Historical Flood Research Articles

Using Sentinel-1 and Google Earth Engine cloud computing for detecting historical flood hazards in tropical urban regions: a case of Dar es Salaam

This study investigates the potential of freely available Sentinel-1 imagery coupled with Google Earth Engine (GEE) for mapping and monitoring flooding in Dar es Salaam. Sentinel-1 images (n = 55) available during the rainy season (March–May) since 2016 were used and processed in GEE. For separating water and land surfaces, we used a histogram-based automatic thresholding method. The binarization accuracy was assessed using confusion matrix based on 1064 randomly generated points in GEE. Overall accuracy of 95% (Kappa = 0.90) were achieved. Dar es Salaam has experienced flood inundation per flood event on average over an area of 50 km2 in March 2019 and 2021. Territories located along the Ocean and inland water shores, built and bare ground were subject to flooding compared to other land cover types. Flooding inundations have been difficult to detect in the city center. With the current temporal and spatial resolution of Sentinel-1, flood detection in city centers remains a challenge yet. However, Sentinel-1 images, coupled with GEE cloud computing simplified flood mapping and monitoring in a large urban region and this approach can be applied in other large cities and their surroundings for countries where data gap and lack of processing tools are critical challenges.

Snow-detonated floods: Assessment of the U.S. midwest march 2019 event

Study regionIowa and the Nishnabotna watershed (Iowa), Midwest U.S. Study focusHistorically, Iowa and the Midwest have faced floods during the summer season. Some historical floods on record are the 2008 and 2013 floods. In March 2019, a meteorological bomb cyclone set the conditions for an unexpected major snow-related flood. This study (1) presents a comprehensive analysis of the March 2019 flood and asses the early-spring peak flows trends, (2) explores the use of a parsimonious hydrological model with a snow component, and (3) validates the model performance for the last 20 years. New Hydrological insightsThe March 2019 event was an extreme flood event that set records on at least 10% of the USGS gauges in Iowa. Moreover, the early spring peak flow analysis showed a significant increasing trend between February and April. In this period, the trend is positive for most gauges, with more than a 30% increase at an annual rate of 4% of the mean yearly peak flow. These findings showed the relevance of snow-detonated floods and their regional understanding. Considering the results' significance, we provided evidence that HLM and a conceptual snow component can represent, forecast, and provide insights regarding snow-detonated events.

Urban waterlogging prediction and risk analysis based on rainfall time series features: A case study of Shenzhen

In recent years, the frequency of extreme weather has increased, and urban waterlogging caused by sudden rainfall has occurred from time to time. With the development of urbanization, a large amount of land has been developed and the proportion of impervious area has increased, intensifying the risk of urban waterlogging. How to use the available meteorological data for accurate prediction and early warning of waterlogging hazards has become a key issue in the field of disaster prevention and risk assessment. In this paper, based on historical meteorological data, we combine domain knowledge and model parameters to experimentally extract rainfall time series related features for future waterlogging depth prediction. A novel waterlogging depth prediction model that applies only rainfall data as input is proposed by machine learning algorithms. By analyzing a large amount of historical flooding monitoring data, a “rainfall-waterlogging amplification factor” based on the geographical features of monitoring stations is constructed to quantify the mapping relationship between rainfall and waterlogging depths at different locations. After the model is trained and corrected by the measured data, the prediction error for short-time rainfall basically reaches within 2 cm. This method improves prediction performance by a factor of 2.5–3 over featureless time series methods. It effectively overcomes the limitations of small coverage of monitoring stations and insufficient historical waterlogging data, and can achieve more accurate short-term waterlogging prediction. At the same time, it can provide reference suggestions for the government to conduct waterlogging risk analysis and add new sensor stations by counting the amplification factor of other locations.

The interaction of topographic slope with various geo-environmental flood-causing factors on flood prediction and susceptibility mapping.

