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The statistics of extreme precipitation over a location of interest are crucial for designing hydraulic structures and mitigating extreme events impact. These statistics emerge from (i) the presence of different storm types, (ii) the different intensity of storms of a given type, (iii) the spatial variability of storms during their life-cycle, combined with (iv) the advection of storms across the domain. Explicit separation of these components could help us establish links between atmospheric dynamics (i.e., the occurrence and frequency of different types of storms) and thermodynamics (i.e., the properties of different storm types) on one side, and the emerging statistics of extremes. Here, we make a first step in this direction by focusing on a semi-arid region in the southeastern Mediterranean in which precipitation is almost solely related to convective processes, minimizing the effect of point (i).We use very-high-resolution (60 m x 60 m, 1 min) weather radar observations to track convective cells during 11 storms that occurred over 2 years (>1200 cells). We mimic rain gauge observation of the tracked cells by sampling the rainfall fields at random locations and we alter advection by applying synthetic velocities to the Lagrangian fields of the cells. This allows us to isolate the impacts of (ii) storm intensity, and (iv) advection. Then, we generate sets of synthetic cells with analogous properties (peak intensity, area, velocity) and different profiles to examine the impact of (iii) spatial variability.We find that the spatial sampling of convective cells occurred during the 11 storms explains most of the variability of extreme precipitation in the region. The extremes emerging from this sampling are well described by Weibull tails. Return levels estimated from the 11 storms using a non-asymptotic extreme value method are comparable to the ones derived from 25 years of rain gauge observations (error in the 100-year return levels

Estimating the frequency of extreme precipitation events, both locally and over extended areas, is key for developing risk reduction measures in present and future climates. Large areas of the world are characterized by sparse or absent rain-gauge networks, which poses significant challenges to the estimation of extreme events in many applications. Remote sensing and reanalysis datasets may contribute to filling some of these gaps, but their use meets some important obstacles: 1) remote sensing/reanalysis rainfall estimates are defined at coarse resolutions, thereby preventing direct validations against ground observations; 2) they usually span a ~20-year observation period, making it difficult to estimate the frequency of large extremes; 3) they suffer from significant uncertainties. Using the novel Metastatistical Extreme Value Distribution (MEVD) and a recent statistical downscaling technique, we compare ground and satellite-based/model estimates of rainfall to quantify the improvement achieved through downscaling in high-quantile quantification. We focus on ocean rainfall observations, which are rarely considered in validating global databases, from the Tao-Triton, Pirata, and Rama buoy networks. We quantify the estimation uncertainty for point extremes associated with the MSWEP rainfall dataset. We find that the MEVD-based extreme value downscaling approach generally improves point extreme estimates.

Cliff bands are common in drylands and their evolution is often influenced by hydrogeomorphic processes. It has been previously suggested that cliff retreat patterns and morphology are affected by the properties and frequency-magnitude relations of rainstorms. However, basic questions on this topic persist because landscape evolution models typically do not account for the surface processes like runoff generation and sediment transport that occur under short-duration (sub-hourly) intense rainfall. Here we test the hypothesis that changes in rainstorm properties can systematically alter cliff retreat patterns and morphology. We developed a novel numerical model that simulates the response of cliffs and associated sub-cliff slopes to various rainstorm regimes to (1) identify dominant cliff morphologies, and (2) examine if extreme rainstorm properties are encoded in the topography. The new model utilizes the Landlab modeling toolkit and includes an explicit novel representation of surface processes that occur during short-duration rainstorms, including cliff-weathering, infiltration, runoff generation, clast fragmentation, and size-dependent sediment transport. Using a suite of numerical experiments, we vary model parameters and rainfall types and simulate changes in cliff retreat patterns and morphology. Our model results agree well with analytical predictions for cliff morphology under a control case of no transport on the sub-cliff slope, indicating a good representation of processes. Furthermore, sensitivity analyses on cases where sediment transport is explicitly included show that cliff evolution is highly dependent on both the grain size of sediment derived from the cliff and the rainfall intensities. These two factors can alter retreat patterns and determine whether and how fast the cliff can be buried under its own sediment. Numerical experiments based on rainfall and field measurements from the central Negev desert (eastern Mediterranean) demonstrate that including the dynamics of high-intensity rainfall and sediment grain size can help explain observed topographic trends. In addition, for a given imposed storm depth, we find that the rainstorm intensities pattern strongly influences both the cliff retreat and its morphology. Short rainstorms with higher intensities are much more erosive than longer storms with lower intensities. This latter case frequently triggers cliff burying. Taken as a whole, our results demonstrate that cliff evolution and morphology are significantly affected by storm-scale sediment transport dynamics and thus highlight the importance of incorporating high-resolution rainfall forcing into landscape evolution models of dryland landforms.

