Extreme rainfall events can trigger flash floods that pose serious risks to communities, infrastructure, and critical services, particularly in arid and rapidly urbanizing environments. In the Kingdom of Saudi Arabia, short hydrological response times, strong spatial variability of precipitation, complex topography, and limited observational data significantly challenge flood early warning capabilities, which affect emergency management at the national scale. Addressing these challenges requires integrated and scalable hydro-meteorological forecasting systems capable of operating across large spatial domains while resolving convective weather events and associated localized flood impacts in urban/suburban areas.This study presents a nationwide, operational flash flood early warning system developed for the Kingdom of Saudi Arabia. The system is designed to provide consistent coverage across the country while capturing fine-scale weather, hydrological and hydrodynamic processes relevant to flash flooding in arid environments. It operates over 137 hydrological domains, representing more than 6,000 outlets, delivering 2D flood simulations at a spatial resolution of 30 m nationwide, with enhanced resolution of up to 2.5 m in selected urban areas.The forecasting framework is structured as an end-to-end modeling chain that links atmospheric forcing, hydrological response, hydraulic flood propagation, and infrastructure impacts. High-resolution numerical weather predictions generated by the Weather Research and Forecasting (WRF) model are combined with real-time radar and rain gauge observations to produce hourly ensemble weather and precipitation forecasts and hindcasts. These meteorological inputs drive a distributed hydrological model (CREST), which simulates runoff generation across arid catchments using spatially explicit information on topography, land cover, soil properties, and drainage networks. A reservoir management module is fully integrated within the modeling chain, allowing the system to account for reservoir storage dynamics, controlled releases, and spillway operations, and to assess the influence of dam infrastructure on downstream flood evolution.Hydrological outputs are used as boundary conditions to a two-dimensional hydrodynamic model, which simulates floodplain dynamics, water depths, and inundation extents.All model components are coupled within a WebGIS-based operational platform that displays deterministic and ensemble weather and hydrologic forecasts, probabilistic flood warnings, and real-time nowcasting products. Flood hazard information is delivered through interactive maps, warning levels, and time series, to support decision- making by civil protection authorities and emergency managers at national and local scales.The functionality and operational performance of the system are demonstrated through its application on a recent extreme rainfall and flash flood events that affected the entire region of Saudi Arabia in the period of December 9-16, 2025. The system successfully captured the timing, spatial extent, and severity of flooding across multiple domains, providing useful lead times and high-resolution inundation maps. This case study highlights the robustness, scalability, and operational value of the framework, demonstrating its potential to enhance flood preparedness through early warning, and risk management across the Kingdom of Saudi Arabia under increasing hydro- meteorological extremes.

Power grids across the United States face intensifying strain from extreme weather events, yet quantitative assessments of resilience are hindered by the disconnection between utility reported outage data and meteorological hazard records. Existing datasets either lack spatial granularity or fail to explicitly link disruptions to operational weather warnings used by forecasters. This Data Descriptor presents a harmonized, event-based archive linking county level customer power outage (hereafter called outage) record from the U.S. Department of Energy’s EAGLE-I platform with National Weather Service (NWS) watches and warnings encoded via the Valid Time Event Code (VTEC) system. The dataset spans the contiguous United States from 2015 through 2024 at hourly resolution. We provide: (i) quality-controlled outage intensity metrics with administrative reporting artifacts removed; (ii) a consolidated, spatially joined hazard record; and (iii) a computed "multi-hazard" index. Technical validation demonstrates that the processing workflow preserves statistical distributions of outage magnitude and duration across diverse climate regimes. This dataset enables reproducible hazard attribution and high-resolution benchmarking of grid performance under compound weather stress.

