SEARCH

SEARCH BY CITATION

Keywords:

  • evaporative demand;
  • pan evaporation;
  • wind speed

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Attribution Using the PenPan Model
  5. 3. Materials and Methods
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[1] Evaporative demand, measured by pan evaporation, has declined in many regions over the last several decades. It is important to understand why. Here we use a generic physical model based on mass and energy balances to attribute pan evaporation changes to changes in radiation, temperature, humidity and wind speed. We tested the approach at 41 Australian sites for the period 1975–2004. Changes in temperature and humidity regimes were generally too small to impact pan evaporation rates. The observed decreases in pan evaporation were mostly due to decreasing wind speed with some regional contributions from decreasing solar irradiance. Decreasing wind speeds of similar magnitude has been reported in the United States, China, the Tibetan Plateau and elsewhere. The pan evaporation record is invaluable in unraveling the aerodynamic and radiative drivers of the hydrologic cycle, and the attribution approach described here can be used for that purpose.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Attribution Using the PenPan Model
  5. 3. Materials and Methods
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[2] Measurements of evaporation from pans have traditionally been used to represent the evaporative demand of the atmosphere when estimating crop water requirements [Doorenbos and Pruitt, 1977; Stanhill, 2002]. Averages over many pans show declines over the last 30 to 50 years with typical rates of −2 to −5 mm a−2 (mm per annum per annum) reported in the USA and across many parts of the former Soviet Union [Peterson et al., 1995; Golubev et al., 2001; Groisman et al., 2004], China [Liu et al., 2004; Chen et al., 2005; Wu et al., 2006], Canada [Burn and Hesch, 2007], Australia [Roderick and Farquhar, 2004], New Zealand [Roderick and Farquhar, 2005] and on the Tibetan plateau [Shenbin et al., 2006; Zhang et al., 2007]. That range is not universal and data from India [Chattopadhyay and Hulme, 1997] and Thailand [Tebakari et al., 2005] show declines of −10 to −12 mm a−2.

[3] Evaporation pans are useful in agro-ecology and hydrology because they are simple robust instruments that integrate the relevant physical factors, namely radiation, temperature, humidity and wind speed, into a single measure of evaporative demand. However, understanding the observed decline in evaporative demand requires that integration be unraveled. Previous efforts to do this have been based on simplified arguments [Roderick and Farquhar, 2002] or calculations that assume a surface of short green grass [Thomas, 2000; Chen et al., 2005; Shenbin et al., 2006] instead of a pan. The preferred approach is to calculate pan evaporation using physical variables and we developed a generic model, called PenPan, for that purpose [Rotstayn et al., 2006]. Here we test the use of that model to attribute changes in pan evaporation to changes in the underlying physical variables.

2. Attribution Using the PenPan Model

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Attribution Using the PenPan Model
  5. 3. Materials and Methods
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[4] The PenPan model is based on Penman's combination equation [Penman, 1948]. It assumes a steady state energy balance, which for a Class A pan requires periods of at least a week and the applications described later use monthly input data. The radiative and aerodynamic components are based on the Linacre [1994] and Thom et al. [1981] models respectively. In brief, the evaporation rate from the pan (Ep, kg m−2 s−1) is expressed as the sum of radiative (Ep,R) and aerodynamic (Ep,A) components,

  • equation image

with s (Pa K−1) the change in saturation vapour pressure (es, Pa) with temperature evaluated at the air temperature (Ta, K) two metres above the ground, Rn (W m−2) the net irradiance of the pan, λ (J kg−1) the latent heat of vaporisation, a (= 2.4 here) the ratio of effective surface areas for heat and vapour transfer, γ (∼67 Pa K−1) the psychrometric constant, D (= esea, Pa) the vapour pressure deficit at two metres and fq(u) (kg m−2 s−1 Pa−1) the vapour transfer function [Thom et al., 1981],

  • equation image

where u (m s−1) is the mean wind speed at two metres above the ground. The net irradiance of the pan is,

  • equation image

The last two terms are the incoming (Rl,in) and outgoing (Rl,out) long-wave irradiance, with Rl,out calculated assuming the pan is a black body radiating at temperature Ta. The first term is the net short-wave irradiance, with Ap (= 0.14 here) the pan albedo and Rsp the incoming short-wave irradiance of the pan. Rsp is greater than the global solar irradiance (Rs) because of additional interception by the walls of the pan [Rotstayn et al., 2006].