Flood properties are known to be sensitive to spatial and temporal patterns of precipitation, which in turn are affected by global warming. In this study, we investigated the effect of global warming on properties of heavy precipitation events (HPEs) in the eastern Mediterranean, focusing on hydrologically-important characteristics, including total precipitation amount, coverage area, precipitation duration and the distribution of rain rates for different durations. Then, we quantified how changes in precipitation due to global warming affect resulting flood properties for small-medium catchments in the study region. We used the weather research and forecasting (WRF) model to simulate 41 HPEsin present and future (end of 21st century; RCP 8.5 scenario) climate conditions and output the precipitation fields at high resolution (1 km2, 10 min). The calibrated GB-HYDRA distributed hydrological model (2). To account for the rainfall spatial uncertainty in the simulations, spatial shifts were applied to the simulated HPEs in a range of 20 km north and south. We found a major decrease in precipitation accumulation (−30% averaged across events) in future HPEs. This decrease results from a substantial reduction of the rain area of storms (−40%) and occurs despite an increase in the mean conditional rain rate (+15%). In addition, the duration of the HPEs decreases (−9%) in future simulations. The above changes were consistent across events. These changes have opposite directions, suggesting that flood properties changes are not trivial. Our simulations indicate a future decrease in both flood volume (-27%) and peak discharge (-20%, non-significant) at the outlet of the catchments. On the other hand, peak discharge is increasing in the future for small sub-catchments (< 5 km2). We currently expand this research to account for expected changes in future antecedent soil moisture conditions and land-use. To conclude: with global warming, HPEs in the eastern Mediterranean are becoming drier and more spatiotemporally concentrated. Consequently, small and larger catchments respond differently to this change, with the former reacting to the increase in rain rates and producing higher flood peak discharge, while the latter reacts more to the reduction in total rainfall, area and duration, and results in lower flood volumes and peaks.

Deciphering aspect-related hillslope asymmetry can enhance our understanding of the influence of climate on Earth’s surface morphology and the linkage between topographic morphology and erosion processes. Although hillslope asymmetry is documented worldwide, the role of microclimatic factors in the evolution of dryland cliffs has received little attention. Here, we address this gap by quantifying aspect-dependent bedrock weathering, slope-rill morphology, and sub-cliff clast transport rates in the hyperarid Negev desert, Israel, based on light detection and ranging (LiDAR)-derived topography, clast-size measurements, and cosmogenic 10Be concentrations. Cliff retreat rates were evaluated using extrapolated profiles from dated talus flatirons and 10Be measurements from the cliff face and sub-cliff sediments. We document systematic, aspect-dependent patterns of south-facing slopes being less steep and finer-grained relative to east- and north-facing aspects. In addition, cliff retreat and clast transport rates on slopes of the south-facing aspect are faster compared to the other aspects. Our data demonstrate that bedrock weathering of the cliff face and the corresponding grain size of cliff-derived clasts delivered to the slopes constitute a first-order control on cliff retreat and sediment transport rates. We demonstrate that the morphology of the cliff and the pattern of bedrock weathering co-vary with the solar radiation flux and hence that cliff evolution in hyperarid regions is modulated by aspect-dependent solar radiation. These results help to better understand interactions between climate and dryland surface processes.

Understanding of flood generating mechanisms is necessary for the largely populated Indian Subcontinent, which observes frequent floods in the large river basins during the Asian monsoon. However, the flood processes linked with the hydrometeorological variability remain unexplored over India. This study examines temporal and spatial variability of hydrometeorological variables (precipitation and surface runoff) prior to large streamflow events to identify the dominated flood processes and contributing areas in the Godavari river basin, the second largest basin in India, using the observed and model-simulated data. Based on the temporal variability in precipitation and surface runoff, we find that long-rain type floods predominantly occur in the semi-humid region of the Godavari basin (Tekra, Pathagudam, Peur, and Polavaram sub-basins). These floods are associated with the long-duration (10-11 days) accumulated precipitation due to the occurrence of multiple high-intensity precipitation events prior to flood. However, floods in the semi-arid Mancherial sub-basin are linked with the short-duration (2-days) accumulated precipitation with single high-intensity precipitation. Apart from temporal variability of hydrometeorological variables, we also analyzed the spatial variability of precipitation and runoff prior to flood events using the empirical orthogonal functions (EOF) analysis. We show that the central and downstream regions of the Godavari basin contribute more in the flood occurrence. Moreover, our analysis shows that the Tekra basin generates more runoff before the flood events compared to the other basins due to the favorable catchment conditions. Overall, our study provides a better understanding of flood processes over a large river basin of the Indian Subcontinent during the monsoonal climate, which can be used for numerous flood control and management applications.