The intricate physical complexity of compound coastal flooding—resulting from the combination of river floods and storm surges—is known for often leading to more severe consequences than independent-driver floods. Damages from this type of flooding are expected to increase due to the impact of climate change on precipitation patterns and coastal storms, coupled with the increasing trends in population growth and economic activities along coastal regions. In the United States, the Federal Emergency Management Agency’s (FEMA) National Flood Insurance Program (NFIP) is the largest provider of flood insurance policies, and currently, more than two million NFIP flood claim transactions (1978 to present) are available to the public for analysis. However, there is a lack of studies that analyze how compound events reflect on insurance claims. In this study, we focus on over 60,000 counties across the entire coastline of the United States to provide an exhaustive analysis of the distribution of economic losses in areas subject to river flooding, coastal flooding, and regions susceptible to compound events. To identify the relative importance of the driving mechanisms (inland vs. coastal flows) for a particular location, we apply a published index [D-Index, readers are referred to the article, https://doi.org/10.1016/j.jhydrol.2023.130278 for details] that is capable of physically attributing the cause of flood depth to either river or coastal drivers, or a combination of both rainfall and storm surge. We focus the analysis on the number of damages reported in the claims, comparing and contrasting claims in counties physically labeled as coastal, river, or compound. By calculating the quantile weight distance (QWD) of the damages from claims in the ‘compound’ counties and claims in the ‘independent-driver’ counties, we further investigate how rainfall and tide characteristics of storm events relate to the NFIP flood claims in the case of compound events. We further quantify differences in QWD by comparing and contrasting FEMA’s high-risk flood zones (identifying the 1-percent annual chance floodplain), where insurance is required for homes financed through federally backed or federally-regulated lenders, and FEMA’s low and moderate-risk flood zones, where flood insurance is not required. In conclusion, this study furnishes invaluable insights into the intricate challenges of assessing compound coastal flooding impacts on insurance claims. The proposed methodology, integrating a flood type-specific mapping system and considering spatial variabilities of inundation characteristics, establishes a robust foundation for a comprehensive and improved flood risk assessment in coastal CONUS. These findings empower coastal communities to proactively manage concealed risks and fortify their resilience against the compounding impacts of environmental forcings. This research offers a proactive and informed strategy to mitigate the potentially disastrous consequences of compound coastal flooding in a changing climate and socio-economic landscape.

High Mountain Asia (HMA) faces heightened vulnerability to natural disasters due to its extreme conditions and the escalating impacts of climate change. Understanding the long-term response of this landscape to hydroclimatic fluctuations is imperative, given the profound effects these changes have on millions of people annually. Heavy rain and the monsoon season bring forth floods and debris flows, resulting in significant damage to crops, infrastructure, and communities and having widespread human impacts. Despite efforts to estimate flood risk locally, traditional techniques often fall short due to the scarcity of high-quality, consistent data, especially in ungauged basins. To overcome this challenge, we propose a novel approach: a geomorphologically guided machine learning (ML) method for mapping flood effects across HMA. Central to our methodology is the life year index (LYI), a systematic measure that quantifies both the financial and the human losses incurred by disasters, specifically for this study fluvial and pluvial flooding. Our model was trained using a dataset comprising over 6000 flood events spanning 1980 to 2020, along with their corresponding 5- and 10-year LYI. Key predictors included (1) 5-year rainfall concentrations derived from ERA5 daily data, (2) a geomorphic classifier based on hydraulic scaling functions derived from high-resolution digital elevation models (DEM), and (3) population density. Results demonstrate the model's effectiveness in identifying flood susceptibility hotspots at a national scale and delineating their evolution from 1980 to 2020. Moreover, the study underscores the severity of hydroclimatic extremes across the entire HMA region. Importantly, the proposed framework is versatile and can be adapted to generate various pluvial and fluvial flood vulnerability and risk maps in ungauged regions.

Floods remain a significant challenge in Saudi Arabia, particularly in rapidly urbanizing and vulnerable regions like Jeddah, Almadinah, and Jazan. This study extends the demonstration of an operational flash flood forecasting system, initially tested during the severe 24 November 2022 event in Jeddah, to the flash floods that struck Makkah, Jeddah and Madinah in January 2025. The system integrates high-resolution numerical weather forecasts from the Weather Research and Forecasting (WRF) model with a distributed hydrological model (CREST) and a 2D hydrodynamic model (HEC-RAS), providing real-time simulations of rainfall-runoff and floodplain dynamics. In these events, the forecasting system demonstrated a satisfactory level of accuracy in predicting flood timing, inundation zones, and peak discharges. The forecasting system consistently provided early warnings, capturing the spatial and temporal characteristics of the floods with remarkable precision. By applying the system across these diverse flood events, this study demonstrates the robustness and scalability of the approach for managing flash flood risks in both urban and rural settings. The outcomes highlight the importance of integrating advanced meteorological and hydrological models to improve flood preparedness and response in Saudi Arabia. This research underscores the need for continued development of forecasting systems to mitigate the impacts of increasingly frequent and intense flash floods across the Kingdom.

