Articles | Volume 21, issue 3
https://doi.org/10.5194/nhess-21-917-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-917-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Mar 2021
Research article |
| 10 Mar 2021
| 10 Mar 2021
Hydrometeorological analysis and forecasting of a 3 d flash-flood-triggering desert rainstorm
Yair Rinat
CORRESPONDING AUTHOR
Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
Francesco Marra
Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
Institute of Atmospheric Sciences and Climate, National Research Council of Italy, ISAC-CNR, Bologna, Italy
Moshe Armon
Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
Asher Metzger
Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
Yoav Levi
Israel Meteorological Service, Beit Dagan, Israel
Pavel Khain
Israel Meteorological Service, Beit Dagan, Israel
Elyakom Vadislavsky
Israel Meteorological Service, Beit Dagan, Israel
Marcelo Rosensaft
Geological Survey of Israel, Jerusalem, Israel
Efrat Morin
Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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Nathalia Correa-Sánchez, Xiaoli Guo Larsén, Eleonora Dallan, Marco Borga, and Francesco Marra
Wind Energ. Sci., 11, 2961–2985, https://doi.org/10.5194/wes-11-2961-2026, https://doi.org/10.5194/wes-11-2961-2026, 2026
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This research presents the first use of the simplified metastatistical extreme value approach for wind extremes, extending it to wind energy applications. We use a categorical framework that combines climate, roughness, and topography to evaluate convection-permitted models. We find that model formulation drives inter-model uncertainties, rather than surface conditions. Convection-permitted models' uncertainty analysis improves the reliability of extreme wind estimates for design parameters.
Ella Thomas, Petr Vohnicky, Marco Borga, Nadav Peleg, and Francesco Marra
Hydrol. Earth Syst. Sci., 30, 4957–4967, https://doi.org/10.5194/hess-30-4957-2026, https://doi.org/10.5194/hess-30-4957-2026, 2026
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Extreme rainfall is expected to grow in magnitude with increasing temperature. We assess whether very rare extremes increase with temperature faster than moderate extremes, and we test methods to include this effect into a model to predict future extremes called TENAX. We find that this dependence on temperature is typically observed but including it in the model without prior information on its magnitude may lead to disproportionately large uncertainty.
Yair Rinat, Moshe Armon, and Efrat Morin
Hydrol. Earth Syst. Sci., 30, 4565–4585, https://doi.org/10.5194/hess-30-4565-2026, https://doi.org/10.5194/hess-30-4565-2026, 2026
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In the coming decades Mediterranean floods are expected to change due to increased urbanization and climate change. We simulate floods under different conditions and show that future rainfall will likely reduce flood magnitudes, but increased urbanization will make them higher. Flood intensification is more pronounced at upstream sections and when soil moisture is high. As future flood trends may change between different regions further research is needed to cope with upcoming challenges.
Rashid Akbary, Eleonora Dallan, Paul C. Astagneau, Raul R. Wood, Francesco Marra, Manuela I. Brunner, and Marco Borga
Hydrol. Earth Syst. Sci., 30, 4117–4139, https://doi.org/10.5194/hess-30-4117-2026, https://doi.org/10.5194/hess-30-4117-2026, 2026
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Heavy short rain can trigger flash floods and debris flows. In this study we evaluated how well climate models reproduce these events in Switzerland. We compared finer and coarser resolution models with high-quality hourly precipitation observations across small to large areas. The finer models better captured where short, intense precipitation occurs, but their errors changed with area size. Flood risk studies should therefore account for these scale-related errors.
Ellina Agayar, Moshe Armon, Michael Sprenger, and Heini Wernli
Nat. Hazards Earth Syst. Sci., 26, 3025–3043, https://doi.org/10.5194/nhess-26-3025-2026, https://doi.org/10.5194/nhess-26-3025-2026, 2026
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This study investigates the hydrometeorological features of the major floods of 2008, 2010, and 2020 in western Ukraine. All cases were linked to upper-level potential vorticity (PV) anomalies. We also conducted a climatological analysis of PV structure associated with 22 summer heavy precipitation events (2000–2022), highlighting their key role in determining the location and intensity of flood-inducing rainfall events in the Carpathians.
Guorong Ling, Hilla Afargan-Gerstman, and Moshe Armon
Weather Clim. Dynam., 7, 915–935, https://doi.org/10.5194/wcd-7-915-2026, https://doi.org/10.5194/wcd-7-915-2026, 2026
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This research examines how well storms that bring heavy rain to the Sahara can be predicted. Using satellite observations and ensemble weather forecasts, we show that predictability varies by season, location, and circulation pattern, with an upper limit of about ten days. These insights can improve early flood warnings and support better planning for scarce water resources in desert regions.
Talia Rosin, Francesco Marra, Marco Gabella, Urs Germann, Daniel Wolfensberger, and Efrat Morin
EGUsphere, https://doi.org/10.5194/egusphere-2026-1800, https://doi.org/10.5194/egusphere-2026-1800, 2026
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Extreme rainfall in mountainous regions is difficult to assess as it varies strongly across space and time, and rain gauges are often too sparse to capture it. Using 9 years of radar data, we analyse summer rainfall extremes across Switzerland for different durations and areas. We show that rainfall intensity depends strongly on scale and topography, and that radar better captures local extremes, improving flood hazard assessment, as demonstrated for three recent major flood-producing storms.
Saran Aadhar, Efrat Morin, and Utkarsh Gupta
EGUsphere, https://doi.org/10.5194/egusphere-2026-339, https://doi.org/10.5194/egusphere-2026-339, 2026
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River basins in India are often affected by flooding during the summer monsoon, putting millions of people and livelihoods at risk. Understanding the processes that cause floods is critical to minimizing flood risk. Our investigations show how rainfall (short and long-duration), surface water flow, and the specific location of the drainage area contribute to flood events in the Godavari River basin using observations and hydrological model data.
Francesco Marra, Eleonora Dallan, Marco Borga, Roberto Greco, and Thom Bogaard
Nat. Hazards Earth Syst. Sci., 25, 5055–5061, https://doi.org/10.5194/nhess-25-5055-2025, https://doi.org/10.5194/nhess-25-5055-2025, 2025
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We highlight an important conceptual difference between the duration used in intensity-duration thresholds and the duration used in the intensity-duration-frequency curves that has been overlooked by the landslide literature so far.
Nathalia Correa-Sánchez, Xiaoli Guo Larsén, Giorgia Fosser, Eleonora Dallan, Marco Borga, and Francesco Marra
Wind Energ. Sci., 10, 2551–2561, https://doi.org/10.5194/wes-10-2551-2025, https://doi.org/10.5194/wes-10-2551-2025, 2025
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We examined the power spectra of wind speed in three convection-permitting models in central Europe and found that these models have a better representation of wind variability characteristics than standard wind datasets like the New European Wind Atlas, due to different simulation approaches, providing more reliable extreme wind predictions.
