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Extreme precipitation events have been extensively applied to the design of social infra structures. Thus, a method to more scientifically estimate the extreme event is required. This paper suggests a method to estimate the extreme precipitation in Korea using a regional climate model. First, several historical extreme events are identified and the most extreme event of Typhoon Rusa (2002) is selected. Second, the selected event is reconstructed through the Weather Research and Forecasting (WRF) model, one of the Regional Climate Models (RCMs). Third, the reconstructed event is maximized by adjusting initial and boundary conditions. Finally, the Probable Maximum Precipitation (PMP) is obtained. The WRF could successfully simulate the observed precipitation in terms of spatial and temporal distribution (R2 = 0.81). The combination of the WRF Single-Moment (WSM 6-class graupel scheme (of microphysics), the Betts-Miller-Janjic scheme (of cumulus parameterization) and the Mellor-Yamada-Janjic Turbulent Kinetic Energy (TKE) scheme (of planetary boundary layer) was determined to be the best combination to reconstruct Typhoon Rusa. The estimated PMP (RCM_PMP) was compared with the existing PMP. The RCM_PMP was generally in good agreement with the PMP. The suggested methodology is expected to provide assessments of the existing PMP and to provide a new alternative for estimating PMP.

Estimation of probable maximum flood (PMF) is a crucial process in water resources management and in the design of large hydraulic structures. However, there are uncertainties in the estimation of hydrologic conditions that contribute to extreme floods. In particular, this is the case in snow‐dominated regions, as surface air temperature and wind speed are understood to have a substantial effect on the magnitude of a flood during a storm event. Motivated by the development of a new approach to investigate and estimate reliable PMF values and in an attempt to resolve the uncertainty issues, this study introduces a physically based modeling approach. For the case study, seven watersheds located in the Sierra‐Nevada mountain range of California, including Cosumnes, Mokelumne, Stanislaus, Tuolumne, Merced, Upper San Joaquin, and Upper Kings were selected. The hydroclimate model was first implemented over the physical boundaries of the study region, and then utilized to simulate possible maximum flood conditions with input from 10 extreme precipitation scenarios. The study results provide evidence of a nonlinear atmospheric–hydrologic system; the extreme 72‐h basin‐averaged precipitation depth was found not to be linearly proportional to 72‐h flow volume equivalent depth. It can also be concluded that a large precipitation depth may not be the sole reason for a large flood event. Temperature and other atmospheric variables also contribute significantly to the production of snowfall and liquid water available for runoff, and to the resulting hydrologic response, such as the flood peak discharge and volume.

The objective of this study is to suggest a methodology for estimating the time-variant probable maximum precipitation. First, the greatest amount of precipitation for a relatively long duration, e.g., a month or longer, defined as the Long-term Probable Maximum Precipitation (LPMP) in the present work was estimated. Then, the procedure for using the LPMP estimates for determining the temporally varied upper limit of precipitation during a certain period of the year, defined as the time-variant PMP (TPMP) in this study was implemented. Hershfield's statistical approach was used to estimate LPMP, i.e., monthly, calendar monthly, and cumulative monthly, for each of 61 weather stations located in South Korea, and nationwide distribution maps of three types of LPMP were made. In particular, using the cumulative monthly PMP estimates and total antecedent precipitation prior to a certain time, the TPMP for a certain duration was forecast for example problems. From the case study for precipitation data at Chungju weather station, TPMP forecasts at certain stormy periods were substantially smaller than the design PMP, which implies that reservoir operation for flood control based on the design PMP may be too restrictive for some period. It is expected that the TPMP can serve as a complementary index to increase the water supply of a reservoir.