This work integrates topographic slope with other geo-environmental flood-causing factors in order to improve the accuracy of flood prediction and susceptibility mapping using logistic regression (LR) model. The work was done for the eastern Jeddah watersheds in Saudi Arabia, where flash floods constitute a danger. A geospatial dataset with 140 historical flood records and twelve geo-environmental flood-causing factors was constructed. A number of significant statistical methods were also applied to provide reliable flood prediction and susceptibility mapping, including Jarque-Bera, Pearson's correlation, multicollinearity, heteroscedasticity, and heterogeneity analyses. The results of the models are validated using the area under curve (AUC) and other seven statistical measures. These statistical measures include accuracy (ACC), sensitivity (SST), specificity (SPF), negative predictive value (NPV), positive predictive value (PPV), root-mean-square error (RMSE), and Cohn's Kappa (K). Results showed that both in training and testing datasets, the LR model with the slope as a moderating variable (LR-SMV) outperformed the classical LR model. For both models (LR and LR-SMV), the adjusted R2 is 88.9 and 89.2%, respectively. The majority of the flood-causing factors in the LR-SMV model had lower Sig. R values than in the LR model. As compared to the LR model, the LR-SMV attained the highest values of PPV (90%), NPV (93%), SST (92%), SPF (90%), ACC (89%), and K (81%), for both training and testing data. Moreover, employing slope as a moderating variable demonstrated its viability and reliability for defining precisely flood-susceptibility zones in order to reduce flood risks.

Restoration and resilience to sea level rise of a salt marsh affected by dieback events

AbstractThe frequency of salt marsh dieback events has increased over the last 25 years with unknown consequences to the resilience of the ecosystem to accelerated sea level rise (SLR). Salt marsh ecosystems impacted by sudden vegetation dieback events were previously thought to recover naturally within a few months to years. In this study, we used a 13‐year collection of remotely sensed imagery to provide evidence that approximately 14% of total marsh area has not revegetated 10 years after a dieback event in Charleston, SC. Dieback onset coincided with a severe drought in 2012, as indicated by the Palmer drought stress index. A second dieback event occurred in 2016 after a historic flood influenced by Hurricane Joaquin in 2015. Unvegetated zones reached nearly 30% of the total marsh area in 2017. We used a light detection and ranging‐derived digital elevation model to determine that most affected areas were associated with lower elevation zones in the interior of the marsh. Further, restoration by grass planting was effective, with pilot‐scale restored plots having greater aboveground biomass than reference sites after two years of transplanting. A positive outcome indicated that the stressors that caused the dieback are no longer present. Despite that, many affected areas have not recovered naturally, even though they are located within the typical elevation range of healthy marshes. A mechanistic modeling approach was used to assess the effects of vegetation dieback on salt marsh resilience to SLR. Predictions indicate that a highly productive restored marsh (2000 g m−2 year−1) would persist at a moderate SLR rate of 60 cm in 100 years, whereas a nonrestored mudflat would lose all its elevation capital after 100 years. Thus, rapid restoration of marsh dieback is critical to avoid further degradation. Also, failure to incorporate the increasing frequency and intensity of extreme climatic events that trigger irreversible marsh diebacks underestimates salt marsh vulnerability to climate change. Finally, at an elevated SLR rate of 122 cm in 100 years, which is most likely an extreme climate change scenario, even highly productive ecosystems augmented by sediment placement would not keep pace with SLR. Thus, climate change mitigation actions are also urgently needed to preserve present‐day marsh ecosystems.

Flash flood susceptibility mapping based on catchments using an improved Blending machine learning approach

Abstract Flash floods are a frequent and highly destructive natural hazard in China. In order to prevent and manage these disasters, it is crucial for decision-makers to create GIS-based flash flood susceptibility maps. In this study, we present an improved Blending approach, RF-Blending (Reserve Feature Blending), which differs from the Blending approach in that it preserves the original feature dataset during meta-learner training. Our objectives were to demonstrate the performance improvement of the RF-Blending approach and to produce flash flood susceptibility maps for all catchments in Jiangxi Province using the RF-Blending approach. The Blending approach employs a double-layer structure consisting of support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) as base learners for level-0, and the output of level-0 is utilized as the meta-feature dataset for the meta-learner in level-1, which is logistic regression (LR). RF-Blending employs the output of level-0 along with the original feature dataset for meta-learner training. To develop flood susceptibility maps, we utilized these approaches in conjunction with historical flash flood points and catchment-based factors. Our results indicate that the RF-Blending approach outperformed the other approaches. These can significantly aid catchment-based flash flood susceptibility mapping and assist managers in controlling and remediating induced damages.