[5] For attribution, the change in pan evaporation rate is given by differentiating equation (1),

  • equation image

The term dEp,A/dt is then partitioned into three components, denoted U*, D*, T* for changes due to changing wind speed, vapour pressure deficit and temperature respectively. The components are defined by,

  • equation image

3. Materials and Methods

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Attribution Using the PenPan Model
  5. 3. Materials and Methods
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[6] Data were collated from existing Australian Bureau of Meteorology (BoM) digital records: class A pan evaporation and wind speed (IDCJDC05.200506), temperature and humidity (IDCJHC02.200506) and radiation (NCCSOL Vers 2.209). We estimated monthly averages when 25 daily observations were flagged as validated by the BoM. Months not satisfying this criterion were omitted. Ta and humidity was measured in Stevenson screens, while u was measured using an anemometer 2 m above the pan. Starting with the 60 or so high quality sites previously identified [Roderick and Farquhar, 2004; Jovanovic et al., 2005], we identified a subset of 41 sites (auxiliary Table S1) having near-complete records of Ep and the observations needed to calculate Ep,A.

[7] As in many regions [Stanhill, 1997] the radiation database [Forgan, 2005] is the most heterogeneous of the meteorological databases. Of the 41 sites, 26 have some measurements of Rs (auxiliary Table S1) in the 1975–2004 period, but only seven have complete 30-year records. Observations of Rl,in are more restrictive with 11 sites having observations, the earliest from 1995. For 1975–2004, we estimated Rl,in at any site having Rs observations using the FAO56 approach [Allen et al., 1998],

  • equation image

with Ro (W m−2) the top of atmosphere solar irradiance, and z (m) the site elevation. Equation (6) includes water vapour but ignores other greenhouse gases (e.g., CO2) and aerosols (e.g., dust). For the 30-year period considered here, the effect of trends in the ignored greenhouse gases on Rl,in should be small compared to the known trends in Ep. Further, recent simulations with the CSIRO climate model suggest that changes in dust-loading over Australia were also small between the 1950s and 1990s [Rotstayn et al., 2007].

[8] Digital metadata (BoM) showed no site location changes at any of the 41 sites. We examined the observations for obvious problems, especially discontinuities due to, for example, unreported changes in site location. At Darwin Airport, there was an obvious problem with u measurements before 1977 (auxiliary Figure S3). All analyses at that site are for 1977–2004. In several other instances, we identified what initially appeared to be suspect Ep observations. For example, at Alice Springs the very low Ep during 1975–1978 look anomalous when viewed in isolation. However, they were quantitatively consistent with the concurrent low values of u, D and Rs (auxiliary Figure S3). The same was found when examining other apparently anomalous situations.

4. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Attribution Using the PenPan Model
  5. 3. Materials and Methods
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

4.1. Evaluation of the PenPan Model

[9] We first used the PenPan model to calculate Ep using complete (post-1995) observations (Rs, Rl,in, Ta, u, es, ea). The agreement between modelled and observed Ep at the 11 sites (auxiliary Figure S1, R2 = 0.95, n = 903, RMSE = 22 mm mth−1) was excellent. Next, we used the available Rl,in observations to evaluate the FAO56 equation. There was no evidence of a change in the slight bias (∼6 W m−2) over time (results not shown) and we concluded that equation (6) was satisfactory for the intended purpose (auxiliary Figure S2, R2 = 0.97, n = 916). Finally, we used equation (6) to estimate Rl,in and thereby calculated Ep at the 26 sites for any month with observations of Rs, Ta, u, es and ea. The comparison with Ep observations was excellent (Figure 1).

