AbstractWhile CMIP5 models robustly project drying of the subtropics and more precipitation in the tropics and subpolar latitudes by the end of the century, the magnitude of these changes in precipitation varies widely across models: for example, some models simulate no drying in the eastern Mediterranean while others simulate more than a 50% reduction in precipitation relative to the model-simulated present-day value. Furthermore, the factors leading to changes in local subtropical precipitation remain unclear. The importance of zonal-mean changes in atmospheric structure for local precipitation changes is explored in 42 CMIP5 models. It is found that up to half of the local intermodel spread over the Mediterranean, northern Mexico, East Asia, southern Africa, southern Australia, and southern South America is related to the intermodel spread in large-scale processes such as the magnitude of globally averaged surface temperature increases, Hadley cell widening, polar amplification, stabilization of the tropical upper troposphere, or changes in the polar stratosphere. Globally averaged surface temperature increases account for intermodel spread in land subtropical drying in the Southern Hemisphere but are not important for land drying adjacent to the Mediterranean. The factors associated with drying over the eastern Mediterranean and western Mediterranean differ, with stabilization of the tropical upper troposphere being a crucial factor for the former only. Differences in precipitation between the western and eastern Mediterranean are also evident on interannual time scales. In contrast, the global factors examined here are unimportant over most of the United States, and more generally over the interior of continents. Much of the rest of the spread can be explained by variations in local relative humidity, a proxy also for zonally asymmetric circulation and thermodynamic changes.

The 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 (\textless10% 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.

Abstract. Catchment scale hydrological studies on drylands are lacking because of the scarcity of consistent data: observations are often available at the plot scale, but their relevance for the catchment scale remains unclear. A database of 24 years of stream gauge discharge and homogeneous high-resolution radar data over the eastern Mediterranean allows to describe the properties of moderate floods over catchments spanning from Desert to Mediterranean climates. Comparing two climatic regions, Desert and Mediterranean, we are able to better identify specific rainfall-runoff properties. Despite the large differences in rainfall forcing between the two regions, the resulting unit peak discharges and runoff coefficients are comparable. In Mediterranean areas rain depth and antecedent conditions are the most important properties to shape flood response. In Deserts, instead, storm core properties display a strong correlation with unit peak discharge and, to a less extent, with runoff coefficient. In this region, an inverse correlation with mean catchment annual precipitation suggests also a strong influence of local surface properties. Preliminary analyses suggest that floods in catchments with wet headwater and dry lower section are more similar to desert catchments, with a strong influence of storm core properties on runoff generation.

Adjustment of weather radar estimates using observed precipitation has been an accepted procedure for decades. Ground observations of precipitation typically come from rain gauges, but can also include data from diverse networks of sensors, with different levels of reliability. This study presents a standardized framework for evaluating adjustment algorithms using synthetically constructed, but realistic, rain grids and weather radar rainfall. Ground observation points are randomly placed throughout the synthetic storm domain and the precipitation for each sensor is extracted from the true rain. Then a subset of the sensors are defined as unreliable, and a log-normal error factor is applied at those locations. This double network of rain sensors could be applicable, for example, when rainfall is derived from signal attenuation between commercial microwave link (CML) antennas. Past research has tested CML observations as a source of precipitation data and validated various radar adjustment algorithms. However, a comprehensive evaluation of adjustment algorithms using accurate gauge data mixed with CML observations at different densities is lacking. Five adjustment algorithms are applied to the synthetic radar grid: Mean Field Bias (MFB), a Multiplicative algorithm, Mixed (additive and multiplicative), Conditional Merge (CondMerge) and Kriging with External Drift (KED). Generation of the synthetic framework, and application of the adjustment algorithms is repeated for 150 realizations. Comparison of coefficient of determination (R2), root mean square error and linear regression for all adjustment procedures over all realizations indicates the following results. Only MFB and KED adjustments performed well when using accurate gauges. The kriging based KED was able to achieve good adjustment also with the addition of error-prone sensors. CondMerge and the Mixed and Multiplicative, however, resulted in poorer adjustments.

