The impact of climate on topography, which is a theme in landscape evolution studies, has been demonstrated, mostly, at mountain range scales and across climate zones. However, in drylands, spatiotemporal discontinuities of rainfall and the crucial role of extreme rainstorms raise questions and challenges in identifying climate properties that govern surface processes. Here, we combine methods to examine hyperarid escarpment sensitivity to storm-scale forcing. Using a high-resolution DEM and field measurements, we analyzed the topography of a 40-km-long escarpment in the Negev desert (Israel). We also used rainfall intensity data from a convection-permitting numerical weather model for storm-scale statistical analysis. We conducted hydrological simulations of synthetic rainstorms, revealing the frequency of sediment mobilization along the sub-cliff slopes. Results show that cliff gradients along the hyperarid escarpment increase systematically from the wetter (90 mm yr−1) southwestern to the drier (45 mm yr−1) northeastern sides. Also, sub-cliff slopes at the southwestern study site are longer and associated with milder gradients and coarser sediments. Storm-scale statistical analysis reveals a trend of increasing extreme (>10 years return-period) intensities toward the northeast site, opposite to the trend in mean annual rainfall. Hydrological simulations based on these statistics indicate a higher frequency of sediment mobilization in the northeast, which can explain the pronounced topographic differences between the sites. The variations in landscape and rainstorm properties across a relatively short distance highlight the sensitivity of arid landforms to extreme events.

The evaluation of erosion risk in dry areas is challenging because erosion is often an outcome of individual rainstorms and is highly dependent on rainfall spatiotemporal patterns and on local land-use and topography. This study integrates a hybrid erosion model with rainfall data from high-resolution weather radar to simulate soil erosion during 22 high-intensity flash-flood generating rainstorms in a Mediterranean watershed (69 km2). We examine erosion over individual hillslopes and their spatial average over the watershed, representing intra-watershed and watershed-scale erosion, respectively. Our objectives are to: (a) determine how intra-watershed erosion corresponds to various physiographic factors (rainfall, land-use, topography); (b) determine which of these factors contributes to intra-watershed erosion the most; (c) quantify the effect of temporal variations in rainfall intensities on storm-scale erosion in relation to land-use type. We use for the first time a hybrid erosion model (K2-RHEM-DWEPP) based on the watershed-scale KINEROS2 model, that integrates the hillslope-scale Dynamic WEPP (DWEPP) and RHEM models, which were individually developed to represent erosion processes in croplands and rangelands, respectively. Watershed-scale storm erosion is best correlated with spatially-averaged 10-minutes maximum intensities (R2 = 0.58), and the correlation decreases for longer durations (R2 ≤ 0.54). When the spatially-averaged 10-minutes maximum intensity is multiplied by the area that contributes sediment, a better correlation with watershed-scale erosion is observed (R2 = 0.75). Hillslope erosion rates are higher when both rainfall intensities and topographic slopes are high, while land-use has a second-order effect. Higher storms maximal intensities result in higher hillslope erosion rates, especially over croplands. Our conclusions are useful to target locations for conservation practices and to better understand the effects of climate change on soil erosion.

Deciphering aspect-related hillslope asymmetry can enhance our understanding of the influence of climate on Earth’s surface morphology and the linkage between topographic morphology and erosion processes. Although hillslope asymmetry is documented worldwide, the role of microclimatic factors in the evolution of dryland cliffs has received little attention. Here, we address this gap by quantifying aspect-dependent bedrock weathering, slope-rill morphology, and subcliff clast transport rates in the hyperarid Negev desert, Israel, based on light detection and ranging (LiDAR)-derived topography, clast-size measurements, and cosmogenic 10Be concentrations. Cliff retreat rates were evaluated using extrapolated profiles from dated talus flatirons and 10Be measurements from the cliff face and sub-cliff sediments. We document systematic, aspect-dependent patterns of south-facing slopes being less steep and finer-grained relative to east and north-facing aspects. In addition, cliff retreat and clast transport rates on slopes of the south-facing aspect are faster compared to the other aspects. Our data demonstrate that bedrock weathering of the cliff face and the corresponding grain size of cliff-derived clasts delivered to the slopes constitute a first-order control on cliff retreat and sediment transport rates. We demonstrate that the morphology of the cliff and the pattern of bedrock weathering co-vary with the solar radiation flux and hence that cliff evolution in hyperarid regions is modulated by aspectdependent solar radiation. These results help to better understand interactions between climate and dryland surface processes.

Among the varied environmental factors that influence insect life-history, temperature has a relatively profound effect that can be mathematically estimated with non-linear equations. 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 a previous study, we developed a temperature-dependent population dynamics model for the global insect-pest Bemisia tabaci, and verified its accuracy under field conditions. In the current work, which focused on Northern Israel, seasonal meteorological forecasts from the ECMWF SEAS5 coupled model were spatially and temporally stochastically downscaled by a weather generator tool using records from ERA5-Land reanalysis and meteorological stations. The local, hourly temperature time series served as input data to a population dynamics model, creating an ensemble of seasonal population forecasts from which probabilistic predictions could be made already at the beginning of the season (which lasts from March to November). Post-hoc evaluation of the seasonal forecast was done using the observed station temperatures as model input. Comparisons to predictions made using climatologic temperatures found the weather generator-based ones much more accurate in predicting the timing of each insect generation, although there was no difference between the two approaches in predicting the population size. Moreover, the weather generator-based predictions highly matched field observations made by pest inspectors during the growing season of 2021. Taken together, our findings indicate that the developed forecasting tool is capable of providing decision makers with the supporting data required for smart seasonal planning and economical- and environmental-driven optimal management of agricultural systems.