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Keywords:

  • water cycle;
  • primary productivity;
  • evapotranspiration;
  • stable water isotopes;
  • watershed hydrology;
  • biosphere

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods and Materials
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[1] Terrestrial water vapor fluxes represent one of the largest movements of mass and energy in the Earth's outer spheres, yet the relative contributions of abiotic water vapor fluxes and those that are regulated solely by the physiology of plants remain poorly constrained. By interpreting differences in the oxygen-18 and deuterium content of precipitation and river water, a methodology was developed to partition plant transpiration (T) from the evaporative flux that occurs directly from soils and water bodies (Ed) and plant surfaces (In). The methodology was applied to fifteen large watersheds in North America, South America, Africa, Australia, and New Guinea, and results indicated that approximately two thirds of the annual water flux from the “water-limited” ecosystems that are typical of higher-latitude regions could be attributed to T. In contrast to “water-limited” watersheds, where T comprised 55% of annual precipitation, T in high-rainfall, densely vegetated regions of the tropics represented a smaller proportion of precipitation and was relatively constant, defining a plateau beyond which additional water input by precipitation did not correspond to higher T values. In response to variable water input by precipitation, estimates of T behaved similarly to net primary productivity, suggesting that in conformity with small-scale measurements, the terrestrial water and carbon cycles are inherently coupled via the biosphere. Although the estimates of T are admittedly first-order, they offer a conceptual perspective on the dynamics of energy exchange between terrestrial systems and the atmosphere, where the carbon cycle is essentially driven by solar energy via the water cycle intermediary.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods and Materials
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

1.1. Overview of the Earth's Climate System

[2] The principal source of energy that drives the dynamics of Earth's outer spheres, including its climate, is unquestionably the Sun, and it is electromagnetic radiation that overwhelmingly dominates energy exchange between the Earth and its cosmic environment [Kandel and Viollier, 2005]. At a radiative balance of 235 W m−2 the Earth would have an average surface temperature of only −19°C, resulting in a perpetually frozen planet [Ruddiman, 2001]. Fortunately, the planetary atmosphere traps sufficient long-wave energy reradiated by the warm Earth's surface (natural greenhouse effect) to raise the surface temperature by about 33°C to a more hospitable average of 14°C. This natural greenhouse effect is overwhelmingly due to water vapor [Chahine, 1992], the principal greenhouse gas, and only to a lesser degree due to the other greenhouse gases, such as CO2, CH4, N2O, and CFCs. Nevertheless, the anthropogenic addition of CO2 since the advent of the Industrial Revolution is believed to have enhanced the global energy balance by approximately 1.5 W m−2, with a compound anthropogenic greenhouse effect of about 2.4 W m−2 [Ramaswamy et al., 2001], yet satellite data for the last decade suggest that a decline in the cloud albedo alone could account for a 2–6 W m−2 enhancement of the short-wave solar energy input into the system [Pallé et al., 2005]. Thus the current scientific and political dispute ultimately boils down to the following: is the additional energy of 2.5 W m−2 that is responsible for the centennial temperature rise of 0.6°C [Houghton et al., 2001] due principally to greenhouse gases or is it due to some external factor, such as the Sun?

[3] Presently, 0.2°C of the rise in temperature over the 20th century is attributed to the observed increase in solar brightness, with the anthropogenic greenhouse effect (0.4°C) related to an increase of anthropogenic greenhouse gases, principally CO2, in the atmosphere [Mitchell et al., 2001]. The attribution of only one third of the centennial temperature increase to solar forcing, despite a good correlation with solar indices [Kristjánsson et al., 2002; Valev, 2006], is based on the empirical observation that, averaged over the 11-year solar cycle, the variability in Total Solar Irradiance is only 0.1% (less than 1.5 W m−2) [Lean, 2005]. An amplifier related to solar dynamics would therefore be required to explain the entire magnitude of the trend. The impact of ultraviolet radiation on ozone and stratospheric dynamics [Reid, 2000] and/or galactic cosmic rays (GCR) [Marsh and Svensmark, 2003] were briefly considered to be such amplifiers, but were dismissed because of the lack of understanding of physical processes, particularly cloud formation, that could point to a climate connection [Ramaswamy et al., 2001]. Note that in regards to the Earth's radiative heat balance we are not dealing with mutually exclusive scenarios, as climate models respond in a similar way to the addition of energy from any source and it is only the relative importance of these potential “drivers” at a variety of timescales that is the contentious issue. Note also that compared to the sizes of the global energy fluxes, and their compound uncertainty (on the order of ±6 W m−2), the apparent centennial to annual trends are at the limit of detectability [Kandel and Viollier, 2005]. It is therefore not likely that the issue of principal climate driver(s) can be resolved by energy balance considerations. Instead, observations based on past climate trends observed over a variety of time/space scales and their compatibility with the celestial versus greenhouse gas records may help to resolve their relative contributions.

[4] A spate of recent empirical observations [Scherer et al., 2006, pp. 427–448, and references therein] demonstrates that the “sun-climate connection is apparent in a plethora of high-fidelity climate indicators” [Lean, 2005] and, as summarized in the Hadley Centre for Climate Prediction and Research review of Gray et al. [2005], the detection/attribution assessments of climate models “suggest that the solar influence on climate is greater than would be anticipated from radiative forcing estimates. This implies that either the radiative forcing is underestimated or there are some processes inadequately represented in those models.” If so, climate modulation by indirect amplifying mechanisms, such as GCR, may play an important role. The most likely pathway for translation of the high-energy particle flux into a climate forcing variable involves the role of clouds [Svensmark et al., 2006; Vieira and da Silva, 2006; Perry, 2007]. In recognition of the potential importance of GCR on clouds, a multiyear experimental program to quantify this process has recently been initiated by European Organization for Nuclear Research (CERN) [Arnold et al., 2004; Kanipe, 2006].

[5] Considering that solar radiation reflected by clouds and the atmosphere accounts for approximately 77 W m−2 and that “evapotranspiration” and precipitation each account for 78 W m−2 [Baede et al., 2001; Stocker et al., 2001], even a minor change in cloudiness could potentially alter the planetary energy balance by more than the disputed 1.5 (or 2.4) W m−2, particularly if the highly effective but contentious role of aerosols (terrestrial and/or GCR generated) is taken into account. In this context, the pattern of dominant energy flow in the planetary system could be viewed from the top-down (Figure 1), instead of bottom-up, thus permitting celestial phenomena to act as the principal (yet not the sole) climate driver over shorter time periods (decades to millennia) as it does over geological time periods. In such a scenario, the relatively small carbon cycle would have to be considered as being superimposed (i.e., “piggybacking”) on the much larger water cycle instead of driving it. That this may be the case is indicated by the observed coincidence of the South African hydrologic regime [Alexander, 2005] and the Southeast Asian monsoon patterns with the record of past solar variability [Bhattacharyya and Narasimha, 2005]. In another example, the overall centennial increase in precipitation over the conterminous U.S. coincides with enhanced solar intensity and most of the inter-annual minima in precipitation coincide with the minima in solar intensity (Figure 2). Regardless of the actual mechanism involved, causation between these terrestrial cycles and solar radiation can only be from the Sun to the Earth, pointing to solar activity as the principal driver of the global water cycle, including moisture advection from the tropics to higher latitudes and the flux of water vapor from the terrestrial biosphere.

