Sub-daily Precipitation Extremes Research Articles

Long‐term evolution and non‐stationary behaviours of daily and subdaily precipitation extremes in China

AbstractThe rise of extreme precipitation events has attracted wide attention throughout the world. However, the analysis of precipitation extremes is not sufficient in the short period of instrumental observation and it is complicated by their non‐stationary behaviours. As a period dominated by natural forcing, the last millennium is helpful for understanding long‐term changes in precipitation extremes. Therefore, this study aimed to (1) investigate changes in daily and subdaily precipitation extremes in China from the last millennium to the end of the twenty‐first century, (2) detect the non‐stationarity of precipitation extremes and (3) compare design storms by using stationary and non‐stationary distributions. The annual maximum 1‐day precipitation (RX1day) and annual maximum 3‐hour precipitation (RX3hour) are used to quantify variations of precipitation extremes. The results show that the trend of RX1day was insignificant for the Medieval Climate Anomaly (MCA, 950–1250) and the Little Ice Age (LIA, 1500–1800). For the historical period (1850–2014), the trend of RX1day and RX3hour was insignificant in most subregions. In addition, the differences for RX1day between the historical period and the MCA/LIA were mostly in the range of −5% to 5%. For the future period (2015–2100), RX1day and RX3hour are projected to increase in almost all land grids compared with the historical period, and they show greater increases in western China than eastern China. The non‐stationarity of RX1day and RX3hour is mainly found under the high‐emission scenario. Moreover, stationary generalized extreme value (GEV) distribution underestimates 50‐year return levels for RX1day and RX3hour in most areas compared with non‐stationary GEV distribution under the high‐emission scenario. Overall, this study suggests that daily and subdaily extreme precipitation will intensify over China in the future. For design storms, non‐stationary methods need to be used for risk management and engineering design under the high‐emission scenario.

Brief communication: A first hydrological investigation of extreme August 2023 floods in Slovenia, Europe

Abstract. Extreme floods occurred from 4 to 6 August 2023 in Slovenia causing three casualties and causing total direct and indirect damage, including post-disaster needs according to the Post-Disaster Needs Assessment (PDNA), close to EUR 10 billion. The atypical summer weather conditions combined with the high air and sea temperatures in the Mediterranean and the high soil moisture led to the most extreme flood event in Slovenia in recent decades. The return periods of both daily and sub-daily precipitation extremes and peak discharges reached 250–500 years, and the runoff coefficient of a typical torrential and mostly forested mesoscale catchment was around 0.5. In addition, flooding, soil erosion, mass movements and river sediment transport processes caused major damage to buildings (more than 12 000 houses) and diverse infrastructure.

Subdaily Extreme Precipitation and Its Linkage to Global Warming Over the Tibetan Plateau

AbstractThe spatiotemporal characteristics of subdaily extreme precipitation over the Tibetan Plateau (TP) have undergone significant changes due to global warming. In this study, we employed the high‐resolution Weather Research and Forecasting regional climate model to conduct a series of historical and projection simulations under representative concentration pathways (RCPs), especially RCP4.5 and RCP8.5. The aim was to investigate the past and future climatologies and spatiotemporal evolution of subdaily precipitation extremes using newly proposed subdaily extreme precipitation indices (EPIs). The results show that projected changes in precipitation amount, particularly during wet hours, exhibit spatial disparaties. Notably, there are significant decreases along the southern border of the TP and over the western TP, while obvious increases are observed over the inner TP. The southeastern TP, western TP, and southern border of the TP are expected to experience less frequent, shorter duration, and more intense precipitation on an hourly basis. The TP, as a whole, has demonstrated significantly increasing trends in moderate‐to‐heavy precipitation frequencies, along with consistent decreasing trends in precipitation events with short, medium, and long durations. Furthermore, it is predicted that the relationship between extreme precipitation and temperature will deviate from the Clausius‐Clapeyron (C‐C) relationship toward the double C‐C relationship in the far‐future under RCP8.5, particularly over the southeastern TP. Additionally, there are robust correlations between the intensity‐related EPIs and elevation. This indicates that, at the local scale, the complex topography of the region may play a crucial role in shaping the nonuniform distribution of precipitation extremes by modulating associated upward motion.