image

Figure 1. Comparison of observed and calculated pan evaporation rates. The PenPan model was forced with observations (Rs, Ta, es, ea, u) with Rl,in calculated using equation (6). Locations (n = 26 sites) shown in the inset where sites denoted Δ are the seven “elite” sites (listed in Table 1). Best fit regression; y = 1.01 x + 7.7, R2 = 0.95, n = 5071 (1:1 line shown). The RMSE is 24 mm mth−1. (Note that 1 mm = 1 kg m−2.)

Download figure to PowerPoint

4.2. Applying the Attribution Approach

[10] In order to test whether the approach was feasible, we first used the seven “elite” sites having complete observations for 1975–2004, where we calculated Ep,A and Ep,R and thereby closed the energy balance. The trends in those two components should sum to the observed Ep trend (equation 4) within error limits. The estimated uncertainty in the model calculations (24 mm mth−1, Figure 1) is equivalent to an uncertainty in the trend estimate over the 30-year period of 1.8 mm a−2 (±1 sd). With that uncertainty, the observed and calculated trends were within 95% confidence intervals at all seven sites (Table 1).

Table 1. Observed (OBS) and Model-Calculated (CALC) Trends in Pan Evaporation Rate (dEp/dt, in mm a−2) at 7 Sites Having Near-Continuous Data for 1975–2004a
SiteOBSCALC = Rad + AeroRadAeroAero Partition
dEp/dtdEp/dtdEp,R/dtdEp,A/dtU*D*T*
  • a

    OBS, observed trends; CALC, model-calculated trends. Pan evaporation rate (dEp/dt) is given in mm a−2. The modelled trend is the sum of the radiative (Rad = dEp,R/dt) and aerodynamic (Aero = dEp,A/dt) components per equation 4. The aerodynamic component is partitioned into components due to changing wind speed (U*), vapour pressure deficit (D*) and temperature (T*) per equation 5. See auxiliary Figure S3 for data and calculations at the sites.

  • b

    Data for 1977–2004.

GERALDTON AIRPORT−4.1−2.20.0−2.2−1.6−0.80.3
DARWIN AIRPORTb−17.0−15.3−6.0−9.3−8.9−0.30.1
ALICE SPRINGS AIRPORT25.821.42.019.416.95.4−1.8
MOUNT GAMBIER AERO−6.1−8.4−0.2−8.2−7.4−1.40.3
ROCKHAMPTON AERO11.07.73.24.50.34.3−0.2
WAGGA WAGGA AMO−1.81.40.50.9−0.51.40.1
MILDURA AIRPORT−8.8−11.90.6−12.5−13.20.60.3

[11] While most pan evaporation sites considered here, or anywhere, do not have radiative observations, we did have near-complete records of the aerodynamic component at each site. To apply the approach at all 41 sites, we estimated the trend in the radiative component as the difference between the trends in observations and aerodynamic components (i.e., dEp,R/dt = dEp/dtdEp,A/dt). The results, including the separation of the aerodynamic component into individual components (U*, D*, T* per equation 5) are shown in Figure 2.

image

Figure 2. Trends in observed pan evaporation rate and its components at 41 sites for the period 1975–2004. (a) Observed pan evaporation rate. (b) Radiative component of pan evaporation rate calculated as the difference between Figures 2a and 2c. (c) Aerodynamic component of pan evaporation rate. The trend in the aerodynamic component is further partitioned (equation 5) into the change due to changing (d) wind speed, (e) vapour pressure deficit, and (f) air temperature. The change in each panel, averaged across all 41 sites is (a) −2.0 mm a−2, (b) +0.6 mm a−2, (c) −2.6 mm a−2, (d) −2.7 mm a−2, (e) 0.0 mm a−2, and (f) 0.0 mm a−2. Details and trends are available for each site in auxiliary Table S1. (Note: The magnitude of the trend is scaled to the dot area per the legend.)

