Abstract. Flood management is adopting a more risk-based approach, whereby flood risk is the product of the probability and consequences of flooding. Two-dimensional flood inundation modeling is a widely used tool to aid flood-risk management. The aim of this study is to develop a flood inundation model that uses historical flow data to produce flood-risk maps, which will help to identify flood protection measures in the rural areas of Sri Lanka. The LISFLOOD-FP model was developed at the basin scale using available historical data, and also through coupling with a hydrological modelling system, to map the inundation extent and depth. Results from the flood inundation model were evaluated using Synthetic Aperture Radar (SAR) images to assess product accuracy. The impacts of flooding on agriculture and livelihoods were analyzed to assess the flood risks. It was identified that most of the areas under paddy cultivation that were located near the middle and downstream part of the river basin are more susceptible to flood risks. This paper also proposes potential countermeasures for future natural disasters to prevent and mitigate possible damages.
Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.
Urban flood hazard model needs rainfall with high spatial and temporal resolutions for flood hazard analysis to better simulate flood dynamics in complex urban environments. However, in many developing countries, such high-quality data are scarce. Data that exist are also spatially biased toward airports and urban areas in general, where these locations may not represent flood-prone areas. One way to gain insight into the rainfall data and its spatial patterns is through numerical weather prediction models. As their performance improves, these might serve as alternative rainfall data sources for producing optimal design storms required for flood hazard modeling in data-scarce areas. To gain such insight, we developed Weather Research and Forecasting (WRF) design storms based on the spatial distribution of high-intensity rainfall events simulated at high spatial and temporal resolutions. Firstly, three known storm events (i.e., 25 June 2012, 13 April 2016, and 16 April 2016) that caused the flood hazard in the study area are simulated using the WRF model. Secondly, the potential gridcell events that are able to trigger the localized flood hazard in the catchment are selected and translated to the WRF design storm form using a quantile expression. Finally, three different WRF design storms per event are constructed: Lower, median, and upper quantiles. The results are compared with the design storms of 2- and 10-year return periods constructed based on the alternating-block method to evaluate differences from a flood hazard assessment point of view. The method is tested in the case of Kampala city, Uganda. The comparison of the design storms indicates that the WRF model design storms properties are in good agreement with the alternating-block design storms. Mainly, the differences between the produced flood characteristics (e.g., hydrographs and the number of flood gird cells) when using WRF lower quantiles (WRFLs) versus 2-year and WRF upper quantiles (WRFUs) versus 10-year alternating-block storms are very minimal. The calculated aggregated performance statistics (F scores) for the simulated flood extent of WRF design storms benchmarked with the alternating-block storms also produced a higher score of 0.9 for both WRF lower quantiles versus 2-year and WRF upper quantile versus 10-year alternating-block storm. The result suggested that the WRF design storms can be considered an added value for flood hazard assessment as they are closer to real systems causing rainfall. However, more research is needed on which area can be considered as a representative area in the catchment. The result has practical application for flood risk assessment, which is the core of integrated flood management.
This study configures the Weather Research and Forecasting (WRF) model with the updated urban fraction for optimal rainfall simulation over Kampala, Uganda. The urban parameter values associated with urban fractions are adjusted based on literature reviews. An extreme rainfall event that triggered a flood hazard in Kampala on 25 June 2012 is used for the model simulation. Observed rainfall from two gauging stations and satellite rainfall from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) are used for model validation. We compared the simulation using the default urban fraction with the updated urban fraction focusing on extreme rainfall amount and spatial-temporal rainfall distribution. Results indicate that the simulated rainfall is overestimated compared to CHIRPS and underestimated when comparing gridcell values with gauging station records. However, the simulation with updated urban fraction shows relatively better results with a lower absolute relative error score than when using default simulation. Our findings indicated that the WRF model configuration with default urban fraction produces rainfall amount and its spatial distribution outside the city boundary. In contrast, the updated urban fraction has peak rainfall events within the urban catchment boundary, indicating that a proper Numerical Weather Prediction rainfall simulation must consider the urban morphological impact. The satellite-derived urban fraction represents a more realistic urban extent and intensity than the default urban fraction and, thus, produces more realistic rainfall characteristics over the city. The use of explicit urban fractions will be crucial for assessing the effects of spatial differences in the urban morphology within an urban fraction, which is vital for understanding the role of urban green areas on the local climate.
