Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

---

Precipitation affects quantity and quality of water resources and agricultural production. Therefore, the estimation and analysis of its spatial-temporal variations is of great importance. In many regions of Iran, limited spatial-temporal information is available due to sparse distribution of monitoring stations and short observational records. On the other hand, dependency of rain-fed and irrigated production systems on precipitation increases the importance of the analysis of spatiotemporal variations of this weather variable. One way to address this limitation is to use regional/global gridded datasets. In this study, monthly precipitation data were obtained from the CRU dataset (developed principally by the UK's Natural Environment Research Council (NERC) and the US Department of Energy) and used to investigate temporal trends in annual, seasonal and monthly precipitations in 675 grid cells (0.5°×0.5°) across Iran over two periods, 1957-1986 and 1987-2016. The results of the previous studies showed that the CRU gridded dataset offers quality data in Iran, especially for trend analysis. Also, the accuracy of the CRU dataset was validated in 14 selected stations regarding monthly precipitations and temporal trends over two different periods, pre-1987 and post-1987. The significance of temporal trends was assessed using a modified version of the rank-based nonparametric Mann-Kendall (MK) test. Trend magnitudes (i.e. slope) were estimated with the Theil-Sen approach and the Trend Free Pre-whitening (TFPW) procedure was applied to remove the effect of serial correlation. The results confirm the acceptable accuracy of the CRU dataset for trend analysis purposes, especially over the last three decades, except in the northern strip of the country (RMSE=10.71mm, R²=0.84). Two 30-year periods (1957-1986 and 1987-2016) were compared in terms of spatial patterns and temporal trends. Annual precipitation over the last three decades (1987-2016) has decreased as compare to the previous 30-year period (1957-1986) in most parts of the country. Over the last three decades, around 42% and 50% of the country’s total area experienced significant and insignificant decreasing trends in annual precipitation, respectively. National average annual precipitation has decreased by 15.78 mm/decade over the same period. Three important regions regarding agricultural production experienced the most significant reductions in annual precipitation: (1) Ardebil, East Azerbaijan, Kurdistan, Kermanshah, Ilam, Lorestan, Zanjan, Hamadan, and parts of West Azerbaijan, Markazi and Gilan (in the west and northwest), (2) Sistan and Baluchestan, Kerman, and southern parts of South Khorasan (in the south and south east), and (3) North Khorasan, northern parts of Razavi Khorasan and east of Golestan (in the east and north east). Reduced annual precipitation was mainly attributed to the reduction in seasonal precipitations in winter and spring, which have critical role in agricultural production and domestic water supply. Temporal trends were also analysed at the monthly scale. January, February, March and December revealed the largest number of grid cells with significant decreasing trends over 1987-2016 while November is the only month with significant number of grid cells experiencing significant increasing trends. The results of this study show that the monthly time series of the CRU TS 4.01 dataset, which has an almost complete spatial and temporal coverage in Iran over the last 60 years, are promising alternatives to weather station observations especially in data-scarce regions of Iran. Analysis of variations and the seasonal and monthly scales help understand the recent climate change and target the most crucial features of it when it comes to formulating adaptation strategies.

Keywords: Mann-Kendall test, Trend analysis, Climate change, Monthly weather data, Interpolation.

Full-Text [DOCX 1953 kb]   (1376 Downloads)    

Add your comments about this article : Your username or Email:

---

---