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NASA NTRS2020

Charles River Watershed Water Resources: Assessing Flooding Vulnerability to Assist High Water Intervention and Urban Planning Programs in the Charles River Watershed

The Charles River watershed intersects 35 municipalities within the Boston Metropolitan Area and has a population of 1.2 million, making it one of the most densely populated watersheds in New England. In recent years, the watershed has observed higher rates of flood inundation, mainly due to increased development, extreme precipitation events, and increased surface runoff. As the frequency of flood events increases and a changing climate poses an ongoing threat to local communities, governments, and organizations in Massachusetts need accurate flood risk assessments. This project partnered with the Charles River Watershed Association, the Town of Natick’s Office of Sustainability, and the Massachusetts Audubon Society to assess the potential for watershed degradation, flood vulnerability, and flood susceptibility in the watershed. The team used Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR), and Sentinel-2 MultiSpectral Instrument (MSI) to assess the feasibility of identifying flood events using remote sensing. After identifying images that overlapped with the reported flood events, the team concluded that it was not feasible to use Earth observation data to detect localized flooding. Instead, the Federal Emergency Management Agency (FEMA) 100-year floodplain was used as a proxy for areas where flooding may occur. The team used statistical analysis and supervised classification to develop a flood susceptibility map, incorporating factors like soil drainage, height above nearest drainage, and topographic wetness index. This was overlaid with demographic and socioeconomic data to create a flood vulnerability map. The flood susceptibility map captured over 2/3 of reported flood events in the watershed, an improvement over the 1/3 of events captured by the FEMA 100-year and 500-year floodplain maps.

DEVELOP Project Summary↗
NASA NTRS2021

Vegetation Monitoring Optimization with Normalized Difference Vegetation Index and Evapotranspiration Using Remote Sensing Measurements and Land Surface Models Over East Africa

The majority of people in East Africa rely on the agro-pastoral system for their livelihood, which is highly vulnerable to droughts and flooding. Agro-pastoral droughts are endemic to the region and are considered the main natural hazard that contributes to food insecurity. Drought begins with rainfall deficit, gradually leading to soil moisture deficit, higher land surface temperature, and finally impacts to vegetation growth. Therefore, monitoring vegetation conditions is essential in understanding the progression of drought, potential effects on food security, and providing early warning information needed for drought mitigation decisions. Because vegetation processes couple the land and atmosphere, monitoring of vegetation conditions requires consideration of both water provision and demand. While there is consensus in using either the Normalized Difference Vegetation Index (NDVI) or evapotranspiration(ET) for vegetation monitoring, a comprehensive assessment optimizing the use of both has not yet been done. Moreover, the evaluation methods for understanding the relationships between NDVI and ET for vegetation monitoring are also limited. Taking these gaps into account we have developed a framework to optimize vegetation monitoring using both NDVI and ET by identifying where they perform the best by using triple collocation and cross-correlation methods. We estimated the random error structure in Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI; ET from the Operational Simplified Surface Energy Balance (SSEBop) model; and ET from land surface models (LSMs). LSM ET and SSEBop ET have been found to be better indicators for vegetation monitoring during extreme drought events, while NDVI could provide better information on vegetation condition during wetter than normal conditions. The random error structures of these variables suggest that LSM ET is most likely to provide important information for vegetation monitoring over low and high ends of the vegetation fraction areas. Over moderate vegetative areas, any of these variables could provide important vegetation information for drought characterization and food security assessments. While this study provides a framework for optimizing vegetation monitoring for drought and food security assessments over East Africa, the framework can be adopted to optimize vegetation monitoring over any other drought and food insecure region of the world.

triple collocation↗
NASA NTRS2020

Increasing anthropogenic methane emissions arise equally from agricultural and fossil fuel sources