Rajani Kumar Pradhan, Yannis Markonis, Francesco Marra, Efthymios I. Nikolopoulos, Simon Michael Papalexiou, and Vincenzo Levizzani
Hydrol. Earth Syst. Sci., 29, 4929–4949, https://doi.org/10.5194/hess-29-4929-2025, https://doi.org/10.5194/hess-29-4929-2025, 2025
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This study compared global satellite and reanalysis precipitation datasets to assess diurnal variability. We found that all datasets capture key diurnal precipitation patterns, with maximum precipitation in the afternoon over land and early morning over the ocean. However, there are differences in the exact timing and amount of precipitation. This suggests that it is better to use a combination of datasets for potential applications rather than relying on a single dataset.
Francesco Marra, Nadav Peleg, Elena Cristiano, Efthymios I. Nikolopoulos, Federica Remondi, and Paolo Tarolli
Nat. Hazards Earth Syst. Sci., 25, 2565–2570, https://doi.org/10.5194/nhess-25-2565-2025, https://doi.org/10.5194/nhess-25-2565-2025, 2025
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Climate change is escalating the risks related to hydro-meteorological extremes. This preface introduces a special issue originating from a European Geosciences Union (EGU) session. It highlights the challenges posed by these extremes, ranging from hazard assessment to mitigation strategies, and covers both water excess events like floods, landslides, and coastal hazards and water deficit events such as droughts and fire weather. The collection aims to advance understanding, improve resilience, and inform policy-making.
Joëlle C. Rieder, Franziska Aemisegger, Elad Dente, and Moshe Armon
Hydrol. Earth Syst. Sci., 29, 1395–1427, https://doi.org/10.5194/hess-29-1395-2025, https://doi.org/10.5194/hess-29-1395-2025, 2025
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The Sahara was wetter in the past and may become wetter in the future. Lake remnants are evidence of the desert’s wetter past. If the Sahara gets wetter in the future, these lakes may serve as a water resource. However, it is unclear how these lakes get filled and how moisture is carried into the desert and converted into rain in the first place. Therefore, we examine processes currently leading to the filling of a dry lake in the Sahara, which can help assess future water availability.
Kevin Kenfack, Francesco Marra, Zéphirin Yepdo Djomou, Lucie Angennes Djiotang Tchotchou, Alain Tchio Tamoffo, and Derbetini Appolinaire Vondou
Weather Clim. Dynam., 5, 1457–1472, https://doi.org/10.5194/wcd-5-1457-2024, https://doi.org/10.5194/wcd-5-1457-2024, 2024
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The results of this study show that moisture advection induced by horizontal wind anomalies and vertical moisture advection induced by vertical velocity anomalies were crucial mechanisms behind the anomalous October 2019 exceptional rainfall increase over western central Africa. The information we derive can be used to support risk assessment and management in the region and to improve our resilience to ongoing climate change.
Baruch Ziv, Uri Dayan, Lidiya Shendrik, and Elyakom Vadislavsky
Nat. Hazards Earth Syst. Sci., 24, 3267–3277, https://doi.org/10.5194/nhess-24-3267-2024, https://doi.org/10.5194/nhess-24-3267-2024, 2024
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The train effect is related to convective cells that pass over the same place. Trains produce heavy rainfall and sometimes floods and are reported in North America during spring and summer. In Israel, 17 trains associated with Cyprus lows were identified by radar images and were found within the cold sector south of the low center and in the left flank of a maximum wind belt; they cross the Israeli coast, with a mean length of 45 km; last 1–3 h; and yield 35 mm of rainfall up to 60 mm.
Talia Rosin, Francesco Marra, and Efrat Morin
Hydrol. Earth Syst. Sci., 28, 3549–3566, https://doi.org/10.5194/hess-28-3549-2024, https://doi.org/10.5194/hess-28-3549-2024, 2024
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Knowledge of extreme precipitation probability at various spatial–temporal scales is crucial. We estimate extreme precipitation return levels at multiple scales (10 min–24 h, 0.25–500 km2) in the eastern Mediterranean using radar data. We show our estimates are comparable to those derived from averaged daily rain gauges. We then explore multi-scale extreme precipitation across coastal, mountainous, and desert regions.
Ellina Agayar, Franziska Aemisegger, Moshe Armon, Alexander Scherrmann, and Heini Wernli
Nat. Hazards Earth Syst. Sci., 24, 2441–2459, https://doi.org/10.5194/nhess-24-2441-2024, https://doi.org/10.5194/nhess-24-2441-2024, 2024
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This study presents the results of a climatological investigation of extreme precipitation events (EPEs) in Ukraine for the period 1979–2019. During all seasons EPEs are associated with pronounced upper-level potential vorticity (PV) anomalies. In addition, we find distinct seasonal and regional differences in moisture sources. Several extreme precipitation cases demonstrate the importance of these processes, complemented by a detailed synoptic analysis.
Shai Abir, Hamish A. McGowan, Yonatan Shaked, Hezi Gildor, Efrat Morin, and Nadav G. Lensky
Atmos. Chem. Phys., 24, 6177–6195, https://doi.org/10.5194/acp-24-6177-2024, https://doi.org/10.5194/acp-24-6177-2024, 2024
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Understanding air–sea heat exchange is vital for studying ocean dynamics. Eddy covariance measurements over the Gulf of Eilat revealed a 3.22 m yr-1 evaporation rate, which is inconsistent with bulk formulae estimations in stable atmospheric conditions, requiring bulk formulae to be revisited in these environments. The surface fluxes have a net cooling effect on the gulf water on an annual mean (-79 W m-2), balanced by a strong exchange flux between the Red Sea and the Gulf of Eilat.
Francesco Marra, Marika Koukoula, Antonio Canale, and Nadav Peleg
Hydrol. Earth Syst. Sci., 28, 375–389, https://doi.org/10.5194/hess-28-375-2024, https://doi.org/10.5194/hess-28-375-2024, 2024
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We present a new physical-based method for estimating extreme sub-hourly precipitation return levels (i.e., intensity–duration–frequency, IDF, curves), which are critical for the estimation of future floods. The proposed model, named TENAX, incorporates temperature as a covariate in a physically consistent manner. It has only a few parameters and can be easily set for any climate station given sub-hourly precipitation and temperature data are available.
Haggai Eyal, Moshe Armon, Yehouda Enzel, and Nadav G. Lensky
Earth Surf. Dynam., 11, 547–574, https://doi.org/10.5194/esurf-11-547-2023, https://doi.org/10.5194/esurf-11-547-2023, 2023
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Extracting paleoenvironmets from sedimentologic and geomorphic records is a main goal in Earth sciences. We study a chain of processes connecting causative Mediterranean cyclones, coeval floods, storm waves generated by mesoscale funneled wind, and coastal gravel transport. This causes northward dispersion of gravel along the modern Dead Sea coast, which has also persisted since the late Pleistocene, resulting in beach berms and fan deltas always being deposited north of channel mouths.
