Future Characteristics of Extreme Precipitation Indicate the Dominance of Frequency Over Intensity: A Multi‐Model Assessment From CMIP6 Across India

Sarkar, Santu; Maity, Rajib

doi:10.1029/2021jd035539

Cited by 18 publications

(17 citation statements)

References 126 publications

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“…The estimated return level helps examine the extreme precipitation, leading to a derived Probable Maximum Flood (PMF), and it will be used to design high-risk infrastructures such as large dams and nuclear power plants where failure would be catastrophic (Visser et al, 2022). And this is consistent with previous findings that showed an increasing and shifting pattern of precipitation over the IRBs during the post-1980 (Chaubey et al, 2022;Sarkar & Maity, 2022). Also, this decline in rainfall over northeast India is consistent with previous researchers (Kuttippurath et al, 2021), which conclude that its due to the changes in land cover and become detrimental to vegetation activity.…”

Section: Changes In Extreme Precipitationsupporting

confidence: 84%

“…In this study, we used a multi-model approach to estimate the future changes in hydroclimate extremes over the IRBs. Many previous researchers have used the multi-model ensemble mean approach to finding the projected precipitation extremes and showed that this is the more robust analysis to estimate extreme events (Sarkar & Maity, 2022). To examine the changes in hydroclimate extremes over the IRBs, we divided the data sets into three-time frames from 2021 to 2040 for Near-future, 2041 to 2060 for Mid-future, and 2081 to 2100 for Far-future.…”

Section: Observed and Projected Data Setsmentioning

confidence: 99%

“…The spatiotemporal changes in hydro‐climate extremes are determined by the frequency and intensity of the daily precipitation events (Salehie et al., 2023; Sarkar & Maity, 2022). According to Huang et al.…”

Section: Introductionmentioning

confidence: 99%

“…To reduce the above uncertainties, a different bias correction method is applied by researchers, in which mean‐based bias correction is found to be more suitable for climate scenarios (Jaiswal et al., 2022; Saha & Sateesh, 2022; Shrestha et al., 2020; Xu et al., 2021). To illustrate the hydroclimate extremes during near, mid, and far future, the study used the indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI) used by the previous researcher (Chaubey et al., 2022; Sarkar & Maity, 2022). The other indices, the Standardized Precipitation Index (SPI), are applied in earlier studies to examine the long‐term dryness and wetness situation (McKee, 1993).…”

Section: Introductionmentioning

confidence: 99%

“…The projected spatial extent of precipitation extremity is determined to increase in large parts of India and about 60%, with a robust increase frequency of extreme events in central India (Shahi et al., 2023). Moreover, the projected frequency is more the western Ghats and the northeast part of India, while the intensity of extreme precipitation is projected to increase in the northwest and peninsular India (Sarkar & Maity, 2022). Also, Lutz et al.…”

Section: Introductionmentioning

confidence: 99%

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Intensification of Extreme Rainfall in Indian River Basin: Using Bias Corrected CMIP6 Climate Data

Chaubey,

Mall

2023

Earth's Future

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The changing frequency of extreme rain events in the past few decades over the Indian river basins (IRBs) contributed to floods and drought and resulted in economic losses and gross domestic product. In this study, we evaluated the performance of 12 Global Circulation Models from the Coupled Model Intercomparison Project Phase 6‐ experiment with India Meteorological Department observed data sets to reproduce the extreme rainfall events as well as project the changes in frequency and intensity of the hydroclimate extremes in future. We found that under low emission scenarios (SSP1‐2.6), the frequency of extreme rainfall is going to increase over the western ghat and northeast IRBs, while an increase in heavy rainfall intensity (14.3%) noticed under SSP2‐4.5 in the upper Ganga and Indus basin. Also, approximately 4%–10% of the heavy rainfall is projected to increase over the western part of IRBs during the Near (2021–2040) and Mid (2041–2060) future. The study explored the new hotspot regions for future urban flooding due to increasing pattern of heavy rainfall in future. Moreover, the lower Ganga basin will experience agricultural drought in near future due to decreasing areal mean rainfall, which needs to be seen by policymakers for managing the excess (less) water. Also, India's northern, central, and western river basins may experience more extremes under high‐emission (SSP5‐8.5) scenarios that indicate challenges to mitigation. The findings of this study highlight the importance of developing long‐term adaptation and mitigation strategies aimed at reducing hydroclimate vulnerability. It emphasizes the need to implement measures that enhance resilience and minimize risks associated with hydroclimate extremes at the basin level.