Climate stabilization remains elusive, with increased greenhouse gas concentrations already increasing global average surface temperatures 1.1°C above pre-industrial levels (World Meteorological Organization 2019). Carbon dioxide (CO2) emissions from fossil fuel use, deforestation, and other anthropogenic sources reached ~ 43 billion metric tonnes in 2019 (Friedlingstein et al 2019, Jackson et al 2019). Storms, floods, and other extreme weather events displaced a record 7 million people in the first half of 2019 (IDMC 2019). When global mean surface temperature four million years ago was 2°C–3°C warmer than today (a likely temperature increase before the end of the century), ice sheets in Greenland and West Antarctica melted and parts of East Antarctica’s ice retreated, causing sea levels to rise 10–20 m (World Meteorological Organization 2019). Methane (CH4) emissions have contributed almost one quarter of the cumulative radiative forcings for CO2, CH4, and N2O (nitrous oxide) combined since 1750 (Etminan et al 2016). Although methane is far less abundant in the atmosphere than CO2, it absorbs thermal infrared radiation much more efficiently and, in consequence, has a global warming potential (GWP) ~86 times stronger per unit mass than CO2 on a 20-year timescale and 28- times more powerful on a 100-year time scale (IPCC 2014). Global average methane concentrations in the atmosphere reached ~1875 parts per billion (ppb) at the end of 2019, more than two-and-a-half times preindustrial levels (Dlugokencky 2020). The largest methane sources include anthropogenic emissions from agriculture, waste, and the extraction and use of fossil fuels as well as natural emissions from wetlands, freshwater systems, and geological sources (Kirschke et al 2013, Saunois et al 2016a, Ganesan et al 2019). Here, we summarize new estimates of the global methane budget based on the analysis of Saunois et al (2020) for the year 2017, the last year of the new Global Methane Budget and the most recent year data are fully available. We compare these estimates to mean values for the reference ‘stabilization’ period of 2000–2006 when atmospheric CH4 concentrations were relatively stable. We present data for sources and sinks and provide insights for the geographical regions and economic sectors where emissions have changed the most over recent decades.

fossil fuel sources↗
NASA NTRS2020

Satellite Gravimetry Improves Seasonal Streamflow Forecast Initialization in Africa

West Africa is one of the poorest regions in the world and highly vulnerable to extreme hydrological events due to the lack of reliable monitoring and forecast systems. For the first time, we demonstrate that initial hydrological conditions informed by satellite-based terrestrial water storage (TWS) estimates improve seasonal streamflow forecasts. TWS variability detected by the Gravity Recovery and Climate Experiment (GRACE) satellites is assimilated into a land surface model during 2003–2016 and used to initialize 6-month hindcasts (i.e., forecasts of past events) during West Africa's wet seasons. We find that GRACE data assimilation (DA) generally increases groundwater and soil moisture storage in the region, resulting in increased evapotranspiration and reduced total runoff. Total runoff is particularly lower at the headwaters of the Niger River, positively impacting streamflow simulations and hindcast initializations. Compared to simulations without GRACE-DA, hindcasts are notably improved at locations draining from large basin areas, in particular, over the Niger River basin, which is consistent with GRACE's coarse spatial resolution. The long memory of groundwater and deep soil moisture, two main TWS components updated by GRACE-DA, is reflected in prolonged improvements in the streamflow hindcasts. Model accuracy at Niamey, Niger, the most populated city where streamflow observations are available, improved up to 33% during the flood season. These new findings directly contribute to ongoing developments in food security, flood potential forecast, and water-related disaster warning systems for Africa.