Stefan Steger, Mateo Moreno, Alice Crespi, Peter James Zellner, Stefano Luigi Gariano, Maria Teresa Brunetti, Massimo Melillo, Silvia Peruccacci, Francesco Marra, Robin Kohrs, Jason Goetz, Volkmar Mair, and Massimiliano Pittore
Nat. Hazards Earth Syst. Sci., 23, 1483–1506, https://doi.org/10.5194/nhess-23-1483-2023, https://doi.org/10.5194/nhess-23-1483-2023, 2023
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We present a novel data-driven modelling approach to determine season-specific critical precipitation conditions for landslide occurrence. It is shown that the amount of precipitation required to trigger a landslide in South Tyrol varies from season to season. In summer, a higher amount of preparatory precipitation is required to trigger a landslide, probably due to denser vegetation and higher temperatures. We derive dynamic thresholds that directly relate to hit rates and false-alarm rates.
Nadav Peleg, Herminia Torelló-Sentelles, Grégoire Mariéthoz, Lionel Benoit, João P. Leitão, and Francesco Marra
Nat. Hazards Earth Syst. Sci., 23, 1233–1240, https://doi.org/10.5194/nhess-23-1233-2023, https://doi.org/10.5194/nhess-23-1233-2023, 2023
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Floods in urban areas are one of the most common natural hazards. Due to climate change enhancing extreme rainfall and cities becoming larger and denser, the impacts of these events are expected to increase. A fast and reliable flood warning system should thus be implemented in flood-prone cities to warn the public of upcoming floods. The purpose of this brief communication is to discuss the potential implementation of low-cost acoustic rainfall sensors in short-term flood warning systems.
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, https://doi.org/10.5194/hess-27-1133-2023, 2023
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Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
Shalev Siman-Tov and Francesco Marra
Nat. Hazards Earth Syst. Sci., 23, 1079–1093, https://doi.org/10.5194/nhess-23-1079-2023, https://doi.org/10.5194/nhess-23-1079-2023, 2023
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Debris flows represent a threat to infrastructure and the population. In arid areas, they are observed when heavy rainfall hits steep slopes with sediments. Here, we use digital surface models and radar rainfall data to detect and characterize the triggering and non-triggering rainfall conditions. We find that rainfall intensity alone is insufficient to explain the triggering. We suggest that antecedent rainfall could represent a critical factor for debris flow triggering in arid regions.
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal, Asher Metzger, Chen Barshai, Dana Weitzner, Dafi Voloshin, Frederik Kratzert, Gal Elidan, Gideon Dror, Gregory Begelman, Grey Nearing, Guy Shalev, Hila Noga, Ira Shavitt, Liora Yuklea, Moriah Royz, Niv Giladi, Nofar Peled Levi, Ofir Reich, Oren Gilon, Ronnie Maor, Shahar Timnat, Tal Shechter, Vladimir Anisimov, Yotam Gigi, Yuval Levin, Zach Moshe, Zvika Ben-Haim, Avinatan Hassidim, and Yossi Matias
Hydrol. Earth Syst. Sci., 26, 4013–4032, https://doi.org/10.5194/hess-26-4013-2022, https://doi.org/10.5194/hess-26-4013-2022, 2022
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Early flood warnings are one of the most effective tools to save lives and goods. Machine learning (ML) models can improve flood prediction accuracy but their use in operational frameworks is limited. The paper presents a flood warning system, operational in India and Bangladesh, that uses ML models for forecasting river stage and flood inundation maps and discusses the models' performances. In 2021, more than 100 million flood alerts were sent to people near rivers over an area of 470 000 km2.
Assaf Hochman, Francesco Marra, Gabriele Messori, Joaquim G. Pinto, Shira Raveh-Rubin, Yizhak Yosef, and Georgios Zittis
Earth Syst. Dynam., 13, 749–777, https://doi.org/10.5194/esd-13-749-2022, https://doi.org/10.5194/esd-13-749-2022, 2022
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Gaining a complete understanding of extreme weather, from its physical drivers to its impacts on society, is important in supporting future risk reduction and adaptation measures. Here, we provide a review of the available scientific literature, knowledge gaps and key open questions in the study of extreme weather events over the vulnerable eastern Mediterranean region.
Francesco Marra, Moshe Armon, and Efrat Morin
Hydrol. Earth Syst. Sci., 26, 1439–1458, https://doi.org/10.5194/hess-26-1439-2022, https://doi.org/10.5194/hess-26-1439-2022, 2022
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We present a new method for quantifying the probability of occurrence of extreme rainfall using radar data, and we use it to examine coastal and orographic effects on extremes. We identify three regimes, directly related to precipitation physical processes, which respond differently to these forcings. The methods and results are of interest for researchers and practitioners using radar for the analysis of extremes, risk managers, water resources managers, and climate change impact studies.
Yoav Ben Dor, Francesco Marra, Moshe Armon, Yehouda Enzel, Achim Brauer, Markus Julius Schwab, and Efrat Morin
Clim. Past, 17, 2653–2677, https://doi.org/10.5194/cp-17-2653-2021, https://doi.org/10.5194/cp-17-2653-2021, 2021
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Laminated sediments from the deepest part of the Dead Sea unravel the hydrological response of the eastern Mediterranean to past climate changes. This study demonstrates the importance of geological archives in complementing modern hydrological measurements that do not fully capture natural hydroclimatic variability, which is crucial to configure for understanding the impact of climate change on the hydrological cycle in subtropical regions.
Cited articles
Alfieri, L., Salamon, P., Pappenberger, F., and Wetterhall, F.: Operational
early warning systems for water-related hazards in Europe, Environ.
