Severe Flooding Research Articles

Enhancing resilience: Understanding the impact of flood hazard and vulnerability on business interruption and losses

Without taking additional measures, flooding is becoming more likely and intense in a changing climate, which causes large economic damage. Households and firms are directly impacted by physical flood damage, but further ripple effects on society occur through business disruptions. By using post-disaster survey data from the 2021 flood event in the Netherlands, this study adds to the literature on business interruption duration and losses after flooding. The current empirical literature on flood impacts on firms is often unable to distinguish separate effects for flooded and non-flooded firms and does not incorporate flood severity and the influence of risk reduction measures. Here, we use multivariate regression models to determine depth-duration functions that describe the relationship between flood hazard characteristics and business interruption duration. This relationship can be used to calibrate flood damage models that capture indirect firm impacts. The prediction of business interruption after flooding allows for differentiation in business interruption between firms within a flooded area, reducing the reliance of these macroeconomic models on restrictive assumptions. Our results indicate that a day of business interruption duration costs a firm on average 0.5 % of their annual revenue; an effect that is stronger for firms with a weaker connection to their region. Flood damage mitigation (FDM) measures taken at the building level do not significantly affect business interruption duration, although further research on this is required. Finally, quick damage compensation is found to reduce business interruption duration and thus revenue losses, calling for higher insurance uptake and rapid and streamlined post-disaster insurance and government compensation.

Analyzing the Impact of Storm ‘Daniel’ and Subsequent Flooding on Thessaly’s Soil Chemistry through Causal Inference

Storm ‘Daniel’ caused the most severe flood phenomenon that Greece has ever experienced, with thousands of hectares of farmland submerged for days. This led to sediment deposition in the inundated areas, which significantly altered the chemical properties of the soil, as revealed by extensive soil sampling and laboratory analysis. The causal relationships between the soil chemical properties and sediment deposition were extracted using the DirectLiNGAM algorithm. The results of the causality analysis showed that the sediment deposition affected the CaCO3 concentration in the soil. Also, causal relationships were identified between CaCO3 and the available phosphorus (P-Olsen), as well as those between the sediment deposit depth and available manganese. The quantified relationships between the soil variables were then used to generate data using a Multiple Linear Perceptron (MLP) regressor for various levels of deposit depth (0, 5, 10, 15, 20, 25, and 30 cm). Then, linear regression equations were fitted across the different levels of deposit depth to determine the effect of the deposit depth on CaCO3, P, and Mn. The results revealed quadratic equations for CaCO3, P, and Mn as follows: 0.001XCaCO32 + 0.08XCaCO3 + 6.42, 0.004XP2 − 0.26XP + 12.29, and 0.003XMn2 − 0.08XMn + 22.47, respectively. The statistical analysis indicated that corn growing in soils with a sediment over 10 cm requires a 31.8% increase in the P rate to prevent yield decline. Additional notifications regarding cropping strategies in the near future are also discussed.

Between Floods and Climate Change: Revisiting the Mishing Community of Majuli Island, Northeast India

The transformation of monsoon rainfall patterns in India, largely attributed to climate change, is leading to more frequent and severe floods. These escalating challenges underscore the imperative of prioritising adaptive measures, given the intrinsic link between humans and climate change. This research conducted in Majuli Island, a highly vulnerable region in India’s northeast, aims to understand current adaptive strategies and assess potential risks from impending physical exposures. Empirical evidence was collected using purposive sampling in two flood-prone villages. The objective was to revisit the Mishing community’s experiences with annual flooding and climate challenges. Thematic analysis interpreted the qualitative findings. Implications for community-based adaptation and sustainable practices are discussed for future flood and climate challenges. The study emphasises strengthening ecosystem-based adaptation through multi-sectoral networking in Majuli Island, Northeast India.

Effects of channel structure at inlet region on temperature distribution of proton exchange membrane fuel cells

Enhancing the temperature distribution uniformity in membrane electrodes is crucial for improving the durability and performance stability of proton exchange membrane fuel cells. In this study, we discovered an entrance region in the cathode membrane electrode of proton exchange membrane fuel cells characterized by a rapid rise in temperature. This inlet region shows a notably higher temperature variation rate compared to other regions. This phenomenon is attributed to the temperature difference between the constant temperature fluid supplied by the channel inlet and the heat generation in the cathode catalyst layer. Thus, the impact of channel structure at the entrance region on the temperature distribution of the cathode catalyst layer is investigated, based on a three-dimensional, non-isothermal, two-phase model. Results show that boosting heat transfer in the inlet region is more effective for reducing the average temperature than in other regions. We proposed five kinds of step arrangement schemes and discovered that the step arrangement in the entrance region of the cathode flow channel can increase the temperature uniformity of the cathode catalyst layer by 9.71% without net power loss. Furthermore, this study examines the impact of length, height of entrance steps, and outlet channel height on heat transfer and identifies the optimum value. When severe water flooding occurs, the gas purge coupled with the step-fronted scheme is essential for improving temperature uniformity.