Heavy precipitation events (HPEs) can lead to deadly and costly natural disasters and, especially in regions where rainfall variability is high, such as the eastern Mediterranean, they are critical to the hydrological budget. Reliable projections of future HPEs are needed, but global climate models are too coarse to explicitly represent rainfall processes during HPEs. In this study we used pseudo global warming high-resolution (1 km2) weather research and forecasting (WRF) model simulations to provide rainfall patterns projections based on simulations of 41 pairs of historic and “future” (end of 21st century) HPEs under global warming conditions (RCP8.5 scenario). Changes in rainfall patterns were analyzed through different properties: storm mean conditional rain rate, storm duration, and rain area. A major decrease in rainfall accumulation occurs in future HPEs (−30% averaged across events). This decrease results from a substantial reduction of the storms rain area (−40%) and duration (−9%), and occurs despite an increase in the mean conditional rain intensity (+15%). The consistency of results across events, driven by varying synoptic conditions, suggests that these changes have low sensitivity to the specific synoptic evolution during the events. Future HPEs in the eastern Mediterranean will therefore likely be drier and more spatiotemporally concentrated, with substantial implications on hydrological outcomes of storms. (For hydrological results see: abstract #EGU22-4777)Armon, M., Marra, F., Enzel, Y., Rostkier‐Edelstein, D., Garfinkel, C. I., Adam, O., et al. (2022). Reduced Rainfall in Future Heavy Precipitation Events Related to Contracted Rain Area Despite Increased Rain Rate. Earth’s Future, 10(1), 1–19. https://doi.org/10.1029/2021ef002397

Climate change impact on floods and water resources is crucial for planning adaptation strategies. This is especially true in Mediterranean regions where a decrease in precipitation and an increase in extreme rain rates are projected. Global climate models and common hydrological models are often too coarse to represent rainfall properties and hydrological processes in these regions due to their scale. Therefore, the current understanding of climate change's impact on hydrological properties and processes in Mediterranean catchments is missing. To resolve this, we utilize the high-resolution (1 km2) weather research and forecasting (WRF) model (abstract #EGU22-1996). Rainfall simulations were input to a distributed hydrological model (

Climatic impact on landscape morphology was previously demonstrated under pronounced gradients in average climatic properties such as mean annual precipitation or temperature. However, in arid areas, where both meteorological observations and rainfall measurements are scarce and the latter is meager, short-term and highly variable in space and time, the determination of meaningful “average climatic” conditions and their variability is challenging. Although it is generally acknowledged that surface processes in arid landscapes should be effected by short-duration rainfall intensities and their extremes, the topographic sensitivity to storm-scale properties were rarely quantified. Here, we attempted to bridge this gap by documenting systematic precipitation variations along a 40 km arid escarpment (Ramon crater) in the central Negev desert (Israel) and their associated topographic signature.We used 0.5 m pixel-1 LiDAR-derived topographic data coupled with field measurements to characterize the morphology of cliffs and slopes along the entire Ramon crater. Sub-hourly rainfall intensities were characterized using an 8-year record of high-resolution, convection-permitting, numerical weather model prediction (NWP). Frequency analyses of rainfall intensity and its spatial variation were conducted using a novel statistical method and used to determine runoff and sediment transport along sub-cliff slopes, through grid-based hydrological simulations of synthetic rainstorms with different frequencies.Our results indicate that due to a pronounced decreasing gradient in the number of rain storms per year, the mean annual rainfall decreases from ~100 mm in the southwest (SW) cliff segment to ~40 mm in the northeast (NE) segment. However, in the drier NE cliff segment, extreme rainfall intensities such as the ones occurring during a storm with a 100-year return period are higher. Topographic cliff gradients and the percentage of exposed bedrock over the cliffs increase toward the drier NE cliff section. Sub-cliff slopes in the NE are systematically straighter, shorter, and associated with a smaller clast sizes relative to the wetter (SW) part of the escarpment. Hydrological simulations reveal that under extreme storms, sediment is mobilized by sheetwash on the NE slopes but is less mobile on the wetter SW slopes. In addition, incised gullies and disconnected talus-flatirons are more frequent in the NE and correlate with the higher erosion efficiency of extreme rainstorms in this zone. Our results indicate that significant morphologic differences can be imprinted in arid landforms due to spatial gradients in the properties of extreme rainstorms.