Sci. Policy, 21, 35–49, https://doi.org/10.1016/j.envsci.2012.01.008, 2012. a
Alfieri, L., Berenguer, M., Knechtl, V., Liechti, K., Sempere-Torres, D., and
Zappa, M.: Flash Flood Forecasting Based on Rainfall Thresholds, in:
Handbook of Hydrometeorological Ensemble Forecasting, Springer, Berlin, Heidelberg,
1–38,
https://doi.org/10.1007/978-3-642-40457-3_49-1, 2015. a, b
Amponsah, W., Marchi, L., Zoccatelli, D., Boni, G., Cavalli, M., Comiti, F.,
Crema, S., Lucía, A., Marra, F., and Borga, M.: Hydrometeorological
Characterization of a Flash Flood Associated with Major Geomorphic Effects:
Assessment of Peak Discharge Uncertainties and Analysis of the Runoff
Response, J. Hydrometeorol., 17, 3063–3077,
https://doi.org/10.1175/JHM-D-16-0081.1, 2016. a
Archer, D. R., Leesch, F., and Harwood, K.: Learning from the extreme River
Tyne flood in January 2005, Water Environ. J., 21, 133–141,
https://doi.org/10.1111/j.1747-6593.2006.00058.x, 2007. a
Armon, M., Dente, E., Smith, J. A., Enzel, Y., and Morin, E.: Synoptic-Scale
Control over Modern Rainfall and Flood Patterns in the Levant Drylands with
Implications for Past Climates, J. Hydrometeorol., 19, 1077–1096,
https://doi.org/10.1175/JHM-D-18-0013.1, 2018. a
Armon, M., Marra, F., Enzel, Y., Rostkier-Edelstein, D., and Morin, E.: Radar-based characterisation of heavy precipitation in the eastern Mediterranean and its representation in a convection-permitting model, Hydrol. Earth Syst. Sci., 24, 1227–1249, https://doi.org/10.5194/hess-24-1227-2020, 2020. a, b
Avni, Y. and Wieler, N.: The geological map of Israel, 1:50,000, Sheet 18-IV: SdeBoqer, Isr. Geol. Surv., Jerusalem, 2013. a
Avni, Y., Bartov, Y., and Sneh, A.: The geological map of Israel, 1:50,000. Sheet 22-I: HarArdon, Isr. Geol. Surv., Jerusalem, 2016. a
Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M.,
and Reinhardt, T.: Operational convective-scale numerical weather prediction
with the COSMO model: Description and sensitivities, Mon. Weather Rev.,
139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a
Barredo, J. I.: Major flood disasters in Europe: 1950–2005, Nat. Hazards,
42, 125–148, https://doi.org/10.1007/s11069-006-9065-2, 2007. a, b
Bartsotas, N. S., Nikolopoulos, E. I., Anagnostou, E. N., Solomos, S., and
Kallos, G.: Moving toward Subkilometer Modeling Grid Spacings: Impacts on
Atmospheric and Hydrological Simulations of Extreme Flash Flood-Inducing
Storms, J. Hydrometeorol., 18, 209–226,
https://doi.org/10.1175/JHM-D-16-0092.1, 2016. a
Belachsen, I., Marra, F., Peleg, N., and Morin, E.: Convective rainfall in a dry climate: relations with synoptic systems and flash-flood generation in the Dead Sea region, Hydrol. Earth Syst. Sci., 21, 5165–5180, https://doi.org/10.5194/hess-21-5165-2017, 2017. a, b, c, d
Benson, M. A. and Dalrymple, T.: General Field and Office Procedures for
Indirect Discharge Measurements, Technical Report, available at:
http://pubs.usgs.gov/twri/twri3-a1/html/pdf/twri_3-A1_a.pdf (last access: 10 May 2020),
1967. a
Berenguer, M., Corral, C., Sánchez-Diezma, R., and Sempere-Torres, D.:
Hydrological Validation of a Radar-Based Nowcasting Technique, J.
Hydrometeorol., 6, 532–549, https://doi.org/10.1175/JHM433.1, 2005. a, b
Borga, M., Boscolo, P., Zanon, F., and Sangati, M.: Hydrometeorological
Analysis of the 29 August 2003 Flash Flood in the Eastern Italian Alps,
J. Hydrometeorol., 8, 1049–1067, https://doi.org/10.1175/JHM593.1, 2007. a
Borga, M., Stoffel, M., Marchi, L., Marra, F., and Jakob, M.: Hydrogeomorphic
response to extreme rainfall in headwater systems: Flash floods and debris
flows, J. Hydrol., 518, 194–205,
https://doi.org/10.1016/j.jhydrol.2014.05.022, 2014. a, b
Borga, M., Comiti, F., Ruin, I., and Marra, F.: Forensic analysis of flash
flood response, Wires, 6, 1–9,
https://doi.org/10.1002/wat2.1338, 2019. a, b, c, d
Bracken, L. J. and Croke, J.: The concept of hydrological connectivity and its
contribution to understanding runoff-dominated geomorphic systems,
Hydrol. Process., 21, 1749–1763, https://doi.org/10.1002/hyp.6313, 2007. a
Brunner, W. G.: HEC-RAS River Analysis System, Hydraulic reference manual, Version 5.0, US Army Crops of Engineers, 2016. a
Brutsaert, W.: Hydrology, An Introducation, Cambridge University Press, New
York, USA, 2005. a
Calvo, R. and Bartov, Y.: Hazeva Group, southern Israel: New observations, and
their implications for its stratigraphy, paleogeography, and
tectono-sedimentary regime, Israel J. Earth Sci., 50, 71–99,
https://doi.org/10.1092/B02L-6K04-UFQL-KUE3, 2001. a
Chappell, C. F.: Quasi-Stationary Convective Events, in: Mesoscale
Meteorology and Forecasting, edited by: Ray, P. S.,
American Meteorological Society, Boston, MA, USA,
289–310,
https://doi.org/10.1007/978-1-935704-20-1_13, 1986. a
Collier, C. G.: Flash flood forecasting: What are the limits of predictability?, Q. J. Roy. Meteor. Soc., 133, 3–23, https://doi.org/10.1002/qj.29, 2007. a, b, c
Creutin, J. D., Borga, M., Gruntfest, E., Lutoff, C., Zoccatelli, D., and Ruin,
I.: A space and time framework for analyzing human anticipation of flash
floods, J. Hydrol., 482, 14–24,
https://doi.org/10.1016/j.jhydrol.2012.11.009, 2013. a, b
Dan, Y., Raz, Z., Yaalon, D. H., and Koyumdjisky, H.: Soil Map of Israel, 1:500,000, Jerusalem, 1975. a
Dayan, U., Lensky, I. M., and Ziv, B.: Atmospheric Conditions Leading to an Exceptional Fatal Flash Flood in the Negev Desert, Israel, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2020-94, in review, 2020. a
Delrieu, G., Ducrocq, V., Gaume, E., Nicol, J., Payrastre, O., Yates, E.,
Kirstetter, P.-E., Andrieu, H., Ayral, P.-A., Bouvier, C., Creutin, J.-D.,
Livet, M., Anquetin, S., Lang, M., Neppel, L., Obled, C., Parent-Du-Chatelet,
J., Saulnier, G.-M., Walpersorf, A., and Wobrock, W.: The catastrophic
flash-flood event of 8–9 September 2002 in the Gard region, France: a first
case study for the Cévennes-Vivarais Mediterranean
Hydrometeorological, J. Hydrometeorol., 6, 34–52,
2005. a
Dey, K. D. and Yan, J.: Extreme Value Modeling and Risk Analysis: Methods and
Applications, CRC Press, Taylor and Francis Group, NY, USA, 2016. a
Dey, S. R., Plant, R. S., Roberts, N. M., and Migliorini, S.: Assessing
spatial precipitation uncertainties in a convective-scale ensemble,
Q. J. Roy. Meteor. Soc., 142, 2935–2948,
https://doi.org/10.1002/qj.2893, 2016. a
