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Journal of Arid Land  2020, Vol. 12 Issue (1): 166-180    DOI: 10.1007/s40333-020-0093-7
Research article     
Uncertainty assessment of potential evapotranspiration in arid areas, as estimated by the Penman-Monteith method
HUA Ding1,2, HAO Xingming1,2,*(), ZHANG Ying1,2, QIN Jingxiu1,2
1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
2 University of Chinese Academy of Sciences Beijing 100049, China
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The Penman-Monteith (PM) method is the most widely used technique to estimate potential worldwide evapotranspiration. However, current research shows that there may be significant errors in the application of this method in arid areas, although questions remain as to the degree of this estimation error and how different surface conditions may affect the estimation error. To address these issues, we evaluated the uncertainty of the PM method under different underlying conditions in an arid area of Northwest China by analyzing data from 84 meteorological stations and various Moderate Resolution Imaging Spectroradiometer (MODIS) products, including land surface temperature and surface albedo. First, we found that when the PM method used air temperature to calculate the slope of the saturation vapor pressure curve, it significantly overestimated the potential evapotranspiration; the mean annual and July-August overestimation was 83.9 and 36.7 mm, respectively. Second, the PM method usually set the surface albedo to a fixed value, which led to the potential evapotranspiration being underestimated; the mean annual underestimation was 27.5 mm, while the overestimation for July to August was 5.3 mm. Third, the PM method significantly overestimated the potential evapotranspiration in the arid area. This difference in estimation was closely related to the underlying surface conditions. For the entire arid zone, the PM method overestimated the potential evapotranspiration by 33.7 mm per year, with an overestimation of 29.0 mm from July to August. The most significant overestimation was evident in the mountainous and plain non-vegetation areas, in which the annual mean overestimation reached 5% and 10%, respectively; during July, there was an estimation of 10% and 20%, respectively. Although the annual evapotranspiration of the plains with better vegetation coverage was slightly underestimated, overestimation still occurred in July and August, with a mean overestimation of approximately 5%. In order to estimate potential evapotranspiration in the arid zone, it is important that we identify a reasonable parameter with which to calibrate the PM formula, such as the slope of the saturation vapor pressure curve, and the surface albedo. We recommend that some parameters must be corrected when using PM in order to estimate potential evapotranspiration in arid regions.

Key wordsPenman-Monteith      parameter correction      surface temperature      albedo      Northwest China     
Received: 13 May 2019      Published: 10 February 2020
Corresponding Authors: Xingming HAO     E-mail:
About author: *Corresponding author: HAO Xingming (E-mail:
Cite this article:

HUA Ding, HAO Xingming, ZHANG Ying, QIN Jingxiu. Uncertainty assessment of potential evapotranspiration in arid areas, as estimated by the Penman-Monteith method. Journal of Arid Land, 2020, 12(1): 166-180.

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Fig. 1 Study area and the distribution of meteorological stations. I, Kizilsu Kirgiz irrigation area; II, Hotan irrigation area; NDVI, Normalized Difference Vegetation Index.
Parameter Type Time resolution (d) Spatial resolution Source
LST MYD11A1 1 1000 m×1000 m NASA
Albedo MCD43A3 1 500 m×500 m NASA
NDVI MYD13A1 16 500 m×500 m NASA
Table 1 Resolutions of major Moderate Resolution Imaging Spectroradiometer products
Crop Growth stage
April May June July August September October
Rice 0.000 0.856 1.540 1.470 0.280 0.000 0.000
Wheat 0.590 1.280 1.028 0.210 0.000 0.000 0.000
Corn 0.267 1.190 1.559 0.790 0.000 0.000 0.000
Cotton 0.351 1.010 1.255 1.250 1.000 0.735 0.300
Walnut 0.722 1.216 1.343 1.065 0.829 1.288 0.273
Fruit forest 0.549 1.158 1.352 1.263 1.190 0.850 0.113
Table 2 Crop coefficients of the major crops in different growth stages
Fig. 2 Overestimation and underestimation of ET0-PM (potential evapotranspiration that was estimated by the original Penman-Monteith (PM) formula) (Δ(f=Ta)) and ET0-Δ (potential evapotranspiration corrected by the slope of the saturation vapor pressure curve) (Δ(f=Ta, Ts)) in 84 meteorological stations during 2003-2017
Fig. 3 Annual variations between Ta (air temperature) and Ts (land surface temperature), Δ(f=Ta) and Δ(f=Ta, Ts), ET0-PM and ET0-Δ in plain no-vegetation area (a, d, g), plain vegetation area (b, e, h) and mountainous area (c, f, i) of arid areas of Northwest China during 2003-2017. The purple shadow in the diagram represents the standard error for Ts, Δ(f=Ta, Ts) and ET0-Δ; the green shadow represents the standard error for Ta, Δ(f=Ta) and ET0-PM. DOY, day of year.
Fig. 4 Overestimation and underestimation of ET0-PM and ET0-AB (estimated by actual surface albedo) from 84 meteorological stations during 2003-2017
Fig. 5 Comparison of ET0-PM and ET0-AB in mountainous area (a, d), plain vegetation area (b, e) and plain no-vegetation area (c, f) of arid areas of Northwest China during 2003-2017
Fig. 6 Overestimation and underestimation of ET0-PM and ET0-Z (simultaneous correction of two parameters, the saturation vapor pressure and albedo) from 84 meteorological stations during 2003-2017
Fig. 7 Comparison of ET0-PM and ET0-Z in mountainous area (a, d), plain vegetation area (b, e) and plain no-vegetation area (c, f) of arid areas of Northwest China during 2003-2017
Fig. 8 Relationships of ET0-PM with ET0-C (the error caused by the original PM method) during 2003-2017
Fig. 9 Comparision of ET0-PM and ET0-Z for pontential evapotranspiration trends, and ETc-PM (based on PM model) and ETc-Z (based on parameter correction) for crop water demand trends for the growth seasons of the two typical irrigation regions in the study area for the period 2003-2017
Fig. 10 Relationships of ET0-PM with corrected ET0, including ET0-∆, ET0-AB and ET0-Z, during2003-2017
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