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Figure 1. A schematic diagram of the principal drivers of the Earth's climate system. The connections between the various components are proposed as a hypothesis for coupling the terrestrial water and carbon cycles via the biosphere. Galactic cosmic rays and aerosols are included, although their roles are more contentious than other aspects of the Earth's climate system.

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Figure 2. (a) Variations in mean annual precipitation (P) and simulated net primary productivity (NPP) for the conterminous United States (figure modified from Nemani et al. [2002b]). (b) Variation of the annual mean sunspot number for the 20th century (http://solarscience.msfc.nasa.gov/SunspotCycle.shtml). Note that except for 1944, minima in precipitation coincide with the minima in sunspot numbers, the latter a proxy for solar variability associated with the 11-year cycle. (c) The total solar open magnetic flux (coronal source flux) derived from the 〈aa〉 geomagnetic index for 1868 to 1996 [Lockwood et al., 1999].

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[6] Almost without exception, the water and carbon cycles are inherently coupled in planetary systems over a variety of time periods, yet in each instance, water is orders of magnitude more abundant than carbon [Veizer, 2005]. For example, over geological timescales, the large-scale dissolution of silicate rocks is considered one of the principal controls of atmospheric CO2 levels which, in turn, alter the Earth's climate [Berner, 2003], yet in actuality, the availability of water is primarily responsible for physical and chemical weathering and these processes would undoubtedly proceed without CO2, albeit with some chemical reactions modified, but not without water. A similar hierarchy is expected to prevail for other aspects of the Earth system that are dependent on solar radiation, yet if solar radiation represents the ultimate driver of terrestrial processes that involve water and carbon and these substances in turn alter the Earth's climate, what would be the specific mechanism by which the terrestrial water and carbon cycles are coupled via the biosphere and how can this alternate hypothesis be tested?

1.2. Principal Controls on the Rate of Primary Productivity in Terrestrial Ecosystems

[7] Regarding the limitations of plant growth in terrestrial ecosystems, numerous model-based [Nemani et al., 2002a, 2002b] and empirical studies [Law et al., 2002; Zheng et al., 2003] have concluded that the rate of NPP is sensitive to a complex interplay between a variety of climatic and environmental factors and that a single variable cannot account for all of the variability in NPP. According to Nemani et al. [2002a], the principal factors are temperature, water availability, and solar radiation. The inter-dependency of water and sunlight as factors affecting plant growth is fairly intuitive, as plants generally grow more rapidly when and/or where water and sunlight are more abundant and vice versa. Altering the balance between these factors generally leads to a proportional change in the rate of NPP until some optimal rate is achieved that is characteristic of ambient environmental and climatic conditions.

[8] In temperate and subtropical regions, the rate of annual terrestrial net primary productivity (NPP) varies in proportion to annual water input by precipitation (P), as plants which grow under conditions of water stress generally utilize additional moisture effectively and additional water resources translate to a proportionally higher rate of NPP [Huxman et al., 2004]. The limitation of NPP by water is evident from the approximately linear relationship between P and NPP data from the Long Term Ecological Research (LTER) network [Knapp and Smith, 2001] and Global Primary Productivity Data Initiative (GPPDI) [Zheng et al., 2001, 2003] (Figure 3). More quantitatively, with respect to the independent variable P, the linear regression models based on the LTER and GPPDI data can be described by the equations, NPP = (0.45 ± 0.11)·(10−3P g C m−2 yr−1 (R2 = 0.77, p < 0.0001, n = 11) and NPP = (0.49 ± 0.05)·(10−3P g C m−2 yr−1 (R2 = 0.88, p < 0.0001, n = 12), respectively. As P values are expressed in units of 103 g H2O m−2 yr−1 whereas NPP values are in units of g C m−2 yr−1, the nearly equivalent slopes of these regression equations indicates that under conditions of water limitation, NPP can be predicted as approximately 0.05% of P in terms of mass.

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Figure 3. The relationship between mean annual precipitation (P) and (a) global, biome-specific net primary productivity (NPP) data from the Global Primary Productivity Data Initiative (GPPDI) [Zheng et al., 2003] (ENL, evergreen needleleaf; DBL, deciduous broadleaf) and (b) NPP data from the Long-Term Ecological Research (LTER) sites in North America [Knapp and Smith, 2001] (ARC, Arctic Tundra; BNZ, Bonanza; CDR, Cedar Creek; HFR, Harvard Forest; HBR, Hubbard Brook; JNR, Jornada; KBS, Kellogg; KNZ, Konza Prairie; NWT, Niwot Ridge; SEV, Sevilleta; SGS, Shortgrass Steppe.

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[9] The data plotted in Figure 3 are admittedly biased toward temperate and boreal regions of the northern hemisphere, yet P does explain most of the variability in annual NPP from the GPPDI and LTER data sets, 88% and 77%, respectively. Hence only a small proportion of the variability needs to be accounted for by other factors. This is congruent with previous assessments, where at a continental scale across a variety of climate, soil, and vegetation types, inter-annual variation in NPP is highly correlated with P, whereas variations in temperature do not influence continental NPP [Nemani et al., 2002a]. In this study, we do not propose that water availability is the only factor that affects the rate of plant growth in terrestrial ecosystems nor do we intend to obfuscate the other factors that can influence the rate of NPP, such as nutrient availability, vegetation type and the level of disturbance, air temperature, and/or the length of the growing season. Asserting that all ecosystems that receive less than 1500 mm of annual water input by P are limited exclusively by water availability is a simplification, as many processes influence the rate of plant growth at smaller scales and over shorter periods of time. Furthermore, although the LTER and GPPDI data are of high quality and were collected over periods of years to decades, they certainly do not represent all possible environmental and climatic conditions that occur on Earth. Nonetheless, we recognize that this study describes the most fundamental limitations on plant growth at a relatively large scale and we are not discounting the importance of other factors by not discussing these details at length, we are merely asserting that water availability alone can explain most of the variability encountered in the data described above.

[10] In contrast to temperate and subtropical regions, NPP in high-rainfall regions of the tropics does not generally increase in proportion to P beyond 1500 mm (Figure 4). For example, NPP for the Luquillo Experimental Forest (955 g C m−2 yr−1) [Clark et al., 2001] corresponds to approximately 0.025% of P (3725 mm) by mass instead of 0.05% of P as predicted by the NPP-P relationship for “water-limited” regions. In Figure 4, NPP for the Luquillo Experimental Forest (LUQ) plots at or below a plateau of NPP values defined by gridded GPPDI data. Consequently, the prediction of NPP based on temperature and precipitation data is not appropriate for tropical ecosystems, as both variables are often consistently high throughout the year [Clark et al., 2001]. Here, the intensity of solar radiation appears to be the likely limiting factor for primary productivity, illustrated in Figure 5 by measurements of Leaf Area Index (LAI), precipitation, and solar radiation in forests of central Amazonia [Saleska et al., 2003; Huete et al., 2006]. Note from Figure 5 that LAI, a proxy for the rate of plant growth, increases during the dry season when solar radiation is most intense, not during the wet season. This relationship suggests that solar radiation is the principal control on the rate of primary productivity in regions where water availability is consistently high throughout the year.

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Figure 4. The relationship between aboveground NPP for the Long-Term Ecological Research (LTER) sites in North America [Knapp and Smith, 2001] and annual precipitation (P). Gridded annual NPP data (grey circles) are from the Global Primary Productivity Data Initiative (GPPDI) [Zheng et al., 2001].