Convection-permitting climate simulations with COSMO-CLM for Germany: Analysis of present and future daily and sub-daily extreme precipitation

Convection-permitting climate simulations with COSMO-CLM for Germany: Analysis of present and future daily and sub-daily extreme precipitation

An Observation-Based Dataset of Global Sub-Daily Precipitation Indices (GSDR-I)

Precipitation indices based on daily gauge observations are well established, openly available and widely used to detect and understand climate change. However, in many areas of climate science and risk management, it has become increasingly important to understand precipitation characteristics, variability and extremes at shorter (sub-daily) durations. Yet, no unified dataset of sub-daily indices has previously been available, due in large part to the lesser availability of suitable observations. Following extensive efforts in data collection and quality control, this study presents a new global dataset of sub-daily precipitation indices calculated from a unique database of 18,591 gauge time series. Developed together with prospective users, the indices describe sub-daily precipitation variability and extremes in terms of intensity, duration and frequency properties. The indices are published for each gauge where possible, alongside a gridded data product based on all gauges. The dataset will be useful in many fields concerned with variability and extremes in the climate system, as well as in climate model evaluation and management of floods and other risks.

Climate change effects on sub-daily precipitation in Spain

ABSTRACT Sub-daily extreme precipitation events are responsible for flash floods which generate impacts that cannot be analysed using daily precipitation information. In this study, we assess the effects of climate change in sub-daily rainfall statistics for Spain. We downscale daily records to the hourly scale using machine learning techniques, and then update the downscaling predictors to generate the sub-daily rainfall projections. We use the atmospheric climate change projections of the Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative to update the downscaling predictors. We do not observe important differences between climate change scenarios, except in the average amounts of precipitation. Climate change will intensify the most extreme sub-daily events overall, as well as increase the number of dry spells, although some dry climates concentrate most of the effects. The intensification will increase the 100-year return period event, especially in those regions where large variances are observed, making flash floods more intense in the future.

New extreme rainfall projections for improved climate resilience of urban drainage systems

Climate services must provide robust estimates of future changes to precipitation extremes to inform flood risk management and to assess the resilience of existing urban drainage systems in a changing climate. We use an ensemble of convection-permitting UK climate projections, UKCP Local to estimate return levels of 1–24 h precipitation extremes, combining state-of-the-art climate and spatial statistical modelling. We produce low, central and high precipitation return level uplifts at 5 km resolution across the UK. On average across the UK, the 30y return level of 1 and 24 h precipitation is projected to increase by 30–45% and 20–40% respectively by 2070 under RCP8.5, for central-to-high estimates. Uplifts are consistently higher over the northern UK. For 1 h extremes, uplifts here are higher than the official guidance for some parts of the UK, where guidelines pre-date UKCP Local. These spatially-varying uplifts are given for durations out to 24 h with uplifts lower for longer duration extremes. We provide physical explanations for these spatial patterns and the variation with duration. The new uplifts improve on previous official guidance that used a single value for all durations and across most UK nations, and was based on climate models with poor representation of sub-daily precipitation extremes. The results translate the latest UK climate change projections into tailored climate adaptation information useful and suitable for the water industry and flood risk management stakeholders. This is critical for ensuring we benefit from the latest advances in climate modelling and build a society that is resilient to a changing climate.

