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Open AccessArticle

Spatiotemporal Variations of Precipitation over Iran Using the High-Resolution and Nearly Four Decades Satellite-Based PERSIANN-CDR Dataset

1
Water Engineering Department, Shiraz University, Shiraz 71964-84334, Iran
2
Center for Hydrometeorology and Remote Sensing (CHRS), The Henry Samueli School of Engineering, Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697, USA
3
Universities Space Research Association, Mountain View, CA 94043, USA
4
Department of Earth System Science, University of California Irvine, 3200 Croul Hall, Irvine, CA 92697-2175, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1584; https://doi.org/10.3390/rs12101584
Received: 16 April 2020 / Revised: 9 May 2020 / Accepted: 13 May 2020 / Published: 16 May 2020
Spatiotemporal precipitation trend analysis provides valuable information for water management decision-making. Satellite-based precipitation products with high spatial and temporal resolution and long records, as opposed to temporally and spatially sparse rain gauge networks, are a suitable alternative to analyze precipitation trends over Iran. This study analyzes the trends in annual, seasonal, and monthly precipitation along with the contribution of each season and month in the annual precipitation over Iran for the 1983–2018 period. For the analyses, the Mann–Kendall test is applied to the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) estimates. The results of annual, seasonal, and monthly precipitation trends indicate that the significant decreases in the monthly precipitation trends in February over the western (March over the western and central-eastern) regions of Iran cause significant effects on winter (spring) and total annual precipitation. Moreover, the increases in the amounts of precipitation during November in the south and south-east regions lead to a remarkable increase in the amount of precipitation during the fall season. The analysis of the contribution of each season and month to annual precipitation in wet and dry years shows that dry years have critical impacts on decreasing monthly precipitation over a particular region. For instance, a remarkable decrease in precipitation amounts is detectable during dry years over the eastern, northeastern, and southwestern regions of Iran during March, April, and December, respectively. The results of this study show that PERSIANN-CDR is a valuable source of information in low-density gauge network areas, capturing spatiotemporal variation of precipitation. View Full-Text
Keywords: trend analysis; precipitation; satellite precipitation product; PERSIANN-CDR; Iran trend analysis; precipitation; satellite precipitation product; PERSIANN-CDR; Iran
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Mosaffa, H.; Sadeghi, M.; Hayatbini, N.; Afzali Gorooh, V.; Akbari Asanjan, A.; Nguyen, P.; Sorooshian, S. Spatiotemporal Variations of Precipitation over Iran Using the High-Resolution and Nearly Four Decades Satellite-Based PERSIANN-CDR Dataset. Remote Sens. 2020, 12, 1584.

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