Current literature suggests that wheat production models are limited either to wide-scale or plot-based predictions ignoring pattern of habitat conditions and surficial hydrological processes. We present here a high-spatial resolution (50 m) non-calibrated GIS-based wheat production model for predictions of aboveground wheat biomass (AGB) and grain yield (GY). The model is an integration of three sub-models, each simulating elemental processes relevant for wheat growth dynamics in water-limited environments: (1) HYDRUS-1D, a finite element model that simulates one-dimensional movement of water in the soil profile; (2) a two-dimensional GIS-based surface runoff model; and (3) a one-dimensional process-driven mechanistic wheat growth model. By integrating the three sub-models, we aimed to achieve a more accurate spatially continuous water balance simulation with a better representation of root zone soil water content (SWC) impacts on plant development. High-resolution grid-based rainfall data from a meteorological radar system were used as input to HYDRUS-1D. Twenty-two commercial wheat fields in Israel were used to validate the model in two seasons (2010/11 and 2011/12). Results show that root zone SWC was accurately simulated by HYDRUS-1D in both seasons, particularly at the top 10-cm soil layer. Observed vs simulated AGB and GY were highly correlated with R2 = 0.93 and 0.72 (RMSE = 171 g m-2 and 70 g m-2) having low biases of -41 g m-2 (8%) and 52 g m-2 (10%), respectively. Model sensitivity test showed that HYDRUS-1D was mainly driven by spatial variability in the input soil characteristics while the integrated wheat production model was mostly affected by rainfall spatial variability indicating the importance of using accurate high-resolution rainfall data as model input. Using the integrated model, we predict decreases in AGB and GY of c. 10.5% and c. 12%, respectively, for 1 °C of warming and c. 7.7% and c. 7.3% for 5% reduction in rainfall amount in our study sites. The suggested model could be used by scientists to better understand the causes of spatial and temporal variability in wheat production and the consequences of future scenarios such as climate change.

The last decade has witnessed the development of methodologies for the post-flood documentation of both hydrogeomorphological and social response to extreme precipitation. These investigations are particularly interesting for the case of flash floods, whose space–time scales make their observations by conventional hydrometeorological monitoring networks particularly challenging. Effective flash flood documentation requires post-flood survey strategies encompassing accurate radar estimation of rainfall, field and remote-sensing observations of the geomorphic processes, indirect reconstruction of peak discharges—as well eyewitness interviews. These latter can give valuable information on both flood dynamics and the related individual and collective responses. This study describes methods for post-flood surveys based on interdisciplinary collaborations between natural and social scientists. These surveys may help to better understand the links between hydrometeorological dynamics and geomorphic processes as well as the relationship between flood dynamics and behavioral response in the context of fast space–time changes of flooding conditions. This article is categorized under: Science of Water > Methods Science of Water > Hydrological Processes A flash flood and its forensic analysis.

The Dead Sea sedimentary fill is the basis for interpreting limnological conditions and regional paleo- hydrology. Such interpretations require an understanding of present-day hydroclimatology to reveal the relative impact of different atmospheric circulation patterns on water and sediment delivery to the Dead Sea. Here we address the most important meteorological conditions governing regional and local rain- storm occurrences, with different discharge characteristics. These meteorological controls over the Dead Sea watershed offer insights into past hydrometeorological processes that could have governed the Dead Sea water budget, seasonal and annual flows, floods, and the resultant sedimentology. Rainfall is typically associated with synoptic-scale circulation patterns forced by an upper-level trough that include Medi- terranean cyclones (MCs), active Red Sea troughs (ARSTs), and active subtropical jets (STJs), although other rainstorms and sub-synoptic processes also affect the region. We point to their relative importance in inflow volume, peak discharges, and delivery of sediments from the various environments of the basin. MCs control the annual water amount discharging into the Dead Sea. A change in their frequency, in- tensity, or latitude can substantially alter the lake water balance. A change in frequency or intensity of ARSTs and STJs affects extreme flood and sediment discharge. Floods reach the lake through (a) the Mediterranean-climate-controlled Lower Jordan River, (b) desert-climate-controlled Nahal HaArava, and (c) the arid wadies draining directly into the Dead Sea, some with wetter headwaters. Floods in the wetter parts of the watershed are mainly controlled by MCs, and characterized by larger frequency, volume, and duration, but lower peak discharges and possibly sediment delivery, than floods in the desert parts, which can be produced by the three synoptic types. ARSTs contribute to heavy rainfall, typically of a spotty nature, in the desert parts of the watershed. STJs are currently rare, but their rainfall accumulation may be greater than the annual mean over a broad area in the southern dry Dead Sea watershed. This article presents a review of recent studies, which is extended with new analyses of meteorological, rainfall and flood data, underlining the importance of the Lower Jordan River in sup- plying water volume to the Dead Sea, as compared to the high-discharge, low-volume floods of the arid part of the watershed. Our analyses will help interpret paleoenvironmental conditions in the Dead Sea sedimentary record, and cope with the region’s changing climate.

