Search NASA⌕ Search

SEARCH · Search NASA

Results for “drought severity”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Land and Land-use Change in the Climate Sensitive High Plains: An Automated Approach with Landsat

The High Plains is an economically important and climatologically sensitive region of the United States and Canada. The High Plains contain 100,000 sq km of Holocene sand dunes and sand sheets that are currently stabilized by natural vegetation. Droughts and the larger threat of global warming are climate phenomena that could cause depletion of natural vegetation and make this region susceptible to sand dune reactivation. The original proposal was directed toward the use of Landsat TM data to establish the state and ongoing changes of the surface in the 1.2 million sq. km, semi-arid High Plains region of the central US, A key objective was to develop a model to predict the reactivation of the 100,000 sq. km of Holocene dunes found on the High Plains during an extended drought. At least one Landsat 5 image per year for 1985, 1988 and 1996 was obtained for 32 scenes on the High Plains to coincide with wet and dry years. Additional Landsat 7 data were acquired for 1999 and 2000 primarily for Colorado and Nebraska. As luck would have it, there was no severe drought during the study period 1985-2000. Attention was focused on developing methods for mapping dry vs. green vegetation on sparsely vegetated rangelands in sandy soils, since these were the areas most susceptible to surface reactivation during a drought.

Goetz, Alexander F.↗
NASA NTRS2017

Remote Linkages to Anomalous Winter Atmospheric Ridging over the Northeastern Pacific

Severe drought in California between 2013 and 2016 has been linked to the multiyear persistence of anomalously high atmospheric pressure over the northeastern Pacific Ocean, which deflected the Pacific storm track northward and suppressed regional precipitation during California's winter 'rainy season.' Multiple hypotheses have emerged regarding why this high pressure ridge near the west coast of North America was so resilient-including unusual sea surface temperature patterns in the Pacific Ocean, reductions in Arctic sea ice, random atmospheric variability, or some combination thereof. Here we explore relationships between previously documented atmospheric conditions over the North Pacific and several potential remote oceanic and cryospheric influences using both observational data and a large ensemble of climate model simulations. Our results suggest that persistent wintertime atmospheric ridging similar to that implicated in California's 2013-2016 drought can at least partially be linked to unusual Pacific sea surface temperatures, and that Pacific Ocean conditions may offer some degree of cool-season foresight in this region despite the presence of substantial internal variability.

California drought;Atmospheric ridging;Ocean-atmos↗
NASA NTRS2016

IN11B-1621: Quantifying How Climate Affects Vegetation in the Amazon Rainforest

Amazon droughts in 2005 and 2010 have raised serious concern about the future of the rainforest. Amazon forests are crucial because of their role as the largest carbon sink in the world which would effect the global warming phenomena with decreased photosynthesis activity. Especially, after a decline in plant growth in 1.68 million km2 forest area during the once-in-a-century severe drought in 2010, it is of primary importance to understand the relationship between different climatic variables and vegetation. In an earlier study, we have shown that non-linear models are better at capturing the relation dynamics of vegetation and climate variables such as temperature and precipitation, compared to linear models. In this research, we learn precise models between vegetation and climatic variables (temperature, precipitation) for normal conditions in the Amazon region using genetic programming based symbolic regression. This is done by removing high elevation and drought affected areas and also considering the slope of the region as one of the important factors while building the model. The model learned reveals new and interesting ways historical and current climate variables affect the vegetation at any location. MAIAC data has been used as a vegetation surrogate in our study. For temperature and precipitation, we have used TRMM and MODIS Land Surface Temperature data sets while learning the non-linear regression model. However, to generalize the model to make it independent of the data source, we perform transfer learning where we regress a regularized least squares to learn the parameters of the non-linear model using other data sources such as the precipitation and temperature from the Climatic Research Center (CRU). This new model is very similar in structure and performance compared to the original learned model and verifies the same claims about the nature of dependency between these climate variables and the vegetation in the Amazon region. As a result of this study, we are able to learn, for the very first time how exactly different climate factors influence vegetation at any location in the Amazon rainforests, independent of the specific sources from which the data has been obtained.

global warming↗
NASA NTRS2002

Is There Really an Intermittent Biennial Oscillation in the Great Plains Low-Level Jet Over Texas?