Across the United States, power grids are increasingly under strain from extreme weather events, such as heatwaves, high winds, and heavy precipitation, that result in frequent, long-duration and widespread power outages. The strain is intensified when these events are compounded, which amplifies their impact and exacerbates the risk of disruptions. To identify weather variables driving outages and cluster regions based on these variables, we employed a self-organizing map (SOM) approach using county-level outage data from 2015 to 2022, obtained using the U.S. Department of Energy’s EAGLE-I platform, and weather data from the ASOS weather station observations. The findings revealed substantial regional differences in outages resulting from variability in heat, wind, and precipitation. In California, for example, high heat coupled with strong wind gusts led to the most severe outages, while in Texas, the primary contributors were high heat followed by heavy precipitation. This research provides a nuanced understanding of weather-induced power outages, offering a data-driven approach for infrastructure planning and resilience efforts. This study underscores the crucial role of continental weather variability in shaping outage patterns and highlights the necessity for region-specific adaptation strategies to enhance grid resilience, considering the rise in heatwaves and compounding weather events.

Compound coastal flooding, resulting from the coincidence of river flood and storm surge, often leads to more severe consequences than either driver alone. With climate change, the frequency and impact of such events are expected to become more frequent and damaging, particularly in rapidly developing coastal areas. In the U.S., the National Flood Insurance Program (NFIP) administered by the Federal Emergency Management Agency (FEMA), has recorded more than 2 million claims since 1978, yet few studies have examined these claims in the context of compound flooding. This study analyzes NFIP claims data from over 60,000 coastal counties to evaluate economic losses from riverine, coastal, and compound flood (CF) events. We attribute causes of flood using a topography-driven indicator, D-Index (Mitu et al., 2023, ), and compare claim frequencies across coastal, river, and compound zones. We also assess how rainfall and tide characteristics influence claims in compound events and compare high-risk FEMA flood zones with lower-risk areas. Across the U.S. coastal regions, counties prone to CF events are associated with higher payouts than counties mostly affected by single flood drivers. Additionally, we find that nearly 50% of coastal counties experience flood damage and claims outside FEMA's Special Flood Hazard Area (SFHA). Our analysis also shows that SFHA underrepresent CF risk for 22%-40% of the counties located in different regions of the coastal U.S. Through this proposed framework, our study offers a robust foundation for improving coastal flood risk assessment to help coastal communities better manage risks associated with compound flooding events.

This study investigated the variability of agricultural drought severity, as depicted by vegetation indices, and the bias in identifying drought events when considering a stationary vs nonstationary climate reference. The work leveraged gridded climate data (NCEP CFSv2, CHIRPS 1981–2022), soil properties (OpenLandMap), satellite imagery (Sentinel2/Landsat, 2000–2022), and future climate projections (NEX-GDDP, 2050) together with local knowledge of selected farms, to augment drought monitoring techniques and identify potential issues for agriculture. For the study domain, significant differences were observed when comparing drought characteristics using stationary and nonstationary drought indexes, with biases being not ubiquitous in either space or time of year. When developing sustainable drought mitigation and adaptation strategies, decision-makers should carefully address this uncertainty to avoid a possible underestimation of drought magnitude. Results showed a drought increase (∼50%) by the mid and late twenty-first century. Projection of future climate highlighted an even more significant impact (∼80%) with a wide variability of risk across the domain. As drought impact was also related to soil organic carbon (SOC), our results suggest that improving SOC content could be a sustainable strategy for enhancing soil drought resilience, especially in areas commonly characterized by low concentrations of organic carbon and nutrients. The analysis highlighted that drought impacts were also modulated by investment in irrigation infrastructure and irrigation efficiency. Researchers and land managers could apply the proposed analysis design to address historical, current and future indicators of vegetation conditions within irrigated regions. By providing spatio-temporal information on the patterns of drought impacts and their bias, this study supports identifying priority regions for targeted drought risk reduction and adaptation options, including water resources and soil management sustainability criteria, to move towards more resilient agricultural systems.