Quantification of model uncertainty in sub-daily extreme precipitation projections

Sub-daily precipitation is projected to intensify more rapidly with global warming, that in turn may lead to frequent disasters such as flash floods, landslides, etc. However, the precipitation projections from Global Climate Model (GCMs) are known to be highly uncertain, with an increasing magnitude at finer spatio-temporal scales. Hence, realistic quantification of the inherent uncertainty is vital to improve the confidence in these projections, for the mitigation/adaptation applications. In the present study, the model uncertainty in sub-daily (3-hourly) precipitation extremes is estimated, by considering 20 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, during four different seasons for the near (2026–45) and far-future (2081–99) periods. An uncertainty quantification approach using “mutual information based independence weight” is proposed in this study, to account the linear and nonlinear dependence between GCMs along with the equitability property. The CMCC-CM model is found to be the most independent model having highest independence weight (0.064) in the ensemble. The uncertainty in northern hemisphere is found to be more during June to August period, whereas in southern hemispheres during December to February period. Overall, there is an increase in model uncertainty in far-future, especially over the equatorial regions. However, the behaviour of different indices is not in agreement with each other. For example, extreme characteristics such as monthly maximum 3hourly-precipitation has increasing uncertainty; whereas the mean characteristic like, simple hourly-precipitation intensity index possesses unchanging uncertainty. Model uncertainty is most sensitive to the precipitation characteristics of the regions, followed by elevation, a significant geophysical factor. The study also highlighted the need for model improvement over the grids having elevation <2 km and above 4 km. The study identifies the possible regions, seasons, factors etc. where model improvement can be made. Moreover, it is anticipated that improvement of resolution, updation of convective parameterization schemes and land surface parameterization schemes in GCMs may enhance the skill of GCMs and can improve the confidence in the projection of precipitation on sub-daily scale. • Seasonal model uncertainty is estimated to assess the reliability in the projections of sub-daily precipitation extremes. • Mutual information-based weighting technique is used to establish the independence weight of models. • Different aspects of sub-daily precipitation exhibit differing uncertainty, in terms of magnitude and also temporal evolution. • Precipitation intensity remains the dominant factor behind model uncertainty, but geophysical influences cannot be ignored. • Reliability on the sub-daily mean characteristics improves with time; whereas that on extreme characteristics declines.

Pre- and post-1975 scaling relationships of monsoon and non-monsoon hourly precipitation extremes with coincident temperature across urban India

• Scaling relationship of hourly precipitation extremes with air temperature and DPT. • Scaling coefficients during monsoon are negative and have dropped post 1975. • Scaling coefficients during non-monsoon are close to CC scaling rate. • Super-CC scaling rates for some locations using DPT as the scaling variable. • Probability of monsoon extremes is higher when conditioned on higher temperatures. This study presents a scaling analysis of extremes in hourly precipitation with coincident temperature in 14 urban locations across India for monsoon as well as non-monsoon seasons, and examines the probabilistic association through bivariate copula. Using gridded hourly rainfall and dew point temperature (DPT) datasets from ERA5 Reanalysis and daily maximum temperature from the India Meteorology Department (IMD), it examines how far the scaling relationships of sub-daily precipitation extremes with temperature deviate from the Clausius-Clapeyron (CC) scaling across different climatic regimes in India. Pre- and post-1975 analyses were conducted to evaluate if the scaling relationships have altered in line with the global climate shift of the 1970s. Findings from the study indicate that during the monsoon, the scaling relationship was negative in all the cities except Pune (PNQ) during the post-1975 period. In fact, in most of the locations (e.g., Bhopal (BHO), Jaipur (JAI) and Mumbai (BOM)), the scaling coefficient for monsoon rainfall extremes decreased after the 1975 global climate shift. During the non-monsoon season, the scaling coefficient was slightly positive almost everywhere and approximately equal to the CC scaling rate, both pre- and post-1975; the only exception being in PNQ, where a negative scaling was observed. The joint probabilistic distributions for BOM showed that the non-exceedance probability of extreme rainfall was higher when conditioned on higher temperatures in the monsoon season; however, the opposite was observed in the non-monsoon season pre-1975. A similar distinct seasonal feature was noticed in a few other locations including New Delhi (DEL). Using DPT as the scaling variable, Chennai (MAA), Guwahati (GAU) and Tiruchirappalli (TRZ) showed positive scaling in both pre- and post-1975 in both monsoon and non-monsoon seasons. Unlike with maximum air temperature, super-CC scaling was observed with DPT in the monsoon season for TRZ (pre-and post-1975) and for MAA and GAU (pre-1975) and also in the non-monsoon season for MAA (pre-and post-1975) and for GAU and BOM (post-1975). This analysis is expected to provide useful insights into the regional and seasonal distribution of hydrological extremes for a subsequent assessment of their impacts on the design of urban infrastructure.

