Integrated Rainfall Modeling and Hydrometeorological Alert System for Extreme Flooding in Kosovo

Integrated Rainfall Modeling and Hydrometeorological Alert System for Extreme Flooding in Kosovo | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated Rainfall Modeling and Hydrometeorological Alert System for Extreme Flooding in Kosovo Lavdim Osmanaj, Ivica Milevski, Bojana Aleksova This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6917578/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 6 You are reading this latest preprint version Abstract Flash floods pose a significant threat to infrastructure and public safety in Kosovo, particularly in urban and river environments. This study analyzes a winter flash flood event that struck the small catchment of Skenderaj on January 19, 2023. The main objective was to develop a flash flood warning system by integrating high-resolution atmospheric modeling, hydrological simulations, and hydrometeorological hazard modeling. A 2-km nested configuration of the ARW model successfully captured the spatial distribution of intense rainfall and localized flooding. The integration of GloFAS forecasts and ERA5 reanalysis data enhanced flood risk assessment and comparative analysis with the EFAS platform, revealing strong agreement in identifying high-risk zones and thereby validating the reliability of the developed system. Additionally, the Flash Flood Potential Index (FFPI) enabled high-resolution mapping of flash flood susceptibility using topographic, soil, land cover, vegetation, and satellite-derived precipitation data. The results for the Klina catchment revealed the precise identification of high-risk zones, allowing for both retrospective and predictive discharge estimation. This geospatially driven approach provides a scalable and operationally viable tool for enhancing regional early warning systems. These findings underscore the effectiveness of coupled weather-hydrology models and geospatial risk assessment tools in enhancing early warning systems, with potential for broader application and further refinement through sensitivity studies. flash flood heavy rain FFPI GEE Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 SIGNIFICANCE STATEMENT This study enhances flash flood prediction in Kosovo by integrating high-resolution atmospheric models (ARW and NMM), hydrological simulations, and hydrometeorological modeling. Focusing on the 2023 floods in Skenderaj and Peja, this analysis demonstrates the ARW model’s accuracy in capturing localized rainfall intensity. Meanwhile, GloFAS and ERA5 data enhance risk assessment by incorporating soil moisture and snowmelt factors. The use of NOTHAS and GIS-based FFPI analysis enables precise identification of high-risk zones. These findings highlight the importance of integrating weather models, hydrology, and geomorphology in flood risk management, providing a scalable framework for early warning systems across Southeastern Europe. Introduction Climate change has intensified extreme weather events globally, including flash floods, which pose severe risks to life, infrastructure, and economies, especially in regions with complex terrain and limited hydrological infrastructure (IPCC, 2021 ; Trenberth, 2011 ; Field et al., 2012; Kundzewicz et al., 2014 ). Characterized by rapid onset and high destructiveness, flash floods are typically triggered by short-duration, high-intensity rainfall events (Fernández and Lutz, 2010 ; Aleksova et al. 2024a , b ). In Kosovo and the broader Balkan Peninsula, convective storms during the warm season are increasingly producing extreme rainfall and flooding (Osmanaj et al., 2023a, b). These evolving hydrometeorological hazards underscore the need for improved forecasting and early warning systems (UNDRR, 2022; Alfieri et al., 2016; Aleksova et al. 2025 ). Numerical Weather Prediction (NWP) tools such as the WRF model are widely used for simulating convective storms and heavy precipitation. However, flash flood modeling remains challenging due to the complex interplay between atmospheric processes and hydrology (Skamarock et al., 2008 ; Lee and Hong, 2006 ; Spiridonov et al., 2020 , 2023 ). Coupling meteorological models with hydrological simulations improves forecasting accuracy by converting rainfall predictions into surface runoff and streamflow estimates (Varlas et al., 2023 ; Zaldi et al., 2022). Global early warning systems like GloFAS and EFAS use real-time meteorological and hydrological data, often assimilating ERA5 reanalysis from ECMWF for model initialization and validation (Alfieri et al., 2013 ; Thielen et al., 2009 ; Harrigan et al., 2020 ; Hersbach et al., 2020 ). Yet, in data-scarce regions like Kosovo, sparse monitoring limits model performance (Hapuarachchi et al., 2011 ; Cluckie & Han, 2000 ). Integration of satellite data such as GPM-IMERG, radar, and GIS tools shows potential for improving flood detection and risk mapping (Giannaros et al., 2022; Aleksova et al. 2024c ). Kosovo experienced two major floods in 2023, including a devastating event on January 19 that struck Skenderaj. These events, exacerbated by climate change and inadequate land-use planning, necessitate the implementation of integrated risk assessments and real-time warning systems. This study evaluates a high-resolution WRF simulation of the January 19 flood, combined with a severe weather alert tool (NOTHAS) and geospatial flood susceptibility mapping. The goal is to improve flood forecasting and inform the development of a scalable, real-time early warning system for Kosovo. The article is structured as follows: Section 2 provides an observational overview of the event, Section 3 details the numerical modeling framework, Section 4 presents simulation results and validates them against observational data, and Section 5 concludes with recommendations for future flood risk management strategies. Methodology The main objectives of this research are: To evaluate the most suitable model configuration for accurately simulating the atmospheric behavior and physical processes associated with torrential rainfall over Kosovo. To develop a prototype for an advanced forecast and hydrometeorological alert tool. To address these objectives, a series of high-resolution simulations were performed using the Weather Research and Forecasting (WRF) model. This included sensitivity testing of physical schemes and resolutions, using ARW triple nested (Kain et al., 2006; Lee & Hong, 2006 ; Han & Hong, 2018 ). Hourly warnings and rainfall outputs were compared with historical observations and flash-flood guidance thresholds (Spiridonov et al., 2020 ; Zaidi et al., 2022 ). a. Meteorological Model Design The WRF model was selected for its robust treatment of atmospheric processes and flexibility in spatial and physical configurations. It supports both single- and nested-domain simulations and includes a wide range of physics options, such as the Thompson microphysics scheme (Thompson et al., 2008 ; Eidhammer & Thompson, 2014 ), Yonsei University PBL (Hong, 2010), Monin-Obukhov surface layer physics (Janjic, 1996), RRTM longwave radiation (Mlawer et al., 1997 ), and Dudhia shortwave radiation (Dudhia, 1989 ). The ARW core (Skamarock et al., 2008 a) was applied in a triple-nested configuration (18 × 6 × 2 km), optimized for resolving convective-scale dynamics over Kosovo. The ARW nested run was configured with time steps of 30, 10, and 3.3 seconds across the three domains, The experiment explored variations in microphysics (e.g., WSM6, Ferrier), cumulus schemes (e.g., Shin & Hong, 2015 ), and vertical levels (32 layers), consistent with prior optimization studies (Chinta et al., 2021 ; Misenis & Zhang, 2010 ; Chawla et al., 2018 ). All simulations were initialized with GDAS/FNL 0.25° global datasets, updated every 6 hours (Elmore et al., 2002 ; Bernadet et al., 2000 ; Levit et al., 2006 ). A spin-up period of 18 hours preceded each run, allowing the model to stabilize before the targeted flood event onset (Jankov et al., 2007 ; Liu et al., 2021 ). b. Methodology for Flash Flood Potential Index (FFPI) modeling Flash floods often occur in small catchments over short durations, necessitating localized modeling beyond the scope of global datasets. This study focuses on Kosovo’s Klina catchment, which experienced a severe flash flood event in January 2023. Recent advancements in remote sensing, GIS, and hydrological modeling have significantly improved flash flood prediction (Pradhan 2009 ; Giustarini et al. 2015 ; Gigović et al. 2017 ). A wide range of methods have been used for flood susceptibility mapping, including multicriteria evaluation (Balogun et al. 2015 ), decision tree analysis (Tehrany et al. 2013 ; Pulvirenti et al. 2011 ), artificial neural networks (Campolo et al. 2003 ), frequency ratio (Rahmati et al. 2016 ), logistic regression (Cao et al. 2019), and support vector machines (Nandi et al. 2016 ). Combining machine learning with multi-criteria decision-making (MCDM) techniques has further enhanced model performance (Zhang et al. 2018 ; Khan et al. 2019 ; Pham et al. 2017 ). In this study, the Flash Flood Potential Index (FFPI)—a method widely used across Europe (Gómez and Kavzoglu 2005 ; Gaume and Borga 2008 )—was applied using a refined formulation adapted from Smith ( 2010 ). The modified FFPI incorporates terrain and land surface parameters and is expressed as: $$\:FFPI=\frac{\left(1.5\bullet\:M+1.05\bullet\:+S+1.25\bullet\:V+1.1\bullet\:E\right)}{5}$$ 1 where M is the slope derived from the 30-m Copernicus GLO-30 DEM (ESA, 2020), calculated as: $$\:M=\frac{10n}{30}$$ 2 L represents land cover class from ESA World Cover (2021), weighted by runoff risk (Panagos et al. 2015 ; Zanaga et al. 2021 ); S = Soil type based on clay content from SoilGrids (Poggio et al. 2021 ); V = Vegetation density, derived using the Bare Soil Index (BSI) from Sentinel-2 bands (Gorelick et al. 2017 ; Yue et al. 2022 ; USGS 2024; Milevski et al. 2024 ), given by: $$\:BSI=\frac{B11+B04-B08-B02}{B11+B04+B08+B02}$$ 3 E is soil erodibility, calculated as the K-factor from the Universal Soil Loss Equation (USLE; Wischmeier & Smith, 1978 ; Renard et al., 1997 ), using: $$\:K=\frac{\left[2.1x{10}^{-4}\bullet\:{M}^{1.14}\bullet\:\left(12-OM\right)+3.25\bullet\:\left(S-2\right)+2.5\bullet\:\left(P-3\right)\right]}{100}$$ 4 Each contributing factor was normalized to a 1–10 scale to maintain consistency and comparability. The inclusion of the K-factor enhanced sensitivity to soil vulnerability by incorporating both textural and structural properties influencing runoff generation. To ensure spatial alignment, all input raster layers—originally ranging from 10 m to 250 m resolution were resampled to a common 30 m resolution and projected to the UTM coordinate system. The final FFPI quantifies flash flood susceptibility by integrating topographic, hydrological, and land surface properties. This composite index supports the identification of vulnerable catchments, informing long-term flood risk assessment and management strategies. Figure 2 illustrates the spatial distribution of key FFPI parameters across Kosovo, including the Klina catchment. Results a. Simulation of rainfall patterns The Advanced Research WRF (ARW) model was configured with a triple-nested domain, using horizontal grid spacings of 18 km, 6 km, and 2 km. For the 18-km and 6-km domains, the simulation incorporated the Thompson-Eidhammer microphysics scheme (2014), the Yonsei University (YSU) planetary boundary layer scheme (Hong, 2010), and a scale- and aerosol-aware convective parameterization scheme (Shin and Hong, 2015 ; Han et al., 2017). The innermost 2 km domain explicitly resolved convection, eliminating the need for convective parameterization. The simulation results primarily focus on total accumulated precipitation over 24-, 48-, and 60-hour periods, as shown in Fig. 3 . The 2-km nested run provided the most detailed and accurate representation of rainfall distribution across Kosovo. The highest precipitation totals were recorded between January 19 at 00 UTC and January 20 at 00 UTC, with the northwestern regions of Kosovo experiencing the most significant rainfall. In Istog, total precipitation exceeded 130 mm, while in Skenderaj—where severe flooding was reported—the accumulated precipitation reached 44.1 mm. The interpretation of these results starts with an analysis of the total precipitation amounts over 24, 48, and 60 hours, derived from the ARW triple-nested run (Fig. 3 ). By comparison, the 6-km simulation captured the general spatial distribution of rainfall but substantially underestimated precipitation totals, with 55.4 mm recorded for Istog and 33.6 mm for Skenderaj. This discrepancy emphasizes the added value of convection-permitting simulations in resolving localized precipitation maxima during convective events. Figure 4 presents the hourly rainfall time series from the ARW 2-km run, which reveals two distinct peaks in Istog: one during the early morning of January 18 (05–06 UTC) and another on January 19 (10–11 UTC), coinciding with the main rainfall event. In Skenderaj, moderate intensity peaks occurred on January 18 between 07:00 and 09:00 UTC, with hourly rates ranging from 4 to 6.4 mm/h. Although the 6-km simulation identified the timing of these events, it consistently underestimated the magnitude of hourly rainfall. During the first 24 hours of the simulation, significant precipitation was confined to the far western mountainous areas of Kosovo. A notable increase in rainfall occurred over the subsequent 24–to 48-hour period. The highest 60-hour accumulation was simulated near Pristina (~ 59.2 mm), while the total in Skenderaj (42.2 mm) closely matched the value from the 2-km run, further confirming the reliability of the high-resolution configuration in accurately capturing event-specific hydrometeorological conditions. b. Hydrological Evaluations This study examines river discharge data from Kosovo's hydrological stations alongside ERA5 reanalysis data from ECMWF to analyze runoff dynamics and assess flood risks. The data, provided by the Global Flood Awareness System (GloFAS), consists of gridded daily hydrological time series driven by meteorological reanalysis data. It provides accurate representations of key hydrological variables, including river discharge, soil moisture, snow water equivalent, and total runoff (both surface and subsurface). The data is derived from the LISFLOOD hydrological model, which operates at a 24-hour timestep with a spatial resolution of 0.05° × 0.05° in its latest iteration. GloFAS provides both near-real-time (ERA5) and consolidated reanalysis data, enabling medium-range and seasonal flood forecasting. By integrating this dataset with observational records, the study enhances the accuracy of flood risk assessments and supports the development of more effective mitigation strategies. The highest river discharge relative to the mean flow occurred on the Black Drim River in northern Albania, coinciding with the region that received the highest accumulated precipitation during the 60-hour simulation period (see Fig. 5 ). On January 19, 2023, the upstream section of the river exceeded a discharge of 1,100 m³/s, which was sustained until January 20, before decreasing to a peak of approximately 937 m³/s on January 21. Between January 18 and 21, 2023, accumulated precipitation from both stratiform and convective rainfall events contributed to an increase in river discharge across Kosovo, albeit at lower magnitudes. The White Drim River reached its peak discharge on January 20, with values ranging between 400 and 450 m³/s. Similarly, the Ibar River in northern Kosovo recorded flows ranging from 350 to 400 m³/s. More detailed insights into the flow rates of rivers in Kosovo during this period can be derived from the high-resolution water discharge forecasts provided by ECMWF as shown in Fig. 6 . The Skenderaj watershed, situated downstream along the Ibar River in western Kosovo, does not directly influence flood genesis; however, it requires further hydrological model analysis for a comprehensive understanding of flood dynamics. The highest runoff water equivalent was observed in the western mountainous regions, with peaks in the southwest. Runoff notably increased on January 20 due to precipitation in the Mitrovica region, reaching 10 kg/m². Snow water equivalent analysis identified two regions with the highest values: the northwestern mountainous areas along the Albanian and Montenegrin borders, and the southern region near Brezovica. Soil moisture was highest in western Kosovo, with values ranging from 0.8 to 0.9 in the north and around 0.7 in flood-prone Skenderaj, indicating