Application of remote sensing precipitation data and the CONNECT algorithm to investigate spatiotemporal variations of heavy precipitation: Case study of major floods across Iran (Spring 2019)

Sadeghi, Mani; Shearer, E. J.; Mosaffa, Hamidreza; Gorooh, Vesta Afzali; Naeini, Matin Rahnamay; Hayatbini, Negin; Katiraie-Boroujerdy, Pari-Sima; Analui, Bita; Nguyen, Phu; Sorooshian, Soroosh

doi:10.1016/j.jhydrol.2021.126569

Cited by 20 publications

(13 citation statements)

References 51 publications

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“…Precipitation measurements within 500 km of TC track data are captured and used to calculate characteristics that describe the event’s hydrometeorological characteristics, namely the mean, 90th, and 99th percentile of precipitation rates, along with the total volume of precipitation and its component over land. The method of using characteristics to describe an event or “object” is known as object-oriented analysis, described in Sellars et al 42 and used in Sellars et al 43 , 44 , Shearer et al 45 , and Sadeghi et al 46 . Characteristics are considered at an annual basis (calendar year) and are calculated per TC basin determined by genesis location: East Pacific (EP), North Atlantic (NA), North Indian (NI), South Indian (SI), South Pacific (SP), and West Pacific (WP), and together as a global aggregate.…”

Section: Introductionmentioning

confidence: 99%

Unveiling four decades of intensifying precipitation from tropical cyclones using satellite measurements

Shearer

Gorooh

Nguyen

et al. 2022

Sci Rep

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Increases in precipitation rates and volumes from tropical cyclones (TCs) caused by anthropogenic warming are predicted by climate modeling studies and have been identified in several high intensity storms occurring over the last half decade. However, it has been difficult to detect historical trends in TC precipitation at time scales long enough to overcome natural climate variability because of limitations in existing precipitation observations. We introduce an experimental global high-resolution climate data record of precipitation produced using infrared satellite imagery and corrected at the monthly scale by a gauge-derived product that shows generally good performance during two hurricane case studies but estimates higher mean precipitation rates in the tropics than the evaluation datasets. General increases in mean and extreme rainfall rates during the study period of 1980–2019 are identified, culminating in a 12–18%/40-year increase in global rainfall rates. Overall, all basins have experienced intensification in precipitation rates. Increases in rainfall rates have boosted the mean precipitation volume of global TCs by 7–15%/year, with the starkest rises seen in the North Atlantic, South Indian, and South Pacific basins (maximum 59–64% over 40 years). In terms of inland rainfall totals, year-by-year trends are generally positive due to increasing TC frequency, slower decay over land, and more intense rainfall, with an alarming increase of 81–85% seen from the strongest global TCs. As the global trend in precipitation rates follows expectations from warming sea surface temperatures (11.1%/°C), we hypothesize that the observed trends could be a result of anthropogenic warming creating greater concentrations of water vapor in the atmosphere, though retrospective studies of TC dynamics over the period are needed to confirm.

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Section: Introductionmentioning

confidence: 99%

Unveiling four decades of intensifying precipitation from tropical cyclones using satellite measurements

Shearer

Gorooh

Nguyen

et al. 2022

Sci Rep

Self Cite

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“…The longest AR path recorded in the region, occurred in 26 March 2019 that stretch from Soudan to south and south west of Iran with a length of 5700 km. the heavy precipitation that occurred in this day involved the most extreme events in terms of volume during the last two decades (Sadeghi et al, 2021). IVT value for AR in all events is above 500 kg/m/s.…”

Section: Resultsmentioning

confidence: 77%

“…Extreme rainfall is a major natural hazard in Middle East that caused severe damage to human lives, agriculture, economic and transportation in this area (Bozkurt et al, 2021;Dezfuli, 2020;Sadeghi et al, 2021). The damages of oods are effective in developing countries (Loudyi and Kantoush, 2020), including Iran.…”

Section: Introductionmentioning

confidence: 99%

Extreme rainfall in south and south west of Iran and its association with Atmospheric Rivers (ARs)

Kamal

Sepandar

Khosravi

2022

Preprint

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Atmospheric rivers (ARs) are long narrow band of high water vapor flux that closely related to heavy rainfall. Flooding from heavy rainfall is one of the major hazards in south and south west of Iran. In this paper, the connection between ARs and heavy rainfall is analyzed by a methodology based on the vertically integrated water vapor transport (IVT). Heavy rainfall days are identified from the Iran Meteorological Organization (IRIMO) from 1987 to 2019. Our results suggest that, all inclusive heavy rainfall events over the south and southwest of Iran are associated with the contributions by the ARs and they matches tend to peak in the cool season. Larger IVT value in the region related to more precipitation and play important role in determining the amount of it. The IVT value intense when the AR crossed over the region. According to the results, there were four major sources of ARs that effected on south and south west of Iran generally include Gulf of Aden, the Red Sea, Arabian Sea and Soudan. The Red Sea is the most important source of moisture those intense ARs on their path in this region.

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“…During March and April 2019 Iran faced three major waves of extreme precipitation, leading to extreme floods with long return periods in large parts of Iran 61 , 62 . Before the 2019 flood many parts of Iran suffered drought and the drying of lakes and rivers for almost 30 years due to climatic change 63 .…”

Section: Case Studymentioning

confidence: 99%

Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages

Zarei

Bozorg‐Haddad

Baghban

et al. 2021

Sci Rep

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Water is stored in reservoirs for various purposes, including regular distribution, flood control, hydropower generation, and meeting the environmental demands of downstream habitats and ecosystems. However, these objectives are often in conflict with each other and make the operation of reservoirs a complex task, particularly during flood periods. An accurate forecast of reservoir inflows is required to evaluate water releases from a reservoir seeking to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. This study aims to improve the informed decisions for reservoirs management and water prerelease before a flood occurs by means of a method for forecasting reservoirs inflow. The forecasting method applies 1- and 2-month time-lag patterns with several Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Artificial Neural Network (ANN), Regression Tree (RT), and Genetic Programming (GP). The proposed method is applied to evaluate the performance of the algorithms in forecasting inflows into the Dez, Karkheh, and Gotvand reservoirs located in Iran during the flood of 2019. Results show that RT, with an average error of 0.43% in forecasting the largest reservoirs inflows in 2019, is superior to the other algorithms, with the Dez and Karkheh reservoir inflows forecasts obtained with the 2-month time-lag pattern, and the Gotvand reservoir inflow forecasts obtained with the 1-month time-lag pattern featuring the best forecasting accuracy. The proposed method exhibits accurate inflow forecasting using SVM and RT. The development of accurate flood-forecasting capability is valuable to reservoir operators and decision-makers who must deal with streamflow forecasts in their quest to reduce flood damages.

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Application of remote sensing precipitation data and the CONNECT algorithm to investigate spatiotemporal variations of heavy precipitation: Case study of major floods across Iran (Spring 2019)

Cited by 20 publications

References 51 publications

Unveiling four decades of intensifying precipitation from tropical cyclones using satellite measurements

Unveiling four decades of intensifying precipitation from tropical cyclones using satellite measurements

Extreme rainfall in south and south west of Iran and its association with Atmospheric Rivers (ARs)

Machine-learning algorithms for forecast-informed reservoir operation (FIRO) to reduce flood damages

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