nasa-gesdisc/es-publications-researchareas huggingface dataset:
Two Centuries of Drought History in the Center of Chihuahua, Mexico (2022)

Droughts are a climatic phenomenon with local, regional, and large-scale repercussions. Historical knowledge of droughts generated by modeled data allows the development of more accurate climate reconstructions to propose better approaches for the management of hydric resources. The objective of this research was to evaluate the association of precipitation and temperature with data from the NLDAS-002 to develop a reconstruction of droughts in the center of Chihuahua, Mexico using the SPEI from tree rings. We also identified the influence of oceanatmospheric phenomena on the reconstructed drought index. The best association among chronologies was obtained with the earlywood band and accumulated seasonal precipitation from November of the previous year to June of the current year (r = 0.82, p < 0.05) and for temperature from January to July (r = 0.81, p < 0.05). The reconstructed drought index extended from 1775 to 2017 (243 years), where seven extreme drought events were identified. We found significant correlations between the reconstructed Standardized Precipitation Evapotranspiration Index and the Pacific Decadal Oscillation (r = 0.46, p < 0.05), Atlantic Multidecadal Oscillation (r = 0.34, p < 0.05), Multivariate El Nino Southern Oscillation Index (r = 0.29, p < 0.05), and Southern Oscillation Index (r = 0.22, p < 0.05). The historical reconstruction of hydroclimatology in the center of Chihuahua is important for planning a long-term assessment and for the management of water resources shared by Mexico and the United States.

Research areas: Droughts
DROUGHT PATTERN ASSESSMENT OVER MARATHWADA, INDIA THROUGH THE DEVELOPMENT OF MULTIVARIATE ADVANCE DROUGHT RESPONSE INDEX (2022)

Abstract. The intensity and frequency with which recent drought events are occurring has made management of food and water a challenge. The situation in Indian subcontinent is no difference. The presented study takes inspiration from such problems to propose a remote sensing based multivariate advance drought response index (ADRI). The proposed multivariate index takes into consideration long term conditions of precipitation, normalized difference vegetation index (NDVI), brightness temperature and soil moisture in a linear way for 8-day drought assessment over drought affected Marathwada region, Maharashtra, India. A value of 33 corresponding to ADRI was found to be normal condition over two decades which corresponds to normal vegetation condition index (VCI) value of 51.1 and 54.2. The last 7 years shows a consistent pattern in the change of regional ADRI values which suggests the years in which agricultural assistance is needed over the region. Drought over the region is found to shift from central to eastern and northern regions in the last 5 years. Temporal analysis for the duration suggests up to 5 percent of the area of Marathwada has been facing severe drought conditions over the last decade while up to 70 percent of the area is experience below normal conditions with varying intensities of water stress. The districts of Latur, Parbhani, Hingoli and Nanded which are downstream river Godavari have been affected the most due to large percentage of land being under agriculture.

Research areas: Droughts
Data mining attribute selection approach for drought modeling: A case study for Greater Horn of Africa (2017)

The objectives of this paper were to 1) develop an empirical method for selecting relevant attributes for modelling drought, and 2) select the most relevant attribute for drought modelling and predictions in the Greater Horn of Africa (GHA). Twenty four attributes from different domain areas were used for this experimental analysis. Two attribute selection algorithms were used for the current study: Principal Component Analysis (PCA) and correlation-based attribute selection (CAS). Using the PCA and CAS algorithms, the 24 attributes were ranked by their merit value. Accordingly, 15 attributes were selected for modelling drought in GHA. The average merit values for the selected attributes ranged from 0.5 to 0.9. Future research may evaluate the developed methodology using relevant classification techniques and quantify the actual information gain from the developed approach.

Research areas: Droughts
Global flood exposure from different sized rivers (2021)

Abstract. There is now a wealth of data to calculate global flood exposure. Available datasets differ in detail and representation of both global population distribution and global flood hazard. Previous studies of global flood risk have used datasets interchangeably without addressing the impacts using different datasets could have on exposure estimates. By calculating flood exposure to different sized rivers using a model-independent geomorphological river flood susceptibility map (RFSM), we show that limits placed on the size of river represented in global flood models result in global flood exposure estimates that differ by more than a factor of 2. The choice of population dataset is found to be equally important and can have enormous impacts on national flood exposure estimates. Up-to-date, high-resolution population data are vital for accurately representing exposure to smaller rivers and will be key in improving the global flood risk picture. Our results inform the appropriate application of these datasets and where further development and research are needed.

