nasa-gesdisc/es-publications-researchareas huggingface dataset:
Drought evaluation over Yangtze River basin based on weighted water storage deficit (2020)

The ability of Gravity Recovery and Climate Experiment (GRACE) to monitor large scale drought events has been explored well during past few years. We develop an alternative method viz.,weighted water storage deficit (WWSD) to characterize drought events over Yangtze river basin (YRB) based on water storage deficit (WSD) method by combining GRACE RL06 sphere harmonic (SH) coefficient data and WaterGAP Global Hydrology Model(WGHM) data. We use component contribution ratio (CCR) of single terrestrial water compartment as weight to compute WWSD, comparing with other commonly employed drought indices and precipitation data from Global Precipitation Measurement(GPM). The results show that precipitation is the major trigger of water storage variation over YRB, which both have significant seasonal change. Our findings also indicate different terrestrial water component contributes distinctly to terrestrial water storage (TWS) variability and responds differently to drought features in YRB according to water component storage deficit (WCSD). Eleven drought events are identified in YRB based on WWSD with 2003, 2006 and 2011 experiencing the severest drought with drought intensity of 1.22 cm, 1.34 cm and 1.02 cm, respectively. Our study suggests that it is more realistic and reasonable to treat water components unequally to construct drought index derived from GRACE.

Research areas: Droughts
Evaluating the benefits of chlorophyll fluorescence for in-season crop productivity forecasting (2021)

Remote sensing of solar-induced chlorophyll fluorescence (SIF) shows promise for monitoring the productivity of global agricultural systems. SIF-based primary productivity metrics have demonstrated higher fidelity to large-scale patterns of crop productivity than reflectance-based vegetation indices when averaged across the growing season. In-season crop yield forecasting typically relies upon reflectance-based vegetation indices, raising the question of whether in-season monitoring could be improved by utilizing SIF. Here, we analyze patterns of US agricultural productivity from USDA surveys and their in-season relationships with coarse-resolution GOME-2 SIF, high-resolution downscaled SIF, SIF-based primary productivity metrics, MODIS NDVI, and MODIS GPP. We find that coarse-resolution SIF-based metrics and NDVI exhibit similar out-of-sample in-season (AprilJuly and AprilAugust) predictive ability, even when spatially filtering higher-resolution NDVI data to cropland areas. The downscaled SIF product performed more poorly than the coarse-resolution SIF, and MODIS GPP performed more poorly than MODIS NDVI. All forecasts are improved by incorporating county fixed effects to control for cross-sectional differences between counties. NDVI-based metrics allow for significantly better yield predictions during drought conditions than SIF-based metrics, suggesting limited added value of SIF for early warning of drought impacts. The benefits of SIF for crop monitoring should be continually evaluated as the frequency and quality of SIF measurements continue to improve.

Research areas: Agriculture
Drought Periods Identification in Ecuador between 2001 and 2018 Using SPEI and MODIS Data (2021)

Drought is a natural phenomenon in which the precipitation amount is below normal in a specific region over a long period. The main objective of this study is to identify periods of drought in Ecuador between 2001 and 2018 using the Standardized Precipitation Evapotranspiration Index (SPEI) and the Normalized Difference Water Index (NDWI) derived from MODIS data. Firstly, the SPEI at a six-month scale and the Runs theory were used to identify periods of drought. Secondly, the NDWI from MOD09A1 MODIS product was used to identify the areas affected by drought.

Research areas: Droughts
OCO-2 Solar-Induced Chlorophyll Fluorescence Variability across Ecoregions of the Amazon Basin and the Extreme Drought Effects of El Nino (20152016) (2020)

Amazonian ecosystems are major biodiversity hotspots and carbon sinks that may lose species to extinction and become carbon sources due to extreme dry or warm conditions. We investigated the seasonal patterns of high-resolution solar-induced chlorophyll fluorescence (SIF) measured by the satellite Orbiting Carbon Observatory-2 (OCO-2) across the Amazonian ecoregions to assess the area s phenology and extreme drought vulnerability. SIF is an indicator of the photosynthetic activity of chlorophyll molecules and is assumed to be directly related to gross primary production (GPP). We analyzed SIF variability in the Amazon basin during the period between September 2014 and December 2018. In particular, we focused on the SIF drought response under the extreme drought period during the strong El Nino in 20152016, as well as the 6-month drought peak period. During the drought s peak months, the SIF decreased and increased with different intensities across the ecoregions of the Amazonian moist broadleaf forest (MBF) biome. Under a high temperature, a high vapor pressure deficit, and extreme drought conditions, the SIF presented differences from 31.1% to +17.6%. Such chlorophyll activity variations have been observed in plant-level measurements of active fluorescence in plants undergoing physiological responses to water or heat stress. Thus, it is plausible that the SIF variations in the ecoregions ecosystems occurred as a result of water and heat stress, and arguably because of drought-driven vegetation mortality and collateral effects in their species composition and community structures. The SIF responses to drought at the ecoregional scale indicate that there are different levels of resilience to drought across MBF ecosystems that the currently used climate- and biome-region scales do not capture. Finally, we identified monthly SIF values of 32 ecoregions, including non-MBF biomes, which may give the first insights into the photosynthetic activity dynamics of Amazonian ecoregions.

