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.
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.
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.
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.
channel, increasing sinuosity. Upstream, near the migrating knickzone channel gradients also increase, incision is more moderate and floods continue to overtop the banks, favoring meander chute cutoffs. The resulting channel has a downstream well-confined meandering segment and an upstream low-sinuosity segment. These new insights regarding spatial differences along an incising channel can improve interpretations of the evolution of ancient planforms and floodplains that responded to base-level decline.
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.
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.
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.
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.
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.
Rainfall in the Levant drylands is scarce, but can potentially generate high-magnitude flash floods. Rainstorms are caused by distinct synoptic-scale circulation patterns: Mediterranean cyclone (MC), active Red Sea trough (ARST) and subtropical jet stream (STJ) disturbances, also termed tropical plumes (TPs). The unique spatiotemporal characteristics of rainstorms and floods for each circulation pattern were identified. Meteorological reanalyses, quantitative precipitation estimates from weather radars, hydrological data, and indicators of geomorphic changes from remote-sensing imagery were used to characterize the chain of hydrometeorological processes leading to distinct flood patterns in the region.Significant differences in the hydrometeorology of these three flood-producing synoptic systems were identified: MC storms draw moisture from the Mediterranean and generate moderate rainfall in the northern part of the region. ARST and TP storms transfer large amounts of moisture from the south, which is converted to rainfall in the hyperarid southernmost parts of the Levant. ARST rainfall is local and intense, whereas TP rainfall is widespread and prolonged due to high precipitation efficiency and large-scale forcing. Thus, TP rainfall generates high-magnitude floods in the largest catchments; integration of numerous basins leads to sediment feeding from the south into the Dead Sea, exhibited in large sediment plumes. Anecdotal observations of the channel with the largest catchment in the region (Nahal HaArava) indicate that TP floods account for noticeable geomorphic changes in the channel. It provides insights into past intervals of increased flash flood frequency characterized by episodes of marked hydrogeomorphic work; such an increase is especially expected during intervals of southerly situated and southwesterly oriented STJs.
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.
The hydroclimatology, hydrometeorology, and hydrology of flash floods in the arid/semiarid southwestern United States are examined through empirical analyses of long-term, high-resolution rainfall and stream gauging observations, together with hydrological modeling analyses of the 19 August 2014 storm based on the Kinematic Runoff and Erosion Model (KINEROS2). The analyses presented here are centered on identifying the structure and evolution of flood-producing storms, as well as the interactions of space–time rainfall variability and basin characteristics in determining the upper-tail properties of rainfall and flood magnitudes over this region. This study focuses on four watersheds in Maricopa County, Arizona, with contrasting geomorphological properties. Flash floods over central Arizona are concentrated in both time and space, reflecting controls of the North American monsoon and complex terrain. Thunderstorm systems during the North American monsoon, as represented by the 19 August 2014 storm, are the dominant flood agents that determine the upper tail of flood frequency over central Arizona and that also shape the envelope curve of floods for watersheds smaller than 250 km2. Flood response for the 19 August 2014 storm is associated with storm elements of comparable spatial extent to the drainage area and slow movement for the three compact, headwater watersheds. Flood response for the elongated and relatively flat Skunk Creek highlights the importance of the spatial distribution of rainfall for transmission losses in arid/semiarid watersheds.
The geomorphic response of channels to base-level fall is an important factor in landscape evolution. To better understand the complex interactions between the factors controlling channel evolution in an emerging continental shelf setting, we use an extensive data set (high-resolution digital elevation models, aerial photographs, and Landsat imagery) of a newly incising, perennial segment of Nahal (Wadi) HaArava, Israel. This channel responds to the rapid and progressive lowering of its base-level, the Dead Sea ( \textgreater 30 m in \~35 years; \~0.5-1.3 m yr -1 ). Progressively evolving longitudinal profiles, channel width, sinuosity, and knickpoint retreat during the last few decades were documented or reconstructed. The results indicate that even under fast base-level fall, rapid delta progradation on top of the shelf and shelf edge can moderate channel mouth slopes and, therefore, largely inhibit channel incision and knickpoint propagation. This channel elongation stage ends when the delta reaches an extended accommodation within the receiving basin and fails to keep the channel mouth slopes as low as the channel bed slopes. Then, processes of incision, narrowing, and meandering begin to shape the channel and expand upstream. When the down-cutting channel encounters a more resistant stratum within the channel substrate, these processes are restricted to a downstream reach by formation of a retreating vertical knickpoint. When the knickpoint and the channel incise to a level below this stratum, a spatially continuous, diffusion-like evolution characterizes the channel's response and source-to-sink transport can be implemented. These results emphasize the mouth slope and channel substrate resistance as the governing factors over long-term channel evolution, whereas flash floods have only local and short-lived impacts in a confined, continuously incising channel. The documented channel response applies to eustatic base-level fall under steepening basin bathymetry, rapid delta progradation, and lithologic variations in the channel substrate.
