## 3 Results and Discussion
The maps show depth variations (color-coded from green for shallow to blue for deep areas), allowing an assessment of risks, the identification of potential flood zones, and the planning of protective measures. coefficients, was performed based on available data, but the limited number of direct field measurements introduces uncertainty into these parameter values. Figure 5: Flood zones on the Uba River in the cities of Shemonaikha, Ust-Talovka, Krasny Yar, and Rechnoye, East Kazakhstan region, for 2019: (**a**) actual flood zone; (**b**) simulated flood zone calculated using the analytical system; for 2020: (**c**) actual flood zone; (**d**) simulated flood zone calculated using the analytical system; for 2021: (**e**) actual flood zone; (**f**) simulated flood zone calculated using the analytical system. In addition, discrepancies may result from the limitations of the observed flood maps themselves. The observed flood extents were derived from satellite imagery, which, despite their high resolution, can be affected by classification errors, such as difficulties distinguishing water surfaces from wet soil or flooded vegetation. These factors can lead to a slight over- or underestimation of actual flooded areas. Furthermore, it is important to note a temporal limitation of the satellite-based validation approach. The Sentinel-1 satellite acquires data with a fixed revisit interval and may not always capture the peak of a flood event due to a timing misalignment. This latency could lead to an under-representation of the maximum inundation area, especially in fast-evolving flood scenarios. Consequently, even with an accurate spatial resolution, satellite imagery might miss short-lived flood peaks or local maxima, introducing additional uncertainty when compared to the simulated maximum flood extent. Despite these sources of uncertainty, the high degree of coincidence between the modeled and observed flood extents (greater than 89% for all analyzed events) indicates that the model provides reliable results for flood hazard assessment in the region. Nevertheless, future improvements could include the use of higher-resolution terrain data, enhanced hydrological monitoring of lateral inflows, and more detailed calibration supported by field surveys to further reduce uncertainties. The accuracy of the model developed in this study, as reflected in the percentage of matching between simulated and observed flood extents (91.89% for 2019, 89.09% for 2020, and 95.91% for 2021), is consistent with or exceeds the results of similar studies conducted in data-limited environments. For example, [PERSON] et al. (2024) [19] applied hydrodynamic modeling for the Tara River using HEC-RAS under conditions of limited hydrological data. Their results demonstrate reliable performance for large flood events, with higher accuracy for these larger events as measured by the Kling-Gupta efficiency (KGE) and Nash-Sutcliffe efficiency (NSE). Similarly, in our study, the largest flood event (2021) showed the highest degree of matching, confirming that event magnitude can positively influence model accuracy when appropriate calibration is performed. Furthermore, [PERSON] et al. (2024) [11] showed that the use of bias-corrected satellite precipitation data (IMERG V7) improved the accuracy of event-based flood simulations in the Sunshui River Basin. While our study did not directly apply distributed precipitation forcing, the integration of satellite-derived spatial data (Sentinel-1 and Sentinel-2), DEMs, and gauging data provides comparable improvements in model reliability. Our approach aligns with these studies in demonstrating that the integration of diverse and complementary datasets significantly enhances the accuracy of flood hazard models in regions where data availability is limited. These comparisons reinforce the robustness of our modeling framework and highlight its relevance for flood hazard assessment and management in East Kazakhstan.