Flood vulnerability and risk assessment of historic urban areas: Vulnerability evaluation, derivation of depth‐damage curves and cost–benefit analysis of flood adaptation measures applied to the historic city centre of Tomar, Portugal

AbstractAround 45% of natural hazards reported worldwide are related to floods, and current indications show that exposure to floods and inherent losses will keep escalating. Historic centres are particularly vulnerable in this context due to the structural and material characteristics of the buildings and because they embrace social and cultural values that must be safeguarded. This article aims to contribute to this research area by presenting and discussing the application of an index‐based methodology specifically tailored to assess flood risk in historic urban centres. The historic city centre of Tomar, Portugal, an area that encompasses over 500 buildings and has a rich history of floods, is used here as a case study. Vulnerability data resulting from the application of the vulnerability assessment approach are then combined with flood hazard—that is, water velocity and depth obtained from flood peaks estimated for 20‐ and 100‐year periods of return—and used to identify the buildings at risk. Finally, a set of depth‐damage curves is derived and used here to carry out a cost–benefit analysis for different flood adaptation measures.

Future changes in water resources, floods and droughts under the joint impact of climate and land-use changes in the Chao Phraya basin, Thailand

Global Climate change and local human activities have profoundly affected the regional hydrological cycle and water resources. It is imperative to explore the potential changes in future water resources and water-related hazards at the regional scale under global warming and local socioeconomic development, while a scientific assessment of future hydrological risks requires reasonable projection of future climate, land use and vegetation changes. In order to improve the traditional statistical downscaling method, this study combines the machine learning and quantile mapping methods to project future climate under four shared socio-economic pathway-representative concentration pathways (SSP-RCP) of the CMIP6. Future land use is projected jointly with the future climate by the CA-Markov model, and the vegetation dynamics are simulated by the Biome-BGC model. Then we employ a physically-based distributed hydrological model to simulate the future hydrological changes in the Upper Chao Phraya basin under the interaction among climate and land use changes and the vegetation dynamics. The results show that under the joint impact of climate and land-use changes, the study area may face increasing water scarcity and more frequent floods and droughts in the future. Water scarcity will reach the worst in the mid-21st century (water resources per capita decrease 34.2% compared to the 2010 s). By the end-21st century, the 100-year historical flood and drought in the study basin will increase by 1.63 times and 0.59 times, respectively, under the SSP126 scenario (the most sustainable pathway), and by 4.55 times and 1.56 times under the SSP370 scenario (the most pessimistic rocky-road pathway). Results demonstrate that climate change is the major cause for more frequent floods and droughts in the future, while afforestation or more sustainable land use management will mitigate the adverse effects of climate change to some extent. This finding is helpful to the local government in managing future water resources, floods, and droughts in the study basin.

A Deep Learning Model of Spatial Distance and Named Entity Recognition (SD-NER) for Flood Mark Text Classification

Information on historical flood levels can be communicated verbally, in documents, or in the form of flood marks. The latter are the most useful from the point of view of public awareness building and mathematical modeling of floods. Information about flood marks can be found in documents, but nowadays, they are starting to appear more often on the Internet. The only problem is finding them. The aim of the presented work is to create a new model for classifying Internet sources using advanced text analysis (including named entity recognition), deep neural networks, and spatial analysis. As a novelty in models of this type, it was proposed to use a matrix of minimum distances between toponyms (rivers and towns/villages) found in the text. The resulting distance matrix for Poland was published as open data. Each of the methods used is well known, but so far, no one has combined them into one ensemble machine learning model in such a way. The proposed SD-NER model achieved an F1 score of 0.920 for the binary classification task, improving the model without this spatial module by 17%. The proposed model can be successfully implemented after minor modifications for other classification tasks where spatial information about toponyms is important.

Quality of Stormwater Infrastructure Systems in Vulnerable Communities: Three Case Studies from Texas.

A properly functioning local stormwater drainage system is essential for mitigating flood risks. This study evaluates the quality of roadside drainage channels in three underserved communities in Texas: the Sunnyside neighborhood in Houston (Harris County), a neighborhood in the City of Rockport (Aransas County), and the Hoehn colonia (Hidalgo County). These communities have a history of flooding, are highly socially vulnerable, and rely on roadside ditches as their principal stormwater drainage system for runoff control. Mobile lidar (Light Detection and Ranging) measurements were collected for 6.09 miles of roadside channels in these communities. The raw lidar measurements were processed to evaluate drainage conditions based on the channel's geometric properties, hydraulic capacity, and level of service. The assessment results are linked to a Geographic Information System (GIS) tool for enhanced visualization. Finally, the paper provides insights regarding the quality of stormwater infrastructure in the study communities and discusses their practical implications.

