diff --git a/app/content/stories/air-quality-and-covid-19.mdx b/app/content/stories/air-quality-and-covid-19.mdx deleted file mode 100644 index 4350cea8..00000000 --- a/app/content/stories/air-quality-and-covid-19.mdx +++ /dev/null @@ -1,286 +0,0 @@ ---- -id: air-quality-and-covid-19 -name: Air Quality and COVID-19 -description: "When governments began implementing shutdowns at the start of the COVID-19 pandemic, scientists wondered how the atmosphere would respond to the sudden change in human behavior." -media: - src: /images/story/air-quality-and-covid-19--discovery-cover.jpg - alt: Clear nightsky with crescent moon above the mountains - author: - name: Benjamin Voros - url: https://unsplash.com/photos/U-Kty6HxcQc -pubDate: 2020-12-01 -taxonomy: - - name: Topics - values: - - Covid 19 ---- - - - - ## Going through changes - When governments began implementing shutdowns at the start of the COVID-19 pandemic, scientists wondered how the atmosphere would respond to the sudden change in human behavior. - - - With people largely confined to their homes to slow the spread of the novel coronavirus, scientists expected there were likely to be fewer cars, planes, and ships burning and emitting fossil fuels. - - - As the pandemic has progressed, these scenarios have largely played out: during the strictest lockdown periods, locations around the world experienced substantial reductions in transportation-related fossil fuel emissions. The impacts on specific air pollutants have been varied and dependent on their lifespan in the atmosphere. - - - Those pollutants with short lifespans, such as nitrogen dioxide (NO2), decreased dramatically and quickly along with emissions. - - - Today, air quality levels are beginning to approach pre-pandemic levels, and scientists are just beginning to dive into the new measurements collected throughout this unprecedented time. - - - ## What Makes Air Quality Good or Bad? - - Cities are easy to spot from space. Choose any large, urban area around the world, and you’re likely to see similar things: dense population centers, complex webs of highways and, more often than not, smog. - - - Smog is the hazy curtain of air that often hangs over cities. It occurs when nitrogen dioxide produced from fossil fuel emissions from gasoline in cars or coal in powerplants chemically reacts with sunlight and other pollutants like carbon monoxide (CO). Thick smog is harmful to breathe and can significantly reduce visibility. - - - During lockdowns, satellites observed sharp reductions in nitrogen dioxide emissions in cities around the world, and smog began to vanish. Skies were bluer, air was cleaner, and, in some places, views previously obscured by air pollution were suddenly revealed. - - - In Los Angeles, NASA scientists detected that nitrogen dioxide levels fell by more than 30% during the height of COVID-related shutdowns. Other large cities around the world experienced similar reductions. - - - ## Cities Experiencing Clearer Air During Lockdowns - - When Chinese authorities suspended travel and closed businesses in late January 2020 in response to the novel coronavirus, Beijing’s local nitrogen dioxide levels fell dramatically. In February 2020, concentrations fell by nearly 30% compared to the previous five-year average. - - - Cities across South America experienced similar declines in nitrogen dioxide. Lima, Peru had some of the most substantial reductions, with nitrogen dioxide levels falling approximately 70% below normal levels. - - - ## Like Flipping a Switch: Lockdowns and NO2 - - Nitrogen dioxide is only one component of air quality: sulfur dioxide (SO2), ozone (O3), formaldehyde (CH2O), and carbon monoxide, along with a host of other atmospheric constituents, also influence the quality of the air we breathe. The difference in nitrogen dioxide is that it has a relatively short lifetime in the atmosphere; once it’s emitted, it only lasts a few hours before it disappears. - - Therefore, once communities entered shutdowns, and people’s mobility was severely restricted, the effect on nitrogen dioxide concentrations was the equivalent of flipping a switch. That is not, however, the case with all air pollutants. - - - ## Seasonal Changes in NO2 - - Even with the strong correlation between nitrogen dioxide and the combustion of fossil fuels, atmospheric concentrations of nitrogen dioxide naturally fluctuate throughout the year, and weather patterns also influence its concentrations. - - - For example, nitrogen dioxide typically falls dramatically during spring and summer months, and rain and wind increase its dispersion, lowering local concentrations. During the COVID-19 pandemic, NASA scientists have been able to attribute the observed changes in nitrogen dioxide to changes in our behavior, and they have been careful to account for any impacts on air pollution that are the result of natural weather variations. - - - ## Seeing Air Pollution from Space - - NASA has used the [Ozone Monitoring Instrument (OMI)](https://aura.gsfc.nasa.gov/omi.html "Explore the OMI product") aboard the Aura satellite to observe global nitrogen dioxide levels since 2004. A joint endeavor between NASA, the Royal Netherlands Meteorological Institute (KNMI) and the Finnish Meteorological Institute (FMI), OMI's longer data record provides important context with which to compare any changes observed during the pandemic. - - NASA scientists are also leveraging other space-based instruments from international partners to study changes in nitrogen dioxide during the pandemic. These include the [TROPOspheric Monitoring Instrument (TROPOMI)](http://www.tropomi.eu/ "Explore the TROPOMI product") aboard the European Commission’s Copernicus Sentinel-5P satellite. Launched in 2016, TROPOMI provides higher resolution observations than OMI. - - - ## Reinforcing Measurements: Nighttime Lights - - Changes in nighttime lights during the pandemic can also be tied to changes in nitrogen dioxide levels if the data are properly processed and interpreted. This is because nitrogen dioxide is primarily emitted from burning fossil fuels, and highways