# Development of Open-Source Tools for Event-Based Hydrological Modelling Using GIS and Python
## 3 Results and Discussion
### General Overview and Application Example  
The previous processes can provide information for performing various advanced hydrological modelling tasks in different coding environments since the ASCII files can be readily accessed using various programming languages. As an example, we present, in this manuscript, cases studies using the QGIS outputs described in previous sections to implement the SCS curve number, the Green-Ampt model [36] and the Muskingum-Cunge routing method [37] within a Python environment. Starting with the SCS curve number, we aimed to assign a curve number to each pixel to derive either an average catchment curve number for lumped modelling or a pixel-specific curve number analysis enabling distributed modelling. We illustrate this using the Aragon River upstream of the Canfranc gauging station (Huesca, Spain). Using the QGIS-derived ASCII land use data from SIOSE, we estimated the curve number and applied the SCS method to compute runoff excess on a per-pixel basis. Figure 17 shows the spatial distribution of the estimated runoff excess for a 60 mm storm event (producing the synthetic hyetograph presented in Figure 18) and the estimated time to runoff initiation for each 2 \(\times\) 2 m pixel under a synthetic hyetograph, assuming runoff begins when rainfall exceeds the initial abstraction (Ia). As shown in Figures 18 and 19, we consider storm events with a volume of 60 mm falling over approximately 20 min. In the context of such storm events, evapotranspiration (approximately 10 mm/day in the most extreme areas and periods of the Ebro River basin) is negligible and can be excluded from the water balance. Figure 18: (**a**) Synthetic hyetograph of a 60 mm storm event; (**b**) spatial distribution of excess runoff as estimated by the curve number method from a 60 mm storm event; (**c**) time taken for the synthetic hyetograph to produce runoff. Similarly, the soil property data from SoilGrids processed within QGIS enabled us to perform simulations based on the Green-Ampt infiltration equation. As a case study, we examined the Urenderro River upstream of the Barindano gauging station (Navarra, Spain). Using the ASCII files containing sand, silt, and clay fractions, we estimated soil textural classes using pedotransferer functions, assigning the Green-Ampt parameters to each pixel. These parameters were retrieved from Carsel and Parrish [40] and aided by [PERSON] [41] and [PERSON] [42] models for the conductivity and water retention curves. Figure 19 displays the maximum aggregated runoff, the time taken for the presented hyteograph to produce runoff and the maximum instantaneous runoff along with its timing. For the kinematic wave model, we simulated the routing of a synthetic hydrograph along the Bailin River from the upstream cross-section to the Sabinanigo gauging station (Huesca, Spain). We processed the QGIS ASCII to obtain the main channel location (Figure 20; 0-1 values), the channel topographical height (Figure 21; 0-z values) and main channel width (0-b values) with \(2*2\) spatial resolution. The longitudinal slope was computed from topographical heights, supposing a straight triangular distance between both consecutive sections while Manning's roughness coefficient and the cross-sections' side slope were estimated from field observations. Figure 22 presents the evolution of the hydrograph for a set of 39 selected cross sections. Figure 23 displays the spatial representation of the maximum flow and the maximum water depth derived from the kinematic wave hydrological simulation over a one pixel scale (the represented information refers to each 2 \(\times\) 2 m pixels with each pixel representing each cross-section located at a 2.8 m distance from the previous one), and Figure 24 is the spatial representation of some hydrological variables. Figure 19: Simulated hyteograph, maximum aggregated runoff, time taken to produce runoff, maximum instant runoff and time at which it was observed. Figure 21: Channel heights from GIS files and computed longitudinal slope.