Estimating urban vulnerability to flood and heat hazards: A case study in the municipality of Thessaloniki, Greece

Continuous urban expansion, the conversion of open land to built-up areas and increased energy consumption have diversified the microclimates of cities. These phenomena combined with climate change hazards increase the vulnerability of cities, in a spatially heterogeneous way. Therefore, cities should become more resilient to those threats, by identifying and prioritising highly vulnerable areas. The main purpose of this study is to develop a spatial-based approach to assess the vulnerability of climate-related hazards in the urban environment of Thessaloniki (Greece). In this context, spatial and temporal patterns of land surface temperature were estimated through the calculation of various spectral indices, to conduct an analytical Urban Heat Island vulnerability assessment. Furthermore, the FloodMap-Pro application was used to identify coastal areas that are vulnerable to sea level rise, while historical floods were digitised in order to identify potential urban (flash) flood zones. The most important outcome of this paper is the creation of an integrated spatial vulnerability index, which identifies the urban areas that are prone to all these hazards. The final vulnerability map illustrates how the city of Thessaloniki is exposed to several climate-related hazards and that many areas/neighbourhoods are prone to one or more risk factors.

A climate-conditioned catastrophe risk model for UK flooding

Abstract. We present a transparent and validated climate-conditioned catastrophe flood model for the UK, that simulates pluvial, fluvial and coastal flood risks at 1 arcsec spatial resolution (∼ 20–25 m). Hazard layers for 10 different return periods are produced over the whole UK for historic, 2020, 2030, 2050 and 2070 conditions using the UK Climate Projections 2018 (UKCP18) climate simulations. From these, monetary losses are computed for five specific global warming levels above pre-industrial values (0.6, 1.1, 1.8, 2.5 and 3.3 ∘C). The analysis contains a greater level of detail and nuance compared to previous work, and represents our current best understanding of the UK's changing flood risk landscape. Validation against historical national return period flood maps yielded critical success index values of 0.65 and 0.76 for England and Wales, respectively, and maximum water levels for the Carlisle 2005 flood were replicated to a root mean square error (RMSE) of 0.41 m without calibration. This level of skill is similar to local modelling with site-specific data. Expected annual damage in 2020 was GBP 730 million, which compares favourably to the observed value of GBP 714 million reported by the Association of British Insurers. Previous UK flood loss estimates based on government data are ∼ 3× higher, and lie well outside our modelled loss distribution, which is plausibly centred on the observations. We estimate that UK 1 % annual probability flood losses were ∼ 6 % greater for the average climate conditions of 2020 (∼ 1.1 ∘C of warming) compared to those of 1990 (∼ 0.6 ∘C of warming), and this increase can be kept to around ∼ 8 % if all countries' COP26 2030 carbon emission reduction pledges and “net zero” commitments are implemented in full. Implementing only the COP26 pledges increases UK 1 % annual probability flood losses by 23 % above average 1990 values, and potentially 37 % in a “worst case” scenario where carbon reduction targets are missed and climate sensitivity is high.

Flood Preparedness in United Kingdom and China: A Comparison Study Focusing on Social and Economic Factors

The paper aims to explore the social and economic factors influencing the flood preparedness of Zhengzhou, China and Leeds, United Kingdom residents. As one of the chosen areas, Zhengzhou experienced a terrible flood with an inaccurate weather forecast and little pre-flood preparedness in July 2021. On the other hand, Leeds has a long history of frequent floods. Wetter winters and stormy weather caused by climate change led to increased floods in Leeds. The consistent seasonal floods keep damaging residents and their properties, leading to economic losses. The authors conducted semi-structured interviews with residents who experienced floods in Zhengzhou and Leeds. Comparative parameters included economic and social, as these factors are hugely different in the study areas. The results showed that factors like education influence the households’ flood preparedness; likewise, economic factors like disposable income also affect the willingness of residents to spend on flood preparedness. Furthermore, the results revealed that with proactive flood management, both communities progressed in minimising the post-adverse effects of floods.

