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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. • Three synoptic systems control rainfall and flood patterns in the Dead Sea watershed. • Winter Mediterranean cyclones control annual rainfall patterns and water budget. • Floods and sedimentology are dominated by rainfall patterns in the watershed. • Paleoenvironmental sedimentary interpretations require present, robust observations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: 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 precipitation 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. Validation 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. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

• Year-round lush oases along arid desert rivers rely only on extreme floods. • Mean annual recharge volume is by far too low to support the aquifers level. • Sand-filled valley in arid desert rivers forms an excellent water storage and oases. • Fast green-up oases along desert rivers by extreme floods remains for 1–2 years. • NDVI can record history of wet/dry flood seasons in remote-ungauged basins. Flood-fed aquifers along the sandy lower reach of the Kuiseb River sustain a 130-km-long green belt of lush oases across the hyperarid Namib desert. This oasis is a year-round source for water creating dense-tall woodland along the narrow corridor of the ephemeral river valley, which, in turn, supports human activity and fauna including during the long dry austral winters and multi-year droughts. Occasional floods, originating at the river's wetter headwaters, travel ∼280 km downstream, before recharging these aquifers. We analyzed the flood-aquifer-vegetation dynamics at-a-site and along the river, determining the relative impact of floods with diverse magnitude and frequency on downstream reaches. We find that flood discharge that feeds the alluvial aquifers also affects vegetation dynamics along the river. The downstream aquifers are fed only by the largest floods that allow the infrequent germination of plants; mean annual recharge volume is too low to support the aquifers level. These short-term vegetation cycles of green-up and then fast senescence in-between floods are easily detected by satellite-derived vegetation index. This index identifies historical floods and their magnitudes in arid and hyperarid regions; specifically, it determines occurrences of large floods in headwater-fed, ephemeral Namib streams as well as in other hyperarid regions. Our study reveals the importance of flood properties on the oasis life cycle, emphasizing the impact of drought and wet years on the Namib's riparian vegetation. [ABSTRACT FROM AUTHOR]

Abstract: A comprehensive investigation of rainstorms and their consequent impacts on landscape evolution is geomorphologically important, but only scant information may be available on exceptional events, because parameters on synoptic conditions, rainstorm, landforms and hydrology for such events may be incomparable with previous knowledge. We studied an exceptional storm on April 2, 2006, in the Ramot Menashe region, Israel. Our investigation of rainfall, landslides, debris flows and channel suggests the effectiveness of such an event on the development of basin-scale morphology. The storm caused damage and casualties although it covered relatively narrow strips. Neither direct rainfall nor runoff measurements exist for the most severely affected area of Ramot Menashe, but the geomorphologic evidence combined with high-resolution meteorological radar data provides the basic understanding of the processes and hazardous conditions which prevailed at the time. In the storm core, based on estimation from meteorological radar data, 263mm of rain fell within 3h with a maximum intensity of 220mmh−1 for 10min, triggering both sporadic landslides at the soil/bedrock contact on the upper slopes and widespread landslides at the fractured/massive bedrock contact on the lower slopes. The 1st order channels on the alternation of chalk and marl also underwent erosion, and the produced sediment deposited on alluvial fans at the confluence with the main channel. The specific peak discharges for catchment size of 0.3–10km2 were 11 to 73m3 s−1 km−2, higher than any recorded floods in the Mediterranean climatic region of Israel. The effectiveness of the flood for geomorphic work, represented by shear stress and stream power per unit boundary area reached 87–398Nm−2 and 212–2134Wm−2, respectively. This kind of analysis can be applied to hazard prediction in other areas under similar geomorphological conditions. [Copyright &y& Elsevier]

Highlights • Simplified formulation of the metastatistical extreme value is proposed. • Parsimonious handling of extremes emerging from multiple underlying processes. • Computationally efficient sensitivity analyses to changes in the underlying processes. • Robust framework for explanatory models, nonstationary analyses, climate projections. Abstract 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. [ABSTRACT FROM AUTHOR]

Highlights • A 50-m spatial resolution wheat production model for semiarid regions is developed. • Model integrates 3 sub-models: crop growth, soil hydrology and runoff models on GIS. • Model was evaluated vs SWC, biomass (AGB) and grain yield (GY) in 22 fields in Israel. • Model shows sensitivity to spatial variations in soils (SWC) and rain (AGB and GY). • Significant AGB and GY decreases were predicted for projected warming and drought in the study area. Abstract 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. [ABSTRACT FROM AUTHOR]

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 (<30% 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. [ABSTRACT FROM AUTHOR]