Monitoring crop responses to drought is crucial for understanding the progressive impact of drought on food production and identifying management practices that can enhance agricultural resilience. This study combined drone-based multispectral data (MDd) with laboratory determination over multiple pilot farms to identify the main soil physical and chemical parameters correlated with a crop health index (SVI- Standardized Vegetation Index), which compares the Normalized Difference Vegetation Index (NDVI) at the observed time to historical (NDVI at similar dates in previous years) values.Significant relationships were found between MDd and selected soil properties for different crops. Differences found at the plot scale were primarily related to texture, organic carbon and total nitrogen content, resulting in heterogeneous responses to droughts. The performance of the proposed indicators was further validated for the same crops by extending the findings across similar climatic regions in Europe, using satellite-based multispectral data (MDs) and field-based soil data from LUCAS (Land Use/Cover Area frame statistical Survey Soil) for 2018, as well as MDs and digital soil data from SoilGrids 2.0 for 2022. Varying drought magnitudes were also considered. The method effectively identified drought-prone areas and distinguished crop health across varying drought intensities, making it a valuable tool for drought monitoring and agricultural planning.

Flooding is predicted to become more frequent in the coming decades because of global climate change. Recent literature has highlighted the importance of river morphodynamics in controlling flood hazards at the local scale. Abrupt and short-term geomorphic changes can occur after major flood-inducing storms. However, there is still a widespread lack of ability to foresee where and when substantial geomorphic changes will occur, as well as their ramifications for future flood hazards. This study sought to gain an understanding of the implications of major storm events for future flood hazards. For this purpose, we developed self-organizing maps (SOMs) to predict post-storm changes in stage–discharge relationships, based on storm characteristics and watershed properties at 3101 stream gages across the contiguous United States (CONUS). We tested and verified a machine learning (ML) model and its feasibility to (1) highlight the variability of geomorphic responses to flood-inducing storms across various climatic and geomorphologic regions across CONUS and (2) understand the impact of these storms on the stage–discharge relationships at gaged sites as a proxy for changes in flood hazard. The established model allows us to select rivers with stage–discharge relationships that are more prone to change after flood-inducing storms, for which flood recurrence intervals should be revised regularly so that hazard assessment can be up to date with the changing conditions. Results from the model show that, even though post-storm changes in channel conveyance are widespread, the impacts on flood hazard vary across CONUS. The influence of channel conveyance variability on flood risk depends on various hydrologic, geomorphologic, and atmospheric parameters characterizing a particular landscape or storm. The proposed framework can serve as a basis for incorporating channel conveyance adjustments into flood hazard assessment.

Saudi Arabia is threatened by recurrent flash floods caused by extreme precipitation events. To mitigate the risks associated with these natural disasters, we implemented an advanced nationwide flash flood forecast system, boosting disaster preparedness and response. A noteworthy feature of this system is its national-scale operational approach, providing comprehensive coverage across the entire country. Using cutting-edge technology, the setup incorporates a state-of-the-art, three-component system that couples an atmospheric model with hydrological and hydrodynamic models to enable the prediction of precipitation patterns and their potential impacts on local communities. This paper showcases the system’s effectiveness during an extreme precipitation event that struck Jeddah on 24 November 2022. The event, recorded as the heaviest rainfall in the region’s history, led to widespread flash floods, highlighting the critical need for accurate and timely forecasting. The flash flood forecast system proved to be an effective tool, enabling authorities to issue warnings well before the flooding, allowing residents to take precautionary measures, and allowing emergency responders to mobilize resources effectively.