Download figure to PowerPoint

[12] Much of the trend in Ep observations (Figure 2a) was due to changes in the aerodynamic component (Figure 2c), and the majority of that was due to changes in wind speed (Figure 2d) with generally minor changes due to changes in both vapour pressure deficit and air temperature (Figures 2e and 2f). However, as expected [Roderick and Farquhar, 2004], there was spatial variation in the results. A notable feature is the decrease in the radiative component shown in the northwest (Figure 2b). Also of note are the two sites showing relatively large increases in Ep in the centre (Alice Springs, 133.89°E, 23.80°S) and south (Woomera, 136.81°E, 31.16°S). At Woomera there were no obvious problems with the data. At Alice Springs, the trend was very sensitive to the starting date because of very low Ep values during 1975–1978 (auxiliary Figure S3).

[13] To put the changes in perspective, the trend in D averaged over all 41 sites was −0.2 Pa a−1 (auxiliary Figure S4) compared to a background average of 1205 Pa, or less than 1% over the 30 years. In contrast, the trend in u averaged over all 41 sites was −0.01 m s−1 a−1 (auxiliary Figure S4) against a background average of 2.3 m s−1: a reduction of 13% over the same period. The change in u was occurring more or less equally in all seasons (auxiliary Figure S5).

5. Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Attribution Using the PenPan Model
  5. 3. Materials and Methods
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[14] Previous research reported a trend in pan evaporation rate, averaged over 61 Australian sites for 1975–2002, of −3.3 mm a−2 [Roderick and Farquhar, 2004]. This was later updated (an addendum is available from the authors) to −3.2 mm a−2 to account for the installation of bird guards. The trend for 1975–2004 over the same 61 sites is lower at −2.4 mm a−2 (results not shown) because of the high pan evaporation rates during the drought conditions prevailing over much of southeast Australia since 2002. For the 41 sites used here, the averaged trend for 1975–2004 was similar at −2.0 mm a−2.

[15] Improvements could be made to the PenPan model, particularly in the calculation of the pan albedo and the treatment of incoming and outgoing long-wave irradiance. Similarly, the meteorological databases are subject to ongoing improvements [Coughlan et al., 2005]. With those caveats, the model performed satisfactorily (Figure 1, Table 1) given the well-known difficulties in making long-term measurements of, and modelling, micrometeorological phenomena. According to the attribution analysis (Figure 2), the reasons for changing pan evaporation differed between sites: there was an indication of a decrease in the radiative component in northwest Australia consistent with increased rainfall and cloud cover in that region [Smith, 2004; Rotstayn et al., 2007]. However, decreases in the aerodynamic component were more important and primarily due to decreasing wind speed. These results are consistent with recent research [Roderick and Farquhar, 2006; Rayner, 2007]. The importance of decreasing wind speed and/or radiation as a reason for decreasing pan evaporation has also been found in the USA [Hobbins, 2004], parts of China [Xu et al., 2006a] and the Tibetan Plateau [Shenbin et al., 2006; Zhang et al., 2007].

[16] Whether the “stilling” reported here is local, i.e., attributable to changes in the immediate environment of the pans (e.g., growing trees or other obstacles progressively obstructing the air flow), or a more regional phenomenon is difficult to assess. Rayner [2007] investigated that by comparing the BoM wind observations against two alternative sources, (1) wind fields in the NCEP reanalysis, and (2) wind calculated using BoM surface air pressure observations. The results were inconclusive because the trends derived from (1) and (2) were inconsistent, and neither result was consistent with the BoM surface observations.