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Figure 5. Measurements of monthly precipitation (P), Leaf Area Index (LAI), and solar radiation in rain forests of the Amazon (modified from Myneni et al. [2007]). Maximum LAI, an indication of the rate of plant growth, occurs during the sunnier, drier season (grey vertical bars), implying that solar radiation is the principal control on the rate of primary productivity in regions where water availability is consistently high year-round.

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[11] The conception that plant growth and water availability are inherently coupled via the process of photosynthesis is not only evident from the relationships described above but has been affirmed by a multitude of field measurements [Bailey, 1940; Buchmann and Schulze, 1999; Law et al., 2002] and crop-yield calculations [Briggs and Shantz, 1914; Sinclair et al., 1984; Zoebl, 2006]. The mechanism by which photosynthesis is controlled by water availability is the process of plant transpiration, that is, the biologically mediated transfer of soil water to the atmosphere via the vascular system of plants. In detail, the process of plant transpiration involves the exchange of water vapor and carbon dioxide between plants and the atmosphere via stomata, small openings on leaf surfaces which may open or close in order to regulate the diffusion of moisture from the leaf and atmospheric carbon dioxide into the leaf where the latter is converted to organic matter [Farquhar et al., 1989; Sellers et al., 1997; Heldt, 2005]. As this process is considered a mechanism by which plants prevent desiccation when stomata are open and the inner leaf is exposed to the comparatively dry, terrestrial atmosphere, the exchange of water vapor and carbon dioxide occurs simultaneously via the same stomata [Nobel, 2005]. However, as the concentration of water vapor in the air space of a leaf in equilibrium with cell water (31,000 parts per million at 25°C) is two orders of magnitude higher than the concentration of CO2 in the atmosphere (about 360 parts per million), a much larger amount of water vapor escapes to the atmosphere in relation to the influx of CO2 via stomata [Heldt, 2005]. This disparity is clear from leaf-scale measurements of plant water-use efficiency which express water flux in units that are three orders of magnitude higher than the associated carbon flux, such as 10−3 moles of carbon per mol of water (mmol C mol−1 H2O) [Masle et al., 2005] or a gram of carbon per kilogram of water (g C kg−1 H2O) [Zoebl, 2006]. Hence, in order to replenish the water that escapes from the leaves in the form of water vapor, water must be taken up continuously through the root system of a plant when leaf stomata are open, which causes a steady flow of water from the root system to the leaves during the process of photosynthesis. By necessity, plants must balance the carbon requirements for growth with the limits imposed by soil moisture availability, thereby maximizing their ability to sequester carbon dioxide from the atmosphere while minimizing water loss via plant transpiration. This relation has given rise to the concept of water-use efficiency, defined as the ratio of carbon fixed via photosynthesis per unit of water flux via transpiration [Nobel, 2005; Heldt, 2005; Masle et al., 2005].

1.3. Study Objectives

[12] Numerous models of the exchange of water, carbon, and heat between the atmosphere and terrestrial systems have utilized the concept of water-use efficiency in order to convert the water vapor flux via plant transpiration to photosynthetic carbon flux [Choudhury et al., 1998; Choudhury, 2000; Chen and Coughenour, 2004; Berry and Roderick, 2004; Kuchment et al., 2006], yet a lack of regional estimates of plant transpiration has essentially precluded the observation of this well-defined relationship at scales larger than individual plants. Moreover, despite recognition that the fluxes of water vapor from the continents each year returns 65% of continental P to the atmosphere [Dai and Trenberth, 2002] and that these fluxes have a discernible influence on atmospheric moisture content [Moreira et al., 1997; Foley et al., 2003; Nobre et al., 2004; Feddema et al., 2005], few quantitative estimates of the relative contributions of evaporation and plant transpiration to this annual terrestrial water vapor flux are available at a regional scale. This is mainly due to the difficulty associated with partitioning these fluxes based on empirical data. Both are types of evaporation, as each involves a thermodynamic phase transition from liquid water to water vapor, yet in this context, we consider direct evaporation (Ed) to represent the abiotic flux of moisture from land surfaces and water bodies, whereas plant transpiration (T) represents the biologically mediated transfer of moisture from soils to the atmosphere via the vascular system of plants.

[13] In the subsequent text, we propose a methodology that enables Ed and T to be partitioned into separate fluxes and apply this methodology to fifteen watersheds in North America, South America, Africa, Australia, and New Guinea that vary in size and the type of vegetation (Figure 6 and Table 1). We purport to offer regional estimates of T in order to test our hypothesis that the terrestrial carbon cycle is superimposed or “piggybacking” on the much larger terrestrial water cycle. For this hypothesis to merit further consideration, estimates of T would have to define a similar trend to that of NPP with respect to P or more specifically, these values would have to vary proportionally to P in regions that receive less than 1500 mm of P and reach a plateau in regions that receive more than 1500 mm of P. Furthermore, the ratio between the flux of water to the atmosphere via T compared to the amount of carbon sequestered from the atmosphere would have to remain close to 1000:1, in conformity with measurements established at much shorter and smaller scales.

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Figure 6. Locations of the selected watersheds in North America, South America, Africa, Australia, and New Guinea.

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Table 1. Selected Watersheds of North America, South America, Africa, Australia, and New Guinea
IDWatershedArea, 103 km2
North America
   1North Saskatchewan River131.0
   2South Saskatchewan River141.0
   3Ottawa River146.0
   4St. Lawrence River774.4
   5Mississippi River2964.0
Africa
   6Bani River101.6
   7Upper Niger River137.0
   8Black Volta River134.0
   9White Volta River93.0
   10Oti River58.7
   11Nyong River26.4
South America
   12Piracicaba River10.9
Australia-Oceania
   13Ok Tedi River5.2
   14Upper Fly River5.2
   15Murray-Darling River991.0

2. Methods and Materials

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods and Materials
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

2.1. Conventional Water Balances

[14] The annual water balance of a closed hydrologic system can be described by the apportionment of mean annual water input by precipitation (P),

  • equation image

where R and ET represent area-standardized estimates of mean river outflow (i.e., runoff) and total evaporation flux to the atmosphere, respectively, and ΔS is the inter-annual change in the proportion of P stored in a watershed. On the basis of measurements of P and R over an extended period of time (years to decades), ΔS can be assumed to be a constant, implying that annual water input by P is balanced by outputs via ET and R. Consequently, (PR) represents an approximation of ET, the collective flux of moisture by direct evaporation flux from soils, water bodies, and vegetated surfaces, and plant transpiration.

[15] Using ArcGIS™ software and monthly precipitation data from a 10′ gridded terrestrial climatology for the period 1961–1990 [New et al., 2000], area-weighted estimates of P were defined by the grid points completely contained by the boundaries of each watershed. Except for the watersheds in New Guinea and South America, R was based on drainage areas and historical records of river flow that were acquired from the online Global River Discharge Database of the Center for Sustainability and the Global Environment, University of Wisconsin-Madison (http://www.sage.wisc.edu/riverdata), B. M. Fekete et al. (University of New Hampshire/Global Runoff Data Centre data, 2000, available at http://www.grdc.sr.unh.edu/), and Vörösmarty et al. [1996]. Other compilations of river flow data exist [Perry et al., 1996; Dai and Trenberth, 2002], but these databases do not include the primary, annual flow data for tributaries of large rivers, such as the Volta River and Niger River, nor did they include data for the Piracicaba River (South America) or Fly River (New Guinea). For the Piracicaba River, river flow data and drainage areas were taken from the online database provided by the Department of Water and Electric Energy of São Paolo (http://www.daee.sp.gov.br) and data for the Ok Tedi and Upper Fly River watersheds was taken from [Ferguson, 2007].

