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Understanding how the space‐time properties of extreme rainfall evolve in response to temperature changes is essential for assessing water‐related hazard risks. However, future sub‐daily rainfall fields, which are the primary trigger of flash floods, are not readily available across locations and warming scenarios due to the high computational cost of convection‐permitting climate models. As an alternative, we develop a Gamma‐based Spatial Quantile Mapping (GSQM) method that uses air temperature as a covariate to morph observed rainfall fields and project an archive of plausible future rainfall fields. When combined with the Stochastic Storm Transposition (SST) method, which estimates rainfall frequency at arbitrary spatial scales using gridded rainfall data, the GSQM‐SST framework provides a computationally efficient approach to project future changes in regional rainfall extremes. Using Beijing as a case study, we employ 22 years of 1‐km hourly rainfall and temperature data to demonstrate this approach. First, the observed scalings governing changes in rainfall fields with temperature are identified across various rainfall intensities. These scalings are then used to morph rainfall intensity, spatial extent, and heterogeneity. Finally, future rainfall extremes for 2‐ to 100‐year return levels under several regional warming levels are estimated by integrating the GSQM and SST methods. Results indicate that hourly extreme rainfall events tend to intensify while becoming more spatially concentrated, resulting in a 4% increase in return levels under 1° ^{\\circ}$C warming in Beijing. The GSQM‐SST approach provides a flexible and physically informed method to project future sub‐daily rainfall extremes, with potential application in regional flood risk assessment.

Increased temperature rates have the potential to change the rainfall regime in a given region, as well as to intensify its extreme events, which may lead to significant and negative socioeconomic and environmental impacts on urban populations. However, knowledge about the extent of changes in rainfall rates in Rio de Janeiro City (RJC) remains incipient; thus, it is necessary applying indices climate change to help better understanding this phenomenon. The aim of the current study is to investigate changes in rainfall distribution and increase in the number of extreme rainfall events in RJC. Daily rainfall data deriving from fifteen weather stations distributed in RJC were analyzed in the RclimDex software and Mann–Kendall test. The analysis has shown increased rainfall rates from the beginning of the series to approximately the first ten years of study. Total rainfall rate has decreased after this period. Rainfall intensity in almost all seasons has decreased after 2005; this outcome has indicated reduced annual rainfall rate and number of wet days. However, there was prevalence of positive trends in daily rainfall rates (Rx1day) and in total rainfall of five consecutive days (Rx5day). The increased number of extreme rainfall events in RJC can cause sudden inundations, floods, runoffs and river overflows with potential to cause landslides and human death due to irregular occupation of hills and slopes.

Accurate catchment level water resource assessment is the base for integrated river basin management. Due to the complexity in model structure and requirement of a large amount of input data for semi-distributed/distributed models, the conceptual models are gaining much attention in catchment modelling these days. The present study compares the performance of three conceptual models, namely GR4J, Australian Water Balance Model (AWBM) and Sacramento for runoff simulation. Four small catchments and one medium catchment in the upper Godavari river basin are selected for this study. Gap-filled daily rainfall data and potential evapotranspiration (PET) measured from the same catchment or adjacent location are the major inputs to these models. These models are calibrated using daily Nash–Sutcliffe efficiency (NSE) with bias penalty as the objective function. GR4J, AWBM and Sacramento models have four, eight and twenty-two parameters, respectively, to optimise during the calibration. Various statistical measures such as NSE, the coefficient of determination, bias and linear correlation coefficient are computed to evaluate the efficacy of model runoff predictions. From the obtained results, it is found that all the models provide satisfactory results at the selected catchments in this study. However, it is found that the performance of GR4J model is more appropriate in terms of prediction and computational efficiency compared to AWBM and Sacramento models.

