Evapotranspiration (ET) is often found to be one of the best climatic correlates of species richness (Currie, 1991; O'Brien, 1993, 1998; Diniz-Filho et al., 2003; Hawkins & Porter, 2003; Hawkins et al., 2003; Kreft & Jetz, 2007), and has also been used in predictive models of species richness for global application (O'Brien, 1998; Field et al., 2005). ET data have been used and found to have explanatory power in other areas of geographical ecology and biogeography including, for example, analyses of traits such as body size (Medina et al., 2007; Olalla-Tárraga & Rodríguez, 2007) and predicted range shifts as a consequence of future climate change (Midgley et al., 2002). ET estimates provide an indication of ecologically important aspects of climate linked to energy supply and, depending on which form of ET is considered, to water balance and plant productivity (Rosenzweig, 1968; Currie, 1991; O'Brien, 1993, 1998, 2006).
How one chooses a model to estimate ET depends not only on how important the potential controls are for the system of study, but also on what data are available to run the model. We provide here: (1) a general description of the basics of ET; (2) a brief review of the use of ET in the macroecological and biogeographical literature; (3) an outline of different types of ET models, including an evaluation of their strengths and weaknesses; (4) an introduction to techniques of measuring ET; and (5) general guidelines for using ET in geographical ecology. We focus primarily on potential evapotranspiration (PET), but include discussion on actual evapotranspiration (AET) where appropriate. Our aims are:
Basics of evapotranspiration
ET is the transfer of liquid water from open water and through plant transpiration to the atmosphere as water vapour. Sublimation, which is the transition of solid water (i.e. ice, snow) to vapour due to low atmospheric pressure (i.e. high altitude), dry air and high sunlight, is generally considered separate from ET. Sources of open water evaporation could include oceans, seas, lakes, rivers, ponds, puddles and water on objects such as plants, buildings, rocks, the soil surface (including movement of water through the soil to the surface) or in the context of measuring devices such as a pan. Transpiration is the loss of water vapour through pores called stomata located on leaves/needles or stems. Plants regulate the opening and closing of their stomata to minimize water loss (closed), yet maximize CO2 absorption (open) for photosynthesis (Zeiger, 1983).
Energy is required to break the strong bonds that hold water molecules together as a liquid – when those bonds break, the individual water molecules may enter the surrounding atmosphere as vapour. If the liquid contains other substances (impurities), then it may have a lower capacity for evaporation (Marek & Straub, 2001). Energy may be in the form of heat, radiation or pressure. Regardless of the availability of energy, water molecules may not be able to enter the atmosphere if the atmosphere is already saturated with moisture (humidity) or if there is no wind (this is not to be confused with the buoyant vertical movement of gas molecules due to free convection) to facilitate the transfer of the molecules from the water source to the atmosphere. The wind itself may be differentially influenced by friction as it passes over smooth versus rough surfaces. Therefore, solar radiation (or, indirectly, air temperature), air humidity and wind speed are the main climate influences on ET (Monteith, 1981; Raupach, 2001). The main vegetative controls include leaf and canopy characteristics, regulation of stomata and rooting dynamics. Finally, soil characteristics control soil moisture retention of precipitation inputs. All of these potential controls vary in influence depending on the system in question, as well as the associated spatial and temporal scales of analysis.
If the atmosphere is not saturated and there is plenty of liquid water at the surface, and there is also sufficient wind to allow transfer of water vapour from the surface to the atmosphere, then it follows that ET will increase with increasing energy provided. Hence actual ET (AET) levels reflect both the energy regime and the water regime, and thus AET is best understood as a water balance variable (Budyko, 1971), which is broadly indicative of plant productivity (Rosenzweig, 1968; Donohue et al., 2007). If there is no water, there is still a potential for ET to occur were water to be added to the system. This potential ET (PET) is a useful concept both for practical application and for scientific – especially ecological – application. In ecological research, PET provides a measure of the energy regime that reflects the capacity for transpiration flow and primary production in circumstances where water is not limiting.
For agriculturalists, accurate estimates of PET can provide knowledge of how much irrigation may be required, for instance, so that crops can maximize photosynthesis without suffering from drought or waterlogged soils (Allen, 1996). PET is often calculated initially for well-watered short grass (called the reference crop), then multiplied by a constant called a crop coefficient to represent the species and developmental stage. Water may be added to crops so that the AET matches the PET, but, by definition, AET never exceeds PET.
