (Australian possums that kill vast numbers of trees in New Zealand’s coniferous forests), and diseases (chestnut blight and Dutch elm disease that reshaped the eastern US; Phytophthora diseases that are changing the landscape in Australia).  Accidental introductions are virtually unpreventable with so many routes of entry that are prohibitive to monitor for small seeds, spores, etc.  Do we have in place the rapid recognition of invasives and immediate control measures for new exotics?


To answer these questions requires a full understanding of the concepts in forest physiology, hydrology, climatology, and other fields that underpin all the phenomena.  Indeed, such an understanding is needed in order to ask the right questions – questions that are meaningful, questions that are capable of validation or falsification, questions whose answers inform effective action.   We must know if we are aiming at the correct targets, and the correct time scales.


III. Some basic phenomena


A. What environmental and physiological factors, including historic legacies, structure forests?  That is, What sets their density, as stem density, basal area index, or leaf area index?  What sets their species composition?  Individual trees experience germination, establishment, maturation, and mortality.   These life-cycle events may occur on the scale of one or a few trees (canopy gap phenomena, when one mature tree dies and leaves a space for regeneration), many trees (e.g., triggered by floods, landslides), or whole stands (such as stand-replacing fires or insect attacks). 


The topics of individual dynamics and stand dynamics are vast in scope, having generated many journal articles, monographs, reports, semi-popular accounts, and popular accounts.  Here we cover some highlights and novel or less-discussed concepts relevant to stand density, water relations, and fire dynamics.


i. Density - a measure, and its regulation. Stand density has several measures.  Common measures are basal area index (stem area per ground area), number density of trees (perhaps by age class), and leaf-area index (LAI, or leaf area per ground area).  A mass establishment event typically follows clearance of an area by fire, harvesting, landslide, or other catastrophic disturbance.  High resource availability favors a dense initial stand, particularly a season of high rainfall.  Individuals grow and begin interfering with each others’ resource acquistion, especially light.  The more resource-stressed individuals die in a process denoted as self-thinning.  Some remarkable regularities occur in the process, such as the “-3/2 law”, that mean plant mass rises as number density, n, falls, as n-3/2.  As an example, as growth (crowding) decreases the number density by a factor of 100, average mass changes by a factor of (1/100)-3/2 = 1000.  The total biomass density increases by the factor (number density)*(average biomass) = (1/100)*1000 = 10.    Detailed physiological reasons for mortality of individual plants, relating performance to local microenvironment and to genotypic variation among the individuals, are just beginning to be explored. 


ii. Leaf area – a measure, and its regulation. The physiology behind development of leaf-area index and its (near) stabilization is better explored.  On the individual plant, development of individual leaves has stages of cell division and cell expansion.  These stages overlap in part.  They respond (at least in herbaceous species such as maize, those best studied to date) via a reduction in one or both processes, to stresses of low water status, low light level, or low humidity.  All these stresses indicate that it is unfavorable at the moment for support of the existing leaf area, thus, even less for added leaf area.  At the level of the whole stand, competing leaf development among all individuals hits limits imposed by availability of water and light, with a modulation of the limit by temperature that affects rates of water use.  A model of LAI acclimating or equilibrating to the environment has been developed and partially tested.  It has been extended to include the effect of increasing atmospheric CO2, which reduces water use rates, among other changes.


Equilibration of LAI (and consequently of basal area) is a response that limits water stress and perhaps nutrient stress.  One may then ask if high density should induce a greater degree of average water stress, with subsequent increases in dangers of fire or insect attack.  If equilibration occurs to a set stress level, the answer would be “no.”  If the tolerated average stress level is higher for smaller plants, the answer would be “yes.” The reality is unknown to date; there are no large-scale and long-term measurements of forest water stress to date, for reasons discussed below.   Extant studies on smaller temporal and spatial scales have indicated some correlation of density with stress, either by observations (1, 2) or using experimental thinning. It is known that high densities that arise from wet years after a disturbance can persist as long as centuries in some forest types.  In brief, self-thinning can be a prolonged process.


iii. Species composition is another major aspect of forest structure.  A few basic aspects of climate have been held to determine biome type and some aspects of species composition, such as dominant functional type or even species.  These pieces of climate are temperature as average or range and precipitation as annual total and possibly in its seasonal distribution. Moreover, individual (tree) species are ascribed to have natural geographic ranges determined by these same factors.  These are certainly gross oversimplifications, so that somewhat deeper models have been developed.  For one, ranges of species are determined as well by competition, as well as by ranges of pests, diseases, pollinators, etc.; potential ranges (fundamental niches) get restricted to actual or realized ranges.  Also, the extremes of climate may be quite a bit more important than the means.  Finally we note that soils matter.  Their texture determines water infiltation rates, their depth and texture determine “plant available water” and stress regimes, and their parent material, among other things, determines some critical nutrient contents.  Soils do develop conjointly with climate, parent material, and vegetation.  The elaborate linkages do not readily allow resolving climate and parent material as the abiotic drivers and soil or vegetation as the result. In all this background of forces that form species composition, mass species replacement do occur and will occur.  Megadroughts are one mechanism, and their prediction, even on a statistical basis (not exactly when they will occur, but with what mean return time at a given intensity), is challenging. 


We have also projected that the human-caused rise of atmospheric CO2 content will directly change species ranges and the species composition at any geographic location.  As CO2 increases, it induces changes in water-use rates, water-use efficiency, nitrogen content, and the efficiency of using nitrogen in photosynthesis and growth.  The changes differ widely among species.  Despite the fundamental importance of each such performance measure to plant success, species vary in the suite of adaptive responses encoded in their genes.  It has been a very long time, 20 million years, by some measures, that CO2 has been as high as now.  Adaptive genetic variation has surely been lost extensively by genetic drift or by selection for other traits that work against adaptation to the return of high CO2.  We believe that some predictions of adaptive and maladaptive responses may be feasible for different plants, not generally as individual species but by functional types defined by, say, stature, lifespan, degree of tolerance of water and nurient stresses, temperature tolerances. These “working regions” for each functional group correlate with climate, which has correlated in the past with CO2. 


iv. Climate change, particularly as an overall warming, is apparent, and its causes are being resolved with greater certainty.  Changes in species composition are widely expected, from a variety of direct and indirect effects.  Extremes of precipitation, both high and low, are increasing (1, 2) over large regions of the globe; these condition survival of species.  Longer warm seasons are changing water use by plants, in some cases causing early depletions of soil water.  Activities of pollinating insects have been shifted, with some mismatches to plant flowering.  This is in part a result of environmental cues such as photoperiod, which plants and animals use to set developmental schedules, becoming more poorly correlated to seasonal growth conditions. Insect pests have become more abundant in some ecosystems, including rather notably pests of conifers in western North America that killed trees over millions of hectars.  More surprises may be in store. 


v. Fire suppression. Another agent that structures stand structure as density and species composition is fire suppression, eliminating a natural agent of self-thinning.  If such causation is correctly identified, how is it that self-thinning is delayed for decades or longer?  Essentially, it is posited that surface fires are normally the dominant agent of self-thinning but that these are prevented.  The simple presence of more fuel of intermediate height (ladder fuel) enhances the probability of fire progression from surface fire to crown fires, which are very infrequent in the fire regime of arid-zone forests.


A number of questions should be answered before we can embark on new, comprehensive management activities (mechanical thinning, prescribed burns) in a useful and cost-effective manner. 



B. What environmental and plant factors, including historic legacies, set the operating points of forests in water and carbon cycles and in climate?


We will assume a rather basic knowledge of plant physiology, to keep the discussion compact.  Elements of this knowledge include that water is primarily taken up by plants at their roots and primarily lost by transpiration at their leaves through myriad small pores called stomata; that CO2 is taken up through the same stomata, passing in as water vapor passes out; that photosynthesis as the source of energy capture for growth occurs primarily in the leaves and responds in general positively to increased availability of light, water, CO2, and mineral nutrients, while response to temperature shows an increase until an optimum is passed.  Many nuances and some counterintuitive phenomena occur but only a few can be detailed here.


i. Leaf transpiration. Water loss at leaves by transpiration is under the control of: 1) the leaf aerial environment (sunlight, humidity, air temperature, windspeed, air pressure, CO2 content of air); 2) water status of the leaves and the roots; and 3) the physiology of the plant – the many small pores in the leaf (stomata), which also pace their operation to the photosynthetic capacity of the leaf.  The action of stomata has evolved to nearly optimize various aspects related to water-use efficiency.  Simply, if water stress develops or if humidity is low, stomata reduce their conductance.  This cuts water loss almost in proportion, but it reduces photosynthesis much less; the water-use efficiency as the ratio of photosynthesis to water loss improves.  Some additional details are given on another page here. 