light up on nighttime satellite imagery when vehicles are present. - - Here we see the illuminated web of highways connecting the Los Angeles metropolitan region. - - Researchers are using night light observations to track variations in energy use, migration, and transportation in response to social distancing and shutdown measures during the pandemic. - - - These data, collected by the [Visible Infrared Imaging Radiometer Suite (VIIRS)](https://www.jpss.noaa.gov/viirs.html "Explore the VIIRS product") instrument aboard the joint NASA-National Oceanic and Atmospheric Administration (NOAA) Suomi-National Polar-orbiting Partnership (NPP) satellite, correlate with changes seen in car traffic on the ground – and, therefore, nitrogen dioxide reductions. - - While this research is still ongoing, the 31% reduction in nitrogen dioxide levels in Los Angeles during the height of pandemic-related lockdowns compared to recent years seems to correspond with a 15% reduction in nighttime lights over highways during the same period. - - - - - - ## Measuring Air Pollution on the Ground at Airports - New research during the pandemic is also looking at how COVID-related travel bans are impacting air quality around airports. Current conditions create a unique opportunity to study airport-related pollutants, especially nitrogen dioxide and formaldehyde. While travel bans and strict regulations around air travel have been in place, air traffic has yet to return to previous levels, and many planes remain grounded. - -
- - - Levels in 10¹⁵ molecules cm⁻². Darker colors indicate higher nitrogen dioxide (NO₂) levels associated and more activity. Lighter colors indicate lower levels of NO₂ and less activity. - -
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- - - Levels in 10¹⁵ molecules cm⁻². Darker colors indicate higher nitrogen dioxide (NO₂) levels associated and more activity. Lighter colors indicate lower levels of NO₂ and less activity. - -
- - They are comparing the on-the-ground sensor information from NASA's [Pandora Project](https://pandora.gsfc.nasa.gov/ "Explore the Pandora Project") with satellite information from TROPOMI. So far, they have found that nitrogen dioxide hotspots in Atlanta shifted from the airport, shown here, to the city center from April-June 2020. - - - - - - By September, however, satellites revealed the airport had reemerged as a dominant nitrogen dioxide emission source. - -
- - - Levels in 10¹⁵ molecules cm⁻². Darker colors indicate higher nitrogen dioxide (NO₂) levels associated and more activity. Lighter colors indicate lower levels of NO₂ and less activity. - -
- - - -
- - - Levels in 10¹⁵ molecules cm⁻². Darker colors indicate higher nitrogen dioxide (NO₂) levels associated and more activity. Lighter colors indicate lower levels of NO₂ and less activity. - -
- - ## Seeing Rebounds in NO2 - - After the initial shock of COVID-related shutdowns in the spring, communities worldwide began to reopen and gradually increase mobility. Cars returned to the road, and travel restrictions slowly eased. These resumptions corresponded with relative increases in nitrogen dioxide levels and other air pollutants, as air quality levels began to return to pre-pandemic levels. - - This demonstrates how quickly atmospheric nitrogen dioxide responds to reductions in emissions. They will persist as long as emissions persist and decline rapidly if emissions are reduced. - - NASA scientists will continue to monitor nitrogen dioxide levels and long-term trends around the world. NASA is expected to launch its [Tropospheric Emissions: Monitoring of Pollution (TEMPO)](http://tempo.si.edu/overview.html "Explore the TEMPO instrument") instrument in 2022, which will provide hourly, high-resolution measurements of nitrogen dioxide, ozone, and other air pollutants across North America, improving future air quality forecasts. - - Explore How COVID-19 Is Affecting Earth's Climate - - \ No newline at end of file diff --git a/app/content/stories/flood-2019.mdx b/app/content/stories/flood-2019.mdx new file mode 100644 index 00000000..b4f3468f --- /dev/null +++ b/app/content/stories/flood-2019.mdx @@ -0,0 +1,243 @@ +--- +id: flood-2019 +name: Flooding in 2019 - Tale of a Terrible Year +description: "NASA Models and Remote Sensing Datasets Capture Cascading Impacts on Midwest Farmers" +media: + src: /public/images/story/flood-2019/flood-2019-background1.png + alt: Saturated/flooded field. + author: + name: NASA EIS Freshwater + url: https://freshwater.eis.smce.nasa.gov/storymap.html +pubDate: 2025-06-20T00:00 +taxonomy: + - name: Topics + values: + - Natural Disasters + - name: Subtopics + values: + - Floods + +--- + + + + Authors: NASA EIS Freshwater1, Andrew Blackford2 + + 1 National Aeronautics and Space Administration Earth Information System + + 2 The University of Alabama in Huntsville + + + + + + ### Timeline + + + + +
+ Flooding timeline. + + 2019 Flooding timeline. + +
+ + + + + This case study is powered by incorporating Earth observations of precipitation from GPM, soil moisture from SMAP, snow depth from AMSR2, and leaf area index from MODIS, and MERRA-2 reanalysis within the Land Information System (LIS) framework. + + +
+ Flooded farm in the Midwest. + + Aerial view of a flooded farm as a result of the expansive flooding in 2019. + +
+ + + + + ### Pre-Flood Conditions + + + + + + ##### Soggy Soils, Frozen Soils + + Across much of the Midwest and Northern Plains, soils were saturated throughout the fall and winter, especially in the Corn Belt. + + !! ADD DATA VIS HERE !! + + Standardized anomalies for root zone soil moisture for August-October 2018 + + + + + + + ##### Huge Blizzard + + GPM IMERG captures a huge blizzard in mid-March that blanketed the region in deep snow. + + !! ADD DATA VIS HERE !! + + GPM IMERG precipitation for March 12-14, 2019 + + + + + + + + ##### Spring Flooding + + The extent of 2019 flooding can be seen over the Missouri River near Omaha, NE when compared with another MODIS-captured scene from 2015. + + !! ADD DATA VIS HERE !! + + SLIDER - MODIS Corrected Reflectance from NASA GIBS (Bands 7-2-1) Left: Mar 2015, Right: Mar 2019 + + + + + + Flood inundation is captured by Sentinel-1 imagery, hosted by the NASA Disasters Mapping Portal. + + !! ADD DATA VIS HERE !! + + Flood water (red pixels) and permanent water (blue pixels) mapped with Sentinel-1. [Data hosted on NASA Disasters Mapping Portal] + + + + + + ##### Rapid Snowmelt + + The rain-on-snow event on the unusually deep snowpack caused rapid snowmelt which intensified spring flooding. + + !! ADD DATA VIS HERE !! + + SLIDER - Snow depth on March 10 (left) before the rain-on-snow event and on March 29, 2019 (right) after the rain-on-snow event + + + + + + The rapid melting of the deep snowpack in mid-March was captured by assimilating snow depth observations from AMSR2 within the LIS framework. + + +