In the 15-year GEOS-1 reanalysis data set, a maximum of interannual variance of low- level meridional flow for the warm season (May through August) occurs over southeast Texas. This variance maximum seems to be dominated by a marked biennial oscillation that occurs only during the first 6 (or possibly 8) years of the reanalysis period (1980-85 or possibly 1980-1987) and then completely disappears by the 9th year. This biennial oscillation seems to be associated with interannual fluctuations in ground wetness, surface temperature and surface pressure gradients over Texas. The periods of drier soil lead to warmer surface temperatures, lower surface pressures, stronger pressure gradients between Texas and the Gulf of Mexico and stronger southerly winds. This intermittent biennial oscillation is also evident in corresponding fields for the the NCEP/NCAR reanalysis data set for the years 1978-1985 (and possibly from 1978- 1987) and 1995-2000, but not during other periods. There are also obvious biennial oscillations evident during these periods in U.S. Climate Division records for the Palmer Drought Severity Index (PDSI) for Texas. Month-by-month correlations of this index with certain el Nino related indices are as high as .45 for the first period and as high as .55 or .6 for the second period for some regions in Texas. The seasonal cycle of the biennial signal in the PDSI and precipitation for the first period suggest that the drought in Texas and Mexico is ended (caused) by a reversal in the sign of anomalies in precipitation rate for the fall/winter season. Analysis of tropical Pacific SST patterns shows a .5 to .75 K biennial oscillation of SSTs along the precipitation-free track to the southwest of the Mexican coast during the fall and winter months of the 1978 to 1985 period that might explain the reversal in precipitation anomalies and hence the entire intermittent biennial oscillation in ground hydrology and low-level flow.

Helfand, H. Mark↗
NASA NTRS2017

Satellite-Based Drought Reporting on the Navajo Nation

The Navajo Nation (NN) is the largest reservation in the US, and faces challenges related to water management during long-term and widespread drought episodes. The Navajo Nation is a federally recognized tribe, which has boundaries within Arizona, New Mexico, and Utah. The Navajo Nation has a land area of over 70,000 square kilometers. The Navajo Nation Department of Water Resources (NNDWR) reports on drought and climatic conditions through the use of regional Standardized Precipitation Index (SPI) values and a network of in-situ rainfall, streamflow, and climate data. However, these data sources lack the spatial detail and consistent measurements needed to provide a coherent understanding of the drought regime within the Nation's regional boundaries. This project, as part of NASA's Western Water Applications Office (WWAO), improves upon the recently developed Drought Severity Assessment Tool (DSAT) to ingest satellite-based precipitation data to generate SPI values for specific administrative boundaries within the reservation. The tool aims to: (1) generate SPI values and summary statistics for regions of interest on various timescales, (2) to visualize SPI values within a web-map application, and (3) produce maps and comparative statistical outputs in the format required for annual drought reporting. The co-development of the DSAT with NN partners is integral to increasing the sustained use of Earth Observations for water management applications. This tool will provide data to support the NN in allocation of drought contingency dollars to the regions most adversely impacted by declines in water availability.

Satellite-based↗
NASA NTRS2023

Drought and Deluge: the Recurrence of Hydroclimate Extremes During the Past 600 Years in Eastern Australia’s Natural Resource Management (NRM) Clusters