An Integrated Extreme Rainfall Modeling Tool (SDExtreme) for Climate Change Impacts and Adaptation

The estimation of the Intensity–Duration–Frequency (IDF) relation is often necessary for the planning and design of various hydraulic structures and design storms. It has been an increasingly greater challenge due to climate change conditions. This paper therefore proposes an integrated extreme rainfall modeling software package (SDExtreme) for constructing the IDF relations at a local site in the context of climate change. The proposed tool is based on a temporal downscaling method to describe the relationships between daily and sub-daily extreme precipitation using the scale-invariance General Extreme Value (GEV) distribution. In addition, SDExtreme provides a modified bootstrap technique to determine confidence intervals (CIs) of the estimated IDF curves for current and the future climate conditions. The feasibility and accuracy of SDExtreme were assessed using rainfall data available from the selected rain gauge stations in Quebec and Ontario provinces (Canada) and climate simulations under three different climate change scenarios provided by the Canadian Earth System Model (CanESM2) and the Canadian Regional Climate Model (CanRCM4).

Modelling Sub-daily Precipitation Extremes with the Blended Generalised Extreme Value Distribution

A new method is proposed for modelling the yearly maxima of sub-daily precipitation, with the aim of producing spatial maps of return level estimates. Yearly precipitation maxima are modelled using a Bayesian hierarchical model with a latent Gaussian field, with the blended generalised extreme value (bGEV) distribution used as a substitute for the more standard generalised extreme value (GEV) distribution. Inference is made less wasteful with a novel two-step procedure that performs separate modelling of the scale parameter of the bGEV distribution using peaks over threshold data. Fast inference is performed using integrated nested Laplace approximations (INLA) together with the stochastic partial differential equation approach, both implemented in R-INLA. Heuristics for improving the numerical stability of R-INLA with the GEV and bGEV distributions are also presented. The model is fitted to yearly maxima of sub-daily precipitation from the south of Norway and is able to quickly produce high-resolution return level maps with uncertainty. The proposed two-step procedure provides an improved model fit over standard inference techniques when modelling the yearly maxima of sub-daily precipitation with the bGEV distribution. Supplementary materials accompanying this paper appear on-line.

Climatology and changes in hourly precipitation extremes over China during 1970–2018

Sub-daily precipitation extremes could intensify with temperature at a higher rate than the scaling for daily precipitation extremes, posing increasing risks to natural ecosystem and human society in the era of global warming. A systematic investigation of the climatology and spatiotemporal changes in sub-daily precipitation extremes is of paramount importance to inform future precipitation projection as well as to guide climate adaptation. Here, leveraging a newly proposed set of sub-daily extreme precipitation indices, we examine the climatology and changes in hourly precipitation extremes in mainland China across the major river basins during the warm period of 1970–2018. Our results show that the southern and eastern parts of China tend to experience more frequent hourly precipitation extremes with larger intensity, and the Pearl river basin has the most frequent and intense extreme precipitation at hourly timescale. The Southeast and Yangtze river basins and the mainland China as a whole have field significantly increasing trends in average and extreme precipitation intensities as well as in extreme precipitation frequencies. The intensification signals in hourly precipitation extremes of mainland China seem to emerge from internal climate variability around 2010, whereas average precipitation intensity since 1970 could become field significant earlier than 1999. Besides, we note a marked shift in the probability distributions of the extreme indices, with a wetting tendency toward more frequent and more intense precipitation extremes from the 1970–1999 period to the recent two decades in the 21st century. Our findings provide an alternative line of evidence for changes in precipitation extremes at hourly timescale over China and could contribute to societal decision-making for climate adaptation.