+ + + Average Snow Depth in the Missouri River Basin during water year 2019 compared to climatology. + +
+ + + +
+ + + Streamflow as measured on the Missouri River at St. Charles, Missouri during water year 2019 compared to climatology. + +
+ + + ##### Record Flooding + + Rapid snowmelt and heavy rainfall resulted in **record streamflows** in spring and summer. + + + + + + ##### Soils Too Wet To Plant + + Wet soil conditions persisted months after the flooding, leaving farmers with no option but to wait for soils to dry out. + + Assimilating downscaled soil moisture from SMAP captures the wet conditions persisting in late-May to early-June. + + !! ADD DATA VIS HERE !! + + Root zone soil moisture anomaly averaged over May 27-June 2, 2019 + + + + + + ### Agricultural Impacts + + ##### Planting Delays Cause Major Farming losses + + The Midwest 2019 flood coincided with the corn and soybean planting season, causing many farmers to delay planting their crops, switch crops, or not plant at all. + + According to the American Farm Bureau Federation, farm subsidies made up 40% of farm income in 2019 — $33 billion out of an expected $88 billion total. + + + + + + + + ##### Earth Observations Show Delayed Planting + + The abnormally low leaf area index (LAI) from MODIS captures delayed planting in the Cornbelt region. + + !! ADD DATA VIS HERE !! + + LAI anomaly for Jun 27, 2019 after the flooding event in spring. + + + + + + ##### Farmers Planted Fewer Acres + + According to USDA 2019 Crop Data, the planted fields were reduced by around 12% compared to the five-year average for corn and soybean. + + !! ADD DATA VIS HERE !! + + Prevented acreage for corn in 2019 (by state), in thousands of acres. + + + + + + ### Conclusions + + The simultaneous use of datasets from **GPM, MODIS, AMSR2, and SMAP** is necessary to capture the causes and extent of the flooding, as well as its impacts on agriculture. + + Using **Earth observations to model and understand extreme events** improves our ability to better manage water resources, plan for and respond to disasters, and assess food production and security across the globe. + + \ No newline at end of file diff --git a/app/content/stories/hurricane-maria-and-ida.mdx b/app/content/stories/hurricane-maria-and-ida.mdx deleted file mode 100644 index de247c29..00000000 --- a/app/content/stories/hurricane-maria-and-ida.mdx +++ /dev/null @@ -1,518 +0,0 @@ ---- -id: hurricane-maria-and-ida -name: Connecting Disaster Recovery with Environmental Justice -description: "Featuring Hurricane María and Hurricane Ida" -media: - src: /images/story/ej--discovery-cover.jpeg - alt: Nighttime view of New Orleans -pubDate: 2022-09-08 -taxonomy: - - name: Topics - values: - - Environmental Justice - - Natural Disasters - - Tropical ---- - - - - - - ## Connecting Disaster Recovery with Environmental Justice: Hurricane María - - Hurricane María made landfall in Puerto Rico as a Category 4 or 5 hurricane on September 20, 2017, leaving a path of destruction in its wake. [Over 1.5 million people on the island lost power, leading to the longest blackout in US history](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218883). Although efforts to repair the damage on the island were extensive, the [areas with the most severe and prolonged impacts were areas of lower socioeconomic status](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0218883). These communities lacked the resources and the representation to repair damage quickly, leading to long-term lack of access to electricity, water, and other critical supplies. - - NASA hosts a wide variety of continuous Earth observation data useful in environmental justice research. This dashboard features a selection of NASA datasets from across the Agency, including socioeconomic data, Earth observation analysis, and other combined datasets. These tools allow users to visualize and download data to understand the environmental issues brought on by Hurricane María. Merging Earth data and socioeconomic data can help communities like those in Puerto Rico to better prepare for and respond to future natural disasters. - - ## Connecting Disaster Recovery with Environmental Justice: Hurricane Ida - - Known as the city that can barely catch its breath between storms, New Orleans experienced another devastating event on August 29, 2021 as Hurricane Ida made landfall as a Category 4 hurricane. The effects of the storm were widespread, causing millions of dollars worth of damage and affecting the lives and homes of millions of people. - - [Disadvantaged communities](https://www.nature.com/articles/d41586-021-02520-8) in Louisiana and across the country already struggle with higher rates of asthma, cancer, and COVID-19 infections. These communities are often hardest-hit by storms like Ida. Research has shown that disadvantaged communities often receive less federal aid than other communities, only prolonging their hardships. NASA is prioritizing open access to environmental justice data such as the datasets in this dashboard in an effort to help communities better prepare for and respond to natural disasters and to help shed light on cases of environmental injustice. - - - - - - - ### Night Light Detection - - **Shining Light on the Aftermath of Hurricanes** - - 30-meter resolution Black Marble HD nighttime lights were visualized for New Orleans, LA and Puerto Rico to highlight the impacts and recovery from the landfalls of Hurricane Ida and Hurricane Maria respectively. For each natural disaster, there is a pre-, during-, and post-event image for baseline comparisons. Within the night lights data layer, brighter areas indicate locations with higher light emissions. The observation data captures how the intensity and distribution of night lights changed during the recovery efforts. - - **Additional Resources** - - * [Explore the Missions](https://blackmarble.gsfc.nasa.gov/) - * [Nighlights Show Slow Recovery from Maria](https://www.earthobservatory.nasa.gov/images/144371/night-lights-show-slow-recovery-from-maria) - * [Satellite Observes Power Outages in New Orleans](https://earthobservatory.nasa.gov/images/148777/satellite-observes-power-outages-in-new-orleans) - - -
