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Spatiotemporal patterns of rainfall are important characteristics that influence runoff generation and flashflood magnitude and require high-resolution measurements to be adequately represented. This need is further emphasized in arid climates, where rainfall is scarce and highly variable. In this study, 24 years of corrected and gaugeadjusted radar rainfall estimates are used to (i) identify the spatial structure and dynamics of convective rain cells in a dry climate region in the Eastern Mediterranean, (ii) to determine their climatology, and (iii) to understand their relation with the governing synoptic systems and with flashflood generation. Rain cells are extracted using a segmentation method and a tracking algorithm, and are clustered into three synoptic patterns according to atmospheric variables from the ERA-Interim reanalysis. On average, the cells are about 90 km2 in size, move 13ms-1 from west to east, and live for 18 min. The Cyprus low accounts for 30% of the events, the low to the east of the study region for 44 %, and the Active Red Sea Trough for 26 %. The Active Red Sea Trough produces shorter rain events composed of rain cells with higher rain intensities, longer lifetime, smaller area, and lower velocities. The area of rain cells is positively correlated with topographic height. The number of cells is negatively correlated with the distance from the shoreline. Rain-cell intensity is negatively correlated with mean annual precipitation. Flash-flood-related events are dominated by rain cells of large size, low velocity, and long lifetime that move downstream with the main axis of the catchments. These results can be further used for stochastic simulations of convective rain storms and serve as input for hydrological models and for flash-flood nowcasting systems. [ABSTRACT FROM AUTHOR]

ABSTRACT 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. [ABSTRACT FROM AUTHOR]

The relationship between air temperature and the spatial characteristics of rainfall at the storm and convective rain cell scales were analyzed using estimates from a long weather radar records over Mediterranean and semiarid regions. The peak intensity of individual convective rain cells was found to increase with temperature, but at a lower rate than the scaling expected from the Clausius-Clapeyron relation, while the area of the individual convective rain cells slightly decreases. 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 toward 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 at warmer temperatures. [ABSTRACT FROM AUTHOR]

• Flood prediction in ungauged catchments using an ED-LSTM model is presented. • The ED-LSTM is applied and tested as a local and regional model. • ED-LSTM outperforms a conceptual hydrological model. • The approach is useful for sparsely gauged catchments with limited training data. Flood prediction in ungauged catchments is usually conducted by hydrological models that are parameterized based on nearby and similar gauged catchments. As an alternative to this process-based modelling, deep learning (DL) models have demonstrated their ability for prediction in ungauged catchments (PUB) with high efficiency. Catchment characteristics, the number of gauged catchments, and their level of hydroclimatic heterogeneity in the training dataset used for model regionalization can directly affect the model's performance. Here, we study the generalization ability of a DL model to these factors by applying an Encoder-Decoder Long Short-Term Memory neural network for a 6-hour lead-time runoff prediction in 35 mountainous catchments in China. By varying the available number of catchments and model settings with different training datasets, namely local, regional, and PUB models, we evaluated the generalization ability of our model. We found that both quantity (i.e. number of gauged catchments available) and heterogeneity of the training dataset used for the DL model are important for improving model performance in the PUB context, due to a data synergy effect. The assessment of the sensitivity to catchment characteristics showed that the model performance is mainly correlated to the local hydro-climatic conditions; the more arid the region, the more likely it is to have a poor model performance for prediction in ungauged catchments. The results suggest that the regional ED-LSTM model is a promising method to predict streamflow from rainfall inputs in PUB, and outline the need for preparing a representative training dataset. [ABSTRACT FROM AUTHOR]