Storm surge and river runoff can result in compound flooding in coastal areas. The impact of these events can be more significant than that generated by each component individually. This paper describes a framework for characterizing flooding during compound events and quantifying the relative importance of surge and river flows in controlling inundation depth (ID). For the analysis, we considered 1051 simulations of historical flood events covering about 40 years in Connecticut (CT), USA, and several simulated storms associated with synthetic climate scenarios. We simulated river discharge time series for each event using a physically based distributed hydrological model and retrieved the storm surge from tidal stations. These time series are used as upstream and downstream boundary conditions in 2D hydrodynamic simulation. We focused the analysis on seven locations along the coast of Connecticut covered by LiDAR-derived 1 m DEM. To capture the variability of inundation characteristics over the full-scale gradient from river to coast, we introduced a new topographic index, D-Index, that distinguishes topologic variabilities. Results from this study highlight that there is a correlation of ID to different drivers in distinct categories of the D-Index, which can correctly label the main source of hazard, either coast, river, or compound. We identified thresholds of standard deviation (sigma) of the D-Index to identify areas where ID strongly correlates with either flow peak and volume, surge peak, or a combination of both. The study also shows that it is possible to use the results obtained from the 1 m analysis to generalize the findings for the entire CT coast to derive zones dominated by surge, river flow, or the compound effect of the two. This study can help coastal communities better understand the risk of the compound impacts of coastal storms, which is essential for increasing climate resilience.

Currently, grassland ecosystems' stability is facing severe challenges due to climate variability and the increased frequency and intensity of extreme droughts. In many grasslands worldwide, closed‐basin ponds supply various ecosystem services by collecting rainfall and runoff. These ponds may provide a significant opportunity to mitigate climate change pressures and improve the resilience of grasslands. Nevertheless, their conservation is hampered by problems auditing these small and dynamic features. Understanding the dynamics that characterise the interactions of ponds and grassland vegetation with climate changes is, therefore, a priority for better quantifying future impacts of climate change on managed‐land systems. This study investigated variations of grassland vegetation quality from 2001 to 2021 in Lessinia Regional Park, a pre‐alpine historical rural landscape in northern Italy. We combined multi‐source data, weather information, and satellite‐derived vegetation quality indices. The work focuses the analysis using a ‘pond landscape dominance’ index, calculated as a proxy of hydrologic connectivity, to illustrate the function of ponds in regulating vegetation. We provided an unprecedented mapping of 459 ponds across the whole area. The results show a progressively decreasing vegetation quality in Lessinia, following rainfall and temperature variability. Overall, however, the analysis highlighted the benefit of water extent and closeness to ponds in providing higher resistance of grasslands against drought, especially during the driest study years. These results demonstrate that this approach has great potential for monitoring the resistance of grasslands in response to drought over large regions. This study supports the importance of identifying multiple co‐benefits and ecosystem services related to ponds, which can help communities cope with climate change and environmental challenges.

Abstract. The exposure of High Mountain Asia (HMA) to disaster risks is heightened by extreme weather conditions and the impacts of climate change. Obtaining knowledge about the long-term response of the landscape to hydroclimatic variations in HMA is paramount, as millions of people are affected by these changes every year. During monsoons, substantial human suffering, and damage to crops and infrastructure in populated communities result from the flooding and debris flow caused by the increase in precipitation extremes each year. Although a few initiatives have undertaken the estimation of flood risk locally, the use of traditional techniques in ungauged basins is, unfortunately, not always possible because of the lack of extensive data required. To address this problem, we present in this study a geomorphologically guided machine learning (ML) approach for mapping flood impacts across HMA. We defined socioeconomic flood impact using the Lifeyears Index (LYI), a systematic index that measures the economic cost and loss of life caused by flooding. This index quantifies the importance of the destruction to infrastructure, capital, and housing in an overall assessment. We trained the proposed model with over 6000 flood events, from 1980 to 2020, and their computed five-year and ten-year LYIs. We used as predictors, (1) the five-year rainfall concentrations (which correlate the magnitude of precipitation events with the time of occurrence) of events retrieved from ERA5 daily data; (2) a geomorphic classifier (flood geomorphic potential) based on hydraulic scaling functions automatically derived from an 8 and 30-meter digital elevation model (DEM) for the region and (3) population. This model proved capable of identifying the hotspots of flood susceptibility on a national scale and showing its variability from 1980 to 2022. The study also highlights the severity of the impacts of hydroclimatic extremes in the entire HMA region. The framework is generic and can be used to derive a wide variety of flood vulnerability and subsequent risk maps in data-scarce regions.