<p>Landslide occurrences are governed by precondition factors and triggering factors. Hence, it is desirable to include physical parameters representing precondition factors in determining thresholds over which landslides are likely to occur. In the case of rainfall-triggered landslides, such parameters include soil properties and land cover information. However, high-resolution data required for a physical-based approach are rarely readily available for a large area, especially in developed countries. Therefore, in developing a landslide early warning system (LEWS) for a large area, rainfall thresholds are derived by optimizing the usage of rainfall datasets.</p><p>This study aims to derive rainfall thresholds from a meteorological perspective regarding rainfall event characteristics (e.g., cumulative rainfall, intensity, duration) that result in trigger the landslides in Progo Catchment in Java, Indonesia. &#160;We explore various hourly rainfall datasets, including rain gauge measurements and satellite-based rainfall products (e.g., the Japan Aerospace Exploration Agency&#8217;s Global Satellite Mapping of Precipitation/GSMaP and the Climate Prediction Center/National Oceanic and Atmospheric Administration&#8217;s morphing technique/ CMORPH), to derive the thresholds. The effect of rainfall event characteristics is assessed by clustering the rainfall event types and preceding conditions associated with different triggering mechanisms leading to the landslide occurrences. The rainfall thresholds are then derived using the frequentist method for each group, hence &#8220;dynamic.&#8221;&#160;</p>
<p>Urbanization affects the initiation and intensification of convective activities by changing local meteorological variables, which alters the atmosphere's convective processes. Therefore, proper urban surface information is required to model the energy partitioning pattern and its contrast with neighboring grid cells. In this study, the mesoscale weather research and forecasting (WRF) model is configured with satellite-derived urban fraction for optimal rainfall simulation and to evaluate its impact on the simulated rainfall over Kampala, Uganda. The WRF urban parameter values associated with the considered urban fraction are adjusted based on the literature reviews. The satellite-derived urban fraction represents the more realistic extent and intensity of the urban class with a more representative urban fraction. Three different simulations are performed to distil the impact of changing urban fractions as well as of adjusting urban parameters: (1) DUF_DUP, which uses the default urban fraction and default urban parameter values, (2) DUF_AUP, which uses the default urban fraction with adjusted urban parameter values, and (3) SUF_AUP, which uses the satellite-derived urban fraction and adjusted urban parameter values. A single extreme rainfall event, which caused a flood hazard in Kampala on 25 June 2012, is used for all three simulations. The simulated peak rainfall and its spatial distribution over the Kampala catchment are evaluated using observed rainfall data from gauging stations and satellite data from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Results indicate that the simulated rainfall is overestimated compared to CHIRPS and underestimated when comparing gridcell values with gauging station records. However, the SUF_AUP simulation shows relatively better results with a lower absolute relative error score compared to the other two simulations. Compared with the default urban fraction, the satellite-derived urban fraction represents the more realistic urban extent and intensity. As a result, SUF_AUP results in a more realistic rainfall simulation compared to when using the default urban fraction. Rainfall analysis for both 24-hour and 2-hour indicates that the presence of an urban landscape alters both the structure and propagation of high-intensity rainfall over the city, mainly due to the impact of the urban landscape on the different meteorological variables leading to modifying mechanisms associated with rainfall.</p><p><em>Keywords: rainfall, Default urban fraction, Kampala, urban parameter, Updated urban fraction, and WRF model</em></p>
To Mama and Kikila i patient throughout this Ph.D. time. Next, I acknowledge a group of people who supported and helped me on this Ph.D. research and shared social life with me during these four years in Enschede.Starting from the beginning, my deepest gratitude goes to my supervisor, Prof. dr. Victor Jetten and dr.ir. Janneke Ettema, for their support, insight, and expertise throughout all these years. Not only have they introduced me to the scientific world, but they also have contributed to educating me to be a better researcher over these years. Indeed, their patience and unlimited advice are what lead to unfolding this thesis.My gratitude also goes to Dr.Gert-Jan Steeneveld from Wageningen University, Meteorology, and Air Quality Section. His expertise and support helped me to further my knowledge of the WRF model. I must also acknowledge Dr.Ronda Reinder from Wageningen Unversity for showing me the WRF software programming and diagnosis to adjust the urban fraction in the WRF model I have used throughout this Ph.D. I want to thank also Dr.Gemechu Fanta Garum from the University of Quebec, Canada, for his assistance in managing static data in the WRF model.My acknowledgment also goes to Dr.Eduardo Perez Molina, a colleague from the PGM department, for developing the land cover fraction of Kampala that I have used throughout this dissertation. I must also thank my co-author Dr. Luigi Lumbardo, for his expertise, insightful comments, and recommendations. I must also thank Dr.Bastian van der Bout for his support and experience sharing on the openLISEM model. My thanks also go to Dr. Harald van der Werff and Dr. Islam Fadel for their technical support on computational facilities. I would like to describe my sincere gratitude to also Professor Cees for editing my thesis.I would like to thank all members of the ESA department and colleagues for their help and support when needed. I always remember the warm discussions we had on social and other issues during tea break. A big thanks to all my wonderful friends and Ph.D. community at ITC, and it's always lovely to be in such an environment. Oscar, thanks for your friendship and also for your help with the Dutch summary. Hakan, Kartika, Yan, and Tunde, it was great to share the office with you guys. Especial thanks to all my current and former Ph.
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