4.7 Article

Pan coefficient sensitivity to environment variables across China

Journal

JOURNAL OF HYDROLOGY
Volume 572, Issue -, Pages 582-591

Publisher

ELSEVIER
DOI: 10.1016/j.jhydrol.2019.03.039

Keywords

Open water evaporation; Pan evaporation; Pan coefficient (K-p); Sensitivity analysis; K-p most sensitive to relative humidity

Funding

  1. National Natural Science Foundation of China [41330529]
  2. Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences [2017A004]
  3. National Key Research and Development Program of China [2017YFC0506603]
  4. General Program of National Natural Science Foundation of China [51679007]
  5. Program for Bingwei Excellent Talents in Institute of Geographic Sciences and Natural Resources Research, CAS [2017RC204]

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Data of open water evaporation (E-ow), such as evaporation of lake and reservoir, have been widely used in hydraulic and hydrological engineering projects, and water resources planning and management in agriculture, forestry and ecology. Because of the low-cost and maneuverability, measuring the evaporation of a pan has been widely regarded as a reliable approach to estimate E-ow through multiplying an appropriate pan coefficient (K-p). K-p is affected by geometry and materials of a pan, and complex surrounding environment variables. However, the relationship between K-p and different environment variables is unknown. Thus, this study chose China D20 pan as an example, used meteorological observations from 767 stations and introduced the latest PenPan model to analyze the sensitivity of K-p to different environment variables. The results show that, the distribution of annual K-p had a strong spatial gradient. For all the stations, annual K-p ranged from 0.31 to 0.89, and decreased gradually from southeast to northwest. The sensitivity analysis shows that for China as a whole, K-p was most sensitive to relative humidity, followed by air temperature, wind speed and sunshine duration. For 767 stations in China, K-p was most sensitive to relative humidity for almost all the stations. For stations north of Yellow River, wind speed and sunshine duration were the next sensitive variables; while for stations south of Yellow River, air temperature was the next sensitive variable. The method introduced in this study could benefit estimating and predicting K-p under future changing environment.

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