Accuracy of reference evapotranspiration (ETo) estimates under data scarcity scenarios in the Iberian Peninsula
Accuracy of reference evapotranspiration (ETo) estimates under data scarcity scenarios in the Iberian Peninsula
复制标题
DOI：
10.1016/j.agwat.2016.12.013
复制
发表时间：
2017-03
影响因子：
6.7
通讯作者：
M. Tomás-Burguera;S. Vicente‐Serrano;M. Grimalt;S. Beguerı́a
中科院分区：
文献类型：
--
作者：
M. Tomás-Burguera;S. Vicente‐Serrano;M. Grimalt;S. Beguerı́a
计算参考作物蒸散量的标准方法是FAO-56 Penman-Monteith（FAO-PM）方法，该方法需要关于气温、辐射、空气湿度和风速的数据。与气温不同，其他变量不太容易获得，阻碍了粮农组织PM的应用。在某些变量不可用的情况下，为找到估算粮农组织-预防机制ETo的最佳方法做了大量努力。FAO-56手册建议根据目前观察到的变量（PM-R）或使用要求较低的Hargreaves和Samani方法（HS）来估计缺失变量。此外，如果缺失的变量是在附近的站点测量的，则可以在应用FAO-PM（PM-IC）之前使用空间插值来估计缺失的数据。本文重点比较，在每月的时间尺度上，这些方法的性能在伊比利亚半岛。通过使用53个气象站的所有数据来计算FAO-PM，数据稀缺的情况下，模拟和测试所提到的方法（PM-R，HS，PM-IC）PM-IC产生了一致的最佳结果，根据一些测试。它产生了最低的平均绝对误差（MAE），为7.56 mm/月，而PM-R产生的值为10.15 mm/月，HS为9.36 mm/月，结果存在偏差。PM-IC也最擅长再现ETO的长期变化和趋势。一个良好的和无偏的估计每月ETo时间序列需要灌溉规划和作物设计。
The standard approach for computing reference crop evapotranspiration (ETo) is the FAO-56 Penman-Monteith (FAO-PM) method, which requires data on air temperature, radiation, air humidity and wind speed. Unlike air temperature the other variables are less frequently available, hindering the application of FAO-PM. A lot of efforts exist to find the best method to estimate FAO-PM ETowhen some variables are not available. The FAO-56 manual recommends to estimate the missing variables based on those currently observed (PM-R), or use the less demanding Hargreaves and Samani method (HS). Additionally, if the missing variables are measured at nearby stations, spatial interpolation can be used to estimate the missing data previous to applying FAO-PM (PM-IC). This paper focuses on the comparison, at the monthly time scale, of the performance of these methods to in the Iberian Peninsula. By using 53 weather stations with all data to calculate FAO-PM, data scarcity scenarios are simulated and the mentioned methods are tested (PM-R, HS, PM-IC)PM-IC yielded consistently the best results according to a number of tests. It yielded the lowest mean absolute error (MAE) at 7.56 mm/month, while PM-R yielded values of 10.15 mm/month and HS 9.36 mm/month and biased results. PM-IC was also best at reproducing the long-term variability and trends in ETo. A good and unbiased estimation of monthly ETotime series are required for irrigation planning and crop design.