Space-time patterns of rainfall are important climatic characteristics that influence runoff generation and flash flood magnitude. Their derivation requires high-resolution measurements to adequately represent the rainfall distribution, and is best provided by remote sensing tools. This need is further emphasized in dry climate regions, where rainfall is scarce and, often, local and highly variable. Our research is focused on understanding the nature of rainfall events in the dry Dead Sea region (Eastern Mediterranean) by identifying and characterizing the spatial structure and the dynamics of convective storm cores (known as rain cells). To do so, we take advantage of 25 years of corrected and gauge-adjusted weather radar data. A statistical analysis of convective rain-cells spatial and temporal characteristics was performed with respect to synoptic pattern, geographical location, and flash flood generation. Rain cells were extracted from radar data using a cell segmentation method and a tracking algorithm and were divided into rain events. A total of 10,500 rain cells, 2650 cell tracks and 424 rain events were elicited. Rain cell properties, such as mean areal and maximal rain intensity, area, life span, direction and speed, were derived. Rain events were clustered, according to several ERA-Interim atmospheric parameters, and associated with three main synoptic patterns: Cyprus Low, Low to the East of the study region and Active Red Sea Trough. The first two originate from the Mediterranean Sea, while the third is an extension of the African monsoon. On average, the convective rain cells in the region are 90 km 2 in size, moving from West to East in 13 ms −1 and living 18 minutes. Several significant differences between rain cells of the various synoptic types were observed. In particular, Active Red Sea Trough rain cells are characterized by higher rain intensities and lower speeds, suggesting a higher flooding potential for small catchments. The north-south negative gradient of mean annual rainfall in the study region was found to be negatively correlated with rain cells intensity and positively correlated with rain cells area. Additional analysis was done for convective rain cells over two nearby catchments located in the central part of the study region, by ascribing some of the rain events to observed flash-flood events. It was found that rain events associated with flash-floods have higher maximal rain cell intensity and lower minimal cell speed than rain events that did not lead to a flash-flood in the watersheds. This information contributes to our understanding of rain patterns over the dry area of the Dead Sea and their connection to flash-floods. The statistical distributions of rain cells properties can be used for high space-time resolution stochastic simulations of rain storms that can serve as an input to hydrological models.
Intensity–duration–frequency (IDF) curves are widely used to quantify the probability of occurrence of rainfall extremes. The usual rain gauge-based approach provides accurate curves for a specific location, but uncertainties arise when ungauged regions are examined or catchment-scale information is required. Remote sensing rainfall records, e.g. from weather radars and satellites, are recently becoming available, providing high-resolution estimates at regional or even global scales; their uncertainty and implications on water resources applications urge to be investigated. This study compares IDF curves from radar and satellite (CMORPH) estimates over the eastern Mediterranean (covering Mediterranean, semiarid, and arid climates) and quantifies the uncertainty related to their limited record on varying climates. We show that radar identifies thicker-tailed distributions than satellite, in particular for short durations, and that the tail of the distributions depends on the spatial and temporal aggregation scales. The spatial correlation between radar IDF and satellite IDF is as high as 0.7 for 2–5-year return period and decreases with longer return periods, especially for short durations. The uncertainty related to the use of short records is important when the record length is comparable to the return period ( ∼ 50, ∼ 100, and ∼ 150 % for Mediterranean, semiarid, and arid climates, respectively). The agreement between IDF curves derived from different sensors on Mediterranean and, to a good extent, semiarid climates, demonstrates the potential of remote sensing datasets and instils confidence on their quantitative use for ungauged areas of the Earth.