[17] Some of the wind speed decreases reported here are no doubt due to local effects. Alternatively, the very widespread nature of the stilling is by itself some evidence of a more robust regional effect. Indeed, the changes reported here are very similar to those reported elsewhere (Table 2). Whilst largely unanticipated in the climate change impacts community, previous analyses have predicted a slowing in the overall circulation rate in tropical regions and, presumably, a reduction in averaged wind speed in those regions with greenhouse warming [Betts, 1998; Held and Soden, 2006; Vecchi et al., 2006]. Although not strictly comparable to surface winds, the summary compiled by Lorenz and DeWeaver [2007] shows that climate models generally predict changes in zonally averaged mid-latitude wind speeds (at 850 hPa) of about −0.5 to −1.5 m s−1 over the 21st Century with largely complementary increases nearer the poles. This would qualitatively fit the pattern in the observations (Table 2, increase in Antarctica, decrease elsewhere). The model projections are equivalent to trends of −0.005 to −0.015 m s−1 a−1 and are of the same order as the observed trends (Table 2). In contrast to the terrestrial-based anemometer records (Table 2), recently reported satellite retrievals indicate increases in oceanic wind speed averaging 0.008 m s−1 a−1 for 1987–2006 [Wentz et al., 2007]. This emphasises the urgent need for research on the wind measurements and the modelling given the scientific importance as well as the widespread interest in wind power generation.

Table 2. Summary of Observed Changes (Represented as a Linear Trend) in Near-Surface Wind Speed (du/dt)a
du/dt, m s−1 a−1LocationDetailsRef.
  • a

    All studies are based on terrestrial anemometer records.

−0.010Australia1975–2004, 41 sitesThis study
−0.005USA1962–1990, 207 sites across the 48 conterminous statesHobbins [2004]
−0.004USA1960–1990, 176 sites across the 48 conterminous statesKlink [1999]
−0.008Yangtze River Catchment, China1960–2000, 150 sitesXu et al. [2006a]
−0.020China1969–2000, 305 sitesXu et al. [2006b]
−0.010Loess Plateau, China1980–2000, 52 sitesMcVicar et al. [2005]
−0.013Tibetan Plateau1960–2000, 101 sitesShenbin et al. [2006]
−0.017Tibetan Plateau1966–2003, 75 sitesZhang et al. 2007]
−0.013Italy∼1955–∼1996, 17 sites on Italian coast. Break point in ∼1975. Trend of ∼−0.026 m s−1 a−1 before and ∼−0.002 m s−1 a−1 after 1975Pirazzoli and Tomasin [2003]
−0.011New Zealand1975–2002, 5 sitesM. L. Roderick (unpublished data, 2005)
−0.017Canada∼1950–∼1990, 4 sites on west coastTuller [2004]
+0.006Antarctica∼1960–∼2000, 11 sitesTurner et al. [2005]

6. Conclusion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Attribution Using the PenPan Model
  5. 3. Materials and Methods
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[18] When forced with radiation, temperature, humidity and wind observations, the PenPan model simulated the pan evaporation observations well. Over Australia, that approach revealed differences between sites, but on the whole, decreasing wind speed was found to be the main reason for decreasing pan evaporation. The observed decrease in wind speed, was similar to the decreases reported over other terrestrial surfaces. Our results show that the extensive world-wide network of pan evaporimeters could be used to recover information about changes in the radiative and aerodynamic drivers of evaporative demand. This would be extremely useful because there are many more pan evaporimeters than radiometers.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Attribution Using the PenPan Model
  5. 3. Materials and Methods
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