- - - Comparison of nightlights data for Puerto Rico pre-landfall (17 July 2017) and post-landfall (20 September 2017) for Hurricane Maria. - -
- - - -
- - - Harmonized Landsat 8 SWIR image provides enhanced contrast to detect flood extent in Puerto Rico before and after Hurricane Maria in 2017. - -
- - ### Flood Detection - - **Watching the Waters Recede** - Harmonized Landsat Sentinel-2 (HLS) imagery shows the impact of flooding for Hurricanes Maria and Ida. This data supports disaster recovery and associated environmental justice issues as the degree and extent of flooding can be monitored in support of recovery efforts. The imagery displayed is a shortwave infrared (SWIR) false color composite that provides enhanced contrast to detect flood extent. In SWIR false color composite imagery, water is identified by dark blue colors, vegetation is bright green, clouds are white, and ice is blue. - - **Additional Resources** - [HLSL30 Dataset Landing Page](https://lpdaac.usgs.gov/products/hlsl30v002/) - [HLSS30 Dataset Landing Page](https://lpdaac.usgs.gov/products/hlss30v002/) - [HLS Webinar with LPDAAC](https://www.youtube.com/watch?v=N2S4KGNo_XY) - - - - - - - ### Blue Tarp Detection - - **Overview** - - The Interagency Implementation and Advanced Concepts Team (IMPACT), based at NASA’s Marshall Space Flight Center in Huntsville, Alabama, undertook an experimental study to determine if the application of artificial intelligence to NASA satellite images could accurately detect the number of blue tarps deployed on rooftops in the aftermath of hurricanes such as María and Ida as a way of predicting the relative severity of damage experienced by the respective local communities. Correlating these detections over time by zip codes and cross-referencing socio-economic data (such as that contained in the Visualization, Exploration, and Data Analysis (VEDA) dashboard), relationships between socio-economic factors and disaster recovery can be identified. - - -
- - - Blue tarp detections in Jefferson Parish, LA on February 12, 2022. - -
- - - - - - ## Approach - - The IMPACT machine learning team identified the bounding boxes for the New Orleans and San Juan regions. They ordered PlanetScope 3 band data from Planet Labs for dates before and after both hurricanes made landfall. They then combined all scenes available for the respective regions for each date. The scenes were segmented using shapefiles for each zip code in the metro areas. In the next step, the team used [US Building Footprints](https://github.com/microsoft/USBuildingFootprints) by Microsoft to limit the detections to just buildings and reduce false positives. A threshold for the color blue was defined and used to segment out blue pixels detected within the building footprints segmentations. For each of the above dates, the model counted the detection of blue pixelated building segments (interpreted to be blue tarps) in each zip code. - - ## "The Ground Truth" - - In order to establish an external benchmark, the team gathered local news reports published in the immediate aftermath of both Hurricane Ida and Hurricane María that included reports of damage. The news reports were grouped by the zip codes of the locations mentioned. Each reported zip code was then assigned an estimated level of damage severity. The damage experienced in a zip code was considered as: - * low impact (L) if the reported damages were minor (e.g. dangling boards or tree debris); - * medium impact (M) if the damages were significant but did not lead to complete loss of structures; or - * high impact (H) if the damages were severe and led to complete loss of structures. - -
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Hurricane Ida
Estimated DamageZip
H70001
H70003
H70094
L70112
M70113
H70114
M70117
M70119
L70130
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Hurricane María
Estimated DamageZip
L00901
L00911
M00912
H00915
M00918
M00920
H00956
H00962
M00979
-
- - Estimated damage by zip code - -
- - A statistical analysis of the rankings demonstrates a high degree of correlation between the maximum number of detected blue tarp counts in each zip code and the level of estimated damage. The three instances where the order of rows should have been reversed, the difference in the blue tarp counts between each pair was not significant: - - * 70114(H) with 70119(M) p=0.177; - * 70113(M) with 70130(L) p=0.452; - * 00912(M) with 00901(L) p=0.478. - - Overall the number of detected blue tarps corresponded with the estimated amount of damage experienced by each zip code based on ground-level reports. - -
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Hurricane Ida
Estimated DamageZipMax. Tarps
H700032112
H700942068
H700011066
M70119883
H70114632
M70117543
L70130213
M70113181
L70112102
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Hurricane María
Estimated DamageZipMax. Tarps
H00915308
H00962242
H00956240
M00979137
M00920110
M0091888
L0090142
M0091240
L0091123
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- - Estimated damage based on number of tarps detected - -
