nasa-gesdisc/es-publications-researchareas huggingface dataset:
A CYGNSSBased Algorithm for the Detection of Inland Waterbodies (2019)

The Cyclone Global Navigation Satellite System (CYGNSS) is a new constellation of eight low Earth orbiting spacecrafts that receive both direct and reflected signals from GPS satellites. Coherent reflection of the GPS signal from standing water over land results in a high surface reflectivity signal in the CYGNSS data. An image processing algorithm is presented, which leverages the surface reflectivity signal to produce a watermask of inland waterbodies at 0.01° × 0.01° spatial resolution. The watermask is compared to hand-drawn maps of inland waterbodies, as well as to the MODIS watermask product. We find that the CYGNSS watermask provides accurate, time-varying maps that are able to resolve changes in lake and river position and extent. With CYGNSS' short return time, watermasks can be generated using as little as half a month of data to produce near-real-time maps of flooding events.

Research areas: Validation
Climate-induced flood inundation for the Arial Khan River of Bangladesh using open-source SWAT and HEC-RAS model for RCP8. 5-SSP5 scenario (2021)

Bangladesh is one of the largest flood-prone deltas of the GBM (GangesBrahmaputraMeghna) basins, and recently, it is categorized as the 7th worst climate-affected country in the world. Future climate change along with economic development, urbanization, and increase in population may worsen this situation manifolds. To cope with future flood situations and lessen probable flood losses, it is essential to develop flood maps of the major flood-prone rivers of Bangladesh considering climate change scenarios. In this study, the flood inundation of the Arial Khan River and its floodplain has been assessed for the predicted climate change scenario of RCP 8.5 (Representative Concentration Pathway 8.5) using open-source mathematical models. A calibrated and validated hydrologic model of GBM basins in SWAT (Soil and Water Assessment Tool) model has been used to estimate the future flow magnitudes at Bahadurabad Transit (Brahmaputra River) and Hardinge Bridge (Ganges River) using extreme emission scenario RCP 8.5. Using the flow magnitude of these two stations as the upstream boundaries, an HEC-RAS 1D model has been set up for the Brahmaputra, Ganges, and Padma rivers for generating future flow magnitude at the offtake of the Arial Khan River. Later, an HEC-RAS 1D-2D coupled model is set up for the Arial Khan River floodplain and flood maps are prepared considering flood depth, duration, and inundation extent. The flood assessment for different projections of RCP 8.5 shows that there is an increasing trend of flood in terms of depth, duration, and inundation from the 2020s to the 2080s. Hence, the floodplain becomes more hazardous by the end of this century. The climate change impact on the projected population for the RCP 8.5 scenario is assessed under SSP5 (Shared Socioeconomic Pathways 5) which indicates that the total flood-affected population will be nearly twice in the 2080s compared to the 2020s. So, future climate change is going to have a dreadful effect on the flood situation of the Arial Khan River floodplain.

Research areas: Floods
Social-media and newspaper reports reveal large-scale meteorological drivers of floods on Sumatra (2020)

Floods are a major contributor to natural disasters in Sumatra. However, atmospheric conditions leading to floods are not well understood due, among other factors, to the lack of a complete record of floods. Here, the 5 year flood record for Sumatra derived from governmental reports, as well as from crowd-sourcing data, based on Twitter messages and local newspapers reports, is created and used to analyze atmospheric phenomena responsible for floods. It is shown, that for the majority of analyzed floods, convectively coupled Kelvin waves, large scale precipitation systems propagating at 12 m/s along the equator, play the critical role. While seasonal and intraseasonal variability can also create conditions favorable for flooding, the enhanced precipitation related to Kelvin waves was found in over 90% of flood events. In 30% of these events precipitation anomalies were attributed to Kelvin waves only. These results indicate the potential for increased predictability of flood risk.