Research areas: Floods
The value of near real-time earth observations for improved flood disaster response (2019)

Information is a critical resource in disaster response scenarios. Data regarding the geographic extent, severity, and socioeconomic impacts of a disaster event can help guide emergency responders and relief operations, particularly when delivered within hours of data acquisition. Information from remote observations provides a valuable tool for assessing conditions on the ground more quickly and efficiently. Here, we evaluate the social value of a near real-time flood impact system using a disaster response case study, and quantify the Value of Information (VOI) of satellite-based observations for rapid response using a hypothetical flooding disaster in Bangkok, Thailand. MODIS imagery from NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) system is used to produce operational estimates of inundation depths and economic damages. These rapid Earth observations are coupled with a decision-analytical model to inform decisions on emergency vehicle routing. Emergency response times from vehicles routed using flood damage data are compared with baseline routes without the benefit of advance information on road conditions. Our results illustrate how the application of near real-time Earth observations can improve the response time and reduce potential encounters with flood hazards when compared with baseline routing strategies. Results indicate a potential significant economic benefit (i.e., millions of dollars) from applying near real-time Earth observations for improved flood disaster response and management.

Research areas: Floods
Using Remote Sensing for Identifying Suitable Areas for Flood Shelter: A Case Study of Thatta, Sindh Pakistan (2020)

The most recurring type of disaster in the world these days is flood because of the spread and extent of its effect on people, among all-natural disasters of the world. Human activities have paved the way for many of these flood behavior to change as they used to be in the past. Pakistan experienced one of the most devastating natural disasters in its history all across the country in 2010, but Thatta district in southern part got severely affected during this flood. For the research, a simple yet efficient methodology Normalized Difference Vegetation Index (NDVI) by using remote sensing images for identifying flood hazard areas was utilized. Geographic Information Systems (GIS) helps in finding shelter areas with a minimum effect of floods. It is essential to realize the importance of mapped results in consideration of manual flood management in future. The method used in this study is robust enough to explain the flood hazard for suggesting suitable shelter sites in case of flooding events. This would help disaster management bodies and other related agencies to formulate the development plans while keeping the hazard areas, which are unsuitable for development due to flood risk in the future.

Research areas: Floods
Datasets of land use change and flood dynamics in the vietnamese mekong delta (2022)

This paper compiles the data associated with a research article published in STOTEN [1]. The data set represents figures, tables, and images illustrating the temporal and spatial distribution of land use and flood dynamics from 2000 to 2020 in the Vietnamese Mekong Delta (VMD). The MODIS imageries were freely accessed online via the NASA website [2] and processed to land use and flood maps based on the algorithms by Sakamoto et al. [3,4]. The MODIS products show a high validation with statistical data and radar satellites [1]. The datasets of flood map and land use, therefore, are available to scientists, engineers, and policy-makers in agricultural management associated with flood management in the VMD. They could be used for policy settings, household livelihood assessment as well as other economic analyses for the VMD region due to the change of land use and flooding dynamics.

Research areas: Floods
Meteorological conditions leading to the 2015 Salgar flash flood: lessons for vulnerable regions in tropical complex terrain (2019)

Abstract. Flash floods are a recurrent hazard for many developing Latin American regions due to their complex mountainous terrain and the rainfall characteristics in the tropics. These regions often lack the timely and high-quality information needed to assess, in real time, the threats to the vulnerable communities due to extreme hydrometeorological events. The systematic assessment of past extreme events allows us to improve our prediction capabilities of flash floods. In May 2015, a flash flood in the La Liboriana basin, municipality of Salgar, Colombia, caused more than 100 casualties and significant infrastructure damage. Despite the data scarcity, the climatological aspects, meteorological conditions, and first-order hydrometeorological mechanisms associated with the La Liboriana flash flood, including orographic intensification and the spatial distribution of the rainfall intensity relative to the basin's geomorphological features, are studied using precipitation information obtained using a weather radar quantitative precipitation estimation (QPE) technique, as well as from satellite products, in situ rain gauges from neighboring basins, quantitative precipitation forecasts (QPFs) from an operational Weather Research and Forecasting Model (WRF) application, and data from reanalysis products. The La Liboriana flash flood took place during a period with negative precipitation anomalies over most of the country as a result of an El Nino event. However, during May 2015, moist easterly flow towards the upper part of La Liboriana caused significant orographic rainfall enhancement. The overall evidence shows an important role of successive precipitation events in a relatively short period and of orography in the spatial distribution of rainfall and its intensification as convective cores approached the steepest topography. There were three consecutive events generating significant rainfall within the La Liboriana basin, and no single precipitation event was exceptionally large enough to generate the flash flood, but rather the combined role of precedent rainfall and the extreme hourly precipitation triggered the event. The results point to key lessons for improving local risk reduction strategies in vulnerable regions with complex terrain.