highly saturated soil from January 18 to 21, 2023. These factors underscore the intricate relationship between precipitation, runoff, snowmelt, and soil saturation in flood risk assessment. c. Assessment of Flash Flood Potential using FFPI In contrast to previous models, which provide broader and coarser-scale (regional to national) estimates of flash flood probability and risks, the FFPI serves as a critical indicator of catchment-based flash flood susceptibility. The calculated FFPI values for Kosovo range from 1.8 to 8.2, with a mean value of 4.7, indicating significant variability in flood risk. Using SAGA GIS software (version 9.7.0) and the Natural Breaks classification method, the FFPI values are categorized into five classes, ranging from 1 (very low susceptibility) to 5 (very high susceptibility), for flash floods. To better assess flash flood susceptibility, a map of average FFPI values per small catchment was created, derived from the GLO-30 DEM. Across Kosovo, 2,114 small catchments, with areas ranging from 30 km² to 100 km², were identified and used as the basis for assessing flash floods (Fig. 7 ). Using the Natural Breaks (Jenks) classification, these catchments were categorized according to their average FFPI values. Calculating the average FFPI per catchment is an effective method for assessing flash flood susceptibility, particularly for regional or watershed-scale analyses. This approach aligns well with hydrological processes, as catchments (or watersheds) are natural units for studying water flow and flood dynamics. Catchments are defined by topography and drainage patterns, making them ideal for flash flood analysis. Averaging the FFPI per catchment integrates spatial variability (e.g., soil properties, land cover, slope) into a single metric, reflecting the overall susceptibility of the catchment to flash floods. This method captures the cumulative effect of runoff generation and flow concentration within a catchment, which is critical for accurate flash flood prediction. The same procedure is applied to the Klina catchment upstream of Skenderaj, which covers an area of 147.2 km². The catchment elevation ranges from 1137 m in the northwestern part to 559 m in the southern downstream area near Skenderaj, with an average elevation of 751 m and a mean slope of 12.1° (21.5%). Following the outlined procedure, the Klina catchment is divided into 273 sub-catchments, each ranging in size from 0.3 to 3.3 km². The map in Fig. 9 clearly shows that the source area of the Klina River catchment, located above 700 m, as well as the regions to the west and east of Skenderaj, are highly susceptible to flash floods. Of the 273 sub-catchments, 98 (covering 40.2 km²) have a mean FFPI value below the average ( 5.5). During the heavy rainfall event on January 19, 2023, the main flood originated from the high-FFPI sub-catchments to the west and east of Skenderaj. The next step involves calculating the peak discharge within these catchments during the flood event, for which precipitation data is required. River discharge is influenced by precipitation that contributes to runoff within a catchment, and the FFPI serves as an indicator of runoff potential. Although FFPI is not directly used in discharge calculations, it can adjust runoff coefficients based on landscape susceptibility to flash floods. River discharge (Q, in m³/s) is the volume of water flowing through a river per unit time, driven by precipitation (P), which becomes runoff after accounting for losses such as infiltration and evaporation. The FFPI reflects runoff potential based on factors like soil erodibility (K-factor), clay content, land cover, vegetation density, and slope. This information helps estimate the runoff coefficient (C), which represents the fraction of precipitation that becomes surface runoff. Discharge can be calculated using the following equation: Q = C ⋅ P ⋅ A / t (6) where: Q is the discharge (m³/s), C is the runoff coefficient (dimensionless, ranging from 0 to 1, influenced by FFPI), P is the precipitation (m, depth over the catchment), A is the catchment area (m²), t is the period (s, typically the duration of the rainfall event, which in this case is 6–7 hours or 21,600 seconds). For the precipitation data, we selected NASA GPM (Global Precipitation Measurement) due to its superior accuracy compared to other datasets, such as CHIRPS and ERA5. The FFPI values are normalized to a range of 0 to 1, with higher values indicating greater runoff potential, corresponding to the runoff coefficient (C). Catchments for Kosovo's rivers are derived from the GLO-30 DEM, and their areas are calculated in square meters. By combining the precipitation data with the normalized FFPI values, the mean discharge for each catchment during the 6-hour flood event is calculated. The results are presented on the map in Fig. 8 . The previously presented FFPI and precipitation-based model calculates discharge during a single, typically heavy, rainfall event. However, to estimate flood intensity, it is important to consider the difference between the high discharge and the discharge during the flood event, as the latter is usually 2–3 times higher. Additionally, this model only estimates river discharge based on past precipitation data from GPM. For flash flood forecasting, precipitation forecasts should replace the historical GPM (or ERA5) data. The Global Forecast System (GFS), produced by NOAA’s National Centers for Environmental Prediction (NCEP), is an ideal dataset for this purpose, available in GEE. The GFS dataset is a numerical weather prediction (NWP) model that provides global forecasts for atmospheric variables, including precipitation. Widely used for weather and hydrological forecasting, the GFS dataset provides forecasts up to 384 hours in advance, with 1-hour intervals for the first 120 hours and 3-hour intervals thereafter. Forecasts are updated every 6 hours. However, one limitation of this dataset is its coarser resolution (~ 27 km), which may smooth rainfall patterns for small catchments (< 100 km²), reducing forecast accuracy beyond 5–7 days. In the GEE script, it is possible to define a forecasting period for GFS (in hours) to anticipate heavy rainfall. The result will be the discharge calculated for the lowest point of each catchment, along with the associated flash flood risk. The static FFPI model, combined with past or forecasted precipitation data (GPM, ERA5, CHIRPS, GFS), provides a highly accurate estimate of flash flood events and the corresponding risks for a given area or catchment, which can be as small as a few km². e. Comparative Analysis and Validation To assess the reliability of the flood forecasting system, a comparative analysis was conducted between the numerical simulations and the outputs of the European Flood Awareness System (EFAS) platform. The results showed strong agreement with the observed precipitation fields (Fig. 9 a), providing a solid foundation for integrating additional parameters that influence flood dynamics. The ARW nested run at a 2 km resolution provided more detailed rainfall patterns, accurately capturing areas of maximum precipitation. While the EFAS flood alert system primarily represents 24-hour observed precipitation, the spatial distribution of rainfall was accurately depicted in other simulations, albeit with some underestimation of total precipitation. For validation of simulated precipitation and temporal intensities, combined daily GPCP satellite-gauge data for Kosovo and ERA5 hourly time-series data were used. The results of this comparison are presented in Appendix C to avoid overloading the main text. The EFAS system, particularly for January 19 and 20, 2023 (Fig. 9 b), highlighted Skenderaj as a high-risk area, situated downstream of the White Drin and Ibar Rivers. This region was identified as having a high probability of exceeding the 5-year maximum precipitation threshold, providing valuable insights into flood formation and propagation. A comparison of the newly developed flood risk mapping system with the EFAS impact assessment revealed a strong correlation between the two, particularly regarding high-risk areas from January 18 to 20, 2023 (Fig. 9 c). The initial results were promising, demonstrating good agreement in flood risk identification. However, further model sensitivity studies are needed to refine assessments of geomorphological factors and test across different geographical regions and flash-flood scenarios. These additional studies will help enhance the robustness and predictive accuracy of the flood risk system. Regarding flood risk probabilities, the EFAS system, particularly for January 19 and 20, 2023 (Fig. 9 b), indicates that the Skenderaj watershed is situated between high-risk areas downstream along the White Drin River and the lower reaches of the Ibar River. This region shows a high probability of exceeding the 5-year maximum total precipitation threshold (red), as derived from the combined ensemble model outputs. In addition to the meteorological factors contributing to flood initiation, this information provides valuable insights into the formation and propagation of flooding in the Skenderaj region. A comparison between the newly developed flood risk mapping system, based on ARW model outputs and a diagnostic algorithm incorporating both meteorological and hydro-geological parameters, reveals a strong correlation with the EFAS impact assessment for the same analysis period (Fig. 9 c). The initial results are highly encouraging, demonstrating explicit agreement in identifying high-risk areas between January 18, 2023, 12 UTC and January 20, 2023, 00 UTC. Conclusions This study offers comprehensive insights into the heavy rainfall event that affected Kosovo in January 2023, with a focus on integrating meteorological, hydrological, and geomorphological factors to assess flash flood risk. The ARW 2-km nested model demonstrated superior accuracy in capturing precipitation patterns, with significantly higher rainfall amounts and more precise temporal intensity peaks compared to the 6-km run, which tended to underestimate rainfall. This improved resolution enabled a more detailed understanding of localized rainfall events, such as those observed in Skenderaj and Istog, where severe flooding had occurred. Hydrological analyses, including river discharge and runoff data, revealed the critical role of precipitation in influencing runoff and river discharge patterns across the region. Elevated flow levels in the Black Drim River, along with increased runoff in the Skenderaj watershed, highlighted potential flood risks, even though the watershed itself was not directly affected by the flood event. The integration of GloFAS data with ERA5 reanalysis proved valuable, emphasizing key hydrological factors such as soil moisture and snowmelt, which are essential for accurate flood predictions. The study also incorporated the Novel Thunderstorm Alert System (NOTHAS) and advanced hydrometeorological modeling to identify areas at high risk of flooding. By integrating these tools with hydrological and geomorphological parameters, the study successfully refined flood risk mapping, categorizing regions into low-, moderate-, and high-risk zones. GIS-based terrain analysis further enhanced flood predictions, enabling more precise identification of areas prone to flash floods and landslides. A significant contribution of this research lies in the application of the Flash Flood Potential Index (FFPI), combined with high-resolution topographic and satellite precipitation data, to assess flash flood susceptibility at the catchment level. The FFPI method proved effective in identifying high-risk areas, as evidenced by the Klina catchment during the January 2023 event, and supports discharge estimation for both past and future events. This approach is highly valuable for regional flood risk mapping and provides a robust foundation for the development of early warning systems, with the potential for further improvement through the integration of forecast data. Moreover, this work marks a step forward in the development of a Geohazard Alert System that integrates not only meteorological and hydrological data but also terrain slope, soil saturation, and land use factors often overlooked in traditional flood forecasting systems. By leveraging the capabilities of Google Earth Engine for terrain modeling, remote sensing, and land surface analysis, the study offers a more integrated and comprehensive approach to understanding flash flood dynamics. However, additional case studies and model sensitivity analyses are necessary to assess the system’s reliability and enhance its robustness for broader operational use. Finally, the comparison between the newly developed flood risk system and the European Flood Awareness System (EFAS) products demonstrated strong spatial agreement in identifying high-risk zones, particularly in the Skenderaj watershed during the January 2023 event. High-resolution ARW simulations accurately captured precipitation patterns, and validation with GPCP and ERA5 data confirmed the model's reliability. While the initial results are promising, further sensitivity analyses are necessary to enhance the model's accuracy across diverse terrains and flash flood scenarios, ensuring its applicability in various regions and future events. Declarations Acknowledgments. We express our sincere appreciation to the Hydrometeorological Institute of Kosovo for their generous provision of precipitation data and daily rainfall distribution for the case study event. Our gratitude extends to the Faculty of Computer Science and Engineering (FINKI) for the invaluable access granted to its advanced computing facilities, which are essential for running our model effectively. Special recognition is reserved for the editor and the anonymous reviewers, whose dedication and time invested in providing insightful reviews and constructive recommendations significantly enriched the quality of our work. Data Availability Statement. The data that support the findings of this study are available from the corresponding author upon request. Conflict of interest. The authors declare that there is no conflict of interest. Author Contribution statement. Conceptualization, I.M., and B.A.; methodology, I.M., and B.A.; software, I.M.; validation, I.M.; formal analysis, I.M., B.A., and L.O.; investigation, I.M., and B.A.; resources, I.M.; data curation, I.M., and B.A.; writing—original draft preparation, B.A.; writing—review and editing, I.M., B.A., and L.O.; visualization, I.M.; supervision, B.A., and L.O.; All authors have read and agreed to the published version of the manuscript. References Aleksova B, Lukić T, Milevski I, Puhar D, Marković SB (2024a) Preliminary assessment of geohazards’ impacts on geodiversity in the Kratovska Reka catchment (North Macedonia). Geosciences 14(3):62. https://doi.org/10.3390/geosciences14030062 Aleksova B, Milevski I, Dragićević S, Lukić T (2024b) GIS-based integrated multi-hazard vulnerability assessment in Makedonska Kamenica Municipality, North Macedonia. Atmosphere 15(7):774. https://doi.org/10.3390/atmos15070774 Aleksova B, Milevski I, Mijalov R, Marković S, Cvetković V, Lukić T (2024c) Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index. Open Geosci 16(1):20220684. https://doi.org/10.1515/geo-2022-0684 Aleksova B, Milevski I, Cvetković VM, Nikolić N (2025) GIS-based assessment of flash flood potential in North Macedonia: Insights from advanced geospatial analytics. https://doi.org/10.20944/preprints202501.0789.v1 . Preprints Alfieri L, Burek P, Dutra E, Krzeminski B, Muraro D, Thielen J, Pappenberger F (2013) GloFAS – global ensemble streamflow forecasting and flood early warning. Hydrol Earth Syst Sci 17:1161–1175. https://doi.org/10.5194/hess-17-1161-2013 Alfieri L, Coauthors (2016) Global projections of river flood risk in a warmer world. Earth’s Future 5:171–182 Balogun A-L, Matori A-N, Hamid-Mosaku AI (2015) A fuzzy multi-criteria decision support system for evaluating subsea oil pipeline routing criteria in East Malaysia. Environ Earth Sci 74:4875–4884. https://doi.org/10.1007/s12665-015-4435-z Bernadet LR, Grasso LD, Nachamkin JE, Finley CA, Cotton WR (2000) Simulating convective events using a high-resolution mesoscale model. J Geophys Res 105:14,963–14982 Cao C, Coauthors (2019) Geospatial analysis of mass-wasting susceptibility of four small catchments in mountainous area of Miyun County. Beijing Int J Environ Res Public Health 16:2801. https://doi.org/10.3390/ijerph16152801 Campolo M, Soldati A, Andreussi P (2003) Artificial neural network approach to flood forecasting in the River Arno. Hydrol Sci J 