Research areas: Floods
Detection of Flash Flood Inundated Areas Using Relative Difference in NDVI from Sentinel-2 Images: A Case Study of the August 2020 Event in Charikar, Afghanistan (2022)

On 26 August 2020, a devastating flash flood struck Charikar city, Parwan province, Afghanistan, causing building damage and killing hundreds of people. Rapid identification and frequent mapping of the flood-affected area are essential for post-disaster support and rapid response. In this study, we used Google Earth Engine to evaluate the performance of automatic detection of flood-inundated areas by using the spectral index technique based on the relative difference in the Normalized Difference Vegetation Index (rdNDVI) between pre- and post-event Sentinel-2 images. We found that rdNDVI was effective in detecting the land cover change from a flash flood event in a semi-arid region in Afghanistan and in providing a reasonable inundation map. The result of the rdNDVI-based flood detection was compared and assessed by visual interpretation of changes in the satellite images. The overall accuracy obtained from the confusion matrix was 88%, and the kappa coefficient was 0.75, indicating that the methodology is recommendable for rapid assessment and mapping of future flash flood events. We also evaluated the NDVIs changes over the course of two years after the event to monitor the recovery process of the affected area. Finally, we performed a digital elevation model-based flow simulation to discuss the applicability of the simulation in identifying hazardous areas for future flood events.

Research areas: Floods
Overview of the Mekong River Basin (2020)

The Lancang-Mekong River, the largest transboundary river in Southeast Asia, flows through six riparian countries, nurtures splendid culture along it. The river is called Lancang River in China, and Mekong River out of China. The Mekong River region is featured by various landscape patterns and high variability of rainfall caused by monsoon cycles. This region is also featured by high density of population, high potential of development and high international attention, which make this study more meaningful. The physical geographical feature of this region is illustrated from the aspects of topography, meteorology and hydrology in this chapter. The Socio-economy characteristics of this region is also reviewed in this chapter to provide background knowledge for the further analysis of flood and drought management. The irrigation projects and hydropower projects, main types of structural measures, are also briefly introduced from the whole basin view.

Research areas: Habitat Conversion/Fragmentation
Home bias in humanitarian aid: The role of regional favoritism in the allocation of international disaster relief (2022)

This paper investigates whether regional favoritism shapes humanitarian aid flows. Using a rich and unique dataset derived from reports of the Office of US Foreign Disaster Assistance (OFDA), we show that substantially larger amounts of aid are disbursed when exogenous natural disasters hit the birth region of the recipient countries political leader. While we find no evidence that US commercial or political interests affect the size of this home bias, the bias is stronger in countries with a weaker bureaucracy and governance, suggesting the absence of effective safeguards in the allocation of aid.

Research areas: Public Health
The flood of 2011 in the lower Chao Phraya valley, Thailand: Study of a long-duration flood through satellite images (2016)

The paper illustrates application of satellite images for studying the anatomy of a long-duration, extensive, and slow flood on the Chao Phraya River in 2011 that inundated Bangkok in its lower reach. The spread of floods in the valley was mapped with MODIS, month by month, from July 2011 to February 2012. A subsampled WorldView-2 mosaic was used to observe part of the valley in detail. The flood in Bangkok was studied with four higher-resolution images from Spot 4, WorldView-2, and GeoEye-1 satellites. We suspect that the floodwaters jumped the banks of the Chao Phraya south of Chai Nat, and then travelled overland and along river channels. The overland passage made it difficult to protect settlements. We also studied sedimentation from the images of this shallow overland flow across the country, which was complicated by the presence of preexisting embankments, other anthropogenic structures, and smaller stream channels. This is a descriptive study but it highlights the nature of flooding that is likely to be repeated in this low flat valley from high rainfall. The pattern of flooding was similar to that of a previous large flood in 1996 recorded in a SPOT 2 image. These floods impact Bangkok periodically, a city of about 10 million people, which started on a levee in a low flat delta, then expanded into backswamps, and is marked with local depressions from groundwater extraction. These slow extensive floods can be mapped from satellite images and properly recorded as an early step in analysis of large floods. Mapping of such floods on ground is logistically impossible. Slow, extensive, and long-lasting floods affect lower valleys and deltas of a number of major rivers, impacting agricultural fields and large populations. These floods are especially disastrous for cities located on low deltas. We submit that basic exercises on satellite images provide valuable introductory information for understanding geomorphology of such floods, and also for structuring plans for flood amelioration. Satellite images at very high resolutions, also used in this study, provide complimentary data to mapping and ground observation. Basin environments that are inundated by large shallow extensive floods are not unusual. In future, climate change is expected to raise the frequency of floods in lower parts of a number of river valleys and deltas, so that for such an environment slow extensive floods may become common and need to be studied. In that sense this is a template for studying large slow floods, arguably more frequent in future.