Research areas: Floods
River flooding in a changing climate: rainfall-discharge trends, controlling factors, and susceptibility mapping for the Mahi catchment, Western India (2021)

The Mahione of the major rivers in Western Indiais subject to frequent major flooding, which severely affects the local economy and infrastructure. Little has been done, however, to assess the flood patterns and severity along its course. Here, the MannKendall and Pettitt tests are used to identify long-term trends of precipitation and peak streamflow at multiple locations in the catchment. Then, flood susceptibility mapping is performed by the analytical hierarchy process, accounting for 14 geomorphic, hydraulic, and geologic factors. The analyses suggest a decline in total precipitation and peak flow discharges at most locations, consistently with the general climatic trend of the area, featuring a weakening summer monsoon. Nonetheless, a significant portion of the catchment area remains highly susceptible to flooding, with stream powers capable of mobilizing boulders up to 1 m in size in extraordinary floods. These results can support the work of engineers and policymakers dealing with floods in the study area, but the proposed methodology can also be applied to other fluvial catchments to evaluate the role of climate trends in modulating flood susceptibility.

Research areas: Floods
Synergistic use of remote sensing, GIS and hydrological models for study of august 2018 Kerala floods (2020)

Abstract. Remote sensing and hydrological models are one of the foremost tools for rapid and comprehensive study of flood hazards and disasters in any parts of the world. Current study is focused on severe 2018 Kerala flood, and is done using various remote sensing data, geospatial tools and combination of hydrological/hydrodynamic/topographical models. Flood mapping is done with pre and post floods remote sensing datasets. For pre-Flood analysis, Normalized Difference Water Index (NDWI) map was prepared on Google Earth Engine (GEE), using Sentinel-2 images for the period of Feb. 2017 to identify permanent water bodies. For post-Flood analysis, GEE was used to download the pre-processed and thermal noise removed Sentinel-1 SAR image for Aug. 9, 2018, Aug. 14 and Aug. 21, 2018 and flood maps were generated using this data. In addition to SAR data, probable flood inundation areas using topography-based flood inundation tool HAND (Height Above Nearest Drainage tool) was also utilized. Hydrological simulation was carried out for all 12 major river sub-basins of Kerala, where floods are reported. Indian Meteorological Department-Global Precipitation Measurement (IMD-GPM) gridded daily data is used as input meteorological data for hydrological simulations. The hydrological simulations results were verified using published Central Water Commission (CWC) reports and reservoirs data for India-WRIS. The hydrodynamic simulation was also performed for simulating the Idukki dam release data and flood condition in downstream areas. Overall, an integrated study and developed approach can be utilized by state and central water and disaster management agencies to develop flood early warning systems.

Research areas: Floods
Increased flooded area and exposure in the White Volta river basin in Western Africa, identified from multi-source remote sensing data (2022)

Accurate information on flood extent and exposure is critical for disaster management in data-scarce, vulnerable regions, such as Sub-Saharan Africa (SSA). However, uncertainties in flood extent affect flood exposure estimates. This study developed a framework to examine the spatiotemporal pattern of floods and to assess flood exposure through utilization of satellite images, ground-based participatory mapping of flood extent, and socio-economic data. Drawing on a case study in the White Volta basin in Western Africa, our results showed that synergetic use of multi-temporal radar and optical satellite data improved flood mapping accuracy (77% overall agreement compared with participatory mapping outputs), in comparison with existing global flood datasets (43% overall agreement for the moderate-resolution imaging spectroradiometer (MODIS) Near Real-Time (NRT) Global Flood Product). Increases in flood extent were observed according to our classified product, as well as two existing global flood products. Similarly, increased flood exposure was also observed, however its estimation remains highly uncertain and sensitive to the input dataset used. Population exposure varied greatly depending on the population dataset used, while the greatest farmland and infrastructure exposure was estimated using a composite flood map derived from three products, with lower exposure estimated from each flood product individually. The study shows that there is considerable scope to develop an accurate flood mapping system in SSA and thereby improve flood exposure assessment and develop mitigation and intervention plans.

Research areas: Floods
Weather radar network benefit model for flash flood casualty reduction (2020)

ABSTRACT A monetized flash flood casualty reduction benefit model is constructed for application to meteorological radar networks. Geospatial regression analyses show that better radar coverage of the causative rainfall improves flash flood warning performance. Enhanced flash flood warning performance is shown to decrease casualty rates. Consequently, these two effects in combination allow a model to be formed that links radar coverage to flash flood casualty rates. When this model is applied to the present-day contiguous U.S. weather radar network, results yield a flash floodbased benefit of $316 million (M) yr1. The remaining benefit pools are more modest ($13 M yr1 for coverage improvement and $69 M yr1 maximum for all areas of radar quantitative precipitation estimation improvements), indicative of the existing weather radar networks effectiveness in supporting the flash flood warning decision process.