Twelve commonly used probability distributions are evaluated to identify the most suitable model that could provide accurate extreme rainfall estimates in Egypt. Three popular parameter estimation methods are applied: the method of moments, L-moments and maximum likelihood. The performance of the models is evaluated based on several numerical and graphical goodness-of-fit criteria. The proposed procedure is applied to annual maximum daily rainfall data from a network of 31 stations located in Egypt. The results indicate that no single distribution performed the best at all stations. Log-Normal, Log-Pearson Type III and Exponential are the top three distributions for the frequency analysis of daily annual extreme rainfalls in Egypt, i.e. they are selected as the “optimum” models for 23%, 19% and 19% of the total stations, respectively. In contrast, the distributions: Normal, Gumbel, Logistic and Generalized Logistic are not suitable for describing the extreme rainfalls in the country. The performances of both L-moments and maximum likelihood methods are almost equal and much better than that of the method of moments. Additionally, Depth-Duration-Frequency curves were established for 18 stations by using the “optimum” model, which can support the design of hydraulic structures. The findings from this study would be helpful for rainfall frequency analysis in similar arid countries.

This study evaluates the impact of the urban heat island (UHI) on the daily and sub-daily monsoon rainfall variabilities in East Asian megacities using the high-resolution ground- and satellite-based observations for the period of 1998–2015. The three representative megacity regions, i.e., Guangdong in China, Seoul/Gyeonggi in Korea, and Tokyo in Japan, are particularly considered. A strong UHI day, defined as a summer day with UHI index greater than one standard deviation, is typically drier than normal especially in the urban area. However, when rainy, a distinct rainfall peak appears in the early afternoon, contrasting to the climatological rainfall distribution with a maximum rainfall in the early morning and a secondary maximum in the late afternoon. A stronger early-afternoon rainfall in the urban area than in the rural area becomes more pronounced as UHI intensity increases beyond a certain threshold value. The UHI-induced extreme rainfall in the afternoon, which is larger than climatology, is also robustly found in all three megacity regions. The impact of the UHI on extreme rainfall is the largest in Tokyo (55–75%), followed by Seoul/Gyeonggi (35–65%) and Guangdong (25–50%). Such regional difference can be partly explained by the difference in the geographical location and urbanization progress. This result suggests that East Asian megacities are likely prone to more extreme UHI-induced rainfall with accelerated urbanization.

Previous observational analyses have shown a declining rainfall trend over Israel, mostly statistically insignificant. The current study, for the period 1975–2020, undermines these findings, and the alarming future projections, and elaborates other ingredients of the rain regime. No trend is found for the annual rainfall, reflecting a balance between a negative trend in the number of rainy days and a positive trend in the daily rainfall intensity, both on the order of 2.0%/decade. In the mid-winter, the rainfall and the daily intensity increased, while both declined in the autumn and spring, implying a contraction of the rainy season. The time span between accumulation of 10% and 90% of the annual rainfall, being 112 days on the average, shortened by 7 days during the study period. This is also expressed by an increase of the Seasonality Index, indicating that the regional climate is shifting from “markedly seasonal with a long dry season” to “most rain in ≤3 months.” The intra-seasonal course of the rainfall trend corresponds to that of the occurrence and intensity of the Cyprus Lows and the Mediterranean Oscillation. The contraction of the rainy season and the increase in the daily intensity have far-reaching environmental impacts in this vulnerable region.

Large-scale atmospheric circulations are a significant driver of rainfall extremes. However, little attention has so far been devoted to understanding how large-scale circulation patterns influence sub-daily rainfall extremes. Using a gauge-based sub-daily rainfall dataset, we investigate the relationship between large-scale circulations and 3-hour extremes (defined here as ≥ 40 mm rainfall in 3 h) for western Europe. A set of 30 weather patterns (WPs) developed by the UK Met Office and reanalysis data of geopotential height at 500 hPa (z500) are used to represent large-scale atmospheric conditions. Strong associations with 3-hour extremes are found for a small number of WPs: over 50% of 3-hour rainfall extremes across Western Europe occur with just 5 WPs. Composites of z500 reveal the WPs resulting in southerly or south-westerly flow along the leading edge of a trough, accompanied by a ridge to the east or northeast, are most favourable for sub-daily rainfall extremes, with a statistically significant difference between the atmospheric conditions on WP days with a 3-hour extreme rainfall event compared to WP non-event days. Given that large-scale circulations are predictable much further in advance than individual extreme rainfall events, these identified relationships could therefore have important implications for forecasting, aiding in the early identification of periods with increased risk of short-duration rainfall extremes.

