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.

End of century projections from Coupled Model Intercomparison Project (CMIP) models show a decrease in precipitation over subtropical oceans that often extends into surrounding land areas, but with...

Heavy precipitation events (HPEs) can lead to deadly and costly natural disasters and are critical to the hydrological budget in regions where rainfall variability is high and water resources depend on individual storms. Thus, reliable projections of such events in the future are needed. To provide high-resolution projections under the RCP8.5 scenario for HPEs at the end of the 21 st century, and to understand the changes in sub-hourly to daily rainfall patterns, weather research and forecasting (WRF) model simulations of 41 historic HPEs in the eastern Mediterranean are compared with "pseudo global warming" simulations of the same events. This paper presents the changes in rainfall patterns in future storms, decomposed into storms' mean conditional rain rate, duration, and area. A major decrease in rainfall accumulation (-30% averaged across events) is found throughout future HPEs. This decrease results from a substantial reduction of the rain area of storms (-40%) and occurs despite an increase in the mean conditional rain intensity (+15%). The duration of the HPEs decreases (-9%) in future simulations. Regionally maximal 10-min rain rates increase (+22%), whereas over most of the region, long-duration rain rates decrease. The consistency of results across events, driven by varying synoptic conditions, suggests that these changes have low sensitivity to the specific synoptic evolution during the events. Future HPEs in the eastern Mediterranean will therefore likely be drier and more spatiotemporally concentrated, with substantial implications on hydrological outcomes of storms. Plain Language Summary Heavy precipitation events are large storms that can recharge freshwater reservoirs, but can also lead to hazardous outcomes such as flash floods. Therefore, understanding the impacts of climate change on such storms is critical. Here, a weather model similar to those used in weather forecasts is used to simulate heavy precipitation events in the eastern Mediterranean. A large collection of storms is simulated in pairs: (1) historic storms, selected for their high impact, and (2) the same storms placed in a global warming scenario projected for the end of the 21 st century. Using these simulations we ask how present-day storms would look like were they to occur at the warmer end of the 21 st century. The future storms are found to produce much less rainfall compared to their historic counterparts. This decrease in rainfall is attributed mainly to the reduction in the area covered by storms' rainfall, and happens despite increasing rainfall intensities. These results suggest that the region will be drier in the future with larger dry areas during storms; however, over short durations, it would rain more intensely over contracted areas-increasing local hazards associated with heavy precipitation events.

Insect physiology is highly dependent on the environmental temperature, and the relationship can be mathematically defined. 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 most cases, however, these functions/models are laboratory-driven and are based on data from constant-temperature experiments. Therefore, they lack an important optimization and validation steps that test their accuracy under field conditions. Here, we developed a realistic and robust regional framework for modeling the field population dynamics of the global insect pest Bemisia tabaci. First, two non-linear functions, development rate (DR) and female reproduction (EN) were fitted to data collected in constant temperature experiments. Next, nine one-generation field experiments were conducted in order to establish a field-derived database of insect performance, representing a variety of growing conditions (different seasons, regions and host plants). Then, sensitivity analyses were performed for identifying the optimal time-scale for which the running-averaged temperatures should be fed to the model. Setting the time to 6 h (i.e., each of the 24-time steps per day represents the last 6 h average) produced the best fit (RMSD score of 1.59 days, 5.7% of the mean) between the field observations and the model simulations. We hypothesize that the 6 h ‘relevant biological time-scale' captures the insect's physiological memory of daily cycling temperature events. Lastly, we evaluated the potential of the developed modeling framework to serve as a decision support tool in pest-management programs by correlating the model predictions with field-observations of three pest control inspectors during 2019. The model successfully predicted the first notable appearance of the insect in the field (completion of the third generation in May). Also, the model correctly identified the sharp rise in abundance (outbreak point) in mid-July (completion of the fifth generation), and the persistent rise in abundance through August and September. Comparing the simulations of the 2018 and 2019 seasons indicated that the model can also serve as a tool for retrospective systematic assessment of major decisions. Taken together, these data demonstrate the model robustness and its potential to provide an excellent decision-making support platform in regional control of pest species.