Novel electrochemical and thermodynamic conditioning approaches and their evaluation for open cathode PEM-FC stacks

The Air-cooled and open cathode proton exchange fuel cell (PEMFC) offers particular advantages in terms of complexity and weight reduction. Therefore, it seems very attractive to support the aviation industry in their de‑carbonisation process. Major challenges in terms of operational stability and reliability hinders this technology to be used in commercial applications.Therefore, this study thoroughly examines the conditioning and optimization of open cathode PEMFC stacks, drawing insights from experimental findings. Key areas of focus include the enduring impact of pre-humidification, the quantification of efficiency enhancements through reconditioning via oxygen starvation, and the refinement of Electrochemical Impedance Spectroscopy (EIS) data analysis under non-stationary conditions.The pre-humidification enhances stack performance by improving cell voltages from 40.09 V to 41.37 V at 30 A, increasing membrane humidity and improving efficiency. Furthermore, the residual water in the stack also functions as evaporative cooling and can assist in limiting the operating temperature of the stack during system start up. However, it is demonstrated that excessive soaking with water leads to severe flooding phenomena at the beginning of operation.A reconditioning period of 100 s through oxygen starvation induces a notable increase in stack voltage, from 41.37 V to 47.79 V at 35 A, which decreases to 43.56 V after 10 min of operation. This corresponds to an average increase in electric energy provision of about 6.2% at constant hydrogen consumption, attributed to PtOx reduction and increased water production.Despite limitations outside the medium frequency range (approximately 10 Hz – 15 kHz) for non-stationary conditions, EIS aids in understanding of the stack behaviour and supports the interpretation of current and voltage results. A novel evaluation method enables the quantitative description of the condition using EIS data. This data reveals a considerable drop (on average about 21,5% for the 10 A point and about 27,4% for the 30 A point) in charge transfer resistances of the fuel cell from initial operation to measurements after overnight soaking and the first series of oxygen starvation recovery.

Assessing the Effects of Flood on Crop and Livestock Production in Bagalkot District of Karnataka, India

Aims: India is the second most flood-affected country, trailing behind Bangladesh. Over the past two decades, Karnataka has been subjected to severe flooding. Keeping the above in view, a research initiative to analyze the effect of floods on crop and livestock production was undertaken.
 Study Design: Multi stage purposive sampling technique was adopted for the study.
 Place and Duration of Study: Bagalkot district of Northern Karnataka was purposively selected for the study.
 Methodology: The study is based on primary data collected from 90 respondents belonging to Ghataprabha and Krishna river basins in Mudhol (45) and Jamakhandi (45) taluks.
 Results: Analysis of cropping patterns reveals sugarcane as the dominant crop with maize also significant. Floods in 2019 caused substantial crop damage, particularly affecting sugarcane and maize, resulting in significant income losses for farmers. The results indicated that the, during 2019, 78.03 and 21.97 per cent of sugarcane and maize areas were affected by floods with a percentage income reduction of 65.84 and 62.87 percent, respectively. The severity of flood was quite less during 2020 where 76.72 and 23.27 percent of sugarcane and maize areas were affected by floods with 29.47 and 33.24 percent, reduction in income respectively.
 Conclusion: Livestock losses were also considerable, with farmers facing economic burdens due to animal deaths and injuries. Soil erosion, predominantly splash and sheet erosion, was prevalent during both years, affecting agricultural productivity. Compensation for losses remained inadequate, highlighting challenges in addressing flood-related damages in the region.

