Monitoring and projection of climate change impact on 24-h probable maximum precipitation in the Southeast of Caspian Sea

Afzali-Gorouh, Zahra; Faridhosseini, Alireza; Bakhtiari, Bahram; Mosaedi, A; Salehnia, Narges

doi:10.1007/s11069-022-05380-1

Cited by 4 publications

(5 citation statements)

References 44 publications

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“…Similar growth caused by a changing climate has been documented in India, Spain, and other parts globally (Sarkar and Maity, 2020;Monjo et al, 2023). However, opposite patterns were also reported in a few regions, possibly due to the reduced actual moisture availability and wind speed by atmospheric dynamic constraints (Afzali-Gorouh et al, 2022;Yin et al, 2023). These inconsistent and contradictory findings imply the complicated mechanism and uncertainty in PMP estimations across regions and underscore the need for a holistic qualification of PMP considering non-stationary climate and at finer spatiotemporal scales.…”

mentioning

confidence: 63%

“…Despite that the changeable PMP under a changing climate has attracted wide attention from hydrologists, most of the previous studies primarily focus on the static scenario comparisons between history and the future (Jakob et al, 2009;Kunkel et al, 2013;Sarkar and Maity, 2020;Monjo et al, 2023;Afzali-Gorouh et al, 2022). Since the return periods corresponding to the PMP values outpace the longest return periods traditionally used in applied climatology products, major water retention and routing structures will likely experience the acute impact of climate change thus highlighting the elusive sense of security inferred from the assessments ignoring the climate-change-induced probabilities of extreme events (Kunkel et al, 2013).…”

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confidence: 99%

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Variation and attribution of probable maximum precipitation of China using high-resolution dataset in a changing climate

Xiong,

Guo,

Yin

et al. 2023

Preprint

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Abstract. Accurate assessment of the probable maximum precipitation (PMP) is crucial in assessing the resilience of high-risk water infrastructures, water resource management, and hydrological hazard mitigation. Conventionally, PMP is estimated based on a static climate assumption and is constrained by the insufficient spatial resolution of ground observations, thus neglecting the spatial heterogeneity and temporal variability of climate systems. Such assumptions are critical, especially for China, which is highly vulnerable to global warming in the premise of existing ~100,000 reservoirs. Here, we use the finest spatiotemporal resolution (1d & 1 km) precipitation dataset to present the spatial distribution of 1d PMP based on the improved Hershfield method. Current reservoir design values, a quasi-global satellite-based PMP database, and in-situ precipitation are used to benchmark against our results. The 35-year running trend from 1961–1995 to 1980–2014 is quantified and partitioned, followed by future projections using the Coupled Model Inter-comparison Project Phase 6 simulations under two scenarios. We find the national PMP generally decreases from Southeast to Northwest and is typically dominated by the high variability of precipitation extremes in North China and high intensity in South China. Though consistent with previous project design values, our PMP calculations present underestimations by comparing with satellite and in-situ results due to differences in spatial scales and computation methods. Inter-annual variability, instead of the intensification of precipitation extremes, dominates the PMP running trends on a national scale. Climate change, mainly attributed to land-atmosphere coupling effects, leads to the widespread increase (>20 %) of PMP across the country under the SSP126 scenario, which is projected to be higher along with the intensification of CO2 emission. Our observation- and modeling-based results can provide valuable implications for water managers under a changing climate.

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mentioning

confidence: 63%

mentioning

confidence: 99%

Variation and attribution of probable maximum precipitation of China using high-resolution dataset in a changing climate

Xiong,

Guo,

Yin

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Similar growth caused by a changing climate has been documented in India, Spain, and other parts globally (Sarkar and Maity, 2020;Monjo et al, 2023). However, opposite patterns were also reported in a few regions, possibly due to the reduced actual moisture availability and wind speed by atmospheric dynamic constraints (Afzali-Gorouh et al, 2022;Yin et al, 2023). These inconsistent and contradictory findings imply complicated mechanisms and uncertainties in PMP estimations across regions and underscore the need for a holistic qualification of PMP considering nonstationary climates and at finer spatiotemporal scales.…”

Section: Introductionmentioning

confidence: 76%

“…Despite the changeable PMP under a changing climate attracting much attention from hydrologists, most of the previous studies primarily focus on the static scenario comparisons between history and the future (Jakob et al, 2009;Kunkel et al, 2013;Sarkar and Maity, 2020;Monjo et al, 2023;Afzali-Gorouh et al, 2022). Since the return periods corresponding to the PMP values outpace the longest return periods traditionally used in applied climatology products, major water retention and routing structures will likely experience the acute impact of climate change.…”

Section: Introductionmentioning

confidence: 99%

Variation and attribution of probable maximum precipitation of China using a high-resolution dataset in a changing climate

Xiong,

Guo,

Yin

et al. 2024

Hydrol. Earth Syst. Sci.

