## 2 Materials and Methods
### Morphological Parameter Estimation  
Aerial photographs and satellite images were integrated into GRASS [67] for estimating morphological characteristics. The methodology used in this study for processing the images is presented in Figure 2. The aerial photos were georeferenced and the river \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multicolumn{2}{c}{**Closest Station to the Dam**} & \multicolumn{3}{c}{**Farthest Station to the Dam**} \\  & \multirow{2}{*}{**Station Name**} & **Downstream** & **Reduction in** & **Resolution Name** & **Downstream** & **Reduction in** \\  & & **Distance (km)** & **Suspended** & **Stisement (\%)** & **Distance (km)** & **Suspended** \\ \hline Prado and & Puente & 42 & 70–73 & Narifno & 279 & 9 \\ Betania & Santander & & & & \\ Salvajina & Salvajina & 1 & 75 & Juanchito & 134 & 30 \\ Urra I & Pascaballos & 6 & 81 & Monteria & 175 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Suspended sediment reduction downstream of the selected dams. channels digitized with a maximum root mean square error (RMSE) of 10 m with the settings of the GRASS GIS (see Figure 2a). Satellite images with a spectral range were processed to identify the river channel width. Based on their spectral characteristics, different indices have been developed for the extraction of water bodies [68]. Considering the geomorphology of the study areas (i.e., high mountain ranges and piedmont plains), the Modified Normalized Difference Water Index (MNDWI; see Figure 2b) was chosen, defined by:  
\[MNDWI=\frac{GREEN-MIR}{GREEN+MIR} \tag{1}\]  
where the \(MIR\) and \(GREEN\) bands must be selected for the different Landsat and Aster images since the band numbers change among missions. All water body extraction methods add additional bodies to the main channel, such as tributaries and effluents, swamps, clouds, and rock bodies, such as outcrops, which need to be removed or cleaned, leaving only one channel. In Colombia, the cloud cover is high, especially in the mountainous areas of all  
Figure 2: Methodology for obtaining the morphological characteristics of rivers: (**a**) georeferencing and digitization of aerial photographs, (**b**) delimitation of the river channel in spectral images, (**c**) delimitation of the river channel in radar images, and (**d**) morphological parameter assessment using the R package cmgo. selected dams. Although the extraction of water bodies with the MNDWI, in general, is for values greater than 0 in the cloudy areas, the selection value may vary between \(-0.2\) and 0, so a constant threshold check is necessary for each image. In the Urra I project sector, due to its high cloudiness, a Sentinel 1-GRD image was used for 2020 (Figure 2c) and corrected by following the procedures proposed by [PERSON] (2019)[PERSON] (2019), a methodology available in the free Sentinel Applications Platform (SNAP) tool ([[https://step.esa.int/main/download/snap-download/](https://step.esa.int/main/download/snap-download/)]([https://step.esa.int/main/download/snap-download/](https://step.esa.int/main/download/snap-download/)); accessed on 30 October 2022). The image was converted from digital pixel values to calibrated SAR backscatter values, which were analyzed with the histogram setting of SNAP, using the low return signal behavior of open water ([PERSON] et al., 2020), allowing us to differentiate water and land. This information was processed in a similar way to the Landsat and Aster images, only leaving the main channel of the river. After obtaining the channel using the methods previously described, the morphological parameters were calculated with the R package cmgo (see Figure 2d) ([PERSON] et al., 2020). This open-source code package is versatile and easy to use. The package gives channel metrics such as width, sinuosity, and slope. The R package cmgo requires separating the channel banks on the left and right sides, information that is used for calculating the width, center-line, and slope of the channel.