Univrsity Of Tehran Press Water and Irrigation Management 2251-6298 13 2 2023 06 22 Examining Different Methods of Daily Rainfall Reconstruction Examining Different Methods of Daily Rainfall Reconstruction 323 340 92516 10.22059/jwim.2023.349023.1021 FA Hanie Sadat Karbasi Department of Water, Waste Water and Environmental Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran. Ali Moridi Department of Water, Waste Water and Environmental Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran. Seyed Saied Mousavi Nadoushani Department of Water, Waste Water and Environmental Engineering, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran. Journal Article 2022 09 22 One of the problems of specialists and designers is the incomplete time series in hydrology studies, which causes errors in the results and complicates the implementation of projects. This issue is more acute in areas where the number of rain gauge stations is limited. Currently, it is common to use statistical methods in order to solve statistical data gaps. The current research aims to evaluate the performance of the method of reconstructing missing values ​​of daily rainfall using the waterData package in R software and the time disaggregation method of reconstructing annual values ​​to daily values ​​in the period from 1990 to 2020 using 43 stations with complete statistics among 87 selected synoptic stations. It was done in Iran. Based on the average values ​​of the evaluation indices for two times disaggregation and reconstruction using the waterData package in R software methods, for the CC index 1 and 0.95 respectively, for the MBE index 0 and -0.01 respectively, for the RMSE index 0.3 and 1.1 respectively, for The NSE index is 0.99 and 0.89, respectively, and the CSI and POD index are 0.94 and 0.63, respectively, which shows the better performance of the time disaggregation method. The average values ​​of Bias and FAR index for two methods are equal to -0.01 and 0, respectively, and indicate the similar performance of the two methods. One of the problems of specialists and designers is the incomplete time series in hydrology studies, which causes errors in the results and complicates the implementation of projects. This issue is more acute in areas where the number of rain gauge stations is limited. Currently, it is common to use statistical methods in order to solve statistical data gaps. The current research aims to evaluate the performance of the method of reconstructing missing values ​​of daily rainfall using the waterData package in R software and the time disaggregation method of reconstructing annual values ​​to daily values ​​in the period from 1990 to 2020 using 43 stations with complete statistics among 87 selected synoptic stations. It was done in Iran. Based on the average values ​​of the evaluation indices for two times disaggregation and reconstruction using the waterData package in R software methods, for the CC index 1 and 0.95 respectively, for the MBE index 0 and -0.01 respectively, for the RMSE index 0.3 and 1.1 respectively, for The NSE index is 0.99 and 0.89, respectively, and the CSI and POD index are 0.94 and 0.63, respectively, which shows the better performance of the time disaggregation method. The average values ​​of Bias and FAR index for two methods are equal to -0.01 and 0, respectively, and indicate the similar performance of the two methods. https://jwim.ut.ac.ir/article_92516_567f99d27702d73910a32a29ec727a40.pdf