Multivariate adaptive regression splines (MARS) applied to daily reference evapotranspiration modeling with limited weather data

ABSTRACT. Estimation of reference evapotranspiration (ETo) is very relevant for water resource management. The Penman-Monteith (PM) equation was proposed by the Food and Agriculture Organization (FAO) as the standard method for estimation of ETo. However, this method requires various weather data, such as air temperature, wind speed, solar radiation and relative humidity, which are often unavailable. Thus, the objective of this study was to compare the performance of multivariate adaptive regression splines (MARS) and alternative equations, in their original and calibrated forms, to estimate daily ETo with limited weather data. Daily data from 2002 to 2016 from 8 Brazilian weather stations were used. ETo was estimated using empirical equations, PM equation with missing data and MARS. Four data availability scenarios were evaluated as follows: temperature only, temperature and solar radiation, temperature and relative humidity, and temperature and wind speed. The MARS models demonstrated superior performance in all scenarios. The models that used solar radiation showed the best performance, followed by those that used relative humidity and, finally, wind speed. The models based only on air temperature had the worst performance.

摘要 参考蒸散量（reference evapotranspiration, ETo）的估算对水资源管理至关重要。联合国粮食及农业组织（Food and Agriculture Organization, FAO）将彭曼-蒙特斯（Penman-Monteith, PM）方程定为ETo估算的标准方法，但该方法需要气温、风速、太阳辐射、相对湿度等多种气象数据，而此类数据往往难以获取。为此，本研究旨在对比多元自适应回归样条（multivariate adaptive regression splines, MARS）模型与其他经验公式（分别采用原始形式与校准形式）在有限气象数据条件下估算逐日ETo的性能。本研究使用了巴西8个气象站2002年至2016年的逐日观测数据，分别通过经验公式、存在缺失数据的PM方程以及MARS模型开展ETo估算。本次研究评估了四种数据可用场景：仅气温、气温与太阳辐射、气温与相对湿度，以及气温与风速。结果表明，MARS模型在所有场景中均表现最优；使用太阳辐射的模型性能最佳，其次为使用相对湿度的模型，最后为使用风速的模型；仅基于气温的模型性能最差。