PET and AET should not be used interchangeably. The PET of the Sahara Desert, for example, is very high because it is hot, windy and dry, but because there is very little water the AET is very low. Moreover, the PET of the arctic tundra is very low because there is little radiation and heat, and the AET is also very low. Both the desert and tundra may have similar values of AET, but very different values of PET and very different ecosystems, functional ecology and diversity.
Hence it can be appreciated that AET and PET measure related but very distinct aspects of the climatological regime (and in the case of AET other aspects, e.g. soil, vegetation cover): PET being essentially an energy variable, while AET reflects the water balance of a place (Stephenson, 1990, 1998). Still, AET and PET are complementary to one another: while AET declines as a wet environment dries, PET increases because the energy that would have been used to drive AET is now available energy in the system (Bouchet, 1963; Morton, 1983). Conversely, a wetter surface can absorb more energy, thus leaving less available energy to drive PET, which is an energy variable. In practice, whilst it is possible to estimate PET using quite simple devices, such as evaporation pans, it is inherently more difficult to measure AET, as it requires sophisticated scaling techniques or expensive micrometeorological eddy flux instrumentation.
Evapotranspiration in the ecological literature
The ecological literature includes large numbers of papers using climatic variables for many different purposes, prominent among which are efforts to relate species diversity to climatic and other potential causal variables. Within the macroecological and biogeographical literature, a wide array of different moisture and energy regime variables has been deployed. Prominent amongst the former are annual or seasonal precipitation, and amongst the latter, annual or seasonal temperature and PET. AET is sometimes classed with the former (water variables) and sometimes the latter (energy variables), but for plants at least, with their dependence on solar energy, thermal conditions within which water is in its liquid state, and on the availability of water, AET is best viewed as a composite water–energy variable (Stephenson, 1990, 1998; O'Brien, 1993, 1998).
For the reasons given above, AET provides only a crude index of the conditions for plant growth. Hence, for many purposes and applications it is preferable to use separate water and energy variables in geographical ecological modelling. For example, in analyses of species richness patterns of woody plants in southern Africa at the macroscale, E. M. O'Brien and colleagues showed that while AET provided higher statistical power on its own than did PET, a two-variable model based on annual rainfall and minimum monthly PET (i.e. Thornthwaite) provided a much stronger basis for building a much more effective general predictive model (O'Brien, 1993, 1998, 2006; Field et al., 2005). The ecological significance of water–energy dynamics and the distinction between AET and PET in this context is discussed in depth by O'Brien (1998, 2006).
For geographical ecology PET is therefore in theory a less ambiguous variable than AET. While AET can often describe more variation in, for example, species richness than PET on its own, when combined in models with water regime variables PET can arguably provide a more powerful and flexible input in model building (see O'Brien, 1998; Field et al., 2005). However, there are many different methods of calculating both AET and PET, meaning that in practice different authors are using metrics with varying properties. For instance, in analyses of coarse-scale spatial patterns in species richness, authors have used PET equations provided by Budyko (1978) (Currie, 1991; Kerr & Packer, 1997, 1999), by Thornthwaite (1948) (O'Brien, 1993, 1998; O'Brien et al., 1998, 2000; Field et al., 2005; Zhao et al., 2006), by Priestley & Taylor (1972) (Hawkins & Porter, 2003; Anderson et al., 2007), by Holdridge (1947) (Bhattarai & Vetaas, 2003; Bhattarai et al., 2004) or other formulae described to varying degrees of clarity (e.g. less clearly described: Hoffman et al., 1994; well described: van Rensburg et al., 2002).
While these analyses span varying taxa, including freshwater fish, amphibians, invertebrates, plants, birds and mammals, which inevitably require separate models to be developed, where efforts have been made to evaluate the same general model structure for a single taxon (cf. Hawkins et al., 2003) comparability may be hampered by the use of different forms of climate data and the use of different PET metrics. For example, see and contrast the papers (cited above) by O'Brien and colleagues, by Currie, and by Bhattarai and colleagues: all are on plants, each team using a different favoured PET method. Similar variability exists in the use of different formulae for estimating AET (for four different choices see Currie, 1991, Hawkins & Porter, 2003, Mönkkönen et al., 2006, and Zhao et al., 2006). A fuller consideration of the interpretation of AET and its value as a proxy for net primary productivity may be found elsewhere (Rosenzweig, 1968; Stephenson, 1990, 1998).