Research areas: Droughts
Insights from a new high-resolution drought atlas for the Caribbean spanning 1950–2016 (2017)

Abstract Climate change is expected to increase the severity and frequency of drought in the Caribbean. Understanding drought variability and its trends is therefore critical for improving resiliency and adaptation capacity of this region, as well as for assessing the dynamics and predictability of regional hydroclimate across spatial and temporal scales. This work introduces a first of its kind high-resolution drought dataset for the Caribbean region from 1950 to 2016, using monthly estimates of the self calibrating Palmer drought severity index (scPDSI), with the physically based PenmanMonteith approximation for the potential evapotranspiration. Statistically downscaled data products, including reanalysis, are employed to establish an historical baseline for characterizing drought from 1950 to the near present. Since 1950, the Caribbean has been affected by severe droughts in 197477, 1997/98, 2009/10, and 201316. Results indicate that the 201316 drought is the most severe event during the time interval analyzed in this work, which agrees with qualitative reports of many meteorological institutions across the Caribbean. Linear trends in the scPDSI show a significant drying in the study area, averaging an scPDSI change of 0.09 decade1 (p < 0.05). However, this trend is not homogenous, and significant trends toward wetter conditions in portions of the study area were observed. Results further indicate a strong influence of both tropical Pacific and North Atlantic oceans in modulating drought variability across the study domain. Finally, this effort is the first step in building high-resolution drought products for the Caribbean to be updated regularly, with the purpose of drought monitoring and eventually seasonal drought prediction.

Research areas: Droughts
Using Metabolic Energy Density Metrics to Understand Differences in (2020)

Terrestrial ecosystems obtain energy in the form of carbon-containing molecules. Quantifying energy acquisition and dissipation throughout an ecosystem may be useful for describing their resistance and resilience to disturbances. Three longleaf pine savannas with different vegetation composition—a result of variation in soil moisture and land use legacy—were used as a case study to test energy-based metrics of ecosystem metabolic function. Available energy from gross ecosystem exchange of CO<SUB>2</SUB> and its dissipation into metabolic energy density (E<SUB>M</SUB>) and energy storage were used to identify differences in drought recovery over an 8-year period. Sites with higher plant functional diversity in the understory stored more energy and lowered their E<SUB>M</SUB> by ~20% when adapting to drought. In contrast, the site with greater abundance of woody understory and overstory species relied on stored energy twice as often as the more diverse sites. The absence of native understory species, due to anthropogenic legacy, prolonged ecosystem-scale drought recovery by 1 year. This study provides the tools to understand differences in site metabolic energy dynamics and has the potential to identify site characteristics that indicate greater vulnerability to disturbances. Metabolic energy density can be applied to any global ecosystem and provides a first step to describe coupled carbon and energy allocation in ecosystems, which may be used to further refine ecological theory and its management implications.

Research areas: Ecosystems, Droughts
Global drought monitoring with big geospatial datasets using Google Earth Engine (2021)

Drought or dryness occurs due to the accumulative effect of certain climatological and hydrological variables over a certain period. Droughts are studied through numerically computed simple or compound indices. Vegetation condition index (VCI) is used for observing the change in vegetation that causes agricultural drought. Since the land surface temperature has minimum influence from cloud contamination and humidity in the air, so the temperature condition index (TCI) is used for studying the temperature change. Dryness or wetness of soil is a major indicator for agriculture and hydrological drought and for that purpose, the index, soil moisture condition index (SMCI), is computed. The deviation of precipitation from normal is a major cause for meteorological droughts and for that purpose, precipitation condition index (PCI) is computed. The years when the indices escalated the dryness situation to severe and extreme are pointed out in this research. Furthermore, an interactive dashboard is generated in the Google Earth Engine (GEE) for users to compute the said indices using country boundary, time period, and ecological mask of their choice: Agriculture Drought Monitoring. Apart from global results, three case studies of droughts (2002 in Australia, 2013 in Brazil, and 2019 in Thailand) computed via the dashboard are discussed in detail in this research.

