Splitting and Length of Years for Improving Tree-Based Models to Predict Reference Crop Evapotranspiration in the Humid Regions of China

Xiaoqiang Liu,Fulai Yan,Guomin Huang,Fucang Zhang,Wenqiang Bai,Lifeng Wu

doi:10.3390/w13233478

Abstract

To improve the accuracy of estimating reference crop evapotranspiration for the efficient management of water resources and the optimal design of irrigation scheduling, the drawback of the traditional FAO-56 Penman–Monteith method requiring complete meteorological input variables needs to be overcome. This study evaluates the effects of using five data splitting strategies and three different time lengths of input datasets on predicting ET0. The random forest (RF) and extreme gradient boosting (XGB) models coupled with a K-fold cross-validation approach were applied to accomplish this objective. The results showed that the accuracy of the RF (R2 = 0.862, RMSE = 0.528, MAE = 0.383, NSE = 0.854) was overall better than that of XGB (R2 = 0.867, RMSE = 0.517, MAE = 0.377, NSE = 0.860) in different input parameters. Both the RF and XGB models with the combination of Tmax, Tmin, and Rs as inputs provided better accuracy on daily ET0 estimation than the corresponding models with other input combinations. Among all the data splitting strategies, S5 (with a 9:1 proportion) showed the optimal performance. Compared with the length of 30 years, the estimation accuracy of the 50-year length with limited data was reduced, while the length of meteorological data of 10 years improved the accuracy in southern China. Nevertheless, the performance of the 10-year data was the worst among the three time spans when considering the independent test. Therefore, to improve the daily ET0 predicting performance of the tree-based models in humid regions of China, the random forest model with datasets of 30 years and the 9:1 data splitting strategy is recommended.

Highlights

Evapotranspiration (ET), the total water consumption of soil evaporation and crop transpiration, is of great significance for water resources planning and management, irrigation systems, land drainage implementation, groundwater research, drought assessment, analysis of farmland environments, and agricultural water management in water shortage areas [1,2,3,4]
The predicting capability of machine learning models for reference evapotranspiration at three levels of time length (2006–2015, 1986–2015, and 1966–2015) was evaluated by the R2, root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe coefficient (NSE), which is largely due to the input of meteorological data, These meteorological data are derived from the FAO-56 Penman–Monteith model
The models with input combination 4 were capable of estimating the daily ET0 with respectable precision, possessing a mean RMSE value of 0.607 mm d−1 and 0.620 mm d−1 in the random forest (RF) and XGB, respectively

Summary

Introduction

Evapotranspiration (ET), the total water consumption of soil evaporation and crop transpiration, is of great significance for water resources planning and management, irrigation systems, land drainage implementation, groundwater research, drought assessment, analysis of farmland environments, and agricultural water management in water shortage areas [1,2,3,4]. In the context of climate change, agricultural water resources are decreasing on a temporal and spatial scale across the world [7]. Crop water use is the key factor of soil water circulation in farmland, which is exceedingly significant regarding the optimal allocation of water resources and the formulation of irrigation systems, and the key to calculate the crop water demand is to determine the evapotranspiration of crops [8,9,10]. Methods for calculating the ET, such as the water balance method [11], the conduction theory of aqueous vapor [12], or using the lysimeter device, are extremely time-consuming and expensive in practice, which limits their applicability.

Objectives

Methods

Results

Discussion

Conclusion