Cross Assessment of Twenty-One Different Methods for Missing Precipitation Data Estimation

Atmosphere

Nadhir Al-Ansari¹  Asaad M. Armanuos²  Zaher Mundher Yaseen³ 

[1] Civil, environmental and natural resources engineering, Lulea University of Technology, 97187 Lulea, Sweden;Irrigation and Hydraulics Engineering Department, Civil Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt;Sustainable Developments in Civil Engineering Research Group, Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam;

关键词: Nile Basin;    missing data;    estimation;    precipitation;    Ethiopia;    classical methods;   

DOI  :  10.3390/atmos11040389

来源: DOAJ

【 摘 要 】

The results of metrological, hydrological, and environmental data analyses are mainly dependent on the reliable estimation of missing data. In this study, 21 classical methods were evaluated to determine the best method for infilling the missing precipitation data in Ethiopia. The monthly data collected from 15 different stations over 34 years from 1980 to 2013 were considered. Homogeneity and trend tests were performed to check the data. The results of the different methods were compared using the mean absolute error (MAE), root-mean-square error (RMSE), coefficient of efficiency (CE), similarity index (S-index), skill score (SS), and Pearson correlation coefficient (r_Pearson). The results of this paper confirmed that the normal ratio (NR), multiple linear regression (MLR), inverse distance weighting (IDW), correlation coefficient weighting (CCW), and arithmetic average (AA) methods are the most reliable methods of those studied. The NR method provides the most accurate estimations with r_Pearson of 0.945, mean absolute error of 22.90 mm, RMSE of 33.695 mm, similarity index of 0.999, CE index of 0.998, and skill score of 0.998. When comparing the observed results and the estimated results from the NR, MLR, IDW, CCW, and AA methods, the MAE and RMSE were found to be low, and high values of CE, S-index, SS, and r_Pearson were achieved. On the other hand, using the closet station (CS), UK traditional, linear regression (LR), expectation maximization (EM), and multiple imputations (MI) methods gave the lowest accuracy, with MAE and RMSE values varying from 30.424 to 47.641 mm and from 49.564 to 58.765 mm, respectively. The results of this study suggest that the recommended methods are applicable for different types of climatic data in Ethiopia and arid regions in other countries around the world.

【 授权许可】

Unknown