[16] Collectively, on the basis of global area and river flow estimates from Dai and Trenberth [2002], the watersheds encompass 5% of global non-ice, nondesert area, and collective annual flow represents 972 km3 or 2.6% of global freshwater discharge to the oceans. Using annual volumetric flow data and gross watershed areas, R was calculated as the mean rate of annual runoff for each watershed and the 95% confidence intervals were reported as estimates of uncertainty. In actuality, the proportion of gross watershed area that contributes to annual river flow on an annual basis probably fluctuates due to intermittent or restricted hydrologic connectivity and this would affect definition of R, but for consistency, gross watershed areas were utilized throughout this study. This may affect the magnitudes of the different water balance components, but not their relative proportions. Corresponding river flow data for the period 1961–1990 were used to match R with P. Where not available, data for the existing durations were used to define R. The records for most rivers were between 20–50 years and annual flow volumes for each river were relatively consistent (i.e., no statistically significant change at the 0.05 level). Only the river flow for the Bani River exhibited a statistically significant decline at the 0.05 level over the period 1960–1988 and therefore the annual river flow for the period 1922–1994 was used. The reason for the use of multiyear precipitation and river flow data is that for relatively long time intervals, hydrologic steady state can be assumed (Figure 7). For the watersheds in New Guinea, P and R were based upon precipitation and streamflow data for stations located within the Ok Tedi and Upper Fly River watersheds for the period 2004–2005. Due to the intensity of daily rainfall in this region and the short-term sensitivity of river flow to rainfall in upstream regions (Figure 8), the assumption of negligible changes in water storage was considered valid despite the short duration of the study. The relatively consistent monthly ratio of R to P for the Ok Tedi and Upper Fly River watersheds (Figure 9) further supports the validity of this assumption [Ferguson, 2007].

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Figure 7. Conventional annual water balances for the selected watersheds. ET often exceeds R, implying that more water may be transferred to atmosphere each year as water vapor than is transferred to the oceans via river water. 1, North Saskatchewan River; 2, South Saskatchewan River; 3, Ottawa River; 4, St. Lawrence River; 5, Mississippi River; 6, Bani River; 7, Upper Niger River; 8, Black Volta River; 9, White Volta River; 10, Oti River; 11, Nyong River; 12, Piracicaba River; 15, Murray-Darling River.

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Figure 8. Rainfall and river runoff for (a) the Upper Fly River and (b) the Ok Tedi. Rainfall exceeds runoff, implying some transfer of water vapor via evaporation and transpiration (see Figure 11 for the locations of rainfall stations and streamflow gauges).

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Figure 9. Mean monthly precipitation (P) and river runoff (R) for the (a) Ok Tedi and (b) Upper Fly River watersheds of central New Guinea, 2004–2005. (PR), an approximation of total annual evaporation (ET), is relatively constant with respect to P and represents 30 – 35% of P on an annual basis for these watersheds.

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2.2. Methodology: Partitioning Evaporation and Transpiration Water Vapor Fluxes

[17] Apportionment of ET from equation (1) into Ed and T is difficult without additional information and for this reason, Ed is often assumed to be negligible in densely vegetated regions by necessity [Fleischbein et al., 2006], implying that the entirety of the terrestrial water vapor flux (less rainfall interception) can be attributed to T. Conversely, in sparsely vegetated regions [Choudhury, 2000] or those covered to a large extent by open water [Machavaram and Krishnamurthy, 1995], Ed is often considered more significant than T. Intuitively, it should be apparent that neither Ed nor T are likely to be completely negligible in most hydrologic systems, yet a quantitative description of their relative contributions to ET often requires the use of nonconventional hydrological techniques, principally the use of the stable isotopes of water as tracers of the hydrologic cycle.

[18] Isotope-based techniques for tracing water movement vary from approaches which entail integration of stable isotope data with computer simulations of the water cycle [Henderson-Sellers et al., 2004; McGuffie and Henderson-Sellers, 2004; Miller et al., 2005; Henderson-Sellers, 2006; Fekete et al., 2006] to field-oriented studies which rely more directly on measurements of ambient moisture [Moreira et al., 1997] or employ hydrologic and isotope mass balance equations and the isotope separation between water input by precipitation and eventual outflow in order to infer the relative contributions of evaporation and transpiration [Gibson et al., 1993; Gibson and Edwards, 2002; Lee and Veizer, 2003; Welp et al., 2005; Mayr et al., 2006; Gammons et al., 2006; Twining et al., 2006; Wolfe et al., 2006; Ferguson et al., 2007; Freitag et al., 2007; Karim et al., 2007]. Each technique is, however, fundamentally based upon the principle that the mass-dependent isotope fractionation that accompanies the phase transitions of water (i.e., evaporation, sublimation, and condensation) causes the predictable apportionment of the heavier, rarer isotopes of water (oxygen-18, 18O; deuterium, 2H) within the principal isotopologues of the water molecule (1H216O, 1H2H16O, 1H218O). By convention, the 18O and 2H content of water samples are hereafter expressed as δ values, representing deviation in parts per thousand (‰) from Vienna-Standard Mean Ocean Water (VSMOW), such that δ2H or δ18O = [Rsample/Rstandard) − 1] × 1000 where R represents 18O/16O and 2H/1H, respectively.

[19] As a consequence of mass-dependent isotope fractionation, δ18O and δ2H values of global precipitation exhibit systematic spatial and temporal variations and collectively define a regression line that is commonly referred to as the global meteoric water line (GMWL, Figure 10) [Craig, 1961; Craig and Gordon, 1965; Dansgaard, 1964; Rozanski et al., 1993]. At a regional scale, mass-dependent isotope fractionation leads to the existence of two linear trends on a conventional δ18O-δ2H diagram, one trend defined by waters which retain their original isotope composition derived from precipitation and a second trend defined by waters which, to some extent, have undergone heavy-isotope enrichment due to evaporation (i.e., surface waters) (Figure 10) [Froehlich et al., 2002; Gibson et al., 2005]. The former trend defines a local meteoric water line (LMWL) with a slope and δ2H-intercept value governed by the prevailing meteorological conditions of the moisture source area, rainout and the trajectory of the air mass, and second-order kinetic effects such as those associated with snow formation and evaporation from raindrops [Clark and Fritz, 1997; Araguás-Araguás et al., 1998]. The second linear trend, commonly referred to as a local evaporation line (LEL), is defined by δ18O and δ2H values of river water collected from the point of outflow from a watershed.

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Figure 10. A conceptual representation of the relationship between a local evaporation line (LEL) defined by δ18O and δ2H values of river water and the global meteoric water line (GMWL). GMWL is based on monthly δ18O and δ2H values of precipitation from each GNIP station used in this study. δ18O and δ2H values of precipitation for Cayenne (French Guyana) and Calgary (Canada) are shown to illustrate differences in the isotope composition of precipitation in the tropics versus higher latitudes. For a given region or watershed, δI and δS represent the mean annual δ18O and δ2H values of water input by precipitation and eventual outflow via river water, respectively (see section 2.2).