48:381–398. https://doi.org/10.1623/hysj.48.3.381.45287 Chang H-I, Kumar A, Niyogi D, Mohanty U, Chen F, Dudhia J (2009) The role of land surface processes on the mesoscale simulation of the July 26, 2005, heavy rain event over Mumbai, India. Global Planet Change 67:87–103 Chawla I, Osuri KK, Mujumdar PP, Niyogi D (2018) Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin. Hydrol Earth Syst Sci 22:1095–1117 Chinta S, Yaswanth SJ, Balaji C (2021) Assessment of WRF model parameter sensitivity for high-intensity precipitation events during the Indian summer monsoon. Earth Space Sci 8. https://doi.org/10.1029/2020ea001471 . e2020EA001471 Cloke H, Pappenberger F, Thielen J, Thiemig V (2013) Operational European flood forecasting. In: Wainwright J, Mulligan M (eds) Environmental Modelling: Finding Simplicity in Complexity, 2nd edn. Wiley, pp 239–247 Cluckie ID, Han D (2000) Fluvial flood forecasting. Water Environ J 14:270–276 Collier CG (2007) Flash flood forecasting: What are the limits of predictability? Quart. J Roy Meteor Soc 133:3–23 da Barcellos CL, P., and, Cataldi M (2020) Flash flood and extreme rainfall forecast through one-way coupling of WRF-SMAP models: Natural hazards in Rio de Janeiro State. Atmosphere 11:834. https://doi.org/10.3390/atmos11080834 Ćurić M, Janc D (2011a) Comparison of modeled and observed accumulated convective precipitation in mountainous and flat land areas. J Hydrometeor 12:245–261. https://doi.org/10.1175/2010JHM1259.1 Ćurić M, Janc D (2011b) Analysis of predicted and observed accumulated convective precipitation in the area with frequent split storms. Hydrol Earth Syst Sci 15:3651–3658. https://doi.org/10.5194/hess-15-3651-2011 Dudhia J (1989) Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107 Elmore KL, Stensrud DJ, Crawford KC (2002) Ensemble cloud model applications to forecast thunderstorms. J Appl Meteor Climatol 41:363–383. https://doi.org/10.1175/1520-0450(2002)0412.0.CO;2 Emerton RE, Stephens EM, Pappenberger F, Pagano TP, Weerts AH, Wood A, Coauthors (2016) Continental and global scale flood forecasting systems. WIREs Water 3:391–418. https://doi.org/10.1002/wat2.1137 European Space Agency (2020) Copernicus DEM - Global and European digital elevation model (GLO-30) . Retrieved from https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model[](https://essd.copernicus.org/data_description_paper.html ) Ferrier B (1994) A double-moment multiple-phase four-class bulk ice scheme. Part 1: Description. J Atmos Sci 51:249–280 Fernández D, Lutz M (2010) Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol 111:90–98 Field CB, Coauthors (2012) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press IPCC (2021) Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press Gao Y, Leung LR, Zhao C, Hagos S (2017) Sensitivity of U.S. summer precipitation to model resolution and convective parameterizations across gray zone resolutions. J Geophys Res Atmos 122:2714–2733 Gaume E, Borga M (2008) Post-flood field investigations in upland catchments after major flash floods: Proposal of a methodology and illustrations. J Flood Risk Manag 1(4):175–189. https://doi.org/10.1111/j.1753-318X.2008.00023.x Gevorgyan A (2018) Convection-permitting simulation of a heavy rainfall event in Armenia using the WRF model. J Geophys Res Atmos 123:11008–11029 Gigović L, Pamučar D, Bajić Z, Drobnjak S (2017) Application of GIS-interval rough AHP methodology for flood hazard mapping in urban areas. Water 9:360. https://doi.org/10.3390/w9050360 Giustarini L, Chini M, Hostache R, Pappenberger F, Matgen P (2015) Flood hazard mapping combining hydrodynamic modeling and multi-annual remote sensing data. Remote Sens 7:14200–14226. https://doi.org/10.3390/rs71114200 Giannaros C, Dafis S, Stefanidis S, Giannaros TM, Koletsis I, Oikonomou C (2023) Hydrometeorological analysis of a flash flood event in an ungauged Mediterranean watershed under an operational forecasting and monitoring context. Meteorological Applications, 29, e2079 Gómez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin. Venezuela Eng Geol 78:11–27. https://doi.org/10.1016/j.enggeo.2004.10.004 Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031[ . ](https://www.mdpi.com/2072-4292/13/24/5019/htm) Harrigan S, Zsoter E, Alfieri L, Prudhomme C, Salamon P, Wetterhall F, Pappenberger F (2020) GloFAS-ERA5 operational global river discharge reanalysis 1979–present. Earth Syst Sci Data 12(2):2043–2060. https://doi.org/10.5194/essd-12-2043-2020 Han JY, Hong SY (2018) Precipitation forecast experiments using the Weather Research and Forecasting (WRF) model at gray-zone resolutions. Weather Forecast 33:1605–1616 Hapuarachchi HAP, Wang QJ, Pagano TC (2011) A review of advances in flash flood forecasting. Hydrol Process 25:2771–2784 Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, Thépaut J-N (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146(730):1999–2049. https://doi.org/10.1002/qj.3803 Hong SY, Lee JW (2009) Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Korea. Atmos Res 93:818–831 Hong SY, Lim JOJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorological Soc 42:129–151 Jankov I, Gallus WA Jr., Segal M, Koch SE (2007) Influence of initial conditions on the WRF–ARW model QPF response to physical parameterization changes. Weather Forecast 22(3):501–519 Jee JB, Kim S (2017) Sensitivity study on high-resolution WRF precipitation forecast for a heavy rainfall event. Atmosphere 8:96 Johor River B (2011) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci. https://doi.org/10.1007/s12665-011-1504-z Khan H, Shafique M, Khan MA, Bacha MA, Shah SU, Calligaris C (2019) Landslide susceptibility assessment using frequency ratio, a case study of Northern Pakistan. Egypt J Remote Sens Space Sci 22:11–24. https://doi.org/10.1016/j.ejrs.2018.03.003 Kumar Samanta R, Bhunia G, Shit P, Pourghasemi HR (2018) Flood susceptibility mapping using geospatial frequency ratio technique: A case study of Subarnarekha River Basin, India. Model Earth Syst Environ 4:395–408. https://doi.org/10.1007/s40808-018-0453-5 Kundzewicz ZW, Kanae S, Seneviratne SI, Handmer J, Nicholls N, Peduzzi P, Mechler R, Bouwer LM, Arnell N, Mach K, Muir-Wood R, Brakenridge GR, Kron W, Benito G, Honda Y, Takahashi K, Sherstyukov B (2014) Flood risk and climate change: Global and regional perspectives. Hydrol Sci J 59(1):1–28 Kumar A, Dudhia J, Rotunno R, Niyogi D, Mohanty UC (2008) Analysis of the 26 July 2005 heavy rain event over Mumbai, India using the Weather Research and Forecasting (WRF) model. Q J R Meteorol Soc 134:1897–1910 Lee JW, Hong SY (2006) A numerical simulation study of orographic effects for a heavy rainfall event over Korea using the WRF model. Atmosphere 16:319–332 Lee TY, Kim YH (2007) Heavy precipitation systems over the Korean Peninsula and their classification. J Korean Meteorological Soc 43:367–396 Lee DK, Kim HR, Hong SY (1998) Heavy rainfall over Korea during 1980–1990. Korean J Atmospheric Sci 1:32–50 Lee MJ, Kang J, Jeon S,Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. 2012 IEEE Int. Geosci. Remote Sens. Symp., Munich (2012) Germany, IEEE, 895–898. https://doi.org/10.1109/IGARSS.2012.6351809 Lekhadiya HS, Jana RK (2018) Analysis of extreme rainfall events with different microphysics and parameterization schemes in the WRF model. Positioning 9:1–11 Levit JJ, Baldwin ME, Bright DR (2006) Examination of convective allowing configurations of the WRF-NMM model for the prediction of severe convective weather: The SPC/NSSL spring program 2004. Weather Forecast 21:167–181 Litta AJ, Mary IS, Mohanty UC, Kiran PS (2012) Comparison of thunderstorm simulations from WRF-NMM and WRF-ARW models over East Indian Region. The Scientific World Journal, 2012, 951870. https://doi.org/10.1100/2012/951870 Liu Y, Chen Y, Chen O, Wang J, Zhuo L, Rico-Ramirez MA, Han D (2021) To develop a progressive multimetric configuration optimization method for WRF simulations of extreme rainfall events over Egypt. J Hydrol 598:126237 Lompar M, Ćurić M, Romanic D (2018) Implementation of a gust front head collapse scheme in the WRF numerical model. Atmos Res 203:231–245. https://doi.org/10.1016/j.atmosres.2017.12.018 Merino A, García-Ortega E, Navarro A, Sánchez JL, Tapiador FJ (2022) WRF hourly evaluation for extreme precipitation events. Atmos Res 274:106215 Milevski I, Aleksova B, Lukić T, Dragićević S, Valjarević A (2024) Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia. Open Geosci 16(1):20220718. https://doi.org/10.1515/geo-2022-0718 Misenis C, Zhang Y (2010) An examination of the sensitivity of WRF/Chem predictions to physical parameterizations, horizontal grid spacing, and nesting options. Atmos Res 97:315–334 Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102:16663–16682. https://doi.org/10.1029/97JD00237 Mohanty U, Routray A, Osuri KK, Prasad SK (2012) A study on simulation of heavy rainfall events over Indian region with ARW 3DVAR modeling system. Pure Appl Geophys 169:381–399 Moya AS, Martínez-Castro D, Flores JL, Silva Y (2018) : A sensitivity study on the influence of parameterization schemes in WRF_ARW model on short- and medium-range precipitation forecasts in the Central Andes of Peru. Adv. Meteor., 2018, 1381092. https://doi.org/10.1155/2018/1381092 Mukerji A, Chatterjee C, Raghuwanshi NS (2009) Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models. J Hydrol Eng 14:647–652. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000044 Nandi A, Mandal A, Wilson M, Smith D (2016) Flood hazard mapping in Jamaica using principal component analysis and logistic regression. Environ Earth Sci 75:465. https://doi.org/10.1007/s12665-016-5276-4 Nohani E, Coauthors (2019) Landslide susceptibility mapping using different GIS-based bivariate models. Water 11:1402. https://doi.org/10.3390/w11071402 Osmanaj L, Spiridonov I, Jakimovski B, Coauthors (2024a) Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Kosovo. Acta Geophys. https://doi.org/10.1007/s11600-024-01365-9 Osmanaj L, Spiridonov I, Jakimovski B, Coauthors (2024b) Three-dimensional cloud-resolving modeling of the flash-flood heavy rainfall event over Kosovo. Acta Geophys. https://doi.org/10.1007/s11600-024-01426-z Panagos P, Borrelli P, Meusburger K, Alewell C, Lugato E, Montanarella L (2015) Estimating the soil erosion cover-management factor at the European scale. Land Use Policy 48:38–50. https://doi.org/10.1016/j.landusepol.2015.05.021[ . ](https://www.mdpi.com/2072-4292/13/24/5019/htm) Pham BT, Bui DT, Prakash I, Dholakia MB (2017) Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149:52–63. https://doi.org/10.1016/j.catena.2016.09.007 Poggio L, de Sousa LM, Batjes NH, Heuvelink GBM, Kempen B, Ribeiro E, Rossiter D (2021) SoilGrids 2.0: Producing soil information for global modeling with unprecedented spatial detail. Geoderma 397:115098. https://doi.org/10.1016/j.geoderma.2021.115098[ . ](https://www.mdpi.com/2071-1050/14/9/5687/html Pradhan B (2009) Flood susceptible mapping and risk area delineation using logistic regression, GIS, and remote sensing. J Spat Hydrol 9:1–18 Pulvirenti L, Pierdicca N, Chini M, Guerriero L (2011) An algorithm for operational flood mapping from synthetic aperture radar (SAR) data using fuzzy logic. Nat Hazards Earth Syst Sci 11:529–540. https://doi.org/10.5194/nhess-11-529-2011 Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province. Iran Geocarto Int 31:42–70. https://doi.org/10.1080/10106049.2015.1041559 Renard KG, Foster GR, Weesies GA, McCool DK, Yoder DC (1997) Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Agriculture Handbook. U.S. Department of Agriculture, p 703 Schwartz C, Glen R, Ryan S, Fossell K, Weisman M (2015) NCAR’s experimental real-time convection-allowing ensemble prediction system. Wea Forecast 30:150904135422005. https://doi.org/10.1175/WAF-D-15-0103.1 Shin HH, Hong SY (2011) Intercomparison of planetary boundary-layer parametrizations in the WRF model for a single day from CASES-99. Bound -Layer Meteor 139:261–281 Shin HH, Hong SY (2015) Representation of the subgrid-scale turbulent transport in convective boundary layers at gray-zone resolutions. Mon Wea Rev 143:250–271 Skamarock WC, Klemp JB (2008a) A time-split non-hydrostatic atmospheric model for weather research and forecasting applications. J Comput Phys 227:3465–3485 Skamarock WC, Klemp JB (2008b) A time-split non-hydrostatic atmospheric model for weather research and forecasting applications. J Comput Phys 227:3465–3485. https://doi.org/10.1016/j.jcp.2007.01.037 Skamarock WC, Weisman ML, Klemp JB (1994) Three-dimensional evolution of simulated long-lived squall lines. J Atmos Sci 51:2563–2584 Skamarock WC, Klemp JB, Dudhia J, and Coauthors (2008a), : A description of the Advanced Research WRF Version 3 (No. NCAR/TN-475 + STR). University Corporation for Atmospheric Research. https://doi.org/10.5065/D68S4MVH Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker D, Duda MG, Powers J (2008) A description of the Advanced Research WRF Version 3 (No. NCAR/TN-475 + STR). University Corporation for Atmospheric Research Smith G (2010) Flash flood potential: Developing a methodology for estimating flash flood risk. J Hydrol 389(3–4):233–247. https://doi.org/10.1016/j.jhydrol.2010.05.013 Sofati I, Nurlatifah A (2019) The prediction of rainfall events using the WRF (Weather Research and Forecasting) model with ensemble technique. Int Conf Ser Earth Environ Sci 374:012036 Song HJ, Sohn BJ (2018) An evaluation of WRF microphysics schemes for simulating the warm-type heavy rain over the Korean Peninsula. J Atmos Sci 54:225–236 Spiridonov V, Ćurić M (2019) Evaluation of Supercell storm triggering factors based on a cloud-resolving model simulation. Asia-Pac J Atmos Sci 55:439–458 Spiridonov V, Baez J, Telenta JB (2020) Prediction of extreme convective rainfall intensities using a free-running 3-D sub-km-scale cloud model initialized from WRF km-scale NWP forecasts. J Atmos Solar-Terr Phys 209:105401. https://doi.org/10.1016/j.jastp.2020.105401 Spiridonov V, Ćurić M, Grcic M, Jakimovski B (2022) Ensemble cloud model application in simulating the catastrophic heavy rainfall event. J Atmos Sci Res 5. https://doi.org/10.30564/jasr.v5i4.5081 Spiridonov V, Ćurić M, Grčić M, Coauthors (2023) Assessment of the WRF model in simulating a catastrophic flash flood. Acta Geophys 71:1347–1359. https://doi.org/10.1007/s11600-023-01032-5 Statistical IM (2018) Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomatics Nat Hazards Risk. https://doi.org/10.1080/19475705.2018.1506509 Sun J, Lee TY (2002) A numerical study of an intense quasi-stationary convection band over the Korean Peninsula. J Meteor Soc Japan 80:1221–1245 Sun C, Liang X-Z (2020) Improving US extreme precipitation simulation: Sensitivity to physics parameterizations. Clim Dyn 54:4891–4918 Sun J, Xue M, Wilson JW, Zawadzki I, Ballard SP, Coauthors (2014) Use of NWP for nowcasting convective precipitation: Recent progress and challenges. Bull Amer Meteor Soc 95:409–426 Tehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule-based decision tree (DT) and a novel ensemble bivariate and multivariate statistical model in GIS. J Hydrol 504:69–79. https://doi.org/10.1016/j.jhydrol.2013.09.034 Tehrany MS, Pradhan B, Jebur MN (2014) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008 Thielen J, Bartholmes J, Ramos MH, de Roo A (2009) The European Flood Alert System – Part 1: Concept and development. Hydrol Earth Syst Sci 13:125–140. https://doi.org/10.5194/hess-13-125-2009 Thompson G, Eidhammer T (2014) A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J Atmos Sci 71(10):3636–3658. https://doi.org/10.1175/JAS-D-13-0305.1 Thompson G, Field PR, Rasmussen RM, Hall WD (2008) Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon Weather Rev 136:5095–5511 Tiwari MK, Chatterjee C (2010) Uncertainty assessment and ensemble flood forecasting using bootstrap-based artificial neural networks (BANNs). J Hydrol 382:20–33. https://doi.org/10.1016/j.jhydrol.2009.12.031 Trenberth KE (2011) Changes in precipitation with climate change. Climate Res 47(1–2):123–138 United Nations Office for Disaster Risk Reduction (UNDRR) (2022) Global Assessment Report on Disaster Risk Reduction 2022. https://www.undrr.org/publication/global-assessment-report-disaster-risk-reduction-2022 Varlas G, Papaioannou G, Papadopoulos A, Markogianni V, Vardakas L, Dimitriou E (2023) Flash flood forecasting using integrated meteorological–hydrological–hydraulic modeling: Application in a Mediterranean river. Environmental Sciences Proceedings, 26(1), 35. https://doi.org/10.3390/environsciproc2023026035 Weisman ML, Davis C, Wang W, Manning KW, Klemp JB (2008) Experiences with 0–36 h explicit convective forecasts with the WRF-ARW