- - - - - - However, when analyzing detected blue tarps as a percentage of total building footprints in each zip code, a similar pattern emerges that suggests the on-the-ground observations and damage assessments do not necessarily capture the complete picture. - - The graphs show a zip code in each of the areas affected by the hurricanes that were estimated as having sustained a low degree of storm damage: 70112 in New Orleans and 00901 in San Juan. As noted previously, in line with the on-the-ground reports, both rank low on the graphs showing the number of blue tarps detected. - - -
- - - Number of tarps detected in the aftermath of Hurricane Ida - -
- - - -
- - - Number of tarps detected in the aftermath of Hurricane Maria - -
- - When the zip codes are analyzed in terms of the number of detected blue - tarps as a percentage of the total building footprints, a pattern emerges in - each of the affected areas. Both 70112 and 00901 jump high up in the damage - estimate, significantly higher than their low designation would have - predicted (p=0.054 and p = 0.009 respectively). - - - - -
- - - Tarps as a percentage of the total buildings in the aftermath of Hurricane - Ida - -
- - - -
- - - Tarps as a percentage of the total buildings in the aftermath of Hurricane - Maria - -
- - - - - ## Findings and Future Avenues of Research - - The ability to detect blue tarp counts for the entirety of a zip code from satellite imagery holds the possibility of surfacing areas of relatively severe damage that may be obscured in ground-level observations. In both these zip codes the small absolute number of damaged buildings and significantly lower building footprint densities (i.e., 761.49/sq. km. in 70112 (p=0.0119) and 713.16/ sq. km. in 00901 (p=0.008)), may have caused these communities to be overlooked by on-the-ground coverage of the storm damage. However, as a percentage of total buildings, the damage sustained in both these zip codes was significantly underestimated by on-the-ground reports when compared to the highest ranked zip codes in medium damage estimate groupings (p=0.012 for 70112 and p=0.004 for 00901). - - These findings have potential environmental justice implications as zip code 70112 and 00901 share similar demographic profiles. Both zip codes are minority majority. The median household income for zip code 70112 is $20,457, and for 00901 it is $14,720 (65.7% and 47.3% the national median respectively). Both zip codes contain a higher than average population density: - * 70112 contains 3,655 residents in a land area of 2.26 square kilometers (an effective density of 4,255/sq. mile). - * 00901 contains 7,080 residents in a land area of 2.54 square kilometers (7,217/sq. mile). - - Both zip codes have indicators that suggest a high degree of multifamily housing. In zip code 70112, 2,563 housing units exist within 1,720 building footprints for an average ratio of 1.49 units per building. There are 4,990 housing units among the 1,810 building footprints in zip code 00901 for an average ratio of 2.76 units per building. In terms of population density, zip code 70112 has a ratio of 2.13 residents per building footprint, and zip code 00901 has 3.91 residents per building footprint. It follows from these residential densities that the number of people directly affected by even an apparently low number of damaged buildings (in terms of raw counts) is not insignificant. - - The environmental justice implications of being able to more quickly identify overlooked storm-damaged communities is considerable. Further research in this area can establish how satellite-based detections can inform and enhance disaster recovery efforts, especially in vulnerable communities. - - - - - - - ## Credits and Sources - - Black Marble data courtesy of [Universities Space Research Association (USRA) Earth from Space Institute (EfSI)](https://www.usra.edu/efsi-our-mission) and NASA Goddard Space Flight Center’s [Terrestrial Information Systems - - Laboratory](https://science.gsfc.nasa.gov/earth/terrestrialinfo/) using VIIRS day-night band data from the Suomi National Polar-orbiting Partnership and Landsat-8 Operational Land Imager (OLI) data from the U.S. Geological Survey - - Citation for HLS data set: Claverie, M., Ju, J., Masek, J. G., Dungan, J. L., Vermote, E. F., Roger, J.-C., Skakun, S. V., & Justice, C. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219, 145-161. - - Masek, J., Ju, J., Roger, J., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., Justice, C. (2021). HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2022-06-16 from https://doi.org/10.5067/HLS/HLSS30.002 - - Masek, J., Ju, J., Roger, J., Skakun, S., Vermote, E., Claverie, M., Dungan, J., Yin, Z., Freitag, B., Justice, C. (2021). HLS Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0 [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2022-06-16 from https://doi.org/10.5067/HLS/HLSL30.002 - - [https://www.wdsu.com/article/parish-to-parish-the-latest-on-what-we-know-ten-days-post-ida/37511472#](https://www.wdsu.com/article/parish-to-parish-the-latest-on-what-we-know-ten-days-post-ida/37511472#) - - [https://www.nola.com/news/hurricane/article_bb0c2268-2144-11ec-8dce-672b7a624bed.html](https://www.nola.com/news/hurricane/article_bb0c2268-2144-11ec-8dce-672b7a624bed.html) - - [https://www.nola.com/multimedia/photos/collection_59f4a7f4-0973-11ec-b836-cfb3d4ff5589.html#20](https://www.nola.com/multimedia/photos/collection_59f4a7f4-0973-11ec-b836-cfb3d4ff5589.html#20) - - [https://www.nola.com/news/hurricane/article_ef41b052-0998-11ec-9c32-97779ca0847b.html](https://www.nola.com/news/hurricane/article_ef41b052-0998-11ec-9c32-97779ca0847b.html) - - [https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1084218&dswid=9465](https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1084218&dswid=9465) - - [https://digitalscholarship.tsu.edu/jpmsp/vol22/iss2/5/](https://digitalscholarship.tsu.edu/jpmsp/vol22/iss2/5/) - - [https://www.unitedstateszipcodes.org/70112/](https://www.unitedstateszipcodes.org/70112/) - - [https://www.unitedstateszipcodes.org/00901/](https://www.unitedstateszipcodes.org/00901/) - - - \ No newline at end of file diff --git a/app/content/stories/lahina-fire.mdx b/app/content/stories/lahina-fire.mdx deleted file mode 100644 index 4c87d466..00000000 --- a/app/content/stories/lahina-fire.mdx +++ /dev/null @@ -1,108 +0,0 @@ ---- -id: lahaina-fire -name: The Devastating August 8th, 2023 Lahaina, Hawai'i Wildfire -description: "A Satellite-Based Overview of the Lahaina Wildfire" -media: - src: /images/story/lahaina-fire-background.jpg - alt: Fire erupting over Lahaina, HI. - author: - name: Matthew Thayer/AP - url: https://www.sfchronicle.com/travel/article/hawaii-fire-maui-lahaina-18289213.php -pubDate: 2023-12-01 -taxonomy: - - name: Topics - values: - - Wildfire ---- - - - - Authors: Trent Cowan[1], Andrew Blackford[1], Udaysankar Nair[1]\ - [1] University of Alabama in Huntsville(UAH) - - ## Introduction - 🚧 This Discovery presents work in progress and not peer-reviewed results! 