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[20] Analogous to the isotope composition of precipitation at a specified location, which is determined by the initial isotope composition of water evaporated at the moisture source and the path of moisture transport to the location of eventual condensation [Jouzel, 2006], river water at a given location within a watershed is primarily determined by the initial isotope composition of water input and the cumulative influence of evaporation upstream, as mass-dependent isotope fractionation causes an evaporating moisture flux to become preferentially depleted in 18O and 2H and leaves the residual liquid in the watershed enriched in these heavier isotopes [Gibson and Edwards, 2002]. Consequently, δ18O and δ2H values of river water collected from the point of outflow from a watershed can be interpreted as a proxy for the residual liquid that remains after evaporation has occurred. In watersheds where direct evaporation from soils and water bodies is volumetrically significant relative to annual water input, δ18O and δ2H values of river water define a LEL with a shallower slope than the LMWL, such that the offset between δ18O and δ2H values of individual samples from the LMWL increases in approximate proportion to the cumulative fraction of water transferred to the atmosphere by evaporation in upstream areas.

[21] According to Gibson and Edwards [2002], the intersection of the LEL and LMWL provides an amount-weighted estimate of the δ18O and δ2H values of water entering a hydrologic system and thus is unaffected by heavy-isotope enrichment due to evaporation within the watershed. Inferences regarding the influence of evaporation within a hydrologic system are usually based upon the relationship between the LMWL and LEL, as these linear features are considered more representative of prevailing hydrologic conditions than individual δ18O and δ2H values. Fundamentally, studies that purport to separate fractionating and nonfractionating water vapor fluxes also rely upon the assumptions that evaporation is incomplete (i.e., a residual, 18O-2H-enriched liquid remains), the isotope composition of soil water is relatively unaffected by the process of water uptake by plants [Moreira et al., 1997; Twining et al., 2006], and that a relatively small number of δ18O and δ2H values are representative of processes occurring over large areas [Gibson et al., 1993, 2005; Fekete et al., 2006]. If these assumptions are deemed valid and only direct evaporation from soils and water bodies affects the isotope composition of water during its movement through a watershed, the proportion of fractionating water vapor fluxes to water input can be resolved by examining the isotope separation between δ18O and δ2H values of water input (i.e., precipitation) and output (i.e., river outflow) from a particular geographical region [Gonfiantini, 1986; Gibson et al., 1993; Lee and Veizer, 2003; Welp et al., 2005; Gammons et al., 2006]. Rivers are often the focus of these studies because their watersheds usually define a closed hydrologic system and the isotope composition of river water reflects, to an extent, the influence of evaporative water vapor fluxes in regions that contribute to river flow.

[22] On the basis of the magnitude of the isotope separation between water input by precipitation (δI) and river outflow (δS), a variety of isotope mass balance equations are proposed to quantify the ratio of evaporation to water input under steady state [Gonfiantini, 1986; Gat and Bowser, 1991; Gibson et al., 1993] and nonsteady state hydrologic conditions [Gibson, 2002]. In situations where volumetric changes are negligible and the volume of water in the hydrologic system can be considered constant (i.e., P = R + ET from equation (1)), a steady state isotope mass balance equation enables quantification of the proportion of evaporation (Ev) with respect to water input (I). Assuming conditions of hydrologic and isotope steady state, [Gonfiantini, 1986] derived an isotope mass balance equation that enables an approximation of the Ev relative to annual water input (I),

  • equation image

where h is the ambient humidity normalized to saturation vapor pressure, α is the equilibrium fractionation factor for oxygen (lnα = 1137T−2 − 0.4156T−1 − 0.00207) and hydrogen (lnα = 24844T−2 − 76.248T−1 + 0.05261) isotopes during evaporation [Friedman and O'Neil, 1977], Δɛ is the kinetic enrichment factor for oxygen [14.2(1 − h)] and hydrogen isotopes [12.5(1 − h)], ɛ = α − 1, and, δI, δA and δS are the mean δ18O (or δ2H) values of precipitation, ambient moisture, and outflow, respectively (see Ferguson et al. [2007] for a more detailed description of equation (2)). The range of δ18O and δ2H values defined by the intersections of the 95% confidence intervals of the LEL with the LMWL was considered an approximation of the uncertainty related to δI, which enabled information on the proportion of variance described by the LEL for each watershed to be incorporated. δS represents the flow-weighted mean δ18O (or δ2H) value of river water at the point of outflow from the watershed (i.e., the river mouth). The 95% confidence intervals for δI and δS were used to approximate the error associated with the Ev/I value from equation (2). Mean annual values for temperature and humidity from [New et al., 2000] were substituted into equation (2) and were used to calculate the isotope fractionation factors for this equation. In order to approximate the isotope composition of atmospheric moisture, isotope equilibrium between atmospheric moisture and mean annual precipitation was assumed such that δA = δI − ɛ* [Gat and Matsui, 1991; Gammons et al., 2006].

[23] The Ev/I values from equation (2) enabled the separation of ET from equation (1) into fractionating water vapor fluxes (i.e., direct evaporation from water bodies and soils, Ed) and nonfractionating water vapor fluxes (i.e., canopy evaporation, In, plus plant transpiration, T). In represents the proportion of precipitation that is caught or intercepted by vegetated surfaces and subsequently evaporated before infiltration into the soil zone can occur. By definition, In represents complete evaporation from plant surfaces (i.e., no residual liquid remains) and thus does not appreciably alter the isotope composition of water entering the soil zone. Consequently, In must be accounted for in order to approximate the proportion of ET attributable to T. In order to calculate In, vegetation within each of the fifteen watersheds was classified on the basis of the Global Land Cover (GLC) 2000 data sets for North America [Latifovic et al., 2002], Africa [Mayaux et al., 2004], Southeast Asia [Stibig et al., 2003] and South America [Eva et al., 2003] and this classification was, by necessity, used to calculate the net water input to each watershed (i.e., PIn). In order to calculate an area-weighted estimate of In for each watershed, the corresponding GLC2000 land-cover class for each grid point from New et al. [2000] was determined using ArcGIS™ software and literature In values were assigned to each point. An area-weighted estimate of In for each watershed was calculated as the sum of In values for each data point completely contained by the watershed boundaries.

[24] Ed was then calculated as the product of the Ev/I value from equation (2) and (PIn), which represents the net water input to the soil zone. T was considered the residual amount of water required to balance equation (1) and the error assigned to T was the propagated errors of P, R, In, and Ed calculated by a sum of squares.

2.3. Sources of Isotope Data

[25] In North America, the selected rivers included the North and South Saskatchewan Rivers (the principal tributaries of the Nelson River, which flows into Hudson Bay), the Mississippi River, the Ottawa River (a tributary of the St. Lawrence River), and the St. Lawrence River (Figure 6). The isotope compositions of river water collected near the mouths of the North Saskatchewan River at Prince Albert and South Saskatchewan River near Saskatoon (Figure 11a) were available from Ferguson et al. [2007]. The isotope compositions of precipitation at Calgary (51.02°N, 114.02°W) and Edmonton (53.57°N, 113.52°W) are from the Global Network of Isotopes in Precipitation (GNIP) network (IAEA/WMO, 2004, http://isohis.iaea.org) and additional isotope data for precipitation at Saskatoon were available from [Ferguson et al., 2007] (Figure 12). For the St. Lawrence River, the isotope composition of river water collected near Cornwall (51.02°N, 114.02°W) was taken from Barth and Veizer [2004] and represents outflow from the Great Lakes region of North America. The isotope composition of precipitation in this region was characterized by data from GNIP stations located at Ottawa (45.32°N, 75.67°W), Atikokan (48.75°N, 91.62°W), Simcoe (42.85°N, 80.27°W), and Chicago (41.78°N, 87.75°W). For the Ottawa River, isotope data for river water were from Carillon, a station located near the river mouth [Telmer and Veizer, 2000; Myre and Hillaire-Marcel, 2004], and isotope data for precipitation from the GNIP station at Ottawa. The isotope composition of river water collected near the mouth of the Mississippi River at Melville was taken from Lee and Veizer [2003] and the isotope composition of precipitation for the Mississippi River watershed was from GNIP stations located at Coshocton (40.36°N, 81.80°W), Chicago (41.78°N, 87.75°W), Calgary (51.02°N, 114.02°W), Hatteras (35.27°N, 75.55°W), and Denver (39.77°N, 104.88°W).

image

Figure 11. (a) North and South Saskatchewan River watersheds in North America, (b) Volta River watershed in West Africa, and (c) sampling locations for river water at Konkonda (Ok Tedi), Kiunga (Upper Fly River), and Nukumba (Middle Fly River) and rainfall at Tabubil.