This study proposes a new approach for quantitively assessing the importance of precipitation features in space and time to predict streamflow discharge (and, hence, sensitivity). For this, we combine well-performing deep-learning (DL) models with interpretability tools.The DL models are composed of convolutional neural networks (CNNs) and long-short term memory (LSTM) networks. Their input is precipitation data distributed over the watershed and taken back in time (other inputs, meteorological and watershed properties, can also be included). Its output is streamflow discharge at a present or future time. Interpretability tools allow learning about the modeled system. We used the Integrated Gradients method that provides a level of importance (IG value) for each space-time precipitation feature for a given streamflow prediction. We applied the models and interpretability tools to several watersheds in the US and India.To understand the importance of precipitation features for flood generation, we compared spatial and temporal patterns of IG for high flows vs. low and medium flows. Our results so far indicate some similar patterns for the two categories of flows, but others are distinctly different. For example, common IG mods exist at short times before the discharge, but mods are substantially different when considered further back in time. Similarly, some spatial cores of high IG appear in both flow categories, but other watershed cores are featured only for high flows. These IG time and space pattern differences are presumably associated with slow and fast flow paths and threshold-runoff mechanisms.There are several advantages to the proposed approach: 1) recent studies have shown DL models to outperform standard process-based hydrological models, 2) given data availability and quality, DL models are much easier to train and validate, compared to process-based hydrological models, and therefore many watersheds can be included in the analysis, 3) DL models do not explicitly represent hydrological processes, and thus sensitivities derived in this approach are assured to represent patterns arise from the data. The main disadvantage of the proposed approach is its limitation to gauged watersheds only; however, large data sets are publicly available to exploit sensitivities of gauged streamflow.It should be stressed out that learning about hydrological sensitivities with DL models is proposed here as a complementary approach to analyzing process-based hydrological models. Even though DL is considered black-box models, together with interpretability tools, they can highlight hard or impossible sensitivities to resolve with standard models.

The yearly exceedance probability of extreme precipitation of multiple durations is crucial for infrastructure design, risk management, and policymaking. Local extremes emerge from the interaction of weather systems with local terrain features such as coastlines and orography; however, multi-duration extremes do not follow exactly the patterns of cumulative precipitation and are still not well understood. High-resolution information from weather radars could help us quantify their patterns better, but traditional extreme value analyses based on radar records were found to be too inaccurate for quantifying the extreme intensities required for impact studies. Here, we propose a novel methodology for extreme precipitation frequency analysis based on relatively short weather radar records, and we use it to investigate the coastal and orographic effects on extreme precipitation of durations between 10 min and 24 h. Combining 11 years of radar data with 10 min rain gauge data in the southeastern Mediterranean, we obtain estimates of the once in 100 years precipitation intensities with ∼26 % standard error, which is lower than those obtained using traditional approaches on rain gauge data. We identify the following three distinct regimes which respond differently to coastal and orographic forcing: short durations (∼10 min), related to peak convective rain rates, hourly durations (∼1 h), related to the yield of individual convective cells, and long durations (∼6–24 h), related to the accumulation of multiple convective cells and to stratiform processes. At short and hourly durations, extreme return levels peak at the coastline, while at longer durations they peak corresponding to the orographic barriers. The distributions tail heaviness is rather uniform above the sea and rapidly changes in presence of orography, with opposing directions at short (decreasing tail heaviness, with a peak at hourly durations) and long (increasing) durations. These distinct effects suggest that short-scale hazards, such as urban pluvial floods, could be more of concern for the coastal regions, while longer-scale hazards, such as flash floods, could be more relevant in mountainous areas., Hydrology and Earth System Sciences, 26 (5), ISSN:1027-5606, ISSN:1607-7938

The operational flood forecasting system by Google was developed to provide accurate real-time flood warnings to agencies and the public, with a focus on riverine floods in large, gauged rivers. It became operational in 2018 and has since expanded geographically. This forecasting system consists of four subsystems: data validation, stage forecasting, inundation modeling, and alert distribution. Machine learning is used for two of the subsystems. Stage forecasting is modeled with the Long Short-Term Memory (LSTM) networks and the Linear models. Flood inundation is computed with the Thresholding and the Manifold models, where the former computes inundation extent and the latter computes both inundation extent and depth. The Manifold model, presented here for the first time, provides a machine-learning alternative to hydraulic modeling of flood inundation. When evaluated on historical data, all models achieve sufficiently high-performance metrics for operational use. The LSTM showed higher skills than the Linear model, while the Thresholding and Manifold models achieved similar performance metrics for modeling inundation extent. During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area of 287,000 km2, home to more than 350M people. More than 100M flood alerts were sent to affected populations, to relevant authorities, and to emergency organizations. Current and future work on the system includes extending coverage to additional flood-prone locations, as well as improving modeling capabilities and accuracy., Comment: 36 pages, 10 figures, 3 tables, 1 supplementary table (9 pages)

