| This article has been：Browse 1718Times Download 3309Times |
scan it! |
|
|
| DOI：10.13522/j.cnki.ggps.2017.0421 |
|
| Analyzing the Uncertainty Induced by Methods Used to Calculate the Missing Data in Time Series: A Case Study Based on Meteorological and Hydrological Data in Small Watershed |
|
SHI Jin, ZHOU Jiaogen, WANG Hui, GAN Lei, SHEN Jianlin, LI Xi, LI Yuyuan,WU Jinshui
|
|
1. College of Engineering, Hunan Agricultural University, Changsha 410128, China; 2. Key Laboratory of Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, China; 3. College of Resources and Environmental Sciences, Hunan Normal University, Changsha 410081, China
|
| Abstract: |
| 【Objective】Incomplete data is common in meteorological and hydrological analysis and this paper analyzed uncertainty caused by estimating such missing date using different interpolation methods.【Method】We take meteorological data, including minimum temperature, maximum temperature, solar radiation; and hydrological data, including rainfall and stream flow, collected from a long-term field experiment in a typical small watershed in a subtropical zone as examples. We developed a computer model to simulate them. The difference between the simulated results using five interpolation methods: the linear interpolation method (LIM), the K-Nearest neighbor interpolation method (KNNM), the polynomial interpolation method (PIM), the spline interpolation method (SIM) and kernel density estimation method (KDEM), was compared. We then analyzed the uncertainty resulted from sampling frequency (daily and monthly) and data missing degree (1%, 5%, 10%, 15%, 20%). Root mean square error (RMSE), absolute mean error (MAE) and the Pearson correlation coefficient (r) were used as criterion to evaluate the five methods. 【Result】 ① All five methods worked well in estimating the missing meteorological data with r varying from 0.62 to 0.99 (P<0.05). In general, the KDEM and PIM were more accurate than other three methods. ② Accuracy of all five methods deteriorated when the sampling time frequency changed from daily to monthly and data missing degree increased. ③The coefficient of variance (CV) of the data sets was significantly correlated with the valuation indexes (RMSE, MAE and r) (P<0.05).【Conclusion】The KDEM and PIM are relatively more reliable, and the coefficient of variance (CV) of data sets is critical to the accuracy of all five interpolation methods. |
| Key words: missing data; interpolation methods; coefficient of variance; uncertainty; time series |
|
|