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image

Figure 12. The δ18O and δ2H values of (a) rainfall and (b) river water at monitoring stations in the North and South Saskatchewan River watersheds (95% confidence intervals are in parentheses). In most of the “water-limited” watersheds, δ18O and δ2H values of river water defined a LEL with a shallower slope than the corresponding LMWL (Table 2), implying that some evaporative enrichment of river water occurs on an annual basis.

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[26] In South America, the Piracicaba River watershed is located in the state of São Paolo in southern Brazil and flows into a tributary of the Paraná River. The isotope composition of river water collected near the river mouth at Artemis (22.67°S, 47.77°W) was available from Martinelli et al. [2004] and the GNIP database (IAEA/WMO, 2004, http://isohis.iaea.org). For precipitation, data from the GNIP station located near Rio de Janeiro (22.90°S, 43.17°W) were used.

[27] In Africa, the Black Volta River, the White Volta River, and the Oti River are the principal tributaries of the Volta River, which flows into Lake Volta in southern Ghana before eventual outflow occurs into the Atlantic Ocean (Figure 11b). The Upper Niger River and the Bani River are headwater tributaries of the Niger River. The Nyong River (Cameroon) flows into the Atlantic Ocean. The isotope composition of river water collected near the mouths of the Black Volta River, White Volta River, and Oti River are from Bamboi, Nawuni, and Sabari, respectively [Freitag et al., 2007] (Figure 13). The isotope composition of river water collected from the Upper Niger River at Banankoro and the Bani River at Douna were available from the GNIR database. Douna is located immediately before the confluence of the Bani River and Upper Niger River whereas the station at Banankoro is located approximately 230 km upstream of this confluence. In the Nyong River watershed, located between the Niger River and Congo River watersheds in Cameroon, the isotope composition of river water collected near the river mouth at Dehane was available from F. Brunet (personal communication, 2006). The isotope composition of precipitation in West Africa was from the GNIP stations at Kano (12.05°N, 8.52°E), Bamako (12.32°N, 7.57°W), and Niamey (13.52°N, 2.08°E), five stations within the Volta River watershed [Freitag et al., 2007], and five stations located within the Nyong River watershed.

image

Figure 13. The δ18O and δ2H values of (a) rainfall and (b) river water at monitoring stations in West Africa.

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[28] The Ok Tedi and Upper Fly River watersheds are located along the southern flank of the Central Cordillera of New Guinea and are tributaries of the Fly River (Figure 11c). The isotope compositions of river water at Konkonda (6.00°N, 141.15°E) and Kiunga (6.12°N, 141.30°E) were used to characterize outflow from the Ok Tedi and Upper Fly River watershed, respectively, and the isotope composition of precipitation at Tabubil (5.26°S, 141.21°E) was considered representative of the headlands region of these rivers [Ferguson, 2007] (Figures 11c and 14).

image

Figure 14. The δ18O and δ2H values of (a) rainfall and (b) river water at monitoring stations in the Fly River watershed, New Guinea (95% confidence intervals are in parentheses). The similarity of LEL and LMWL in watersheds covered primarily by tropical rain forest suggests that the volume of water “lost” via direct evaporation from water bodies and soils (Ed) may be much less than the nonfractionating water vapor fluxes in these regions (i.e., T plus In).

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[29] In Australia, outflow from the Murray-Darling River watershed was characterized by samples collected from several stations located below Rufus Junction [Simpson and Herczeg, 1991]. The isotope composition of precipitation was characterized by data from GNIP stations located near Brisbane (27.43°S, 153.07°E), Adelaide (34.92°S, 138.57°E) and Melbourne (37.82°S, 144.97°E).

[30] All discussed isotope data for precipitation and river waters are included as auxiliary material (see Tables S1 and S2).

3. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods and Materials
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[31] The equations for the local evaporation lines (LEL) and local meteoric water lines (LMWL) for the studied watersheds are summarized in Table 2 and the input parameters for equation (2) are summarized in Table 3 with Ev/I values. Estimates of the water balance components for the studied watersheds are summarized in Table 4, including annual precipitation (P), river runoff (R), rainfall interception (In), direct evaporation from soils and water bodies (Ed), and plant transpiration (T). Note that T was standardized to the vegetated proportion of each watershed, whereas the other water balance components were standardized to the total watershed area. Consequently, (R + In + T + Ed) exceeds P for some watersheds. Each component of the annual water balances are expressed in mm, where 1 mm = 103 g H2O m−2 yr−1.

Table 2. Summary of Local Evaporation Lines and Local Meteoric Water Lines for the Selected Watersheds of North America, South America, Africa, Australia, and New Guineaa
WatershedWater Input by PrecipitationOutflow at the River Mouth
LMWLbLELcLocationnd
  • a

    LEL, local evaporation lines; LMWL, local meteoric water lines.

  • b

    Defined by δ18O and δ2H values of regional precipitation (see section 2).

  • c

    Defined by δ18O and δ2H values of river water (see section 2).

  • d

    Refers to number of data points used for LEL.

North America
   North Saskatchewanδ2H = 7.9 ± 0.1·δ18O + 3.4δ2H = 5.5 ± 0.8·δ18O − 45.1Prince Albert35
   South Saskatchewanδ2H = 7.9 ± 0.1·δ18O + 3.4δ2H = 4.3 ± 0.8·δ18O − 61.5Saskatoon35
   Ottawaδ2H = 7.5 ± 0.2·δ18O + 6.1δ2H = 6.1 ± 0.5·δ18O − 15.5Carillon15
   St. Lawrenceδ2H = 7.5 ± 0.2·δ18O + 4.5δ2H = 6.9 ± 0.6·δ18O − 2.8Cornwall25
   Mississippiδ2H = 7.5 ± 1.6·δ18O − 0.4δ2H = 6.0 ± 1.8·δ18O − 3.0Melville13
Africa
   Baniδ2H = 6.2 ± 0.6·δ18O − 0.4δ2H = 4.3 ± 0.3·δ18O − 6.2Douna27
   Upper Nigerδ2H = 6.2 ± 0.6·δ18O − 0.4δ2H = 4.8 ± 0.2·δ18O − 4.1Banankoro29
   Black Voltaδ2H = 6.1 ± 0.8·δ18O − 0.6δ2H = 4.7 ± 0.7·δ18O − 7.1Bondouku26
   White Voltaδ2H = 6.1 ± 0.8·δ18O − 0.6δ2H = 4.8 ± 0.9·δ18O − 6.0Tamale22
   Otiδ2H = 6.1 ± 0.8·δ18O − 0.6δ2H = 4.9 ± 0.7·δ18O − 6.3Sokode23
   Nyongδ2H = 8.1 ± 0.2·δ18O + 10.9δ2H = 7.7 ± 1.1·δ18O + 7.7Dehane31
South America
   Piracicabaδ2H = 9.1 ± 1.3·δ18O + 16.7δ2H = 6.3 ± 0.6·δ18O − 0.9Artemis34
Australia-Oceania
   Ok Tediδ2H = 8.2 ± 0.1·δ18O + 11.8δ2H = 6.7 ± 0.8·δ18O − 1.1Konkonda26
   Upper Flyδ2H = 8.2 ± 0.1·δ18O + 11.8δ2H = 7.3 ± 0.8·δ18O + 3.3Kiunga22
   Murray-Darlingδ2H = 8.1 ± 0.7·δ18O + 12.7δ2H = 5.1 ± 0.5·δ18O − 9.0Rufus Junction21
Table 3. Summary of Input Parameters for δ18O and Ev/I Values for the Selected Watersheds of North America, South America, Africa, Australia, and New Guinea
WatershedT, Kh, %δI, ‰δA, ‰ɛ, ‰Δɛ, ‰δS, ‰Ev/I, %
  • a