Heavy precipitation events (HPEs) can lead to deadly and costly natural disasters and are critical to the hydrological budget in regions where rainfall variability is high and water resources depend on individual storms. Thus, reliable projections of such events in the future are needed. To provide high‐resolution projections under the RCP8.5 scenario for HPEs at the end of the 21st century, and to understand the changes in sub‐hourly to daily rainfall patterns, weather research and forecasting (WRF) model simulations of 41 historic HPEs in the eastern Mediterranean are compared with “pseudo global warming” simulations of the same events. This paper presents the changes in rainfall patterns in future storms, decomposed into storms' mean conditional rain rate, duration, and area. A major decrease in rainfall accumulation (−30% averaged across events) is found throughout future HPEs. This decrease results from a substantial reduction of the rain area of storms (−40%) and occurs despite an increase in the mean conditional rain intensity (+15%). The duration of the HPEs decreases (−9%) in future simulations. Regionally maximal 10‐min rain rates increase (+22%), whereas over most of the region, long‐duration rain rates decrease. The consistency of results across events, driven by varying synoptic conditions, suggests that these changes have low sensitivity to the specific synoptic evolution during the events. Future HPEs in the eastern Mediterranean will therefore likely be drier and more spatiotemporally concentrated, with substantial implications on hydrological outcomes of storms.

Talus-pediment slopes are a common morphologic feature in arid areas and constitute a prominent runoff and sediment source at the watershed and channel scales. The evolution of talus-pediment sequences (talus flatirons) was often linked to climatic cycles, although the physical processes that may account for such a link remained obscure. Our approach is to integrate field measurements, high-resolution radar rainfall data and numerical modeling to link the frequency of storms and the resulted hillslope runoff and sediment transport. We present a quantitative hydrometeorological analysis of rainstorms and their geomorphic impact, potentially involved in the evolution of arid talus-pediment slopes in the Negev desert (Israel). Artificial rainstorms were designed based on intensity-duration-frequency curves and simulated in the field using a rainfall simulator. Then, the obtained experimental results were up-scaled to the entire slope length using a fully distributed hydrological model. In addition, natural storms and their hydro-geomorphic impacts were monitored using X-band radar and time-lapse cameras.These integrated analyses constrain the rainfall threshold for local runoff generation at rain intensity of 14-22 mm h-1 for a duration of 5 min for the study area conditions. We characterized small-scale runoff-generating convective rain cells using an X-band radar and found that small convective cells (~30 km2), having extremely high internal spatial gradients in rainfall intensity and low velocity (-1), have the potential to generate local hillslope runoff. The frequency of local runoff-producing rainstorms is ~1-3 per year, but most of these storms activate only small parts of the hillslope. Modeling results indicate that a full extent hillslope runoff occurs under much rarer rainstorms of at least 100-years return interval (1% or less). During such rainstorms, the shear stress produced by the runoff flow (sheetwash) is capable of transporting surface clasts at a distance of ~80 m downslope. However, transport of coarse clasts in the upper parts of the slopes is most probably gravitationally controlled. The erosion efficiency of discrete rare events (1% or less) on the lower part of the slopes highlights their potential to trigger incision and lead to cliff dissection. This study results support the hypothesis that a climatic shift in terms of the properties and frequency of extreme rainstorms, rather than the common views of it as changing precipitation means, can play an important role in shaping and in transforming landscapes in such arid setting.

While 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.

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.

Flash flooding in the arid/semiarid southwestern United States is frequently associated with convective rainfall during the North American monsoon. In this study, we examine flood-producing storms in central Arizona based on analyses of dense rain gauge observations and stream gauging records as well as North American Regional Reanalysis fields. Our storm catalog consists of 102 storm events during the period of 1988–2014. Synoptic conditions for flood-producing storms are characterized based on principal component analyses. Four dominant synoptic modes are identified, with the first two modes explaining approximately 50% of the variance of the 500-hPa geopotential height. The transitional synoptic pattern from the North American monsoon regime to midlatitude systems is a critical large-scale feature for extreme rainfall and flooding in central Arizona. Contrasting spatial rainfall organizations and storm environment under the four synoptic modes highlights the role of interactions among synoptic conditions, mesoscale processes, and complex terrains in determining space–time variability of convective activities and flash flood hazards in central Arizona. We characterize structure and evolution properties of flood-producing storms based on storm tracking algorithms and 3D radar reflectivity. Fast-moving storm elements can be important ingredients for flash floods in the arid/semiarid southwestern United States. Contrasting storm properties for cloudburst storms highlight the wide spectrum of convective intensities for extreme rain rates in the arid/semiarid southwestern United States and exhibit comparable vertical structures to their counterparts in the eastern United States.

The Dead Sea sedimentary fill is the basis for interpreting limnological conditions and regional paleohydrology. 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 rainstorm 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 Mediterranean 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, intensity, 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 supplying 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.