Research areas: Floods
Flooding dynamics on the lower Amazon floodplain: 1. Hydraulic controls (2014)

Modeling the routing of flood waters across large floodplains is challenging because flows respond to dynamic hydraulic controls from complex geomorphology, vegetation, and multiple water sources. In this study, we analyzed the topographic and hydrologic controls of inundation dynamics of a large floodplain unit (2440 km<SUP>2</SUP>) along the lower Amazon River. We combined land topography derived from the Shuttle Radar Topography Mission (SRTM) with underwater topography derived from an extensive echo-sounding survey to generate a seamless digital elevation model (DEM). Floodplain inundation was simulated using LISFLOOD-FP, which combines one-dimensional river routing with two-dimensional overland flow, and a local hydrological model. For the first time, accurate simulation of filling and drainage of an Amazon floodplain was achieved with quantification of changes in water elevation, flooding extent, and river-floodplain exchange. We examined the role of diffuse overbank versus channelized flows on river-floodplain exchange. Diffuse overbank flows represent 93% of total river to floodplain discharge and 54% of floodplain to river discharge. Floodplain discharge during high-water was four times higher than field observation values when the SRTM v.4 DEM with no correction was used for simulation because of a -4.4 m elevation bias originating from residual motion errors of the SRTM interferometric baseline.

Research areas: Floods
Humanitarian need drives multilateral disaster aid (2021)

Significance
            Threats to human livelihoods resulting from natural hazards are increasing due to climate change. Climate-related disasters such as floods, storms, and droughts have destroyed shelter, reduced crop yields, harmed livestock, and fueled conflict, especially in developing countries. The key finding is that UN aid in the aftermath of climate-related disasters is largely driven by humanitarian need. The UN seems able to fend off donor states strategic interest and allocate more aid after disasters where hazard severity is greater and need is more pressing. Based on this finding, we argue that the UN lives up to its stated principles of neutrality, impartiality, and independence in disaster aid, corroborating the legitimacy of the UN in allocating disaster aid.
          , 
            As the climate changes, human livelihoods will increasingly be threatened by extreme weather events. To provide adequate disaster relief, states extensively rely on multilateral institutions, in particular the United Nations (UN). However, the determinants of this multilateral disaster aid channeled through the UN are poorly understood. To fill this gap, we examine the determinants of UN disaster aid using a dataset on UN aid covering almost 2,000 climate-related disasters occurring between 2006 and 2017. We make two principal contributions. First, we add to research on disaster impacts by linking existing disaster data from the Emergency Events Database (EM-DAT) to a meteorological reanalysis. We generate a uniquely global hazard severity measure that is comparable across different climate-related disaster types, and assess and bolster measurement validity of EM-DAT climate-related disasters. Second, by combining these data with social data on aid and its correlates, we contribute to the literature on aid disbursements. We show that UN disaster aid is primarily shaped by humanitarian considerations, rather than by strategic donor interests. These results are supported by a series of regression and out-of-sample prediction analyses and appear consistent with the view that multilateral institutions are able to shield aid allocation decisions from particular state interests to ensure that aid is motivated by need.

