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.

An accurate flood detection method is essential for obtaining areas of irrigated rice fields affected by flooding. This paper aims to distinguish between rice fields with flooding and rice fields with agronomic inundation using MODerate resolution Imaging Spectroradiometer (MODIS) 8 day 500 m spatial resolution (MOD09A1) imageries over irrigated rice fields with complex cropping patterns in West Java. Over the past decade, Enhanced Vegetation Index (EVI) ≤ 0.1 derived from moderate resolution remote sensing imageries has been used for detecting flooding in irrigated rice fields. Without additional farming information, this paper argues that EVI ≤ 0.1 cannot estimate flood areas correctly, given the existence of both hazardous flooding and non-hazardous agronomic inundation in irrigated rice fields. Adding a threshold of 40-day duration representing land preparation and transplanting activities enables EVI ≤ 0.1 to distinguish between agronomic inundation and flooding in irrigated rice fields. The difference in the Start of Season (SOS) between the wet planting season 2013/2014 and long-term average (2000–2015) shows that the Overall Accuracy (OA) and F1 scores are 75.96% and 81.74%, respectively. The confusion matrix using the respondents’ reports shows OA of 80.5% and Kappa of 60.16%. The quality of flood maps is partly influenced by environmental processes, human decisions, and mixed pixels.

Geologic remote sensing studies often targets surface cover that is supposed to be invariant or only changing on a geological timescale. In terms of surface material characteristics, this holds for rocks and minerals, but only to a lesser degree for soils (including alluvium, colluvium, regolith or weathered outcrop) and not for vegetation cover, for example. A view unobstructed by clouds, vegetation or fire scars is essential for a persistent observation of surface mineralogy. Sensors with a continuous multi-temporal operation (e.g., Landsat 8 OLI and Sentinel-2 MSI) can provide the data volume needed to come to an optimal seasonal acquisition and the application of data fusion approaches to create an unobstructed view. However, the acquisition environment always changes over time, driven by seasonal changes, illumination changes and the weather. Consequently, the creation of an unobstructed view does not necessarily lead to a repeatable measurement. In this paper, we evaluate the influence of weather and resulting soil moisture conditions over a 3-year period, with alternating dry and wet periods, on the variance of several “geological” spectral indices in a semi-arid area. Sentinel-2 MSI data are chosen to calculate band ratios for green vegetation, ferric and ferrous iron oxide mineralogy and hydroxyl bearing alteration (clay) mineralogy. The data were used “as provided”, meaning that the performance of the atmospheric correction and geometric accuracy is not changed. The results are shown as time-series for selected areas that include solid rock, beach sand, bare soil and natural vegetation surfaces. Results show that spectral index values vary not only between dry and wet periods, but also within dry periods longer than 45 days, as a result of changing soil moisture conditions long after a last rain event has passed. In terms of repeatability of measurements, an overall low soil-moisture level is more important for long-term stability of spectral index values than the occurrence of minor rain events. In terms of creating an unobstructed view, we found that thresholds for NDVI should not be higher than 0.1 when masking vegetation in geological remote sensing, which is lower than what usually is indicated in literature. In conclusion, multi-temporal data are not only important to study dynamic Earth processes, but also to improve mapping of surfaces that are seemingly invariant. As this work is based on a few selected pixels, the obtained results should be considered only indicative and not as a numerical truth. We conclude that multi-temporal data can be used to create an unobstructed view, but also to select the data that give the most repeatability of measurements. Images selection should not be based on a certain number of days without rain in the days preceding data acquisition but aim for the lowest soil moisture conditions. Consequently, weather data should be incorporated to come to an optimal selection of remote sensing imagery, and also when analyzing multi-temporal data.

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.

Various stakeholders seek effective methods to communicate the potential impacts of tropical cyclone (TC) rainfall and subsequent flood hazards. While current methods, such as Intensity–Duration–Frequency curves, offer insights, they do not fully capture TC rainfall complexity and variability. This research introduces an innovative workflow utilizing GPM-IMERG satellite precipitation estimates to cluster TC rainfall spatial–temporal patterns, thereby illustrating their potential for flood hazard assessment by simulating associated flood responses. The methodology is tested using rainfall time series from a single TC as it traversed a 500 km diameter buffer zone around Dominica. Spatial partitional clustering with K-means identified the spatial clusters of rainfall time series with similar temporal patterns. The optimal value of K = 4 was most suitable for grouping the rainfall time series of the tested TC. Representative precipitation signals (RPSs) from the quantile analysis generalized the cluster temporal patterns. RPSs served as the rainfall input for the openLISEM, an event-based hydrological model simulating related flood characteristics. The tested TC exhibited three spatially distinct levels of rainfall magnitude, i.e., extreme, intermediate, and least intense, each resulting in different flood responses. Therefore, TC rainfall varies in space and time, affecting local flood hazards; flood assessments should incorporate variability to improve response and recovery.