The lack of knowledge on precipitation frequency over ungauged areas introduces a significant source of uncertainty in relevant engineering designs and risk estimation procedures. Radar-based observations offer precipitation information over ungauged areas and thus have gained increasing attention as a potential solution to this problem. However, due to their relative short data records and inherent uncertainty sources, their ability to provide accurate estimates on the frequency of precipitation extremes requires evaluation. This study involves the evaluation of at-site precipitation frequency estimates from NEXRAD Stage IV radar precipitation dataset. We derive precipitation annual maxima series from the 16yrs record (2002-2017) of NEXRAD and we compare against 539 long-term (50yrs) hourly gauge records. In addition, Intensity-Duration-Frequency (IDF) curves are estimated from both radar and gauge dataset and compared. IDF estimation is based on fitting the Generalize Extreme Value distribution to annual precipitation maxima. Evaluation is carried out over the contiguous United States and results are grouped and presented for five dominant climate classes and for a range of return period and precipitation durations. NEXRAD was shown to overestimate intensities at shorter durations (1- and 3-hr) and low quantiles, while it tends to underestimate higher quantiles at longer durations (24hr). In addition, evaluation of the IDF curves estimated from NEXRAD revealed a distinct geographic dependence with certain regions exhibiting a tendency to overestimation (e.g. east of the Rocky Mountains) or underestimation (Midwest). Overall, this analysis suggests that, while significant discrepancies may exist, there are several cases where NEXRAD provide estimates within the uncertainty bounds of the reference rain gauge dataset. The climate/geographic region and the temporal duration are important aspects to consider. Findings provided in this work on these aspects will hopefully serve as a general guideline for those interested in using NEXRAD estimates for further research or applications on precipitation extremes.

Information on extreme precipitation is essential to managing weather-related risks and designing hydraulic structures. Research attention to frequency analyses based on remotely sensed precipitation datasets, such as those obtained from weather radars and satellites, has been rapidly increasing owing to their potential to provide information for ungauged regions worldwide. Together with the ability to measure the areal scale directly, these analyses promise to overcome the sampling limitations of traditional methods based on rain gauges. This focused review of the literature depicts the state of the art after a decade of efforts, and identifies the crucial gaps in knowledge and methodology that currently hinder the quantitative use of remotely sensed datasets in water resources system design and operation. It concludes by highlighting a set of research directions promising immediate impact with regard to the separation of the sources of uncertainty currently affecting applications based on remotely sensed datasets, the development of statistical methods that can cope with the peculiar characteristics of these datasets, and the improvement of validation methods. Important gains in knowledge are expected from the explicit inclusion of the areal dimension in the analyses and from the fine-scale investigation of extreme precipitation climatology.

Rainfall thresholds for landslides occurrence derived in real applications tend to be lower than the ones one would obtain using exact data. This letter shows how the use of coarse temporal resolution rainfall data causes a systematic overestimation of the duration of the triggering rainfall events that directly contributes to thresholds underestimation. A numeri- cal experiment is devised to quantify this systematic effect for the relevant case of power- law depth/intensity–duration thresholds. In the examined conditions, i.e., the frequentist method at 5% non-exceedance probability level, \~ 70% underestimation of the scale param- eter and \~ 60% overestimation of the shape parameter of the thresholds is to be expected using daily resolution rainfall data, but the exact quantification depends on the specific characteristics of each study case. The underestimation increases as the temporal resolu- tion becomes larger than the expected minimal duration of the triggering events. Under operational conditions, sensitivity analyses based on the methods and datasets of interest are advised.