Recent extremes of flood and drought across Australia have raised questions about the recurrence of such rare events and highlighted the importance of understanding multi-decadal climate variability. However, instrumental records over the past century are too short to adequately characterise climate variability on multi-decadal and longer timescales or robustly determine extreme event frequencies and their duration. Palaeoclimate reconstructions can provide much-needed information to help address this problem. Here, we use the 600-year hydroclimate record captured in the eastern Australian and New Zealand Drought Atlas (ANZDA) to analyse drought and pluvial frequency trends for East Australian Natural Resource Management (NRM) clusters. This partitioning of the drought atlas grid points into recognised biophysical areas (i.e. NRM clusters) revealed their differences and similarities in drought intensity and pluvial events over time. We find sustained multi-decadal periods of a wet–dry geographic 'seesaw' between eastern to central and southern NRMs (e.g. 1550–1600 CE and 1700–1750 CE). In contrast, other periods reveal spatially consistent wetting (e.g. 1500–1550 CE) or drying (e.g. 1750–1800 CE). Emerging hot spot analysis further shows that some areas that appear naturally buffered from severe drought during the instrumental period have a greater exposure risk when the longer 600-year record is considered. These findings are particularly relevant to management plans when dealing with the impacts of climate extremes developed at regional scales. Our results demonstrate that integrating and extending instrumental records with palaeoclimate datasets will become increasingly important for developing robust and locally specific extreme event frequency information for managing water resources.

Australian hydroclimate↗
NASA NTRS2020

The European Russia Drought Atlas (1400-2016 CE)

We present the European Russia Drought Atlas (ERDA) that covers the East European Plain to the Ural Mountains from 1400–2016 CE. Like the Old World Drought Atlas (OWDA) for the Euro-Mediterranean region, the ERDA is a one-half degree gridded reconstruction of summer Palmer Drought Severity Indices estimated from a network of annual tree-ring chronologies. Ensemble point-by-point regression is used to generate the ERDA with the identical protocols used for developing the OWDA. Split calibration/validation tests of the ERDA indicate that it has significant skill over most of its domain and is much more skillful than the OWDA where they overlap in the western part of ERDA domain. Comparisons to historical droughts over European Russia additionally support the ERDA’s overall validity. The ERDA has been spatially smoothed and infilled using a local regression method to yield a spatially complete drought atlas back to 1400 CE. EOF analysis indicates that there are three principal modes of hydroclimatic variability in the ERDA. After Varimax rotation, these modes correlate significantly with independent climate data sets extending back to the late nineteenth century in a physically interpretable way and relate to atmospheric circulation dynamics of droughts and heatwaves over European Russia based on more recent instrumental data.

Cook, Edward R.↗
NASA NTRS2014

Abrupt Increases in Amazonian Tree Mortality Due to Drought-Fire Interactions

Interactions between climate and land-use change may drive widespread degradation of Amazonian forests. High-intensity fires associated with extreme weather events could accelerate this degradation by abruptly increasing tree mortality, but this process remains poorly understood. Here we present, to our knowledge, the first field-based evidence of a tipping point in Amazon forests due to altered fire regimes. Based on results of a large-scale, longterm experiment with annual and triennial burn regimes (B1yr and B3yr, respectively) in the Amazon, we found abrupt increases in fire-induced tree mortality (226 and 462%) during a severe drought event, when fuel loads and air temperatures were substantially higher and relative humidity was lower than long-term averages. This threshold mortality response had a cascading effect, causing sharp declines in canopy cover (23 and 31%) and aboveground live biomass (12 and 30%) and favoring widespread invasion by flammable grasses across the forest edge area (80 and 63%), where fires were most intense (e.g., 220 and 820 kW x m(exp −1)). During the droughts of 2007 and 2010, regional forest fires burned 12 and 5% of southeastern Amazon forests, respectively, compared with less than 1% in nondrought years. These results show that a few extreme drought events, coupled with forest fragmentation and anthropogenic ignition sources, are already causing widespread fire-induced tree mortality and forest degradation across southeastern Amazon forests. Future projections of vegetation responses to climate change across drier portions of the Amazon require more than simulation of global climate forcing alone and must also include interactions of extreme weather events, fire, and land-use change.