Augusto Getirana↗
NASA NTRS2019

Analysis of A Severe Dust Storm and Its Impact on Air Quality Conditions Using Wrf-Chem Modeling, Satellite Imagery, and Ground Observations

This study presents a comprehensive analysis of an extreme dust event recorded in the Arabian Peninsula and the United Arab Emirates (UAE) between 31 March and 3 April 2015. Simulations of the dust event with the Weather Research and Forecasting model coupled with the Chemistry module (WRF-Chem) were analyzed and verified using MSG-SEVIRI imagery and aerosol optical depth (AOD) from the recent 1-km Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm for MODIS Terra/Aqua. Data from the National Centers for Atmospheric Prediction/National Center for Atmospheric Research (NCEP/NCAR) and the upper-air radiosonde observations were used to understand the synoptic of the event. In addition, the impact of the event on atmospheric and air quality conditions is investigated. The Air Quality Index (AQI) was calculated prior, during, and after the event to assess the degradation of air quality conditions. Simulated temperature, relative humidity, wind speed, and surface radiation were compared to observations at six monitoring stations in the UAE giving R2 values of 0.84, 0.63, 0.60, and 0.84, respectively. From 1 to 2 April 2015, both observations and simulations showed an average drop in temperature from 33 to 26 °C and radiance reduction from about 950 to 520 Wm−2. The AOD modeled by WRF-Chem showed a good correlation with Aerosol Robotic Network (AERONET) measurements in the UAE with R2 of 0.83. The AQI over the UAE reached hazardous levels during the peak of the dust event before rapidly decreasing to moderate–good air quality levels. This work is the first attempt to demonstrate the potential of using WRF-Chem to estimate AQI over the UAE along with two satellite products (MODIS-MAIAC and MSG-SEVIRI) for dust detection and tracking.

dust storm↗
NASA NTRS2021

Fairfax Water Resources: Estimating Urban Flood Susceptibility, Historical Flooding Extent, and Land Cover Change in Fairfax County, Virginia to Aid in Flood Mitigation Planning

Between 2000 and 2020, Fairfax County, Virginia experienced extreme weather events that caused severe flooding and degradation of roads, businesses, and other public property. A single flood event on July 8th, 2019 resulted in $14.8 million in damages. These flood events routinely impact the community, often resulting in power outages, school closures, and downed trees. The Fairfax County Department of Public Works and Environmental Services partnered with DEVELOP to explore how remotely sensed data could be integrated to support its current flood mitigation efforts. This project used environmental factors such as elevation, slope, and topographic wetness index from Earth observation derived data to map flood susceptibility. We utilized Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-Band Synthetic Aperture Radar (C-SAR), and Sentinel-2 Multispectral Instrument (MSI) to map historic flooding events in Fairfax County. These maps will support flood management practices for the Fairfax County Department of Public Works and Environmental Services through the integration of remotely sensed data. Our results show that developed areas in the county are more susceptible to flooding, coinciding with analysis of flood factors, which indicated that imperviousness and tree canopy were the most influential drivers in flood susceptibility. Other results show that using Earth observations to map historical flooding is limited in urban areas due to false positives from SAR imagery between water and shadows. Further research is necessary to evolve the historical flood mapping technique if Earth observations are to be incorporated in future historical flood analysis.

Kaitlynn Hietpas↗
NASA NTRS2020

The GPM Ground Validation Program

We present a detailed overview of the structure and activities associated with the NASA-led ground-validation component of the NASA-JAXA Global Pre­cipitation Measurement (GPM) mission. The overarching philosophy and approaches for NASA's GV program are presented with primary focus placed on aspects of direct validation and a summary of physical validation campaigns and results. We describe a spectrum of key instruments, methods, field campaigns and data products developed and used by NASA's GV team to verify GPM level-2 precipitation products in rain and snow. We describe the tools and analysis framework used to confirm that NASA's Level-I science requirements for GPM are met by the GPM Core Observatory. Examples of routine validation activities related to verification of Integrated Multi­satellitE Retrievals for GPM (IMERG) products for two different regions of the globe (Korea and the US) are provided, and a brief analysis related to IMERG performance in the extreme rainfall event associated with Hurricane Florence is discussed.

precipitation↗
NASA NTRS2019

Measuring Metapopulation Characteristics of the Southeastern Beach Mouse on Kennedy Space Center using a Dynamic Habitat Occupancy Approach

