Evaluating three non-gauge-corrected satellite precipitation estimates by a regional gauge interpolated dataset over Iran

Eini, Mohammad Reza; Olyaei, Mohammad Ali; Kamyab, Taraneh; Teymoori, Javad; Brocca, Luca; Piniewski, Mikołaj

doi:10.1016/j.ejrh.2021.100942

Cited by 18 publications

(7 citation statements)

References 64 publications

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“…The estimated values of the categorical indices (POD, FAR, CSI, and SR) of the SM2Rain-ASCAT product were better than all the other products. There was a better performance of this product in the plain areas of Iran [43]. The overall detection skill of the CHIRPS product in terms of the POD, CSI, and FAR was very poor compared to the other evaluated products (Figure 12).…”

Section: Discussionmentioning

confidence: 91%

Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

Anjum

Irfan

Waseem

et al. 2022

Water

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This study compares the performance of four satellite-based rainfall products (SRPs) (PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0) in a semi-arid subtropical region. As a case study, Punjab Province of Pakistan was considered for this assessment. Using observations from in-situ meteorological stations, the uncertainty in daily, monthly, seasonal, and annual rainfall estimates of SRPs at pixel and regional scales during 2010–2018 were examined. Several evaluation indices (Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias, and relative Bias (rBias), as well as categorical indices (Probability of Detection (POD), Critical Success Index (CSI), and False Alarm Ration (FAR)) were used to assess the performance of the SRPs. The following findings were found: (1) CHIRPS-2.0 and SM2RAIN-ASCAT products were capable of tracking the spatiotemporal variability of observed rainfall, (2) all SRPs had higher overall performances in the northwestern parts of the province than the other parts, (3) all SRP estimates were in better agreement with ground-based monthly observations than daily records, and (4) on the seasonal scale, CHIRPS-2.0 and SM2RAIN-ASCAT were better than PERSIANN-CCS and PERSIANN. In all seasons, CHIRPS-2.0 and SM2RAIN-ASCAT outperformed PERSIANN-CCS and PERSIANN-CDR. Based on our findings, we recommend that hydrometeorological investigations in Pakistan’s Punjab Province employ monthly estimates of CHIRPS-2.0 and SM2RAIN-ASCAT products.

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Section: Discussionmentioning

confidence: 91%

Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

Anjum

Irfan

Waseem

et al. 2022

Water

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“…Very intensive moisture stress can be noticed almost in the entire study area in 2008 and 2021. In addition, the time series of VCI during the period 2000-2021 Iran's 20-year average annual rainfall is 250 mm [58]; this statistic shows that our country's annual rainfall is much lower than the world. The average rainfall in Kerman province is 140 mm per year [59], according to the recorded statistics; this amount has decreased in recent years.…”

Section: A Spatial Patterns Of Multiple Single-factor Indices Includi...mentioning

confidence: 90%

Assessment of Spatiotemporal Characteristic of Droughts Using In Situ and Remote Sensing-Based Drought Indices

Jalayer

Sharifi

Abbasi‐Moghadam

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Drought has been identified as one of the significant complicated natural disasters exacerbated by land degradation and climate change. Hence, monitoring drought and evaluating its spatiotemporal dynamics are essential to manage regional drought conditions and protecting the natural environment. In this study, various single remote sensing-based drought indices including soil moisture condition index (SMCI), precipitation condition index (PCI), temperature condition index (TCI), and vegetation condition index (VCI) and combined RS-based drought Indices including optimized meteorological drought index (OMDI) and synthesized drought index (SDI) have been used to investigate the spatiotemporal variations of meteorological and agricultural droughts between 2000 and 2021 in Iran. The in situ drought indices, including the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) series of 1, 3, 6, 12, and 24 months were utilized to verify remote sensing-based drought indices and evaluate their applicability for analyzing drought conditions. The results indicated that the correlation coefficients of the in situ drought indices with the combined drought indices are higher than the RS-based single drought indexes. Generally, single-factor drought indexes, including VCI, TCI, PCI, and SMCI, have specific characteristics. The PCI and SMCI have an acceptable correlation with the short-term SPI and SPEI and are more applicable to monitoring short-term drought conditions. Further, the TCI has better performance in monitoring long-term drought conditions in Iran. This research concluded that the central, eastern, and southeastern parts of Iran mainly were experiencing exceptional and extreme drought conditions as the worst agricultural and meteorological drought conditions observed in the years 2008 and 2021 in the region during the last 20 years. The results also showed that, in 2019 and 2020, most areas of Iran had higher OMDI and

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“…Due to its precise spatial and temporal properties, SPPs have been extensively used to analyze the life cycle of extreme precipitation events [22]. However, due to retrieval procedures, indirect measurements, and numerical model parameters, SPPs have errors and biases [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Evaluation of Near-Real-Time Satellite-Based Precipitation: PDIR-Now over Saudi Arabia

Alharbi,

Dao,

Jimenez Arellano

et al. 2024

Remote Sensing

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In the past decade, Saudi Arabia has witnessed a surge in flash floods, resulting in significant losses of lives and property. This raises a need for accurate near-real-time precipitation estimates. Satellite products offer precipitation data with high spatial and temporal resolutions. Among these, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Dynamic Infrared Rain Rate near-real-time (PDIR-Now) stands out as a novel, global, and long-term resource. In this study, a rigorous comparative analysis was conducted from 2017 to 2022, contrasting PDIR-Now with rain gauge data. This analysis employs six metrics to assess the accuracy of PDIR-Now across various daily rainfall rates and four yearly extreme precipitation indices. The findings reveal that PDIR-Now slightly underestimates light precipitation but significantly underestimates heavy precipitation. Challenges arise in regions characterized by orographic rainfall patterns in the southwestern area of Saudi Arabia, emphasizing the importance of spatial resolution and topographical considerations. While PDIR-Now successfully captures annual maximum 1-day and 5-day precipitation measurements across rain gauge locations, it exhibits limitations in the length of wet and dry spells. This research highlights the potential of PDIR-Now as a valuable tool for precipitation estimation, offering valuable insights for hydrological, climatological, and water resource management studies.

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Evaluating three non-gauge-corrected satellite precipitation estimates by a regional gauge interpolated dataset over Iran

Cited by 18 publications

References 64 publications

Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

Assessment of Spatiotemporal Characteristic of Droughts Using In Situ and Remote Sensing-Based Drought Indices

Comprehensive Evaluation of Near-Real-Time Satellite-Based Precipitation: PDIR-Now over Saudi Arabia

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