Research areas: Floods
Influence of El Niño-Southern Oscillation on Global Coastal Flooding (2018)

Anomalous atmosphere-ocean conditions in the tropical Pacific associated with the El Niño-Southern Oscillation (ENSO) drive interannual variations in mean and extreme sea levels. Climate change may lead to more frequent extreme ENSO events in the future. Therefore, it is important to enhance our understanding of ENSO's influence on coastal flood impacts. We assessed ENSO's influence on extreme sea levels using a global reanalysis of tides and storm surges. This allows for a full coverage of the global coastline from 1979 to 2014. A mean sea level component is added to account for steric effects. This results in a substantial improvement in the representation of the seasonal and interannual variability. Our results show significant correlations across the Pacific between ENSO and extreme sea levels (expressed as 95th annual percentiles), which is consistent with previous studies based on tide gauge observations. Average anomalies in the annual percentiles over El Niño years compared to neutral years show similar patterns. When examining total sea levels, results are largely statistically insignificant. This is because in many regions large tidal variability dominates over the other components. Combining sea levels with an inundation and impact model shows that ENSO has a significant but small effect on the number of people potentially exposed to flooding at the globally aggregated scale. Our results demonstrate that a model-based approach allows for an assessment of the influence of ENSO on coastal flood impacts and could be used to assess impacts of future changes in ENSO.

Research areas: Floods
Flood susceptibility mapping of the Western Ghat coastal belt using multi-source geospatial data and analytical hierarchy process (AHP) (2020)

Progressive environmental and climate change have altogether expanded hydro-meteorological hazards everywhere throughout the world. In recent times, floods have gained increasing global significance as a result of their destructive nature and for causing monetary and human losses. Hitherto, flood mapping studies in India have been profoundly constrained because of the absence of a comprehensive database coordinating flood attributes, for example, peak discharge at river gauges or geospatial data. The present investigation, for the first time, applies a large number of multi-sourced environmental flood conditioning factors (viz. elevation, slope, distance from drainage, drainage density, flow accumulation, topographic wetness index, rainfall, land use, soil texture, topographic ruggedness index and geology) and socio-economic parameters (viz. population density, literacy rate, literate population density, and road density) using the analytical hierarchy process approach to prepare flood susceptibility, vulnerability, and flood risk maps of the entire Western Ghat coastal belt, India. Essential findings of this study demonstrate that a critical percentage of area is involving high (28%) and very high (22%) flood susceptibility. About 25% area shows high flood risk and these regions need immediate attention. Efficiency testing of the flood susceptibility map indicates high precision, utilizing area under the curve (AUC) through a considerably high value (0.84). The output flood risk map can be useful for planners, managers, and regulatory bodies to manage and mitigate flood incidents along the Western Ghat rivers. Even though the methodology applied in this study is highly simple, yet it shows high accuracy, and subsequently, can be utilized in other regions for expert knowledge-based flood mapping.

Research areas: Floods
Implications of flood disturbance for conservation and management of giant panda habitat in human-modified landscapes (2019)

As certain extreme weather events are becoming frequent and intense, conservationists must identify areas across species' ranges recurrently affected, especially with regard to threatened species. Focusing on the giant panda (Ailuropoda melanoleuca) and historical flood frequency distribution, we determined overlaps between panda distribution affected by floods and nature reserves. We also examined the correspondence between areas subject to high flood exposure densities, areas with high panda habitat use, and areas that exhibit high human density. Of the 67 reserves established for giant panda conservation 7 included areas with the highest flood exposure densities while having a mean exposure ranging between 20 and 75%. In Sichuan province up to 32% of areas of high habitat use were subject to low flood density, and 10% overlapped with areas subject to high flood density. We also found that 40% of the total area with high human density was subject to a high flood density. Our findings indicate that high frequency of flooding is affecting areas of nature reserves where people are rather than areas which pandas are using more intensively. In areas occupied by pandas, strategies should remain focus on mitigating habitat degradation and fragmentation caused by human activities that can also reduce habitat resilience to floods. Management aimed at reducing vulnerability and enhancing resilience in flood-prone areas is warranted if we are to prevent negative indirect impacts on panda habitat driven by human responses to increasingly frequent and intense extreme weather events in the coming decades.

Research areas: Floods