🚧 - On August 8, 2023, the city of Lahaina, Hawai’i located on the island of Maui faced a devastating wildfire, leaving destruction and despair in its wake. The city of over 13,000 residents bore witness to a tragedy that would go down as the deadliest U.S. wildfire since 1918. A downed powerline on Lahainaluna Road initiated the wildfire. Though initially extinguished, the fire was reinvigorated by strong winds near the surface caused by air funneling through the channel between Maui and Molokai; a phenomenon known as a [gap wind](https://glossary.ametsoc.org/wiki/Gap_wind). - - The National Weather Service reported wind gusts as high as 67 mph in the area, which helped to quickly spread the wildfire across much of Lahaina during the afternoon hours of August 8. The intense winds was further aided by a sharp pressure gradient caused by [Hurricane Dora](https://www.google.com/url?q=https://www.earthdata.nasa.gov/worldview/worldview-image-archive/hurricane-dora-6-aug-2023&sa=D&source=docs&ust=1698703849605505&usg=AOvVaw2v_a0o1c-R2PBY6AQEbNrB), a Category 4 hurricane approximately 500 miles south of the islands when the fire began. As Hurricane Dora exerted its influence, the gap wind persisted from August 7 to 9, creating ideal conditions for the rapid progression and expansion of a small brush fire to a wildfire that would consume much of the town of Lahaina. - - - - -
- US Drought Monitor, August 8th, 2023. - - US Drought Monitor Index on August 8th, 2023 over the Hawaiian Islands, with darker colors indicating worse drought conditions (source: drought.gov). - -
- - - Furthermore, a considerable portion of Maui is inherently more prone to drought conditions compared to the other Hawaiian Islands, mainly because the mountainous terrain obstructs beneficial rainfall. The US Drought Monitor Index on August 8th, 2023, underscores the prevalent dry conditions preceding the disaster. Much of the island of Maui was experiencing extreme drought conditions and Lahaina was in a severe drought as defined by the Drought Monitor Index. These drought conditions dried out the vegetation across the island and provided a natural fuel source that facilitated the rapid spread of the Lahaina Fire through the surrounding fields and into the city. This event serves as a depiction of cascading or compounding disasters, where several independent disasters converge, amplifying the magnitude and impact of the crisis. - - - - - - ## Satellite Analysis of the Lahaina Wildfire - Thermal imagery acquired by the Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) aboard the joint NASA/USGS Landsat-8 satellite detected ongoing fires across much of the city of Lahaina during its overpass at 11:35 PM Hawaiian Daylight Time on August 8 (shown above). The OLI and TIRS instruments aboard the Landsat satellites take observations from several different wavelengths that can be used to better understand changes to land and vegetation from natural disasters. The Burned Area Index (BAIS-2) derived using NASA’s Harmonized Landsat and Sentinel-2 (HLS) products on August 13, 2023, clearly identifies the regions most affected by the fire. The areas with the highest probability of being burned are the scorched fields just uphill from the city. Note that population centers typically have lower BAIS2 values because the change in the land surface characteristics isn't as distinct as land covered with vegetation. - - Another technique used to identify the impacts of natural disasters are false-color composite images. False-color composite imagery replaces the traditional red, green, and blue wavelength bands that correspond to how our eyes see with other wavelength bands. Fires are particularly sensitive to the near infrared (IR) and shortwave IR wavelengths. When the red, green, and blue bands are replaced with the shortwave IR, near IR, and red bands, respectively, land areas most impacted by the fire are much darker in the image than unaffected areas. - - -
- - - Landsat-8 nighttime thermal imagery captured on August 8, 2023 reveals fires in and around Lahaina. The Burned Area Index for Sentinel-2 (BAIS-2) derived from Harmonized Landsat Sentinel-2 data captured on the August 13, 2023 illustrates the extent of the damage. - -
- - - -
- - - Harmonized Landsat Sentinel-2 false color composite imagery of Maui before (August 8, 2013) and after the fire (August 13, 2013). - -
- - - - - According to the Pacific Disaster Center (PDC) and Federal Emergency Management Agency (FEMA), 2,170 acres were burned by the Lahaina Fire, destroying 2,207 structures — 86% of which were residential buildings. The fire is estimated to have caused $5.52 billion in damages, and 97 fatalities have been confirmed as a direct result of the fire. As the city continues to recover from the wildfire tragedy, satellite data can be used to identify areas in need of resources and monitor precursor conditions such as drought in the future. - - - - - - ## Additional Resources - [HLS Landing Page](https://lpdaac.usgs.gov/data/get-started-data/collection-overview/missions/harmonized-landsat-sentinel-2-hls-overview/) - - [PDC/FEMA report](https://www.mauicounty.gov/CivicAlerts.aspx?AID=12683) - - [Glossary of Meteorology on gap wind](https://glossary.ametsoc.org/wiki/Gap_wind) - - [List of Deadliest