model. Weather Forecast 23:407–437 Wilson JW, Roberts RD (2006) Summary of convective storm initiation and during IHOP: Observational and modeling perspective. Mon Weather Rev 134:23–47 Wischmeier WH, Smith DD (1978) Predicting rainfall erosion losses: A guide to conservation planning. Agriculture Handbook. U.S. Department of Agriculture, p 537 Xue M, Kong F, Thomas KW, Gao J, Wang Y, Coauthors (2008) CAPS real-time storm-scale ensemble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2008 Spring Experiment. In Preprints, 24th Conference on Severe Local Storms. American Meteorological Society, Savannah, GA. [CD-ROM 12.2] Yue H, Liu Y, Wang H (2022) Monitoring bare soil and vegetation areas using Sentinel-2 images and machine learning in Google Earth Engine: A case study of the Yellow River Basin. Remote Sens 14(19):4836. https://doi.org/10.3390/rs14194836[ . ](https://www.mdpi.com/2072-4292/13/24/5019/htm Zaharia L, Costache R, Prăvălie R, Ioana-Toroimac G (2017) Mapping flash flood potential in the Carpathian Basin using a GIS-based multicriteria approach. Environ Earth Sci 76(14):489. https://doi.org/10.1007/s12665-017-6822-3 Zaidi SM, Gisen JIA, Eltahan M, Yu Q, Misbari S, Ngien SK (2022) Assessment of Weather Research and Forecasting (WRF) physical schemes parameterization to predict moderate to extreme rainfall in poorly gauged basin. Sustainability 14(19):12624. https://doi.org/10.3390/su141912624 Zanaga D, Van De Kerchove R, De Keersmaecker W, Souverijns N, Brockmann C, Quast R, Arino O (2021) ESA WorldCover 10 m 2021 v100. Zenodo . https://doi.org/10.5281/zenodo.5571936[] (https://www.mdpi.com/2072-4292/13/24/5019/htm ) Zhang D-L, Gao K, Parsons DB (1989) Numerical simulation of an intense squall line during 10–11 June 1985 PRE-STORM. Part I: Model verification. Mon Weather Rev 117:960–994 Zheng Y, Xue M, Li B, Chen J, Tao Z (2016) Spatial characteristics of extreme rainfall over China with hourly through 24-hour accumulation periods based on national-level hourly rain gauge data. Adv Atmos Sci 33:1218–1232 Zhang T, Han L, Chen W, Shahabi H (2018) Hybrid integration approach of entropy with logistic regression and support vector machine for landslide susceptibility modeling. Entropy 20:884. https://doi.org/10.3390/e20110884 Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major revisions 05 Nov, 2025 Reviewers agreed at journal 08 Jul, 2025 Reviewers invited by journal 03 Jul, 2025 Editor invited by journal 24 Jun, 2025 First submitted to journal 22 Jun, 2025 Editor assigned by journal 19 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6917578","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480125644,"identity":"72d3c30c-c5ec-47a4-a3ad-3053812a96fb","order_by":0,"name":"Lavdim Osmanaj","email":"","orcid":"","institution":"University of Pristina: Universiteti i Prishtines Hasan Prishtina","correspondingAuthor":false,"prefix":"","firstName":"Lavdim","middleName":"","lastName":"Osmanaj","suffix":""},{"id":480125645,"identity":"b7718cc4-f6f6-4655-9b94-04a1b7f31cc6","order_by":1,"name":"Ivica Milevski","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACNgbGBiAlIQMkmBkSKkAUcwNRWnggWs6AKEb8WmAAooWxDcQmoIVPIrnxw88cCx5+ifTHBg/n1UbztwO1/KjYhtthEonNkr3bJHgkZ+QYJyRuO5474zBjA2PPmdu4tfAcbGPgBWoxOHOG+UDitmO5DUAtzIxt+LUw/gVqsT9z/PGBxDnHcucT1MLe2MYMtoW9AeiwhprcDURoaZaWBWqRON5jbJBw7EDuRqCWg/j8It/M/vDj2211cvzN7I8lf9TU5c47f/jggx8VuLWgg8Ng8gDR6oGgjhTFo2AUjIJRMEIAAOP7VbaAcA1VAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-9275-9174","institution":"Ss Cyril and Methodius University in Skopje: Saints Cyril and Methodius University in Skopje","correspondingAuthor":true,"prefix":"","firstName":"Ivica","middleName":"","lastName":"Milevski","suffix":""},{"id":480125646,"identity":"80dc1a7d-df30-4927-8538-697b72373b54","order_by":2,"name":"Bojana Aleksova","email":"","orcid":"","institution":"University of Novi Sad: Univerzitet u Novom Sadu","correspondingAuthor":false,"prefix":"","firstName":"Bojana","middleName":"","lastName":"Aleksova","suffix":""}],"badges":[],"createdAt":"2025-06-17 22:28:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6917578/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6917578/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86205586,"identity":"739e7627-c3fb-4f43-9641-c428d188e2ef","added_by":"auto","created_at":"2025-07-08 02:45:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":258429,"visible":true,"origin":"","legend":"\u003cp\u003eWRF-ARW (ARW) triple-nested model domain configuration: the outermost domain (D1) covers central Europe with an 18 km grid resolution, the intermediate domain (D2) captures Southeast Europe at 6 km resolution, and the innermost domain (D3) focuses on Kosovo with a 2 km grid.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6917578/v1/6534d6cabf87b0ade85ee5f5.png"},{"id":86205578,"identity":"b916c484-07de-4ff8-8705-fb8be11517bf","added_by":"auto","created_at":"2025-07-08 02:45:32","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":749831,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of key FFPI factors for the Klina catchment, Kosovo. a. Slope (M) from Copernicus GLO-30 DEM, classified from 1 (gentle) to 10 (steep); (b) Land Cover (L) from ESA World Cover 2021, indicating runoff potential; (c) Soil Texture (S) from SoilGrids clay content, showing flash flood susceptibility; (d) Vegetation Density (V) from Sentinel-2 BSI, where higher values indicate sparse vegetation and higher erosion risk.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6917578/v1/78959a6c67e3c3f949e9cafb.jpeg"},{"id":86205577,"identity":"3b10ae7b-5c85-4a64-ab8a-228c64c12b22","added_by":"auto","created_at":"2025-07-08 02:45:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":456311,"visible":true,"origin":"","legend":"\u003cp\u003eARW forecast of total accumulated precipitation (mm) over 24, 48, and 60 hours, with the 2-km run shown in the upper panels and the 6-km run in the lower panels.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6917578/v1/9570ca8ee6e96f298bdf58c0.png"},{"id":86205579,"identity":"23038dfc-04a7-4ae1-8669-e8b00aaee3ba","added_by":"auto","created_at":"2025-07-08 02:45:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":261181,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of relative rainfall intensities during the simulation period, based on the ARW 2-km run. The graph shows hourly precipitation rates for Istog and Skenderaj, highlighting the timing and intensity of rainfall peaks during the event.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6917578/v1/5f88a7485fb6fe8f974e9d4a.png"},{"id":86205582,"identity":"264e7b4d-7d66-4587-a381-ac2d132b37a6","added_by":"auto","created_at":"2025-07-08 02:45:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2004757,"visible":true,"origin":"","legend":"\u003cp\u003eMean discharge in the last 24 hours (m³ s¹); runoff water equivalent (kg m²); snow depth water equivalent (kg m²); and soil wetness index. Valid: 19–21 January 2023, 00:00 UTC. Credit: \u003ca href=\"https://ewds.climate.copernicus.eu/datasets/cems-glofas-historical?tab=download\" target=\"_new\"\u003eCopernicus Climate Data Store\u003c/a\u003e.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6917578/v1/7d8cb49c4524ce82ad05b39a.png"},{"id":86205584,"identity":"9dc54a48-feef-4292-9490-648e8dc42964","added_by":"auto","created_at":"2025-07-08 02:45:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":590910,"visible":true,"origin":"","legend":"\u003cp\u003eMean River discharge of Kosovo from 19-21 Jan 2023 with a high-resolution model (ECMWF)\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6917578/v1/52b9032fa8fe32855868490f.png"},{"id":86205585,"identity":"11cfe915-6fe9-48f6-b6db-6de02e89b52b","added_by":"auto","created_at":"2025-07-08 02:45:32","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":234979,"visible":true,"origin":"","legend":"\u003cp\u003eMap of average FFPI values at the catchment scale for Kosovo (b) and the Klina watershed (a), indicating flash flood susceptibility.\u003c/p\u003e","description":"","filename":"image7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6917578/v1/c422fb36709c12b7b181f019.jpg"},{"id":86205591,"identity":"c68c32e5-fbf3-4843-9222-01962a7e3b3f","added_by":"auto","created_at":"2025-07-08 02:45:33","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":422494,"visible":true,"origin":"","legend":"\u003cp\u003eModel of runoff (a) and mean discharge per catchment in Kosovo (b) during the heavy rainfall event on January 19, 2023.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6917578/v1/2e920c7bfc48f8babaeeb219.jpeg"},{"id":86205590,"identity":"9f320130-05ce-4d91-8588-8a94f697a12d","added_by":"auto","created_at":"2025-07-08 02:45:33","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":3095244,"visible":true,"origin":"","legend":"\u003cp\u003eEuropean Flood Awareness System (EFAS) Valid: 19-21 Jan 2023 00:00 UTC a) Observed precipitation (mm); b) Flood probability and threshold level exceedance ongoing; c) Rapid impact assessment. Layers show the main catchments and major rivers in the central domain of Kosovo.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-6917578/v1/327d8ade429dc65a1b115739.png"},{"id":86207243,"identity":"0f95244b-a4cc-4845-8a5f-29f787245474","added_by":"auto","created_at":"2025-07-08 03:09:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8213878,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6917578/v1/a0f97199-47f6-4965-b0f8-001ae07265b6.pdf"}],"financialInterests":"","formattedTitle":"Integrated Rainfall Modeling and Hydrometeorological Alert System for Extreme Flooding in Kosovo","fulltext":[{"header":" SIGNIFICANCE STATEMENT","content":"\u003cp\u003eThis study enhances flash flood prediction in Kosovo by integrating high-resolution atmospheric models (ARW and NMM), hydrological simulations, and hydrometeorological modeling. Focusing on the 2023 floods in Skenderaj and Peja, this analysis demonstrates the ARW model’s accuracy in capturing localized rainfall intensity. Meanwhile, GloFAS and ERA5 data enhance risk assessment by incorporating soil moisture and snowmelt factors. The use of NOTHAS and GIS-based FFPI analysis enables precise identification of high-risk zones. These findings highlight the importance of integrating weather models, hydrology, and geomorphology in flood risk management, providing a scalable framework for early warning systems across Southeastern Europe.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eClimate change has intensified extreme weather events globally, including flash floods, which pose severe risks to life, infrastructure, and economies, especially in regions with complex terrain and limited hydrological infrastructure (IPCC, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Trenberth, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Field et al., 2012; Kundzewicz et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Characterized by rapid onset and high destructiveness, flash floods are typically triggered by short-duration, high-intensity rainfall events (Fern\u0026aacute;ndez and Lutz, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Aleksova et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024a\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003eb\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Kosovo and the broader Balkan Peninsula, convective storms during the warm season are increasingly producing extreme rainfall and flooding (Osmanaj et al., 2023a, b). These evolving hydrometeorological hazards underscore the need for improved forecasting and early warning systems (UNDRR, 2022; Alfieri et al., 2016; Aleksova et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Numerical Weather Prediction (NWP) tools such as the WRF model are widely used for simulating convective storms and heavy precipitation. However, flash flood modeling remains challenging due to the complex interplay between atmospheric processes and hydrology (Skamarock et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lee and Hong, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Spiridonov et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCoupling meteorological models with hydrological simulations improves forecasting accuracy by converting rainfall predictions into surface runoff and streamflow estimates (Varlas et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zaldi et al., 2022). Global early warning systems like GloFAS and EFAS use real-time meteorological and hydrological data, often assimilating ERA5 reanalysis from ECMWF for model initialization and validation (Alfieri et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Thielen et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Harrigan et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hersbach et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Yet, in data-scarce regions like Kosovo, sparse monitoring limits model performance (Hapuarachchi et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cluckie \u0026amp; Han, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Integration of satellite data such as GPM-IMERG, radar, and GIS tools shows potential for improving flood detection and risk mapping (Giannaros et al., 2022; Aleksova et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024c\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eKosovo experienced two major floods in 2023, including a devastating event on January 19 that struck Skenderaj. These events, exacerbated by climate change and inadequate land-use planning, necessitate the implementation of integrated risk assessments and real-time warning systems. This study evaluates a high-resolution WRF simulation of the January 19 flood, combined with a severe weather alert tool (NOTHAS) and geospatial flood susceptibility mapping. The goal is to improve flood forecasting and inform the development of a scalable, real-time early warning system for Kosovo. The article is structured as follows: Section 2 provides an observational overview of the event, Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e3\u003c/span\u003e details the numerical modeling framework, Section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents simulation results and validates them against observational data, and Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e5\u003c/span\u003e concludes with recommendations for future flood risk management strategies.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe main objectives of this research are:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eTo evaluate the most suitable model configuration for accurately simulating the atmospheric behavior and physical processes associated with torrential rainfall over Kosovo.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTo develop a prototype for an advanced forecast and hydrometeorological alert tool.