Analysis of annual and monthly precipitation trends based on long-term data (Case study: Zohreh–Jirahi Basin in Iran)

Abstract

Based on data from 1966 to 2018, the present study analyses precipitation trends in the Zohreh–Jirahi basin in Iran. The homogeneity of data was estimated based on the Kolmogorov–Smirnov method. Missing data were reconstructed using inverse distance weighted (IDW) and ordinary-linear-kriging methods. The results were evaluated by the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). A multidimensional raster was generated containing precipitation values related to the statistical period years, and time-series data were produced in array format per unit area. Based on the period range, 34,267 points underwent the time-series analysis, and the trend change was assessed based on the Mann–Kendall method. The Sen’s slope was evaluated using yearly and monthly scales in these points, followed by producing raster maps. The IDW was selected with an R² value of 0.95 as the optimal method of missing data reconstruction in this time period based on the model evaluation. The results showed no significant trends on a yearly scale, but on the monthly scale, December–April–November–August–September showed the highest ascending trends, respectively, February–December–March–May exhibited the highest descending trend, respectively, and June–July–September showed no trends. The maximum and minimum mean Sen’s Slope were estimated for December (0.213) and February (–0.68), respectively. Water resources management is inevitable, particularly in the agricultural sector as the main consumer with macro socioeconomic dimensions. Given the notable impact of water supply time to optimise and increase productivity, the current study can contribute to revising the cultivation patterns and time in the region. The recharge of aquifers, the storage process, and the consumption process should be compatible with forthcoming changes.