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Accurate short-term rainfall–runoff prediction is essential for flood mitigation and safety of hydraulic structures and infrastructures. This study investigates the capability of four machine learning methods (MLM), optimal pruning extreme learning machine (OPELM), multivariate adaptive regression spline (MARS), M5 model tree (M5Tree, and hybridized MARS and Kmeans algorithm (MARS-Kmeans), in hourly rainfall–runoff modeling (considering 1-, 6- and 12-h horizons). Their results are compared with a conceptual method, Event-Based Approach for Small and Ungauged Basins (EBA4SUB) and multi-linear regression (MLR). Hourly rainfall and runoff data gathered from Ilme River watershed, Germany, were divided into two equal parts, and MLM were validated considering each part by swapping training and testing datasets. MLM were compared with EBA4SUB using four events and with respect to three statistics, root-mean-square errors (RMSE), mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE). Comparison results revealed that the newly developed hybridized MARS-Kmeans method performed superior to the OPELM, MARS, M5Tree and MLR methods in prediction of 1-, 6- and 12-h ahead runoff. Comparison with conceptual method showed that all the machine learning models outperformed the EBA4SUB and OPELM provided slightly better performance than the other three alternatives in event-based rainfall–runoff modeling.Graphic abstract

The 2020 Meiyu season has received extensive attention due to its record-breaking rainfall in the Yangtze–River Huai Basin (YHRB) region of China. Although its rainfall features have been well studied on various time scales, the sub-hourly/hourly rainfall features are unknown. In this study, a wavelet analysis was applied to 1 min rainfall data from 480 national rain gauges across the YHRB, and hourly synoptic patterns during the Meiyu season were grouped using an obliquely rotated principal component analysis in T-mode (PCT). The results suggest that variances on the sub-hourly and hourly scales contributed 63.4% of the 2020 Meiyu rainfall. The hourly synoptic variations in the Meiyu season can be categorized into three major patterns: weak synoptic forcing (P1), a convergence line (P2), and a vortex (P3). The rainfalls under P1 were spatially dispersed over the YHRB and on the shortest time scale, with a 70.4% variance from sub-hourly to hourly rainfalls. P2 had a peak wavelet variance around 30 min–1 h, with rainfalls concentrated to the south of the convergent line. The rainfalls under P3 were locally distributed with a longer duration of around 1–4 h. Compared with the climate mean, hourly rainfall frequencies are indispensable to understanding the 2020 accumulated Meiyu rainfall anomaly. This research highlights the dominant role of synoptic patterns on the temporal and spatial features of the Meiyu rainfall.

The warning accuracy, false alarm rate and timeliness of regional geological hazard early warning models (GHEWMs) have an important impact on significantly reducing the damage caused by geological hazards. Most of the existing regional GHEWMs are based on forecast rainfall. Due to the influence of rainfall forecast accuracy and other factors, its early warning accuracy, false alarm rate and timeliness are still difficult to meet the needs of engineering applications such as disaster avoidance, mitigation and prevention of geological hazards. Therefore, this paper proposes a regional GHEWM based on the hourly rainfall series (HRS) of real-time automatic rainfall stations. Based on the data of 689 geological hazards that have occurred in Huangshan City from 2018 to 2021 and the corresponding rainfall data of automatic rainfall stations, the model uses the dynamic time warping (DTW) algorithm on the Spark big data platform to extract the historical HRS of each geological hazard and calculates the highest similarity between it and the current HRS in parallel. By coupling the probability of occurrence of geological hazards and the highest similarity of the above-mentioned HRS, a regional GHEWM based on real-time rainfall big data is finally constructed. The research results show that the model's early warning accuracy reaches 85%, and the false alarm rate is only 15%, which can predict the possibility of geological hazards after the next 3 h.

The timing of short extreme rainstorm, which was usually thought to occur on midsummer afternoons, was investigated to improve future mitigation options for infrastructure and safety from localised flash flooding. Using a peak-over-threshold approach, the timing of 10- and 60-min extreme events was filtered from high-resolution rainfall series assessing diurnal, seasonal, and annual distributions and analysed for spatial variations and prevailing atmospheric circulation types (CTs). The diurnal distribution showed a clear deviation from that of the entire rainfall regime. A complex spatial pattern was identified with distinct timing signatures of storms in the northern (mostly afternoon) and southern regions (a bimodal distribution with a second peak in the early morning) of Germany and a more homogenous diurnal distribution of events across the central regions. Most storms occurred in summer, but 42% of 10-min events occurred outside the summer months (June–July–August). A distinct annual clustering of extremes was identified, which varied distinctly between the 10- and 60-min extremes, indicating that the sub-hourly and hourly events were far from running conterminously. The timing of extreme events on the investigated time scales was not dominated by the occurrence of specific CTs in most cases, suggesting that other factors control these extremes.