MODIS↗
NASA NTRS2014

Global Warming and 21st Century Drying

Global warming is expected to increase the frequency and intensity of droughts in the twenty-first century, but the relative contributions from changes in moisture supply (precipitation) versus evaporative demand (potential evapotranspiration; PET) have not been comprehensively assessed. Using output from a suite of general circulation model (GCM) simulations from phase 5 of the Coupled Model Intercomparison Project, projected twentyfirst century drying and wetting trends are investigated using two offline indices of surface moisture balance: the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI). PDSI and SPEI projections using precipitation and Penman- Monteith based PET changes from the GCMs generally agree, showing robust cross-model drying in western North America, Central America, the Mediterranean, southern Africa, and the Amazon and robust wetting occurring in the Northern Hemisphere high latitudes and east Africa (PDSI only). The SPEI is more sensitive to PET changes than the PDSI, especially in arid regions such as the Sahara and Middle East. Regional drying and wetting patterns largely mirror the spatially heterogeneous response of precipitation in the models, although drying in the PDSI and SPEI calculations extends beyond the regions of reduced precipitation. This expansion of drying areas is attributed to globally widespread increases in PET, caused by increases in surface net radiation and the vapor pressure deficit. Increased PET not only intensifies drying in areas where precipitation is already reduced, it also drives areas into drought that would otherwise experience little drying or even wetting from precipitation trends alone. This PET amplification effect is largest in the Northern Hemisphere mid-latitudes, and is especially pronounced in western North America, Europe, and southeast China. Compared to PDSI projections using precipitation changes only, the projections incorporating both precipitation and PET changes increase the percentage of global land area projected to experience at least moderate drying (PDSI standard deviation of < or = -1) by the end of the twenty-first century from 12 to 30 %. PET induced moderate drying is even more severe in the SPEI projections (SPEI standard deviation of > or = -1; 11 to 44 %), although this is likely less meaningful because much of the PET induced drying in the SPEI occurs in the aforementioned arid regions. Integrated accounting of both the supply and demand sides of the surface moisture balance is therefore critical for characterizing the full range of projected drought risks tied to increasing greenhouse gases and associated warming of the climate system.

moisture↗
NASA NTRS2021

Recovery: Fast and Slow—Vegetation Response During the 2012–2016 California Drought

The 2012–2016 California Drought severely impacted natural vegetation across a wide range of environmental gradient. Although several studies have reported an increase in plant water stress and mortality, the spatiotemporal variations of ecosystem productivity responses and the associated environmental and biological drivers remain unclear. Here, using Enhanced Vegetation Index from the Moderate resolution imaging spectrometer, we found that 45% of the natural ecosystems showed an abrupt change (breakpoint [BP]) in productivity during 2012–2016. There were three major contrasting temporal patterns of productivity responses: (i) a steady increase under higher temperature followed by a decline due to accumulated moisture depletion (high elevation forest) or temperature decrease (high elevation nonforest), (ii) gradual decline during the drought followed by a rapid recovery within 1 year after drought stress was partially relieved, and (iii) both a gradual decline and an abrupt decline. The magnitude of abrupt changes was negatively correlated (r = −0.80, p < 0.001) with initial gradual changes. Overall, changes during BP offset, on average, 57% of the preceding gradual responses. The spatial variability in ecosystem response patterns is driven by both environmental and biological factors. Particularly, for forests, positive BP was driven by increasing rainfall and decreasing temperature, while negative BP was mainly driven by the precipitation anomaly. By 2019, 33% of the natural vegetation have recovered to the level of EVI in 2010. Ecosystem responses to multiyear droughts can influence ecosystem dynamics in a complex pattern. Multiple ecohydrological factors should be considered to understand and predict the long-term drought impacts on ecosystems.

Xi Yang↗
NASA NTRS2001

Spectral Unmixing of Vegetation, Soil and Dry Carbon in Arid Regions: Comparing Multispectral and Hyperspectral Observations

Remote sensing of vegetation cover and condition is critically needed to understand the impacts of land use and climate variability in and and semi-arid regions. However, remote sensing of vegetation change in these environments is difficult for several reasons. First, individual plant canopies are typically small and do not reach the spatial scale of typical Landsat-like satellite image pixels. Second, the phenological status and subsequent dry carbon (or non-photosynthetic) fraction of plant canopies varies dramatically in both space and time throughout and and semi-arid regions. Detection of only the 'green' part of the vegetation using a metric such as the normalized difference vegetation index (NDVI) thus yields limited information on the presence and condition of plants in these ecosystems. Monitoring of both photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) is needed to understand a range of ecosystem characteristics including vegetation presence, cover and abundance, physiological and biogeochemical functioning, drought severity, fire fuel load, disturbance events and recovery from disturbance.

