Explore the vast range of titles available.

Many shallow landslides and debris flows are precipitation initiated. Therefore, regional landslide hazard assessment is often based on empirically derived precipitation intensity-duration (ID) thresholds and landslide inventories. Generally, two features of precipitation events are plotted and labeled with (shallow) landslide occurrence or non-occurrence. Hereafter, a separation line or zone is drawn, mostly in logarithmic space. The practical background of ID is that often only meteorological information is available when analyzing (non-)occurrence of shallow landslides and, at the same time, it could be that precipitation information is a good proxy for both meteorological trigger and hydrological cause. Although applied in many case studies, this approach suffers from many false positives as well as limited physical process understanding. Some first steps towards a more hydrologically based approach have been proposed in the past, but these efforts received limited follow-up. Therefore, the objective of our paper is to (a) critically analyze the concept of precipitation ID thresholds for shallow landslides and debris flows from a hydro-meteorological point of view and (b) propose a trigger–cause conceptual framework for lumped regional hydro-meteorological hazard assessment based on published examples and associated discussion. We discuss the ID thresholds in relation to return periods of precipitation, soil physics, and slope and catchment water balance. With this paper, we aim to contribute to the development of a stronger conceptual model for regional landslide hazard assessment based on physical process understanding and empirical data.

This paper presents an improved method of using threshold of peak rainfall intensity for robust flood/flash flood evaluation and warnings in the state of São Paulo, Brazil. The improvements involve the use of two tolerance levels and the delineating of an intermediate threshold by incorporating an exponential curve that relates rainfall intensity and Antecedent Precipitation Index (API). The application of the tolerance levels presents an average increase of 14% in the Probability of Detection (POD) of flood and flash flood occurrences above the upper threshold. Moreover, a considerable exclusion (63%) of non-occurrences of floods and flash floods in between the two thresholds significantly reduce the number of false alarms. The intermediate threshold using the exponential curves also exhibits improvements for almost all time steps of both hydrological hazards, with the best results found for floods correlating 8-h peak intensity and 8 days API, with POD and Positive Predictive Value (PPV) values equal to 81% and 82%, respectively. This study provides strong indications that the new proposed rainfall threshold-based approach can help reduce the uncertainties in predicting the occurrences of floods and flash floods.

In the present paper, the variabilities and long-term trends of summer monsoon rainfall for different intensity bins (dry, low, moderate, high, very high, and extreme) are studied for five homogeneous regions, namely Northeast India (NEI), Northcentral India (NCI), Northwest India (NWI), Westcentral India (WCI), and Peninsular India (PI) for 118 years (1901–2018). The study was carried out based on gridded rainfall data from the India Meteorological Department (IMD). The rainfall characteristics such as number of rainy days, percentage contribution, and periodicity of rainfall intensity classes are analysed and found to be different in different homogeneous regions. The long-term trend (1901–2018) of total rainfall showed a significant increasing trend (19.9 mm decade−1) in NEI and significant decreasing trends in NCI (9.6 mm decade−1) and PI (4.9 mm decade−1). Analysis on rainfall intensity indicates a significant increasing trend for high, very high, and extreme classes in NEI, a significant increasing trend for dry, and a decreasing trend for moderate and very high classes over NCI and PI. From correlation analysis among the homogeneous regions, it is found that the rain events in different intensity classes show different relationships, which indicate the regional heterogeneity in rainfall characteristics. It is also important to note that an increase in rainfall contribution from very high and extreme classes was found over NEI, NWI, and WCI in the multidecadal period of 1991–2018, while NCI showed a decrease during this period; however, in NCI, a drastic increase for these intensity bins is distinct during the 1961–1990 multidecadal period. In addition to the trends and variabilities, we also explored spatial heterogeneity of different rainfall intensity categories, and found remarkable differences from one homogeneous region to another.