    Ev/I value undefined for Nyong River watershed.

North America
   North Saskatchewan274.970−20.4 ± 3.0−20.411.54.3−18.7 ± 0.38.5 ± 1
   South Saskatchewan276.366−18.2 ± 3.3−29.611.44.8−15.9 ± 0.211.7 ± 2
   Ottawa276.775−13.0 ± 2.2−24.311.33.6−10.9 ± 0.311.8 ± 2
   St. Lawrence278.675−11.6 ± 1.0−22.811.13.6−7.4 ± 0.634.3 ± 4
   Mississippi283.266−5.1 ± 1.1−17.910.74.8−6.1 ± 0.45.0 ± 2
Africa
   Bani299.858−3.0 ± 0.3−12.29.26.0−1.3 ± 1.28.3 ± 8
   Upper Niger299.858−3.2 ± 0.3−12.49.26.0−2.2 ± 0.54.7 ± 2
   Black Volta301.357−4.3 ± 0.8−13.49.16.0−1.6 ± 0.914.1 ± 5
   White Volta301.556−5.1 ± 1.1−14.29.16.2−2.0 ± 0.716.2 ± 7
   Oti300.961−4.4 ± 0.8−13.69.15.5−1.6 ± 0.815.2 ± 9
   Nyonga298.980--9.32.8−2.6 ± 0.2-
South America
   Piracicaba293.177−6.2 ± 1.1−16.09.83.3−5.8 ± 0.22.1 ± 0
Australia-Oceania
   Ok Tedi298.583−8.6 ± 1.0−17.99.32.4−7.6 ± 0.55.8 ± 1
   Upper Fly298.983−9.4 ± 1.0−18.79.32.4−8.4 ± 0.75.8 ± 1
   Murray-Darling293.247−7.2 ± 0.9−17.09.87.5−3.0 ± 0.721.6 ± 6
Table 4. Components of the Annual Water Balances for the Selected Watersheds of North America, South America, Africa, Australia, and New Guinea
WatershedAnnual Valuesa Expressed in mm or 103 g H2O m−2 yr−1
PRInEdT
  • a

    P, precipitation; R, river runoff; In, interception; Ed, evaporation; T, transpiration.

North America
   North Saskatchewan461 ± 2159 ± 27137 ± 1328 ± 3248 ± 37
   South Saskatchewan430 ± 2359 ± 23111 ± 1937 ± 8229 ± 39
   Ottawa948 ± 20418 ± 112177 ± 5591 ± 18277 ± 128
   St. Lawrence859 ± 9312 ± 10140 ± 24247 ± 30276 ± 41
   Mississippi796 ± 12177 ± 19212 ± 3129 ± 11384 ± 40
Africa
   Bani1068 ± 23100 ± 26192 ± 8282 ± 73694 ± 115
   Upper Niger1545 ± 70488 ± 28268 ± 10660 ± 29729 ± 133
   Black Volta955 ± 1760 ± 12166 ± 72111 ± 39618 ± 85
   White Volta969 ± 1985 ± 17107 ± 46140 ± 58641 ± 78
   Oti970 ± 19189 ± 38110 ± 41131 ± 78540 ± 98
   Nyong1835 ± 76527 ± 42178 ± 1610 ± 01130 ± 183
South America
   Piracicaba1468 ± 169355 ± 56202 ± 8927 ± 6979 ± 199
Australia-Oceania
   Ok Tedi7056 ± 6964583 ± 8161129 ± 226344 ± 591000 ± 1098
   Upper Fly6540 ± 6484032 ± 696961 ± 209324 ± 561223 ± 975
   Murray-Darling506 ± 138 ± 384 ± 4091 ± 26325 ± 50

4. Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods and Materials
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[32] A fundamental aspect of this study and others that utilize P and R as the framework for the annual water balance is that P exceeds R, yet a quantitative description of the magnitude of this excess is limited by considerations of the extent of internal hydrologic connectivity, the extrapolation of point measurements of precipitation to larger areas, and the validity of assumptions regarding of hydrologic steady state, yet previous assessments of similar scale have demonstrated that results of this type are informative [Zeng, 1999; Liu et al., 2006]. Despite the inherent sources of uncertainty, it can be reasonably inferred that for the watersheds in this study, the difference between P and R represents an approximation of ET and further, that ET often exceeds R in magnitude (Figure 7).

[33] Excluding those watersheds that are covered primarily by tropical rain forest, R generally comprised only a small proportion of P (Figure 7 and Table 4). Hence a greater quantity of water is transferred to the atmosphere as water vapor via ET than is delivered to the oceans via R. A linear regression model for T with respect to ET for the “water-limited” watersheds (P < 1500 mm) can be described by the equation, T = (0.71 ± 0.06) · ET mm (R2 = 0.89, p < 0.0001, n = 12) (Figure 15), which indicates that T represents the major conduit for moisture transfer from the continents to the atmosphere and that the other forms of evaporation represent a comparatively small proportion of ET by comparison.

image

Figure 15. The relationship between T and ET in watersheds of North America, South America, Africa, and Australia that receive less than 1500 mm of P. On the basis of the regression model, T represents approximately two thirds of ET in these “water-limited” watersheds. Symbols: blue, North America; red, Africa, green, Australia, yellow, South America.

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[34] As outlined in section 1.2, water and carbon fluxes are inherently coupled via the process of photosynthesis and estimates of NPP vary in proportion to water input by P (Figure 3). Further, leaf-scale measurements of plant water-use indicate that, in terms of mass, the flux of water vapor via transpiration is several orders of magnitude larger than the concurrent amount of carbon fixed during photosynthesis. Hence, in section 1.3, it was hypothesized that if water availability imposes the main restraint on plant growth in “water-limited” regions, then estimates of T and NPP would be expected to define similar trends with respect to P. More specifically, T values would have to vary proportionally to P in regions that receive less than 1500 mm of P and reach a plateau in regions that receive more than 1500 mm of P.

