Kallush et al. (2025) Flash flood dynamics in arid areas at the Sub-Basin Scale: The Ze’elim Basin, Israel

Identification

• Journal: Journal of Hydrology
• Year: 2025
• Date: 2025-11-09
• Authors: Amit Kallush, Davide Zoccatelli, Eran Halfi, Daniel Cadol, Talia Rosin, Jonathan B. Laronne
• DOI: 10.1016/j.jhydrol.2025.134581

Research Groups

• Ben-Gurion University of the Negev, Department of Earth and Environmental Sciences, Beer Sheva, Israel
• Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg
• Dead Sea and Arava Science Center, Masada, Israel
• Department of Earth & Environmental Science, New Mexico Institute of Mining and Technology, Socorro, USA
• The Fredy and Nadine Herrmann Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
• Ben-Gurion University of the Negev, Department of Environmental, Geoinformatics and Urban Planning Sciences, Beer Sheva, Israel

Short Summary

This study quantitatively evaluates rainfall-runoff relationships at the sub-basin scale in the hyper-arid Ze’elim Basin, Israel, finding that rainfall depth reliably predicts runoff depth, and peak discharge correlates more strongly with rain-core coverage (spatial extent of intense rainfall) than with point-maximum rainfall intensity.

Objective

• To provide the first quantitative evaluation of rainfall–runoff relationships at the sub-basin scale in a hyper-arid environment, addressing the scarcity of event-scale observations, the lack of sub-basin monitoring, and the limited quantitative evaluation of rainfall spatial organization.
• Hypotheses: (i) Rainfall depth is the primary control on runoff volume, with runoff coefficients varying systematically with basin size. (ii) Metrics describing spatial rainfall organization (e.g., rain-core coverage) better explain peak discharge magnitude than point-based rainfall intensity. (iii) Event-scale analyses will reveal variability and departures from long-term averaged rainfall–runoff relationships, reflecting spatial rainfall localization and downstream transmission losses.

Study Configuration

• Spatial Scale: Ze’elim Basin (245 km² excluding the alluvial fan) in the Judean Desert, Israel. The study utilized a dense sub-basin monitoring network comprising eight sub-basins ranging from 10.8 km² to 246 km². Radar data were processed to a 500 m x 500 m spatial resolution.
• Temporal Scale: Three hydrological years (2020–21, 2021–22, and 2022–23), analyzing twelve distinct flash-flood events. Radar data had a temporal resolution of approximately 5 minutes.

Methodology and Data

• Models used:
  • Manning’s equation for calculating water discharge.
  • Z-R relationship (Z = 316 • R^1.5) for radar Quantitative Precipitation Estimation (QPE).
  • Mean Bias Removal method for radar data calibration using rain gauge observations.
  • Power-law scaling (Q = αA^θ) for analyzing peak discharge as a function of catchment area.
• Data sources:
  • Observation: A network of 11 tipping-bucket rain gauges (0.1 mm or 0.2 mm resolution) and eight water stage gauges (pressure transducers: Solinst Levelogger 5, Seba Dipper-PT) with ±5 mm accuracy. Barometric pressure sensors were used for compensation. Cross-sectional and longitudinal profiles were surveyed using a Trimble S6 Total Station or manual theodolite. Bed grain size distributions were determined using a Wolman pebble plate.
  • Remote Sensing/Reanalysis: IMS (Israel Meteorological Service) C-band weather radar data (Bet-Dagan, near Tel Aviv) with a ~5 minute temporal resolution and 500 m x 500 m spatial resolution. Daily IMS rain gauge archive data were used for radar bias adjustment.

Main Results

• Rainfall depth is a reliable predictor of runoff depth, with this relationship strengthening in larger sub-basins (P5-P8) where localized variability is smoothed. Smaller basins (P1s-P4s) exhibit less distinct relationships.
• Peak discharge correlates more strongly with Rain Core Coverage (RCC), defined as the fraction of a sub-basin covered by ≥10 mm/h rainfall for more than 10 minutes, than with point-maximum rainfall intensity. RCC is identified as a reliable predictor of peak discharge across various sub-basin scales.
• Runoff coefficients range from 0.01 to 0.30, generally decreasing with increasing catchment size, and fit a negative power-law relationship (R² = 0.91 for small events, 0.68 for large events).
• Event-scale peak discharge relationships with catchment area (Q = αA^θ) show high variability. While peak discharge generally increases with catchment size (θ between 0.02 and 0.98), localized storms can cause larger flows in smaller catchments, leading to negative θ values (between -0.91 and -0.13), which deviates from long-term average predictions.
• Significant transmission losses were observed, ranging from 75–79% between stations P7 and P8, and 37–47% between P1s and P5 for the analyzed events, highlighting their impact on downstream runoff.
• The rain position index indicates that rainfall storms tend to concentrate in the upper regions of the sub-basins. Lag times are longer for larger, eastern sub-basins, consistent with the typical west-to-east progression of storms in the region.
• Contrary to previous studies, runoff indices exhibited a weak correlation with maximum rainfall intensity.

Contributions

• Provides the first quantitative evaluation of event-scale rainfall-runoff relationships at the sub-basin scale in a hyper-arid environment, utilizing a uniquely dense and high-resolution monitoring network.
• Challenges established assumptions about rainfall-runoff relationships, particularly those derived from humid regions, by empirically demonstrating the critical role of spatial rainfall organization and transmission losses in governing event-scale responses in hyper-arid basins.
• Identifies rainfall depth as a robust predictor for runoff volume and Rain Core Coverage (RCC) as a promising predictor for peak discharge, offering valuable insights for flash-flood assessment in data-scarce drylands.
• Highlights the scientific and practical value of intermediate-scale (sub-basin) monitoring networks for reconciling fine-scale hydrological processes with broader catchment responses and improving flash-flood understanding and prediction.
• Offers transferable predictors and monitoring strategies that can enhance flood assessment in other arid and semi-arid environments.

Funding

• Israel Ministry of Environmental Protection
• Ben-Gurion University of the Negev Kreitman School
• Ben-Gurion University of the Negev, Department of Earth and Environmental Sciences (scholarship program)
• Israel-US Basic Science Foundation (grant 2018619)
• Dead Sea Works Company
• Dead Sea Arava Science Center

Citation

@article{Kallush2025Flash,
  author = {Kallush, Amit and Zoccatelli, Davide and Halfi, Eran and Cadol, Daniel and Rosin, Talia and Laronne, Jonathan B.},
  title = {Flash flood dynamics in arid areas at the Sub-Basin Scale: The Ze’elim Basin, Israel},
  journal = {Journal of Hydrology},
  year = {2025},
  doi = {10.1016/j.jhydrol.2025.134581},
  url = {https://doi.org/10.1016/j.jhydrol.2025.134581}
}