Extreme rainfall has changed in frequency and intensity as a result of climate change, and its impact on nature and society is far greater than that of rainfall change. Scientifically defining extreme rainfall events and obtaining the design rainfall is of great significance to analyze the impact of climate change on extreme precipitation events. Existing definitions of extreme precipitation events have usually focused on rainfall amounts at fixed durations rather than complete events with variable durations. Using sub-daily precipitation and runoff data covering 1971–2018 from 17 stations over the Jingle sub-basin, we constructed an appropriate copula function to define extreme persistent rainfall events under bivariate analysis and obtained the rainfall process of the designed rainfall under different return periods. The results showed that (1) the joint distribution based on Copula was more accurate than the traditional univariate probability methods for identifying extreme rainfall events with a threshold of 90%. (2) The Copula joint distribution established in the basin can well calculate the designed extreme rainfall under the return periods of 100 a, 50 a, 30 a, 25 a, 10 a and 5 a. (3) The rainfall intensity duration model had a good simulation effect on the rainfall intensity duration distribution of the basin.

The summer rainfall amount over East China is expected to increase along with a strengthening of the East Asian summer monsoon in a warmer climate. However, how the seasonality of precipitation will respond to global warming remains uncertain and is closely related to monsoon circulation. Here, we project future changes in multiple intra-seasonal rainfall characteristics over East China under 1.5 °C, 2 °C, 2.5 °C, and 3 °C of global warming above pre-industrial levels based on coupled model intercomparison project phase 6 multi-model projections. Both the onset and cessation dates over South China are likely to be delayed in a warmer climate, resulting in a later shift of the rainy season. In contrast, advanced cessation dates are projected over Northeast China with high model consensus. As for rainfall characteristics within the rainy season, total rainy season rainfall is expected to increase over the whole East China domain, with remarkable enhancement of heavy rainfall intensity. Further analysis indicates that continuous warming over a 1.5 °C warmer climate is projected to further increase total rainy season rainfall and enhance heavy rainfall intensity, with a magnitude at least twice as large with additional warming of 0.5 to 1.5 °C. Also, changes in cessation dates over South and Northeast China are projected to be enhanced significantly. These results together indicate the vital need to slow down global warming to reduce potential adverse impacts on agricultural and socioeconomic development.

We used empirical–statistical downscaling to derive local statistics for 24 h and sub-daily precipitation over the Nordic countries, based on large-scale information provided by global climate models. The local statistics included probabilities for heavy precipitation and intensity–duration–frequency (IDF) curves for sub-daily rainfall. The downscaling was based on estimating key parameters defining the shape of mathematical curves describing probabilities and return values, namely the annual wet-day frequency, fw, and the wet-day mean precipitation, μ. Both parameters were used as predictands representing local precipitation statistics as well as predictors representing large-scale conditions. We used multi-model ensembles of global climate model (CMIP6) simulations, calibrated on the ERA5 reanalysis, to derive local projections and future outlooks. Our analysis included an evaluation of how well the global climate models reproduced the predictors in addition to assessing the quality of downscaled precipitation statistics. The evaluation suggested that present global climate models capture essential aspects of the covariance, and there was a good match between annual wet-day frequency and wet-day mean precipitation derived from ERA5 on the one hand and local rain gauges in the Nordic region on the other. Furthermore, the ensemble downscaled results for annual fw and μ were approximately normally distributed, which may justify using the ensemble mean and standard deviation to describe the ensemble spread. Hence, our efforts provide a demonstration for how empirical–statistical downscaling can be used to provide practical information on heavy rainfall, which subsequently may be used for impact studies. Future projections for the Nordic region indicated little increase in precipitation due to more wet days, but most of the contribution comes from increased mean intensity. The west coast of Norway had the highest probabilities of receiving more than 30 mm d−1 precipitation, but the strongest relative trend in this probability was projected over northern Finland. Furthermore, the highest estimates for trends in 10-year and 25-year return values were projected over western Norway, where they were high from the outset. Our results also suggested that future precipitation intensity is sensitive to future emissions, whereas the wet-day frequency is less sensitive.