The quantification of spatial rainfall is critical for distributed hydrological modeling. Rainfall spatial patterns generated by similar weather conditions can be extremely diverse. This variability can have a significant impact on hydrological processes. Stochastic simulation allows generating multiple realizations of spatial rainfall or filling missing data. The simulated data can then be used as input for numerical models to study the uncertainty on hydrological forecasts. In this paper, we use the direct sampling technique to generate stochastic simulations of high-resolution (1 km) daily rainfall fields, conditioned by elevation and weather state. The technique associates historical radar estimates to variables describing the daily weather conditions, such as the rainfall type and mean intensity, and selects radar images accordingly to form a con- ditional training image set of each day. Rainfall fields are then generated by resampling pixels from these images. The simulation at each location is conditioned by neighbor patterns of rainfall amount and eleva- tion. The technique is tested on the simulation of daily rainfall amount for the eastern Mediterranean. The results show that it can generate realistic rainfall fields for different weather types, preserving the temporal weather pattern, the spatial features, and the complex relation with elevation. The concept of conditional training image provides added value to multiple-point simulation techniques dealing with extremely non- stationary heterogeneities and extensive data sets.
In this study we investigate the scaling of precipitation extremes with temperature in the Mediterranean region by assessing against observations the present day and future regional climate simulations performed in the frame of the HyMeX and MED-CORDEX programs. Over the 1979–2008 period, despite differences in quantitative precipitation simulation across the various models, the change in precipitation extremes with respect to temperature is robust and consistent. The spatial variability of the temperature–precipitation extremes relationship displays a hook shape across the Mediterranean, with negative slope at high temperatures and a slope following Clausius–Clapeyron (CC)-scaling at low temperatures. The temperature at which the slope of the temperature–precipitation extreme relation sharply changes (or temperature break), ranges from about 20 \$\backslash$textdegree\C in the western Mediterranean to \textless10 \$\backslash$textdegree\C in Greece. In addition, this slope is always negative in the arid regions of the Mediterranean. The scaling of the simulated precipitation extremes is insensitive to ocean–atmosphere coupling, while it depends very weakly on the resolution at high temperatures for short precipitation accumulation times. In future climate scenario simulations covering the 2070–2100 period, the temperature break shifts to higher temperatures by a value which is on average the mean regional temperature change due to global warming. The slope of the simulated future temperature–precipitation extremes relationship is close to CC-scaling at temperatures below the temperature break, while at high temperatures, the negative slope is close, but somewhat flatter or steeper, than in the current climate depending on the model. Overall, models predict more intense precipitation extremes in the future. Adjusting the temperature–precipitation extremes relationship in the present climate using the CC law and the temperature shift in the future allows the recovery of the temperature–precipitation extremes relationship in the future climate. This implies negligible regional changes of relative humidity in the future despite the large warming and drying over the Mediterranean. This suggests that the Mediterranean Sea is the primary source of moisture which counteracts the drying and warming impacts on relative humidity in parts of the Mediterranean region.
This work evaluates two numerical warning indicators of severe weather. These indicators, the MKI and RDI indices, were developed within the framework of the EU- funded FLASH project which studies flash flood events in the Mediterranean Basin. The MKI (Modified K-Index) is a modification of the K-Index, which expresses probability of lightning activity, and the RDI (Rain Dynamical Index) is the integrated upward moisture flux. The indices were tested on 59 episodes which occurred during nine rainstorms in Israel, Greece, Spain, Italy, and Cyprus. The data for calculation of the indices included rain cell identification derived from microwave radiometer imagery of polar orbiting NOAA satellites, rain RADAR data, and lightning activity from the international ZEUS detection system. Atmospheric data with 0.5? 9 0.5? spatial resolution and 6-h time res- olution were used for the calculation and the display of the two indices. The indices were tested by calculating the spatially correlating locations with high index values and actual locations of intense rain and intense lightning. The RDI detected both event types: rain and lightning, with a statistically significant success rate and a low rate of false results. The MKI was successful in indicating intense lightning activity, but the rate of correct indi- cations was not statistically significant and there was a high rate of false indications. The results suggest that the RDI computed with output of weather prediction models is a potentially good predictor of torrential rain and therefore can predict flash floods caused by such rain in the Mediterranean region.