Meteorological conditions have greater control over transpiration rates than does physiology (stomatal control).   Solar energy delivery is the most important condition.  Unless a plant canopy is quite stressed for water or nutrients, transpiration rate typically changes in parallel with the flux of solar energy to the leaves.  For a specified leaf area, one can visualize all the different contributions to control by computing changes in E under the normal range of variations in all factors – energy flux density in sunlight, air temperature, humidity, windspeed, CO2 mixing ratio, air pressure, and stomatal conductance.  In this exercise, one must use consistent, coupled models of energy balance of a leaf (or of all leaves in a canopy), stomatal control, and photosynthesis.  We have developed a Fortran program for this.


ii. Leaf area development. Stomatal conductance (gs) is nonetheless the primary short-term physiological control over transpiration.  In the long term, leaf-area development exerts the greatest control: a full vegetative canopy readily evapotranspires at twice or more the rate of bare, wet ground, which has a low aerodynamic or boundary-layer conductance for water vapor.  Strong control of leaf area development is adaptive, in the biological sense of contributing to fitness as leaving descendants.  The plant must survive to do so, and must partition its resources to seeds or other propagules efficiently, not growing leaves and other non-reproductive parts beyond necessity.  Individual leaves develop by division of small sets of original cells (meristems) and their volume expansion.  In some species, these processes have been discerned in great detail.  Division and expansion proceed for limited times as measured by thermal time, which slows down at low temperatures and passes rapidly at high temperatures.  Both processes are permanently debited (and leaves are thus smaller) at low light availability, low humidity, or during tissue water stress.  Such responses are adaptive.  For example, water stress indicates that current leaf area is minimally supportable; extra leaf area is not, so that its development should be reduced.  In extreme cases, plants lose leaves.  Many species are adapted to do so without damage to their later growth potential.  Examples include trees in tropical dry forests as in southern Africa or central America.


iii. Responses of stomata. To compare vegetation types with comparable leaf area development, we return to considering stomatal control.  Stomata respond to light, humidity, temperature, CO2 concentration in air, windspeed, air pressure, and water stress. The metabolic and biophysical steps that mediate the responses are still being eludicated.  Nonetheless, simple regularities have emerged: gs reponds in direction proportion to humidity at the leaf surface and in inverse proportion to CO2 mixing ratio at the leaf surface.  All other responses – to light, temperature – fold into a direct response to photosynthetic rate that is driven by these environmental factors.  The observed responses act to maintain good water-use efficiency (ratio of photosynthesis performed to water lost) or more general optima, and they are apparent at the level of the whole stand as well.  Stomata also respond to signals of water stress, with a notabl decline in conductance.  In herbaceous plants, this signal appears to be largely a hormone, abscisic acid, made in the roots in response to stress.  In trees, a larger signal may derive from the water status of the leaf itself.


IV. Water in forests


A. Water balance of individuals and stands


i. Roots. Plants maintain high function, or, at least, viability, by the ability of root uptake of water to match transpirational water loss.  For most plants replenishment of root-accessible water relies on precipitation.  The episodic nature of precipitation inputs leads to episodic shortfalls in water uptake, abetted by limitations on root development and function.  Such shortfalls require control of transpirational water loss by stomata, and, more so, curtailment of leaf area development that was noted in Sec. above.   Root development and function is an extensive topic that cannot be detailed here.  One point to note is that stress can permanently cut root function (hydraulic conductivity) by a process of air entry into plants’ water-conducting vessels.  This possibility makes stomatal control yet more important.


ii. Control varies with scale. The balance of factors that control water use by plants shifts with scale, going from leaf to plant to stand to landscape.   We noted earlier that stomatal control is the most important physiological control.  Control can be described by a few physiological parameters: one is a “slope” function, the factor of proportionality between stomatal conductance and an index comprised of photosynthetic capacity, humidity, and CO2 mixing ratio at the leaf surface.  Another is the intercept of the relation between gs and the index just noted. A yet more important parameter is maximal photosynthetic capacity  Vc,max.   We have analyzed field data on trees growing in a dense stand on the banks of the Rio Grande to illustrate this. We used a complete model of the physiological processes and meteorological processes to predict daily water use per tree as two physiological parameters varied over their plausible ranges – Vc,max and the major determinant of stomatal conductance, the slope mBB in the Ball-Berry model of gs.   Our figure shows that Vc,max is even more important than stomatal control.  The availability of soil nutrients, especially nitrogen, strongly constrains this photosynthetic capacity – and leaf area development - and is thus a strong determinant of transpiration rate.   N-poor boreal forest soils support both low photosynthesis and low transpiration rates, even in warm conditions.


iii.  Using all the water.  Of course, leaf area index is a major scaling factor for transpiration, also.  Limitations on water and nutrient availability constrain leaf area development and, thus, transpiration also.  Deserts are examples of water use scaled to water supply; leaf area indices over a whole landscape in a desert are less than unity, and may be nearly zero in the driest deserts.  We may state that water use is“closely” scaled to water supply.  Plants have evolved to use as much water as possible on the time scale of a whole season; there is no adaptive value in leaving water unused, particularly for competitors.  Careful measurements confirm this.  There is an evolutionary challenge in metering water use over the short term during any season, to avoid severe water stress that threatens function and, ultimately, life of the plant.  On the converse side to episodes of water-supply deficits, water may be delivered at rates exceeding both plant use and storage in soil.  Runoff results, forming one major route of generating surface waters, the rivers and lakes.  (The other route is infiltration of water into soil with subsequent movement below the surface to springs.)  On a global average, about 1/3 of precipitation on land becomes runoff.  There are notable variations among the continents, including the perhaps surprising result that Australia has a greater runoff fraction than Africa, the driest continent by this measure.


iv. Large scales. At larger spatial scales, stomatal control is diluted by the imposition of additional resistances to moving water vapor from the leaf interior to ambient air.  Two of these are the “aerodynamic” conductances (or resistances) of boundary layers of restricted air movement that form around individual leaves and over whole plant canopies.  In an extreme case, stomatal control becomes negligible and only meteorological control remains.  At the scale of entire watersheds, stomatal control is not obvious at all.   It has been found in Russian and US studies that yearly evapotranspiration (plant transpiration plus evaporation of water from bare soil) scales to annual precipitation, P, in a form that is a bit subtle and a bit complicated.   The form involves net radiation, Rn, a measure not only of solar energy input but also gains and losses of thermal radiation, and the form involves the power 1/a of (1+[P/Rn]a).   Where did stomatal control, and leaf area control, disappear?  Our interpretation is that, on a landscape average, gs and LAI both acclimate to match this function of precipitation.  Why it is in the complicated form is not yet to be discerned.  Also not clear is why nutrient status does not enter; two biomes of the same total P can have very different nutrient availability yet appear to be covered by the same relation that does not involve nutrient status, or does so in an even more obscure fashion than for gs and LAI.   These scalings must be important for forests and for understanding the diverse operating conditions of diverse forest types.


B. Water stress and its stable and unstable forms of occurrence


i. Stress is inevitable. Water stress is formative of forests, strongly contributing to determining their establishment, persistence, species composition, fire regime, and the balance of water use and surface water yield.   Episodes of water stress are inevitable in virtually all ecosystems, even in the tropics, given: 1) the stochastic nature of precipitation or other water supplies, and 2) the competitive disadvantage to a plant from reducing water use to a stress-avoiding minimum; water is saved for competitors as well.  Consequently, plants have various behaviors, which we may call evolved strategies, to deal with water stress.  These may be categorized as: 1) stress avoidance, by developing access to privileged water supplies, typically by developing deep root systems.  These are metabolically costly, particularly in construction, so that there are some elaborate tradeoffs, and they are far from common; 2) stress escape, by timing the plant’s life cycle to wet seasons.  Many ephemeral plants do this; 3) stress resistance, by controlling water uptake and water loss stringently to limit the depth and duration of stress.  Some draconian actions may be involved, including shedding of leaves in a dry season; 4) stress tolerance, which is the ability to undergo significant drops in water status (water potential) while retaining future function and assuring a high probability of survival.  Some desert shrubs have remarkable tolerance of low cellular water potential that includes protection against additional damage such as photoinhibition of idling photosynthetic systems.


ii. Extreme events. Water stress may be ubiquitous but it varies seasonally, and by ecosystem type, and by species within an ecosystem, and so on.  It also can develop into an extreme event.  This is a concept that needs extreme care in its defintion in order to be useful; among other things, extremity is a continuum, not a category.  Moreover, an extreme event is a specific sequence (of temperature, precipitation, etc.) rather than a point event; the same high temperature may be of no consequence in summer but lethal in early spring, because acclimation is critical and it depends upon time sequences in the environment.    No evolved physiology and developmental program of a genotype assures survival of extremes nor preservation of reproductive potential, which is the ultimate measure of fitness.  There are stand-replacing droughts even in natural systems when one considers long time scales, such as millenia.   In lesser extremes, many individuals suffer damage or mortality because they established in poorer microsites (soils, topography, competitor density) or because their genotype did not confer sufficient stress responses.  More than abiotic water relations are important in mortality events; insects attacking drought-stressed trees have mediated the greatest one-year loss of forests in history and a massive reinjection of CO2 into the atmosphere from decay of dead material.


iii. Stress is multi-dimensional. Thus, we must phrase questions about water stress, or any stress, with care to develop the full context.  In particular, there will be a regime of water stress that is natural and sustainable by the species in the ecosystem.  In general, we in the scientific community have very little knowledge of these regimes.  We propose that the description of stress must be expanded to at least three dimensions (see also 1, 2).  Stress episodes have depths (quantifiable as, say, the amount of the drop in water potential), durations, and recurrence frequencies or, more generally, recurrence probability distributions.  In complementary fashion, there are regimes of plant stress responses.  A given “toolbox” of stress responses encoded genetically and epigenetically in an individual plant enables it to cope with a variety of stress regimes, with varying consequences to growth, fitness, and survival probability in each regime.  We expect that sustainable stands cluster in some region of the 3-dimensional “stress space,” a concept we may borrow from mechanics.  If we lack a knowledge of stress regimes, associated coping strategies, and fitness consequences of the combinations of stress regime and strategy, we cannot interpret the exhibition of stress symptoms as being normal or not, or adaptive or not, or as having specific consequences for hazards of fire or insect attack or the like.  We can gain this knowledge, nonetheless.  It requires, first, observation of natural regimes, their causation (as, by weather), and plant responses to them.


iv. Stress is distinct from drought. Water stress may be associated with drought, but equating them is inviting confusion by attributing causation without critical thinking.  Drought is a meteorological phenomenon, an almost-always temporary shift to a regime of lesser precipitation.  It may contribute to water stress in a plant or stand or landscape, but so also might the chance location of an individual or stand on an unfavorable microsite, or so might human management practices. 


v. Equilibrating water use. We offer two concepts that should be useful in discerning natural stress regimes.  One is equilibrium leaf area index, noted earlier.  Another, which we believe is new in this document, is equilbrium evapotranspiration, or, to avoid confusion with the a term of the same name in micrometeorology, acclimated evapotranspiration (AcET).  We may extend the concept of equilibrium LAI to add the concept of equilibrium stomatal conductance.  The mean value of gs over a longer period may be shifted by stress responses. Probably more commonly, a shift in gs and thus in ET between times or between locations results from acclimation of photosynthetic capacity of leaves, to which gs has been observed to scale fairly strictly.   Photosynthetic capacity requires nutrient investment in leaves, so that nutrient availability certainly helps to mold capacity and AcET.  Low photosynthetic capacities in the nitrogen-poor boreal forest appear to exemplify this limitation. The role of water in setting photosynthetic capacity is less clear to date.  In one example, we have found (publ. in prep.) that leaves of the desert shrub, creosotebush (Larrea tridentata) exhibit modest photosynthetic rates per leaf area while having extremely high amounts of nitrogen per leaf area.  They have abundant N in soil but limited water.