[19] We thank Alan Beswick, John Carter, Edward Linacre, David Rayner and Blair Trewin for helpful discussions and Alison Saunders for expert assistance with acquiring and processing the data. We acknowledge funding from the Managing Climate Variability Program managed by Land and Water Australia (MLR, GDF), the Australian Greenhouse Office (LDR) and a Gary Comer Award (GDF). We acknowledge the BoM and especially the numerous BoM observers whose work formed the ultimate basis of this study.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Attribution Using the PenPan Model
  5. 3. Materials and Methods
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information
  • Allen, R. G., L. S. Pereira, D. Raes, and M. Smith (1998), Crop evapotranspiration: Guidelines for computing crop water requirements, Irrig. Drainage Pap. 56, Food and Agriculture Organization, Rome.
  • Betts, A. K. (1998), Climate-convection feedbacks: Some further issues, Clim. Change, 39, 3538.
  • Burn, D. H., and N. M. Hesch (2007), Trends in evaporation for the Canadian Prairies, J. Hydrol., 336, 6173.
  • Chattopadhyay, N., and M. Hulme (1997), Evaporation and potential evapotranspiration in India under conditions of recent and future climate change, Agric. For. Meteorol., 87, 5573.
  • Chen, D., G. Gao, C.-Y. Xu, J. Guo, and G. Ren (2005), Comparison of the Thornthwaite method and pan data with the standard Penman-Monteith estimates of reference evapotranspiration in China, Clim. Res., 28, 123132.
  • Coughlan, M., K. Braganza, D. Collins, D. Jones, B. Jovanovic, and B. Trewin (2005), Observed climate change in Australia, paper presented at Greenhouse 2005 Conference, Commonw. Sci. and Ind. Res. Org., Melbourne, Victoria.
  • Doorenbos, J., and W. O. Pruitt (1977), Crop water requirements, Irrig. Drainage Pap. 24, Food and Agric. Org., Rome.
  • Forgan, B. W. (2005), Australian solar and terrestrial network data, paper presented at Pan Evaporation: An Example of the Detection and Attribution of Trends in Climate Variables Workshop, Aust. Acad. of Sci., Canberra, ACT, Australia.
  • Golubev, V. S., J. H. Lawrimore, P. Y. Groisman, N. A. Speranskaya, S. A. Zhuravin, M. J. Menne, T. C. Peterson, and R. W. Malone (2001), Evaporation changes over the contiguous United States and the former USSR: A reassessment, Geophys. Res. Lett., 28(13), 26652668.
  • Groisman, P. Y., R. W. Knight, T. R. Karl, D. R. Easterling, B. Sun, and J. Lawrimore (2004), Contemporary changes of the hydrological cycle over the contiguous United States: Trends derived from in situ observations, J. Hydrometeorol., 5, 6485.
  • Held, I. M., and B. J. Soden (2006), Robust responses of the hydrological cycle to global warming, J. Clim., 19, 56865699.
  • Hobbins, M. T. (2004), Regional evapotranspiration and pan evaporation: complementary interactions and long-term trends across the conterminous United States, Ph.D. thesis, Colo. State Univ., Fort Collins.
  • Jovanovic, B., D. A. Jones, and N. Nicholls (2005), A historical monthly pan-evaporation dataset for Australia, paper presented at 16th Biennial Congress, Aust. Inst. of Phys., Canberra, ACT, Australia.
  • Klink, K. (1999), Trends in mean monthly maximum and minimum surface wind speeds in the coterminous United States, 1961 to 1990, Clim. Res., 13, 193205.
  • Linacre, E. T. (1994), Estimating U.S. class A pan evaporation from few climate data, Water Int., 19, 514.
  • Liu, B., M. Xu, M. Henderson, and W. Gong (2004), A spatial analysis of pan evaporation trends in China, 1955–2000, J. Geophys. Res., 109, D15102, doi:10.1029/2004JD004511.
  • Lorenz, D. J., and E. DeWeaver (2007), The response of the extratropical hydrological cycle to global warming, J. Clim., 20, 34703484.
  • McVicar, T. R., L. T. Li, T. G. Van Niel, M. F. Hutchinson, X. M. Mu, and Z. H. Liu (2005), Spatially distributing 21 years of monthly hydrometeorological data in China: Spatio-temporal analysis of FAO-56 crop reference evapotranspiration and pan evaporation in the context of climate change, Land and Water Tech. Rep. 8/05, Commonw. Sci. and Ind. Res. Org., Canberra, ACT, Australia.
  • Penman, H. L. (1948), Natural evaporation from open water, bare soil and grass, Proc. R. Soc., Ser. A, 193, 120145.