A globally applicable framework for compound flood hazard modeling

Abstract. Coastal river deltas are susceptible to flooding from pluvial, fluvial, and coastal flood drivers. Compound floods, which result from the co-occurrence of two or more of these drivers, typically exacerbate impacts compared to floods from a single driver. While several global flood models have been developed, these do not account for compound flooding. Local-scale compound flood models provide state-of-the-art analyses but are hard to scale to other regions as these typically are based on local datasets. Hence, there is a need for globally applicable compound flood hazard modeling. We develop, validate, and apply a framework for compound flood hazard modeling that accounts for interactions between all drivers. It consists of the high-resolution 2D hydrodynamic Super-Fast INundation of CoastS (SFINCS) model, which is automatically set up from global datasets and coupled with a global hydrodynamic river routing model and a global surge and tide model. To test the framework, we simulate two historical compound flood events, Tropical Cyclone Idai and Tropical Cyclone Eloise in the Sofala province of Mozambique, and compare the simulated flood extents to satellite-derived extents on multiple days for both events. Compared to the global CaMa-Flood model, the globally applicable model generally performs better in terms of the critical success index (−0.01–0.09) and hit rate (0.11–0.22) but worse in terms of the false-alarm ratio (0.04–0.14). Furthermore, the simulated flood depth maps are more realistic due to better floodplain connectivity and provide a more comprehensive picture as direct coastal flooding and pluvial flooding are simulated. Using the new framework, we determine the dominant flood drivers and transition zones between flood drivers. These vary significantly between both events because of differences in the magnitude of and time lag between the flood drivers. We argue that a wide range of plausible events should be investigated to obtain a robust understanding of compound flood interactions, which is important to understand for flood adaptation, preparedness, and response. As the model setup and coupling is automated, reproducible, and globally applicable, the presented framework is a promising step forward towards large-scale compound flood hazard modeling.

Geochemistry of a paleo-oxbow lake sediments and its implications for the late Holocene extreme overbank flooding history of the Yellow River within the Zoige Basin, NE Tibetan Plateau

Paleo-oxbow lake sediments can provide archives to reconstruct paleo-channel evolution and flooding history of the river. Multi-proxy approaches including detailed sediment stratigraphy, sedimentology and geochemistry have been implemented in a high-resolution sedimentary section of paleo-oxbow lake of the Yellow River within the Zoige Basin on the NE Tibetan Plateau, to reconstruct regional environmental changes and extreme overbank flooding history. Our results suggest that not only traditional sedimentological proxies, but also chemical elements can be applied in defining sequences with different genetic types, especially the paleoflood deposits in the paleo-oxbow profile. Two units of late-Holocence extreme overbank flooding deposits (OFDs) are identified in terms of the significantly higher proportions of sand, high contents of SiO2, Na2O, Ba, low contents of Al2O3, Fe2O3, MgO, K2O, Ti, Rb and high values of Zr/Fe, Zr/Rb ratios. These extraordinary flood events within the Zoige Basin were dated back to 2,960 ± 240–2,870 ± 270 a and 1840 ± 200–1700 ± 160 a, in response to the mid-Holocene climatic optimum to the late Holocene and the Dark Age Cold Period (DACP). And the strong rainfall caused by the abnormal atmospheric circulation during the period of climate transition and abrupt change may led to the frequent occurrence of extreme flood events in the source region of the Yellow River. These findings are important for understanding the response of regional fluvial system to high climatic instability and provide a new perspective for us to analyze the risk of flood disasters on the Tibetan Plateau under the background of climate change.

The Treatiseon Waters by Cornaro (1560) and a quantitative assessment of the historical sea surges “Acqua Alta” in Venice

In the sixteenth century, Cornaro wrote a Treatise on Waters with personal observations and conclusions regarding the Lagoon of Venice, e.g., the sea level rise over the centuries, the difference between normal tides for astronomical forces and storm surges driven by meteorological factors, and water exchanges between the Lagoon and the Sea. He witnessed the continuous rise of the sea level since the Middle Ages and listed some public works made to adapt to this challenge, i.e., raising city paving and floors, and rebuilding bridges that had become too low. Cornaro dealt with the mark left by the algae on walls that was kept as an official (zero) reference of sea level. Using this key to measure flooding depths, and knowing the relationship between the algae front and mean sea level, a revision of the historical floods (from 1240 to 1867) is made to assess precise depths. During the deepest floods, it was possible to reach San Marco square by gondola and float on the square. The draught of past gondola types has been another key to interpret flood depths. From 1200 to 1500, the most extreme flooding depths were higher than that of 1966, i.e., the highest in the instrumental record since 1871; from 1500 to 1799, they have been quite homogeneous, close to the value observed in 1966; in the nineteenth century, they returned to be higher than that in 1966. Over eight centuries, the deepest historical floods exceeded 7 times by 40 cm the 1966 extreme depth. The city should be prepared to face this risk.