Dingman, S. L.: Physical hydrology, Waveland Press, Long Grove, IL, USA, https://doi.org/10.1177/030913337800200111, 2015. a
Doms, G., Förstner, J., Heise, E., Herzog, H.-J., Mironov, D.,
Raschendorfer, M., Reinhardt, T., Ritter, B., Schrodin, R., Schulz, J.-P.,
and Vogel, G.: A Description of the Nonhydrostatic Regional COSMO Model Part II: Physical Parameterization, Deutscher Wetterdienst, Offenbach, Germany, Technical Report, p. 139, 2011. a
Doocy, S., Daniels, A., Murray, S., and Kirsch, T. D.: The Human Impact of
Floods: a Historical Review of Events and Systematic Literature Review, PLOS
Currents Disasters, 1, 1–32,
https://doi.org/10.1371/currents.dis.f4deb457904936b07c09daa 98ee8171a, 2013. a, b, c
Doswell, A. C., Brooks, E. H., and Madox, A. R.: Flash flood forecasting: An
ingredients-based methodology, Weather Forecast., 11, 560–581,
1996. a
Downer, C. W. and Ogden, F. L.: GSSHA – User's Manual, Version 1.43, p. 190, 2002. a
DWD – Deutscher Wetterdienst: Wetter und Klima – Deutscher Wetterdienst – Startseite, https://www.dwd.de, last access: 10 May 2020. a
EMS Mekorot Projects Ltd: https://www.emsmekorotprojects.com/, last access: 24 January 2021. a
Engman, E. T.: Roughness Coefficients for Routing Surface Runoff, J. Irrig. Drainage Eng., https://doi.org/10.1061/(ASCE)0733-9437(1986)112:1(39), 1986. a, b
Faurès, J. M., Goodrich, D. C., Woolhiser, D. A., and Sorooshian, S.:
Impact of small-scale spatial rainfall variability on runoff modeling,
J. Hydrol., 173, 309–326, https://doi.org/10.1016/0022-1694(95)02704-S,
1995. a
Foody, G. M., Ghoneim, E. M., and Arnell, N. W.: Predicting locations
sensitive to flash flooding in an arid environment, J. Hydrol.,
292, 48–58, https://doi.org/10.1016/j.jhydrol.2003.12.045, 2004. a
Gaume, E. and Borga, M.: Post-flood field investigations in upland catchments
after major flash floods: proposal of a methodology and illustrations,
J. Flood Risk Manag., 1, 175–189,
https://doi.org/10.1111/j.1753-318x.2008.00023.x, 2008. a
Gaume, E., Bain, V., Bernardara, P., Newinger, O., Barbuc, M., Bateman, A.,
Blaškovičová, L., Blöschl, G., Borga, M., Dumitrescu,
A., Daliakopoulos, I., Garcia, J., Irimescu, A., Kohnova, S., Koutroulis, A.,
Marchi, L., Matreata, S., Medina, V., Preciso, E., Sempere-Torres, D.,
Stancalie, G., Szolgay, J., Tsanis, I., Velasco, D., and Viglione, A.: A
compilation of data on European flash floods, J. Hydrol., 367,
70–78, https://doi.org/10.1016/j.jhydrol.2008.12.028, 2009. a, b, c, d
Gaume, E., Borga, M., Llasat, M., Maouche, S., Lang, M., and Diakakis, M.:
Mediterranean extreme floods and flash floods. Into Hydro-meteorological
extremes, The Mediterranean Region under Climate Change, A Scientific
Update, edited by: Thiébault, S. and Moatti, J.-P., IRD Éditions, Marseille, France, 133–144, 2016. a
Goldreich, Y.: The Climate of Israel, Observations, Research and
Imlementation, Springer, New York, USA, 2003. a
Greenbaum, N., Schwartz, U., Schick, A., and Enzel, Y.: Paleofloods and the
Estimation of Long Term Transmission Losses and Recharge to the Lower Nahal
Zin Alluvial Aquifer, Negev Desert, Israel, in: Ancient floods, modern
hazards: principles and applications of paleoflood hydrologydrology, edited
by: House, P., Webb, R., Baker, V., and Levish, D.,
NY, USA, 311–328,
2002a. a, b
Greenbaum, N., Schwartz, U., Schick, P. A., and Enzel, Y.: Paleo floods and
the Estimation of Long Term Transmission Losses and Recharge to the Lower
Nahal Zin Alluvial Aquifer, Negev Desert, Israel, in: Ancient Floods, Modern
Hazards Principles and Applications of Paleoflood Hydrology, Water Science
and Application, edited by: House, P. K., Webb, H. R., Baker, R. V., and
Levish, R. D., American Geophysical Union, Washington, DC, USA,
311–328, 2002b. a
Greenbaum, N., Ben-Zvi, A., Haviv, I., and Enzel, Y.: The hydrology and
paleohydrology of the Dead Sea tributaries, Geol. S. Am.
S., 401, 63–93, https://doi.org/10.1130/2006.2401(05), 2006. a
Grodek, T., Jacoby, Y., Morin, E., and Katz, O.: Effectiveness of exceptional
rainstorms on a small Mediterranean basin, Geomorphology, 159–160, 156–168,
https://doi.org/10.1016/j.geomorph.2012.03.016, 2012. a
Gruntfest, E. and Handmer, J.: Coping With Flash Floods, Springer Science and Business Media, Dordrecht, Netherlands,
https://doi.org/10.1016/j.irfa.2004.01.004, 2001. a
Haan, C. T. C. T.: Statistical methods in hydrology, Iowa State Press,
Iowa, USA,
2002. a
Hagelin, S., Son, J., Swinbank, R., McCabe, A., Robertsa, N., and Tennanta, W.:
The Met Office convective-scale ensemble, MOGREPS-UK, Q. J. Roy. Meteor. Soc., 143, 2846–2861, https://doi.org/10.1002/qj.3135,
2017. a
Hirsch, F.: The geological map of Israel, 1:50,000. Sheet 19-II: HamakhteshHaqatan, Isr. Geol. Surv., Jerusalem, 1995. a
Hosking, J. R., Wallis, J. R., and Wood, E. F.: Estimation of the generalized
extreme-value distribution by the method of probability-weighted moments,
Technometrics, 27, 251–261, https://doi.org/10.1080/00401706.1985.10488049, 1985. a
Inbar, M.: Natural disasters in israel, availabe at:
https://geo.hevra.haifa.ac.il/~inh/index.php/en/disasters-en/floods-en (last access: 10 May 2020),
2019. a
Israel Hydrological service: https://www.gov.il/he/departments/water_authority, last access: 24 January 2021 (in Hebrew). a
Kahana, R., Ziv, B., Enzel, Y., and Dayan, U.: Synoptic climatology of major
floods in the Negev Desert, Israel, Int. J. Climatol.,
22, 867–882, https://doi.org/10.1002/joc.766, 2002. a
Kampf, S. K., Faulconer, J., Shaw, J. R., Lefsky, M., Wagenbrenner, J. W., and
Cooper, D. J.: Rainfall Thresholds for Flow Generation in Desert Ephemeral
Streams, Water Resour. Res., 54, 9935–9950,
https://doi.org/10.1029/2018WR023714, 2018. a
Karklinsky, M. and Morin, E.: Spatial characteristics of radar-derived
convective rain cells over southern Israel, Meteorol. Z., 15,
513–520, https://doi.org/10.1127/0941-2948/2006/0153, 2006. a
Khain, P., Levi, Y., Shtivelman, A., and Vadislavsky, E.: Improving the
precipitation forecast over the Eastern Mediterranean using a smoothed
time-lagged ensemble, Meteorol. Appl., 27, 1–17,
https://doi.org/10.1002/met.1840, 2019. a, b, c
Kidd, C., Becker, A., Huffman, G. J., Muller, C. L., Joe, P.,
Skofronick-Jackson, G., and Kirschbaum, D. B.: So, how much of the Earth's
surface is covered by rain gauges?, B. Am. Meteorol.