General quality of urban greenery as a city indicator in Brazil in comparison with central Europe

The evidence for climate change and its associated consequences, such as severe droughts, floods, and storms, exposed the need to investigate the ways that countries can cope with them, especially Brazil, which is a continental nation. Moreover, the COVID-19 pandemic contributed to the urgency to consider global uncertainties. To audit it, the Pondělíček method, an indicator of the general quality of urban greenery (IOKZM), was employed. This approach considers four factors to indicate if one city is sustainable: city altitude [m], average annual temperature [°C], average annual precipitation [mm], and the total greenery area [km2] per inhabitant per year. The outcomes demonstrate that, regardless independently of the Brazilian region, geographic localization, estimated altitude range, or precipitation range may not strongly influence the BIOKZM. The impact measured in the indicator is significantly influenced by the greenery areas in Brazil. Those estimated areas are much smaller than European Union ones. Teresina’s Fkes demonstrates that if the greenery area is more than 5 (i.e., more than 50 percent), the IOKZM will be a positive outcome. The IOKZM calculated for Brazilian cities is interesting precisely because of their location in the southern hemisphere, as all the evidence of climate change subtly suggests that the climate in the southern hemisphere changes in a different way. Hence, this article can be used as an initial tool to assess whether the chosen Brazilian cities (Belém, São Luís, Guarulhos, Campinas, Porto Alegre, Teresina, and João Pessoa) can be considered “smart cities”. It is a step towards guiding mayors and other public figures in charge to create and maintain smart cities in Brazil. In addition, further studies should be conducted to contribute to this collaborative effort.

Predicting affective and cognitive learning outcomes: A quantitative analysis using climate change vectors

Recent changes in the atmospheric conditions have exerted a lot of pressure on the surface of the earth causing attitudinal change, fear of survival as well as pattern of living. Schools are not out of these influences from the changes in climatic conditions. These study, therefore. examines the nexus between climate change vectors such as classroom temperature variation, persistent drought, severe flood occurrence on class attendance, class participation and academic performance among secondary school students. The study adopted an ex post facto research design, and a total of 1,881 were used for data collection. Two research instruments, the climate change variation scale and class attendance, class participation, and academic performance scale were used for data collection. The instrument was validated using factor analysis to assess the dimensionality of the items as well as obtain factors using component analysis and varimax rotation. To assess principal the mode fitness and acceptability, the confirmatory factor analysis (CFA) was carried out using the maximum likelihood estimation method, and the factor loadings from exploratory factor analysis and CFA were not too different. The collected data were analyzed using the simple and multilinear regression techniques. The results showed that relatively, persistent, severe flooding and classroom temperature variation contributes significantly to class participation, class attendance, and academic performance among students. Similarly, the variables; severe flood, drought and classroom temperature when compositely examined contributes to the variation in class attendance, class participation and academic performance among students. Implications and recommendations of the study were stated.

Attitudes toward moving or staying and buying flood insurance to face varied flood threats in a floodplain community

Demographics and possession of flood insurance are among the factors influencing flood response and recovery actions adopted by individuals, such as deciding to either move out of their homes or stay. It is essential to consider residents' attitudes to help policies prepare for the social and economic impacts of flooding. In some urban areas situated in floodplains, climate change has caused an increase in the severity of flooding. This research project focused on residents living within riverine floodplain areas delimited by FEMA and their attitudes regarding relocation under flood varied threats. A household survey in the United States was conducted in Corvallis, Oregon, which presented participants with different flood threat scenarios involving varying water depths and frequencies. After selecting predictors by applying a collinearity constraint, logistic regression was used to find significant predictors of individuals' attitudes toward moving out of their homes or staying at home and buying or upgrading flood insurance under varied flood threats. The results showed that some low or unknown-magnitude flood scenarios produced statistically significant models to predict mobility, but high-magnitude flooding did not yield statistically significant models. For example, flood insurance possession was the only significant predictor for staying after flooding in various scenarios. A more robust understanding of these interdependencies should increase the effectiveness of growing awareness of environmental threats and participation in risk-reducing behaviors.

Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution

An effective flood early warning system is vital to take action to save lives and protect properties in urban areas which are increasingly prone to flooding. Despite substantial progress in flood early warning systems, limited available and accessible data often impede their advancement and reliability. Engaging communities affected by flooding can help address data and information gaps in flood early warning systems, facilitated by appropriate methods. This study developed and evaluated a flood threshold combination method to support a community-based flood early warning system in the Akaki catchment, home to Addis Ababa, the capital city of Ethiopia. Various flood threshold combinations were formulated, calibrated and validated by integrating multiple sources of data: rainfall, antecedent precipitation index estimates, Sentinel-1 Synthetic Aperture Radar satellite time series of flood extent, long-term simulated streamflow, citizen science data, river water level and three days lead-time numerical weather prediction rainfall forecast. During validation, the rainfall and river water level threshold combination outperformed other threshold combinations with probability of detection, false alarm ratio, and critical success index estimates of 0.74, 0.18 and 0.63, respectively. The flood threshold combination showed high detection performance for most flooding conditions. Flood forecasts with a 1-day lead-time exhibited a high likelihood in detecting historical severe flood events. The study provides a tested methodology for selecting suitable flood threshold-combinations, enhance the engagement of citizen scientists in a community–based flood early warning system in urban communities.