Characterizing recurrent flood hazards in the Himalayan foothill region through data-driven modelling

Floods are frequently occurring hazards in the Himalayan foothills that primarily affect the physical environment and socio-economic profile of the local people. To this end, we used some data-driven models and techniques such as multi-criteria decision analysis, bivariate statistical method, machine learning, and sub-watershed prioritization by morphometric analysis (SWPMA) in the Kaljani River basin for the flood susceptibility mapping (FSM). The study used 200 non-flood points and 200 historical flood points for developing the testing (30%) and training (70%) datasets. Multicollinearity diagnostic test (VIF) and information gain ratio (IGR) were used to measure the consistency of the selected twelve influencing factors. Rainfall, drainage density, clay content, distance from the river and elevation were the most important drivers of the selected influencing parameters. We found that the high and very high FSM areas of the basin were around 32.24% for random forest (RF), 47.46% for frequency ratio (FR), 46.41% for analytical hierarchic process (AHP), and 40.5% for SWPMA. Most of these areas lie in Alipurduar town surrounding areas and the lower part of the basin. The ROC (receiver operating characteristic), MAE (mean absolute error), RMSE (root mean square error), Kappa index and Pearson’s correlation coefficients were employed for the performance efficiency of four data-driven models. Achieved ROC values of the RF model, FR model, AHP model and SWPMA model were at 92.4%, 86.6%, 83.1% and 79.4% respectively. Compared to all four methods the machine learning approach, the RF method performed most accurately. This study can help researchers for choosing the right approaches to the FSM. The present FSM may be instrumental before the various stakeholders while designing the flood evacuation plan, preparation of future flood shelters, and planning for future development for the local community.

A Multi-Sensor Satellite Approach to Characterize the Volcanic Deposits Emitted during Etna’s Lava Fountaining: The 2020–2022 Study Case

Between December 2020 and February 2022, the South East Crater of Etna has been the source of numerous eruptions, mostly characterized by the emission of lava fountains, pyroclastic material and short-lasting lava flows. Here we estimate the volume and distribution of the lava deposits by elaborating multi-source satellite imagery. SEVIRI data have been elaborated using CL-HOTSAT to estimate the lava volume emitted during each event and calculate the cumulative volume; Pléiades and WorldView-1 data have been used to derive Digital Surface Models, whose differences provide thickness distributions and hence volumes of the volcanic deposits. We find a good agreement, with the total average lava volume obtained by SEVIRI reaching 73.2 × 106 m3 and the one from optical data amounting to 67.7 × 106 m3. This proves the robustness of both techniques and the accuracy of the volume estimates, which provide important information on the lava flooding history and evolution of the volcano.

Flood detection using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and extreme precipitation data

Abstract. A complete global flood event record would aid researchers to analyze the distribution of global floods and, thus, better formulate and manage disaster prevention and reduction policies. This study used Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days globally between 1 April 2002 and 31 August 2016; these results were then further compared and validated with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that GRACE-based flood days could cover 81 % of the flood events in the DFO database, 87 % of flood events extracted by MODIS and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection greater than or equal to 0.5 reached 62 % among 261 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. Finally, we provided flood day products with a 1∘ spatial resolution covering the range between 60∘ S and 60∘ N from 1 April 2002 to 31 August 2016; these products can be obtained from https://doi.org/10.5281/zenodo.6831384 (Zhang et al., 2022b). Thus, this research contributes a data foundation for the mechanistic analysis and attribution of global flood events.

Non-governmental organizations’ motivation to diversify: self-interest or operation-related? Evidence from Uganda

Abstract Understanding the mechanisms that guide non-governmental organizations’ (NGOs) managerial decisions is a key to effective development policies. One fundamentally strategic decision is the number of activities an NGO offers. We provide a conceptual framework based on the agency theory to study the motivations underlying strategic decisions of development NGOs in Uganda. We test whether diversifying into many activities is driven by operational reasons or by personal gains of NGO managers. Following a historic flood in 2007, NGOs that rely more on contractual income offer fewer activities than their counterparts in less affected areas. The results support theoretical explanations that operational motives such as risk-reduction or cost complementarity dominate personal and for-profit-like motives. Our article contributes to the debates around the ethical and governmental foundation of the non-profit sector, highlighting the different roles of personal and operational aspects in the decision-making process.