A limited number of studies have focused on the hydroclimate dynamics of tropical Caribbean islands. The present study aims to analyze the rainfall regime in Barbados. CHIRPS gridded dataset, at a resolution of 0.05°×0.05°, providing daily rainfall data from January 1981 until 2018 was used. The variables analyzed were the annual and seasonal maximum rainfall, the total annual and seasonal rainfall, and the number of rainy days per year and per season. Potential change points in rainfall time-series were detected with a Bayesian multiple change point detection procedure. Time series were then analyzed for detection of trends using the modified Mann-Kendall test. The true temporal slopes of the rainfall time series were obtained with the Theil-Sen’s statistic. The links between rainfall and various global climate oscillation indices were also investigated. Results indicate that no change points or significant trends were observed in the annual rainfall time series. However, it was found that some climate indices have a strong correlation with precipitation on the island, especially for the total rainfall and the number of rainy days. A stationary and non-stationary frequency analysis is carried out on the rainfall annual variables using climate oscillation indices as covariates, and uncertainties on quantile estimates are identified. It is shown that non-stationary models lead to a better fit to rainfall data. Empirical mode decomposition (EMD) is used for the long-term prediction of hydro-climatic time series. Rainfall annual time series were extended with this method for a period of 20 years. Results indicate that, within that period, annual maximum rainfall will increase by about 12 mm (or 0.6 mm/year), total annual rainfall will increase by about 200 mm (or 10 mm/year) and the number of rainy days per year will see a slight decrease by about 3 days (or 0.15 day/year).

The quality control of weather data is a necessity and a responsibility of meteorological services that store, distribute, and use these data. In the present work, a newly designed quality control procedure for daily rainfall data is presented after it has been adjusted and tested with more than 107 data from 1726 daily rainfall measurement sites in Catalonia. It is applicable to data from different origins (e.g., automatic weather stations or manual historical measurements). The procedure is focused on relative comparison of daily data with reference stations that are automatically selected after an initial estimation of their quality and a proximity study regarding location and correlation. The presented procedure has been verified taking advantage of an available network in the study area that has been routinely quality controlled by technicians of the Meteorological Service of Catalonia. The newly designed quality control procedure for daily precipitation yields good results, especially for extreme values: type I error under 10% is found for values up to 150 mm (error decreasing for lower values) and type II error is under 16% when reported values are twice a measure of 50 mm or more (error decreasing for more extreme values). After the application of the quality control procedure, a selection of series with the minimum desired quality is achieved.

This paper presents an integrated approach to simulate flooding and inundation for small- and medium-sized coastal river basins where measured data are not available or scarce. By coupling the rainfall–runoff model, the one-dimensional and two-dimensional models, and the integration of these with global tide model, satellite precipitation products, and synthetic aperture radar imageries, a comprehensive flood modeling system for Tra Bong river basin selected as a case study was set up and operated. Particularly, in this study, the lumped conceptual model was transformed into the semi-distributed model to increase the parameter sets of donor basins for applying the physical similarity approach. The temporal downscaling technique was applied to disaggregate daily rainfall data using satellite-based precipitation products. To select an appropriate satellite-derived rainfall product, two high temporal-spatial resolution products (0.1 × 0.1 degrees and 1 h) including GSMaP_GNRT6 and CMORPH_CRT were examined at 1-day and 1-h resolutions by comparing with ground-measured rainfall. The CMORPH_CRT product showed better performance in terms of statistical errors such as Correlation Coefficient, Probability of Detection, False Alarm Ratio, and Critical Success Index. Land cover/land use, flood extent, and flood depths derived from Sentinel-1A imageries and a digital elevation model were employed to determine the surface roughness and validate the flood modeling. The results obtained from the modeling system were found to be in good agreement with collected data in terms of NSE (0.3–0.8), RMSE (0.19–0.94), RPE (− 213 to 0.7%), F1 (0.55), and F2 (0.37). Subsequently, various scenarios of flood frequency with 10-, 20-, 50-, and 100-year return periods under the probability analysis of extreme values were developed to create the flood hazard maps for the study area. The flood hazards were then investigated based on the flood intensity classification of depth, duration, and velocity. These hazard maps are significantly important for flood hazard assessments or flood risk assessments. This study demonstrated that applying advanced hydrodynamic models on computing flood inundation and flood hazard analysis in data-scarce and ungauged coastal river basins is completely feasible. This study provides an approach that can be used also for other ungauged river basins to better understand flooding and inundation through flood hazard mapping.