Assessment of the impacts of climate change on the construction of homogeneous climatic regions and ensemble climate projections using CMIP6 data over Pakistan

In the 21st century, the global environment faces significant threats due to climate change, ranging from floods and droughts to heat waves and other extreme weather events. In recent decades, Pakistan has experienced severe flooding and prolonged droughts, compounding challenges related to livelihoods, public health, and population displacement. The availability of precise and robust climate change information is paramount in addressing and mitigating the adverse impacts on a region's environment and its communities. The present study intends to assess the effects of climate change on the development of homogenous climate regions (HCRs) and then developed ensemble climate projections under the shared socioeconomic pathways (SSPs) using CMIP6 data. In the first step, HCRs were developed by integrating the cluster analysis approach and L-moment technique, identifying five distinct climatic regions. HCRs were developed using both observed data and ensemble baseline data which allowed us to thoroughly evaluate the ensemble data's performance. Subsequently, we extended our analysis to the future period (2015–2044) using ensemble climate data according to SSP2–4.5 and SSP5–8.5. Our findings indicate that, under both SSPs, certain stations exhibit discordant climatic trends in the future due to climate change. Ensemble climate projections were developed over each region for both SSPs for the duration of 2015–2044. A significant increase of 2–3.20 °C has been noted in the summer season in zone 3 and zone 5 (a region that has one of the world's largest glaciers) in maximum as well as minimum temperatures. Temperature has upward trend in the other region as well while precipitation has a mixed trend in all regions. The insights gleaned from this study hold significance and offer valuable guidance for decision-makers involved in water management, agricultural planning, and disaster management.

Flood hazard mapping using a GIS-based morphometric analysis approach in arid regions, a case study in the Red Sea Region, Egypt

Flash floods are a major threat to life and properties in arid regions. In recent decades, Egypt has experienced severe flash floods that have caused significant damage across the country, including the Red Sea region. The aim of this study is to map the flood hazards in flood-prone areas along the Red Sea region using a Geographic Information System (GIS)-based morphometric analysis approach. To evaluate the flood hazard degree, the adopted methodology considers various morphometric parameters such as basin area, slope, sinuosity index, shape factor, drainage intensity, circularity ratio, and curve number. GIS techniques were employed to delineate the watershed and the drainage network. The delineated watershed was used together with the digitized maps of soil and land use types to estimate the curve number and the morphometric parameters for each subbasin. The flood hazard degrees are calculated based on the considered morphometric parameters and distinguished based on a five-degree scale ranging from very low to very high. Results indicate that 47% of the study area has a very high flood hazard degree. Furthermore, morphometric analysis results align with the runoff results simulated by a hydrological model, where, for example, basins with a high to very high hazard degree exhibited high runoff. This suggests the influence of physical characteristics on the hydrological behavior of the watershed and further validates the morphometric analysis presented in this work. The results presented here can help policy planners and decision-makers develop appropriate measures to mitigate flash floods and achieve sustainable development in arid regions.

Sensitivity analysis of coastal cities to effects of rainstorm and flood disasters.

Heavy rains and floods cause human, material, and economic damage in cities worldwide. The severity of flooding has intensified due to accelerating urbanization. While much of the existing research on flood hazards emphasizes simulation and assessment, the correlation between indicators has yet to be explored. This study employs the Tree Gaussian Process sensitivity analysis method. Through rigorous sampling and correlation analysis, the model identifies critical determinants. Significantly, factors such as the water supply penetration rate (Var3), water pipeline density in built-up areas (Var4), centralized treatment rate of sewage treatment plants (Var6), agricultural land for forestry (Var13), and urban, village, and industrial and mining land (Var15) stand out as primary influencers on the flood-affected populace. These variables reflect a city's flood management capability and its dedication to resource stewardship and ecological equilibrium, underscoring its critical role in flood risk assessment and strategic mitigation. The study further illuminates that the interplay of these variables can exacerbate flood consequences, suggesting a compounded impact when variables operate in tandem. Recognizing these synergistic effects reveals a more pronounced flood threat than previously estimated, indicating that viewing these factors in silos might underrepresent the risk involved.