  • Peterson, T. C., V. S. Golubev, and P. Y. Groisman (1995), Evaporation losing its strength, Nature, 377, 687688.
  • Pirazzoli, P. A., and A. Tomasin (2003), Recent near-surface wind changes in the central Mediterranean and Adriatic areas, Int. J. Climatol., 23, 963973.
  • Rayner, D. P. (2007), Wind run changes are the dominant factor affecting pan evaporation trends in Australia, J. Clim., 20, 33793394.
  • Roderick, M. L., and G. D. Farquhar (2002), The cause of decreased pan evaporation over the past 50 years, Science, 298, 14101411.
  • Roderick, M. L., and G. D. Farquhar (2004), Changes in Australian pan evaporation from 1970 to 2002, Int. J. Climatol., 24, 10771090.
  • Roderick, M. L., and G. D. Farquhar (2005), Changes in New Zealand pan evaporation since the 1970s, Int. J. Climatol., 25, 20312039.
  • Roderick, M. L., and G. D. Farquhar (2006), A physical analysis of changes in Australian pan evaporation, technical report, Land and Water Australia, Canberra, ACT, Australia.
  • Rotstayn, L. D., M. L. Roderick, and G. D. Farquhar (2006), A simple pan-evaporation model for analysis of climate simulations: Evaluation over Australia, Geophys. Res. Lett., 33, L17715, doi:10.1029/2006GL027114.
  • Rotstayn, L. D., et al. (2007), Have Australian rainfall and cloudiness increased due to the remote effects of Asian anthropogenic aerosols? J. Geophys. Res., 112, D09202, doi:10.1029/2006JD007712.
  • Shenbin, C., L. Yunfeng, and A. Thomas (2006), Climatic change on the Tibetan plateau: Potential evapotranspiration trends from 1961–2000, Clim. Change, 76, 291319.
  • Smith, I. N. (2004), An assessment of recent trends in Australian rainfall, Aust. Meteorol. Mag., 53, 163173.
  • Stanhill, G. (1997), Physics and stamp collecting: Comments arising from “The NOAA integrated surface irradiance study (ISIS)—A new surface radiation monitoring program, Bull. Am. Meteorol. Soc., 78, 28722873.
  • Stanhill, G. (2002), Is the class A evaporation pan still the most practical and accurate meteorological method for determining irrigation water requirements? Agric. For. Meteorol., 112, 233236.
  • Tebakari, T., J. Yoshitani, and C. Suvanpimol (2005), Time-space trend analysis in pan evaporation over Kingdom of Thailand, J. Hydrol. Eng., 10(3), 205215.
  • Thom, A. S., J. L. Thony, and M. Vauclin (1981), On the proper employment of evaporation pans and atmometers in estimating potential transpiration, Q. J. R. Meteorol. Soc., 107, 711736.
  • Thomas, A. (2000), Spatial and temporal characteristics of potential evapotranspiration trends over China, Int. J. Climatol., 20, 381396.
  • Tuller, S. E. (2004), Measured wind speed trends on the west coast of Canada, Int. J. Climatol., 24, 13591374.
  • Turner, J., S. R. Colwell, G. J. Marshall, T. A. Lachlan-Cope, A. M. Carleton, P. D. Jones, V. Lagun, P. A. Reid, and S. Iagovkina (2005), Antarctic climate change during the last 50 years, Int. J. Climatol., 25, 279294.
  • Vecchi, G. A., B. J. Soden, A. T. Wittenberg, I. M. Held, A. Leetmaa, and M. J. Harrison (2006), Weakening of tropical Pacific atmospheric circulation due to anthropogenic forcing, Nature, 441, 7376.
  • Wentz, F. J., L. Ricciardulli, K. Hilburn, and C. Mears (2007), How much more rain will global warming bring? Science, 317, 233235.
  • Wu, S., Y. Yin, D. Zheng, and Q. Yang (2006), Moisture conditions and climate trends in China during the period 1971–2000, Int. J. Climatol., 26, 193206.
  • Xu, C.-Y., L. Gong, T. Jiang, D. Chen, and V. P. Singh (2006a), Analysis of spatial distribution and temporal trend of reference evapotranspiration and pan evaporation in Changjiang (Yangtze River) catchment, J. Hydrol., 327, 8193.
  • Xu, M., C.-P. Chang, C. Fu, Y. Qi, A. Robock, D. Robinson, and H. Zhang (2006b), Steady decline of east Asian monsoon winds, 1969–2000: Evidence from direct ground measurements of wind speed, J. Geophys. Res., 111, D24111, doi:10.1029/2006JD007337.
  • Zhang, Y., C. Liu, Y. Tang, and Y. Yang (2007), Trends in pan evaporation and reference and actual evapotranspiration across the Tibetan plateau, J. Geophys. Res., 112, D12110, doi:10.1029/2006JD008161.

