Articles
Natural Hazards (15730840)121(6)pp. 6467-6497
Satellite precipitation products (SPPs) with high spatial and temporal resolution are considered as a new source of precipitation data to monitor drought events, particularly for data-sparse areas. However, they should be extensively validated against ground-based data before their utilization. In this study, three (SPPs) including Integrated Multi-satellite Retrievals for GPM (GPM IMERG), Climate Prediction Center morphing technique (CMORPH), and PERSIANN Dynamic Infrared–Rain Rate (PDIR-Now) are examined extensively on multiple spatiotemporal scales in precipitation estimation as well as their utility for drought monitoring across the southwestern Iran over 2001–2021. The Asfezari gridded precipitation data developed from a dense of rainfall gauges were used as the reference dataset. The results suggest that IMERG Final Run Version 7 (IMERG-F hereafter) outperforms the other products in representing spatiotemporal patterns of precipitation followed by PDIR-Now and CMORPH based on the statistical indices including relative bias (RB), correlation coefficient (CC), and root mean square error (RMSE). CMORPH product substantially underestimates precipitation values over the elevated regions. The results also suggest that IMERG-F shows the best performance with ground-based data for drought monitoring particularly for the 6 month SPI time scale. IMERG-F was also superior in tests involving correct, false, and missing drought detection. Ultimately, our results show that satellite-based precipitation products can be quite useful in drought monitoring, particularly in areas like southwestern Iran where droughts are frequent and may become more frequent in years to come. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.
Remote Sensing (20724292)16(15)
The growing concerns about floods have highlighted the need for accurate and detailed precipitation data as extreme precipitation occurrences can lead to catastrophic floods, resulting in significant economic losses and casualties. Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM IMERG) is a commonly used high-resolution gridded precipitation dataset and is recognized as trustworthy alternative sources of precipitation data. The aim of this study is to comprehensively evaluate the performance of GPM IMERG Early (IMERG-E), Late (IMERG-L), and Final Run (IMERG-F) in precipitation estimation and their capability in detecting extreme rainfall indices over southwestern Iran during 2001–2020. The Asfezari gridded precipitation data, which are developed using a dense of ground-based observation, were utilized as the reference dataset. The findings indicate that IMERG-F performs reasonably well in capturing many extreme precipitation events (defined by various indices). All three products showed a better performance in capturing fixed and non-threshold precipitation indices across the study region. The findings also revealed that both IMERG-E and IMERG-L have problems in rainfall estimation over elevated areas showing values of overestimations. Examining the effect of land cover type on the accuracy of the precipitation products suggests that both IMERG-E and IMERG-L show large and highly unrealistic overestimations over inland water bodies and permanent wetlands. The results of the current study highlight the potential of IMERG-F as a valuable source of data for precipitation monitoring in the region. © 2024 by the authors.
Advances in Space Research (02731177)71(3)pp. 1451-1472
The goal of this study is to assess the performance of four widely-used satellite precipitation products in capturing extreme precipitation indices across Iran over the period 2001–2018; these products include GPM IMERG (Integrated Multi-Satellite Retrievals for Global Precipitation Measurement), TRMM 3B42 (Tropical Rainfall Measuring Mission), CHIRPS (Climate Hazards Center InfraRed Precipitation with Station data), and PERSIANN-CDR (Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record). For this aim, a national gridded precipitation dataset was developed using a dense network of rain gauges as a reference dataset. The results suggest that the IMERG product outperforms the other three precipitation products in capturing extreme precipitation indices both temporally and spatially. TRMM 3B42 data show promising results in identifying many extreme indices, while the CHIRPS and PERSIANN-CDR products show less performance in accurately generating many of the extreme precipitation indices. © 2022 COSPAR
International Journal of Climatology (10970088)42(4)pp. 2039-2064
Satellite remote-sensing products with high spatial and temporal resolution are viable sources of precipitation information, especially for data-sparse and remote regions. The aim of this study is to examine the performance of four precipitation products including Integrated Multi-satellite Retrievals for GPM (GPM IMERG), Tropical Rainfall Measuring Mission (TRMM 3B43), Climate Hazards Centre InfraRed Precipitation with Station data (CHIRPS), and European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) in estimating precipitation and capturing meteorological droughts over Iran for the time span from 2001 to 2019. For this aim, a ground-based gridded precipitation dataset was constructed over the country using a dense network of quality-controlled rain gauges as a reference dataset. Different statistical metrics including the correlation coefficient (CC), the bias, the relative bias, and the root mean square error (RMSE) were applied to evaluate the performance of the products. The results suggest that GPM IMERG and TRMM 3B43 outperform CHIRPS and ERA5 in capturing the spatial distribution of precipitation and meteorological drought events across the country. The estimates of precipitation from the four products are seasonally influenced, with the least accurate precipitation estimates during summer season over the southern shores of the Caspian Sea. The GPM IMERG and TRMM 3B43, with higher CC and lower RMSE, show better performance in detecting drought events at both short and long time scales while the CHIRPS demonstrates the least accuracy. Spatially, all of the products show the best performance in identifying drought events over western and southwestern regions. © 2021 Royal Meteorological Society
Theoretical and Applied Climatology (14344483)150(1-2)pp. 389-403
The recent droughts in Iran have contributed to declining runoff, diminishing lake water levels, and rising salt levels due to lower runoff. This study aims to quantify how the recent changes affected to investigate the trend of land surface albedo in Iran between 2000 and 2018. Accessing field data from the Iran is difficult, and thus the common understanding of climate change in the region is strongly based on satellite data. We use remotely sensed data of land surface albedo, land surface temperature (LST), number of snow-covered days (SCDs), normalized difference vegetation index (NDVI), and the land cover type, obtained from Moderate Resolution Imaging Spectroradiometer (MODIS). The results show decreasing trends in albedo and SCDs by − 0.02 and − 0.52, respectively, and upward trends in the LST and NDVI data by 0.07 °C and 0.009, respectively. Due to the recent drought condition in Iran, SCDs decreased significantly, which might explain the reason behind the albedo decreases in winter time in Iran. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.