Wildfires](https://www.npr.org/2023/08/15/1193710165/maui-wildfires-deadliest-us-history) - - [NASA's Fire Information for Resourse Management System (FIRMS)](https://www.earthdata.nasa.gov/learn/articles/firms-lfta-data) - - [AI Aiding in Lahaina Fire Recovery](https://www.earthdata.nasa.gov/news/access-maui-fire-recovery) - - - diff --git a/app/content/stories/ogallala-aquifer.mdx b/app/content/stories/ogallala-aquifer.mdx new file mode 100644 index 00000000..dd51113d --- /dev/null +++ b/app/content/stories/ogallala-aquifer.mdx @@ -0,0 +1,201 @@ +--- +id: ogallala-aquifer +name: The Human Footprint on the Ogallala Aquifer +description: "NASA Models and Datasets Capture Irrigation and Groundwater Depletion Impacts" +media: + src: /public/images/story/ogallala-aquifer/ogallala-aquifer-background1.png + alt: Satellite remote sensing data showing irrigated fields. + author: + name: NASA EIS Freshwater + url: https://freshwater.eis.smce.nasa.gov/storymap.html +pubDate: 2025-06-20T00:00 +taxonomy: + - name: Topics + values: + - Agriculture + - Water Resources + - name: Subtopics + values: + - Groundwater + - Hydrology + - Land Use + +--- + + + + Authors: NASA EIS Freshwater1, Andrew Blackford2 + + 1 National Aeronautics and Space Administration Earth Information System + + 2 The University of Alabama in Huntsville + + + + + + ### Groundwater and Irrigation + + ##### Largest U.S. groundwater resource is rapidly depleting + + The Ogallala Aquifer provides over 30% of the water for irrigation used in the country. However, overuse is rapidly depleting this aquifer, threatening those who depend on this resource. + + +
+ Center-pivot low-energy sprinkler irrigation in Nebraska. + + Center-pivot low-energy sprinkler irrigation in Nebraska. + +
+ + + +
+ Ogallala Aquifer Extent. + + Ogallala Aquifer Extent. + +
+ + + ##### Declining Water Levels + + Groundwater levels have declined as irrigation and agriculture have intensified in the region. + + + + + + + !! ADD DATA VIS HERE !! + + Annual TWS change (GRACE) and annual irrigation fraction map (Landsat) + + + + + + ### Modeling Irrigation + + NASA's land surface hydrology model **assimilates inputs from multiple sensors** to model irrigation. + + + + +
+ Ogallala Aquifer Flowchart. + + Flowchart of how to use NASA satellite remote sensing datasets to model and monitor irrigation practices. + +
+ + + + + ### Impacts on the Aquifer + + ##### Groundwater depletion is captured by LIS + + Storage depletion modeled by assimilating GRACE observations within the Land Information System (LIS) matches well with USGS observations. + + +
+ USGS observations of groundwater storage changes for 2002-2015. + + USGS observations of groundwater storage changes for 2002-2015. + +
+ + + + + + !! ADD DATA VIS HERE !! + + Swipe the slider for modeled groundwater storage with GRACE data assimilation (DA) (left) and without GRACE DA (right) + + + + +
+ Time series of terrestrial water storage trends modeled using GRACE data assimilation in the LIS framework for the Northern Ogallala + + Time series of terrestrial water storage trends modeled using GRACE data assimilation in the LIS framework for the Northern Ogallala. + +
+ + + ##### Contrasting Management Impacts in the Northern and Southern Ogallala + + For the Northern Ogallala, sufficient precipitation helps prevent the depletion of the aquifer caused by water withdrawals. + + Without assimilating GRACE observations, the model tends to underestimate groundwater recharge. So, assimilation is essential to capture water storage recovery after the 2012 drought. + + + + + + + !! ADD DATA VIS HERE !! + + Terrestrial water storage trends modeled using GRACE data assimilation in the LIS framework for the Northern Ogallala + + + + + + In contrast, for the Southern Ogallala, severe groundwater depletion is observed due to pumping. + + Assimilating GRACE TWS and MODIS LAI contributes to a **better estimation of irrigation withdrawal** and captures its impact on long-term water storage decline. + + +
+ Time series of terrestrial water storage trends modeled using GRACE data assimilation in the LIS framework for the Southern Ogallala + + Time series of terrestrial water storage trends modeled using GRACE data assimilation in the LIS framework for the Southern Ogallala. + +
+ + + + + + !! ADD DATA VIS HERE !! + + Terrestrial water storage trends modeled using GRACE data assimilation in the LIS framework for the Southern Ogallala + + + + + + ### Conclusions + + Remote sensing data from GRACE, MODIS, and GPM plays a **key role in understanding changes in the Ogallala Aquifer** fromirrigation and other uses. + + Decline in groundwater storage in the Southern Ogallala Aquifer is well-captured by leveraging **NASA models and datasets**. + + diff --git a/app/content/stories/plains-drought.mdx b/app/content/stories/plains-drought.mdx new file mode 100644 index 00000000..33b9ea86 --- /dev/null +++ b/app/content/stories/plains-drought.mdx @@ -0,0 +1,454 @@ +--- +id: plains-drought +name: A Tale of Two Droughts +description: "Progression and Impacts of Flash Droughts in the Great Plains as captured by NASA Models and Earth Observations" +media: + src: /public/images/story/plains-drought/plains-drought-background1.png + alt: Cracked ground stressed by ongoing drought conditions. + author: + name: NASA EIS Freshwater + url: https://freshwater.eis.smce.nasa.gov/storymap.html +pubDate: 2025-06-18T00:00 +taxonomy: + - name: Topics + values: + - Natural Disasters + - name: Subtopics + values: + - Drought + +--- + + + + Authors: NASA EIS Freshwater1, Andrew Blackford2 + + 1 National Aeronautics and Space Administration Earth Information System + + 2 The University of Alabama in Huntsville + + + + + + ### Introduction + + The Northern Great Plains region is increasingly experiencing **flash droughts** - those with more rapid onset and intensification. + + Here we compare two such flash droughts by incorporating remote sensing observations from GPM, MODIS, SMAP, and AMSR2 within the Land Information System (LIS) framework. + + +
+ Region of interest. + + Northern Great Plains (Montana, North Dakota and South Dakota). + +