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 us to describe the properties of floods over catchments spanning from desert to Mediterranean climates, and we note that the data set is mostly of moderate intensity floods. Comparing two climatic regions, desert and Mediterranean, we can 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. Rain depth and antecedent conditions are the most important properties to shape flood response in Mediterranean areas. In deserts, instead, storm core properties display a strong correlation with unit peak discharge and, to a lesser 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.

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 Mediterranean, 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 quantiles 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.

Projections of extreme precipitation based on modern climate models suffer from large uncertainties. Specifically, unresolved physics and natural variability limit the ability of climate models to ...

Hyperarid (< 80 mm yr-1) soils in hot deserts are characterized by accumulations of soluble salts (gypsum and halite) in diagnostic horizons as a result of limited moisture availability. In most desert terrains, the source for pedogenic gypsum and halite is atmospheric dust and rainwater. The interplay between climatic properties such as frequency and intensity of rain events, rainfall composition, dust flux, and evaporation rates, govern the depth and concentration of these salts. Better understanding of these relationships can improve our estimation of regional paleoenvironmental and paleoclimate conditions. Up to date, only empirical correlations between annual rainfall and pedogenic salt horizons are available.The goals of this study are to: 1) quantify rates of pedogenic gypsum accumulation with time and the role of controlling climatic conditions that govern its accumulation, 2) estimate the most likely climatic scenarios that led to the formation of the diagnostic gypsic horizon developed in late Pleistocene (~ 60 ka) abandoned alluvial fan surfaces in the hyperarid Negev desert, southern Israel. To achieve these goals, we constructed a compartment model that simulates gypsum accumulation in soil and tests its sensitivity to various changes in the long-term climate properties. The model predicts gypsum content and depth of accumulation in the soil profile over thousands of years and more. The input parameters are stochastically simulated rainstorms, evaporation, dust flux, and sulfate concentration in rainwater, at daily time steps. The model was tested and calibrated using data of Holocene (< 11 ka) soil profiles developed on stable alluvial fans in the hyperarid Negev. With the assumption that the climate during the Holocene was not much different than today (i.e., mean annual rainfall < 50 mm). Sensitivity analyses indicate that gypsum accumulation is highly sensitive to mean annual rainfall and sulfate concentration in rainwater. Synthetic gypsum profiles were calculated using different climate scenarios and compared to late Pleistocene soils. Our results suggest that: (a) gypsum accumulation in late Pleistocene soils cannot occur simply by extending current climate conditions for a much longer duration. (b) The plausible climate scenarios for the late Pleistocene must include additional rain input (1.5 – 2.0 times than mean annual rainfall today) and increased sulfate concentration in rainwater (2.0 – 2.5 times than today) to successfully reconstruct the observed accumulated gypsum in mature (60 – 12 Ka) soil profiles.

Preparedness to natural hazards in mountainous areas strongly relies on the knowledge of extreme rainfall probability. The presence of mountains influences the motion of air masses, thereby modifying the storms characteristics. Here, we use a novel statistical approach to quantify the orographic impact on the probability of occurrence of extreme rainfall of short duration (10-min to 6-hour). We find that mountains tend to decrease the mean annual maximum intensities at sub-hourly scales, thereby confirming the previously reported “reversed orographic effect”, and tend to decrease the tail heaviness, thereby decreasing the extremely high intensities such as the events occurring on average once in 100 years. The second effect is however non-monotonic, in that it increases between 10 minute and 1 hour and diminishes between 1 and 6 hours. Sub-hourly extremes could thus be higher than what can be estimated from hourly data alone, implying that the scaling assumptions typically adopted for risk assessment may systematically underestimate the risk of short-duration extremes

Heavy precipitation events (HPEs) in the densely populated eastern Mediterranean trigger natural hazards, such as flash floods and urban flooding. However, they also supply critical amounts of fresh water to this desert-bounded region. The impact of global warming on such events is thus vital to the inhabitants of the region. HPEs are poorly represented in global climate models, leading to large uncertainty in their sensitivity to climate change. Is total rainfall in HPEs decreasing, as projected for the mean annual rainfall? Are short duration rain rates decreasing, or rather increasing as expected from the higher atmospheric moisture content? Where are the changes more pronounced, near the sea or farther inland towards the desert? To answer these questions, we have identified 41 historical HPEs from a long weather radar record (1990-2014) and simulated them in the same resolution (1 km2) using the convection-permitting weather research and forecasting (WRF) model. Results were validated versus the radar data, and served as a control group to simulations of the same events under ‘pseudo global warming’ (PGW) conditions. The PGW methodology we use imposes results from the ensemble mean of 29 Coupled Model Intercomparison Project Phase 5 (CMIP5) models for the end of the century on the initial and boundary conditions of each event simulated. The results indicate that HPEs in the future may become more temporally focused: they are 6% shorter and exhibit maximum local short-duration rain rates which are ~20% higher on average, with larger values over the sea and the wetter part of the region, and smaller over the desert. However, they are also much drier; total precipitation during the future-simulated HPEs decreases substantially (~-20%) throughout the eastern Mediterranean. The meteorological factors leading to this decrease include shallower cyclones and the projected differential land-sea warming, which causes reduced relative humidity over land. These changing rainfall patterns are expected to amplify water scarcity – a known nexus of conflict and strife in the region – highlighting the urgent need for deeper knowledge, and the implementation of adaptation and mitigation strategies.

