Is it possible to assess the flood risk of a small river with limited available data? Why do people not pay much attention to flood risk and are surprised at the same time? Are there solutions to prevent flood risk? Can the science of hydrology and watershed management help humans in this field?

Flood risk modeling of small watercourses is challenging when only limited input data are available. Therefore, this study assessed the flood characteristics of a small river (Tarna River: entire watershed-C, upper-VS, middle-TMS, and lower section-TOS) from 1990 to 2019. The assessment focused on modeling, model calibration, and validation using feature event-based time-series data in data-scarce environments. We showed that since the 2000s, the number of high-water levels above 250 cm, and the frequency of three flood types had increased. Flood simulation results showed the largest flooded area in the TMS section, followed by the VS, and then the TOS. The outcomes from the VS, TMS, and TOS sections did not exhibit superior performance compared to the C area. Models performed well for larger flood events, with Kling Gupta Efficiency corresponding well to NRMSE and Nash-Sutcliffe efficiency metrics. Accordingly, flood events characterized by the longest duration and high-water levels yielded outstanding results across all areas, followed by moderate flood events with good accuracy. Normal water level events exhibited significant deviations from the reference across all sections. In summary, despite the event-based modeling challenges in data-limited environments, such models can still mitigate potential flood events and improve decision-making processes. Keywords Hydrological modeling · HEC-RAS · Model accuracy · Flood events.

The impacts of climate change are becoming increasingly perceptible owing to shifting average temperature values, the emergence of anomalies, and the growing frequency of extreme weather events. Consequently, natural disasters, societal changes, and economic shifts occurred. Studies indicate that Earth’s surface temperature exhibits an increasing trend [1]. Previous research has also confirmed that due to decreasing summer precipitation and increasing spring evaporation, the values of temperature anomalies and

