The effect of climate change on the Eastern Mediterranean (EM) region, a region that reflects a transition between Mediterranean and semi-arid climates, was examined. This transition region is affected by global changes such as the expansion of the Hadley cell, which leads to a poleward shift of the subtropical dry zone. The Hadley cell expansion forces the migration of jet streams and storm tracks poleward from their standard course, potentially increasing regional desertification. This article focuses on the northern coastline of Israel along the EM region where most wet synoptic systems (i.e. systems that may lead to precipitation) are generated. The current climate was compared to the predicted mid-21st century climate based on Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) RCP4.5 and RCP8.5 scenarios using four Coupled Model Intercomparison Project Phase 5 (CMIP5) models. A warming of 1.1–2.6 °C was predicted for this region. The models predicted that rain in the region will become less frequent, with a reduction of 1.2–3.4% in 6-h intervals classified as wet synoptic systems and a 10–22% reduction in wet events. They further predicted that the maximum wet event duration in the mid-21st century would become shorter relative to the current climate, implying that extremely long wet systems will become less frequent. Three of the models predicted shrinking of the wet season length by up to 15%. All models predicted an increasing occurrence frequency of Active Red Sea Troughs (ARSTs) for the RCP8.5 scenario by up to 11% by the mid-21st century. For the RCP4.5 scenario, a similar increase of up to 6% was predicted by two of the models.

In ancient times human activities were tightly related and sensitive to rainfall amounts and seasonal distribution. East Mediterranean settlements were concentrated around numerous small to large springs, such as the Judean Mountains area. The goals of this study were to determine (a) the sensitivity of total discharge, recession curve, and response time of such springs to annual precipitation patterns, and (b) how spring hydrology responds to series of drought or wet years and to transitions from drought to normal and/or wet episodes (and vice versa). These goals were achieved by setting a finite-element hydro-geological flow model for selected perched springs that characterize the numerous springs throughout the carbonate karst terrain in the Judean Mountains. In addition, we estimated the effect of proposed regional past climate changes on the springs; in so doing, we transfer climate change to community size, livelihood and economic strength that were highly dependent on agricultural productivity. The results of the hydro-geological model revealed that these mountainous communities had the potential to prosper during historically wetter episodes and were probably adapted to short-term variability in annual rainfall. However, moderate to extreme droughts lasting only a few years could have led to a partial or even total abandonment of the springs as focal sites of intensive agricultural production. Spring drying eliminated the primary cause for the location of settlement. This occurred simultaneously in numerous settlements around the mountains of the southern Levant and therefore, must have caused dramatic economic and societal changes in the entire region, perhaps even resonating afar.

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 modeling framework is formulated and applied to assess the sensitivity of the hydrological regime of two catchments in a convective rainfall environment with respect to projected climate change. The study uses likely rainfall scenarios with high spatiotemporal resolution that are dependent on projected changes in the driving regional meteorological synoptic systems. The framework was applied to a case study in two medium-sized Mediterranean catchments in Israel, affected by convective rainfall, by combining the HiReS-WG rainfall generator and the SAC-SMA hydrological model. The projected climate change impact on the hydrological regime was examined for the RCP4.5 and RCP8.5 emission scenarios, comparing the historical (beginning of the 21st century) and future (mid-21st-century) periods from three general circulation model simulations available from CMIP5. Focusing on changes in the occurrence frequency of regional synoptic systems and their impact on rainfall and streamflow patterns, we find that the mean annual rainfall over the catchments is projected to be reduced by 15% (outer range 2–23%) and 18% (7–25%) for the RCP4.5 sand RCP8.5 emission scenarios, respectively. The mean annual streamflow volumes are projected to be reduced by 45% (10–60%) and 47% (16–66%). The average events’ streamflow volumes for a given event rainfall depth are projected to be lower by a factor of 1.4–2.1. Moreover, the streamflow season in these ephemeral streams is projected to be shorter by 22% and 26–28% for the RCP4.5 and RCP8.5, respectively. The amplification in reduction of streamflow volumes relative to rainfall amounts is related to the projected reduction in soil moisture, as a result of fewer rainfall events and longer dry spells between rainfall events during the wet season. The dominant factors for the projected reduction in rainfall amount were the reduction in occurrence of wet synoptic systems and the shortening of the wet synoptic systems durations. Changes in the occurrence frequency of the two dominant types of the regional wet synoptic systems (active Red Sea trough and Mediterranean low) were found to have a minor impact on the total rainfall.