The long-term trends in the occurrence frequency of pre-summer daytime and nocturnal extreme hourly rainfall (EXHR) during 1988-2018 in Hong Kong and their spatial distributions are examined and analyzed. Despite a significant increasing trend observed in the occurrence frequency of pre-summer EXHRs during the investigated period, the increase in daytime and nocturnal EXHRs show distinct spatial patterns. Nocturnal EXHRs show uniform increasing trends over the entire Hong Kong. However, the increase in daytime EXHRs is concentrated over the northern or eastern areas of Hong Kong, indicating a downstream shift of pre-summer EXHRs in Hong Kong with regard to the prevailing southwesterly monsoonal flows in south China. The clustering of weather types associated with daytime and nocturnal EXHRs further reveals that the increase in EXHRs over Hong Kong are mainly contributed by the increase of the events associated with southwesterly monsoonal flows with relatively high speeds. During the past few decades, the southwesterly monsoonal flows at coastal south China have undergone a substantial weakening due to the increased surface roughness induced by the urbanization over the Guangdong-Hong Kong-Macau Greater Bay Area since 1990s, leading to enhanced low-level convergence and thus significant increase in EXHRs at coastal south China. Meanwhile, daytime sea-wind circulation at coastal south China is markedly enhanced during the investigated period, which is the main reason for the northward shift of daytime EXHRs in Hong Kong. In addition, the blocked southwesterly monsoonal flows at coastal south China are detoured eastward, leading to stronger convergence and increase in EXHRs at eastern coast of Hong Kong, especially during daytime, when the easterly sea winds prevail at the region.

The aim of this paper is to present a stochastic model to generate sub-hourly rainfall events at a given point. Historical events used as the input have been extracted by the sub-hourly rainfall series available for a defined rain gauge station based on a fixed inter-event time and selected if their average intensity was larger than a critical fixed one. The sub-hourly events generated by applying the proposed methodology are completely stochastic and their main characteristics, i.e., shape, duration and average intensity, have been derived as a function of the statistics of the historical events analyzed. In order to characterize the shape, dimensionless hyetographs have been derived. They have been statistically modelled by using the Beta cumulative distribution. Average intensity and duration of the historical events were first modelled separately by fitting several probability distributions and selecting the best one using the more common statistical criteria. Then, their correlation was modelled using the Frank’s copula. In order to test the methodology, two sites in Sicily, Italy, where 10 min’ recorded rainfall data were available, were analyzed. Finally, comparison between the statistics of the simulated events and those of the measured data demonstrates the good performance of the model.

An extremely heavy rainfall event occurred in Zhengzhou, China, on 20 July 2021 and produced an hourly rainfall rate of 201.9 mm, which broke the station record for mainland China. Based on radar observations and a convection-permitting simulation using the WRF-ARW model, this paper investigates the multiscale processes, especially those at the mesoscale, that support the extreme observed hourly rainfall. Results show that the extreme rainfall occurred in an environment characteristic of warm-sector heavy rainfall, with abundant warm moist air transported from the ocean by an abnormally northward-displaced western Pacific subtropical high and Typhoon In-Fa (2021). However, rather than through back building and echo training of convective cells often found in warm-sector heavy rainfall events, this extreme hourly rainfall event was caused by a single, quasi-stationary storm in Zhengzhou. Scale separation analysis reveals that the extreme-rain-producing storm was supported and maintained by the dynamic lifting of low-level converging flows from the north, south, and east of the storm. The low-level northerly flow originated from a mesoscale barrier jet on the eastern slope of the Taihang Mountain due to terrain blocking of large-scale easterly flows, which reached an overall balance with the southerly winds in association with a low-level meso- β -scale vortex located to the west of Zhengzhou. The large-scale easterly inflows that fed the deep convection via transport of thermodynamically unstable air into the storm prevented the eastward propagation of the weak, shallow cold pool. As a result, the convective storm was nearly stationary over Zhengzhou, resulting in record-breaking hourly precipitation.