Soc., 98, 69–78, https://doi.org/10.1175/BAMS-D-14-00283.1, 2017. a, b
Lange, J.: Dynamics of transmission losses in large arid stream channel,
J. Hydrol., 306, 112–126, https://doi.org/10.1016/j.jhydrol.2004.09.016,
2005. a
Lange, J., and Leibundgut, C: Surface runoff and sediment dynamics in arid and semi‐arid regions, in Understanding Water in a Dry Environment, edited by: Simmers, I., Taylor & Francis, Lisse, the Netherlands, 115–150, 2003. a
Lange, J., Leibundgut, C., Greenbaun, N., and Schick, A.: A noncalibrated
model for large, arid catchments, Water Resour. Res., 35, 2161–2172,
https://doi.org/10.1029/1999WR900038, 1999. a, b, c
Larsen, M. C., Conde, M. T. V., and Clark, R. A.: Landslide hazards associated
with flash-foods, with examples from the December, 1999 disaster in
Venezuela, in: Coping with Flash Floods, edited by: Gruntfest, E. and
Handmer, J., Springer Science and Business Media, Dordrecht, Netherlands,
259–275, 2001. a
Liechti, K. and Zappa, M.: Verification of Short-Range Hydrological
Forecasts, in: Handbook of Hydrometeorological Ensemble Forecasting, edited
by: Qingyun, D., Pappenberger, F., Wood, A., Cloke, H. L., and Schaake, J. C., Springer, Berlin and Heidelberg, Germany, 954–974,
2019. a
Limerinos, T. J.: Determination of the Manning Coefficient From Measured Bed
Roughness in Natural Channels, Technical Report, Geological Survey, Washington, DC, USA, 47 pp., 1970. a
Marani, M. and Ignaccolo, M.: A metastatistical approach to rainfall
extremes, Adv. Water Resour., 79, 121–126,
https://doi.org/10.1016/j.advwatres.2015.03.001, 2015. a
Marra, F. and Morin, E.: Autocorrelation structure of convective rainfall in
semiarid-arid climate derived from high-resolution X-Band radar estimates,
Atmos. Res., 200, 126–138, https://doi.org/10.1016/j.atmosres.2017.09.020,
2018. a, b
Marra, F., Morin, E., Peleg, N., Mei, Y., and Anagnostou, E. N.: Intensity–duration–frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean, Hydrol. Earth Syst. Sci., 21, 2389–2404, https://doi.org/10.5194/hess-21-2389-2017, 2017. a
Marra, F., Nikolopoulos, E. I., Anagnostou, E. N., and Morin, E.:
Metastatistical Extreme Value analysis of hourly rainfall from short
records: Estimation of high quantiles and impact of measurement errors,
Adv. Water Resour.,
117, 27–39,
https://doi.org/10.1016/j.advwatres.2018.05.001, 2018. a, b
Marra, F., Nikolopoulos, E. I., Anagnostou, E. N., Bárdossy, A., and
Morin, E.: Precipitation frequency analysis from remotely sensed datasets: A
focused review, J. Hydrol., 574, 699–705, https://doi.org/10.1016/j.jhydrol.2019.04.081, 2019a. a, b, c, d
Marra, F., Zoccatelli, D., Armon, M., and Morin, E.: A simplified MEV
formulation to model extremes emerging from multiple nonstationary underlying
processes, Adv. Water Resour., 127, 280–290,
https://doi.org/10.1016/j.advwatres.2019.04.002, 2019b. a
Martens, B., Cabus, P., De Jongh, I., and Verhoest, N. E.: Merging weather
radar observations with ground-based measurements of rainfall using an
adaptive multiquadric surface fitting algorithm, J. Hydrol., 500,
84–96, https://doi.org/10.1016/j.jhydrol.2013.07.011, 2013. a
MeteoSwiss – Federal Office of Meteorological and Climatology MeteoSwiss:
availabe at: http://www.meteoswiss.admin.ch, last access: 10 May 2020. a
Met Office: availabe at: https://www.metoffice.gov.uk/, last access: 10 May 2020. a
Michaud, J. D. and Sorooshian, S.: Effect of rainfall‐sampling errors on
simulations of desert flash floods, Water Resour. Res., 30,
2765–2775, https://doi.org/10.1029/94WR01273, 1994. a
Miniussi, A., and Marani, M. Estimation of daily rainfall extremes through the metastatistical extreme value distribution: Uncertainty minimization and implications for trend detection, Water Resour. Res., 56, e2019WR026535, https://doi.org/10.1029/2019WR026535, 2020. a
Mishra, S. and Singh, V. P.: Soil Conservation Service Curve Number (SCS-CN)
Methodology, Water Science and Technology Library, Springer Science and Business Media, Dordrecht, The Netherlands,
514 pp.,
https://doi.org/10.1007/978-94-017-0147-1,
2003. a
Moawad, M. B.: Analysis of the flash flood occurred on 18 January 2010 in wadi
El Arish, Egypt (a case study), Geomat. Nat. Haz. Risk, 4,
254–274, https://doi.org/10.1080/19475705.2012.731657, 2013. a
Moawad, M. B., Omar Abdel Aziz, A., and Mamtimin, B.: Flash floods in the
Sahara: a case study for the 28 January 2013 flood in Qena, Egypt,
Geomat. Nat. Haz. Risk, 7, 215–236,
https://doi.org/10.1080/19475705.2014.885467, 2016. a
Montgomery, D. R. and Gran, K. B.: Downstream variations in the width of
bedrock channels, Water Resour. Res., 37, 1841–1846,
https://doi.org/10.1029/2000WR900393, 2001. a
Morin, E. and Yakir, H.: Hydrological impact and potential flooding of
convective rain cells in a semi-arid environment, Hydrolog. Sci.
J., 57, 1–10, https://doi.org/10.1080/02626667.2013.841315, 2014. a, b
Morin, E., Enzel, Y., Shamir, U., and Garti, R.: The characteristic time scale
for basin hydrological response using radar data, J. Hydrol., 252,
85–99, https://doi.org/10.1016/S0022-1694(01)00451-6, 2001. a
Morin, E., Goodrich, D. C., Maddox, R. A., Gao, X., Gupta, H. V., and
Sorooshian, S.: Spatial patterns in thunderstorm rainfall events and their
coupling with watershed hydrological response, Adv. Water Resour.,
29, 843–860, https://doi.org/10.1016/j.advwatres.2005.07.014, 2006. a
Morin, E., Grodek, T., Dahan, O., Benito, G., Kulls, C., Jacoby, Y.,
Langenhove, G. V., Seely, M., and Enzel, Y.: Flood routing and alluvial
aquifer recharge along the ephemeral arid Kuiseb River, Namibia, J.