This paper examines the spatiotemporal characteristics of convective rain cells over the eastern Mediterranean (northern Israel) and their relationship to synoptic patterns. Information on rain cell features was extracted from high-resolution weather radar data. The radar-gauge adjustment, validation, cell segmentation and tracking techniques are discussed at length at the beginning of the paper. Convective rain cells were clustered into three synoptic types (two winter lows—deep Cyprus lows and shallow lows—and one tropical intrusion, Active Red Sea Trough) using several NCEP/NCAR parameters, and empirical distributions were computed for their spatial and temporal features. In the study region, it was found that the Active Red Sea Trough rain cells are larger, live for less time and possess lower rain intensities than the rain cells generated by the winter lows. The Cyprus low rain cells were found to be less intense and slightly larger on average than the shallow low rain cells. It was further discovered that the preferential orientation of the rain cells is associated with the direction and velocity of the wind. The effect of distance from the coastline was also examined. An increase in the number and area of the rain cells near the coastline was observed, presumably due to the sea breeze convection. The mean rainfall intensity was found to peak near the shore and decrease with distance inland. This information is of great importance for understanding rain patterns and can be further applied in exploring the hydrological responses of the basins in this region

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.

Insect physiology is highly dependent on the environmental temperature, and the relationship can be mathematically defined. Thus, many models that aim to predict insect-pest population dynamics, use meteorological data as input to descriptive functions that predict the development rate, survival and reproduction of pest populations. In most cases, however, these functions/models are laboratory-driven and are based on data from constant-temperature experiments. Therefore, they lack an important optimization and validation steps that test their accuracy under field conditions. Here, we developed a realistic and robust regional framework for modeling the field population dynamics of the global insect pest Bemisia tabaci. First, two non-linear functions, development rate (DR) and female reproduction (EN) were fitted to data collected in constant temperature experiments. Next, nine one-generation field experiments were conducted in order to establish a field-derived database of insect performance, representing a variety of growing conditions (different seasons, regions and host plants). Then, sensitivity analyses were performed for identifying the optimal time-scale for which the running-averaged temperatures should be fed to the model. Setting the time to 6 h (i.e., each of the 24-time steps per day represents the last 6 h average) produced the best fit (RMSD score of 1.59 days, 5.7% of the mean) between the field observations and the model simulations. We hypothesize that the 6 h ‘relevant biological time-scale’ captures the insect’s physiological memory of daily cycling temperature events. Lastly, we evaluated the potential of the developed modeling framework to serve as a decision support tool in pest-management programs by correlating the model predictions with field-observations of three pest control inspectors during 2019. The model successfully predicted the first notable appearance of the insect in the field (completion of the third generation in May). Also, the model correctly identified the sharp rise in abundance (outbreak point) in mid-July (completion of the fifth generation), and the persistent rise in abundance through August and September. Comparing the simulations of the 2018 and 2019 seasons indicated that the model can also serve as a tool for retrospective systematic assessment of major decisions. Taken together, these data demonstrate the model robustness and its potential to provide an excellent decision-making support platform in regional control of pest species.

Meteorological radar is a remote sensing system that provides rainfall estimations at high spatial and temporal resolutions. The radar-based rainfall intensities (R) are calculated from the observed radar reflectivities (Z). Often, rain gauge rainfall observations are used in combination with the radar data to find the optimal parameters in the Z–R transformation equation. The scale dependency of the power-law Z–R parameters when estimated from radar reflectivity and rain gauge intensity data is explored herein. The multiplicative (a) and exponent (b) parameters are said to be “scale dependent” if applying the observed and calculated rainfall intensities to objective function at different scale results in different “optimal” parameters. Radar and gauge data were analyzed from convective storms over a midsize, semiarid, and well-equipped watershed. Using the root-mean-square difference (rmsd) objective function, a significant scale dependency was observed. Increased time- and space scales resulted in a considerable increase of the a parameter and decrease of the b parameter. Two sources of uncertainties related to scale dependency were examined: 1) observational uncertainties, which were studied both experimentally and with simplified models that allow representation of observation errors; and 2) model uncertainties. It was found that observational errors are mainly (but not only) associated with positive bias of the b parameter that is reduced with integration, at least for small scales. Model errors also result in scale dependency, but the trend is less systematic, as in the case of observational errors. It is concluded that identification of optimal scale for Z–R relationship determination requires further knowledge of reflectivity and rain-intensity error structure.