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Abstract. Accurate assessment of the probable maximum precipitation (PMP) is crucial in assessing the resilience of high-risk water infrastructures, water resource management, and hydrological hazard mitigation. Conventionally, PMP is estimated based on a static climate assumption and is constrained by the insufficient spatial resolution of ground observations, thus neglecting the spatial heterogeneity and temporal variability of climate systems. Such assumptions are critical, especially for China, which is highly vulnerable to global warming in ∼ 100 000 existing reservoirs. Here, we use the finest-spatiotemporal-resolution (1 d and 1 km) precipitation dataset from an ensemble of machine learning algorithms to present the spatial distribution of 1 d PMP based on the improved Hershfield method. Current reservoir design values, a quasi-global satellite-based PMP database, and in situ precipitation are used to benchmark against our results. The 35-year running trend from 1961–1995 to 1980–2014 is quantified and partitioned, followed by future projections using the Coupled Model Inter-comparison Project Phase 6 simulations under two scenarios. We find that the national PMP generally decreases from southeast to northwest and is typically dominated by the high variability of precipitation extremes in northern China and high intensity in southern China. Though consistent with previous project design values, our PMP calculations present underestimations by comparing them with satellite and in situ results due to differences in spatial scales and computation methods. Interannual variability, instead of the intensification of precipitation extremes, dominates the PMP running trends on a national scale. Climate change, mainly attributed to land–atmosphere coupling effects, leads to a widespread increase (> 20 %) in PMP across the country under the SSP126 scenario, which is projected to be higher along with the intensification of CO2 emissions. Our observation- and modeling-based results can provide valuable implications for water managers under a changing climate.

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“…Unlike previous studies that mostly rely on global or regional climate models with coarse spatial resolutions (∼10 km or beyond, Afzali-Gorouh et al, 2022;Beauchamp et al, 2013;Kunkel et al, 2013) or focus on long-duration rainfall extremes (daily scale or beyond, e.g., Gangrade et al, 2018;Hiraga et al, 2021), our convection-permitting WRF simulations with the spatial resolution of 1 km allow us to examine rainfall structures at fine spatial scales (e.g., less than 100 km 2 ) along with short durations (e.g., sub-hourly and hourly). Empirical analyses show substantially faster intensification of sub-hourly rainfall extremes than those at sub-daily scales (Ayat et al, 2022), implying the scale-dependence of rainfall response to anthropogenic climate change (Fowler et al, 2021).…”

mentioning

confidence: 93%

Response of Extreme Rainfall to Atmospheric Warming and Wetting: Implications for Hydrologic Designs Under a Changing Climate

Zhang

Yang

et al. 2023

JGR Atmospheres

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Understanding the processes of rainfall extremes and their response to anthropogenic climate change is pivotal for improved adaptation of unprecedented flood hazards around the world. Here we take the record‐breaking 20 July 2021 storm over central China as an example. We investigate the response of this particular storm to atmospheric warming (i.e., increase in air temperature) and wetting (i.e., increase in atmospheric moisture content) based on a series of convection‐permitting model simulations. Our results show non‐monotonic changes of the space‐time rainfall variability to either increased temperature or atmospheric moisture content. The most extreme rain rate is produced when relative humidity is increased by 20%–40% or temperature is increased by less than 2°C. The non‐monotonic rainfall response is more clearly revealed at fine spatial (100–1,000 km2) and temporal scales (less than 6 hr) rather than over the entire domain (∼104 km2) and aggregated over the storm duration (around 2 days). This is mainly attributable to the distinct feedbacks from atmospheric dynamics (i.e., moisture convergence and interaction with regional topography) rather than regulated by thermodynamic changes alone. Atmospheric warming poses notable changes in the vertical structure of storm cells, contributing to reduced areal reduction factors at small spatial scales and short durations, while atmospheric wetting additionally modifies storm evolution properties. Our modeling analyses challenge the existing practices for hydrologic designs under a changing climate, highlighting particular vulnerability for cities or small basins to short‐duration rainfall extremes and the resultant flash flood hazards.

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Monitoring and projection of climate change impact on 24-h probable maximum precipitation in the Southeast of Caspian Sea

Cited by 4 publications

References 44 publications

Variation and attribution of probable maximum precipitation of China using high-resolution dataset in a changing climate

Variation and attribution of probable maximum precipitation of China using high-resolution dataset in a changing climate

Variation and attribution of probable maximum precipitation of China using a high-resolution dataset in a changing climate

Response of Extreme Rainfall to Atmospheric Warming and Wetting: Implications for Hydrologic Designs Under a Changing Climate

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