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Section: Changes In Extreme Precipitationsupporting

confidence: 84%

Section: Observed and Projected Data Setsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Intensification of Extreme Rainfall in Indian River Basin: Using Bias Corrected CMIP6 Climate Data

Chaubey,

Mall

2023

Earth's Future

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Quantification of uncertainties in projections of extreme daily precipitation simulated by CMIP6 GCMs over homogeneous regions of India

Nair,

Rajesh,

Sahai

et al. 2023

Intl Journal of Climatology

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Global climate model (GCM) projections are subject to significant uncertainties. Quantifying uncertainties in climate change projections improves credibility and makes climate data more reliable. This study aims to quantify the uncertainties in projected extreme precipitation during the 21st century over the homogeneous rainfall regions of India simulated by Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs. The percentile‐based square root error variance (SREV) method estimates model, scenario and ensemble uncertainties in projections of extreme precipitation. The uncertainty is investigated at four thresholds: 95th, 99th, 99.9th and 100th percentiles. The results show that the wet northeast region has a greater SREV, which is consistent with previous studies. At 99th and 99.9th percentiles, relative model SREV is dominant over the northeast (NE) region. However, at the 95th percentile high relative model SREV is found over the northwest (NW) region during southwest (June, July, August and September) and NE (October, November and December) monsoon seasons. Model uncertainty is the main source of uncertainty, followed by scenario and ensemble uncertainties. The study indicates that the arid NW region in India has a higher level of uncertainty than other regions with homogeneous rainfall. These findings will assist policymakers in planning infrastructure development in arid regions of India.

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Characteristics and projected changes in maximum daily precipitation across the globe

Uchale,

Singh

2024

Quart J Royal Meteoro Soc

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Research indicates a notable rise in extreme precipitation events across the globe in association with a warmed climate. A considerable chunk of such assessments focuses either on global land areas or specific regions, and these have often been reported on an annual basis. However, it is crucial to note that significant monthly variations occur in the precipitation regimes, especially during seasonal transitions. This research evaluates the statistical characteristics of extreme precipitation globally in observations and Coupled Model Intercomparison Project Phase 6 model projections. Our assessment focuses on maximum daily precipitation on an annual, seasonal, and monthly scale globally for both historical and future climate in the 21st century. The historical assessment is conducted based on the 1980–2014 period, whereas the future climate is sectioned into near future (2040–2060) and far future (2070–2090) focusing on two emission scenarios: SSP245 and SSP585. Monthly assessments of maximum daily precipitation indicate an expansion in the probability density curve's width starting from the boreal winter season going into the peak‐to‐late summer season both in observations and model simulations. Transitioning between unimodal and bimodal distributions in density curves indicates the appearance of one more class of grid points that receive higher precipitation; for example, over the land. Despite large inter‐model spread, future projections assessed based on multimodel mean reveal a substantial rise in maximum daily precipitation in a warmer climate. The reported increase in the intensity of maximum daily precipitation compared with historical climate outweighs the decrease by at least a factor of 1.2 in both scenarios, which is, further, more prominent in SSP585. A large decrease and increase in the intensity of extremes indicates that it may become more variable and intense in future. Additionally, land regions, particularly along the intertropical convergence zone and global monsoon domains, are expected to exhibit more pronounced intensification in future, whereas the decline is mostly confined to tropical–subtropical oceanic regions.

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Future Characteristics of Extreme Precipitation Indicate the Dominance of Frequency Over Intensity: A Multi‐Model Assessment From CMIP6 Across India

Abstract: The intensification of the global hydrological cycle is the most consistent and ubiquitous signature of the changing scenario of climate in this 21st century's world (

Cited by 18 publications

References 126 publications

Intensification of Extreme Rainfall in Indian River Basin: Using Bias Corrected CMIP6 Climate Data

Intensification of Extreme Rainfall in Indian River Basin: Using Bias Corrected CMIP6 Climate Data

Quantification of uncertainties in projections of extreme daily precipitation simulated by CMIP6 GCMs over homogeneous regions of India

Characteristics and projected changes in maximum daily precipitation across the globe

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