The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products provide quasi-global (60° N–60° S) precipitation estimates, beginning March 2014, from the combined use of passive microwave (PMW) and infrared (IR) satellites comprising the GPM constellation. The IMERG products are available in the form of near-real-time data, i.e., IMERG Early and Late, and in the form of post-real-time research data, i.e., IMERG Final, after monthly rain gauge analysis is received and taken into account. In this study, IMERG version 3 Early, Late, and Final (IMERG-E,IMERG-L, and IMERG-F) half-hourly rainfall estimates are compared with gauge-based gridded rainfall data from the WegenerNet Feldbach region (WEGN) high-density climate station network in southeastern Austria. The comparison is conducted over two IMERG 0.1°  ×  0.1° grid cells, entirely covered by 40 and 39 WEGN stations each, using data from the extended summer season (April–October) for the first two years of the GPM mission. The entire data are divided into two rainfall intensity ranges (low and high) and two seasons (warm and hot), and we evaluate the performance of IMERG, using both statistical and graphical methods. Results show that IMERG-F rainfall estimates are in the best overall agreement with the WEGN data, followed by IMERG-L and IMERG-E estimates, particularly for the hot season. We also illustrate, through rainfall event cases, how insufficient PMW sources and errors in motion vectors can lead to wide discrepancies in the IMERG estimates. Finally, by applying the method of Villarini and Krajewski (2007), we find that IMERG-F half-hourly rainfall estimates can be regarded as a 25 min gauge accumulation, with an offset of +40 min relative to its nominal time.

In many areas of the world, the prediction of rainfall-induced landslides is usually carried out by means of empirical rainfall thresholds. Their definition is complicated by several issues, among which are the evaluation and quantification of diverse uncertainties resulting from data and methods. Threshold effectiveness and reliability strongly depend on the quality and quantity of rainfall measurements and landslide information used as input. In this work, the influence of the temporal resolution of rainfall measurements on the calculation of landslide-triggering rainfall thresholds is evaluated and discussed. For the purpose, hourly rainfall measurements collected by 172 rain gauges and geographical and temporal information on the occurrence of 561 rainfall-induced landslides in Liguria region (northern Italy) in the period 2004–2014 are used. To assess the impact of different temporal resolutions on the thresholds, rainfall measurements are clustered in increasing bins of 1, 3, 6, 12 and 24 h. A comprehensive tool is applied to each dataset to automatically reconstruct the rainfall conditions responsible for the failures and to calculate frequentist cumulated event rainfall–rainfall duration (ED) thresholds. Then, using a quantitative procedure, the calculated ED thresholds are validated. The main finding of the work is that the use of rainfall measurements with different temporal resolutions results in considerable variations of the shape and the validity range of the thresholds. Decreasing the rainfall temporal resolution, thresholds with smaller intercepts, higher slopes, shorter ranges of validity and higher uncertainties are obtained. On the other hand, it seems that the rainfall temporal resolution does not influence the validation procedure and the threshold performance indicators. Overall, the use of rainfall data with coarse temporal resolution causes a systematic underestimation of thresholds at short durations, resulting in relevant drawbacks (e.g. false alarms) if the thresholds are implemented in operational systems for landslide prediction.

Based on the hourly rainfall gauge data and ERA5 reanalysis for the period 1980–2020, typical synoptic patterns responsible for summer regional hourly extreme precipitation events (RHEPE) over the middle and lower Yangtze River basin have been objectively identified using a circulation clustering method. It is found that the Meiyu front with different locations and intensities imbedded in the East Asian summer monsoon, and landfalling typhoons are the leading contributors. As the dominant synoptic pattern, the Meiyu front pattern is associated with ∼92% of the total RHEPE occurrence and can be categorized into a southerly strong‐Meiyu type and a northerly weak‐Meiyu type. The RHEPE occurrence shows a predominant morning peak associated with the southerly strong‐Meiyu type and a secondary late afternoon peak related to the northerly weak‐Meiyu type, in which the Meiyu front is pushed northward by the strengthened western North Pacific subtropical high accompanied by accelerated low‐level southwesterly flow. Plain Language Summary Using ERA5 reanalysis and hourly gauge rainfall measurements in summers of 1980–2020, this study investigates the driving mechanisms and temporal variation of summer regional hourly extreme rainfall events over the middle and lower Yangtze River basin. Typical synoptic patterns responsible for the summer regional hourly rainfall extremes are objectively identified using spectral clustering analysis. The Meiyu front with different locations and intensities imbedded in East Asian summer monsoon and the landfalling typhoons are the synoptic patterns leading to the regional hourly rainfall extremes. The diurnal twin peaks (morning and late afternoon) in the occurrence of regional hourly rainfall extremes are related to the Meiyu front with different locations and intensities. The results from this investigation may help improve the prediction and climate risk assessment of regional extreme rainfall events. Key Points The Meiyu front imbedded in the East Asian summer monsoon and landfalling typhoons are the leading contributors of summer regional hourly extreme precipitation events (RHEPE) over middle and lower Yangtze River basin The Meiyu front pattern can be sorted into a southerly type with strong Meiyu front and a northerly type with weak Meiyu front The northerly weak Meiyu front pattern with active convection contributes the most to the afternoon diurnal peak of RHEPE occurrence