In the southeast of Spain heavy storms can occur in the late summer and early autumn, during which great amounts of rain pours down. During a storm that took place in September 2019, over 300 mil- limetres of rain fell in just five hours. Throughout the 2019 flooding event, the Rambla de la Carrasquilla has overflowed its banks and the adjacent agricultural lands flooded and large amounts of water ac- cumulated in the streets of Los Nietos. Since the 2019 flood, improvements to the river system of the Rambla de la Carrasquilla have been made. For instance: a relatively small culvert was replaced with a bigger one in 2021, to increase the discharge capacity. Due to global warming, the probability of occurrence of the heavy storms will increase and thus the probability of flooding. However, the lack of historical data makes it hard to assess the influence of different storms and the replacement of the cul- vert on future floods. Numerical models can be used to make predictions of floodings and gain insight in storm impacts. The aim of this report is summarized by the question: How can the flood areas and peak water levels of the Rambla de la Carrasquilla and its catchment be obtained by simulating three different scenarios based on peak discharges using HEC-RAS models?. At first a spatial analysis of the river system was done using QGIS. Hereafter a 1D and 2D model were constructed with the help of a Digital Terrain Model (DTM). QGIS and RiverGIS were used to define the geometry of the system for the 1D model, where the 2D model geometry was constructed in HEC-RAS itself. The dimensions of the structures in the river were measured using photogrammetry, due to which dimensions could be obtained with an absolute error of around 2 cm. Fieldwork was carried out to estimate the surface roughness of the main channel and parts of the flood plains.
The results consist of different floodmaps that were made using 1D and 2D HEC-RAS models with different settings and boundary conditions. For the 1D model, one run was done using a single reach for the whole river and another including a bifurcation for the downstream part of the river. It can be concluded that the 1D model approach is not very suitable for our research as it is unable to accurately construct the flooded areas. The 2D model performed better and thus was used to further investigate the influence of different model parameters and inputs. Important take-away points are that the time step is of large influence for finding a stable solution, which is found around Δ𝑡 = 1 [second] in this report. The influence of the surface roughness of the river bed and floodplains was investigated by using Manning’s n. These results showed clearly that a higher value for Manning’s n leads to a larger flooded area. When investigating the influence of structures (two bridges, a pipeline and a culvert) on the flooded area, it is found that the structures do not have a great influence. The reason for this is that at critical points, the river already overflows its banks when the flow is not hindered by these structures. Adding the structures makes this only slightly worse. Furthermore, the 2021 culvert only showed a slight improvement in flooded area with respect to the 2019 culvert.
In order to further calibrate the HEC-RAS models, a comparison was made with floodmaps that were constructed by the Spanish government. Both studies show similar results, although the government used a Digital Terrain Model with a higher resolution, which can have a large impact on the outcome. However, this government study cannot be used for validation of the models in this research, as the results are based on hypothetical situations instead of empirical data as well.
For further research it is recommended to investigate more accurate hydrological boundary conditions, based on empirical rainfall data. For both boundary conditions a constant value was taken, but the behaviour over time could change the output of the results. Besides that, it could be interesting to study the exact effect of using a Digital Terrain Model with a higher resolution for the Rambla de la Carrasquilla. As this can have a direct influence on the size of the flooded area. Furthermore, the approaches used in this research can be used for investigating different flood mitigation solutions. These solutions, for example heightening of the river banks or widening of the bed, can be implemented in the 2D model before applying them in real life.

Providing farmers with the tools to monitor their crops continuously and reliably can aid the scaling of global food production to meet the ever-growing demand. The optical Normalised Difference Vegetation Index (NDVI) is commonly used to monitor crop greenness as an indicator for biomass, but it is limited by clouds and signal saturation. Synthetic Aperture Radar (SAR) imagery, which is not hampered by clouds, can be used to complement the NDVI. The Biomass Proxy (BP) combines the NDVI and SAR data, but spatial biomass estimations still strongly depend on the NDVI and therefore face the same limitations.
This study aims to assess the potential value of spatial SAR data to approximate the in-field biomass distribution. The SAR signal from Sentinel-1 and the NDVI signal from Sentinel-2 were analysed temporally and spatially for fields of maize, barley, oat, and spring wheat in the Dutch province of Flevoland. It was assumed that consistent SAR patterns in the spatial signal correspond to biophysical changes in the monitored crops. A framework to detect these patterns and include them in the BP was created based on combining cluster detection with spatial autocorrelation. The components of this framework demonstrate that backscatter intensity, phenological stage and crop type influence the probability of consistent patterns and that consistent patterns could not be observed from the spatial NDVI signal. Moreover, it was found that the BP’s sensitivity to the input signals depends on crop type. With the knowledge of when and where consistent patterns occur, targeted research can be done to understand the spatial SAR signal better and, thereby, optimally use all available information.