The Cape Canaveral Barrier Island Complex (CCBIC) represents the largest (72 km) continuous coastal habitat supporting the core population of the threatened southeastern beach mouse (Peromyscus polionotus niveiventris) in Florida. Despite protection throughout the subspecies’ remaining range, storm activity during the past two decades has de-stabilized coastal dune and coastal strand habitat required for beach mouse survival. Our long-term goal is to provide information to the management process to ensure a sustainable population on the CCBIC in light of sea-level rise, extreme storm events, and continued land development supporting the multi-user spaceport.Because beach mice populations are affected by storms (hurricanes, tropical storms, nor’easters), it is important to understand the species-habitat relations during periods with and without storm impacts. Conventional wisdom is that coastal dune and coastal strand are the preferred habitats, but coastal scrub habitat may be important because it is less affected by storm impacts (e.g., overwash, escarpment, sand deposition, etc.) and thus may serve as a refugia. In addition, genetic studies have shown some evidence for reduced dispersal between regions on CCBIC (Kalkvik et al., 2012; Zimmerman et al., 2015). We compared site occupancy dynamics for coastal dune, coastal strand, and coastal scrub habitat over three quarters (seasons) and determined the pattern of spatial correlation in habitat occupancy dynamics for locations at a range of distances in coastal dune, coastal strand, and coastal scrub habitats along the Kennedy Space Center (KSC) coastline (Figures1 and 2). Although habitat occupancy is very useful for understanding species-habitat relationships,in its simplest form it ignores differences in abundance between sites. Because information on abundance may inform management efforts and provide knowledge regarding status of the population, we tested measuring abundance state based on the number of detections in tracking tubes. Small mammal populations often fluctuate in a cyclic manner over time. These fluctuations make populations more susceptible to extinction and make trend detection more difficult. Efficient methods of measuring abundance across time and space are needed for monitoring trends.The purpose of this project was to:1.Estimate southeastern beach mouse habitat occupancy parameters in coastal dune, coastal strand, and coastal scrub habitats using a combination of tracking tubes and traps; 2.Model the spatial relationships of the effect of habitat (coastal dune, coastal strand, and coastal scrub) on parameters as a function of distance; and 3.Compare the reliability and efficiency of our survey methods and evaluate the potential to develop an abundance index.

Eric Douglas Stolen↗
NASA NTRS2021

Maya Forest Water Resources I: Using NASA Earth Observations to Map Forested Inundation in the Maya Forest

As climate change increases the severity and frequency of extreme weather events in the tropics, it is vital for the safety of local communities and the health of ecosystems to monitor seasonal inundation. Forested inundation affects the ability of forested wetlands to provide ecosystem services, such as flood mitigation, water filtration, carbon storage, and erosion mitigation. While ground-based monitoring has traditionally been used to map inundation extent, those methods are costly and time-intensive. The NASA DEVELOP team focused on seasonal inundation throughout 2008 in the Maya Forest, when changes in inundation were drastic. To monitor seasonal inundation, our team used in situ field data and Earth observations from Landsat 7 Enhanced Thematic Mapper (ETM+), Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 1, Shuttle Radar Topography Mission (SRTM), and products from the Ice, Cloud, and Land Elevation Satellite (ICESat). The team applied a Random Forest algorithm to Landsat 7 imagery, generating an object-level land cover classification with an overall accuracy of 72.1% and forest class with 100% recall and 78% precision. The team applied L-band backscatter thresholds from existing literature to forest-masked ALOS imagery and refined the thresholds in an iterative process using field data and hydrology models to delineate seasonal inundation extent. These publicly available data products help end users from Belize’s Land Information Center (LIC) and Forest Department, Guatemala’s Center for Monitoring and Evaluation (CEMEC), and Mexico’s El Colegio de la Frontera Sur (ECOSUR) to inform land management and protect community infrastructure.

Madelyn Savan↗
NASA NTRS2021

DroughtCast: A Machine Learning Forecast of the United States Drought Monitor

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods toforecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.