Radar-based estimates of rainfall rates and accumulations are one of the principal tools used by the National Weather Service (NWS) to identify areas of extreme precipitation that could lead to flooding. Radar-based rainfall estimates have been compared to gauge observations for 13 convective storm events over a densely instrumented, experimental watershed to derive an accurate reflectivity–rainfall rate (i.e., Z–R) relationship for these events. The resultant Z–R relationship, which is much different than the NWS operational Z–R, has been examined for a separate, independent event that occurred over a different location. For all events studied, the NWS operational Z–R significantly overestimates rainfall compared to gauge measurements. The gauge data from the experimental network, the NWS operational rain estimates, and the improved estimates resulting from this study have been input into a hydrologic model to “predict” watershed runoff for an intense event. Rainfall data from the gauges and from the derived Z–R relation produce predictions in relatively good agreement with observed streamflows. The NWS Z–R estimates lead to predicted peak discharge rates that are more than twice as large as the observed discharges. These results were consistent over a relatively wide range of subwatershed areas (4–148 km2). The experimentally derived Z–R relationship may provide more accurate radar estimates for convective storms over the southwest United States than does the operational convective Z–R used by the NWS. These initial results suggest that the generic NWS Z–R relation, used nationally for convective storms, might be substantially improved for regional application.

A spatial rainfall model was applied to radar data of air mass thunderstorms to yield a rainstorm representation as a set of convective rain cells. The modeled rainfall was used as input into hydrological model, instead of the standard radar-grid data. This approach allows a comprehensive linkage between runoff responses and rainfall structures

Abstractt Spatio-temporal storm properties have a large impact on catchment hydrological response. The sensitivity of simulated flash floods to convective rain-cell characteristics is examined for an extreme storm event over a 94 km2 semi-arid catchment in southern Israel. High space–time resolution weather radar data were used to derive and model convective rain cells that then served as input into a hydrological model. Based on alterations of location, direction and speed of a major rain cell, identified as the flooding cell for this case, the impacts on catchment rainfall and generated flood were examined. Global sensitivity analysis was applied to identify the most important factors affecting the flash flood peak discharge at the catchment outlet. We found that the flood peak discharge could be increased three-fold by relatively small changes in rain-cell characteristics. We assessed that the maximum flash flood magnitude that this single rain cell can produce is 175 m3/s, and, taking into account the...

Fresh water resources, human societies, and ecosystems are expected to be strongly impacted by climate change, with precipitation trends being one of the most important elements that will be closely monitored. However, the natural variability of precipitation data can often mask existing trends such that the results appear as statistically insignificant. Information on the limitations of trend detection is important for risk assessment and for decision making related to adaption strategies under inherent uncertainties. This paper reports on an effort to quantify and map minimal detectable absolute trends in annual precipitation data series on a global scale. Monte Carlo simulations were conducted to generate realizations of trended precipitation data for different precipitation means and coefficients of variance, and the MannKendall method was applied for detecting the trend significance. Global Precipitation Climatology Centre (GPCC) VASClimO data was used to compute the mean and coefficient of variance of annual precipitation over land and to map minimal detectable absolute trends. It was found that relatively high magnitude trends (positive or negative) have a low chance of being detected as a result of high natural variance of the precipitation data. The largest undetectable trends were found for the tropics. Arid and semiarid regions also present high relative values in terms of percent change from the mean annual precipitation. Although the present analysis is based on several simplified assumptions, the goal was to point out an inherent problem of potentially undetectable high absolute trends that must be considered in analyzing precipitation data series and assessing risks in adaption strategies to climate change.

Weather radar systems provide detailed information on spatial rainfall patterns known to play a significant role in runoff generation processes. In the current study, we present an innovative approach to exploit spatial rainfall information of air mass thunderstorms and link it with a watershed hydrological model. Observed radar data are decomposed into sets of rain cells conceptualized as circular Gaussian elements and the associated rain cell parameters, namely, location, maximal intensity and decay factor, are input into a hydrological model. Rain cells were retrieved from radar data for several thunderstorms over southern Arizona. Spatial characteristics of the resulting rain fields were evaluated using data from a dense rain gauge network. For an extreme case study in a semi-arid watershed, rain cells were derived and fed as input into a hydrological model to compute runoff response. A major factor in this event was found to be a single intense rain cell (out of the five cells decomposed from the storm). The path of this cell near watershed tributaries and toward the outlet enhanced generation of high flow. Furthermore, sensitivity analysis to cell characteristics indicated that peak discharge could be a factor of two higher if the cell was initiated just a few kilometers aside.