Can the acclimated ET of a stand be exceeded, escaping physiological and developmental controls within individual plants, such that water stress becomes greater in one or more of its dimensions of depth, duration, or frequency, and among many individuals in a whole stand?  One condition in which such excess stress has been invoked as occurring and with attribution of a cause is a high density of even-aged plants.  High density may occur naturally after a disturbance such as a stand-replacing fire is followed by abundant precipitation, allowing establishment of many more individual plants than is normal.  Indeed, stand density does show legacies of high-establishment years for as long a centuries.  Can stand density lead to greater rates of transpiration per ground area, thus, to depleting soil water more rapidly between precipitation events?  Because transpiration is more controlled by energy delivery (sunlight interception) than by physiological (stomatal) control, modestly higher ET might occur.  A full program of modelling and field measurement would be needed to answer these questions.  Correspondingly, the concepts of equilibrium LAI and acclimated ET do need to be generalized to account for their recovery from disequilibrium.  Perhaps most simply, one could define the characteristic time of recovery, or relaxation time.


C. Quantifying stress over large scales


En route to answering the questions just posed and later questions about fire and insect attacks, we must be able to quantify water stress responses of large numbers of individuals or of whole stands. 


Direct measures of stress vary in the identity of the process studied.  Some measures are of water potential itself, others are of photosynthetic pigment changes, others sense transpiration, leaf temperature, or CO2 uptake from bulk air.  Changes in plant development are also used, ranging from delays in development to loss of leaves.  Methods vary as well in applicability to large-scale detection by virtue of the effort and time required, or the degree to which direct access to plants is required, particularly to elevated parts of tall canopies. 


D. Remote sensing for stress and other measures of vegetation status


i. Pros and cons. Large-scale measurements are often taken, and with good reason, to be the purview of remote sensing (RS) from satellites or aircraft.  Satellite RS offers global coverage, as well as repeat coverage at known intervals and access to the aerial parts of the canopy.  Tradeoffs include: inability to resolve single trees or other plants, such that the resultant averaging can hide some phenomena; long intervals (16 days) between repeat coverage for the high-resolution imagery such as ASTER; fixed overpass time that disallows resolution of daily time courses of plant responses; obscuration of signals by clouds or by atmospheric aerosols whose effects cannot be completely corrected.  Aircraft RS offers imagery that is much more detailed spatially.  The most advanced systems offer numerous wavelength bands that indicate very diverse physiological conditions.  Repeat coverage is challenging; spatial coverage is limited, and the operational expense is high.


ii. Electromagnetic signals. In remote sensing, the only signals available are optical in the widest sense, that is, from electromagnetic radiation, covering not only the visible spectrum but also infrared, thermal infrared, and microwaves.  Most RS is passive, in the sense that the satellite or aircraft does not emit a probe beam.  Instead, RS relies on reflectance of sunlight or spontaneous emission of thermal radiation by plants and soil. 


Fortunately, many changes in physiological status include changes in reflectance.  These occur in discrete ranges of wavelength or wavebands.  A great body of research allows interpretation of these signals with varying degrees of confidence that they each represent unique plant responses.  A number of these signals or spectral indices are outlined on a separate page here.  These indices do have explanatory power for carbon gain in both forests and woodlands, for one.  Some have significant limitations, particularly if they are measured in narrow wavebands that require hyperspectral sensors.  Satellites and many portable sensors have broader bands that dilute the signal.  There are some surrogates (1, 2) using combinations of a number of these broader bands.  Indices may have to be calibrated for individual tree species, adding a demand for additional ground-based information.  In some species, the relation to stress is weak and less useful.  Finally, indices may depend on illumination geometry  that variably samples leaves that are heterogeneous by location in the canopy and by time (refs. 1, 2).


iii. Using multiple signals. There are other RS measures that use satellite-measured combinations of many reflectances and thermal infrared emissions in order to estimate evapotranspiration itself.  Changes in ET indicate changes in stress as well as the direct participation of vegetation in the water cycle and climate system.  Most of the methods use the radiation measurements to indicate energy exchanges between the vegetated surface and the air, the soil, or the sky (by radiation).  The energy lost to the air by evapotranspiration is determined as the remainder in the total energy budget.  This remainder can be the small difference between larger quantities that have associated error limits, so that the error in estimating ET is larger yet.  One critical part of the energy budget is the transfer of so-called sensible heat (that is, transfer that involves changes in temperature, not changes in phase of water between liquid and vapor).  This is proportional to the temperature difference between vegetation and air.  The amount of error in determining this difference can be large, as noted above.  Several methods reduce the problem by calibrating this difference with two locations in a scene, one that is hot (maximum heat transfer, essentially zero ET) and one that is cold (maximum ET).  The method has several weak assumptions that limit its utility in comparing different locations (it is useful comparing different times at one site) and over forests that have very small differences between vegetation and air, or over sparse vegetation where (hot) soil dominates the signal.  Other models have been proposed and tested. The technique is maturing, so that it is becoming practical to identify water stress by remote sensing.  We thus have hopes for using the acclimated ET index in the effort to determine the natural stress regime of different vegetation types.


iv. Active remote sensing is feasible also, with more limited times and areas of deployment. One promising technique with direct relevance to vegetation water stress is chlorophyll fluorescence.  A laser beam is directed at leaves.  Chorophyll molecules, with the participation of auxiliary pigments, absorb the beam, as they also absorb sunlight.  A small but readily measured fraction of the energy is re-emitted from chlorophyll as fluorescence.  The amount of fluorescence and its timing can be interpreted in great detail.  For vegetation surveys, rapid scanning is needed, limiting the type of information that can be derived, but it is highly informative about stress.   Surveys with such instrumentation (1, 2, 3) can cover areas of intermediate spatial scale, up to a number of square km.  For accurate representation of the status of large geographic areas, researchers must carefully choose where samples are taken, as by using hierarchical sampling (1, 2, 3, 4). Other active sensing methods, primarily for structure, include radar and its analog using light beams, LIDAR.  Pulses of microwaves or of light are reflected; the time for the return signal indicates the distance travelled, hence, the variations in height of the surface.  The height variations are often related to the patterns of tree crowns or other vegetation patterns, hence, to vegetation amount and type.  Yet another active system uses microwave pulses whose reflections are detected with information about the wave phase, not only the amplitude.  The geometric structure of the surface doing the reflecting can be inferred, though with much complexity.


v. Sensing mass. One final type of remote sensing that we will mention is not based on light or other electromagnetic radiation, but on gravity.  In the GRACE experiments (an acronym for Gravity Recovery And Climate Experiment), two satellites follow each other in orbit by 220 km.  Local changes in mass in the earth below, such as water accumulating in soils, speed up the lead satellite as it approaches and retard it after passage, and similarly for the following satellite.  The changes in satellite separation are measured to micrometes and allow detection of changes in soil water content as small as mm depth.  The resolution by spatial extent is crude, about 400 km, but this is useful in large-scale studies of weather and climate, including monsoonal rains such as in the US Southwest.


E. Long-term, integrated measures of stresses and resource use


i. Growth. Reduction in overall growth is certainly an indication of stress.  This may be practical to measure in very limited circumstances, requiring, among other things, that individuals have been identified from their origin.  More practical measures exist.  Tree rings can be related to specific years and seasons.  Their width indicates how favorable each year was, in that weather but also that tree’s microsite, which may have had significantly different water, nutrient, or light availability from the stand as a whole. 


ii. Isotopic composition of tissue. Even more informative than gross ring width is its composition of carbon and oxygen isotopes.  In brief, carbon on earth exists primarily as 2 stable isotopes, carbon-12 (12C) and carbon-13 (13C) that differ only in the number of neutrons in the nucleus.  The average fraction of 13C is 1.1%.  The isotopes behave almost identically in chemical reactions; the heavier isotope is, among other things, slightly slower to react, as explained by quantum mechanics (my writeup?).  Consequently, photosynthetic reaction of CO2 with water to give sugars and then final biomass shows a depletion of the 13C/12C ratio in plant tissues.  This ratio can be measured to exquisite precision by modern instrumentation (mass spectrometers).  The discrimination in photosynthesis occurs in two major steps – diffusion of CO2 through the stomatal pores (a lesser discrimination), and the enzymatic reactions (much greater discrimination).  If stomatal conductance is reduced by tissue water stress or by low atmospheric humidity, the lesser discrimination during diffusion predominates.  The 13C/12C ratio is higher than in unstressed conditions.  The change in discrimination is a reliable indicator of reduction in stomatal conductance over the period that the biomass accumulated.  Leaf tissue can be fractionated to resolve components made recently (days).  Tree rings can be analyzed as a whole.  This measurement resolves water stress (as a combination of low humidity and tissue water stress) from other factors such as low temperature or high cloudiness.