Soil erosion affects agricultural landscapes worldwide, threatening food security and ecosystem viability. In arable environments, soil loss is primarily caused by short, intense rainstorms, typically characterized by high spatiotemporal variability. The complexity of erosive events challenges modeling efforts and explicit inclusion of extreme events in long-term risk assessment is missing. This study is intended to bridge this gap by quantifying the discrete and cumulative impacts of rainstorms on plot-scale soil erosion and providing storm-scale erosion risk analyses for a cropland region in northern Israel. Central to our analyses is the coupling of (1) a stochastic rainfall generator able to reproduce extremes down to 5-minute temporal resolutions; (2) a processes-based event-scale cropland erosion model (Dynamic WEPP, DWEPP); and, (3) a state-of-the-art frequency analysis method that explicitly accounts for rainstorms occurrence and properties. To our knowledge, this is the first study in which DWEPP runoff and soil loss are calibrated at the plot-scale on cropland (NSE is 0.82 and 0.79 for event runoff and sediment, respectively). We generated 300-year stochastic simulations of event runoff and sediment yield based on synthetic precipitation time series. Based on this data, the mean annual soil erosion in the study site is 0.1 kg m−2 [1.1 t ha−1]. Results of the risk analysis indicate that individual extreme rainstorms (>50 return period), characterized by high rainfall intensities (30-minute maximal intensity > $\sim$60 mm h−1) and high rainfall depth (>$\sim$200 mm), can trigger soil losses even one order of magnitude higher than the annual mean. The erosion efficiency of these rainstorms is mainly controlled by the short-duration (≤30 min) maximal intensities. The results demonstrate the importance of incorporating the impact of extreme events into soil conservation and management tools. We expect our methodology to be valuable for investigating future changes in soil erosion with changing climate.

Meandering channels and valleys are dominant landscape features on Earth. Their morphology and remnants potentially indicate past base-level fluctuations and changing regional slopes. The prevailing presence of meandering segments in low-slope areas somewhat confuses the physically based relationships between slope and channel meandering. This relationship underlies a fundamental debate: do incised sinuous channels actively develop during steepening of a regional slope, or do they inherit the planform of a preexisting sinuous channel through vertical incision? This question was previously explored through reconstructed evolution of meandering rivers, numerical simulations, and controlled, scaled-down laboratory experiments. Here, we study a rare, field-scale set of a dozen adjacent perennial channels, evolving in recent decades in a homogeneous erodible substrate in response to the Dead Sea level fall (> 30 m over 40 years). These channels are fed by perennial springs and have no drainage basin or previous fluvial history; they initiated straight and transformed into incising meandering channels following the emergence of the preexisting lake bathymetry, which resulted in increased channel lengths and regional slopes at different rates for each channel. This field setting allows testing the impact of changing regional slope on the sinuosity of a stream in the following cases: (a) relatively long and low-gradient shelf-like margins, (b) a sharp increase in the basinward gradient at the shelf-slope transition, and (c) gradually steepening slopes. Under a stable and low valley slope, the channels mainly incise vertically, inheriting a preexisting sinuous pattern. When the regional slope steepens, the channels start to meander, accompanying the vertical incision. The highest sinuosity evolved in the steepest channel, which also developed the deepest and widest valley. These results emphasize the amplifying impact of steepening regional slope on sinuosity. This holds when the flow is confined and chute cutoffs are scarce.

Abstract Orographic impact on extreme subdaily precipitation is critical for risk management but remains insufficiently understood due to complicated atmosphere-orography interactions and large uncertainties. We investigate the problem adopting a framework able to reduce uncertainties and isolate the systematic interaction of Mediterranean cyclones with a regular orographic barrier. The average decrease with elevation reported for hourly extremes is found enhanced at subhourly durations. Tail heaviness of 10-min intensities is negligibly affected by orography, suggesting self-similarity of the distributions at the convective scale. Orography decreases the tail heaviness at longer durations, with a maximum impact around hourly scales. These observations are explained by an orographically induced redistribution of precipitation toward stratiform-like processes, and by the succession of convective cores in multihour extremes. Our results imply a breaking of scale-invariance at subhourly durations, with important implications for natural hazards management in mountainous areas.

Abstract Projections of extreme precipitation based on modern climate models suffer from large uncertainties. Specifically, unresolved physics and natural variability limit the ability of climate models to provide actionable information on impacts and risks at the regional, watershed and city scales relevant for practical applications. Here, we show that the interaction of precipitating systems with local features can constrain the statistical description of extreme precipitation. These observational constraints can be used to project local extremes of low yearly exceedance probability (e.g., 100-year events) using synoptic-scale information from climate models, which is generally represented more accurately than the local scales, and without requiring climate models to explicitly resolve extremes. The novel approach, demonstrated here over the south-eastern Mediterranean, offers a path for improving the predictability of local statistics of extremes in a changing climate, independent of pending improvements in climate models at regional and local scales.