Machine Learning↗
NASA NTRSDate not supplied

The Application of Remote Sensing using NASA Earth Observations paired with Sociodemographic Indicators to Identify Communities Most Susceptible to Urban Heat Exposure in Austin, Texas

In recent years, Austin, Texas has experienced an increase in population and urban development. Additionally, the City’s climate—already characterized by periods of extreme heat and drought—continues to change. As temperatures and demand for utilities and cooling resources rise, the number of heat-related deaths and illnesses in socially vulnerable populations (e.g., older, lower-income populations) is expected to increase. The City of Austin, The University of Texas at Austin (UT Austin), and The University of Texas Health Science Center at Houston (UT Health) partnered with NASA DEVELOP to examine the distribution of urban heat throughout the City. This project used land surface temperature, greenness, plant water content, and urban surface material analysis parameters derived from NASA Earth observations from Landsat 8 Operational Land Imager (OLI), Landsat 8 Thermal Infrared Sensor (TIRS), and Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The project team produced three different indices to create a spatial analysis for the study area including a social vulnerability index (SoVI), heat exposure index (HEI), and an overall heat priority index (HPI) score. This overall score was determined with a weighted analysis of heat-related environmental variables from NASA Earth observations and socioeconomic data from the 2019 American Community Survey. Based on the HPI score, the project team identified 121 census block groups out of 605 total that are designated as being most at risk of adverse impacts from extreme heat events. To test the sensitivity of the HPI, the team used a Monte Carlo analysis using different approaches for geographic scale, variable inclusion, census uncertainty, and index aggregation. Based on the sensitivity analysis, the resulting HPI score showed the metric was consistent with the baseline HPI. This provided increased confidence the score can be used as a tool to make informed infrastructure improvement plans in targeted areas (e.g., siting of cooling centers) and ensure equitable sustainable development.

Ryan Hammock↗
NASA NTRSDate not supplied

Assessing Flooding Vulnerability to Assist High Water Intervention and Urban Planning Programs in the Charles River Watershed with NASA DEVELOP

The Charles River watershed intersects 35 municipalities within the Boston Metropolitan Area and has a total population of 1.2 million, making it one of the most densely populated watersheds in New England. In recent years, the watershed has observed higher rates of flood inundation, mainly due to increased development, extreme precipitation events, and increased surface runoff. As the frequency of flooding events increases and a changing climate poses an ongoing threat to local communities, governments and organizations in Massachusetts are in need of accurate flood risk assessments. NASA DEVELOP partnered with the Charles River Watershed Association, the Town of Natick’s Office of Sustainability, and the Massachusetts Audubon Society to assess flood vulnerability and susceptibility in the watershed. The team used Landsat 5 Thematic Mapper, Landsat 8 Operational Land Imager, Sentinel-1 C-Band Synthetic Aperture Radar, and Sentinel-2 MultiSpectral Instrument to assess the feasibility of identifying the extent of past flood events using remote sensing. After identifying images that overlapped with reported flood events, the team concluded that it was not feasible to use Earth observation data to detect localized flooding in the time available for this study. Instead, the Federal Emergency Management Agency (FEMA) 100-year floodplain was used as a proxy for areas where flooding may occur. The team used statistical regression analysis and validation and supervised classification to develop a flood susceptibility map, incorporating several flood conditioning factors. The susceptibility maps were calibrated to various thresholds, including two that highlight hypothetical flooding under more liberal and more conservative planning scenarios. These were overlaid with demographic and socioeconomic data to create flood vulnerability maps. The team’s flood susceptibility maps showed an improvement in capturing known flood events over the FEMA 100-year and 500-year floodplain maps. These results will be improved with the addition of stormwater drainage mapping and precipitation data. Results can be used to fill in the gaps to help the stakeholders understand their communities’ vulnerability and susceptibility to flooding and improve their preparedness plans.