Analysis of extreme hydrometeorological events is important for characterizing and better understanding the meteorological conditions that can generate severe rainstorms and the consequent catastrophic flooding. According to several studies (e.g., Alpert et al., 2004; Wittenberg et al., 2007), the occurrence of such extreme events is increasing over the eastern Mediterranean although total rain amounts are generally decreasing. The current study presents an analysis of an extreme event utilizing different methodologies: (a) synoptic maps and high resolution satellite imagery for atmospheric condition analysis; (b) rainfall analysis by rain gauges data; (c) meteorological radar rainfall calibration and analysis; (d) field measurements for estimating maximum peak discharges; and, (e) high resolution aerial photographs together with field surveying for quantifying the geomorphic impacts. The unusual storm occurred over Israel between 30 March and 2 April, 2006. Heavy rainfall produced more than 100mm in some locations in only few hours and more than 200mm in the major core area. Extreme rain intensities with recurrence intervals of more than 100 years were found for durations of 1 h and more as well as for the daily rain depth values. In the most severely affected area,Wadi Ara, extreme flash floods caused damages and casualties. Specific peak discharges were as high as 10–30m3/s/km2 for catchments of the size of 1–10 km2, values larger than any recorded floods in similar climatic regions in Israel.

viously applied in the Alps of Europe. Adjustment coefficients have been derived for 28 rainfall periods using 59 independent gauges of a quality-checked training data set. The validation was based on an independent data set composed of gauges located in eleven 20 ? 20 km2 validation areas, which are representative of different climate, topography and radar distance conditions. The WR and WMR methods were found preferable with a slight better performance of the latter. Furthermore, a novel approach has been adopted in this study, whereby radar estimates are considered useable if they provide information that is better than gauge-only estimates. The latter was derived by spatial interpolation of the gauges belonging to the training data set. Note that these gauges are outside the validation areas. As for the radar-adjusted estimates, gauge-derived estimates were assessed against gauge data in the validation areas. It was found that radar-based estimates are better for the validation areas at the dry climate regime. At distances larger than 100 km, the radar underestimation becomes too large in the two northern validation areas, while in the southern one radar data are still better than gauge interpolation. It is concluded that in ungauged areas of Israel it is preferable to use WMR-adjusted (or alternatively, simply WR-adjusted) radar echoes rather than the standard bulk adjustment method and for dry ungauged areas it is preferable over the conventional gauge-interpolated values derived from point measurements, which are outside the areas themselves. The WR and WMR adjustment methods provide useful rain depth estimates for rainfall periods for the examined areas but within the limitation stated above.

Flash floods caused by convective rain storms are highly sensitive to the space–time character- istics of rain cells. In this study we exploit the high space–time resolution of the radar data to study the characteristics of the rain cells and their impact on flash flood magnitudes. A rain cell model is applied to the radar data of an actual storm and the rain fields represented by the model further serve as input into a hydrological model. Global sensitivity analysis is applied to identify the most important factors affecting the flash flood peak discharge. As a case study we tested an extreme storm event over a semi-arid catchment in southern Israel. The rain cell model was found to simulate the rain storm adequately. We found that relatively small changes in the rain cell’s location, speed and direction could cause a three-fold increase in flash flood peak discharge at the catchment outlet.

Flash-flood warning models can save lives and protect various kinds of infrastructure. In dry climate regions, rainfall is highly variable and can be of high-intensity. Since rain gauge networks in such areas are sparse, rainfall information derived from weather radar systems can provide useful input for flash- flood models. This paper presents a flash-flood warning model which utilizes radar rainfall data and applies it to two catchments that drain into the dry Dead Sea region. Radar-based quantitative precipita- tion estimates (QPEs) were derived using a rain gauge adjustment approach, either on a daily basis (allowing the adjustment factor to change over time, assuming available real-time gauge data) or using a constant factor value (derived from rain gauge data) over the entire period of the analysis. The QPEs served as input for a continuous hydrological model that represents the main hydrological processes in the region, namely infiltration, flow routing and transmission losses. The infiltration function is applied in a distributed mode while the routing and transmission loss functions are applied in a lumped mode. Model parameters were found by calibration based on the 5 years of data for one of the catchments. Val- idation was performed for a subsequent 5-year period for the same catchment and then for an entire 10- year record for the second catchment. The probability of detection and false alarm rates for the validation cases were reasonable. Probabilistic flash-flood prediction is presented applying Monte Carlo simulations with an uncertainty range for the QPEs and model parameters. With low probability thresholds, one can maintain more than 70% detection with no more than 30% false alarms. The study demonstrates that a flash-flood warning model is feasible for catchments in the area studied.