Monitoring crop physiological responses to drought is crucial to understand progressive impacts on food production and identify resilient and sustainable irrigation practices. Although many climate research experiments provide valuable data, long-term measurements of soil properties and plant characteristics at the field scale are not always affordable. We combined drone-based multispectral remote sensing with measurements of soil properties over multiple pilot farms, where soil sampling was performed for each plot during the drone survey. Our goal was to determine if drone-based indices capture drought stress responses of different crops (maize and sugar beet) and whether responses are affected by soil physical and chemical characteristics (e.g., texture, density, porosity, moisture, pH, electrical conductivity, organic carbon and total nitrogen contents, availability of micro and macro nutrients). Significant relationships were found between vegetation indices and soil features for different crop types. Differences found at the field scale were related mostly to organic carbon content and resulted in heterogeneous responses to irrigation practices. Our spatial variability analysis pointed out an overall homogeneous response for areas submitted to severe and moderate drought having similar soil properties, independently of the crop type. More investigation is needed to address the possible effect of local practices (e.g., fertilization, amendment, tillage) at the field scale. The feasibility of carrying out systematic drone flights coincidentally or close to-ground campaigns will reveal the consistency of the observed spatial patterns in the long run.

•This research considers the variability of daily concentrations of precipitation, flows and evapotranspiration.•5th and 95th percentiles show precipitation deficit and extremes respectively in time.•For floods and hydrological droughts, we identify the thresholds at the 95th and 5th percentiles of river flows respectively.•Precipitation deficit, precipitation extremes and river flood show latitudinal and longitudinal variability.•Gini captures spatio-temporal uniformity of flood events better than the hydrological drought.

<p>Compound floods, particularly in estuaries and coastal areas, are gaining increasing attention among the recent extreme climatic events. Understanding which driver dominates inundation depth (ID) is still an open question. In this study, a detailed and extended assessment of flood damages from 2009 to 2022 is conducted across the USA coast, based on the National Flood Insurance Program (NFIP) insurance claims records and historical storm events that occurred during the corresponding period.</p> <p>To identify the relative importance of the driving mechanisms (inland vs. coastal flows) for a particular location, we propose an index [hereafter named D-Index] that identifies the topology of the local draining potentials to either the closest river, or to the coast. The D-Index captures the topographic control over ID, and it considers the vertical hydrologic distance between a location and its nearest water body, either a river stream, or the coastline.</p> <p>The D-index was initially developed and validated considering 1051 simulations of historical flood events covering a time span of 40 years in Connecticut (CT), USA, and several simulated storms associated with future climate scenarios. For the analysis, we simulated river discharge time series for each event using a physically based distributed hydrological model and retrieved the storm surge from tidal stations. These time series are used as upstream and downstream boundary conditions for 2D hydrodynamic simulations. We focused the analysis on seven locations along the coast of CT for which we had available LIDAR-derived 1m DEM. To capture the variability of inundation characteristics over the full-scale gradient from river to coast, we highlight the correlation of ID to different drivers in distinct categories of the D-Index. We identified thresholds of standard deviation of the D-Index to identify areas where ID strongly correlates with either of the flood drivers. For validation, we demonstrated that it is possible to use the results obtained from the 1 m analysis to generalize the findings using coarser (still high quality) resolution DEM for the entire CT coast to derive zones dominated by surge, river flow, or the compound effect of both. The areas mapped as surge dominated based on the D-index overlap well with the SLOSH ranking.</p> <p>We demonstrated the actual impacts of major events, e.g., Irene (2011) and Sandy (2012), to analyze the differences in the corresponding claims data by detecting the underlying flood drivers. To date, the claim records have been investigated based on individual drivers, for example flood caused either by excessive river flow or by coastal flooding. Hence, it is crucial to assess how compound flooding reflect on insurance flood claim records. The results obtained in this study demonstrate the potential of integrating a flood type-specific mapping system into a compound flood impact estimation. The outcome of this study will be helpful for the coastal communities to better understand their risk to the compounding impacts of various environmental forcings (heavy precipitation, surge, and the effect of sea level rise), which is important for increasing their resilience to future compound flooding events.</p>