Flash floods have long been common in Asian cities, with recent increases in urbanization and extreme rainfall driving increasingly severe and frequent events. Floods in urban areas cause significant damage to infrastructure, communities and the environment. Numerical modelling of flood inundation offers detailed information necessary for managing flood risk in such contexts. This study presents a calibrated flood inundation model using referenced photos, an assessment of the influence of four extreme rainfall events on water depth and inundation area in the Hanoi central area. Four types of historical and extreme rainfall were input into the inundation model. The modeled results for a 2008 flood event with 9 referenced stations resulted in an R 2 of 0.6 compared to observations. The water depth at the different locations was simulated under the four extreme rainfall types. The flood inundation under the Probable Maximum Precipitation presents the highest risk in terms of water depth and inundation area. These results provide insights into managing flood risk, designing flood prevention measures, and appropriately locating pump stations.

Design flood corresponding to probable maximum precipitation (PMP) is often used in risk assessment of large hydraulic structures. PMP is widely estimated using the Hershfield method (HM) when reasonably long precipitation records are available, but other hydrometeorological data are unavailable/limited. Uncertainty in HM‐based PMP estimates can arise from multiple sources, but the literature lacks methodologies to identify significant contributors to the uncertainty and determine the overall uncertainty bounds. To address this research gap, a novel framework based on imprecise probability (IP) theory is proposed. It facilitates quantifying the overall uncertainty in PMP estimates arising from multiple sources and discerning contributions from individual sources and their combinations. Uncertainties analyzed include those due to (i) the sampling effect of precipitation observations, and (ii) 24 combinations of options for (a) defining a meaningful region/zone, (b) envelope curve space preparation, and (c) the curve construction. The effectiveness of the proposed framework in determining IP bounds of PMP estimates is illustrated through case studies on two major flood‐prone river basins (Mahanadi and Godavari) in India and the Brazos River basin in the United States. Results revealed that the highest contributor to the overall uncertainty in PMP estimates is the combined/interaction component of all four uncertainty sources for the Indian river basins and the statistical sampling effect for the United States basin. Whereas the least contribution to uncertainty is consistently from envelope curve construction. Options/guidelines are provided to reduce the uncertainty (interval between IP bounds) arising from different sources in PMP estimation with HM.

In mid-April 2024, an extreme rainfall event struck Oman and the United Arab Emirates (UAE), causing unprecedented flooding, significant infrastructure damage, and loss of life. Characterized by intense and sustained rainfall over a few days, this event highlighted the region’s vulnerability to extreme weather phenomena. This study examines the rainfall estimated by two satellite rainfall products during this event, with a detailed analysis focusing on the watershed encompassing the city of Al Ain. A rain gauge in this watershed recorded a historic 24-hour rainfall of 254.8 mm, approximately 75% of the area’s previously estimated probable maximum precipitation (PMP). Other gauges in the watershed also recorded substantial rainfall amounts, breaking previous records, while satellite products significantly underestimated the actual rainfall. A physically-based distributed hydrologic model simulated the resulting flood, indicating a low runoff ratio of about 7.14% due to high infiltration rates. Despite this, significant flooding occurred in urbanized parts of the watershed. This discrepancy highlights the complexity of urban hydrology and the challenges of predicting flood extents in urban areas. The findings underscore the need for improved flood forecasting systems in the UAE, emphasizing enhanced satellite rainfall estimation accuracy and advanced modeling approaches for better urban flood management and mitigation. Integrating new technologies and methodologies in urban flood forecasting is crucial for enhancing resilience against future extreme weather events, ensuring the safety and preparedness of vulnerable regions like the UAE.

The shift in climate regimes around 1970s caused an overall enhancement of precipitation extremes across the globe with a specific spatial distribution pattern. We used gridded observational-reanalysis precipitation dataset and two important extreme precipitation measures, namely Annual Maximum Daily Precipitation (AMDP) and Probable Maximum Precipitation (PMP). AMDP is reported to increase for almost two-third of the global land area. The variability of AMDP is found to increase more than its mean that eventually results in increased PMP almost worldwide, less near equator and maximum around mid-latitudes. Continent-wise, such increase in AMDP and PMP is true for all continents except some parts of Africa. The zone-wise analysis (dividing the globe into nine precipitation zones) reveals that zones of ‘moderate precipitation’ and ‘moderate seasonality’ exhibit the maximum increases in PMP. Recent increased in pole-ward heat and moisture transport as a result of Arctic Amplification may be associated with such spatial redistribution of precipitation extremes in the northern hemisphere.