Isotopes of oxygen, hydrogen, nitrogen, and sulfur can be measured in tissues as well as in stem water and water flux into the air.  Much other information can be extracted, for example, about which sources of water or nutrients that the plant has used.


F. Direct measurement of fluxes of water vapor, CO2, and heat in the air


i. Eddy covariance. Transpired water goes into the atmosphere, and the fluxes can be measured at a given point from towers where sensors record the vapor concentration and the vertical wind velocity very frequently, about 20 times per second.  In this technique of eddy covariance, the integral of vapor concentration multiplied by vertical windspeed yields the cumulative transport of water vapor, that is cumulative ET.  When sensors are also deployed to measure CO2 in the air, one can get net CO2 exchanges or fluxes between the landscape and the air.  Because parcels of air arrive at the sensors from various points upwind at different times, eddy covariance measures ET averaged over a “footprint” distributed over the land.  The footprint shifts position with atmospheric condition (stability) and with wind direction.  Thus, the technique does not resolve individual trees.  However, it gives continuous records over time.  More than 400 towers are in place around the globe in a group called Fluxnet.  Cooperative data analysis has enabled the resolution of large-scale changes in ET, such as happened during the European heat wave of 2003.  The Fluxnet towers also measure CO2 fluxes, informing studies of the carbon cycle and its role in climate.  Some specialized towers resolve different isotopes of carbon.  The isotopic ratio reflects differences in flux from different vegetation groups (so-called C3 vs. C4 plants, which differ in the enzymatic pathways that begin photosynthesis).  The ratio also reflects vegetation stress that changes stomatal conductance.


ii. Limitations. The accuracy and areas of applicability of eddy covariance has its limitations, as do all technologies.  Foremost, and still puzzling, is the general inability of eddy covariance systems to account for the balance of energy on the landscape.  The net input of energy comes as radiation, both shortwave (sunlight) and thermal infrared, debited for energy flowing into soil as heat.  This should equal the energy that goes into heating the air (sensible heat) and into evapotranspiration, with some corrections for heat staying transiently within the stand to heat the stand itself (wood, primarily).  The degree of energy closure, as the outputs divided by the inputs, is disturbingly low on average, about 75% among all the Ameriflux sites.  About ten possible causes have been investigated, with no conclusions possible to date.  The common resolution is to scale up both sensible heat flux and ET by the same factor, but it is not known how appropriate this is.  Problems are excerbated by placement of the systems on complex terrain (hills).  Additionally, systems suffer data gaps when individual sensors fail. 


iii. LIDAR. An even more elaborate system for measuring the flux of water vapor in air is dual-beam LIDAR that is aimed over the canopy, traversing the air.  One beam is absorbed by water vapor, the other is not.  Incremental differences in beam strength are detected by the scattering of the beams back to a detector.  The spatial profiles of water vapor content, with the aid of data on winds and atmospheric structure, can be processed to indicate water vapor fluxes in the air – ET in great detail.  The system is very expensive and rather immobile, and its senses small areas (100’s of meters).


G. Ruminations about our abilities to quantify plant, stand, and regional water relations


i. Inadequate precision in single methods. There has been remarkable progress in the technology and data analysis methods for measuring many aspect of plant stress and plant participation in water and carbon cycles.  Many of these methods apply well to forests, our topic here.  While we can’t review all of this here, we can offer some observations about the adequacy of various methods, singly and in combination.  First, it is necessary to disabuse ourselves of the idea that remote sensing will suffice to quantify the water cycle across the globe.  Consider the challenge of estimating surface water yield as runoff (plus some deep percolation with later emergence of water at springs).  On a global average, about 30% of precipitation over land runs off into surface waters.  To predict runoff with a relative precision of 50% (far below the precision of ground-based stream gauges, where they exist) requires an absolute accuracy of 15% in estimating precipitation minus ET.  (We also refer readers to discussions of the distinction between accuracy and precision.)  The current precision of ET estimation from satellite remote sensing is often greatly overstated, by restricting consideration to full vegetative cover only, and to repeat measures on one site, and the like.  A precision of 20% may be realistic, particularly in view of the calibration methods such as eddy covariance having yet lower precision.   At this point, we already have exceeded the desired error bound.  Now add the imprecision in estimating precipitation.  Even ground-based raingauges suffer here, not nearly as much from their own operation as from inadequate spatial sampling.  In convective storms that account for perhaps half of all precipitation but variably by location – the rest being stratiform precipition – the distribution of amount is highly varied across the landscape.  An impractically large array of raingauges may be needed for high precision.  Radar detection of precipitation, such as by the NEXRAD system, offers coverage of large areas on moderately fine scales (1 km), but calibration has proved extremely difficult.  Most areas of the globe lack the radar systems, so that even rougher surrogates must be used, notably cloud-top temperatures.


ii. What is the solution?  Most certainly, it is coupling of remote-sensing and ground-based methods.  Remote sensing may offer relative measures of ET and precipitation that are wholly inadequate to predict runoff by themselves, but the results may be entered into hydrological models, often very empirical, that are calibrated to, say, stream gauges.  Will these joint efforts be pursued adequately and in timely fashion?  One of us attended a NASA workshop in 1991 at Columbia, MD, at which the NASA teams admitted that data were not being made available in sufficiently usable form nor with adequate ground validation.  Our admittedly anecdotal view is that progress has been incremental.  Some campaigns have made intensive efforts to combine ground and remote sensing.  On the other hand, many efforts emphasize intercomparing models to each other, not to ground reality.  The impetus for better ground validation surely needs to come from the broad array of end-users, including large agencies.  Fragmentation of responsibilities among agencies have frustrated this, as have simple interagency competitions.  This is documented a bit more in our essay on water issues.


V. Revisiting some specific couplings of forests to climate


Water balance and water stress merited a lengthy development above because of their importance for forest structure per se, plus fire dynamics, water yield, and more.  Forests, in turn, have direct effects on water and carbon cycles that are important for determining climate. 


A. Three major effects are: forest absorption of sunlight, or albedo; sequestration of carbon that reduces the atmosphere’s content of CO2 as a greenhouse gas; and evapotranspiration that helps load the atmosphere with water vapor as a potent greenhouse gas and as precipitable water.  Early model studies implicated the absorption of sunlight by dark boreal forests as contributing significantly to air temperature on the scale of very large regions.  Recent studies indicate that adding forest area in these latitudes would have a net warming effect.  Admittedly, tree growth removes CO2 from the air and reduces the warming that CO2 causes by trapping thermal radiation.  However, trees that replace highly reflective snowcover for long seasons directly warm that air as a larger effect.  Trees are not always helpful against global warming.  [As a side note, vegetation and specifically forests have done much over the last hundreds of millions of years to sequester carbon, massively reducing its content in air – one might say making their own lives harder by reducing CO2 as a photosynthetic substrate, as well as cooling the climate.  The decay-resistance of wood allowed burial of much carbon as coal.  Also, plant roots exuded CO2 into soil air spaces, acidifying soil water and accelerating rock weathering into soil that leads ultimately to carbon being sequestered as carbonate deposits in sediments.  Time scales matter a great deal.]


B. Carbon sinks. In most locations, nonetheless, growth of stable, dense biomass is a major contribution to removing CO2 from the air.  Regrowth of North American forests that were heavily harvested up to the 1950’s has created a major carbon sink, exceeding 10% of all the CO2 injection from fossil fuel use and deforestation globally.  Whole nations such as Australia have embarked on tree-planting programs.  There is, of course, a limit to how much growth can be sustained.  Forests reach saturating biomass densities.  Unless logs are buried – which has been suggested, net CO2 sequestration then ceases.  A prominent global-change researcher has remarked in private that all the efforts at afforestation and reforestation might gain us the equivalent of only 2 years of human-cause CO2 addition to the air.  Even on a global scale, then, forests are insufficient in themselves to mitigate climate change.


C. Carbon sources. Forests also burn with some regularity, participating in the 4% of all biomass that burns annually.  Burning consequently represents a major flow in the carbon cycle.  This is made clearer by the observation that photosynthesis annually fixes a mass of carbon about 10% as large as the standing biomass, so that burning represents 40% of the return flux of CO2 to the air.  This is much greater than before the expansion of human populations.  Regrettably, it is likely to continue or to increase.


D. Water recycling. While CO2 circulates globally, water vapor has more regional circulation, even if large-scale.   Transpiration by all vegetation is responsible for about 2/3 of total water injection to the air from the land; the other 1/3 is from direct evaporation of water from wet soil and water bodies or sublimation of ice.  Forests dominate the biomass of vegetation on earth and co-dominate total transpiring area, along with woodlands and grasslands, so they clearly are critical in the land-to-air part of the water cycle.  In the other part of the water cycle, total ET over land supplies about one-fifth of water for precipitation globally (less than the 29% fraction of the earth’s surface as land; land has less ET and less precipitation than ocean, on average).  In some regions, the return of water from ET to precipitation occurs in relatively short distances, on the order of 1000 km.  The recycling ratio over a given downwind distance is high in areas of rainforest, notably Amazonia.  Large-area deforestation measurably reduces precipitation in regions coupled by weather patterns.  As deforestation continues – a lamentable near-certainty – predictions are for significantly lower precipitation and higher air temperatures.  The reversibility of these changes is dubious, on the grounds that soil-surface conditions become inhospitable for many rainforest species to germinate and establish.


E. Surface water. Also critically for the water cycle, forests condition runoff to rivers by their action to intercept snow and rain.  They also retard overland water flows, allowing greater infiltration of precipitation into soils and reducing soil erosion that affects water quality and ecosystem stability itself.  These ecosystem services make it an object of great concern that forest losses are huge, particularly in poor tropical and subtopical nations, such as Haiti and Madagascar.  Worldwide, river degradation has greatly reduced water utility, including the effect on fish stocks.


