Reference evapotranspiration (ET0) is an important parameter to characterize hydrological water cycle and energy balance. An extremely heavy rainstorm occurred in Zhengzhou City, Henan Province on 20 July 2021, causing heavy casualties and economic losses. One of the important reasons for this rainstorm was abnormal water circulation. The purpose of this study is to estimate ET0 accurately and avoid extreme disasters caused by abnormal water cycles. This study compared and analyzed the accuracy and robustness of ET0 prediction based on the improved Levenberg–Marquardt (L-M) model based on artificial neural network and the genetic algorithm-backward neural network (GA-BP) model. The model uses seven weather stations in Zhengzhou, including mountain climate and plain climate. By utilizing the Pearson correlation analysis technique, six distinct input scenarios were identified, and the efficacy of the model was assessed using evaluation metrics, including RMSE, MAE, NSE, and SI. The results show that the estimation accuracy of the L-M model is better than that of the GA-BP model; when the number of input meteorological parameters is the same, the combined simulation effect including wind speed is the best; the R2 of L-M3 and L-M4 are 0.9285 and 0.9675, respectively; Models can accurately estimate ET0 with limited data.
Daily reference evapotranspiration (ET0) is the most crucial link in estimating crop water demand. In this study, Levenberg-Marquardt (L-M), Genetic Algorithm-Back Propagation (GA-BP) and Partial Least Squares Regression (PLSR) models were introduced to calculate the ET0 values, Based on the Pearson Correlation analysis method, five meteorological factors were obtained, which were combined into six different input scenarios. Compared with the values that calculated by the the Penman Monteith (PM) formula. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash-Sutcliffe Efficiency (NSE), and Scatter Index (SI) were used to evaluate the simulation performance of the models. The results showed that the simulation effect of the L-M model is better than that of the GA-BP model and PLSR model in all scenarios. PLSR model has the worst performance. The SI index of L-M6 was 46.69% lower than that of GA-BP6 and 65.78% lower than that of PLSR6. When the input factors are 3, the simulation effect of the input wind speed, the maximum temperature and the minimum temperature is the best. L-M model and GA-BP model can predict the ET0 in the region with a lack of meteorological data. This study provides an important reference for high-precision prediction of ET0 under different input combinations of meteorological factors.
In this study, a framework model (TPEM) for evaluating the temporal and spatial variation of urban precipitation is established. TPEM includes seven calculation methods at the same time. Taking the annual precipitation and flood season precipitation data of 8 meteorological stations in Zhengzhou, China from 1960 to 2020 as an example, the trend and period of rain island effect in Zhengzhou are analyzed from the annual and flood season rainfall scale, and the distance method is introduced to quantitatively evaluate the rain island effect. The results show that the rain island effect in the central urban area of Zhengzhou has an sudden increase trend in 1997 and 2004 respectively, and the increasing trend of rain island effect on the scale of annual and flood season rainfall lasts for 4 and 10 years respectively; At the same time, it has fluctuation periods of 8–10a, 16–18a, 3–5a, 17–20a, 5–7a, and 2–3a respectively. The rain island effect in Zhengzhou is concentrated in the flood season. The research can provide a scientific basis for cities to deal with the rain island effect in the future.
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