Hydrol., 368, 262–275, https://doi.org/10.1016/j.jhydrol.2009.02.015,
2009a. a, b
Morin, E., Jacoby, Y., Navon, S., and Bet-Halachmi, E.: Towards flash-flood
prediction in the dry Dead Sea region utilizing radar rainfall information,
Adv. Water Resour., 32, 1066–1076,
https://doi.org/10.1016/j.advwatres.2008.11.011, 2009b. a
Morin, E., Marra, F., and Armon, M.: Dryland Precipitation Climatology from
Satellite Observations, in: Satellite Precipitation Measurement: Volume 2,
edited by: Levizzani, V., Kidd, C., Kirschbaum, D. B., Kummerow, C. D.,
Nakamura, K., and Turk, F. J., Springer International
Publishing, Cham, UK,
843–859, https://doi.org/10.1007/978-3-030-35798-6_19, 2020. a, b
Nouh, M.: Flood hydrograph estimation from arid catchment morphology,
Hydrol. Process., 4, 103–120, https://doi.org/10.1002/hyp.3360040202, 1990. a
Overeem, A., Buishand, A., and Holleman, I.: Rainfall depth-duration-frequency
curves and their uncertainties, J. Hydrol., 348, 124–134,
https://doi.org/10.1016/j.jhydrol.2007.09.044, 2008. a
Peckham, S. D.: A New Algorithm for Creating DEMs with Smooth Elevation
Profiles, Proceedings of Geomorphometry, 2, 34–37, 2009. a
Petrucci, O., Aceto, L., Bianchi, C., Bigot, V., Brázdil, R., Pereira,
S., Kahraman, A., Kiliç, Ö., Kotroni, V., Llasat, M. C.,
Llasat-Botija, M., Papagiannaki, K., Pasqua, A. A., Řehoř, J., Geli, J. R.,
Salvati, P., Vinet, F., and Zêzere, J. L.: Flood fatalities in Europe,
1980–2018: Variability, features, and lessons to learn, Water
11, 1682, https://doi.org/10.3390/w11081682, 2019. a
Pilgrim, D. H., Chapman, T. G., and Doran, D. G.: Problems of rainfall-runoff
modelling in arid and semiarid regions, Hydrolog. Sci. J., 33,
379–400, https://doi.org/10.1080/02626668809491261, 1988. a, b, c
Prein, A. F., Langhans, W., Fosser, G., Ferrone, A., Ban, N., Goergen, K.,
Keller, M., Tölle, M., Gutjahr, O., Feser, F., Brisson, E., Kollet, S.,
Schmidli, J., Van Lipzig, N. P., and Leung, R.: A review on regional
convection-permitting climate modeling: Demonstrations, prospects, and
challenges, Rev. Geophys., 53, 323–361, https://doi.org/10.1002/2014RG000475,
2015. a
Rinat, Y. and Armon, M.: Desert Flash Flood, 26-Apr-2018, TIB-AV-Portal, https://doi.org/10.5446/47152, 2020. a
Roberts, N.: Assessing the spatial and temporal variation in the skill of
precipitation forecasts from an NWP model, Meteorol. Appl., 15,
163–169, https://doi.org/10.1002/met.57, 2008. a
Roded, R.: The geological map of Israel, 1:50,000, Sheet 19-III: Oron, Isr. Geol. Surv., Jerusalem, 1982. a
Roded, R.: The geological map of Israel, 1:50,000. Sheet 19-I: Dimona, Isr. Geol. Surv., Jerusalem, 1996. a
Sadeh, Y., Cohen, H., Maman, S., and Blumberg, D. G.: Evaluation of manning's
n roughness coefficient in arid environments by using SAR backscatter,
Remote Sens., 10, 1–14, https://doi.org/10.3390/rs10101505, 2018. a
Schwartz, C. S. and Sobash, R. A.: Generating Probabilistic Forecasts from
Convection-Allowing Ensembles Using Neighborhood Approaches: A Review and
Recommendations, Mon. Weather Rev., 145, 3379–3418,
https://doi.org/10.1175/MWR-D-16-0400.1, 2017. a
Schwartz, U.: Surface and near‐surface responses to floods in a large channel (Nahal Zin) in the context of an alluvial aquifer in a hyper‐arid environment, PhD diss., Department of Geography, Hebrew University of Jerusalem, Israel, 2001. a
S.C.S: Soil Conservation Service, National engineering handbook, section 4,
Hydrology, Technical Report, US Department of Agriculture, Washington DC, USA, 127 pp., 1972. a
Sempere-Torres, D., Berenguer, M., Corral, C., and Sanchez-Diezma, R.:
Improvements on flow forecasting using precipitation nowcasting based in
radar advection techniques: assessment of predictability and uncertainty
propagation, 32nd Conference on Radar Meteorology,
Albuquerque, USA,
2005. a
Shamir, E., Ben-Moshe, L., Ronen, A., Grodek, T., Enzel, Y., Georgakakos, K. P., and Morin, E.: Geomorphology-based index for detecting minimal flood stages in arid alluvial streams, Hydrol. Earth Syst. Sci., 17, 1021–1034, https://doi.org/10.5194/hess-17-1021-2013, 2013. a
Shammout, M., Shatanawi, M., and Nelson, J.: Curve Number Applications for
Restoration the Zarqa River Basin, Sustainability, 10, 586,
https://doi.org/10.3390/su10030586, 2018. a
Shmilovitz, Y., Morin, E., Rinat, Y., Haviv, I., Carmi, G., Mushkin, A., and
Enzel, Y.: Linking frequency of rainstorms, runoff generation and sediment
transport across hyperarid talus-pediment slopes, Earth Surf. Proc.
Land., 45, 1644–1659, https://doi.org/10.1002/esp.4836, 2020. a, b, c
Sivakumar B.: Chaos in hydrology: bridging determinism and stochasticity, Springer, the Netherlands, https://doi.org/10.1007/978-90-481-2552-4, 2017. a
Sneh, A., Bartov, Y., Weissbrod, T., and Rosensaft, M.:
Geological Map of Israel, 1:200,000. Isr. Geol. Surv., 4 sheets, 1998. a
Sobash, R. A., Kain, J. S., Bright, D. R., Dean, A. R., Coniglio, M. C., and
Weiss, S. J.: Probabilistic forecast guidance for severe thunderstorms based
on the identification of extreme phenomena in convection-allowing model
forecasts, Weather Forecast., 26, 714–728,
https://doi.org/10.1175/WAF-D-10-05046.1, 2011. a
Sokol, Z. and Zacharov, P.: Nowcasting of precipitation by an NWP model using
assimilation of extrapolated radar reflectivity, Q. J.