Flexible and consistent quantile estimation for intensity–duration–frequency curves

Abstract. Assessing the relationship between the intensity, duration, and frequency (IDF) of extreme precipitation is required for the design of water management systems. However, when modeling sub-daily precipitation extremes, there are commonly only short observation time series available. This problem can be overcome by applying the duration-dependent formulation of the generalized extreme value (GEV) distribution which fits an IDF model with a range of durations simultaneously. The originally proposed duration-dependent GEV model exhibits a power-law-like behavior of the quantiles and takes care of a deviation from this scaling relation (curvature) for sub-hourly durations (Koutsoyiannis et al., 1998). We suggest that a more flexible model might be required to model a wide range of durations (1 min to 5 d). Therefore, we extend the model with the following two features: (i) different slopes for different quantiles (multiscaling) and (ii) the deviation from the power law for large durations (flattening), which is newly introduced in this study. Based on the quantile skill score, we investigate the performance of the resulting flexible model with respect to the benefit of the individual features (curvature, multiscaling, and flattening) with simulated and empirical data. We provide detailed information on the duration and probability ranges for which specific features or a systematic combination of features leads to improvements for stations in a case study area in the Wupper catchment (Germany). Our results show that allowing curvature or multiscaling improves the model only for very short or long durations, respectively, but leads to disadvantages in modeling the other duration ranges. In contrast, allowing flattening on average leads to an improvement for medium durations between 1 h and 1 d, without affecting other duration regimes. Overall, the new parametric form offers a flexible and enhanced performance model for consistently describing IDF relations over a wide range of durations, which has not been done before as most existing studies focus on durations longer than 1 h or day and do not address the deviation from the power law for very long durations (2–5 d).

Analysis of Sub-Daily Precipitation for the PannEx Region

The PannEx is a GEWEX-initiated, community driven research network in the Pannonian Basin. One of the main scientific issues to address in PannEx is the investigation of precipitation extremes. Meteorological Services in the PannEx area collected the hourly precipitation data and commonly used a computer program, which was developed in the INTENSE project, to produce a set of global hydro-climatic indices. Calculations are carried out on data aggregated 1-, 3- and 6-h intervals. Selected indices are analyzed in this paper to assess the general climatology of the short-term precipitation in the Pannonian basin. The following indices are illustrated on maps and graphs: the annual mean and maxima of 1-h, 3-h and 6-h sums, the count of 3-hr periods greater than 20 mm thresholds, the maximum length of wet hours, the timing of wettest hour and the 1-h precipitation intensity. The seasonal trends of the 1-h precipitation intensity were tested from 1998 to 2019. Analysis of sub-daily precipitation has been limited by the availability of data on a global or a regional scale. The international effort made in this work through collaboration in the PannEx initiative contributes to enlarging the data availability for regional and global analysis of sub-daily precipitation extremes.

Internal variability and temperature scaling of future sub-daily rainfall return levels over Europe

The range of sub-daily extreme precipitation due to internal variability is quantified within the single model initial-condition large ensemble featuring 50 members of the Canadian regional climate model, version 5 (CRCM5) under the high-emission scenario representative concentration pathway 8.5. Ten-year return levels of sub-daily precipitation are calculated for three future periods (2010–2039, 2040–2069, 2070–2099) and hourly to 24-hourly aggregations over a European domain. The return levels are found to increase by 4%–8% for every future 30 year period averaged for the study area, where short-duration rainfall intensities increase to a greater extent than longer-duration rainfall intensities. The ranges between the median of the 50 members and the 5th and 95th quantile amount to −15.6%–19.3%, −16.0%–20.1%, and −16.5%–20.9% for the near, mid and far future, respectively. It is also shown that the scaling of the precipitation increase with temperature (Clausius–Clapeyron scaling) exhibits substantial variations between the 50 CRCM5 members at regional aggregations. These findings illustrate the large impact of internal variability on the uncertainty of extreme precipitation return level estimates. Here, regions of significant changes are identified, where future median extreme precipitation exceeds the 95th quantile of the reference period (1980–2009). These regions are located in northern Europe, central Europe and the eastern part of the Mediterranean.