This paper presents a Simplified Metastatistical Extreme Value formulation (SMEV) able to model hydro- meteorological extremes emerging from multiple underlying processes. The formulation explicitly includes the average intensity and probability of occurrence of the processes allowing to parsimoniously model changes in these quantities to quantify changes in the probability of occurrence of extremes. SMEV allows (a) frequency analyses of extremes emerging from multiple underlying processes and (b) computationally efficient analyses of the sensitivity of extreme quantiles to changes in the characteristics of the underlying processes; moreover, (c) it provides a robust framework for explanatory models, nonstationary frequency analyses, and climate projections. The methodology is applied to daily precipitation data from long recording stations in the eastern Mediter-ranean, using Weibull distributions to model daily precipitation amounts generated by two classes of synoptic systems. At-site application of SMEV provides spatially consistent estimates of extreme quantiles, in line with regional GEV estimates and generally characterized by reduced uncertainties. The sensitivity of extreme quan- tiles to changes and uncertainty in the intensity and yearly occurrences of events generated by different synoptic classes is examined, and an application of SMEV for the projection of future extremes is provided.

Small scale rainfall variability is a key factor driving runoff response in fast responding systems, such as mountainous, urban and arid catchments. In this paper, the spatial–temporal autocorrelation structure of convective rainfall is derived with extremely high resolutions (60 m, 1 min) using estimates from an X-Band weather radar recently installed in a semiarid-arid area. The 2-dimensional spatial autocorrelation of convective rainfall fields and the temporal autocorrelation of point-wise and distributed rainfall fields are examined. The autocorrelation structures are characterized by spatial anisotropy, correlation distances \~ 1.5–2.8 km and rarely exceeding 5 km, and time-correlation distances \~ 1.8–6.4 min and rarely exceeding 10 min. The observed spatial variability is expected to negatively affect estimates from rain gauges and microwave links rather than satellite and C-/S-Band radars; conversely, the temporal variability is expected to negatively affect remote sensing estimates rather than rain gauges. The presented results provide quantitative information for stochastic weather generators, cloud-resolving models, dryland hydrologic and agricultural models, and multi-sensor merging techniques.

Floods comprise a dominant hydroclimatic phenomenon in aridlands with significant implications for humans, infrastructure, and landscape evolution worldwide. The study of short-term hydroclimatic variability, such as floods, and its forecasting for episodes of changing climate therefore poses a dominant challenge for the scientific community, and predominantly relies on modeling. Testing the capabilities of climate models to properly describe past and forecast future short-term hydroclimatic phenomena such as floods requires verification against suitable geological archives. However, determining flood frequency during changing climate is rarely achieved, because modern and paleoflood records, especially in arid regions, are often too short or discontinuous. Thus, coeval independent climate reconstructions and paleoflood records are required to further understand the impact of climate change on flood generation. Dead Sea lake levels reflect the mean centennial-millennial hydrological budget in the eastern Mediterranean. In contrast, floods in the large watersheds draining directly into the Dead Sea, are linked to short-term synoptic circulation patterns reflecting hydroclimatic variability. These two very different records are combined in this study to resolve flood frequency during opposing mean climates. Two 700-year-long, seasonally-resolved flood time series constructed from late Pleistocene Dead Sea varved sediments, coeval with significant Dead Sea lake level variations are reported. These series demonstrate that episodes of rising lake levels are characterized by higher frequency of floods, shorter intervals between years of multiple floods, and asignificantly larger number of years that experienced multiple floods. In addition, floods cluster into intervals of intense flooding, characterized by 75% and 20% increased frequency above their respective background frequencies during rising and falling lake-levels, respectively. Mean centennial precipitation in the eastern Mediterranean is therefore coupled with drastic changes in flood frequencies. These drastic changes in flood frequencies are linked to changes in the track, depth, and frequency of mid-latitude eastern Mediterranean cyclones, determining mean climatology resulting in wetter and drier regional climatic episodes.