Soil erosion affects agricultural landscapes worldwide, threatening food security and ecosystem viability. In arable environments, soil loss is primarily caused by short, intense rainstorms, typically characterized by high spatiotemporal variability. The complexity of erosive events challenges modeling efforts and explicit inclusion of extreme events in long-term risk assessment is missing. This study is intended to bridge this gap by quantifying the discrete and cumulative impacts of rainstorms on plot-scale soil erosion and providing storm-scale erosion risk analyses for a cropland region in northern Israel. Central to our analyses is the coupling of (1) a stochastic rainfall generator able to reproduce extremes down to 5-minute temporal resolutions; (2) a processes-based event-scale cropland erosion model (Dynamic WEPP, DWEPP); and, (3) a state-of-the-art frequency analysis method that explicitly accounts for rainstorms occurrence and properties. To our knowledge, this is the first study in which DWEPP runoff and soil loss are calibrated at the plot-scale on cropland (NSE is 0.82 and 0.79 for event runoff and sediment, respectively). We generated 300-year stochastic simulations of event runoff and sediment yield based on synthetic precipitation time series. Based on this data, the mean annual soil erosion in the study site is 0.1 kg m-2 [1.1 t ha-1]. Results of the risk analysis indicate that individual extreme rainstorms (>50 return period), characterized by high rainfall intensities (30-minute maximal intensity > ~60 mm h-1) and high rainfall depth (>~200 mm), can trigger soil losses even one order of magnitude higher than the annual mean. The erosion efficiency of these rainstorms is mainly controlled by the short-duration (≤30 min) maximal intensities. The results demonstrate the importance of incorporating the impact of extreme events into soil conservation and management tools. We expect our methodology to be valuable for investigating future changes in soil erosion with changing climate.

Flash floods are among the most devastating and lethal natural hazards. In 2018, three flash-flood episodes resulted in 46 casualties in the deserts of Israel and Jordan alone. This paper presents the hydrometeorological analysis and forecasting of a substantial storm (25–27 April 2018) that hit an arid desert basin (Zin, ∼1400 km2, southern Israel) claiming 12 human lives. This paper aims to (a) spatially assess the severity of the storm, (b) quantify the timescale of the hydrological response, and (c) evaluate the available operational precipitation forecasting. Return periods of the storm's maximal rain intensities were derived locally at 1 km2 resolution using weather radar data and a novel statistical methodology. A high-resolution grid-based hydrological model was used to study the intra-basin flash-flood magnitudes which were consistent with direct information from witnesses. The model was further used to examine the hydrological response to different forecast scenarios. A small portion of the basin (1 %–20 %) experienced extreme precipitation intensities (75- to 100-year return period), resulting in a local hydrological response of a high magnitude (10- to 50-year return period). Hillslope runoff, initiated minutes after the intense rainfall occurred, reached the streams and resulted in peak discharge within tens of minutes. Available deterministic operational precipitation forecasts poorly predicted the hydrological response in the studied basins (tens to hundreds of square kilometers) mostly due to location inaccuracy. There was no gain from assimilating radar estimates in the numerical weather prediction model. Therefore, we suggest using deterministic forecasts with caution as it might lead to fatal decision making. To cope with such errors, a novel cost-effective methodology is applied by spatially shifting the forecasted precipitation fields. In this way, flash-flood occurrences were captured in most of the subbasins, resulting in few false alarms.