Synoptic structure of a sub-daily extreme precipitation and flood event in Thohoyandou, north-eastern South Africa

An extreme sub-daily precipitation event produced about 300 mm of rainfall in less than 4 h overnight from 13–14 February 2019 resulting in high floods in Thohoyandou, a small town northeast of South Africa. We employed station, radar, satellite and reanalysis datasets to investigate the rainfall, circulation and thermodynamic fields and understand the meteorological structure of the extreme event via a multiscale analysis. The large-scale synoptic environment was characterized by a mid-tropospheric tropical-temperate trough and attendant cloud band coupled to a surface high ridging over the southeast coast of the country. We found that whilst heavy rainfall (>50 mm/24 h) was widespread ahead of the upper trough, extreme amounts (∼100 mm/h) were localized due to a cloudburst. A small perturbation to the favorable large scale mid-tropospheric environment also contributed to localized heavy rainfall. The south-north pressure gradient was steepened by a surface low over southern Mozambique resulting in enhanced moisture fluxes deriving from the southwest Indian Ocean. The interaction of prevailing surface winds and a low-level jet with the steep topography of the adjacent Soutpansberg Mountain Range enhanced low-level convergence and lifting in the area. We also show that the highest rainfalls were uphill of the location of flooding which was contained in a poorly drained valley. Whereas the Unified Model forecasts appeared accurate for the large-scale pattern of heavy rainfall in the area, the rainfall peak was generally underestimated, whilst the timing of extreme rainfall was delayed in the 18Z simulation, which is used by forecasters operationally. Our findings contribute to understanding the occurrence of extreme weather events over northeastern South Africa and also how models treat them, towards natural disaster risk reduction.

Evaluation of the Tail of the Probability Distribution of Daily and Subdaily Precipitation in CMIP6 Models

AbstractDaily and subdaily precipitation extremes in historical phase 6 of the Coupled Model Intercomparison Project (CMIP6) simulations are evaluated against satellite-based observational estimates. Extremes are defined as the precipitation amount exceeded everyxyears, ranging from 0.01 to 10, encompassing the rarest events that are detectable in the observational record without noisy results. With increasing temporal resolution there is an increased discrepancy between models and observations: for daily extremes, the multimodel median underestimates the highest percentiles by about a third, and for 3-hourly extremes by about 75% in the tropics. The novelty of the current study is that, to understand the model spread, we evaluate the 3D structure of the atmosphere when extremes occur. In midlatitudes, where extremes are simulated predominantly explicitly, the intuitive relationship exists whereby higher-resolution models produce larger extremes (r= −0.49), via greater vertical velocity. In the tropics, the convective fraction (the fraction of precipitation simulated directly from the convective scheme) is more relevant. For models below 60% convective fraction, precipitation amount decreases with convective fraction (r= −0.63), but above 75% convective fraction, this relationship breaks down. In the lower-convective-fraction models, there is more moisture in the lower troposphere, closer to saturation. In the higher-convective-fraction models, there is deeper convection and higher cloud tops, which appears to be more physical. Thus, the low-convective models are mostly closer to the observations of extreme precipitation in the tropics, but likely for the wrong reasons. These intermodel differences in the environment in which extremes are simulated hold clues into how parameterizations could be modified in general circulation models to produce more credible twenty-first-century projections.