Research areas: Public Health
Urban ecosystems and heavy rainfall – A Flood Regulating Ecosystem Service modelling approach for extreme events on the local scale (2022)

Increasing urbanisation in combination with a rise in the frequency and intensity of heavy rain events increase the risk of urban flooding. Flood Regulating Ecosystem Services (FRES) address the capacity of ecosystems to reduce the flood hazard and lower damage. FRES can be estimated by quantification of supply (provision of a service by an ecosystem) and demand (need for specific ES by society). However, FRES for pluvial floods in cities have rarely been studied and there is a gap in research and methods on FRES supply and demand quantification.
            In this study, we assessed FRES of an urban district in the City of Rostock (Germany) for a one-hour heavy rainfall event using the hydrological model LEAFlood. The hydrological model delivered the FRES supply indicators of soil water retention and water retained by canopies (interception). An intersection of the potential demand (based on indicators of population density, land reference value, monuments and infrastructure) and the modelled surface water depth revealed the actual demand. Comparing the actual demand and supply indicated the budget of FRES to identify unmet demand and supply surplus.
            Results show highest mean FRES supply on greened areas of forests, woodlands and green areas, resulting in a supply surplus. Whereas, sealed areas (paved surface where water cannot infiltrate into the soil), such as settlements, urban dense areas, traffic areas and industry, have an unmet demand resulting from low supply and relatively high actual demand.
            With the hydrological model LEAFlood, single landscape elements on the urban scale can be evaluated regarding their FRES and interception can be considered. Both are important for FRES assessment in urban areas. In contrast to flood risk maps, the study of FRES gives the opportunity to take into account the contribution of nature to flood regulation benefits for the socio-economic system. The visualisation of FRES supply and demand balance helps urban planners to identify hotspots and reduce potential impacts of urban pluvial flooding with ecosystem-based adaptations.

Research areas: Floods
Putting the poorly documented 1998 GLOF disaster in Shakhimardan River valley (Alay Range, Kyrgyzstan/Uzbekistan) into perspective (2020)

On July 8, 1998, the deadliest glacier lake outburst flood (GLOF) in Central Asia for at least the last 100 years occurred in the Shakhimardan catchment, Kyrgyzstan. Most of the >100 victims were, however, killed in the Uzbek enclave of Shakhimardan, i.e. in the downstream part of this transboundary catchment. No warnings were issued between the two countries. In addition, due to political tensions, access to the site was impossible and a detailed assessment of the disaster could not be realized until now. Using remote sensing, we show that the lake at the origin of the Shakhimardan event appeared in the 1960s and drained periodically, without, however, causing damage to downstream areas before it eventually disappeared in the late 1980s. Based on post-event videos, we conclude that the GLOF-producing depression was again filled with a lake, estimated at 20  1.2  103 m2 in area, before the disaster. The lake burst was likely driven by the rapidly rising air temperatures and the melting of snow/ice in late June and early July. The GLOF first travelled as a debris flow for 17 km, then continued as a debris flood in the increasingly flatter channel for another 20 km. Interestingly, the mean weighted channel angle in the areas of erosion was extremely low at 6.7. The flood continued further downstream for ~100 km from its source. Today, 32 lakes (total area ~300  103 m2 in 2018) exist in the catchment, with several of the larger lakes (>5  103 m2) showing signs of instability. We therefore call for a systematic monitoring of environments like the Shakhimardan catchment, as well as for the installation of early warning systems at critical sites, with exchange of data between the Kyrgyz and Uzbek disaster risk management units, so as to mitigate existing and evolving GLOF risks.

Research areas: Floods
The importance of infrastructure and national demand to represent (2022)

A new US hydroeconomic model quantifies water stress accounting for infrastructure. A third of US counties are unable to meet their water demands from renewable sources. Water stress is greatly overestimated by metrics based only on local runoff. Reservoirs reduce both total water supply failure and its intra-annual variability. Over 40% of counties overcome deficits during dry months due to existing reservoirs.