Civil engineers design infrastructure exposed to hydrometeorological hazards, such as hydroelectric dams, using probable maximum precipitation (PMP) estimates. Current PMP estimation methods have several flaws: some required variables are not directly observable and rely on a series of approximations; uncertainty is not always accounted for and can be complex to quantify; climate change, which exacerbates extreme precipitation events, is difficult to incorporate; and subjective choices increase estimation variability. In this paper, we derive a statistical model from the World Meteorological Organization's PMP definition and use it for estimation. This novel approach leverages the Pearson Type-I distribution, a generalization of the Beta distribution over an arbitrary interval, allowing for uncertainty quantification and the incorporation of climate change effects. Multiple estimation procedures are considered, including the method of moments, maximum likelihood, and Bayesian estimation. However, statistical PMP estimation remains challenging because a short-tailed model is applied to typically heavy-tailed precipitation data. The performance of the proposed approach is assessed through a simulation study and applied to actual precipitation data from two nearby stations in Canada. Finally, we provide and discuss recommendations for best practices in PMP estimation.

The assessment of the impacts of extreme floods is important for dealing with residual risk, particularly for critical infrastructure management and for insurance purposes. Thus, modelling of the probable maximum flood (PMF) from probable maximum precipitation (PMP) by coupling hydrological and hydraulic models has gained interest in recent years. Herein, we examine whether variability in precipitation patterns exceeds or is below selected uncertainty factors in flood loss estimation and if the flood losses within a river basin are related to the probable maximum discharge at the basin outlet. We developed a model experiment with an ensemble of probable maximum precipitation scenarios created by Monte Carlo simulations. For each rainfall pattern, we computed the flood losses with a model chain and benchmarked the effects of variability in rainfall distribution with other model uncertainties. The results show that flood losses vary considerably within the river basin and depend on the timing and superimposition of the flood peaks from the basin's sub-catchments. In addition to the flood hazard component, the other components of flood risk, exposure, and vulnerability contribute remarkably to the overall variability. This leads to the conclusion that the estimation of the probable maximum expectable flood losses in a river basin should not be based exclusively on the PMF. Consequently, the basin-specific sensitivities to different precipitation patterns and the spatial organization of the settlements within the river basin need to be considered in the analyses of probable maximum flood losses.

Extreme short‐duration rainfall in the Mid‐Atlantic region of the US is examined through polarimetric radar analyses of storms that produced rainfall accumulations exceeding 1,000‐year values for time scales less than 3 hr. Polarimetric radar analyses of Mid‐Atlantic cloudbursts focus on dynamical processes associated with updrafts and downdrafts, microphysical processes associated with extreme rainfall rates and mesoscale processes associated with structure, motion and evolution of convective systems over short time scales and small spatial scales. Dynamical processes associated with updrafts and downdrafts play a key role in determining the spatial and temporal distribution of extreme rainfall and in dictating errors in radar rainfall estimates through the effects of vertical motion. The microphysics of extreme short‐duration rainfall exhibit a mix of cold and warm rain processes, with cold rain processes contributing to cycles of growth and decay in raindrop size distributions. Analyses are designed to address critical research problems linked to modernizing methods for estimating Probable Maximum Precipitation (PMP). Polarimetric radar provides an important path for estimating rainfall for PMP‐magnitude storms. We compare rainfall analyses from recent storms in the Mid‐Atlantic with cloudburst rainfall from the pre‐radar era, including storms that produced record or near‐record rainfall accumulations for the US and the world. Rainfall accumulations at time scales shorter than 3 hr for polarimetric era storms are large relative to rainfall frequency results, but modest in comparison with rainfall maxima from historical cloudbursts in the Mid‐Atlantic.