Asner, Gregory P.↗
NASA NTRS2007

Remote Sensing of Terrestrial Water Storage and Application to Drought Monitoring

Terrestrial water storage (TWS) consists of groundwater, soil moisture and permafrost, surface water, snow and ice, and wet biomass. TWS variability tends to be dominated by snow and ice in polar and alpine regions, by soil moisture in mid-latitudes, and by surface water in wet, tropical regions such as the Amazon (Rodell and Famiglietti, 2001; Bates et al., 2007). Drought may be defined as a period of abnormally dry weather long enough to cause significant deficits in one or more of the TWS components. Thus, along with observations of the agricultural and socioeconomic impacts, measurements of TWS and its components enable quantification of drought severity. Each of the TWS components exhibits significant spatial variability, while installation and maintenance of sufficiently dense monitoring networks is costly and labor-intensive. Thus satellite remote sensing is an appealing alternative to traditional measurement techniques. Several current remote sensing instruments are able to detect variations in one or more TWS variables, including the Advanced Microwave Scanning Radiometer (AMSR) on NASA's Aqua satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra and Aqua. Future satellite missions have been proposed to improve this capability, including the European Space Agency's Soil Moisture Ocean Salinity mission (SMOS) and the Soil Moisture Active Passive (SMAP), Surface Water Ocean Topography (SWOT), and Snow and Cold Land Processes (SCLP) missions recommended by the US National Academy of Science's Decadal Survey for Earth Science (NRC, 2007). However, only one remote sensing technology is able to monitor changes in TWS from the land surface to the base of the deepest aquifer: satellite gravimetry. This paper focuses on NASA's Gravity Recovery and Climate Experiment mission (GRACE; http://www.csr.utexas.edu/grace/) and its potential as a tool for drought monitoring.

Rodell, Matt↗
NASA NTRS2023

Modeling Global Indices for Estimating Non-Photosynthetic Vegetation Cover

Non-photosynthetic vegetation (NPV) includes plant litter, senesced leaves, and crop residues. NPV plays an essential role in terrestrial ecosystem processes, and is an important indicator of drought severity, ecosystem disturbance, agricultural resilience, and wildfire danger. Current moderate spatial resolution multispectral satellite systems (e.g., Landsat and Sentinel-2) have only a single band in the 2000–2500 nm shortwave infrared “SWIR2” range where non-pigment biochemical constituents of NPV, including cellulose and lignin, have important spectral absorption features. Thus, these current systems have suboptimal capabilities for characterizing NPV cover. This research used simulated spectral mixtures accounting for variability among NPV and soils to evaluate globally-appropriate hyperspectral and multispectral indices for estimation of fractional NPV cover. The Continuum Interpolated NPV Depth Index (CINDI), a weighted ratio index measuring lignocellulose absorption near 2100 nm, was found to produce the lowest error in estimating NPV cover. CINDI was less sensitive to variability in soil spectra and green vegetation cover than competing indices. While CINDI was sensitive to the relative water content of soil and NPV, this sensitivity allowed for correcting error in estimated NPV cover as water content increased. CINDI bands were less capable than Dual Absorption NPV Index (DANI) bands for maintaining continuity with the heritage Landsat SWIR2 band, but combining multiple CINDI bands demonstrated adequate continuity. Three SWIR2 bands with band centers at 2038, 2108, and 2211 nm can provide superior capabilities for future moderate resolution multispectral/superspectral systems targeting NPV monitoring, including the next generation Landsat mission (Landsat Next). These bands and the associated CINDI index provide potential for global NPV monitoring using a constellation of future superspectral sensors and imaging spectrometers, with applications including improving soil management, preventing land degradation, evaluating impacts of drought, mapping ecosystem disturbance, and assessing wildfire danger.