Research areas: Droughts
Multi-Temporal Analysis and Trends of the Drought Based on MODIS Data in (2020)

The aim of this study is to analyze the performance of the Drought Severity Index (DSI) in Romania and its validation based on other data sources (meteorological data, soil moisture content (SMC), agricultural production). Also, it is to assess the drought based on a multi-temporal analysis and trends of the DSI obtained from Terra MODIS satellite images. DSI is a standardized product based on evapotranspiration (ET) and the Normalized Difference Vegetation Index (NDVI), highlighting the differences over a certain period of time compared to the average. The study areas are located in Romania: three important agricultural lands (Oltenia Plain, Baragan Plain and Banat Plain), which have different environmental characteristics. MODIS products have been used over a period of 19 years (20012019) during the vegetation season of the agricultural crops (AprilSeptember). The results point out that those agricultural areas from the Baragan Plain and Oltenia Plain were more affected by drought than those from Banat Plain, especially in the years 2002, 2007 and 2012. Also, the drought intensity and the agricultural surfaces affected by drought decreased in the first part of the vegetation season (MarchMay) and increased in the last part (AugustSeptember) in all three study areas analyzed. All these results are confirmed by those of the Standardized Precipitation Evapotranspiration Index (SPEI) and Soil Moisture Anomaly (SMA) indices.

Research areas: Droughts
Drought Legacy in Sub‐Seasonal Vegetation State and Sensitivity to Climate Over the Northern Hemisphere (2022)

Droughts affect ecosystems at multiple time scales, but their sub-seasonal legacy effects on vegetation activity remain unclear. Combining the satellite-based enhanced vegetation index MODIS EVI with a novel location-specific definition of the growing season, we quantify drought impacts on sub-seasonal vegetation activity and the subsequent recovery in the Northern Hemisphere. Drought legacy effects are quantified as changes in post-drought greenness and sensitivity to climate. We find that greenness losses under severe drought are partially compensated by a ∼+5% greening within 2-6 growing-season months following the droughts, both in woody and herbaceous vegetation but at different timings. In addition, post-drought sensitivity of herbaceous vegetation to hydrological conditions increases noticeably at high latitudes compared with the local normal conditions, regardless of the choice of drought time scales. In general, the legacy effects on sensitivity are larger in herbaceous vegetation than in woody vegetation.

Research areas: Droughts
Climate Variability of Atmospheric Rivers and Droughts over the West (2021)

Water resources are crucial to the livelihood and sustainability of the general public across the western United States. This study covers the timespan of both the third driest drought in Californian history between 2012 and 2015 as well as the extreme atmospheric river year in 20162017. The evaluation of vertical moisture profiles using Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Radio Occultation (RO) data, National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis of 500 hPa geopotential heights, 1000500 hPa thickness, Optimum Interpolation (OI) Sea Surface Temperature (SST), NOAA/NDBC buoy data, and NASA, MEaSUREs, Gridded Sea Surface Height Anomalies (SSHA) were performed. The daily COSMIC time evolution from 2006 through 2015 showed a flat to slightly upward trend of both temperature and water vapor profiles through the entirety of the western US drought. Subsequently, a significant increase of temperatures and water vapor were recorded in early 2016 before the extreme Atmospheric River (AR) season of 20162017. The quantitative analyses suggest that warmer SST and higher SSHA lead to an increase of heat fluxes from the ocean into the troposphere, which forces thickness changes and thus the position of troughs in the geopotential height field changes afterwards, consequently pushing the trough eastward over the Pacific Northwest and potentially leading to an active AR year in the western US. It appears that regional COSMIC RO moisture profiles, seasonal SST, and SLH anomalies may serve as a precursor for seasonal or sub-seasonal precipitation outlook along the western US.

Research areas: Droughts
Interactive biogenic emissions and drought stress  effects on atmospheric composition in NASA GISS ModelE (2022)