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Attribution Using the PenPan Model
  5. 3. Materials and Methods
  6. 4. Results
  7. 5. Discussion
  8. 6. Conclusion
  9. Acknowledgments
  10. References
  11. Supporting Information

Auxiliary material for this article contains additional figures and a table.

Auxiliary material files may require downloading to a local drive depending on platform, browser, configuration, and size. To open auxiliary materials in a browser, click on the label. To download, Right-click and select “Save Target As…” (PC) or CTRL-click and select “Download Link to Disk” (Mac).

See Plugins for a list of applications and supported file formats.

Additional file information is provided in the readme.txt.

FilenameFormatSizeDescription
grl23644-sup-0001-readme.txtplain text document3Kreadme.txt
grl23644-sup-0002-ts01.txtplain text document14KTable S1. Trends and averages in annual pan evaporation, and model-based calculations of the radiative and aerodynamic components of pan evaporation at 41 sites for the period 1975–2004 along with the number of observations used in the respective calculations.
grl23644-sup-0003-fs01.epsPS document153KFigure S1. Comparison of observed and calculated pan evaporation rates.
grl23644-sup-0004-fs02.epsPS document150KFigure S2. Comparison of observed and calculated incoming long wave irradiance (Rl,in).
grl23644-sup-0005-fs03a.epsPS document85KFigure S3a. Monthly time series for Geraldton Airport.
grl23644-sup-0006-fs03b.epsPS document82KFigure S3b. Monthly time series for Darwin Airport.
grl23644-sup-0007-fs03c.epsPS document87KFigure S3c. Monthly time series for Alice Springs Airport.
grl23644-sup-0008-fs03d.epsPS document84KFigure S3d. Monthly time series for Mount Gambier Aero.
grl23644-sup-0009-fs03e.epsPS document79KFigure S3e. Monthly time series for Rockhampton Aero.
grl23644-sup-0010-fs03f.epsPS document80KFigure S3f. Monthly time series for Wagga Wagga Amo.
grl23644-sup-0011-fs03g.epsPS document84KFigure S3g. Monthly time series for Mildura Airport.
grl23644-sup-0012-fs04.epsPS document91KFigure S4. Trends in wind speed and vapour pressure deficit at 41 sites for the period 1975–2004.
grl23644-sup-0013-fs05.epsPS document123KFigure S5. Trends in wind speed at 41 sites for the period 1975–2004 in four seasons (DJF, MAM, JJA, SON).
grl23644-sup-0014-t01.txtplain text document1KTab-delimited Table 1.
grl23644-sup-0015-t02.txtplain text document1KTab-delimited Table 2.

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.