Index optimization↗
NASA NTRS2021

Changing Hydroclimate Dynamics and the 19th to 20th Century Wetting Trend in the English Channel Region of Northwest Europe

Northwestern Europe has experienced a trend of increasingly wet winters over the past 150 years, with few explanations for what may have driven this hydroclimatic change. Here we use the Old World Drought Atlas (OWDA), a tree-ring based reconstruction of the self-calibrating Palmer Drought Severity Index (scPDSI), to examine this wetting trend and place it in a longer hydroclimatic context. We find that scPDSI variability in northwestern Europe is strongly correlated with the leading mode of the OWDA during the last millennium (1000–2012). This leading mode, here named the ‘English Channel’ (EC) mode, has pronounced variability on interannual to centennial timescales and has an expression in scPDSI similar to that of the East Atlantic teleconnection pattern. A shift in the EC mode from a prolonged negative phase to more neutral conditions during the 19th and 20th centuries is associated with the wetting trend over its area of influence in England, Wales, and much of northern continental Europe. The EC mode is the dominant scPDSI mode from approximately 1000–1850, after which its dominance waned in favor of the secondary ‘North–South’ (NS) mode, which has an expression in scPDSI similar to that of the winter North Atlantic Oscillation (NAO). We examine the dynamical nature of both of these modes and how they vary on interannual to centennial timescales. Our results provide insight into the nature of hydroclimate variability in Europe before the widespread availability of instrumental observations.

hydroclimate↗
NASA NTRS1976

ERTS-B applications to Minnesota resource management

The author has identified the following significant results. The shape, pattern, and extent of surface water (e.g. lakes) can be readily mapped. Comparing detailed maps of several lakes in Itasca County with the areas classified as water by the LANDSAT data shows that some lakes have changed considerably since they were mapped. Due to several droughts this year (1976), the water level in most lakes has dropped. At this time, it seems feasible that LANDSAT digital tape data estimate lake water level change, due to the 1976 drought conditions.

Sizer, J. E.↗
NASA NTRS1980

Relationship of crop radiance to alfalfa agronomic values

Red and photographic infrared spectral data of alfalfa were collected at the time of the third and fourth cuttings using a hand-held radiometer for the earlier alfalfa cutting. Significant linear and non-linear correlation coefficients were found between the spectral variables and plant height, biomass, forage water content, and estimated canopy cover. For the alfalfa of the later cutting, which had experienced a period of severe drought stress which limited growth, the spectral variables were found to be highly correlated with the estimated drought scores.

Tucker, C. J.↗
NASA NTRS1983

Integration of environmental and spectral data for sunflower stress determination

Stress in sunflowers was assessed in western and northwestern Minnesota. Weekly ground observations (acquired in 1980 and 1981) were analyzed in concert with large scale aerial photography and concurrent LANDSAT data. Using multidate supervised and unsupervised classification procedures, it was found that all crops grown in association with sunflowers in the study area are spectrally separable from one another. Under conditions of extreme drought, severely stressed plants were differentiable from those not severely stressed, but between-crop separation was not possible. Initial regression analyses to estimate sunflower seed yield showed a sensitivity to environmental stress during the flowering and seed development stages. One of the most important biological factors related to sunflower production in the Red River Valley area was found to be the extent and severity of insect infestations.

Lillesand, T.↗
NASA NTRS1990

Monitoring land surface change over semi-arid regions using multispectral satellite data

Visible reflectance and surface temperature are derived from observations by the AVHRR on the NOAA-7 and NOAA-9 satellites and microwave emission at 37-GHz by the SMMR on Nimbus-7 satellite over the Sahel and Sudan zones. The AVHRR data is for the period January 1982 to December 1986, while the SMMR data is for the period January 1979 to December 1986. Rainfall data show that both the Sahel and Sudan zones experienced a particularly severe drought during 1984, and thus the present analysis shows the patterns leading to and recovering from the 1984 drought. Interrelationships among these multispectral data and the ways these relationships change in response to drought are evaluated in relation to field observations and heat balance models.

Choudhury, B. J.↗