the frequency of summer droughts in Central Europe and the Carpathian Basin show an increasing trend [2–4]. The global frequency of natural disasters, particularly the occurrence of severe floods, has increased [5, 6]. Largescale inundations from river flooding have caused extensive damage and have had a global impact on economic activities, built environments, infrastructure, ecosystems, agriculture, and the natural environment [5]. However, major river floods pose a threat and even smaller, rapidly occurring river floods can lead to considerable damage. This phenomenon has also been observed in Hungary [7, 8]. Early intervention, predictive systems, and hydrological models can significantly assist in the event of sudden floods. The current conventional tools present challenges in terms of timely forecasting, making it necessary to introduce new methods. Flood preparedness and innovative flood management can help mitigate damage [6]. The use of numerical models has gained prominence in research [9, 10]. In hydrological studies, modeling offers the opportunity for both permanent and non-permanent runs, enabling the examination of temporal and spatial variations in events and modeling of various flood discharges [11]. However, it is crucial to understand the applications and objectives of these models.Hydrodynamic models allow the tracking of flow, determination of water depths, simulation of flood wave propagation, and simulation of precipitation runoff at the watershed scale. The location and extent of the potential inundation areas can be defined. A comprehensive understanding of a flood event can only be achieved by analyzing and drawing conclusions after merging the information [11, 12]. For small watercourses, several factors complicate the construction of models, determination of their accuracy, and assessment of the relevance of the results. The first and most important factor is the resolution and accuracy of the available basic data because it is necessary to use a more detailed DTM base for a reconstructed flood wave during the recession of a small watercourse [13, 14]. When modeling small watercourses, it is essential to define the banks as accurately as possible and mark terrain features to determine sensitive bank sections and load levels as accurately as possible. Determining the load levels is a vital part of operational forecasting. One illustrative example is the spring flood on the Tisza River in 2000, where Highway 41 had to be cut during the flood because this road section functioned as a dam element. The bridges and culverts were unable to drain a large amount of water, resulting in flooding of seven settlements. The defenses had to face a flood of unprecedented magnitude on the Tisza River, and flood waves also formed on the tributaries due to the effects of the backwater, while the dyke breached in 2001 further supported the further development of localization plans [7, 15]. Another complicating factor is the absence of gauging stations during a receding flood wave, resulting in significant information gaps. As emphasized by Sziebert and Zellei [16], flood flow measurements aim to facilitate understanding through measurement series and analysis, which we deemed necessary in this study. Current studies suggest utilizing international databases and new remote sensing techniques when creating models and hydrological studies that ensure data reliability and uniformity, and consequently, the widespread acceptance and relevance of the results [17, 18]. Studies have been conducted on the possibility of applying hydrological modeling at both national and international levels. However, there is still room for refining this information and expanding our experience [12, 19–22]. HEC-RAS (USACE-CEIWRHEC) is a freely accessible, internationally recognized, and widely used program for modeling hydrological processes. The model is applicable to both complex studies and small watercourses and is compatible with other geoinformation software. Previous research has demonstrated that HECRAS is particularly suitable for river channel modeling, cross-sectional representation, and combined analysis of multiple parameters and is capable of simulating surface profiles during various flood events [11, 23, 24]. Field measurements and digital modeling can be effectively combined for surface water flow modeling [20], and integrating HEC-RAS with other models has proven to be suitable for improving the accuracy of traditional forecasts and serves as a valuable tool for flood risk management [25]. However, the results of studies on data-scarce and large-scale watercourses require further investigation [22, 23]. Flood modeling has already been conducted for Hungary’s large rivers, and in recent decades, the models have been improved with an increasing focus on detailed surveying and mapping. Special and important roles are given to understanding the formation and changes of flood waves in the Tisza and Dan-ube Rivers [26, 27]. Kovács et al. [28] and colleagues demonstrated the significance of floods using the example of the Tisza River. Nagy [29] stated that floodplains are one of the most important elements in the runoff of flood events, and that surveying and modeling these areas is an urgent task to ensure long-term flood safety. Gashi et al. [30] investigated the lower reaches of the Drava River, emphasizing how the runoff from flood events influenced channel evolution. However, smaller streams remain in the background and in many cases, understanding of flood situations is incomplete and the Tarna river is a representative example. The data-poor environment includes lack of high-resolution topographic data, few water gauge stations, few official cross-sections, accurate delineation of the riverbank, a database of tributary flow rates, and the extent of vegetation along the river. We aimed to provide a flood inundation model for the most relevant flood events that occurred within the watershed of the Tarna River (Hungary) using a comprehensive approach that utilized integrated data sources, including satellite imagery, water gauge data, and land-use information. Although various attempts have been made to model larger rivers, modeling flood situations in smaller rivers with limited observed data remains a challenge, while the risk of sudden, large-scale floods in lowland and hilly regions in Hungary is high [9]. From this perspective, the Tarna Watershed is of particular importance. Although the watercourse is a relatively small river, floods and flash floods have already caused damage in this area. Flood surges rapidly inundate an area and their duration typically spans a few days. In extreme cases, the descending water volume can reach up to 50–70 million m3 . Although flood modeling (especially HEC-RAS) is not a new topic, examining and integrating data-scarce segments is still a research field in which substantial knowledge is missing. The lack of observed data remains an issue for catchments of smaller rivers and poses a significant risk for inappropriate projection possibilities to plan prevention. This research represents an innovative advancement at a region-specific level as our findings demonstrate that HECRAS models are suitable for modeling small streams even with varying terrain conditions. The research process can serve as a potential scenario for modeling and predicting flood disasters in similarly data-poor environments with similarly diverse terrain conditions. Accordingly, our main objective was to provide flood risk assessment of a small river by assessing the possibilities of modeling, model calibration, and validation considering topographic features based on event-based time-series data in data-scarce environments. Accordingly, our main objective was to provide flood risk assessment of a small river by assessing the possibilities of modeling, model calibration, and validation considering topographic features based on event-based timeseries data in data-scarce environments. We had the following hypotheses: (i) number of flood events had increased in the recent decades, and (ii) the river section as well as (iii) the water level (i.e. flood intensity) have relevant effect on the accuracy of the models.