VI. Forest fires per se


A. Very brief overview of fire ecologically and in human affairs


Burning is not only a major element of the carbon cycle.  It is also an agent for changing habitats for many species, eliminating some habitats, creating others, and generally diversifying habitat.  Forest fire is an object of concern for humans, as a danger and as a tool of land and wildlife management.  Humans have radically changed fire regimes, generally by increasing fire frequency and timing.  This has transformed the vegetation of whole continents, perhaps most notably Australia over the last 40,000 years of human habitation.  Humans also have reduced fire frequency in a number of regions.  Consequences include not only changes in plant establishment but even soil fertility.


We turn our attention now to forest fire among the topics above.  The topic is very large, so we focus on several issues.  We may ask how forest structure and function are related to fire regime, fire hazard to humans and their activities, and insect attack that kills trees and may increase fire probability.  Conversely, how does fire contribute to structuring forests?  We may ask several questions with yet finer focus.  What is the role of water stress in the probability of ignition and in the pattern of fire propagation?  What effect does stand density and related management have on water stress and on fire dynamics directly?  What are positive and negative effects of changed fire regimes on the ecosystem services offered by forests?   How will climate change alter forest structure, stress regimes, and fire regimes?


B. Fire regimes


A number of monographs and reports cover the basic physical and biological determinants of fire.  A recent report addresses the issue of overdense dry western forests in North America.  The combustible masses or fuels in a forest can be divided into crowns (live and dead matter in the top or canopy), surface fuels (understory live and dead matter as grass, forbs, and shrubs), and ground fuels (organic matter in soil).  These fuels vary in their probability of ignition and ability to propagate flames or smoldering combustion.  Variation arises from dry-matter composition and from moisture content.  Together with weather conditions and topography, the fuel load and condition determines the fire intensity and its location.  A notable distinction is between surface fires and crown fires.  In forests such as the aformentioned dry forests, surface fires are frequent in normal ecological conditions.  They burn the understory but do minimal damage to mature trees.  Fires that reach the crown are more common in wetter regions that naturally build up denser understories that act as “ladder fuels” for fire to reach the crowns.  Moist fuel has lower energy release and flame height, such that flame extension to the canopy is less likely in a given canopy structure.  Crown fires are often stand-replacing, consuming all vegetation in a patchwork of large and small areas that may cover a very large area overall. 


Natural ignition of forest fires is commonly by lightning strikes.  Humans cause additional fires by accident or as arson.  In western Canada, the resultant fires are more numerous but burn less area in total annually.   Propagation of the fire has a number of stochastic components in wind fields and such, and is necessarily hard to predict.  Models with sufficient resolution for detailed prediction demand data volumes that can only be attained on limited areas.


C. Vegetation adaptation to fire


Both kinds of fires have recurred naturally for thousands to millions of years, variously by location.  They generate diverse habitats in the recovery or successional stages.  They generate concurrently cycles of other ecosystem services such as surface water yield (more runoff on burned areas, e.g.).  Tree species native to a region show diverse adaptations to withstanding fire or regenerating after fire.  Fire-resistant bark protects some species as mature trees.  Some species have fruits and cones that release seeds only after fire to start regeneration; others regenerate from vegetative parts underground.  Yet others, notably the eucalypts of Australia, have evolved high flammability that is thought to drive the burning of competitors that have lower post-fire regeneration capabilities.  Tree physiology and development is not the full story.  The recovery of other plants is important in the cyclic restoration of soil condition (stability against erosion, nutrient and water relations).  The recovery of microbes is also important, particularly the mycorrhizal fungi that aid plant nutrient acquisition.  Fire intensities may temporarily kill microbes, engendering a longer recovery cycle.


D. Conditions for fire ignition and propagation


Fire conditions in any given forest vary both naturally and artificially, and on a range of time scales.  Episodes of precipitation vary the fuel moisture content, which is a good predictor of ignition probability.  Surrogates for fuel moisture are commonly used in monitoring systems, either using ground weather data or satellite remote sensing.  In the longer term, stand density varies.  High densities can arise from wet years following fire and can persist for many decades.  Fire suppression by human management can lead to a buildup of surface fuels and ladder fuels.  The consequent change to a regime of crown fires and large fires is a concern over approximately 40 to 60% of the dry forests of the western US.


E. Monitoring of fuel conditions


It is important to estimate both fuel amount and moisture content on large areas.  Ground-based surveys are sufficiently labor-intensive as to require extensive interpolation of transect measurements.  Satellite remote sensing has poor spatial resolution and a modest ability to detect water stress in canopy tissue, which has only variable correlation with the moisture of surface fuel.  Hyperspectral remote sensing from aircraft or pointable satellites can reveal more detail but is expensive and has a low repeat frequency.  Fuel density can be estimated with better precision using LIDAR that reveals surface roughness.  It has the same problems of expense and repeat frequency for large areas.  Fuel moisture is often estimated from ground data on weather with water-balance models (long download).  In summary, fire condition remains challenging to estimate.


F. Monitoring of extant fires and post-fire effects


Ground-based observations and aircraft overflights have long been used to monitor fires in progress.  More recently, satellite detection of hot pixels has been used in a highly automated system that maps fires in near-real time.  All three techniques are employed in monitoring post-fire conditions of vegetation and soil.  LIDAR has been used as well to monitor soil conditions, particularly erosion in post-fire rains.


G. Management, before, during, and after fire


This is a very large topic, both conceptually and economically.  Over and above the need for monitoring as noted, forest managers and emergency agencies prepare firebreaks, firefighting and evacuation routes, and the like.  Modification of forest conditions is the largest potential expense.  In dry western forests of the US, mechanical thinning is one option being tried, but costs  that can exceed $4000 per hectare are insupportable over large areas.  Thinning treatment in a near-checkerboard pattern that prevents fire “percolationreduces costs by at least a factor of three but has not been adopted by US agencies.  Prescribed burns may be less expensive but difficult to control in many circumstances.  Cost comparisons are still in progress.  Post-fire treatment varies from allowing natural processes alone to extensive revegetation with grasses (often non-native, with attendant problems of invasiveness) to reduce soil erosion. 


H. Climate change affecting fire regime


Higher temperatures are correlated, if imperfectly, with greater evaporation from wet surfaces (of dead fuels) and lower fuel moisture.  The temperatures act directly on instantaneous rates of evaporation and indirectly by extending the season of active evapotranspiration.  Perhaps more important is alteration of the precipitation regime.  The hydrologic cycle is become more active globally, as expected, but precipitation has trends upward in some regions and downward in others.  Intensity of precipitation has similarly bidirectional trends (1, 2).  Vegetation growth itself is enhanced by elevated CO2, acting to increase fuel loads.  Vegetation water status is affected variously by species and location, ranging from minimally changed to enhanced by virtue of reduced stomatal conductance.  Drought and high temperature in the case of conifer dieoffs in western North America might be related to climate change.  If so, an increasing frequency of events is projected.  The dieoff was not related to higher fire incidence, fortuitously.  The canopy fuel load of dead leaves was reduced within a year by needle drop.  Studies of the whole range of climate effects on fire regimes are incomplete, if pressing.




Agee JK, Wright CS, Williamson N, Huff, MH. 2002. Foliar moisture content of Pacific Northwest vegetation and its relation to wildland fire behavior. Forest Ecol. Manage. 167: 57– 66.

Allen CD, Breshears DD. 1998. Drought-induced shift of a forest–woodland ecotone: Rapid landscape response to climate variation Proc. Nat. Acad. Sci. USA 95: 14839–14842.

Ananyev G, Kolber ZS, Klimov D, Falkowski PG, Berry JA, Rascher U, Martin R, Osmond B. 2005. Remote sensing of heterogeneity in photosynthetic efficiency, electron transport and dissipation of excess light in Populus deltoides stands under ambient and elevated CO2 concentrations, and in a tropical forest canopy, using a new laser-induced fluorescence transient device. Global Change Biol. 11: 1195-1206.

Andales A, Wang J, Sammis TW, Mexal JG, Simmons LJ, Miller DR, Gutschick VP. 2006. A model of pecan tree growth for the management of pruning and irrigation. Agric. Water Manage.: 77-88.

Asner GP, Hughes RF, Vitousek PM, Knapp DE, Kennedy-Bowdoin T, Boardman J, Martin RE, Eastwood M, Green RO. 2008. Invasive plants transform the three-dimensional structure of rain forests.  Proc. Nat. Acad. Sci. USA 105: 4519-4523.

Asner GP, Nepstad D, Cardinot G, Ray D. 2004. Drought stress and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy. Proc. Nat. Acad. Sci. (USA) 101: 6039-6044.

Aukema BH, Carroll AL, Zhu J, Raffa KF, Sickely TA, Taylor SW. 2006. Landscape level analysis of mountain pine beetle in British Columbia, Canada: spatiotemporal development and spatial synchrony within the present outbreak. Ecography 29: 427-441.

Baldocchi D, Finnigan J, Wilson K, Paw U KT, Falge E. 2000. On measuring net ecosystem carbon exchange over tall vegetation on complex terrain. Boundary-Layer Meteorol. 96: 257-291.

Ball JT, Woodrow IE, Berry JA. 1987. A model predicting stomatal conductance and its contribution to the control of photosynthesis under different environmental conditions. In: Biggins J (Ed.), Progress in Photosynthesis Research, vol. IV. Martinus, Nijhoff, Dordrecht, The Netherlands. Pp.221–224.

Bastiaanssen WGM. 2000. SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey. J. Hydrol. 229: 87-100.

Betts RA, Falloon PD, Goldewijk KK, Ramankutty N. 2007.  Biogeophysical effects of land use on climate: Model simulations of radiative forcing and large-scale temperature change. Agric. Forest Meteorol. 142: 216-233.

