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Climatic Change

, Volume 120, Issue 4, pp 915–930 | Cite as

Methodological differences in projected potential evapotranspiration

  • Stephanie A. McAfee
Article

Abstract

There is growing concern that the higher temperatures expected with climate change will exacerbate drought extent, duration and severity by enhancing evaporative demand. Temperature-based estimates of potential evapotranspiration (PET) are popular for many eminently practical reasons and have served well in many research and management settings. However, a number of recent publications have questioned whether it is appropriate to use temperature-based PET estimates for long-term evaporative demand and drought projections, demonstrating that PET does not always track temperature. Where precipitation changes are modest, methodologically driven differences in the magnitude or direction of PET trends could lead to contrasting drought projections. Here I calculate PET by three methods (Hamon, Priestley-Taylor and Penman) and evaluate whether different techniques introduce disparities in the sign of PET change, the degree of model agreement, or the magnitude of those changes. Changes in temperature-based Hamon PET were more significantly and consistently positive than trends in PET estimated by other methods, and where methods agreed that summer PET would increase, trends in temperature-based PET were often larger in magnitude. The discrepancies in PET trends appear to derive from regional changes in incoming shortwave radiation, wind speed and humidity -- phenomena simpler equations cannot capture. Because multiple variables can influence trends in PET, it may be more justifiable to use data-intensive methods, where the source(s) of uncertainty can be identified, rather than using simpler methods that could mask important trends.

Keywords

Wind Speed Vapor Pressure Deficit Specific Humidity Drought Index Diurnal Temperature Range 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

I would like to thank John Walsh, Scott Rupp and three anonymous reviewers for helpful comments on the manuscript. This research was performed while the author was a postdoctoral fellow funded by the USGS Alaska Climate Science Center.

Supplementary material

10584_2013_864_Fig9_ESM.jpg (123 kb)
Supp Fig 1

Comparison of changes in Priestley-Taylor and Penman potential evapotranspiration in terms of statistical significance, sign and consistency between models. White areas indicate qualitative agreement between methods. Pale yellow denotes areas where one method does not show a consistent statistically significant change, and the other method suggests a statistically significant change in at least half of the models. Gold shading represents areas where one method shows no consistent statistically significant change, while the other suggests that there is a statistically significant change, but that models disagree about the sign of that change. Orange areas indicate that one method finds a statistically significant change with a high degree of model agreement, and the other method a statistically significant change, but model disagreement. Red denotes areas where the two methods find robust and statistically significant trends of opposite signs. (JPEG 132 kb)

10584_2013_864_MOESM1_ESM.tif (2.3 mb)
ESM 1 High resolution image (TIFF 2349 kb)
10584_2013_864_Fig10_ESM.jpg (133 kb)
Supp Fig 2

Change in Priestley-Taylor potential evapotranspiration (PET) minus the change in Penman PET (mm day-1) where at least five models show a statistically significant (p ≤ 0.05) increase in PET by both methods. Stippling indicates that the values are significantly different (p ≤ 0.05). White areas denote that fewer than five models showed a statistically significant change in PET by both methods. (JPEG 144 kb)

10584_2013_864_MOESM2_ESM.tif (2.9 mb)
ESM 2 High resolution image (TIFF 2980 kb)
10584_2013_864_Fig11_ESM.jpg (144 kb)
Supp Fig 3

Changes in cloud cover (%) between 2002-2011 and 2079–2098 under the A1B scenario. Colors show the mean change in cloud cover across all models. Stippling indicates that at least 80 % of the models projecting a significant (p ≤ 0.05) change agree in sign. White areas indicate that although at least half of the models project a significant change in cloud cover, they do not agree on the direction of change. Lack of stippling over colored areas of the map indicated that less than 50 % of models projected a significant change in cloud cover. (JPEG 154 kb)

10584_2013_864_MOESM3_ESM.tif (3.2 mb)
ESM 3 High resolution image (TIFF 3318 kb)
10584_2013_864_Fig12_ESM.jpg (154 kb)
Supp Fig 4

As in Supp Fig 3, but for vapor pressure deficit (kPa) (JPEG 139 kb)

10584_2013_864_MOESM4_ESM.tif (3.1 mb)
ESM 4 High resolution image (TIFF 3165 kb)
10584_2013_864_Fig13_ESM.jpg (139 kb)
Supp Fig 5

As in Supp Fig 3, but for Thornthwaite PET (mm day-1) (JPEG 134 kb)

10584_2013_864_MOESM5_ESM.tif (3.5 mb)
ESM 5 High resolution image (TIFF 3611 kb)
10584_2013_864_Fig14_ESM.jpg (135 kb)
Supp Fig 6

As in Supp Fig 3, but for Oudin PET (mm day-1) (JPEG 115 kb)

10584_2013_864_MOESM6_ESM.tif (2.9 mb)
ESM 6 High resolution image (TIFF 2950 kb)
10584_2013_864_Fig15_ESM.jpg (115 kb)
Supp Fig 7

Number of models (out of 17) indicating a statistically significant (p ≤ 0.05) decreased in Priestley-Taylor PET. (JPEG 113 kb)

10584_2013_864_MOESM7_ESM.tif (3.5 mb)
ESM 7 High resolution image (TIFF 3634 kb)
10584_2013_864_Fig16_ESM.jpg (114 kb)
Supp Fig 8

As in Supp Fig 7, but for Penman PET. (JPEG 89 kb)

10584_2013_864_MOESM8_ESM.tif (3.4 mb)
ESM 8 High resolution image (TIFF 3437 kb)
10584_2013_864_Fig17_ESM.jpg (89 kb)
Supp Fig 9

As in Supp Fig 7, but for Hamon PET (JPEG 122 kb)

10584_2013_864_MOESM9_ESM.tif (2 mb)
ESM 9 High resolution image (TIFF 2085 kb)
10584_2013_864_MOESM10_ESM.docx (66 kb)
ESM 10 (DOCX 66 kb)

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Scenarios Network for Alaska and Arctic PlanningUniversity of Alaska Fairbanks, USGS Alaska Climate Science CenterAnchorageUSA

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