## 1 Introduction
The authors of the article [10] presented a 2D modeling concept created in their study, whereby a cascade of 25 2D models was developed in the MIKE21 software, which covers a 600 km-long stretch of the Odra River (Poland) with an area of 5700 km\({}^{2}\). A constant grid resolution of 4-6 m was used in the modeling. The models were applied for multiple purposes, first to develop the flood hazard and risk maps for major cities, then to validate historical flood data and stage-discharge relationships at monitoring stations and to verify design discharges through flood routing. In a study by [PERSON], [PERSON], [PERSON]. [11], the applicability of hourly data from the Global Precipitation Measurement Mission (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG V6 and IMERG V7) for event-based flood modeling in the Songshui River basin in southwest China using the hydrological modeling system (HEC-HMS) was assessed. Land stations were used to adjust the data, which significantly improved the accuracy of flood modeling. The paper [12] presents two empirical methods for predicting downstream hydraulic variables based on observed flow data: a linear programming (LP) model and a convolutional neural network (CNN). In this paper, we apply both empirical models to the Colorado River system at a site on the Green River downstream of the Yampa River confluence and Flaming Gorge Dam and compare it with the physics-based Streamflow Synthesis and Reservoir Regulation (SSARR) model. The results show that both proposed models significantly outperform the SSARR model. Moreover, the CNN model outperforms the LP model for hourly forecasts, while both perform similarly for daily forecasts. In [13], the applicability of the k-nearest neighbor (KNN) algorithm for forecasting the mean daily discharge of the Ramis River at the Ramis gauging station is investigated. A dataset including the basin-wide mean rainfall and the mean daily discharge obtained from hydrometeorological stations with different lags was used as input to the KNN machine learning algorithm. The performance of the KNN algorithm was quantitatively evaluated using hydrological capability metrics, such as the mean absolute percentage error (MAPE), anomaly correlation coefficient (ACC), Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE'), and spectral angle (SA). The flow forecasting results of the Ramis River using the k-nearest neighbor machine learning algorithm achieved a high level of reliability with flow lags of one and two days and precipitation of three days. The authors of the study [14] showed that 1D flood modeling overestimated water depth and flow velocity compared to 2D flood modeling. This is due to the fact that the 2D model used an implicit algorithm for determining the final volume, which is more accurate and stable compared to 1D modeling. The authors of the work [15] emphasize that the highest peak flow results were obtained using the HEC-HMS method among the three SUH formulas and HEC-RAS calculations. The deviation of the peak flow value using SUH, HEC-HMS, and HEC-RAS was less than 10%. At the same time, the authors of the work [16] used an integrated modeling approach to combine the soil and water assessment tool (SWAT 2012 version) and the river analysis system of the Hydrological Engineering Center (HEC-RAS 5.0.7 version). SWAT was used to generate the effective rainfall amounts required to run 2D rainfall hydrodynamic simulations on the HEC-RAS grid. The approach presented by the authors in [17] is also noteworthy, who studied hydrological analyses using the HEC-HMS software and flood analyses using HEC-RAS (Ras Mapper). As a result of the HEC-HMS modeling, a flood flow restoration plan was obtained for 5, 10, and 25 years, which was 185 m\({}^{3}\)/s, 208.7 m\({}^{3}\)/s, and 234.3 m\({}^{3}\)/s. The results of the HEC-RAS modeling show that the flooded area with repeated flood flows after 5, 10, and 25 years was 857.08 ha, 885.62 ha, and 979.59 ha.