Trista Brophy↗
NASA NTRS2021

Atmospheric River Precipitation Contributed to Rapid Increases in Surface Height of the West Antarctic Ice Sheet in 2019

Estimating the relative contributions of the atmospheric and dynamic components of ice-sheet mass balance is critical for improving projections of future sea level rise. Existing estimates of changes in Antarctic ice-sheet height, which can be used to infer changes in mass, are only accurate at multiyear time scales. However, NASA's Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) laser altimetry mission now allows us to accurately measure changes in ice-sheet height at subannual time scales. Here, we use ICESat-2 data to estimate height changes over Antarctica between April 2019 and June 2020. These data show widespread increases in surface height over West Antarctica during the 2019 austral winter. Using climate reanalysis data, we show that 41% of increases in height during winter were from snow accumulation via extreme precipitation events—63% of these events were associated with landfalling atmospheric rivers (ARs) which occurred only 5.1% of the time.

Susheel Adusumilli↗
NASA NTRS2022

Spatial Downscaling of GOES-R Land Surface Temperature over Urban Regions: A Case Study for New York City

The surface urban heat island (SUHI) effect is among the major environmental issues encountered in urban regions. To better predict the dynamics of the SUHI and its impacts on extreme heat events, an accurate characterization of the surface energy balance in urban regions is needed. However, the ability to improve understanding of the surface energy balance is limited by the heterogeneity of surfaces in urban areas. This study aims to enhance the understanding of the urban surface energy budget through an innovation in the use of land surface temperature (LST) observations from remote sensing satellites. A LST database with 5–min temporal and 30–m spatial resolution is developed by spatial downscaling of the Geostationary Operational Environmental Satellites—R (GOES–R) series LST product over New York City (NYC). The new downscaling method, known as the Spatial Downscaling Method (SDM), benefits from the fine spatial resolution of Landsat–8 and high temporal resolution of GOES–R, and considers the temporal variation in LST for each land cover type separately. Preliminary results show that the SDM can reproduce the temporal and spatial variability of LST over NYC reasonably well and the downscaled LST has a spatial root mean square error (RMSE) of the order of 2 K as compared to the independent Landsat–8 observations. The SDM shows smaller RMSE of 1.93 K over the tree canopy land cover, whereas RMSE is 2.19 K for built–up areas. The overall results indicate that the SDM has potential to estimate LST at finer spatial and temporal scales over urban regions.

land surface temperature↗
NASA NTRS2022

Strong Gravity Waves Associated With Tonga Volcano Eruption Revealed by Saber Observations

Using the SABER temperature profiles from January 6 to 21 2022, we studied the mesospheric gravity waves (GWs) associated with the Tonga volcano eruption on January 15. We observed that the eruption induced strong GWs in the mesosphere. Detailed analysis shows that there were strong GWs with amplitudes greater than 30 K (twice the usual GWs) on January 15. These GWs have dominant vertical wavelengths of 13.9–25.5 km and horizontal speeds of 44–81 ms(exp −1), and they have intrinsic periods (momentum flux per unit mass) of ∼2–5 hr (4–320 m(exp 2)s(exp −2)). The strong but slow GWs observed here are opposite to the fast GWs observed at different atmospheric layers after the Tonga volcano eruption, and are stronger than the GWs associated with other volcanic eruption and extreme weather events. Such that one can get a more comprehensive picture on GWs excited by a powerful source.

Mesospheric temperature↗
NASA NTRS2022

What's in a Name? On the Use and Significance of the Term “Polar Vortex”

Mainstream and popular science media are rife with misunderstandings about what a “polar vortex” is. The term most aptly describes the stratospheric polar vortex, a single feature dominating the cool-season circulation from ∼15–50 km. Regional jet stream variations dominate the tropospheric circulation, which is not well-described as a polar vortex; indeed, there is no single consistent definition of a tropospheric polar vortex in the literature. Stratospheric polar vortex disturbances profoundly influence extreme weather events, including cold air outbreaks (CAOs). How the stratospheric polar vortex affects tropospheric jets, whose local excursions drive CAOs, is not fully understood. Public facing parts of publications describing research on this topic are not always clear about how the “polar vortex” is defined; greater clarity could improve communications both within the community and with non-specialist audiences.