Precipitation extremes and associated hydrological hazards pose a significant global risk to society and economy. To be effective, mitigation strategies require the best possible estimation of the intensity and frequency of precipitation extremes. Traditional approaches to precipitation frequency analysis rely on long-term records from in-situ observations, which are limited in terms of global coverage. Satellite-based precipitation products provide global coverage, but errors in these estimates may lead to large biases in the quantification of extremes. Previous studies have demonstrated the ability of the novel Metastatistical Extreme Value Distribution (MEVD) framework to provide robust estimates of high quantiles in the presence of short-term data records and the uncertainties typical of remote sensing precipitation products. Here, we evaluate MEVD-based precipitation frequency analyses for four widely used quasi-global precipitation products (IMERG-v6, GSMaP-v6, CMORPH-v1.0, and MSWEP-v2) over high-density gauge networks in five hydroclimatic regions (Austria, Italy, Florida, Texas, and Arizona). We show dependence of MEVD-based estimation error on the characteristics of each dataset and the hydroclimatic region. Additionally, we evaluate the sub-grid variability of extreme precipitation and demonstrate the impact of spatial scale mismatch (that is, single in-situ gauge versus satellite pixel) on the frequency analysis of extremes. This work provides an assessment of the use of MEVD for estimating precipitation extremes from globally available datasets and an understanding of the variability of sub-daily precipitation extremes in different hydroclimatic regions of the world.

At site flood frequency analysis (FFA) in arid/semi-arid watersheds poses unique challenges to researchers and practitioners due to the generally limited data records. This study presents a comprehensive evaluation of FFA in arid/semi-arid watersheds in relation to the unique characteristics of these regions, such as the limited number of floods occurring each year and the large variability of the flood peak discharges. Study cases in Israel and the US are examined and compared with non-arid watersheds, characterized by Mediterranean climate, and with synthetic flood records. Results show that the tail of extreme value distributions describing arid/semi-arid watersheds is found to be heavier than the one describing Mediterranean watersheds. The number of yearly floods and the variability of flood peak discharge are shown to have a crucial impact on the accuracy of the quantile estimates with smaller number of events per year and larger coefficient of variation of flood peak discharge being related to larger errors in the estimated quantiles. Partial duration series approach provides a slightly reduced bias in the estimates, but should not be blindly preferred over annual maxima series as it presents comparable estimation uncertainty. In general, the generalized extreme value and the generalized Pareto distribution are found to be non-optimal choices for the examined arid/semi-arid watersheds.

The metastatistical extreme value approach proved promising in the frequency analysis of daily precipitation from ordinary events, outperforming traditional methods based on sampled extremes. However, subdaily applications are currently restrained by two knowledge gaps: It is not known if ordinary events can be consistently examined over durations, and it is not clear to what extent their entire distributions represent extremes. We propose here a unified definition of ordinary events across durations and suggest the simplified metastatistical extreme value formulation for dealing with extremes emerging from the tail, rather than the entire distributions, of ordinary events. This unified framework provides robust estimates of extreme quantiles (\textless10% error on the 100 yr from a 26 yr long record) and allows representations in which ordinary and extreme events share the scaling exponent. Future applications could improve our knowledge of subdaily extreme precipitation and help investigate the impact of local factors and climatic forcing on their frequency.

Abstract Water volume estimates of shallow desert lakes are the basis for water balance calculations, important both for water resource management and paleohydrology/climatology. Water volumes are typically inferred from bathymetry mapping; however, being shallow, ephemeral and remote, bathymetric surveys are scarce in such lakes. We propose a new, remote-sensing based, method to derive the bathymetry of such lakes using the relation between water occurrence, during \textgreater30-yr of optical satellite data, and accurate elevation measurements from the new Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). We demonstrate our method at three locations where we map bathymetries with \~0.3 m error. This method complements other remotely sensed, bathymetry-mapping methods as it can be applied to: (a) complex lake systems with sub-basins, (b) remote lakes with no in-situ records, and (c) flooded lakes. The proposed method can be easily implemented in other shallow lakes as it builds on publically accessible global data sets.

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.