Roy. Meteor. Soc., 138, 1072–1082, https://doi.org/10.1002/qj.970, 2012. a
Stephan, K., Klink, S., and Schraff, C.: Assimilation of radar-derived rain
rates into the convective-scale model COSMO-DE at DWD, Q. J.
Roy. Meteor. Soc., 134, 1315–1326, https://doi.org/10.1002/qj.269,
2008. a, b, c
Tarolli, P., Borga, M., Morin, E., and Delrieu, G.: Analysis of flash flood regimes in the North-Western and South-Eastern Mediterranean regions, Nat. Hazards Earth Syst. Sci., 12, 1255–1265, https://doi.org/10.5194/nhess-12-1255-2012, 2012. a, b, c
Theis, S. E., Hense, A., and Damrath, U.: Probabilistic precipitation
forecasts from a deterministic model: a pragmatic approach, Meteorol.
Appl., 268, 257–268, https://doi.org/10.1017/S1350482705001763, 2005. a
Tooth, S.: Process, form and change in dryland rivers: A review of recent
research, Earth Sci. Rev., 51, 67–107,
https://doi.org/10.1016/S0012-8252(00)00014-3, 2000. a, b
USGS: Estimating Basin Lagtime and Hydrograph-Timing Indexes Used to
Characterize Stormflows, Technical Report, availabe at:
http://pubs.usgs.gov/sir/2012/5110/pdf/sir2012-5110_text.pdf (last access: 10 May 2020),
2012. a
Vetter, T., Rieger, A. K., and Nicolay, A.: Disconnected runoff contributing
areas: Evidence provided by ancient watershed management systems in arid
north-eastern Marmarica (NW-Egypt), Geomorphology, 212, 41–57, https://doi.org/10.1016/j.geomorph.2013.10.002,
2014. a, b
Vincendon, B., Ducrocq, V., Nuissier, O., and Vié, B.: Perturbation of convection-permitting NWP forecasts for flash-flood ensemble forecasting, Nat. Hazards Earth Syst. Sci., 11, 1529–1544, https://doi.org/10.5194/nhess-11-1529-2011, 2011. a
Vinet, F., Bigot, V., Petrucci, O., Papagiannaki, K., Llasat, M. C., Kotroni,
V., Boissier, L., Aceto, L., Grimalt, M., Llasat-Botija, M., Pasqua, A. A.,
Rossello, J., Kiliç, Ö., Kahraman, A., and Tramblay, Y.: Mapping
flood-related mortality in the mediterranean basin. Results from the MEFF
v2.0 DB, Water, 11, p. 2196, https://doi.org/10.3390/w11102196, 2019. a
Wagener, T., Gupta, H., Yatheendradas, S., Goodrich, D., Unkrich, C., and
Schaffner, M.: Understanding sources of uncertainty in flash-flood
forecasting for semi-arid regions, IAHS-AISH, 313,
204–212, 2007. a
Walters, O. M.: Transmission losses in arid region, J. Hydraul.
Eng., 116, 129–138, 1990. a
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, J.
Am. Stat. Assoc., 102, p. 380, https://doi.org/10.1198/jasa.2007.s163, 2006. a
Wittenberg, L., Kutiel, H., Greenbaum, N., and Inbar, M.: Short-term changes
in the magnitude, frequency and temporal distribution of floods in the
Eastern Mediterranean region during the last 45 years – Nahal Oren, Mt.
Carmel, Israel, Geomorphology, 84, 181–191,
https://doi.org/10.1016/j.geomorph.2006.01.046, 2007. a
Yair, A. and Kossovsky, A.: Climate and surface properties: Hydrological
response of small arid and semi-arid watersheds, Geomorphology, 42, 43–57,
https://doi.org/10.1016/S0169-555X(01)00072-1, 2002. a
Yair, A. and Lavee, H.: Application of the concept of partial area
contribution to small arid watersheds, Z. Gemorphologie,
29, 71–82, 1981. a
Yair, A. and Raz-Yassif, N.: Hydrological processes in a small arid catchment:
Scale effects of rainfall and slope length, Geomorphology, 61, 155–169,
https://doi.org/10.1016/j.geomorph.2003.12.003, 2004. a, b
Yair, A., Sharon, D., and Lavee, H.: Trends in runoff and erosion processes
over an arid limestone hillside, northern negev, israel, Hydrol.
Sci. Bull., 25, 243–255, https://doi.org/10.1080/02626668009491932, 1980. a
Yakir, H. and Morin, E.: Hydrologic response of a semi-arid watershed to spatial and temporal characteristics of convective rain cells, Hydrol. Earth Syst. Sci., 15, 393–404, https://doi.org/10.5194/hess-15-393-2011, 2011. a, b, c
Yang, L., Smith, J., Baeck, M. L., Morin, E., and Goodrich, D. C.: Flash
Flooding in Arid/Semiarid Regions: Dissecting the Hydrometeorology and
Hydrology of the 19 August 2014 Storm and Flood Hydroclimatology in Arizona,
J. Hydrometeorol., 18, 3103–3123, https://doi.org/10.1175/jhm-d-17-0089.1,
2017.
a, b
Yechieli, Y., Elron, E., and Sneh, A.: The geological map of Israel, 1:50,000, Sheet 19-IV,20-III: NeotHakikar, Isr. Geol. Surv., Jerusalem, 1994. a
Zilberman, E. and Avni, Y.,: The geological map of Israel, 1:50,000, Sheet 21-II: MizpeRamon, Isr. Geol. Surv., Jerusalem, 2004. a
Zoccatelli, D., Marra, F., Armon, M., Rinat, Y., Smith, J. A., and Morin, E.: Contrasting rainfall-runoff characteristics of floods in desert and Mediterranean basins, Hydrol. Earth Syst. Sci., 23, 2665–2678, https://doi.org/10.5194/hess-23-2665-2019, 2019. a, b, c, d
Zoccatelli, D., Marra, F., Smith, J., Goodrich, D., Unkrich, C., Rosensaft, M.,
and Morin, E.: Hydrological modelling in desert areas of the eastern
Mediterranean, J. Hydrol., 587, 124879,
https://doi.org/10.1016/j.jhydrol.2020.124879, 2020. a, b
Zorzetto, E., Botter, G., and Marani, M.: On the emergence of rainfall
extremes from ordinary events, Geophys. Res. Lett., 43, 8076–8082,
https://doi.org/10.1002/2016GL069445, 2016. a
Short summary
Flash floods are among the most devastating and lethal natural hazards worldwide. The study of such events is important as flash floods are poorly understood and documented processes, especially in deserts. A small portion of the studied basin (1 %–20 %) experienced extreme rainfall intensities resulting in local flash floods of high magnitudes. Flash floods started and reached their peak within tens of minutes. Forecasts poorly predicted the flash floods mostly due to location inaccuracy.
Flash floods are among the most devastating and lethal natural hazards worldwide. The study of...
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