Binns, W.O. 1986. Forestry and fresh waters: problems and remedies" in Effects of Land Use on Fresh Waters. ed. L.G. Solbe. Ellis Horwood Limited, Chichester. p. 365.

Biondi F, Kozubowski TJ, Panorska AK. 2005. A new model for quantifying climate episodes. Int. J. Climatol. 25: 1253–1264.

Bonan, GB, D Pollard, and SL Thompson. 1992. Effects of boreal forest vegetation on global climate. Nature 359: 716-718.

Breda N, Granier A, Aussenac  G. 1995. Effects of thinning on soil and tree water relations, transpiration and growth in an oak forest (Quercus petraea (Matt) Liebl). Tree Physiol. 15: 295-306.

Breshears DD, Cobb NS, Rich PM, Price KP, Allen CD, Balics RG, Romme WH, Kastens JH, Floyd ML, Belnap J, Anderson JJ, Myers OB, Meyer CW. 2005. Regional vegetation die-off in response to global-change-type drought. Proc. Nat. Acad. USA 102: 15144-15148.

Brown PM, Wu R. 2005. Climate and disturbance forcing of episodic tree recruitment in a southwestern ponderosa pine landscape. Ecology 86: 3030-3038.

Brugnoli E, Hubick KT, von Caemmerer S, Wong, SC, Farquhar GD. 1988. Correlation between the carbon isotope discrimination in leaf starch and sugars of C3  plants and the ratio of intercellular and atmospheric partial pressures of carbon dioxide. Plant Physiol. 88: 1418-1424.

Buckley KJ, Walterscheid JC, Loftin SR, Kuyumjian GA. 2003.  Final Progress Report on Los Alamos National Laboratory Cerro Grande Fire Rehabilitation Activities. Los Alamos National Laboratory.

Bunn AG, Lawrence RL, Bellante GJ, Waggoner LA, Graumlich LJ. 2003. Spatial variation in distribution and growth patterns of old growth strip-bark pines. Arctic Antarc. Alpine Res. 35: 323-330.

Burrows SN, Gower ST, Clayton MK, Mackay DS, Ahl DE, Norman JM, Diak, G. 2002. Application of geostatistics to characterize leaf area index (LAI) from flux tower to landscape scales using a cyclic sampling design. Ecosystems 5: 667-679.

Casagrandi R, Rinaldi S. 1999. A minimal model for forest fire regimes. Am. Nat. 153: 527-539.

Catling DC, Claire MW. 2005. How Earth's atmosphere evolved to an oxic state: A status report. Earth Planetary Sci. Lett. 237: 1-20.

Cheng KS, Lin YC, Liou, JJ. 2008. Rain-gauge network evaluation and augmentation using geostatistics. Hydrol. Processes 22: 2554-2564.

Choudhury BJ. 1989. Estimating evaporation and carbon assimilation using infrared temperature data: Vistas in modeling, p. 628-690, In: Asrar G (ed.) Theory and Applications in Optical Remote Sensing. John Wiley, New York. Pp. 628-690.

Choudhury BJ. Evaluation of an empirical equation for annual evaporation using field observations and results from a biophysical model. 1999. J. Hydrology 216: 99-110.

Clark JS. 1991. Disturbance and population structure on the shifting mosaic landscape. Ecology 72: 1119-1137.

Cohen WB, Spies TA, Alig RJ, Oetter DR, Maiersperger TK. 2002. Characterizing 23 years (1972-95) of stand replacement disturbance in western Oregon forests with Landsat imagery. Ecosystems 5: 122-137.

Cooper DI, Eichinger WE, Kao J, Hipps L, Reisner J, Smith S, Schaeffer SM, Williams DG. 2000. Spatial and temporal properties of water vapor and latent energy flux over a riparian canopy. Agric. Forest Meteorol. 105: 161-183.

Cowan IR, and GD Farquhar (1977) Stomatal diffusion in relation to leaf metabolism and environment. Symp. Soc. Exper. Biol. 31: 471-505.

De Santis A, Vaughan P, Chuvieco E. 2006. Foliage moisture estimation from one-dimensional and two-dimensional spectroradiometry for fire danger assessment. J. Geophys. Res. – Biogeosci. 111G: G04S03.

Dobrowski SZ, Pushnik JC, Zarco-Tejada PJ, Ustin SL. 2005. Simple reflectance indices track heat and water stress-induced changes in steady-state chlorophyll fluorescence at the canopy scale. Remote Sens. Environ. 97: 403-414.

Drolet GG, Huemmrich KF, Hall FG, Middleton EM, Black TA, Barr AG, Margolis HA. 2005. A MODIS-derived photochemical reflectance index to detect inter-annual variations in the photosynthetic light-use efficiency of a boreal deciduous forest. Remote Sens. Environ. 98: 212-224.

Ehleringer JR, Hall AE, Farquhar GD (eds.). 1993. Stable isotopes and plant carbon/water relations. Academic, San Diego.

Ehrler WL, Nakayama FS. 1984. Water stress status in guayule as measured by relative leaf water content.Crop Sci. 24: 61-66

Eitel JUH, Gessler PE, Smith AMS, Robberecht R. 2006.  Suitability of existing and novel spectral indices to remotely detect water stress in Populus spp. Forest Ecol. Manage. 229: 170-182.

Evain S, Flexas J, Moya I. 2004. A new instrument for passive remote sensing. 2. Measurement of leaf and canopy fluorescence changes at 531 nm and their relationship with photosynthesis and chlorophyll fluorescence. Remote Sens. Environ. 91: 175-185.

Evans JR, Sharkey TD, Berry JA, Farquhar GD. 1986. Carbon isotope discrimination measured concurrently with gas exchange to investigate CO2 diffusion in leaves of higher plants. Aust. J. Plant Physiol. 13: 281-292.

Fan J, Yao Q. and Q. Yao. 2003. Nonlinear Time Series. Springer, New York.

Farquhar GD,von Caemmerer S, Berry JA. 1980. A biochemical model of photosynthetic CO2  assimilation in leaves of C3 species.Planta 149: 78-90.

Fensholt R, Sandholt I, Rasmussen MS, Stisen S, Diouf A. 2006. Evaulation of satellite-based primary production modelling in the semi-arid Sahel. Remote Sens. Environ. 105: 173-188.

Fettig CJ, Klepzig KD, Billings RF, Munson AS, Nebeker TE, Negron JF, Nowak JT. 2007. The effectiveness of vegetation management practices for prevention and control of bark beetle infestations in coniferous forests of the western and southern United States.  Forest Ecol. Manage. 238: 24-53.

Floyd ML, Hanna DD, Romme WH. 2004. Historical and recent fire regimes in Pinon-Juniper woodlands on Mesa Verde, Colorado, USA. Forest Ecol. Manage. 198: 269-289.

Frelich LE. 2002. Forest Dynamics and Disturbance Regimes : Studies from Temperate Evergreen-Deciduous Forests. Cambridge Univ. Press.

French AN, Jacob F, Anderson MC, Kustas WP, Timmermans W, Gieske A, Su Z, Su F, McCabe MF, Li F, Prueger J, Brunsell N. 2005. Surface energy fluxes with the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) at the Iowa 2002 SMACEX site (USA). Remote Sens. Environ. 99: 55-65.

Fry N. 2001. Stress space: striated faults, deformation twins, and their constraints on paleospace. J. Struc. Geol. 23: 1-9.

Gamon JA, Serrano L, Surfus JS. 1997. The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levelsOecologia 112 : 492-501.

Gebremichael M, Barros AP. 2006. Evaluation of MODIS gross primary productivity (GPP) in tropical monsoon regions. Remote Sens. Environ. 100: 150-166.

Gonzalez J, Valdes JB. 2004. The mean frequency of recurrence of in-time-multidimensional events for drought analyses. Natural Haz. Earth Sys. Sci. 4: 17-28.

Gower  ST, Kucharik CJ, Norman JM. 1999. Direct and indirect estimation of leaf area index, f(APAR), and net primary production of terrestrial ecosystems. Remote Sens. Environ. 70: 29-51.

Granier A, Reichstein M, Breda N, Janssens IA, Falge E, Ciais P, Grunwald T, Aubinet M, Berbigier P, Bernhofer C, Buchmann N, Facini O, Grassi G, Heinesch B, Ilvesniemi H, Keronen P, Knohl A, Kostner B, Lagergren F, Lindroth A, Longdoz B, Loustau D, Mateus J, Montagnani L, Nys C, Moors E, Papale D, Peiffer M, Pilegaard K, Pita G, Pumpanen J, Rambal S, Rebmann C, Rodrigues A, Seufert G, Tenhunen J, Vesala I, Wang Q. 2007. Evidence for soil water control on carbon and water dynamics in European forests during the extremely dry year, 2003. Agric. For. Meteorol. 143: 123-145.

Granier C, Tardieu F. 1999. Water deficit and spatial pattern of leaf development. Variability of responses can be simulated using a simple model of leaf development. Plant Physiol 119: 609-619.

Gray ST, Betancourt JL, Fastie CL, Jackson ST. 2003. Patterns and sources of multidecadal oscillations in drought-sensitive tree-ring records from the central and southern Rocky Mountains. Geophys. Res. Lett. 6: 1316.

Gray ST, Betancourt JL, Jackson ST, Eddy RG. 2006. Role of multidecadal climate variability in a range extension of pinyon pine. Ecology 87: 1124-1130.

Groisman PY, Knight RW, Easterling DR, Karl TR, Hegerl GC, Razuvaev VAN. 2005.
Trends in intense precipitation in the climate record. J. Climate 18: 1326-1350.

Guo JM, Trotter, CM. 2004. Estimating photosynthetic light-use efficiency using the photochemical reflectance index: variations among species. Func. Plant Biol. 31: 255-265.