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo address these objectives, a series of high-resolution simulations were performed using the Weather Research and Forecasting (WRF) model. This included sensitivity testing of physical schemes and resolutions, using ARW triple nested (Kain et al., 2006; Lee \u0026amp; Hong, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e; Han \u0026amp; Hong, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Hourly warnings and rainfall outputs were compared with historical observations and flash-flood guidance thresholds (Spiridonov et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zaidi et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ea. Meteorological Model Design\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe WRF model was selected for its robust treatment of atmospheric processes and flexibility in spatial and physical configurations. It supports both single- and nested-domain simulations and includes a wide range of physics options, such as the Thompson microphysics scheme (Thompson et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Eidhammer \u0026amp; Thompson, \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), Yonsei University PBL (Hong, 2010), Monin-Obukhov surface layer physics (Janjic, 1996), RRTM longwave radiation (Mlawer et al., \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e), and Dudhia shortwave radiation (Dudhia, \u003cspan class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe ARW core (Skamarock et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003ea) was applied in a triple-nested configuration (18 \u0026times; 6 \u0026times; 2 km), optimized for resolving convective-scale dynamics over Kosovo. The ARW nested run was configured with time steps of 30, 10, and 3.3 seconds across the three domains, The experiment explored variations in microphysics (e.g., WSM6, Ferrier), cumulus schemes (e.g., Shin \u0026amp; Hong, \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), and vertical levels (32 layers), consistent with prior optimization studies (Chinta et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Misenis \u0026amp; Zhang, \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chawla et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAll simulations were initialized with GDAS/FNL 0.25\u0026deg; global datasets, updated every 6 hours (Elmore et al., \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e; Bernadet et al., \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Levit et al., \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). A spin-up period of 18 hours preceded each run, allowing the model to stabilize before the targeted flood event onset (Jankov et al., \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Liu et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eb. Methodology for Flash Flood Potential Index (FFPI) modeling\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFlash floods often occur in small catchments over short durations, necessitating localized modeling beyond the scope of global datasets. This study focuses on Kosovo\u0026rsquo;s Klina catchment, which experienced a severe flash flood event in January 2023. Recent advancements in remote sensing, GIS, and hydrological modeling have significantly improved flash flood prediction (Pradhan \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Giustarini et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gigović et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA wide range of methods have been used for flood susceptibility mapping, including multicriteria evaluation (Balogun et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), decision tree analysis (Tehrany et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pulvirenti et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), artificial neural networks (Campolo et al. \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e), frequency ratio (Rahmati et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), logistic regression (Cao et al. 2019), and support vector machines (Nandi et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Combining machine learning with multi-criteria decision-making (MCDM) techniques has further enhanced model performance (Zhang et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Khan et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pham et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this study, the Flash Flood Potential Index (FFPI)\u0026mdash;a method widely used across Europe (G\u0026oacute;mez and Kavzoglu \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e; Gaume and Borga \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u0026mdash;was applied using a refined formulation adapted from Smith (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). The modified FFPI incorporates terrain and land surface parameters and is expressed as:\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$$\\:FFPI=\\frac{\\left(1.5\\bullet\\:M+1.05\\bullet\\:+S+1.25\\bullet\\:V+1.1\\bullet\\:E\\right)}{5}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere \u003cstrong\u003eM\u003c/strong\u003e is the slope derived from the 30-m Copernicus GLO-30 DEM (ESA, 2020), calculated as:\u003c/p\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ2\" class=\"mathdisplay\"\u003e$$\\:M=\\frac{10n}{30}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eL\u003c/strong\u003e represents land cover class from ESA World Cover (2021), weighted by runoff risk (Panagos et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zanaga et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); S\u0026thinsp;=\u0026thinsp;Soil type based on clay content from SoilGrids (Poggio et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e); V\u0026thinsp;=\u0026thinsp;Vegetation density, derived using the Bare Soil Index (BSI) from Sentinel-2 bands (Gorelick et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yue et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; USGS 2024; Milevski et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), given by:\u003c/p\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ3\" class=\"mathdisplay\"\u003e$$\\:BSI=\\frac{B11+B04-B08-B02}{B11+B04+B08+B02}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e is soil erodibility, calculated as the K-factor from the Universal Soil Loss Equation (USLE; Wischmeier \u0026amp; Smith, \u003cspan class=\"CitationRef\"\u003e1978\u003c/span\u003e; Renard et al., \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e), using:\u003c/p\u003e\n\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ4\" class=\"mathdisplay\"\u003e$$\\:K=\\frac{\\left[2.1x{10}^{-4}\\bullet\\:{M}^{1.14}\\bullet\\:\\left(12-OM\\right)+3.25\\bullet\\:\\left(S-2\\right)+2.5\\bullet\\:\\left(P-3\\right)\\right]}{100}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eEach contributing factor was normalized to a 1\u0026ndash;10 scale to maintain consistency and comparability. The inclusion of the K-factor enhanced sensitivity to soil vulnerability by incorporating both textural and structural properties influencing runoff generation.\u003c/p\u003e\n\u003cp\u003eTo ensure spatial alignment, all input raster layers\u0026mdash;originally ranging from 10 m to 250 m resolution were resampled to a common 30 m resolution and projected to the UTM coordinate system. The final FFPI quantifies flash flood susceptibility by integrating topographic, hydrological, and land surface properties. This composite index supports the identification of vulnerable catchments, informing long-term flood risk assessment and management strategies. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the spatial distribution of key FFPI parameters across Kosovo, including the Klina catchment.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cem\u003ea. Simulation of rainfall patterns\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe Advanced Research WRF (ARW) model was configured with a triple-nested domain, using horizontal grid spacings of 18 km, 6 km, and 2 km. For the 18-km and 6-km domains, the simulation incorporated the Thompson-Eidhammer microphysics scheme (2014), the Yonsei University (YSU) planetary boundary layer scheme (Hong, 2010), and a scale- and aerosol-aware convective parameterization scheme (Shin and Hong, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Han et al., 2017). The innermost 2 km domain explicitly resolved convection, eliminating the need for convective parameterization.\u003c/p\u003e \u003cp\u003eThe simulation results primarily focus on total accumulated precipitation over 24-, 48-, and 60-hour periods, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The 2-km nested run provided the most detailed and accurate representation of rainfall distribution across Kosovo. The highest precipitation totals were recorded between January 19 at 00 UTC and January 20 at 00 UTC, with the northwestern regions of Kosovo experiencing the most significant rainfall. In Istog, total precipitation exceeded 130 mm, while in Skenderaj\u0026mdash;where severe flooding was reported\u0026mdash;the accumulated precipitation reached 44.1 mm. The interpretation of these results starts with an analysis of the total precipitation amounts over 24, 48, and 60 hours, derived from the ARW triple-nested run (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBy comparison, the 6-km simulation captured the general spatial distribution of rainfall but substantially underestimated precipitation totals, with 55.4 mm recorded for Istog and 33.6 mm for Skenderaj. This discrepancy emphasizes the added value of convection-permitting simulations in resolving localized precipitation maxima during convective events. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the hourly rainfall time series from the ARW 2-km run, which reveals two distinct peaks in Istog: one during the early morning of January 18 (05\u0026ndash;06 UTC) and another on January 19 (10\u0026ndash;11 UTC), coinciding with the main rainfall event. In Skenderaj, moderate intensity peaks occurred on January 18 between 07:00 and 09:00 UTC, with hourly rates ranging from 4 to 6.4 mm/h. Although the 6-km simulation identified the timing of these events, it consistently underestimated the magnitude of hourly rainfall.\u003c/p\u003e \u003cp\u003eDuring the first 24 hours of the simulation, significant precipitation was confined to the far western mountainous areas of Kosovo. A notable increase in rainfall occurred over the subsequent 24\u0026ndash;to 48-hour period. The highest 60-hour accumulation was simulated near Pristina (~\u0026thinsp;59.2 mm), while the total in Skenderaj (42.2 mm) closely matched the value from the 2-km run, further confirming the reliability of the high-resolution configuration in accurately capturing event-specific hydrometeorological conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eb. Hydrological Evaluations\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThis study examines river discharge data from Kosovo's hydrological stations alongside ERA5 reanalysis data from ECMWF to analyze runoff dynamics and assess flood risks. The data, provided by the Global Flood Awareness System (GloFAS), consists of gridded daily hydrological time series driven by meteorological reanalysis data. It provides accurate representations of key hydrological variables, including river discharge, soil moisture, snow water equivalent, and total runoff (both surface and subsurface).\u003c/p\u003e \u003cp\u003eThe data is derived from the LISFLOOD hydrological model, which operates at a 24-hour timestep with a spatial resolution of 0.05\u0026deg; \u0026times; 0.05\u0026deg; in its latest iteration. GloFAS provides both near-real-time (ERA5) and consolidated reanalysis data, enabling medium-range and seasonal flood forecasting. By integrating this dataset with observational records, the study enhances the accuracy of flood risk assessments and supports the development of more effective mitigation strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe highest river discharge relative to the mean flow occurred on the Black Drim River in northern Albania, coinciding with the region that received the highest accumulated precipitation during the 60-hour simulation period (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). On January 19, 2023, the upstream section of the river exceeded a discharge of 1,100 m\u0026sup3;/s, which was sustained until January 20, before decreasing to a peak of approximately 937 m\u0026sup3;/s on January 21. Between January 18 and 21, 2023, accumulated precipitation from both stratiform and convective rainfall events contributed to an increase in river discharge across Kosovo, albeit at lower magnitudes. The White Drim River reached its peak discharge on January 20, with values ranging between 400 and 450 m\u0026sup3;/s. Similarly, the Ibar River in northern Kosovo recorded flows ranging from 350 to 400 m\u0026sup3;/s.\u003c/p\u003e \u003cp\u003eMore detailed insights into the flow rates of rivers in Kosovo during this period can be derived from the high-resolution water discharge forecasts provided by ECMWF as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Skenderaj watershed, situated downstream along the Ibar River in western Kosovo, does not directly influence flood genesis; however, it requires further hydrological model analysis for a comprehensive understanding of flood dynamics. The highest runoff water equivalent was observed in the western mountainous regions, with peaks in the southwest. Runoff notably increased on January 20 due to precipitation in the Mitrovica region, reaching 10 kg/m\u0026sup2;. Snow water equivalent analysis identified two regions with the highest values: the northwestern mountainous areas along the Albanian and Montenegrin borders, and the southern region near Brezovica. Soil moisture was highest in western Kosovo, with values ranging from 0.8 to 0.9 in the north and around 0.7 in flood-prone Skenderaj, indicating highly saturated soil from January 18 to 21, 2023. These factors underscore the intricate relationship between precipitation, runoff, snowmelt, and soil saturation in flood risk assessment.\u003c/p\u003e \u003cp\u003e \u003cem\u003ec. Assessment of Flash Flood Potential using FFPI\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn contrast to previous models, which provide broader and coarser-scale (regional to national) estimates of flash flood probability and risks, the FFPI serves as a critical indicator of catchment-based flash flood susceptibility. The calculated FFPI values for Kosovo range from 1.8 to 8.2, with a mean value of 4.7, indicating significant variability in flood risk. Using SAGA GIS software (version 9.7.0) and the Natural Breaks classification method, the FFPI values are categorized into five classes, ranging from 1 (very low susceptibility) to 5 (very high susceptibility), for flash floods.\u003c/p\u003e \u003cp\u003eTo better assess flash flood susceptibility, a map of average FFPI values per small catchment was created, derived from the GLO-30 DEM. Across Kosovo, 2,114 small catchments, with areas ranging from 30 km\u0026sup2; to 100 km\u0026sup2;, were identified and used as the basis for assessing flash floods (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Using the Natural Breaks (Jenks) classification, these catchments were categorized according to their average FFPI values. Calculating the average FFPI per catchment is an effective method for assessing flash flood susceptibility, particularly for regional or watershed-scale analyses. This approach aligns well with hydrological processes, as catchments (or watersheds) are natural units for studying water flow and flood dynamics. Catchments are defined by topography and drainage patterns, making them ideal for flash flood analysis. Averaging the FFPI per catchment integrates spatial variability (e.g., soil properties, land cover, slope) into a single metric, reflecting the overall susceptibility of the catchment to flash floods. This method captures the cumulative effect of runoff generation and flow concentration within a catchment, which is critical for accurate flash flood prediction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe same procedure is applied to the Klina catchment upstream of Skenderaj, which covers an area of 147.2 km\u0026sup2;. The catchment elevation ranges from 1137 m in the northwestern part to 559 m in the southern downstream area near Skenderaj, with an average elevation of 751 m and a mean slope of 12.1\u0026deg; (21.5%). Following the outlined procedure, the Klina catchment is divided into 273 sub-catchments, each ranging in size from 0.3 to 3.3 km\u0026sup2;. The map in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e clearly shows that the source area of the Klina River catchment, located above 700 m, as well as the regions to the west and east of Skenderaj, are highly susceptible to flash floods. Of the 273 sub-catchments, 98 (covering 40.2 km\u0026sup2;) have a mean FFPI value below the average (\u0026lt;\u0026thinsp;4.7), while another 60 (covering 40.6 km\u0026sup2;) exhibit moderate to high FFPI values (\u0026gt;\u0026thinsp;5.5). During the heavy rainfall event on January 19, 2023, the main flood originated from the high-FFPI sub-catchments to the west and east of Skenderaj.\u003c/p\u003e \u003cp\u003eThe next step involves calculating the peak discharge within these catchments during the flood event, for which precipitation data is required. River discharge is influenced by precipitation that contributes to runoff within a catchment, and the FFPI serves as an indicator of runoff potential. Although FFPI is not directly used in discharge calculations, it can adjust runoff coefficients based on landscape susceptibility to flash floods. River discharge (Q, in m\u0026sup3;/s) is the volume of water flowing through a river per unit time, driven by precipitation (P), which becomes runoff after accounting for losses such as infiltration and evaporation. The FFPI reflects runoff potential based on factors like soil erodibility (K-factor), clay content, land cover, vegetation density, and slope. This information helps estimate the runoff coefficient (C), which represents the fraction of precipitation that becomes surface runoff. Discharge can be calculated using the following equation:\u003c/p\u003e \u003cp\u003eQ\u0026thinsp;=\u0026thinsp;C \u0026sdot; P \u0026sdot; A / t (6)\u003c/p\u003e \u003cp\u003ewhere:\u003c/p\u003e \u003cp\u003eQ is the discharge (m\u0026sup3;/s),\u003c/p\u003e \u003cp\u003eC is the runoff coefficient (dimensionless, ranging from 0 to 1, influenced by FFPI),\u003c/p\u003e \u003cp\u003eP is the precipitation (m, depth over the catchment),\u003c/p\u003e \u003cp\u003eA is the catchment area (m\u0026sup2;),\u003c/p\u003e \u003cp\u003et is the period (s, typically the duration of the rainfall event, which in this case is 6\u0026ndash;7 hours or 21,600 seconds).