Ten-year return levels of sub-daily extreme precipitation over Europe

Abstract. Information on the frequency and intensity of extreme precipitation is required by public authorities, civil security departments, and engineers for the design of buildings and the dimensioning of water management and drainage schemes. Especially for sub-daily resolutions, at which many extreme precipitation events occur, the observational data are sparse in space and time, distributed heterogeneously over Europe, and often not publicly available. We therefore consider it necessary to provide an impact-orientated data set of 10-year rainfall return levels over Europe based on climate model simulations and evaluate its quality. Hence, to standardize procedures and provide comparable results, we apply a high-resolution single-model large ensemble (SMILE) of the Canadian Regional Climate Model version 5 (CRCM5) with 50 members in order to assess the frequency of heavy-precipitation events over Europe between 1980 and 2009. The application of a SMILE enables a robust estimation of extreme-rainfall return levels with the 50 members of 30-year climate simulations providing 1500 years of rainfall data. As the 50 members only differ due to the internal variability in the climate system, the impact of internal variability on the return level values can be quantified. We present 10-year rainfall return levels of hourly to 24 h durations with a spatial resolution of 0.11∘ (12.5 km), which are compared to a large data set of observation-based rainfall return levels of 16 European countries. This observation-based data set was newly compiled and homogenized for this study from 32 different sources. The rainfall return levels of the CRCM5 are able to reproduce the general spatial pattern of extreme precipitation for all sub-daily durations with Spearman's rank correlation coefficients &gt;0.76 for the area covered by observations. Also, the rainfall intensity of the observational data set is in the range of the climate-model-generated intensities in 60 % (77 %, 78 %, 83 %, 78 %) of the area for hourly (3, 6, 12, 24 h) durations. This results in biases between −16.3 % (hourly) to +8.2 % (24 h) averaged over the study area. The range, which is introduced by the application of 50 members, shows a spread of −15 % to +18 % around the median. We conclude that our data set shows good agreement with the observations for 3 to 24 h durations in large parts of the study area. However, for an hourly duration and topographically complex regions such as the Alps and Norway, we argue that higher-resolution climate model simulations are needed to improve the results. The 10-year return level data are publicly available (Poschlod, 2020; https://doi.org/10.5281/zenodo.3878887).

Evaluation of MEVD-based precipitation frequency analyses from quasi-global precipitation datasets against dense rain gauge networks

Precipitation extremes and associated hydrological hazards pose a significant global risk to society and economy. To be effective, mitigation strategies require the best possible estimation of the intensity and frequency of precipitation extremes. Traditional approaches to precipitation frequency analysis rely on long-term records from in-situ observations, which are limited in terms of global coverage. Satellite-based precipitation products provide global coverage, but errors in these estimates may lead to large biases in the quantification of extremes. Previous studies have demonstrated the ability of the novel Metastatistical Extreme Value Distribution (MEVD) framework to provide robust estimates of high quantiles in the presence of short-term data records and the uncertainties typical of remote sensing precipitation products. Here, we evaluate MEVD-based precipitation frequency analyses for four widely used quasi-global precipitation products (IMERG-v6, GSMaP-v6, CMORPH-v1.0, and MSWEP-v2) over high-density gauge networks in five hydroclimatic regions (Austria, Italy, Florida, Texas, and Arizona). We show dependence of MEVD-based estimation error on the characteristics of each dataset and the hydroclimatic region. Additionally, we evaluate the sub-grid variability of extreme precipitation and demonstrate the impact of spatial scale mismatch (that is, single in-situ gauge versus satellite pixel) on the frequency analysis of extremes. This work provides an assessment of the use of MEVD for estimating precipitation extremes from globally available datasets and an understanding of the variability of sub-daily precipitation extremes in different hydroclimatic regions of the world.

A Unified Framework for Extreme Subdaily Precipitation Frequency Analyses Based on Ordinary Events

AbstractThe metastatistical extreme value approach proved promising in the frequency analysis of daily precipitation from ordinary events, outperforming traditional methods based on sampled extremes. However, subdaily applications are currently restrained by two knowledge gaps: It is not known if ordinary events can be consistently examined over durations, and it is not clear to what extent their entire distributions represent extremes. We propose here a unified definition of ordinary events across durations and suggest the simplified metastatistical extreme value formulation for dealing with extremes emerging from the tail, rather than the entire distributions, of ordinary events. This unified framework provides robust estimates of extreme quantiles (&lt;10% error on the 100 yr from a 26 yr long record) and allows representations in which ordinary and extreme events share the scaling exponent. Future applications could improve our knowledge of subdaily extreme precipitation and help investigate the impact of local factors and climatic forcing on their frequency.