Gutschick VP. 1987. A Functional Biology of Crop Plants. Croom Helm, London /Timber Press, Beaverton, OR.

Gutschick VP. 2007.  Plant acclimation to elevated CO2 - from simple regularities to biogeographic chaos. Ecol. Mod. 200: 433-451.

Gutschick VP, BassiriRad H. 2003. Tansley Review: Extreme events as shaping physiology, ecology, and evolution of plants: toward a unified definition and evaluation of their consequences. New Phytologist 160: 21-42.

Gutschick VP, Simmoneau T. 2002. Modelling stomatal conductance of field-grown sunflower under varying soil water status and leaf environment: comparison of three models of response to leaf environment and coupling with an ABA-based model of response to soil drying. Plant Cell Environ. 25: 1423-1434.

Hall AE, Mutters RG, Hubick KT, Farquhar GD. 1990. Genotypic differences in carbon isotope discrimination under wet and dry field conditions. Crop Sci. 30: 300-305.

Hall FG, Knapp DE, Huemmrich KF. 1997. Physically based classification and satellite mapping of biophysical characteristics in the southern boreal forest. J. Geophys. Res. _ Atmos. 102D: 29567-29580.

Hartmann DL. 1994. Global Physical Climatology. Academic, San Diego.

Haxeltine A, Prentice IC, Creswell ID. 1996. A coupled carbon and water flux model to predict vegetation structure. J. Veg. Sci. 7: 651-666.

Huntington TG. 2006. Evidence for intensification of the global water cycle. J. Hydrol. 319: 83-95.

Ishihara M, Matsunaga T, Tamura M. 2005. Improved estimation of photochemical reflectance index using MODIS ocean bands. In: Remote Sensing for Agriculture, Ecosystems, and Hydrology VII.  Owe M, D’Urso G (eds.). D'Urso, Guido. Proceedings of the SPIE, Volume 5976, pp. 386-393

McNaughton KG, Jarvis PG. 1991. Effects of spatial scale on stomatal control of transpiration. Agric. Forest Meteorol. 54: 279-302.

Justice CO, Giglio L,  Korontzi, S, Owens J, Morisette JT, Roy D, Descloitres J, Alleaume S, Petitcolin F, Kaufman Y. 2002. The MODIS fire products. Remote Sens.  Environ. 83: 244-262.

Kashian DM, Turner MG, Romme WH. 2005. Variability in leaf area and stemwood increment along a 300-year lodgepole pine chronosequence. Ecosystems 8: 48-61.

Kergoat L. 1998. A model for hydrological equilibrium of leaf area index on a global scale. J. Hydrol. 212-213: 268-286.

Kergoat L, Lafont S, Douville H, Berthelot B, Dedieu G, Planton S, Rover JF. 2002. Impact of doubled CO2 on global-scale leaf area index and transpiration. J. Geophys. Res. – Atmos. 107D: 4808-4825.

King DA, Ball MC. 1998. A model of frost impacts on seasonal photosynthesis of Eucalyptus pauciflora. Aust. J. Plant Physiol. 25: 27-37.

Kokaly RF, Rockwell BW, Haire SL, King TVV. 2007. Characterization of post-fire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing. Remote Sens. Environ. 106: 305-325.

Kolb TE, Holmberg KM, Wagner MR, Stone JE. 1998. Regulation of ponderosa pine foliar physiology and insect resistance mechanisms by basal area treatments. Tree Physiol. 18: 375-381.

Kustas WP, French AN, Hartfield JL, Jackson TJ, Moran MS, Rango A, Ritchie JC, Schmugge TJ. 2003. Remote sensing research in hydrometeorology. J. Photogramm. Engin. Remote Sens. 69: 631-646.

Lafon CW, Kutac M. 2003. Effects of ice storms, southern pine beetle infestation, and fire on table mountain pine forests of southwestern Virginia. Phys. Geogr. 24: 502-519.

Leblon B. 2005. Monitoring forest fire danger with remote sensing. Natural Hazards 35: 343-359.

Le Dantec V, Dufrene E, Saugier B. 2000. Interannual and spatial variation in maximum leaf area index of temperate deciduous stands. Forest Ecol. Manage. 134: 71-82.

Lee KS, Cohen WB, Kennedy RE, Maiersperger TK, Gower ST. 2004. Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sens. Environ. 91: 508-520.

Loehle C, LeBlanc D. 1996. Model-based assessments of climate change effects on forests: a critical review. Ecological Modelling 90: 1–31.

Lutze JL, Roden JS, Holly CJ, Wolfe J, Egerton JJG, Ball MC. 1998. Elevated atmospheric CO2 promotes frost damage in evergreen tree seedlings. Plant Cell Environ. 21: 631-635.

Mabry TJ, Hunziker JH, DiFeo DR Jr (eds.). 1977. Creosote Bush: Biology and Chemistry of Larrea in New World Deserts. Dowden, Hutchinson, and Ross, Stroudsburg, PA

Macias M, Andreu L, Bosch O, Camerero JJ, Gutierrez E. 2006. Increasing aridity is enhancing silver fir (Abies alba Mill.) water stress in its southwestern distribution limit. Clim. Change 79: 289-313.

Maggi S, Rinaldi S. 2006. A second-order impact model for forest fire regimes. Theoret. Popul. Biol. 70: 174-182.

Martens SN, Breshears DD, Barnes FJ. 2001. Development of species dominance along an elevational gradient: Population dynamics of Pinus edulis and Juniperus monosperma. Int. J. Plant Sci. 162: 777–783.

Masek JG. 2001. Stability of boreal forest stands during recent climate change: evidence from Landsat satellite imagery. J. Biogeog. 28: 967-976.

McDowell NG, Bowling DR, Schauer A, Irvine J,  Bond BJ, Law BE, Ehleringer JR. 2004. Associations between carbon isotope ratios of ecosystem respiration, water availability and canopy conductance. Global Change Biol. 10: 1767-1784.

McDowell NG, Adams HD, Bailey JD, Kolb TE.  2007. The role of stand density on growth efficiency, leaf area index, and resin flow in southwestern ponderosa pine forests. Can. J. For. Res. 37: 343–355.

McDowell N, Pockman WT, Allen CD, Breshears DD, Cobb N, Kolb T, Plaut J, Sperry J, West A, Williams DG, et. al. 2008. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytol. 178: 719-739.

Moritz MA. 1997. Analyzing extreme disturbance events: fire in Los Padres National Forest. Ecol. Appl. 7: 1252-1262.

Mu QZ, Zhao MS, Heinsch FA, Liu ML, Tian HQ, Running SW. 2007. Evaluating water stress controls on primary production in biogeochemical and remote-sensing based models. J. Geophys. Res. – Biogeosci. 112G: G01012.

Mueller RC, Scudder CM, Porter ME, Trotter RT, Gehring CA, Whitham TG. 2005. Differential tree mortality in response to severe drought: evidence for long-term vegetation shifts. J. Ecol. 93: 1085-1093.

Mutanga O, Skidmore AK. 2007. Red-edge shift and biochemical content in grass canopies. J. Photogramm. Remote Sens. 62: 34-42.

Naor A, Gal Y, Peres M. 2006. The inherent variability of water stress indicators in apple, nectarine and pear orchards, and the validity of a leaf-selection procedure for water potential measurements. Irrig. Sci. 24: 129-135.

Niinemets Ü. 2007.  Photosynthesis and resource distribution through plant canopies. Plant Cell Environ. 30: 1052-1071.

Niinemets Ü, Sonninen E, Tobias M. 2004. Canopy gradients in leaf intercellular CO2 mole fractions revisited : interactions between leaf irradiance and water stress need consideration. Plant Cell Environ. 27: 569-583.

Norman JM, Anderson MC, Kustas WP, French AN, Mecikalski J, Torn R, Diak GR, Schmugge TJ, Tanner BCW. 2003. Remote sensing of surface energy fluxes at 10-m pixel resolutions. Water Reso. Res. 39: 1221.

Norman JM, Tanner CB. 1969. Transient light measurements in plant canopies. Agron. J.61: 847-849.

Nowak RS, Ellsworth DS, Smith SD. 2004. Functional responses of plants to elevated atmospheric CO2 – do photosynthetic and productivity data from FACE experiments support early predictions? New Phytol. 162: 352-280.

Nowak RS, Zitzer SF, Babcock D, Smith-Longozo V, Charlet TN, Coleman JS, Seemann JR, Smith SD. 2004. Elevated atmospheric CO2 does not conserve soil water in the Mojave Desert. Ecology 85: 93-99.

Oberhuber W, Kofler W. 2000. Topographic influences on radial growth of Scots pine (Pinus sylvestris L.) at small spatial scales. Plant Ecol. 146: 231-240.

Parker TJ, Clancy KM, Mathiasen RL. 2006.  Interactions among fire, insects and pathogens in coniferous forests of the interior western United States and Canada. Agric. Forest Entomol. 8: 167-189.

Pearcy RW, Ehleringer J, Mooney HA, Rundel PW (eds.). 1989. Plant Physiological Ecology: Field Methods and Instrumentation. Chapman and Hall, London.

Perez-Priego O, Zarco-Tejada PJ, Miller JR, Sepulcre-Canto G, Fereres E. 2005. Detection of water stress in orchard trees with a high-resolution spectrometer through cholorophyll fluorescence in-filling of the O-2-A band. IEEE Trans. Geosci. Remote Sens. 43: 2860-2869.

Peters DPC, Mariotto I, Havstad KM, Murray LW. 2006. Spatial variation in remnant grasses after a grassland-to-shrubland state change: Implications for restoration. Rangeland Ecol. Manage. 59: 343-350.

Poorter L, Markesteijn L. 2008. Seedling traits determine drought tolerance of tropical tree species. Biotropica 40: 321-331.