[35] In watersheds that received less than 1500 mm of P, the relationship between T and P could be described by the regression equation T = (0.55 ± 0.08) · P g H2O m−2 yr−1 (R2 = 0.86, p < 0.0001, n = 12) (Figure 16a). Note that the slope of this equation is nearly equivalent to that of the NPP-P relationships described in section 1.2. The similarity of P-T and P-NPP relationships suggest that water availability represents the fundamental restraint on the rate of plant growth in most temperate and subtropical ecosystems, as higher P corresponds to higher T and NPP. Moreover, the similar behavior of T (in 103 g H2O m−2 yr−1) and NPP (in g C m−2 yr−1) in response to changes in P (in 103 g H2O m−2 yr−1) supports the assertion that the fluxes of carbon and water are inherently coupled via process of photosynthesis and that the water flux exceeds the carbon flux by several orders of magnitude in terms of mass. Although not intended as a quantitative assessment of the exact ratio, T and NPP can be predicted as approximately 55% and 0.05% of P, respectively. This disparity between T and NPP (approximately 1000 to 1) conforms to observations at leaf or plant scales despite T values representing water efflux from the multitude of vegetation types within a particular watershed.

image

Figure 16. (a) The relationship between mean annual precipitation (P) and plant transpiration (T) for watersheds that receive less than 1500 mm of P. (b) The relationship between P and T for watersheds in North America, South America, Africa, Australia, and New Guinea (shaded region represents P > 5500 mm). Uncertainty for T as in Table 4. GPPDI NPP data shown as grey circles. 1, North Saskatchewan River; 2, South Saskatchewan River; 3, Ottawa River; 4, St. Lawrence River; 5, Mississippi River; 6, Bani River; 7, Upper Niger River; 8, Black Volta River; 9, White Volta River; 10, Oti River; 11, Nyong River; 12, Piracicaba River; 13, Ok Tedi; 14, Upper Fly River; 15, Murray-Darling River.

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[36] In regions covered primarily by tropical rain forest, the availability of water and the intensity of solar radiation promote more rapid rates of photosynthesis and NPP values generally reach a plateau beyond which additional water input by P does not translate to higher rates of NPP (Figure 4). Estimates of T for the Ok Tedi and Upper Fly River watersheds appear to affirm the existence of a similar plateau for T in high rainfall regions of the tropics (Figure 16b), as estimates of P for these watersheds were high (7056 mm and 6540 mm, respectively) yet T comprised only 10–15% of water input by P (Table 4). Despite the uncertainty associated with estimates of T for the Ok Tedi and Upper Fly River watersheds, which precludes a quantitative comparison of T (the error associated with T was reduced to 25–30 if the uncertainties associated with P and R were not considered), a plateau of T values from 800–1200 mm is implied and broadly affirms the proposal by Leigh [1999] that T is relatively constant in tropical rain forests, as in these regions, water availability does not represent a rate-limiting factor to plant growth.

[37] Instead of water, the intensity of incident solar radiation is the probable control on NPP and T in regions covered predominantly by tropical rain forests [Saleska et al., 2003; Huete et al., 2006; Mynenei et al., 2007]. For example, in rain forests of the central Amazon, higher rates of plant growth were observed during the drier, sunnier seasons compared to the rainier, cloudier season (Figure 5). Huete et al. [2006] attributed more rapid rates of plant growth during periods of intense solar radiation to the ability of trees to access deeper soil water horizons via their root systems. Consequently, the pattern illustrated in Figure 5 for LAI could be interpreted as indicative of the intraannual variability of T in tropical rain forests, with higher rates of T during periods of intense solar radiation and vice versa.

[38] On the basis of the relationships described above, it seems reasonable to infer that T and NPP are both generally limited by water availability in most temperate and subtropical regions (P < 1500 mm), whereas in tropical regions where P exceeds 1500 mm, the intensity of incident solar radiation represents the limiting factor. Hence the plateau of T and NPP in the tropics represents the process of photosynthesis operating near a saturation point with respect to incident solar energy flux. Fundamentally, it must therefore be explicitly recognized that solar radiation represents the ultimate driver of the plant growth in any ecosystem, yet it is only in the tropics where water does not impose a restraint that this relationship can be observed.

5. Summary and Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods and Materials
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[39] In fifteen large watersheds, was interpreted the difference between annual water input by precipitation (P) and outflow via rivers (R) as a proxy for total evaporation (ET), which represents the collective flux of water vapor from plants, soils, and water bodies. In the majority of the studied watersheds, excluding those covered primarily by tropical rain forest, ET generally exceeded R, implying that more water is transferred from the continents to the atmosphere as water vapor than is discharged to the oceans via rivers. By incorporating additional information on the distribution of stable water isotopes (δ18O and δ2H) in precipitation and river water, we further apportioned ET into direct evaporation from soils and water bodies (Ed) and plant transpiration (T), the plant-mediated flux of water vapor that is controlled by photosynthesis.

[40] In watersheds that receive less than 1500 mm of P, T could be predicted as approximately 55% of water input by P or 71% of ET. The pattern defined by T values with respect to P was similar to that of net primary productivity (NPP), yet the units of measurement reveal the disparity in mass, as NPP was expressed in units of g C m−2 yr−1 whereas 1 mm of T represents 103 g H2O m−2 yr−1. In conformity with small-scale measurements of water-use by plants, regional estimates of T were thus three orders of magnitude larger than NPP. This emphasizes the inter-dependency of terrestrial water and carbon cycling, yet also reveals how small the flux of atmospheric carbon to the terrestrial biosphere is compared to the concurrent amount of water vapor released to the atmosphere during photosynthesis.

[41] In watersheds covered primarily by tropical rain forest where annual water input by P exceeds 1500 mm, T was larger in magnitude (900–1200 mm) than in “water-limited” regions, but T comprised a smaller proportion of P compared to R. In these watersheds, T (and NPP) values remained relatively constant, implying that plants in the high-rainfall regions may function near a physiologic threshold beyond which additional water resources are not required for optimal rates of growth. Instead, T and NPP appear to be limited by the intensity of solar radiation.

[42] Estimates of T and their relation to P and NPP support the assertion that moisture limitation represents the main restraint on the rate of plant growth in most ecosystems. Given the close correspondence between T and NPP and the disparity in terms of mass exchange between these fluxes, it seems probable that the larger water cycle controls the much smaller carbon cycle, not vice versa, and other limitations to plant growth, such as atmospheric carbon dioxide, nutrients, and temperature are likely superimposed phenomena. Intuitively, the validity of this assertion should be apparent and cannot be considered novel, as biological studies have established the connection between the water and carbon cycles at a variety of spatial and temporal scales. Nonetheless, few regional estimates of T are available and despite the limitations inherent to this methodology, results are informative and emphasize the inter-dependency between solar radiation, water vapor, and carbon dioxide in determining the interaction between the terrestrial biosphere and the atmosphere, and ultimately, the Earth's climate.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods and Materials
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[43] Funding was provided to both authors by the Natural Science and Engineering Research Council of Canada (NSERC) and the Canadian Foundation for Advanced Research (CFAR) to J.V. We thank Frederic Brunet for data from the Nyong River watershed and Ajaz Karim for bringing to our attention the relationships described in Figure 2.

References

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  2. Abstract
  3. 1. Introduction
  4. 2. Methods and Materials
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Methods and Materials
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

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jgrd13789-sup-0002-ts01.txtplain text document2KTable S1. Percentage interception values with respect to annual gross precipitation.
jgrd13789-sup-0003-ts02.txtplain text document15KTable S2. Isotope data for river water interpreted in the study.
jgrd13789-sup-0004-t01.txtplain text document1KTab-delimited Table 1.
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