# Development of Open-Source Tools for Event-Based Hydrological Modelling Using GIS and Python
## 3 Results and Discussion
### Discussion  
The integration of QGIS with Python routines for simulating event-based hydrological processes offers a flexible open-source framework for catchment-scale analyses. The pro  
Figure 23: Profile of the peak flow, maximum velocity, maximum water depth and Froude number across the cross-sections. Figure 24: Spatial representation of the peak flow, maximum velocity, maximum water depth and Froude number across the cross-sections. posed methodology demonstrates that producing GIS-based information to be processed in Python can improve the performance of events-based hydrological analysis in that environment. The main assumption underlying our approach is that spatial information can be used directly to parameterise hydrological models. The proposed framework can improve the model parametrisation by for example leveraging the delineation of catchments and retrieving land uses or soil physical properties to be directly processed in Python. By taking advantage of QGIS's graphical interface and geoprocessing tools, users can efficiently manage spatial datasets, while Python scripts enable automated parameter calculation, event-based simulation and results post-processing within the same environment. However, due to this integration, the proposed methodology is highly sensitive to the availability, accuracy and spatial resolution of the input data which can limit the performance of the hydrological model outputs. Since the QGIS outputs from processing spatial information are used to parametrise hydrological models, the greater the accuracy and quality of the spatial information, the higher the potential for improved model outputs. This is the main source of the uncertainty of the methodological process. The methodological framework we present in this manuscript, unlike proprietary software packages such as ArcHydro [43], HEC-GeoHMS [44] or semi-proprietary frameworks like MIKE SHE [45], allows for a better control over the hydrological modelling chain, from spatial data pre-processing to computation and visualisation. While other hydrological models do not allow for spatially distributed parametrisation, this proposal has the potential to improve model parameterisation, as it is directly linked to the resolution of the spatial data, which can enhance the performance of (semi-)distributed models and increase the accuracy of the representative values in lumped models. Conversely, this also implies that unrepresentative or erroneous spatial information will lead to poor parameterisation. As mentioned previously, we have presented a set of common routines for producing data that can be easily managed in Python and used for the specific models presented here, as well as for many other models. For example, the time of concentration models presented in [46; 47; 48; 49] can be easily computed within the Python environment using the GIS outputs we have provided. Similarly, the infiltration models of [PERSON] [50] and [PERSON] [51] can be parameterised using the outputs presented in this manuscript, following the approaches in [52; 53; 54]. Paying particular attention to the specific hydrological models used in this manuscript, it should be noted that we have not carried out specific validations for the examples presented; therefore, the outcomes cannot be compared with actual observations. Specific model calibration or validation based on the proposed framework could be performed in future work, for example, to analyse the accuracy of parameter estimates. Similarly, given the short duration of the storm events considered (see Figures 17 and 18), we have neglected the effect of evapotranspiration incorporated in the theoretical definition of the water balance presented in Equations (2) and (3). Future studies using the proposed methodology could extend the duration of the analysed events and incorporate the effect of evapotranspiration in the water balance. During the application of this workflow, we identified certain limitations, particularly convergence issues in some QGIS tools when processing highly complex geometries.