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

Runoff and flash flood generation are very sensitive to rainfall’s$\backslash$nspatial and temporal variability. The increasing use of radar and$\backslash$nsatellite data in hydrological applications, due to the sparse$\backslash$ndistribution of rain gauges over most catchments worldwide, requires$\backslash$nfurthering our knowledge of the uncertainties of these data. In 2011, a$\backslash$nnew super-dense network of rain gauges containing 14 stations, each with$\backslash$ntwo side-by-side gauges, was installed within a 4 km(2) study area near$\backslash$nKibbutz Galed in northern Israel. This network was established for a$\backslash$ndetailed exploration of the uncertainties and errors regarding rainfall$\backslash$nvariability within a common pixel size of data obtained from remote$\backslash$nsensing systems for timescales of 1 min to daily. In this paper, we$\backslash$npresent the analysis of the first year’s record collected from this$\backslash$nnetwork and from the Shacham weather radar, located 63 km from the study$\backslash$narea. The gauge-rainfall spatial correlation and uncertainty were$\backslash$nexamined along with the estimated radar error. The nugget parameter of$\backslash$nthe inter-gauge rainfall correlations was high (0.92 on the 1 min scale)$\backslash$nand increased as the timescale increased. The variance reduction factor$\backslash$n(VRF), representing the uncertainty from averaging a number of rain$\backslash$nstations per pixel, ranged from 1.6% for the 1 min timescale to 0.07%$\backslash$nfor the daily scale. It was also found that at least three rain stations$\backslash$nare needed to adequately represent the rainfall (VRF\textless 5 %) on a typical$\backslash$nradar pixel scale. The difference between radar and rain gauge rainfall$\backslash$nwas mainly attributed to radar estimation errors, while the gauge$\backslash$nsampling error contributed up to 20% to the total difference. The ratio$\backslash$nof radar rainfall to gauge-areal-averaged rainfall, expressed by the$\backslash$nerror distribution scatter parameter, decreased from 5.27 dB for 3 min$\backslash$ntimescale to 3.21 dB for the daily scale. The analysis of the radar$\backslash$nerrors and uncertainties suggest that a temporal scale of at least 10$\backslash$nmin should be used for hydrological applications of the radar data.$\backslash$nRainfall measurements collected with this dense rain gauge network will$\backslash$nbe used for further examination of small-scale rainfall’s spatial and$\backslash$ntemporal variability in the coming years.

A new stochastic high-resolution synoptically conditioned weather generator (HiReS-WG) appropriate for climate regimes with a substantial proportion of convective rainfall is presented. The simu- lated rain fields are of high spatial (0.53 0.5 km2) and temporal (5 min) resolution and can be used for most hydrological applications. The WG is composed of four modules: the synoptic generator, the motion vector generator, the convective rain cell generator, and the low-intensity rainfall generator. The HiReS-WG was applied to a study region on the northwestern Israeli coastline in the Eastern Mediterranean, for which 12 year weather radar and synoptic data were extensively analyzed to derive probability distributions of con- vective rain cells and other rainfall properties for different synoptic classifications; these distributions were used as input to the HiReS-WG. Simulated rainfall data for 300 years were evaluated for annual rain depth, season timing, wet-/dry-period durations, rain-intensity distributions, and spatial correlations. In general, the WG well represented the above properties compared to radar and rain-gauge observations from the studied region, with one limitation—an inability to reproduce the most extreme cases. The HiReS-WG is a good tool to study catchments’ hydrological responses to variations in rainfall, especially small-size to medium-size catchments, and it can also be linked to climate models to force the prevailing synoptic conditions.

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.

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.

The transformation of rainfall into runoff at a basin outlet is the combined effect of many hydrological processes, which occur at a wide range of spatial and temporal scales. However, determining the scale of the combined hydrological response of the basin is still problematic and concepts for its definition are yet to be identified. In this paper high-resolution meteorological radar data are used for the determination of a characteristic temporal scale for the hydrological response of the basin - the ’response time scale’ (Ts*). Ts* is defined as the time scale at which the pattern of the time-averaged radar rainfall hietograph is most similar to the pattern of the measured outlet runoff hydrograph. The existence of such similarity at a relatively stable time scale for a specific basin indicates that it is an intrinsic property of the basin and is related to its hydrological response. The identification of the response time scale is carried out by analysis of observations only, without assuming a specific rainfall-runoff model. Ts* is examined in four small basins (10-100 km2) in Israel. The spatial scale is assumed as the entire basin. For all analyzed basins a stable response time scale is identified. Relatively short time scales are found for the urban and arid basins (15-30 min), while for the rural basins longer time scale are identified (90-180 min). The issues of relationship between the response time scale and basin properties and modeling at the response time scale have yet to be determined. ?? 2001 Elsevier Science B.V. All rights reserved.

Flood water infiltrates ephemeral channels, recharging local and regional aquifers, and it is the main water source in hyperarid regions. Quantitative estimations of these resources are limited by the scarcity of data from such regions. The floods of the Kuiseb River in the Namib Desert have been monitored for 46 years, providing a unique data set of flow hydrographs from one of the world’s hyperarid regions. The study objectives were to: (1) subject the records to quality control; (2) model flood routing and transmission losses; and (3) study the relationships between flood characteristics, river characteristics and recharge into the aquifers. After rigorous quality-testing of the original gauge-station data, a flood-routing model based on kinematic flow with components accounting for channel-bed infiltration was constructed and applied to the data. A simplified module added to this routing model estimates aquifer recharge from the infiltrating flood water. Most of the model parameters were obtained from field surveys and GIS analyses. Two of the model parameters-Manning’s roughness coefficient and the constant infiltration rate-were calibrated based on the high-quality measured flow data set, providing values of 0.025 and 8.5 mm/h, respectively. This infiltration rate is in agreement with that estimated from extensive direct TDR-based moisture measurements in the vadose zone under the Kuiseb River channel, and is low relative to those reported for other sites. The model was later verified with additional flood data and observed groundwater levels in boreholes. Sensitivity analysis showed the important role of large and medium floods in aquifer recharge. To generalize from the studied river to other streams with diverse conditions, we demonstrate that with increasing in infiltration rate, channel length or active channel width, the relative contribution of high-magnitude floods to recharge also increases, whereas medium and small floods contribute less, often not reaching the downstream parts of the arid ephemeral river at all. For example, more than three-quarters of the floods reaching the downstream Kuiseb River (with an infiltration rate of 8.5 mm/h) would not have reached similar distances in rivers with all other properties similar but with infiltration rates of 50 mm/h. The recharge volume in the downstream segment in the case of higher infiltration is mainly contributed by floods with magnitude ???93rd percentile, compared to floods in the 63rd percentile at an infiltration rate of 8.5 mm/h. ?? 2009 Elsevier B.V. All rights reserved.

A new characteristic timescale of a catchment is presented, the response timescale (RTS). It is a range of averaging time intervals which, when applied to catchment rainfall, yield smoothed time series that best approximate that of the resultant runoff. In determining the RTS, nothing is assumed about the nature of the rainfall-runoff transformation. In addition, this new measure is shown to be robust against measurement errors. An objective, automatic, observations-based algorithm is described that introduces the concept of peaks density for the estimation of RTS. Estimation is exemplified for single and multiple rainfall-runoff events through application to small catchments in Panama and Israel. In all cases, relatively stable values of response timescale are obtained. It is concluded that at least for the case studies, the response timescale is an intrinsic characteristic of the catchment and it is generally expected to be different from the catchment lag time and time of concentration. INDEX

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.