+ + + +
+ + + + United States Drought Monitor progression during the summers of 2016 and 2017. + +
+ + + ### Drought Overview + + In July 2017, 'exceptional' drought conditions spread over the region, resulting in the most devastating flash drought of recent times. The drought of Summer 2016, with lower severity and shorter duration, also impacted farmers and agriculture around the Black Hills of South Dakota. + + + + + + Considering potential impacts of drought to agriculture, economies and ecosystems, a better understanding of **how droughts evolve is critical for decision-makers**. + + +
+ USDM drought conditions time series. + + Time series from the United States Drought Monitor of Northern Great Plains drought conditions. + +
+ + + + + ### Drought Mechanisms + + The onset and evolution of a flash drought typically follows this sequence of events: + + + + +
+ Evolution of a flash drought. + + Typical evolution of a flash drought. + +
+ + + + + ### Evolution of the Droughts + + + + + + ##### What Triggered the Two Droughts? + + In 2017, the unusually low precipitation during the normally rainy season resulted in a very rapid onset of drought conditions. + + +
+ + + + Precipitation standardized anomalies. + +
+ + + + + + +
+ Precipitation anomaly time series. + + Standardized anomalies for GPM precipitation over Northern Great Plains, 2017 highlighting large negative anomalies during May-July. + +
+ + + GPM captured the large negative anomalies in precipitation over the region for May-July, 2017. + + + + + + + + ### Extreme Heat Triggered the 2016 Drought + In contrast, the 2016 flash drought was mainly a consequence of an **extreme heat-wave** that started in early March. + + +
+ + + + Temperature standardized anomalies. + +
+ + + + + +
+ Temperature anomaly time series. + + Standardized anomalies for temperature showing the early March heat-wave, while precipitation was above normal in March-April. + +
+ + + +
+ + + + Depletion of root zone soil moisture during the 2017 drought. + +
+ + + ### Unusually Dry Soils in 2017 + + Below-average precipitation during May-July of 2017 led to **rapid depletion of soil moisture**, causing an exceptional _**precipitation-deficit-driven**_ flash drought in the region. + + + + + +
+ + + Depletion of root zone soil moisture anomalies during the 2017 drought, compared to 2016's anomalies. + +
+ + + + + ### High Evaporative Demands in 2016 + + In contrast, abnormally high temperatures led to **increased evaporative demands at the onset** (compared to 2017), resulting in a _**heat-wave-driven**_ flash drought of 2016. + + +
+ + + + Anomalies in vegetation transpiration demands for 2016. + +
+ + + + +
+ + + Depletion of evapotranspirtation anomalies during the 2017 drought, compared to 2016's anomalies. + +
+ + + + + ### Vegetation Stress Seen From Space + + + + +
+ + + + Spatial progression of LAI anomalies during the droughts of 2016 and 2017. + +
+ + + + + The dry soils in 2017 caused increased vegetation stress, seen from MODIS-derived leaf area index (LAI) anomalies. + + +
+ Leaf area index (LAI) anomaly time series. + + Large negative LAI anomalies showing increased vegetation stress caused by dry soils during the 2017 drought. + +
+ + + +
+ ET and Leaf area index (LAI) anomaly time series. + + Negative LAI anomalies caused by high evaporative demands at the onset of the 2016 drought. + +
+ + + ##### While in 2016... + + Increased evaporative demands significantly stressed the vegetation and impacted crop health. + + The LAI in 2016, however, was less anomalous when compared to 2017. + + + + +
+ + + + Spatial progression of LAI anomalies during the droughts of 2016 and 2017. + +
+ + + + + ### Aftermath of the Droughts + + The aftermath of the 2017 drought severely damaged field crops like wheat, sparked massive wildfires, and compromised water resources. + + According to NOAA’s National Centers for Environmental Information, an estimated $2.7 billion of economic losses resulted from the 2017 Northern Great Plains drought. + + + + + + ##### Lodgepole Complex Fire + + Massive wildfire erupted on July 19, 2017 and spread over 270,000 acres in Montana. + + +
+ Lodgepole Complex Fire + + Lodgepole Complex Fire. + +
+ + + + + ##### Sensing Fire from MODIS + + The extent of the Lodgepole Fire was captured by MODIS Terra Reflectance scenes, obtained from NASA GIBS. + + !! ADD DATA VIS HERE !! + + MODIS Terra Corrected Reflectance Bands 7-2-1; Left: Pre-fire July 13, Right: Active fire July 23. + + The MODIS fire product MCD64A1 also captures the extent of the wildfire. + + !! ADD DATA VIS HERE !! + + Areas labelled as 'burned' in MCD64A1 between July 19 and July 26, 2017. + + + + + + + ### Damaged Crops + + 2017 drought also caused extensive impacts to agriculture. Field crops including wheat were severely damaged. + + Data from USDA reveals 30% lower production of wheat in 2017 compared to the five-year average. + + +
+ Crop production time series + + United States Department of Agriculture crop production time series. + +
+ + + + + ### Conclusions + + Integrating datasets from **GPM, MODIS, AMSR2, and SMAP is necessary** to capture a more complete evolution of droughts. + + Remote sensing datasets also help capture the impacts of flash droughts on **agriculture and post-drought wildfires**. + + + + + + + + #### References + https://research.noaa.gov/article/ArtMID/587/ArticleID/2460/Climate-change-to-make-events-like-2017-Northern-Plains-drought-more-likely + + https://research.noaa.gov/article/ArtMID/587/ArticleID/2472/Climate-change-to-make-hot-droughts-hotter-in-the-US-southern-plains + + https://droughtmonitor.unl.edu/DmData/TimeSeries.aspx + + https://quickstats.nass.usda.gov/ + + https://calmit.unl.edu/drought + + \ No newline at end of file diff --git a/public/charts/story/flood-2019-snow-depth.csv b/public/charts/story/flood-2019-snow-depth.csv new file mode 100644 index 00000000..4696e415 --- /dev/null +++ b/public/charts/story/flood-2019-snow-depth.csv @@ -0,0 +1,731 @@ +DateTime,Snow Depth 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