Prolonged drought moderates flood effects on soil nutrient pools across a rainfall gradient

Climate change is manifesting through increased intensity and frequency of extreme events such as droughts and floods. Drought causes significant stress by limiting water availability, resulting in reduced plant growth and belowground carbon (C) inputs as well as plant nutrient uptake and microbial activity, thus impacting C and nutrient cycling. Severe floods can increase leaching of nutrients, which affect nutrient availability for plants and microorganisms potentially compounding drought-induced effects on C and nutrient cycles. However, few studies have assessed the interactions between drought and floods, constraining our ability to predict ecosystem-level effects of projected climate shifts toward fewer but larger rainfall events with longer dry spells between events. We conducted a study to assess how long-term drought moderates the effect of flooding on soil C, nitrogen (N) and phosphorus (P) pools across a rainfall gradient in south-eastern Australia. We collected soil monoliths from field rainfall manipulation facilities with a 5–7 years history of simulated reduced rainfall at three sites representing a rainfall gradient (∼200–800 mm) and exposed them to a 1-in-100-year flood event under laboratory conditions. We assessed nutrients and C in soil and microbial pools before and after the flood and in flood-induced leachates. The results showed that flooding caused significantly greater losses of N in leachates, with greater effect sizes in the drought treatments compared to ambient rainfall. This response was particularly pronounced for nitrate (152 % increase on average), especially at the site with the highest mean annual precipitation (>25-fold increase). This suggest that drought may significantly reduce the capacity of ecosystems to retain N following flood events, particularly in mesic ecosystems where C and nutrient pools are greater.

Coping self-efficacy mediates effects of posttraumatic distress on communal coping in parent-adolescence dyads after floods.

Social cognitive theory provides a framework of human agency during environmental challenges, with coping self-efficacy (CSE) as an important construct underlying adaptation. We examined two alternative models involving CSE as a mediator of the association between posttraumatic stress symptoms (PTSS) and communal coping among parent-youth dyads after severe floods using Bayesian dyadic multilevel modeling. The first model included PTSS as the independent variable and communal coping as the dependent variable (disaster distress model). The independent and dependent variables were replaced for each other in the second model (communal coping model). We used data from 485 parent-youth dyads who experienced floods between 2015 and 2016 in Texas, USA. Parents of children (69% women) aged 10-19 years old, and their oldest child (53% male; Mean age = 13.75) in that age range were recruited. We assessed PTSS, CSE, and communal coping for parents and youths. Results favored the disaster distress model over the communal coping model. In the disaster distress model, results demonstrated that CSE declines as PTSS increases, predicting decreased communal coping. This mediation effect of CSE is stronger for youths compared to parents, indicating that children's CSE is affected more by PTSS.

Increasingly frequent co-occurrence of drought or pluvial in multiple tributaries of the Yangtze River basin

Drought and pluvial respectively represent a region experiencing a period of water deficit and surplus relative to its normal state. The co-occurrence of drought and pluvial across numerous tributaries of a large river would trigger widespread water shortage and extreme flooding. However, little is known about such spatially concurrent hydrological extremes within large river basins throughout historical periods. Here, we select ten tributaries of the Yangtze River basin (YRB) and employ the VineCopula model to assess the probability of simultaneous drought and pluvial using precipitation observations during 1960–2019 from the perspective of dry and rainy seasons. From 1960 to 1989 to 1990–2019, the probability of two or more tributaries with simultaneous drought decreases from 37.7% to 25.3% during the dry season while increases from 26.6% to 27.3% during the rainy season. For the case of simultaneous pluvial, the probability increases in both dry (from 19.2% to 36.7%) and rainy (from 26.2% to 29.6%) seasons. The notable shifts in the probabilities of concurrent drought and pluvial within YRB highlight an escalating threat of widespread water scarcity and heightened risks of severe flooding. These changes necessitate immediate attention and proactive measures to address the imminent challenges faced by both natural and societal systems along the Yangtze River.