During the complex dynamic interactions between rainfall and basin properties, different portions of the basin produce runoff at different moments. Capturing this spatiotemporal variability is important for flood analysis, but knowledge of this subject is limited. The presented research aims at improving the understanding of runoff-contributing areas (RCA; hillslope sections from which water flows, reaches the stream network, and consequently the basin outlet) and at examining their relationship with the magnitude of a flash flood’s peak discharge. A distributed hydrological model (GB-HYDRA) that enables computing RCA and flood discharge was developed. The model was applied to four medium-size basins (18–69 km2) in a Mediterranean climate and 59 flash flood events were analyzed. The correlation between basin input flux (basin area multiplied by the basin maximal rain intensity averaged over the time of concentration) and output flux (observed peak discharge) was poor (R2= 0.16). However, using a newly developed index, termed IRCA, to calculate the input flux accounting only for the RCA extent and rainfall intensity over it, resulted in a substantially higher correlation (R2= 0.64) across a wide range of flood magnitudes. The highest correlation was found using a 50-min time window, which is shorter than the time of concentration. Flood events were categorized according to their magnitude and the differences of several factors among the groups were examined. Pre-storm soil moisture content was found to be similar for all event magnitudes; however, pre-peak soil moisture content was substantially different between moderate and large–extreme events. Other important properties that differed between magnitudes were: RCA extent and its averaged rain intensity and ratio of convective rainfall. Finally, areas with land-uses characterized by low runoff potential became dominant and contributed mainly during large and extreme events. Although the RCA and its extent full potential is yet to be fulfilled, it is proposed as a significant tool for understanding processes of flash flood generation at the basin scale in future research.

Identifying climates favoring extreme weather phenomena is a primary aim of paleoclimate and paleohydrological research. Here, we present a well-dated, late Holocene Dead Sea sediment record of debris flows covering 3.3 to 1.9 cal ka BP. Twenty-three graded layers deposited in shallow waters near the western Dead Sea shore were identified by microfacies analysis. These layers represent distal subaquatic deposits of debris flows triggered by torrential rainstorms over the adjacent western Dead Sea escarpment. Modern debris flows on this escarpment are induced by rare rainstorms with intensities exceeding \textgreater30mm h-1 for at least one hour and originate primarily from the Active Red Sea Trough synoptic pattern. The observed late Holocene clustering of such debris flows during a regional drought indicates an increased influence of Active Red Sea Troughs resulting from a shift in synoptic atmospheric circulation patterns. This shift likely decreased the passages of eastern Mediterranean cyclones, leading to drier conditions, but favored rainstorms triggered by the Active Red Sea Trough. This is in accord with present-day meteorological data showing an increased frequency of torrential rainstorms in regions of drier climate. Hence, this study provides conclusive evidence for a shift in synoptic atmospheric circulation patterns during a late Holocene drought.

Abstract. This paper describes an integrated, high-resolution dataset of hydro-meteorological variables (rainfall and discharge) concerning a number of high-intensity flash floods that occurred in Europe and in the Mediterranean region from 1991 to 2015. This type of dataset is rare in the scientific literature because flash floods are typically poorly observed hydrological extremes. Valuable features of the dataset (hereinafter referred to as the EuroMedeFF database) include (i) its coverage of varied hydro-climatic regions, ranging from Continental Europe through the Mediterranean to Arid climates, (ii) the high space–time resolution radar rainfall estimates, and (iii) the dense spatial sampling of the flood response, by observed hydrographs and/or flood peak estimates from post-flood surveys. Flash floods included in the database are selected based on the limited upstream catchment areas (up to 3000km2), the limited storm durations (up to 2 days), and the unit peak flood magnitude. The EuroMedeFF database comprises 49 events that occurred in France, Israel, Italy, Romania, Germany and Slovenia, and constitutes a sample of rainfall and flood discharge extremes in different climates. The dataset may be of help to hydrologists as well as other scientific communities because it offers benchmark data for the identification and analysis of the hydro-meteorological causative processes, evaluation of flash flood hydrological models and for hydro-meteorological forecast systems. The dataset also provides a template for the analysis of the space–time variability of flash flood triggering rainfall fields and of the effects of their estimation on the flood response modelling. The dataset is made available to the public with the following DOI: https://doi.org/10.6096/MISTRALS-HyMeX.1493.

