Advances in flash flood modelling in urbanized and data scarce-areas
Due to the COVID-19 crisis the PhD defence of Yakob Umer will take place (partly) online.
The PhD defence can be followed by a live stream.
Yakob Umer is a PhD student in the department of Applied Earth Sciences (AES). His supervisor is prof.dr. V.G. Jetten from the Faculty of Geo-Information Science and Earth Observation (ITC).
Strategies to cope with floods, for instance, integrated flood management, require proper flood hazard assessment. Such flood hazard assessment relies on the flood modelling approaches, which require high-quality and quantity data to produce realistic flood hazard maps. In many cities in the developing countries, also known as data scarce-areas, flood modelling is challenging as there is sparse or no observation on rainfall data and soil information used for proper model development, model calibration, and validation. However, open-source geospatial data and the NWP model rainfall product can overcome the data scarcity problem. Therefore, this Ph.D. thesis aimed to explore publicly available geospatial datasets and their integration with hydro-meteorological modelling systems to overcome the data-scarcity challenges, specifically, to explore the data of extreme rainfall, soil, and land-cover information for flash flood modelling in the urbanized catchment. The findings are summarized into three phases, as discussed in the following.
The first phase of this Ph.D. study focuses on exploring soil information used for flash flood modelling in the urbanized catchment. Accordingly, the soil information that is determining the infiltration processes (i.e., Ksat, porosity, initial condition, soil matric suction, and soil depth) is derived following three different soil databases: (1) FAO soil map (SMFAO); (2) soil map derived based on the soil-landscape relationships (SMLS), and (3) soil map derived from the SoilGrids database (SMSG)). The soil information derived from these data sources is believed to be overcome the data limitation problem. However, the open-source soil databases cannot correctly consider the local features (e.g., wetlands, fragmented vegetation cover, and soil compaction), which would lead to the data quality problem. Therefore, in this study, the local features' influence on the derived soil information is numerically adjusted by incorporating the land cover data derived from the satellite image. The derived soil information is then used as the input to openLISEM integrated flood modelling system to assess their impact on flood dynamics in the urbanized catchment. The impact analysis is evaluated as the compacted and uncompacted soil condition. The results indicated that the flood dynamics are highly sensitive to different soil databases. The incorporation of soil compaction into the soil information has the largest impact on the flood dynamics in the catchment. This study showed that open-source data choice strongly influences both the simulated quantity and spatial variability of the infiltration, which directly affects runoff and flooding. On top of that, the effect of sealing and compaction is equally essential and nearly outweighs the differences caused by the use of different soil databases.
The second part of the Ph.D. thesis is to model and analyze the high-intensity rainfall product using the WRF model, which combined two main research objectives (chap.3 & 4 in the document). The first objective is to evaluate the satellite-driven urban fraction appropriateness in the WRF model for simulating high-intensity rainfall events in urbanized areas. Three different simulations are performed in order to distill the impact of changing urban fractions and adjusted urban parameters on the simulated rainfall: The first simulation (1) StandardWPS, which is carried out using the default urban fraction with the default urban parameters; (2) UFD_Parameter, which is using the default urban fraction, but with adjusted urban parameters; and (3) Updated, which is with the updated urban fraction based on the Landsat 2016 image and the adjusted urban parameters. All model simulations are configured at high spatial (1 km) and temporal (10-minute) resolutions forced with the latest ERA5 global reanalysis dataset. The model result was validated using the rainfall observation from the gauging station and CHIRPS data. The results showed that the simulated rainfall performs better with a relatively lower error when using the updated urban fraction. The satellite-derived urban map represents a more realistic extent and intensity of the urban fraction with a heterogeneous urban fraction, which results in more realistic rainfall simulations. The second objective of the second part of the Ph.D. is to evaluate the suitability of the WRF model in simulating high-intensity rainfall events. Here, we evaluated the procedure to select the appropriate WRF parameterization combination for proper high-intensity rainfall simulation through the sensitivity analysis. The WRF model set up with the updated urban fraction is used for the WRF model simulation as the combination of microphysics, cumulus, and planetary boundary layer (i.e., MP-CP-PBL procedure). The result showed that the WRF model's ability to simulate the HIRE that can be used for flash flood modelling is highly determined by the appropriate selection of the parametrization combinations.
The last phase of this Ph.D. study focuses on examining the WRF rainfall product's applicability for urban flood hazard assessment by proposing a new methodology to select the representative gridcell-rainfall events from three known WRF simulated rainfall events (HIREs). The two-step procedure is followed. Firstly, the potential gridcell-rainfall events from the WRF simulated HIREs are selected based on the given criteria. Secondly, the representative gridcell-rainfall events as a design storm of a given return period are defined using the quantile function where the quantile function is applied to the cumulative rainfall amount of each of the selected potential gridcell-rainfall events. Finally, three different gridcell-rainfall events representing the design storms varying between T = 2 and 10-year return periods are extracted for each three HIRE. The developed design storms are then compared with the design storms from the pre-established Intensity-Duration-Frequency (IDF) curves in terms of their 24-hour total rainfall amount (mm), peak intensity (mm/hr), and the time to peak intensity (minute). The constructed design storms are then applied to the openLISEM model for flood hazard modelling in Kampala's upper Lubigi catchment. The derived design storm can give an insight into the applicability and usability of the numerical weather prediction model outputs for flood modelling in the data-scarce areas.
In general, the results of this study indicates that open-source database such as SoilGrids and their combination with satellite-driven land-cover data can provide soil information needed for flood modelling in the data-scarce area. Moreover, the high-intensity rainfall that has the potential to trigger the localized flood can be produced using the mesoscale WRF model. However, the procedure to improve the performance of the NWP model in simulating high-intensity rainfall must be taken into consideration. The MP-CP-PBl procedure followed in this study and updating the urban fraction certainly improved the performance of the WRF model to simulate high-intensity rainfall. The WRF data-assimilation and model coupling system can further improve the model's performance in simulating the events.