A moderately chocked estuary: Influence of a constriction on the water level variations of the Wouri estuary (Cameroon)

We study the water-level (WL) evolution in the Douala basin (DB), a sub-basin of the Wouri estuary, separated from the ocean by a natural constriction and influenced by tides and river inflow. Our objective is to assess whether floods in Douala city could result from water overflow from the Douala basin due to Wouri river floods. We first evaluate the constriction’s damping effect by analyzing tide amplitude variations from the ocean to the basin. A simplified model for basin WL variation is developed, incorporating a dissipation parameter (Г) that is evaluated using weak tidal amplitude modulation from the ocean to the basin over the dry season of 2023. Results in the DB reveal nondimensional choking-parameter (P) values of 12–19, indicating moderate dissipation. The basin's relatively small area results in a nondimensional river discharge parameter (S) of 0–12, indicating high river discharge regimes. Subsequently, the model is used to evaluate the mean and maximum WL, as well as tide amplitude variations of the DB, as functions of river flow and ocean tides. The mean WL increases, but the maximum tide amplitude decreases as river input increases. The maximum WL in the basin always exceeds the maximum amplitude of ocean tides but is only significantly higher when river input is above 1000 m3/s. In current conditions, where maximum observed river input is around 1800 m3/s, it is unlikely that flooding will extend beyond the basin borders into the city of Douala. For a moderate increase (20%) in future maximum river fluxes, it is also unlikely that the DB will overflow into Douala city. Only a drastic increase in mean ocean level and river fluxes (which are possible scenarios associated with climate change) could potentially lead to a significant rise in basin WL, resulting in severe flooding.

Playing the Water Cycle “Game”: Data From Space for A Safer Planet

As Earth’s climate continues to warm, extreme weather events like severe storms and floods are becoming more common, endangering many lives. To help keep people safe, we have developed a computer model called DTE Hydrology—kind of like a scientific video game—that can warn people if flooding is likely in their area. This model is known as a “digital twin” of the Earth because it is almost like having a computer-based copy of the planet! Measurements of rainfall and soil moisture can be collected both on Earth and from satellites, and the model can use these data to predict the likelihood of flooding in a given area. We can also use DTE Hydrology to “experiment” on Earth’s digital twin, to learn more about what causes flooding. In the future, we hope to use this system to save even more lives by predicting landslides and monitoring wildfires, too.

Flood hazard comparison based on geomorphic flood index and hydraulic HEC-RAS (Case study in Ciliwung Watershed, Jakarta)

The Jakarta Special Capital Region is a highly vulnerable area to floods due to its location on wetlands laced by 13 major rivers and borders the Java Sea, with more than 40% of its land below sea level and groundwater extraction leading to the ground sinking. The city has experienced several major flood disasters, and climate change has increased the risk, frequency, and severity of flooding in Jakarta. The present study aims to identify areas with flood potential in the Jakarta Special Capital Region (Provinsi DKI Jakarta) using the Geospatial Flood Index (GFI) method recommended by the National Disaster Management Agency (BNPB). The GFI method is an alternative rapid assessment that utilizes the Digital Elevation Model for National Spatial (DEMNAS) data with an 8-meter spatial resolution and employs ARCGIS and QGIS software to identify areas with high potential for flooding and the extent of inundation. To assess the effectiveness of this method, a comparison is made with the Hydraulic HECRAS model for the section from the Automatic Water Level Recorder (AWLR) MT. Hartono to the Manggarai Flood Gate, considering flood return periods Q50 and Q100. The modeling results indicate that the inundation area estimated by the conventional GFI modeling is 150% larger than that the HECRAS Hydraulic model predicted.

Temporal and spatial satellite data augmentation for deep learning-based rainfall nowcasting

Abstract The significance of improving rainfall prediction methods has escalated due to climate change-induced flash floods and severe flooding. In this study, rainfall nowcasting has been studied utilizing NASA Giovanni satellite-derived precipitation products and the convolutional long short-term memory (ConvLSTM) approach. The goal of the study is to assess the impact of data augmentation on flood nowcasting. Due to data requirements of deep learning-based prediction methods, data augmentation is performed using eight different interpolation techniques. Spatial, temporal, and spatio-temporal interpolated rainfall data are used to conduct a comparative analysis of the results obtained through nowcasting rainfall. This research examines two catastrophic floods that transpired in Türkiye Marmara Region in 2009 and the Central Black Sea Region in 2021, which are selected as the focal case studies. The Marmara and Black Sea regions are prone to frequent flooding, which, due to the dense population, has devastating consequences. Furthermore, these regions exhibit distinct topographical characteristics and precipitation patterns, and the frontal systems that impact them are also dissimilar. The nowcast results for the two regions exhibit a significant difference. Although data augmentation significantly reduced the error values by 59% for one region, it did not yield the same effectiveness for the other region.