Predicting flash floods in the arid region of the Arabia Peninsula poses unique challenges to researchers and practitioners due to the generally limited data records and field observations. The rapid onset of these events hinders mitigation measures and limits timely decisions, resulting in fatalities and property losses. To improve our predictive capability, we deployed a flash flood forecasting system that integrates numerical weather forecasts from the Weather Research and Forecasting (WRF) model with a distributed hydrological model, the Coupled Routing and Excess STorage (CREST), and a 2D hydrodynamic model (HEC-RAS). The atmospheric component runs at cloud-resolving scales (1.6 km) to incorporate local features and strong convection. The hydrological and hydrodynamic models run at variable spatiotemporal resolution: the rainfall-runoff generation runs at 500 meter-by-hourly, routing at 30 meter-by-hourly, while the floodplain dynamics are computed at 30 meter-by-hourly. The significant differences in computational demands dictate the domain differences: CREST runs over large natural basins while HEC-RAS runs over small urban sub-basins associated with dense infrastructures and exposure.The effectiveness of the operational national scale flash flood forecasting system is evaluated in this study for the extreme precipitation event that hit Jeddah on 24 November 2022. The event was the heaviest ever recorded in the area, causing widespread flash floods across Jeddah's urban and rural areas.The atmospheric component forecast is compared to the NASA satellite precipitation product (IMERG Late) and radar-rainfall estimates that were bias-adjusted based on in situ gauge observations. Since no hydrological observations were available to the authors for this event, discharge obtained from the gauge-adjusted radar-rainfall data, which represents the benchmark precipitation, was used as a reference to assess the skill of the WRF-based flood forecasts. Finally, the effectiveness of the warning system was compared to reported localized flood incidents at the street or neighborhood level by the public ('crowd source').The results of this study reveal an excellent temporal and spatial agreement between the forecasted precipitation from WRF and the bias-adjusted radar-rainfall estimates. The same conclusions cannot be drawn for the IMERG Late data. The satellite product seems to overestimate precipitation in most cases, which is consistent with the findings of several prior satellite validation studies. Comparing the flood quantiles for the Nov. 24th flood event indicates that the WRF-driven flood peak discharge properties agree with the radar-based ones. The differences between the flood characteristics (hydrographs peak, timing, and flood volume) when using WRF-forecasted versus radar-based benchmark precipitation were also minimal. The simulated flood inundation could capture the broad patterns of inundated areas at the city level: a high degree of agreement was reached, and more than 95% of the reported incidents across the city districts fell within the forecasted high or extreme warnings provided by the operational system on Nov. 23rd, at 12.30; therefore, more than 12 hours ahead. The importance of the study comes from the fact that it provides an effective solution and a state-of-the-art methodology to forecast such types of extreme rainfall events, which can cause major flash floods in the urban areas of Saudi Arabia.

Drought and surface water ponding (DSP) are one of the major natural hazards affecting crop production, especially in low-land irrigated areas. This work focus on an irrigated area in north-eastern Italy, a territory of about 400k ha, part of the central Veneto, where water demands is met through a mechanical and well-regulated widespread distribution of water resources. For this complex landscape, reliance on weather data alone is not sufficient to monitor areas of DSP, particularly when these data can be i) untimely, sparse, and incomplete, and ii) water inflows are mechanically controlled, with varying flow exchanges, not necessarily reflecting climatic fluctuations. Augmenting climatic data with satellite images to identify the location and severity of DSP phenomena, therefore, is a must for complete, up-to-date, and comprehensive coverage of current crop conditions. The objective of this research is to apply and standardize open source data to augment DSP-monitoring techniques. The study was conducted with 5 years (2015-2021) of Sentinel2-10m satellite images. Z-scores of the NDVI distribution are used to estimate the probability of occurrence of the present vegetation condition at a given location relative to the possible range of vegetative vigor, historically. This information is coupled with soil data, topographic information, and accurate information on the system water fluxes, to identify and target locations more susceptible to DSP. Findings indicate that the framework, along with other monitoring tools, is useful for assessing the extent and severity of DSP at a spatial resolution of 10m. The framework is capable of providing a near-real-time indicator of vegetation conditions within irrigated regions, and, more specifically, areas of varying water management conditions. The present study is founded by the Consorzio LEB, Cologna Veneta, Italy