Flash floods are among the most devastating and lethal natural hazards. In 2018, three flash-flood episodes resulted in 46 casualties in the deserts of Israel and Jordan alone. This paper presents the hydrometeorological analysis and forecasting of a substantial storm (25–27 Apr 2018) that hit an arid desert basin (Zin, ~ 1400 km2, southern Israel), claiming 12 human lives. Our aim was to: (a) spatially assess the severity of the storm, (b) quantify the time scale of the hydrological response, and (c) evaluate the available operational precipitation forecasting. Return periods of the storm's maximal rain intensities were derived locally, at 1-km2 resolution, using weather radar data and a novel statistical methodology. A high-resolution grid-based hydrological model was used to study the intra-basin flash-flood magnitudes, which were consistent with direct information from witnesses. The model was further used to examine the hydrological response to different forecast scenarios. A small portion of the basin (1–20 %) experienced extreme precipitation intensities (75- to 100-year return period), resulting in a local hydrological response of a high magnitude (10- to 50-year return period). Hillslope runoff, initiated minutes after the intense rainfall occurred, reached the streams and resulted in peak discharge within tens of minutes. Available deterministic operational precipitation forecasts poorly predicted the hydrological response in the studied basins (tens to hundreds of km2) mostly due to location inaccuracy. There was no gain from assimilating radar estimates in the numerical weather-prediction model. Therefore, we suggest using deterministic forecasts with caution as it might lead to fatal decision making. To cope with such errors a novel cost-effective methodology is applied by spatially shifting the forecasted precipitation fields. In this way, flash-flood occurrences were captured in most of the sub-basins, resulting in few false alarms.

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.

Water volume estimates of shallow desert lakes are the basis for water balance calculations, important both for water resource management and paleohydrology/climatology. Water volumes are typically...

Many of the world’s drylands are characterized by interior drainage systems that terminate at shallow desert lakes or playas. Except for episodic flooding these largely ephemeral water bodies, remain mostly dry. Surveying and mapping their respective floor topography in a suitable resolution for calculating water balance is a difficult and laborious task. As this is crucial for water resources management and reconstructing paleoenvironmental conditions, diverse methods and efforts were applied. However, detailed, high-quality bathymetric surveys in such environments are rare and have only been conducted in a few such lakes. This is primarily due to their shallow, low-relief, large areas, and often remote characteristics, which complicate application of conventional topographic surveying techniques. Therefore, satellite-based remote sensing is an essential complementary approach for deriving bathymetry of such lakes.Global digital elevation models, such as NASA’s Shuttle Radar Topography Mission (SRTM) or ASTER’s GDEM, are unsuitable for accurate measurements of these ephemeral lakes, mainly because of their impenetrability to water and their high signal-to-noise ratio in the low-relief environments. Recent studies addressed these complications by combining remote sensing data with local calibrations of in-situ measurements, or alternatively, by relating shoreline altitudes with precise altimetry. This approach requires a spatial interpolation of individual measurements. Therefore, it is prone to errors that demand intensive efforts to be reduced; even then the errors may remain larger than the actual depth of a flooded lake. Moreover, such methods are hard to apply in complex lakes with multiple sub-basins.To tackle these problems, we developed a simple methodology to derive a high-resolution (30 m per pixel) bathymetry of shallow desert lakes. In this new method we combine two sources of data: a >30-yr record of Landsat-derived surface water occurrence data; and accurate high-resolution elevation data, acquired by the NASA’s recently launched ICESat-2 satellite (Ice, Cloud, and Land Elevation Satellite-2). We test the proposed new method over three ephemeral lakes around the world: Lake Eyre, Australia, with its complex shallow lake system, consisting of a few sub-basins; Sabkhat Al-Mellah, Algeria; Lago Coipasa, Bolivia. The accuracy of the resulted bathymetric maps was evaluated using cross-validation of ICESat-2 scans, yielding low RMSD values of ~0.3 m, versus ~2.5 m of the SRTM data (validated through other ICESat-2 scans). At Lago Coipasa, we show that bathymetry was effectively determined even when the lake was full of water (up to a few meters depth). This high-resolution, low-error bathymetry mapping complement other globally available topographic data.

Observations from the past century and projections for the end of this century exhibit a decrease in precipitation over the Eastern Mediterranean Sea and surrounding land areas, but the magnitude of the expected drying is unknown. Changes in precipitation are controlled by both thermodynamic (moist) and dynamic (dry) processes, but the relative contributions of these processes, in particular on regional scales, is not well understood. Previous studies have analyzed the ability of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) multi-model mean to represent the spatial and seasonal patterns of the Mediterranean hydroclimate. A wide spread exists among the individual models, which can be exploited to better understand the factors controlling future climate. Garfinkel et al. (2020)[i] found that large-scale mechanisms contribute about 50% of the model spread in Eastern Mediterranean drying. This study further explores the variance across models in projected changes of the moisture budget by decomposing them into mean dynamic, mean thermodynamic and transient components. These components are then related to the variance across models in projected large-scale processes. Through these analyses, uncertainties regarding future changes in precipitation can be reduced.[i] Garfinkel, C. I. et al. (2020) ‘The role of zonally averaged climate change in contributing to inter-model spread in CMIP5 predicted local precipitation changes’, Journal of Climate, 33, pp. 1141–1154. doi: 10.1175/JCLI-D-19-0232.1.