Stratospheric Polar Vortex↗
NASA NTRS2022

Detecting the Greatest Changes in Global Satellite-Based Precipitation Observations

In recent years, the analysis of abrupt and non-abrupt changes in precipitation has received much attention due to the importance of climate change-related issues (e.g., extreme climate events). In this study, we used a novel segmentation algorithm, DBEST (Detecting Breakpoints and Estimating Segments in Trend), to analyze the greatest changes in precipitation using a monthly pixel-based satellite precipitation dataset (TRMM 3B43) at three different scales: (i) global, (ii) continental, and (iii) climate zone, during the 1998–2019 period. We found significant breakpoints, 14.1%, both in the form of abrupt and non-abrupt changes, in the global scale precipitation at the 0.05 significance level. Most of the abrupt changes were observed near the Equator in the Pacific Ocean and Asian continent, relative to the rest of the globe. Most detected breakpoints occurred during the 1998–1999 and 2009–2011 periods on the global scale. The average precipitation change for the detected breakpoint was ±100 mm, with some regions reaching ±3000 mm. For instance, most portions of northern Africa and Asia experienced major changes of approximately +100 mm. In contrast, most of the South Pacific and South Atlantic Ocean experienced changes of −100 mm during the studied period. Our findings indicated that the larger areas of Africa (23.9%), Asia (22.9%), and Australia (15.4%) experienced significant precipitation breakpoints compared to North America (11.6%), South America (9.3%), Europe (8.3%), and Oceania (9.6%). Furthermore, we found that the majority of detected significant breakpoints occurred in the arid (31.6%) and polar (24.1%) climate zones, while the least significant breakpoints were found for snow-covered (11.5%), equatorial (7.5%), and warm temperate (7.7%) climate zones. Positive breakpoints’ temporal coverage in the arid (54.0%) and equatorial (51.9%) climates were more than those in other climates zones. Here, the findings indicated that large areas of Africa and Asia experienced significant changes in precipitation (−250 to +250 mm). Compared to the average state (trend during a specific period), the greatest changes in precipitation were more abrupt and unpredictable, which might impose a severe threat to the ecology, environment, and natural resources.

Breakpoint↗
NASA NTRSDate not supplied

Assessing Environmental and Socioeconomic Factors of Urban Flood Vulnerability in Kansas City, Kansas

Pluvial flooding, over-saturated ground, and drainage systems disproportionately impact historically marginalized urban neighborhoods during extreme rainfall events. These communities are impacted by physical and socioeconomic factors that make them vulnerable to flooding events, such as high concentrations of impervious landcover, high precipitation rates, and a combined sewer system framework. Despite known vulnerability to environmental hazards, understanding potential pluvial street-level flooding events are largely unknown. Using the open-source National Capital Project’s Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Flood Risk Mitigation model, NASA DEVELOP examined neighborhood scale runoff retention and potential economic damages for risk mapping throughout Kansas City, Kansas. The generated outputs aid in identifying vulnerable neighborhoods susceptible to flooding and in need of future intervention. Previous studies have applied this model framework to understand urban flood vulnerability regarding ecosystem services, urban planning, and flood mitigation strategies. We utilized the InVEST outputs to develop indices pertaining to environmental justice factors of race, socioeconomic status, social vulnerability, and health. The findings indicate that historically redlined neighborhoods in Kansas City, Kansas face disproportional impacts from flood events and are subject to greater environmental stressors. This research provides an approach to utilizing an open-source flood vulnerability model to empower neighborhood-scale environmental justice analysis, enhancing the local communities' understanding of present-day impacts of historical environmental injustices.

Hadwynne Gross↗