Rainfall intensity at a specific time is an important factor affecting the occurrence of combined sewer overflow (CSO). In this study, a statistical analysis of rainfall intensity frequency considering rainfall time (or cross-section) in the diurnal cycle were conducted based on the original 10-year rainfall intensity time series (the temporal resolution is 5 min). First, the stationarity of two different types of time series was evaluated by Augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) test, including the original rainfall time series and the diurnal cycle time series of five statistical characteristics (mean value (Mean), standard deviation (Std), coefficient of variation (Cv), skewness coefficient (Cs) and kurtosis coefficient (Kurt)). Moreover, the cumulative distribution function (CDF) of rainfall intensity at different cross-sections was analyzed. Finally, the best-fitting CDF of cross-section was used to quantify the CSO overflow frequency in the diurnal cycle under different thresholds. Results revealed that: (1) The original 10-year rainfall time series was second-order stationary time series. (2) The diurnal cycle time series of rainfall intensity statistics (Mean and Std) were non-stationary while those of rainfall intensity statistics (Cv, Cs and Kurt) were second-order stationary. (3) CDF of rainfall intensity at different cross-sections can be elaborated by the Generalized exponential distribution (Genexpon) and Generalized Pareto distribution (GPD) (R2 > 0.914). (4) CSO overflow has a high probability of occurring in three time intervals: (4:0–5:25), (15:35 − 16:40), and (20:30 − 22:55).HighlightsThe original rainfall time series was second-order stationary.The diurnal cycle time series of Mean and Std were non-stationary.The diurnal cycle time series of Cv, Cs and Kurt were second-order stationary.CDF of rainfall intensity can be expressed as the generalized exponential distribution and generalized pareto distribution.CSO overflow has a high probability of occurring in three time intervals.

Intensity–duration–frequency (IDF) statistics describing extreme rainfall intensities in Norway were analysed with the purpose of investigating how the shape of the curves is influenced by geographical conditions and local climate characteristics. To this end, principal component analysis (PCA) was used to quantify salient information about the IDF curves, and a Bayesian linear regression was used to study the dependency of the shapes on climatological and geographical information. Our analysis indicated that the shapes of IDF curves in Norway are influenced by both geographical conditions and 24 h precipitation statistics. Based on this analysis, an empirical model was constructed to predict IDF curves in locations with insufficient sub-hourly rain gauge data. Our new method was also compared with a recently proposed formula for estimating sub-daily rainfall intensity based on 24 h rain gauge data. We found that a Bayesian inference of a PCA representation of IDF curves provides a promising strategy for estimating sub-daily return levels for rainfall.

West African Sahel and Soudan extreme rainfall events are impactful when strong mesoscale convective systems (MCSs) produce large amounts of rainfall in short periods. NASA IMERG rainfall estimates and the ERA5 reanalysis are examined to understand where the top 100 highest 12Z–12Z 24-h rainfall totals and MCS storm genesis occur, and to assess the relative importance of environmental conditions in their generation including the influence of atmospheric moisture and vertical wind shear. Most of the top 100 events are located south of 14° N over the Soudan. Events cluster over three regions, namely, Mali, Burkina Faso, and northern Nigeria. The associated MCSs are typically not locally generated, forming instead at distances greater than 100 km upstream. Composites reveal that a significant increase in atmospheric moisture content occurs prior to development, but there is no evidence of significant changes in the 600–925 hPa vertical wind shear. This indicates that changes in vertical wind shear are less influential in extreme storm development than atmospheric moisture preconditioning. The top 10 events are further evaluated. A change in these storms’ direction and speed near the maximum rainfall location is common, suggesting the MCSs are reorganizing around peak rainfall intensity time. Three atmospheric conditions are associated with these events. They are (1) moisture preconditioning of the atmosphere, (2) interaction of the storm in the wake of a region of anticyclonic flow, and (3) interaction of the storm in the wake of a region of anticyclonic flow and the Sahel/tropical dryline boundary.

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.