AbstractThis study contributes to the understanding of the relationship between air temperature and convection by analyzing the characteristics of rainfall at the storm and convective rain cell scales. High spatial-temporal resolution (1-km, 5-min) estimates from a uniquely long weather radar record (24-year) were coupled with near-surface air temperature over Mediterranean and semiarid regions in the eastern Mediterranean. In the examined temperature range (5 to 25°C), the peak intensity of individual convective rain cells was found to increase with temperature, but at lower rate than the 7%°C-1 scaling expected from the Clausius-Clapeyron relation, while the area of the individual convective rain cells slightly decrease or, at most, remains unchanged. At the storm-scale, the areal convective rainfall was found to increase with warmer temperatures, whereas the areal non-convective rainfall and the storm-wide area decrease. This suggests an enhanced moisture convergence from the storm-wide extent towards the convective rain cells. Results indicate a reduction in the total rainfall amounts and an increased heterogeneity of the spatial structure of the storm rainfall for temperatures increasing up to 25°C. Thermodynamic conditions, analyzed using convective available potential energy, were determined to be similar between Mediterranean and semiarid regions. Limitation in the atmospheric moisture availability when shifting from Mediterranean to semiarid climates was detected and explains the suppression of the intensity of the convective rain cells when moving towards drier regions. The relationships obtained in this study are relevant for nearby regions characterized by Mediterranean and semiarid climates.

A novel quantitative assessment of late Holocene precipitation in the Levant is presented, including mean and variance of annual precipitation and their trends. A stochastic framework was utilized and allowed, possibly for the first time, linking high-quality, reconstructed rises/declines in Dead Sea levels with precipitation trends in its watershed. We determined the change in mean annual precipitation for 12 specific intervals over the past 4500 yr, concluding that: (1) the twentieth century was substantially wetter than most of the late Holocene; (2) a representative reference value of mean annual precipitation is 75% of the present-day parameter; (3) during the late Holocene, mean annual precipitation ranged between -17 and +66% of the reference value (-37 to +25% of present-day conditions); (4) the driest intervals were 1500–1200 BC and AD 755–890, and the wettest intervals were 2500–2460 BC, 130–40 BC, AD 350–490, and AD 1770–1940; (5) lake-level rises and declines probably occurred in response to trends in precipitation means and are less likely to occur when precipitation mean is constant; (6) average trends in mean annual precipitation during intervals of >=200 yr did not exceed 15mm per decade. The precipitation trends probably reflect shifts in eastern Mediterranean cyclone tracks.

The Sahara was wetter and greener during multiple interglacial periods of the Quaternary, when some have suggested it featured very large (mega) lakes, ranging in surface area from 30,000 to 350,000km2. In this paper, we review the physical and biological evidence for these large lakes, especially during the African Humid Period (AHP) 11–5 ka. Megalake systems from around the world provide a checklist of diagnostic features, such as multiple well-defined shore- line benches, wave-rounded beach gravels where coarse material is present, landscape smoothing by lacustrine sediment, large-scale deltaic deposits, and in places, tufas encrusting shorelines. Our survey reveals no clear evidence of these fea- tures in the Sahara, except in the Chad basin. Hydrologic modeling of the proposed megalakes requires mean annual rain- fall >=1.2 m/yr and a northward displacement of tropical rainfall belts by >=1000 km. Such a profound displacement is not supported by other paleo-climate proxies and comprehensive climate models, challenging the existence of megalakes in the Sahara. Rather than megalakes, isolated wetlands and small lakes are more consistent with the Sahelo-Sudanian paleoenvironment that prevailed in the Sahara during the AHP. A pale-green and discontinuously wet Sahara is the like- lier context for human migrations out of Africa during the late Quaternary.

This study expands the Metastatistical Extreme Value (MEV) framework to sub-daily rainfall frequency analysis and compares it to extreme value theory methods in presence of short records and measurement errors. Ordinary events are identified based on the temporal autocorrelation of hourly data and modeled with a Weibull distribution. MEV is compared to extreme value theory methods in the estimation of long return period quantiles from actual data (160 rain gauges with at least 60-year record in the contiguous United States) and on synthetic data perturbed with measurement errors typical of remote sensing rainfall estimation. MEV tends to underestimate the 100-year return period quantiles of hourly rainfall when 5–20 years of actual data are used, but presents diminished uncertainty. When a good model of the ordinary events and adequate number of events per year are available, MEV is able to provide information on the 100-year return period quantiles from 10–20, or even 5 years of data with significantly reduced uncertainty (\textless30% uncertainty for 5-year records). MEV estimates of 100-year return period quantiles from short records are much less sensitive than extreme value theory methods to additive/multiplicative errors, presence of cap values in the estimates, and missing of extreme values. Results from this study strongly support the use of MEV for rainfall frequency analyses based on remotely sensed datasets.