Evapotranspiration plays an inevitable role in various fields of hydrology and agriculture. Reference evapotranspiration (ET0) is mostly applied in irrigation planning and monitoring. An accurate estimation of ET0 contributes to decision and policymaking processes governing water resource management, efficiency, and productivity. Direct measurements of ET0, however, are difficult to achieve, often requiring empirical methods. The Penman–Monteith FAO56 (PM-FAO56) method, for example, is still considered to be the best way of estimating ET0 in most regions of the globe. However, it requires a large number of meteorological variables, often restricting its applicability in regions with poor or missing meteorological observations. Furthermore, the objectivity of some elements of the empirical equations often used can be highly variable from region to region. The result is a need to find an alternative, objective method that can more accurately estimate ET0 in regions of interest. This study was conducted in the Hexi corridor, Northwest China. In it we aimed to evaluate the applicability of 32 simple empirical ET0 models designed under different climatic conditions with different data inputs requirements. The models evaluated in this study are classified into three types of methods based on temperature, solar radiation, and mass transfer. The performance of 32 simple equations compared to the PM-FAO56 model is evaluated based on model evaluation techniques including root mean square error (RMSE), mean absolute error (MAE), percentage bias (PBIAS), and Nash–Sutcliffe efficiency (NSE). The results show that the World Meteorological Organization (WMO) and the Mahringer (MAHR) models perform well and are ranked as the best alternative methods to estimate daily and monthly ET0 in the Hexi corridor. The WMO and MAHR performed well with monthly mean RMSE = 0.46 mm and 0.56 mm, PBIAS = 12.1% and −11.0%, and NSE = 0.93 and 0.93, before calibration, respectively. After calibration, both models showed significant improvements with approximately equal PBIAS of −2.5%, NSE = 0.99, and RMSE of 0.24 m. Calibration also significantly reduced the PBIAS of the Romanenko (ROM) method by 82.12% and increased the NSE by 16.7%.

Exploring the variance in reference evapotranspiration (ET 0 ) and its dominant influencing factors is important for climate change, hydrological cycles and water management. Temperature (T), wind speed (U 2 ), net radiation (R n ) and actual vapour pressure (e a ) are the major climatic input variables in Penman-Monteith equation. Previous studies have successfully applied the total differential method to determine the relative contributions of changes in these variables to variation in ET 0 on different timescales. However, the interaction of climatic variables has not been incorporated into this method. Taking the inland river basin of Northwest China as the study area, we extended the total differential method to decompose the ET 0 variance into temporal variance and covariance of T, U 2 , R n and e a on intra-annual and annual scales during 1960-2017. The results indicated that the variance in ET 0 on the intra-annual scale was larger than that on the annual scale. Among the four single climatic variables, U 2 variance made larger contributions to intra-annual and annual ET 0 variance with relative contributions of 6.8% and 1.1%, respectively. However, the interaction of climatic variables played a dominant role in the variation in ET 0 . Specifically, on intra-annual scale, coupled U 2 and R n primarily controlled the ET 0 variance (76.1%), followed by coupled R n and e a (9.6%); but their positive effects were weakened by the negative effects of coupled T and e a (À2.3%). On annual scale, coupled U 2 and R n still governed the variance in ET 0 (97.3%), but the effects were also weakened by other groups of interaction effects. Furthermore, ET 0 was highly related to NDVI on an intra-annual scale (R 2 = 0.68, p = 0.00), indicating the strong effect of seasonal vegetation dynamics on ET 0 variance. This study provides new insights to quantitatively assess the interaction effects of environmental factors on key hydrometeorological variables.

Climate extremes pose significant natural threats to socioeconomic activities. Accurate prediction of short-term climate (STC) can provide relevant departments with warnings to effectively reduce this threat. To accurately predict STC in China, this study utilizes machine learning algorithms, particularly the random forest (RF) model, to evaluate the role of both natural and anthropogenic factors. Monthly temperature and precipitation data from 160 meteorological stations spanning China, as well as natural climate factors and an economic activity index, were obtained to perform a seasonal hindcast of air temperature and precipitation observed from 1979 to 2018. Our focus was to predict the seasonal mean temperature and precipitation, specifically the summer (June, July, and August (JJA)) and winter (December, January, and February (DJF)) air temperature and precipitation anomalies using forecast factors from the preceding season. Results show that a comprehensive consideration of both natural and anthropogenic effects provides a more accurate fit to the observed climate trends compared to using only one factor. When both factors were integrated, the model scores (coefficient of determination) exceeded 0.95, close to 1.00, which is significantly higher than those of natural (0.86 for temperature, 0.85 for precipitation) or anthropogenic (0.90 for temperature and 0.50 for precipitation) factors alone. Furthermore, we also attempted to predict similar components for 2019 and 2020. The average relative error between predictions and observations was less than 10%, indicating that this integrated model’s performance exhibited a significant improvement in predicting the STC. The findings of this study underscore the importance of accounting for both natural and anthropogenic factors in predicting climate trends to inform sustainable decision-making in China.