Research areas: Land Surface/Agriculture Indicators
Extreme flood events in the Bolivian Amazon wetlands (2016)

Study region
The Amazonian wetlands of Bolivia, known as the Llanos de Moxos, are believed to play a crucial role in regulating the upper Madeira hydrological cycle, the most important southern tributary of the Amazon River. Because the area is vast and sparsely populated, the hydrological functioning of the wetlands is poorly known.
Study focus
We analyzed the hydrometeorological configurations that led to the major floods of 2007, 2008 and 2014. These data, together with flood mapping derived from remote sensing images, were used to understand the dynamics of the Llanos during the three flood events.
New hydrological insights for the region
The results showed that large floods are the result of the superimposition of flood waves from major sub-basins of the region. As a previous study suggested, the dynamics of the floods are controlled by an exogenous process, created by the flood wave originating in the Andes piedmont that travels through the Mamore River; and by an endogenous process, which is the runoff originating in the Llanos. Our study showed that the first process is evident only at the initial phase of the floods, and although important for attenuating the rising flood wave, it is of lesser importance compared to the endogenous process. We conclude that the endogenous process controls the magnitude and duration of major floods.

Research areas: Floods
Comparison of satellite passive microwave with modeled snow water equivalent estimates in the Red River of the North Basin (2019)

The Red River of the North basin (RRB) is vulnerable to spring snowmelt flooding because of its flat terrain, low permeability soils, and the presence of river ice jams resulting from the river's northward flow direction. The onset and magnitude of major flood events in the RRB have been very difficult to forecast, in part due to limited field observations of snow water equivalent (SWE). Coarse-resolution (25-km) passive microwave observations from satellite instruments are well suited for the monitoring of SWE. Despite routine use in the Earth sciences community to document the impacts of climate change, the use of passive microwave observations in operational flood forecasting is rare. This paper compares daily satellite passive microwave SWE observations from special sensor microwave/imager (SSM/I) and special sensor microwave imager/sounder (SSMIS), advanced microwave scanning radiometer for earth observing system (AMSR-E), and advanced microwave scanning radiometer 2 (AMSR2) from 2003 to 2016 to modeled output from the SNOw Data Assimilation System (SNODAS) and Global Snow Monitoring for Climate Research-2 (GlobSnow-2) in the RRB to determine the differences between the remotely sensed SWE estimates and the model products currently used in flood forecasting. Results show statistically significant differences between the satellite observations and SNODAS in the northern region of the basin that were not evident in the southern region. Satellite estimates of peak SWE in the forecast subbasins in the northern region were 40-125% higher than the model results which points to the lack of ground observations used to constrain the model simulations. This paper recommends that satellite SWE observations should be considered for improving operational snowmelt forecasting in the RRB.

Research areas: Floods
Addressing spatio-temporal resolution constraints in Landsat and MODIS-based mapping of large-scale floodplain inundation dynamics (2018)

Recent studies have developed novel long-term records of surface water (SW) maps on continental and global scales but due to the spatial and temporal resolution constraints of available satellite sensors, they are either of high spatial and low temporal resolution or vice versa. In this study, we address this limitation by exploring two approaches for generating an 8-day series of Landsat resolution (30 m) SW maps for three floodplain sites in south-eastern Australia during the 2010 La Nina Floods. Firstly, we applied a generalized additive regression model (GAM) that empirically relates Landsat-based SW extent to in-situ river flow to then predict additional time steps. Secondly, we used the STARFM and ESTARFM blending algorithms for predicting the Open Water Likelihood at 8-daily intervals and 30 m resolution from Landsat and MODIS imagery. ESTARFM outperformed STARFM and best blending accuracies were achieved in the floodplain site with the slowest changes in inundation extent through time. There was good agreement between the blended and statistically-modeled 8-day SW series and both series provided new and temporally consistent information about changes in inundation extent throughout the flooding cycles. After careful consideration of accuracy limitations and model assumptions, these SW records hold great potential for assimilation into hydrodynamic and hydrological models as well as improved management of terrestrial freshwater ecosystems.

Research areas: Floods