Porporato A, Daly E, Rodriguez-Iturbe I. 2004. Soil water balance and ecosystem response to climate change. Am. Nat. 164: 625-632.

Prince SD, Steininger MK. 1999. Biophysical stratification of the Amazon basin. Global Change Biol. 5: 1-22.

Pu RL, Gong P, Biging GS, Larrieu MR. 2003. Extraction of red-edge optical parameters from Hyperion data for estimation of forest leaf area index. IEEE Trans. Geosci. Remote Sens. 41: 916-921.

Pushnik JC, Garcia-Ibilcieta D, Bauer S, Anderson PD, Bell J, Houpis JL. 1999.Biochemical responses and altered genetic expression patterns in ponderosa pine (Pinus ponderosa Doug ex P. Laws) grown under elevated CO2. Water Air Soil Pollu. 116: 413-422.

Pushnik JC, Demaree RS, Houpis JLJ, Flory WB, Bauer SM, Anderson PD. 1995. The effect of elevated carbon dioxide on a Sierra-Nevadan dominant species: Pinus ponderosa. J. Biogeog. 22: 249-254.

Raffalli-Delerce G, Masson-Delmotte V, Dupouey JL, Stievenard M, Breda N, Moisselin JM. 2004. Reconstruction of summer droughts using tree-ring cellulose isotopes: a calibration study with living oaks from Brittany (western France). Tellus B – Chem. Phys. Meteorol. 56: 160-174.

Randerson JT, Liu H, Flanner MG, Chambers SD, Jin Y, Hess PG, Pfister G, Mack MC, Treseder KK, Welp LR, Chapin FS, Harden JW, Goulden ML, Lyons E, Neff JC, Schuur EAG, Zender CS. 2006. Impact of boreal forest fire on climate warming. Science 314: 1130-1132.

Rascher U, Nichol CJ, Small C, Hendricks L. 2007. Monitoring spatio-temporal dynamics of photosynthesis with a portable hyperspectral imaging system. J. Photogramm. Engin. Remote Sens. 73: 45-56.

Richardson AD, Berlyn GP, Gregoire TG. 2001. Spectral reflectance of Picea rubens (Pinaceae) and Abies balsamea (Pinaceae) needles along an elevational gradient, Mt. Moosilauke, New Hampshire, USA. Am. J. Bot. 88: 667-676.

Roden JS, .Bowling DR, McDowell, Bond BJ, Ehleringer JR. 2005. :Carbon and oxygen isotope ratios of tree ring cellulose along a precipitation transect in Oregon, United States. J. Geophys. Res. – Biogeosci. 110G:

Rodriguez-Iturbe I. 2005. Ecohydrology of Water-Controlled Ecosystems : Soil Moisture and Plant Dynamics. Cambridge Univ. Press.

Sakata M, Suzuki K, Koshiji T. 2001. Variations of wood δ13C for the past 50 years in declining Siebold’s beech (Fagus crenata) forests. Environ. Experim. Bot. 45: 33-41.

Salehi M. 1992. Optimal sampling design under a spatial correlation model. J. Statis. Planning Inference 32: 385-399.

Schmugge TJ,  Kustas WP, Humes, KS. 1998. Monitoring land surface fluxes using ASTER observations. IEEE Trans. Geosci. Remote Sens. 5: 1421-1430.

Schulze ED. 1986. Carbon dioxide and water vapor exchange in response to drought in the atmosphere and in the soil.  Annu. Rev. Plant Physiol. 37: 247-274.

Schwartzman D. 1999. Life, Temperature, and the Earth. Columbia University Press, New York.

Schymanski SJ, Roderick ML, Sivapalan M, Hutley LB, Beringer J. 2008. A canopy-scale test of the optimal water-use hypothesis. Plant Cell Environ. 31: 97-111.

Sellers PJ, Randall DR, Collatz GJ, Berry JA, Field CB, Dazlich DA, Collelo GD. 1996. A revised land-surface parameterization (SiB2) for GCMs: Part 1. Model formulation. J. Climate 9: 676– 705.

Sepulcre-Canto G, Zarco-Tejada PJ, Jimenez-Munoz JC, Sobrino JA, Soriano MA, Fereres E, Vega V, Pastor M. 2007. Monitoring yield and fruit quality parameters in open-canopy tree crops under water stress.  Implications for ASTER. Remote Sens. Environ. 107: 455-470.

Sharkey TD, Bernacchi CJ, Farquhar GD, Singaas EL. 2007. Fitting photosynthetic carbon dioxide response curves for C3 leaves. Plant Cell Environ. 30: 1035-1040.

Shugart HH. 1984. A Theory of Forest Dynamics : the Ecological Implications of Forest Succession Models. Springer, New York.

Skowronski N, Clark K, Nelson R, Hom J. 2007. Remotely sensed measurements of forest structure and fuel loads in the Pinelands of New Jersey. Remote Sens. Environ. 108: 123-129.

Small EE. 2001.The influence of soil moisture anomalies on variability of the North American monsoon system. Geophys. Res. Lett. 28: 139-142.

Smil V. 2002. The Earth’s Biosphere. MIT Press, Cambridge, MA.  See esp. pp. 183, 191 ff.

Sperry JS. 2000. Hydraulic constraints on plant gas exchange. Agric. For. Meteorol. 104:13-23.

Stephenson NL. 1990. Climatic control of vegetation distribution: the role of the water balance. Am. Nat. 135:649–670

Stewart JD, Elabidine AZ, Bernier PY. 1995.Stomatal and mesophyll limitations of photosynthesis in black spruce seedlings during multiple cycles of drought. Tree Physiol. 15: 57-64.

Steyaert LT, Hall FG, Loveland TR. 1997. Land cover mapping, fire regeneration, and scaling studies in the Canadian boreal forest with 1 km AVHRR and Landsat TM data . J. Geophys. Res. – Atmos. 102D: 29581-29598.

Stimson HC, Breshears DD, Ustin SL, Kefauver SC. 2005. Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma. Remote Sens. Environ. 96: 108-118.

Stone DA, Allen, MR. 2005. The end-to-end attribution problem: From emissions to impacts. Climate Change 71: 303-318.

Tardieu F, Simonneau T. 1998. Variability among species of stomatal control under fluctuating soil water status and evaporative demand: modelling isohydric and anisohydric behaviours. J. Exp. Bot. 49: 419-432.

Trenberth KE, Dai AG, Rasmussen RM, Parsons DB. 2003. The changing character of precipitation. Bull. Am. Meteorol. Soc. 84: 1205-

Trimble, S.W., and F.H. Weirich. 1987. Reforestation reduces streamflow in the southeastern United States. Journal of Soil and Water Conservation 42: 274-276.

Turner MG, Romme WH, Gardner RH. 1999. Prefire heterogeneity, fire severity, and early postfire plant reestablishment in subalpine forests of Yellowstone National Park, Wyoming. Int. J. Wildland Fire 9: 21-36.

Turner MG, Romme WH, Tinker DB. 2003. Surprises and lessons from the 1988 Yellowstone fires. Front. Ecol. Environ. 1: 351-358.

Urban O. 2003. Physiological impacts of elevated CO2 concentration ranging from molecular to whole plant responses. Photosynthetica 41: 9-20.

van der Werf  GR, Randerson JT, Giglio L, Collatz GJ, Kasibhatla PS, Arellano AF. 2006. :Interannual variability in global biomass burning emissions from 1997 to 2004. Atmos. Chem. Phys. 6: 3423-3441.

Veblen TT, Kitzberge T, Donnegan J. 2000. Climatic and human influences on fire regimes in ponderosa pine forests in the Colorado Front Range. Ecol. Appl. 10: 1178-1195.

Vidal A, Devauxros C. 1995. Evaluating forest fire hazard with a Landsat TM derived water stress index. Agric. For. Meteorol. 77:  207-224.

Walvoord MA, Phillips FM,  Stonestrom DA, Evans RD, Hartsough PC, Newman BD, Striegl RG. 2003. A reservoir of nitrate beneath desert soils. Science 302: 1021-1024.

Wang J, Miller DR, Sammis w:st="on">Miller DR, Sammis TW, Simmons LJ, Gutschick VP. In press. Sensitivity analysis of remote sensing evapotranspiration algorithm: energy balance for land.  Remote Sens. Environ.

Wilson K, Goldstein A, Falge E, Aubinet M, Baldocchi D, Berbigier P, Bernhofer C, Ceulemans R, Dolman H, Field C, Grelle A, Ibrom A, Law BE, Kowalski A, Meyers T, Moncrieff J, Monson R, Oechel W, Tenhunen J, Valentini R, Verma S. 2002. Energy balance closure at FLUXNET sites. Agric. For. Meteorol. 113: 223-243.

Wilson   KB, Baldocchi DD, Hanson PJ. 2000. Quantifying stomatal and non-stomatal limitations to carbon assimilation resulting from leaf aging and drought in mature deciduous tree species. Tree Physiol. 12: 787-797.

Xiao XM, Zhang QY, Saleska S, Hutyra L, De Camargo P, Wofsy S, Frolking S, Boles S, Keller M, Moore B. 2005. Satellite-based modeling of gross primary production in a seasonally moist tropical evergreen forest. Remote Sens. Environ. 94: 105-122.

Xie HJ, Zhou XB, Hendrickx JMH, Vivoni ER, Guan HD, Tian YQ, Small EE. 2006.  Evaluation of NEXRAD Stage III precipitation data over a semiarid region. J. Am. Water Reso. Assoc. 42: 237-256.

Yakir D, Sternberg LDL. 2000. The use of stable isotopes to study ecosystem gas exchange. Oecologia 123: 297-311. Zarco-Tejada PJ, Pushnik JC, Dobrowski S, Ustin SL. 2003. Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sens. Environ. 84: 283-294.