\u003c/p\u003e \u003cp\u003eFor the precipitation data, we selected NASA GPM (Global Precipitation Measurement) due to its superior accuracy compared to other datasets, such as CHIRPS and ERA5. The FFPI values are normalized to a range of 0 to 1, with higher values indicating greater runoff potential, corresponding to the runoff coefficient (C). Catchments for Kosovo's rivers are derived from the GLO-30 DEM, and their areas are calculated in square meters. By combining the precipitation data with the normalized FFPI values, the mean discharge for each catchment during the 6-hour flood event is calculated. The results are presented on the map in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe previously presented FFPI and precipitation-based model calculates discharge during a single, typically heavy, rainfall event. However, to estimate flood intensity, it is important to consider the difference between the high discharge and the discharge during the flood event, as the latter is usually 2\u0026ndash;3 times higher. Additionally, this model only estimates river discharge based on past precipitation data from GPM. For flash flood forecasting, precipitation forecasts should replace the historical GPM (or ERA5) data. The Global Forecast System (GFS), produced by NOAA\u0026rsquo;s National Centers for Environmental Prediction (NCEP), is an ideal dataset for this purpose, available in GEE. The GFS dataset is a numerical weather prediction (NWP) model that provides global forecasts for atmospheric variables, including precipitation. Widely used for weather and hydrological forecasting, the GFS dataset provides forecasts up to 384 hours in advance, with 1-hour intervals for the first 120 hours and 3-hour intervals thereafter. Forecasts are updated every 6 hours.\u003c/p\u003e \u003cp\u003eHowever, one limitation of this dataset is its coarser resolution (~\u0026thinsp;27 km), which may smooth rainfall patterns for small catchments (\u0026lt;\u0026thinsp;100 km\u0026sup2;), reducing forecast accuracy beyond 5\u0026ndash;7 days. In the GEE script, it is possible to define a forecasting period for GFS (in hours) to anticipate heavy rainfall. The result will be the discharge calculated for the lowest point of each catchment, along with the associated flash flood risk. The static FFPI model, combined with past or forecasted precipitation data (GPM, ERA5, CHIRPS, GFS), provides a highly accurate estimate of flash flood events and the corresponding risks for a given area or catchment, which can be as small as a few km\u0026sup2;.\u003c/p\u003e \u003cp\u003e \u003cem\u003ee. Comparative Analysis and Validation\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTo assess the reliability of the flood forecasting system, a comparative analysis was conducted between the numerical simulations and the outputs of the European Flood Awareness System (EFAS) platform. The results showed strong agreement with the observed precipitation fields (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea), providing a solid foundation for integrating additional parameters that influence flood dynamics. The ARW nested run at a 2 km resolution provided more detailed rainfall patterns, accurately capturing areas of maximum precipitation. While the EFAS flood alert system primarily represents 24-hour observed precipitation, the spatial distribution of rainfall was accurately depicted in other simulations, albeit with some underestimation of total precipitation.\u003c/p\u003e \u003cp\u003eFor validation of simulated precipitation and temporal intensities, combined daily GPCP satellite-gauge data for Kosovo and ERA5 hourly time-series data were used. The results of this comparison are presented in Appendix C to avoid overloading the main text. The EFAS system, particularly for January 19 and 20, 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb), highlighted Skenderaj as a high-risk area, situated downstream of the White Drin and Ibar Rivers. This region was identified as having a high probability of exceeding the 5-year maximum precipitation threshold, providing valuable insights into flood formation and propagation. A comparison of the newly developed flood risk mapping system with the EFAS impact assessment revealed a strong correlation between the two, particularly regarding high-risk areas from January 18 to 20, 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec). The initial results were promising, demonstrating good agreement in flood risk identification. However, further model sensitivity studies are needed to refine assessments of geomorphological factors and test across different geographical regions and flash-flood scenarios. These additional studies will help enhance the robustness and predictive accuracy of the flood risk system.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding flood risk probabilities, the EFAS system, particularly for January 19 and 20, 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb), indicates that the Skenderaj watershed is situated between high-risk areas downstream along the White Drin River and the lower reaches of the Ibar River. This region shows a high probability of exceeding the 5-year maximum total precipitation threshold (red), as derived from the combined ensemble model outputs. In addition to the meteorological factors contributing to flood initiation, this information provides valuable insights into the formation and propagation of flooding in the Skenderaj region.\u003c/p\u003e \u003cp\u003eA comparison between the newly developed flood risk mapping system, based on ARW model outputs and a diagnostic algorithm incorporating both meteorological and hydro-geological parameters, reveals a strong correlation with the EFAS impact assessment for the same analysis period (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec). The initial results are highly encouraging, demonstrating explicit agreement in identifying high-risk areas between January 18, 2023, 12 UTC and January 20, 2023, 00 UTC.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study offers comprehensive insights into the heavy rainfall event that affected Kosovo in January 2023, with a focus on integrating meteorological, hydrological, and geomorphological factors to assess flash flood risk. The ARW 2-km nested model demonstrated superior accuracy in capturing precipitation patterns, with significantly higher rainfall amounts and more precise temporal intensity peaks compared to the 6-km run, which tended to underestimate rainfall. This improved resolution enabled a more detailed understanding of localized rainfall events, such as those observed in Skenderaj and Istog, where severe flooding had occurred. Hydrological analyses, including river discharge and runoff data, revealed the critical role of precipitation in influencing runoff and river discharge patterns across the region. Elevated flow levels in the Black Drim River, along with increased runoff in the Skenderaj watershed, highlighted potential flood risks, even though the watershed itself was not directly affected by the flood event. The integration of GloFAS data with ERA5 reanalysis proved valuable, emphasizing key hydrological factors such as soil moisture and snowmelt, which are essential for accurate flood predictions.\u003c/p\u003e \u003cp\u003eThe study also incorporated the Novel Thunderstorm Alert System (NOTHAS) and advanced hydrometeorological modeling to identify areas at high risk of flooding. By integrating these tools with hydrological and geomorphological parameters, the study successfully refined flood risk mapping, categorizing regions into low-, moderate-, and high-risk zones. GIS-based terrain analysis further enhanced flood predictions, enabling more precise identification of areas prone to flash floods and landslides.\u003c/p\u003e \u003cp\u003eA significant contribution of this research lies in the application of the Flash Flood Potential Index (FFPI), combined with high-resolution topographic and satellite precipitation data, to assess flash flood susceptibility at the catchment level. The FFPI method proved effective in identifying high-risk areas, as evidenced by the Klina catchment during the January 2023 event, and supports discharge estimation for both past and future events. This approach is highly valuable for regional flood risk mapping and provides a robust foundation for the development of early warning systems, with the potential for further improvement through the integration of forecast data.\u003c/p\u003e \u003cp\u003eMoreover, this work marks a step forward in the development of a Geohazard Alert System that integrates not only meteorological and hydrological data but also terrain slope, soil saturation, and land use factors often overlooked in traditional flood forecasting systems. By leveraging the capabilities of Google Earth Engine for terrain modeling, remote sensing, and land surface analysis, the study offers a more integrated and comprehensive approach to understanding flash flood dynamics. However, additional case studies and model sensitivity analyses are necessary to assess the system\u0026rsquo;s reliability and enhance its robustness for broader operational use.\u003c/p\u003e \u003cp\u003eFinally, the comparison between the newly developed flood risk system and the European Flood Awareness System (EFAS) products demonstrated strong spatial agreement in identifying high-risk zones, particularly in the Skenderaj watershed during the January 2023 event. High-resolution ARW simulations accurately captured precipitation patterns, and validation with GPCP and ERA5 data confirmed the model's reliability. While the initial results are promising, further sensitivity analyses are necessary to enhance the model's accuracy across diverse terrains and flash flood scenarios, ensuring its applicability in various regions and future events.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments.\u003c/h2\u003e \u003cp\u003eWe express our sincere appreciation to the Hydrometeorological Institute of Kosovo for their generous provision of precipitation data and daily rainfall distribution for the case study event. Our gratitude extends to the Faculty of Computer Science and Engineering (FINKI) for the invaluable access granted to its advanced computing facilities, which are essential for running our model effectively. Special recognition is reserved for the editor and the anonymous reviewers, whose dedication and time invested in providing insightful reviews and constructive recommendations significantly enriched the quality of our work.\u003c/p\u003e\u003ch2\u003eData Availability Statement.\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon request.\u003c/p\u003e \u003cp\u003eConflict of interest.\u003c/p\u003e \u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e \u003cp\u003eAuthor Contribution statement.\u003c/p\u003e \u003cp\u003eConceptualization, I.M., and B.A.; methodology, I.M., and B.A.; software, I.M.; validation, I.M.; formal analysis, I.M., B.A., and L.O.; investigation, I.M., and B.A.; resources, I.M.; data curation, I.M., and B.A.; writing\u0026mdash;original draft preparation, B.A.; writing\u0026mdash;review and editing, I.M., B.A., and L.O.; visualization, I.M.; supervision, B.A., and L.O.; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAleksova B, Lukić T, Milevski I, Puhar D, Marković SB (2024a) Preliminary assessment of geohazards\u0026rsquo; impacts on geodiversity in the Kratovska Reka catchment (North Macedonia). Geosciences 14(3):62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/geosciences14030062\u003c/span\u003e\u003cspan address=\"10.3390/geosciences14030062\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAleksova B, Milevski I, Dragićević S, Lukić T (2024b) GIS-based integrated multi-hazard vulnerability assessment in Makedonska Kamenica Municipality, North Macedonia. Atmosphere 15(7):774. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/atmos15070774\u003c/span\u003e\u003cspan address=\"10.3390/atmos15070774\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAleksova B, Milevski I, Mijalov R, Marković S, Cvetković V, Lukić T (2024c) Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index. Open Geosci 16(1):20220684. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/geo-2022-0684\u003c/span\u003e\u003cspan address=\"10.1515/geo-2022-0684\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAleksova B, Milevski I, Cvetković VM, Nikolić N (2025) GIS-based assessment of flash flood potential in North Macedonia: Insights from advanced geospatial analytics. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.20944/preprints202501.0789.v1\u003c/span\u003e\u003cspan address=\"10.20944/preprints202501.0789.v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Preprints\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlfieri L, Burek P, Dutra E, Krzeminski B, Muraro D, Thielen J, Pappenberger F (2013) GloFAS \u0026ndash; global ensemble streamflow forecasting and flood early warning. Hydrol Earth Syst Sci 17:1161\u0026ndash;1175. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/hess-17-1161-2013\u003c/span\u003e\u003cspan address=\"10.5194/hess-17-1161-2013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlfieri L, Coauthors (2016) Global projections of river flood risk in a warmer world. Earth\u0026rsquo;s Future 5:171\u0026ndash;182\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalogun A-L, Matori A-N, Hamid-Mosaku AI (2015) A fuzzy multi-criteria decision support system for evaluating subsea oil pipeline routing criteria in East Malaysia. Environ Earth Sci 74:4875\u0026ndash;4884. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-015-4435-z\u003c/span\u003e\u003cspan address=\"10.1007/s12665-015-4435-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBernadet LR, Grasso LD, Nachamkin JE, Finley CA, Cotton WR (2000) Simulating convective events using a high-resolution mesoscale model. J Geophys Res 105:14,963\u0026ndash;14982\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao C, Coauthors (2019) Geospatial analysis of mass-wasting susceptibility of four small catchments in mountainous area of Miyun County. Beijing Int J Environ Res Public Health 16:2801. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph16152801\u003c/span\u003e\u003cspan address=\"10.3390/ijerph16152801\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampolo M, Soldati A, Andreussi P (2003) Artificial neural network approach to flood forecasting in the River Arno. Hydrol Sci J 48:381\u0026ndash;398. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1623/hysj.48.3.381.45287\u003c/span\u003e\u003cspan address=\"10.1623/hysj.48.3.381.45287\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang H-I, Kumar A, Niyogi D, Mohanty U, Chen F, Dudhia J (2009) The role of land surface processes on the mesoscale simulation of the July 26, 2005, heavy rain event over Mumbai, India. Global Planet Change 67:87\u0026ndash;103\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChawla I, Osuri KK, Mujumdar PP, Niyogi D (2018) Assessment of the Weather Research and Forecasting (WRF) model for simulation of extreme rainfall events in the upper Ganga Basin. Hydrol Earth Syst Sci 22:1095\u0026ndash;1117\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChinta S, Yaswanth SJ, Balaji C (2021) Assessment of WRF model parameter sensitivity for high-intensity precipitation events during the Indian summer monsoon. Earth Space Sci 8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2020ea001471\u003c/span\u003e\u003cspan address=\"10.1029/2020ea001471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. e2020EA001471\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCloke H, Pappenberger F, Thielen J, Thiemig V (2013) Operational European flood forecasting. In: Wainwright J, Mulligan M (eds) Environmental Modelling: Finding Simplicity in Complexity, 2nd edn. Wiley, pp 239\u0026ndash;247\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCluckie ID, Han D (2000) Fluvial flood forecasting. Water Environ J 14:270\u0026ndash;276\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollier CG (2007) Flash flood forecasting: What are the limits of predictability? Quart. J Roy Meteor Soc 133:3\u0026ndash;23\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eda Barcellos CL, P., and, Cataldi M (2020) Flash flood and extreme rainfall forecast through one-way coupling of WRF-SMAP models: Natural hazards in Rio de Janeiro State. Atmosphere 11:834. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/atmos11080834\u003c/span\u003e\u003cspan address=\"10.3390/atmos11080834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eĆurić M, Janc D (2011a) Comparison of modeled and observed accumulated convective precipitation in mountainous and flat land areas. J Hydrometeor 12:245\u0026ndash;261. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/2010JHM1259.1\u003c/span\u003e\u003cspan address=\"10.1175/2010JHM1259.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eĆurić M, Janc D (2011b) Analysis of predicted and observed accumulated convective precipitation in the area with frequent split storms. Hydrol Earth Syst Sci 15:3651\u0026ndash;3658. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/hess-15-3651-2011\u003c/span\u003e\u003cspan address=\"10.5194/hess-15-3651-2011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDudhia J (1989) Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077\u0026ndash;3107\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElmore KL, Stensrud DJ, Crawford KC (2002) Ensemble cloud model applications to forecast thunderstorms. J Appl Meteor Climatol 41:363\u0026ndash;383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/1520-0450(2002)041\u0026lt;0363:ECMATF\u0026gt;2.0.CO;2\u003c/span\u003e\u003cspan address=\"10.1175/1520-0450(2002)041%3C0363:ECMATF%3E2.0.CO;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmerton RE, Stephens EM, Pappenberger F, Pagano TP, Weerts AH, Wood A, Coauthors (2016) Continental and global scale flood forecasting systems. WIREs Water 3:391\u0026ndash;418. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/wat2.1137\u003c/span\u003e\u003cspan address=\"10.1002/wat2.1137\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Space Agency (2020) \u003cem\u003eCopernicus DEM - Global and European digital elevation model (GLO-30)\u003c/em\u003e. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model[](https://essd.copernicus.org/data_description_paper.html\u003c/span\u003e\u003cspan address=\"https://spacedata.copernicus.eu/collections/copernicus-digital-elevation-model[](https://essd.copernicus.org/data_description_paper.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerrier B (1994) A double-moment multiple-phase four-class bulk ice scheme. Part 1: Description. J Atmos Sci 51:249\u0026ndash;280\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez D, Lutz M (2010) Urban flood hazard zoning in Tucum\u0026aacute;n Province, Argentina, using GIS and multicriteria decision analysis. Eng Geol 111:90\u0026ndash;98\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eField CB, Coauthors (2012) Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. Special Report of the Intergovernmental Panel on Climate Change. Cambridge University Press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPCC (2021) Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao Y, Leung LR, Zhao C, Hagos S (2017) Sensitivity of U.S. summer precipitation to model resolution and convective parameterizations across gray zone resolutions. J Geophys Res Atmos 122:2714\u0026ndash;2733\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaume E, Borga M (2008) Post-flood field investigations in upland catchments after major flash floods: Proposal of a methodology and illustrations. J Flood Risk Manag 1(4):175\u0026ndash;189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1753-318X.2008.00023.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1753-318X.2008.00023.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGevorgyan A (2018) Convection-permitting simulation of a heavy rainfall event in Armenia using the WRF model. J Geophys Res Atmos 123:11008\u0026ndash;11029\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGigović L, Pamučar D, Bajić Z, Drobnjak S (2017) Application of GIS-interval rough AHP methodology for flood hazard mapping in urban areas. Water 9:360. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w9050360\u003c/span\u003e\u003cspan address=\"10.3390/w9050360\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiustarini L, Chini M, Hostache R, Pappenberger F, Matgen P (2015) Flood hazard mapping combining hydrodynamic modeling and multi-annual remote sensing data. Remote Sens 7:14200\u0026ndash;14226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs71114200\u003c/span\u003e\u003cspan address=\"10.3390/rs71114200\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiannaros C, Dafis S, Stefanidis S, Giannaros TM, Koletsis I, Oikonomou C (2023) Hydrometeorological analysis of a flash flood event in an ungauged Mediterranean watershed under an operational forecasting and monitoring context. Meteorological Applications, 29, e2079\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;mez H, Kavzoglu T (2005) Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin. Venezuela Eng Geol 78:11\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.enggeo.2004.10.004\u003c/span\u003e\u003cspan address=\"10.1016/j.enggeo.2004.10.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rse.2017.06.031[\u003c/span\u003e\u003cspan address=\"10.1016/j.rse.2017.06.031[\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e](https://www.mdpi.com/2072-4292/13/24/5019/htm)\u003c/span\u003e\u003cspan address=\"http://](https://www.mdpi.com/2072-4292/13/24/5019/htm)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrigan S, Zsoter E, Alfieri L, Prudhomme C, Salamon P, Wetterhall F, Pappenberger F (2020) GloFAS-ERA5 operational global river discharge reanalysis 1979\u0026ndash;present. Earth Syst Sci Data 12(2):2043\u0026ndash;2060. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/essd-12-2043-2020\u003c/span\u003e\u003cspan address=\"10.5194/essd-12-2043-2020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan JY, Hong SY (2018) Precipitation forecast experiments using the Weather Research and Forecasting (WRF) model at gray-zone resolutions. Weather Forecast 33:1605\u0026ndash;1616\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHapuarachchi HAP, Wang QJ, Pagano TC (2011) A review of advances in flash flood forecasting. Hydrol Process 25:2771\u0026ndash;2784\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHersbach H, Bell B, Berrisford P, Hirahara S, Hor\u0026aacute;nyi A, Mu\u0026ntilde;oz-Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A, Soci C, Abdalla S, Abellan X, Balsamo G, Bechtold P, Biavati G, Bidlot J, Bonavita M, Th\u0026eacute;paut J-N (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146(730):1999\u0026ndash;2049. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/qj.3803\u003c/span\u003e\u003cspan address=\"10.1002/qj.3803\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong SY, Lee JW (2009) Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Korea. Atmos Res 93:818\u0026ndash;831\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong SY, Lim JOJ (2006) The WRF single-moment 6-class microphysics scheme (WSM6). J Korean Meteorological Soc 42:129\u0026ndash;151\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJankov I, Gallus WA Jr., Segal M, Koch SE (2007) Influence of initial conditions on the WRF\u0026ndash;ARW model QPF response to physical parameterization changes. Weather Forecast 22(3):501\u0026ndash;519\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJee JB, Kim S (2017) Sensitivity study on high-resolution WRF precipitation forecast for a heavy rainfall event. Atmosphere 8:96\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohor River B (2011) An artificial neural network model for flood simulation using GIS: Johor River Basin, Malaysia. Environ Earth Sci. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-011-1504-z\u003c/span\u003e\u003cspan address=\"10.1007/s12665-011-1504-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan H, Shafique M, Khan MA, Bacha MA, Shah SU, Calligaris C (2019) Landslide susceptibility assessment using frequency ratio, a case study of Northern Pakistan. Egypt J Remote Sens Space Sci 22:11\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejrs.2018.03.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ejrs.2018.03.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar Samanta R, Bhunia G, Shit P, Pourghasemi HR (2018) Flood susceptibility mapping using geospatial frequency ratio technique: A case study of Subarnarekha River Basin, India. Model Earth Syst Environ 4:395\u0026ndash;408. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40808-018-0453-5\u003c/span\u003e\u003cspan address=\"10.1007/s40808-018-0453-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKundzewicz ZW, Kanae S, Seneviratne SI, Handmer J, Nicholls N, Peduzzi P, Mechler R, Bouwer LM, Arnell N, Mach K, Muir-Wood R, Brakenridge GR, Kron W, Benito G, Honda Y, Takahashi K, Sherstyukov B (2014) Flood risk and climate change: Global and regional perspectives. Hydrol Sci J 59(1):1\u0026ndash;28\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar A, Dudhia J, Rotunno R, Niyogi D, Mohanty UC (2008) Analysis of the 26 July 2005 heavy rain event over Mumbai, India using the Weather Research and Forecasting (WRF) model. Q J R Meteorol Soc 134:1897\u0026ndash;1910\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JW, Hong SY (2006) A numerical simulation study of orographic effects for a heavy rainfall event over Korea using the WRF model. Atmosphere 16:319\u0026ndash;332\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee TY, Kim YH (2007) Heavy precipitation systems over the Korean Peninsula and their classification. J Korean Meteorological Soc 43:367\u0026ndash;396\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee DK, Kim HR, Hong SY (1998) Heavy rainfall over Korea during 1980\u0026ndash;1990. Korean J Atmospheric Sci 1:32\u0026ndash;50\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee MJ, Kang J, Jeon S,Application of frequency ratio model and validation for predictive flooded area susceptibility mapping using GIS. 2012 IEEE Int. Geosci. Remote Sens. Symp., Munich (2012) Germany, IEEE, 895\u0026ndash;898. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/IGARSS.2012.6351809\u003c/span\u003e\u003cspan address=\"10.1109/IGARSS.2012.6351809\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLekhadiya HS, Jana RK (2018) Analysis of extreme rainfall events with different microphysics and parameterization schemes in the WRF model. Positioning 9:1\u0026ndash;11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevit JJ, Baldwin ME, Bright DR (2006) Examination of convective allowing configurations of the WRF-NMM model for the prediction of severe convective weather: The SPC/NSSL spring program 2004. Weather Forecast 21:167\u0026ndash;181\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLitta AJ, Mary IS, Mohanty UC, Kiran PS (2012) Comparison of thunderstorm simulations from WRF-NMM and WRF-ARW models over East Indian Region. The Scientific World Journal, 2012, 951870. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1100/2012/951870\u003c/span\u003e\u003cspan address=\"10.1100/2012/951870\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Chen Y, Chen O, Wang J, Zhuo L, Rico-Ramirez MA, Han D (2021) To develop a progressive multimetric configuration optimization method for WRF simulations of extreme rainfall events over Egypt. J Hydrol 598:126237\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLompar M, Ćurić M, Romanic D (2018) Implementation of a gust front head collapse scheme in the WRF numerical model. Atmos Res 203:231\u0026ndash;245. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atmosres.2017.12.018\u003c/span\u003e\u003cspan address=\"10.1016/j.atmosres.2017.12.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMerino A, Garc\u0026iacute;a-Ortega E, Navarro A, S\u0026aacute;nchez JL, Tapiador FJ (2022) WRF hourly evaluation for extreme precipitation events. Atmos Res 274:106215\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilevski I, Aleksova B, Lukić T, Dragićević S, Valjarević A (2024) Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia. Open Geosci 16(1):20220718. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/geo-2022-0718\u003c/span\u003e\u003cspan address=\"10.1515/geo-2022-0718\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMisenis C, Zhang Y (2010) An examination of the sensitivity of WRF/Chem predictions to physical parameterizations, horizontal grid spacing, and nesting options. Atmos Res 97:315\u0026ndash;334\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102:16663\u0026ndash;16682. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/97JD00237\u003c/span\u003e\u003cspan address=\"10.1029/97JD00237\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohanty U, Routray A, Osuri KK, Prasad SK (2012) A study on simulation of heavy rainfall events over Indian region with ARW 3DVAR modeling system. Pure Appl Geophys 169:381\u0026ndash;399\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoya AS, Mart\u0026iacute;nez-Castro D, Flores JL, Silva Y (2018) : A sensitivity study on the influence of parameterization schemes in WRF_ARW model on short- and medium-range precipitation forecasts in the Central Andes of Peru. Adv. Meteor., 2018, 1381092. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2018/1381092\u003c/span\u003e\u003cspan address=\"10.1155/2018/1381092\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukerji A, Chatterjee C, Raghuwanshi NS (2009) Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models. J Hydrol Eng 14:647\u0026ndash;652. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1061/(ASCE)HE.1943-5584.0000044\u003c/span\u003e\u003cspan address=\"10.1061/(ASCE)HE.1943-5584.0000044\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNandi A, Mandal A, Wilson M, Smith D (2016) Flood hazard mapping in Jamaica using principal component analysis and logistic regression. Environ Earth Sci 75:465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-016-5276-4\u003c/span\u003e\u003cspan address=\"10.1007/s12665-016-5276-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNohani E, Coauthors (2019) Landslide susceptibility mapping using different GIS-based bivariate models. Water 11:1402. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w11071402\u003c/span\u003e\u003cspan address=\"10.3390/w11071402\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsmanaj L, Spiridonov I, Jakimovski B, Coauthors (2024a) Assessment of the WRF model in reproducing a flash-flood heavy rainfall event over Kosovo. Acta Geophys. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11600-024-01365-9\u003c/span\u003e\u003cspan address=\"10.1007/s11600-024-01365-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsmanaj L, Spiridonov I, Jakimovski B, Coauthors (2024b) Three-dimensional cloud-resolving modeling of the flash-flood heavy rainfall event over Kosovo. Acta Geophys. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11600-024-01426-z\u003c/span\u003e\u003cspan address=\"10.1007/s11600-024-01426-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePanagos P, Borrelli P, Meusburger K, Alewell C, Lugato E, Montanarella L (2015) Estimating the soil erosion cover-management factor at the European scale. Land Use Policy 48:38\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.landusepol.2015.05.021[\u003c/span\u003e\u003cspan address=\"10.1016/j.landusepol.2015.05.021[\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e](https://www.mdpi.com/2072-4292/13/24/5019/htm)\u003c/span\u003e\u003cspan address=\"http://](https://www.mdpi.com/2072-4292/13/24/5019/htm)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePham BT, Bui DT, Prakash I, Dholakia MB (2017) Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. CATENA 149:52\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.catena.2016.09.007\u003c/span\u003e\u003cspan address=\"10.1016/j.catena.2016.09.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoggio L, de Sousa LM, Batjes NH, Heuvelink GBM, Kempen B, Ribeiro E, Rossiter D (2021) SoilGrids 2.0: Producing soil information for global modeling with unprecedented spatial detail. Geoderma 397:115098. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.geoderma.2021.115098[\u003c/span\u003e\u003cspan address=\"10.1016/j.geoderma.2021.115098[\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e](https://www.mdpi.com/2071-1050/14/9/5687/html\u003c/span\u003e\u003cspan address=\"http://](https://www.mdpi.com/2071-1050/14/9/5687/html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePradhan B (2009) Flood susceptible mapping and risk area delineation using logistic regression, GIS, and remote sensing. J Spat Hydrol 9:1\u0026ndash;18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePulvirenti L, Pierdicca N, Chini M, Guerriero L (2011) An algorithm for operational flood mapping from synthetic aperture radar (SAR) data using fuzzy logic. Nat Hazards Earth Syst Sci 11:529\u0026ndash;540. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/nhess-11-529-2011\u003c/span\u003e\u003cspan address=\"10.5194/nhess-11-529-2011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province. Iran Geocarto Int 31:42\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10106049.2015.1041559\u003c/span\u003e\u003cspan address=\"10.1080/10106049.2015.1041559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRenard KG, Foster GR, Weesies GA, McCool DK, Yoder DC (1997) Predicting soil erosion by water: A guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Agriculture Handbook. U.S. Department of Agriculture, p 703\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwartz C, Glen R, Ryan S, Fossell K, Weisman M (2015) NCAR\u0026rsquo;s experimental real-time convection-allowing ensemble prediction system. Wea Forecast 30:150904135422005. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/WAF-D-15-0103.1\u003c/span\u003e\u003cspan address=\"10.1175/WAF-D-15-0103.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin HH, Hong SY (2011) Intercomparison of planetary boundary-layer parametrizations in the WRF model for a single day from CASES-99. Bound -Layer Meteor 139:261\u0026ndash;281\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin HH, Hong SY (2015) Representation of the subgrid-scale turbulent transport in convective boundary layers at gray-zone resolutions. Mon Wea Rev 143:250\u0026ndash;271\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkamarock WC, Klemp JB (2008a) A time-split non-hydrostatic atmospheric model for weather research and forecasting applications. J Comput Phys 227:3465\u0026ndash;3485\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkamarock WC, Klemp JB (2008b) A time-split non-hydrostatic atmospheric model for weather research and forecasting applications. J Comput Phys 227:3465\u0026ndash;3485. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jcp.2007.01.037\u003c/span\u003e\u003cspan address=\"10.1016/j.jcp.2007.01.037\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkamarock WC, Weisman ML, Klemp JB (1994) Three-dimensional evolution of simulated long-lived squall lines. J Atmos Sci 51:2563\u0026ndash;2584\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkamarock WC, Klemp JB, Dudhia J, and Coauthors (2008a), : A description of the Advanced Research WRF Version 3 (No. NCAR/TN-475\u0026thinsp;+\u0026thinsp;STR). University Corporation for Atmospheric Research. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5065/D68S4MVH\u003c/span\u003e\u003cspan address=\"10.5065/D68S4MVH\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkamarock WC, Klemp JB, Dudhia J, Gill DO, Barker D, Duda MG, Powers J (2008) A description of the Advanced Research WRF Version 3 (No. NCAR/TN-475\u0026thinsp;+\u0026thinsp;STR). University Corporation for Atmospheric Research\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith G (2010) Flash flood potential: Developing a methodology for estimating flash flood risk. J Hydrol 389(3\u0026ndash;4):233\u0026ndash;247. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhydrol.2010.05.013\u003c/span\u003e\u003cspan address=\"10.1016/j.jhydrol.2010.05.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSofati I, Nurlatifah A (2019) The prediction of rainfall events using the WRF (Weather Research and Forecasting) model with ensemble technique. Int Conf Ser Earth Environ Sci 374:012036\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong HJ, Sohn BJ (2018) An evaluation of WRF microphysics schemes for simulating the warm-type heavy rain over the Korean Peninsula. J Atmos Sci 54:225\u0026ndash;236\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpiridonov V, Ćurić M (2019) Evaluation of Supercell storm triggering factors based on a cloud-resolving model simulation. Asia-Pac J Atmos Sci 55:439\u0026ndash;458\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpiridonov V, Baez J, Telenta JB (2020) Prediction of extreme convective rainfall intensities using a free-running 3-D sub-km-scale cloud model initialized from WRF km-scale NWP forecasts. J Atmos Solar-Terr Phys 209:105401. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jastp.2020.105401\u003c/span\u003e\u003cspan address=\"10.1016/j.jastp.2020.105401\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpiridonov V, Ćurić M, Grcic M, Jakimovski B (2022) Ensemble cloud model application in simulating the catastrophic heavy rainfall event. J Atmos Sci Res 5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.30564/jasr.v5i4.5081\u003c/span\u003e\u003cspan address=\"10.30564/jasr.v5i4.5081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpiridonov V, Ćurić M, Grčić M, Coauthors (2023) Assessment of the WRF model in simulating a catastrophic flash flood. Acta Geophys 71:1347\u0026ndash;1359. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11600-023-01032-5\u003c/span\u003e\u003cspan address=\"10.1007/s11600-023-01032-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatistical IM (2018) Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomatics Nat Hazards Risk. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/19475705.2018.1506509\u003c/span\u003e\u003cspan address=\"10.1080/19475705.2018.1506509\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun J, Lee TY (2002) A numerical study of an intense quasi-stationary convection band over the Korean Peninsula. J Meteor Soc Japan 80:1221\u0026ndash;1245\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun C, Liang X-Z (2020) Improving US extreme precipitation simulation: Sensitivity to physics parameterizations. Clim Dyn 54:4891\u0026ndash;4918\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun J, Xue M, Wilson JW, Zawadzki I, Ballard SP, Coauthors (2014) Use of NWP for nowcasting convective precipitation: Recent progress and challenges. Bull Amer Meteor Soc 95:409\u0026ndash;426\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule-based decision tree (DT) and a novel ensemble bivariate and multivariate statistical model in GIS. J Hydrol 504:69\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhydrol.2013.09.034\u003c/span\u003e\u003cspan address=\"10.1016/j.jhydrol.2013.09.034\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTehrany MS, Pradhan B, Jebur MN (2014) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332\u0026ndash;343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhydrol.2014.03.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jhydrol.2014.03.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThielen J, Bartholmes J, Ramos MH, de Roo A (2009) The European Flood Alert System \u0026ndash; Part 1: Concept and development. Hydrol Earth Syst Sci 13:125\u0026ndash;140. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/hess-13-125-2009\u003c/span\u003e\u003cspan address=\"10.5194/hess-13-125-2009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson G, Eidhammer T (2014) A study of aerosol impacts on clouds and precipitation development in a large winter cyclone. J Atmos Sci 71(10):3636\u0026ndash;3658. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JAS-D-13-0305.1\u003c/span\u003e\u003cspan address=\"10.1175/JAS-D-13-0305.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson G, Field PR, Rasmussen RM, Hall WD (2008) Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon Weather Rev 136:5095\u0026ndash;5511\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTiwari MK, Chatterjee C (2010) Uncertainty assessment and ensemble flood forecasting using bootstrap-based artificial neural networks (BANNs). J Hydrol 382:20\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jhydrol.2009.12.031\u003c/span\u003e\u003cspan address=\"10.1016/j.jhydrol.2009.12.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrenberth KE (2011) Changes in precipitation with climate change. Climate Res 47(1\u0026ndash;2):123\u0026ndash;138\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Office for Disaster Risk Reduction (UNDRR) (2022) Global Assessment Report on Disaster Risk Reduction 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.undrr.org/publication/global-assessment-report-disaster-risk-reduction-2022\u003c/span\u003e\u003cspan address=\"https://www.undrr.org/publication/global-assessment-report-disaster-risk-reduction-2022\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarlas G, Papaioannou G, Papadopoulos A, Markogianni V, Vardakas L, Dimitriou E (2023) Flash flood forecasting using integrated meteorological\u0026ndash;hydrological\u0026ndash;hydraulic modeling: Application in a Mediterranean river. Environmental Sciences Proceedings, 26(1), 35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/environsciproc2023026035\u003c/span\u003e\u003cspan address=\"10.3390/environsciproc2023026035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeisman ML, Davis C, Wang W, Manning KW, Klemp JB (2008) Experiences with 0\u0026ndash;36 h explicit convective forecasts with the WRF-ARW model. Weather Forecast 23:407\u0026ndash;437\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilson JW, Roberts RD (2006) Summary of convective storm initiation and during IHOP: Observational and modeling perspective. Mon Weather Rev 134:23\u0026ndash;47\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWischmeier WH, Smith DD (1978) Predicting rainfall erosion losses: A guide to conservation planning. Agriculture Handbook. U.S. Department of Agriculture, p 537\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXue M, Kong F, Thomas KW, Gao J, Wang Y, Coauthors (2008) CAPS real-time storm-scale ensemble and high-resolution forecasts as part of the NOAA Hazardous Weather Testbed 2008 Spring Experiment. In Preprints, 24th Conference on Severe Local Storms. American Meteorological Society, Savannah, GA. [CD-ROM 12.2]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYue H, Liu Y, Wang H (2022) Monitoring bare soil and vegetation areas using Sentinel-2 images and machine learning in Google Earth Engine: A case study of the Yellow River Basin. Remote Sens 14(19):4836. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/rs14194836[\u003c/span\u003e\u003cspan address=\"10.3390/rs14194836[\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e](https://www.mdpi.com/2072-4292/13/24/5019/htm\u003c/span\u003e\u003cspan address=\"http://](https://www.mdpi.com/2072-4292/13/24/5019/htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaharia L, Costache R, Prăvălie R, Ioana-Toroimac G (2017) Mapping flash flood potential in the Carpathian Basin using a GIS-based multicriteria approach. Environ Earth Sci 76(14):489. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12665-017-6822-3\u003c/span\u003e\u003cspan address=\"10.1007/s12665-017-6822-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaidi SM, Gisen JIA, Eltahan M, Yu Q, Misbari S, Ngien SK (2022) Assessment of Weather Research and Forecasting (WRF) physical schemes parameterization to predict moderate to extreme rainfall in poorly gauged basin. Sustainability 14(19):12624. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su141912624\u003c/span\u003e\u003cspan address=\"10.3390/su141912624\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZanaga D, Van De Kerchove R, De Keersmaecker W, Souverijns N, Brockmann C, Quast R, Arino O (2021) ESA WorldCover 10 m 2021 v100. \u003cem\u003eZenodo\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.5571936[]\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.5571936[]\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e(https://www.mdpi.com/2072-4292/13/24/5019/htm\u003c/span\u003e\u003cspan address=\"http://(https://www.mdpi.com/2072-4292/13/24/5019/htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang D-L, Gao K, Parsons DB (1989) Numerical simulation of an intense squall line during 10\u0026ndash;11 June 1985 PRE-STORM. Part I: Model verification. Mon Weather Rev 117:960\u0026ndash;994\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Y, Xue M, Li B, Chen J, Tao Z (2016) Spatial characteristics of extreme rainfall over China with hourly through 24-hour accumulation periods based on national-level hourly rain gauge data. Adv Atmos Sci 33:1218\u0026ndash;1232\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang T, Han L, Chen W, Shahabi H (2018) Hybrid integration approach of entropy with logistic regression and support vector machine for landslide susceptibility modeling. Entropy 20:884. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/e20110884\u003c/span\u003e\u003cspan address=\"10.3390/e20110884\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"acta-geophysica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agph","sideBox":"Learn more about [Acta Geophysica](http://link.springer.com/journal/11600)","snPcode":"11600","submissionUrl":"https://www.editorialmanager.com/agph/default2.aspx","title":"Acta Geophysica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"flash flood, heavy rain, FFPI, GEE","lastPublishedDoi":"10.21203/rs.3.rs-6917578/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6917578/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFlash floods pose a significant threat to infrastructure and public safety in Kosovo, particularly in urban and river environments. This study analyzes a winter flash flood event that struck the small catchment of Skenderaj on January 19, 2023. The main objective was to develop a flash flood warning system by integrating high-resolution atmospheric modeling, hydrological simulations, and hydrometeorological hazard modeling. A 2-km nested configuration of the ARW model successfully captured the spatial distribution of intense rainfall and localized flooding. The integration of GloFAS forecasts and ERA5 reanalysis data enhanced flood risk assessment and comparative analysis with the EFAS platform, revealing strong agreement in identifying high-risk zones and thereby validating the reliability of the developed system. Additionally, the Flash Flood Potential Index (FFPI) enabled high-resolution mapping of flash flood susceptibility using topographic, soil, land cover, vegetation, and satellite-derived precipitation data. The results for the Klina catchment revealed the precise identification of high-risk zones, allowing for both retrospective and predictive discharge estimation. This geospatially driven approach provides a scalable and operationally viable tool for enhancing regional early warning systems. These findings underscore the effectiveness of coupled weather-hydrology models and geospatial risk assessment tools in enhancing early warning systems, with potential for broader application and further refinement through sensitivity studies.\u003c/p\u003e","manuscriptTitle":"Integrated Rainfall Modeling and Hydrometeorological Alert System for Extreme Flooding in Kosovo","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-08 02:45:27","doi":"10.21203/rs.3.rs-6917578/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-11-05T14:07:40+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-07-08T05:13:16+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-03T10:25:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Acta Geophysica","date":"2025-06-24T08:55:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"Acta Geophysica","date":"2025-06-22T18:20:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-19T13:24:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"acta-geophysica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agph","sideBox":"Learn more about [Acta Geophysica](http://link.springer.com/journal/11600)","snPcode":"11600","submissionUrl":"https://www.editorialmanager.com/agph/default2.aspx","title":"Acta Geophysica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3c888c59-a9ee-405c-8940-1b1fdbd1b9eb","owner":[],"postedDate":"July 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-24T20:54:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-08 02:45:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6917578","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6917578","identity":"rs-6917578","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}