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Daily pan evaporation modeling from local and cross-station data using three tree-based machine learning models
Journal of Hydrology ( IF 6.4 ) Pub Date : 2018-11-01 , DOI: 10.1016/j.jhydrol.2018.09.055 Xianghui Lu , Yan Ju , Lifeng Wu , Junliang Fan , Fucang Zhang , Zhijun Li
Journal of Hydrology ( IF 6.4 ) Pub Date : 2018-11-01 , DOI: 10.1016/j.jhydrol.2018.09.055 Xianghui Lu , Yan Ju , Lifeng Wu , Junliang Fan , Fucang Zhang , Zhijun Li
Abstract Accurate estimation of pan evaporation (Ep) is required for many applications, e.g., water resources management, irrigation system design and hydrological modeling. However, the estimation of Ep for a target station can be difficult as a result of partial or complete lack of local meteorological data under many conditions. In this study, daily Ep was estimated from local (target-station) and cross-station data in the Poyang Lake Watershed of China using four empirical models and three tree-based machine learning models, including M5 model tree (M5Tree), random forests (RFs) and gradient boosting decision tree (GBDT). Daily meteorological data during 2001–2010 from 16 weather stations were used to train the models, while the data from 2011 to 2015 were used for testing. Two cross-station applications were considered between each of the 16 stations and the other 15 stations. The results showed that the radiation-based Priestley-Taylor model (on average RMSE = 1.13 mm d−1, NSE = 0.53, R2 = 0.57, MBE = 0.21 mm d−1) gave the most accurate daily Ep estimates among the four empirical models during testing, while the mass transfer-based Trabert model (on average RMSE = 1.38 mm d−1, NSE = 0.25, R2 = 0.46, MBE = 0.65 mm d−1) performed worst. The GBDT model outperformed the RFs model, M5Tree model and the empirical models under the same input combinations in terms of prediction accuracy (on average RMSE = 0.86 mm d−1, NSE = 0.68, R2 = 0.73, MBE = 0.07 mm d−1) and model stability (average percentage increase in testing RMSE = 16.3%). The RMSE values generally increased with the increase in the distance of two cross stations. A distance of less than 100 km between two cross stations is highly recommended for cross-station applications with satisfactory prediction accuracy (median percentage increase in RMSE
更新日期：2018-11-01
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