Abstract. Drought is a hydroclimatic extreme that causes perturbations to the terrestrial biosphere and acts as a stressor on vegetation, affecting emissions patterns. During severe drought, isoprene emissions are reduced. In this paper, we focus on capturing this reduction signal by implementing a new percentile isoprene drought stress (yd) algorithm in NASA GISS ModelE based on the MEGAN3 (Model of Emissions of Gases and Aerosols from Nature Version 3) approach as a function of a photosynthetic parameter (Vc,max) and water stress (). Four global transient simulations from 20032013 are used to demonstrate the effect without yd (Default_ModelE) and with online yd (DroughtStress_ModelE). DroughtStress_ModelE is evaluated against the observed isoprene measurements at the Missouri Ozarks AmeriFlux (MOFLUX) site during the 2012 severe drought where improvements in the correlation coefficient indicate it is a suitable drought stress parameterization to capture the reduction signal during severe drought. The application of yd globally leads to a decadal average reduction of 2.7 %, which is equivalent to 14.6 Tg yr1 of isoprene. The changes have larger impacts in regions such as the southeastern US. DroughtStress_ModelE is validated using the satellite HCHO column from the Ozone Monitoring Instrument (OMI) and surface O3 observations across regions of the US to examine the effect of drought on atmospheric composition. It was found that the inclusion of isoprene drought stress reduced the overestimation of HCHO in Default_ModelE during the 2007 and 2011 southeastern US droughts and led to improvements in simulated O3 during drought periods. We conclude that isoprene drought stress should be tuned on a model-by-model basis because the variables used in the parameterization responses are relative to the land surface model hydrology scheme (LSM) and the effects of yd application could be larger than seen here due to ModelE not having large biases of isoprene during severe drought.

Research areas: Droughts
A statistical approach towards defining national-scale meteorological droughts in India using crop data (2020)

Abstract
            In recent years, several drought indices have been developed and used to monitor local to regional scale droughts on various temporal scales. However, to our knowledge, these indices do not possess generalized criteria to define a threshold in which to declare a national-scale drought. We present a statistical methodology to identify national-scale meteorological drought years in India. We implement a Superposed Epoch Analysis and bootstrap analysis to estimate annual cereal crop production losses as a result of widespread meteorological drought events. For this purpose, the meteorological definition of drought based on the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), in combination with the countrys cropland area and cereal crops production, is used. The results demonstrate that a national-scale meteorological drought is defined if approximately 19% or more of Indias cropland is affected by meteorological drought (SPI3 and SPEI3 equal to or less than 1.00) throughout the monsoon season (JuneSeptember). According to this analysis, depending on the indicator data used, a total of 18to 20 national-scale meteorological droughts were identified in India during 19642015, causing a 3.61% to 3.93% composite decrease in cereal crops production. The years which were commonly identified as national scale meteorological droughts over cropland by using different approaches are 1965, 1972, 1987, 2002, and 2009. A similar statistical approach can also be used to define drought thresholds at various spatial scales using the drought indices most applicable to the purpose and scale of study.

Research areas: Droughts
Evaluating a generic drought index as a predictive tool for aflatoxin contamination of corn: From plot to regional level (2018)

Corn (Zea mays L.) kernel infection by Aspergillus flavus and subsequent aflatoxin accumulation in grain can have a deleterious effect on both humans and animals that consume contaminated grain. Predicting the aflatoxin risk is challenging due to complex interactions of biotic and abiotic stress factors that govern and exacerbate the phenomenon. The goal of this study was to determine whether a drought index could be used to predict the risk for pre-harvest aflatoxin contamination in corn. Risk assessment was approached at: 1) field (plot) level with data obtained from an in-field controlled experiment (Mississippi study), and 2) state level, where corn fields were sampled at a county level (Georgia study). The data used for this study consisted of historical records on aflatoxin contamination collected over thirteen growing seasons from 2000 to 2011, 2013, and 2014 at Mississippi State, Mississippi (1), and from random corn fields in 53 counties across Georgia between 1977 and 2004 (2). A controlled experiment was conducted at Mississippi with two soil types (a Leeper silty clay loam and a Myatt loam), and three commercial hybrids characterized by different susceptibility levels to aflatoxin contamination. The Agricultural Reference Index for Drought (ARID), a generic drought index for calculating drought on daily basis was evaluated as an aflatoxin risk prediction tool. Mid-silk day was selected to split each growing season into two time periods, which were further divided into positive and negative weeks representing weeks after and before mid-silk, respectively. Weekly ARID factors were calculated for all periods to evaluate the in-season alterations in aflatoxin risk. In both studies, multiple logistic regression models were used to predict aflatoxin risk as a function of the weekly ARID values. In Mississippi, risk level changes were additionally tested according to soil type and corn hybrid aflatoxin susceptibility. The United States Food and Drug Administration restricts corn grain consumption by humans and young animals if the contamination level is above 20 μg/kg; thus, this threshold (20 μg/kg) was selected to develop a binary dependent variable for the logistic model from the raw aflatoxin data. The results revealed that ARID might be used as a predictive tool to assess aflatoxin risk, soil type and hybrid susceptibility to aflatoxin contamination were statistically significant independent factors, and there are critical week windows during the growing season when changes in drought conditions affect the likelihood for aflatoxin contamination. These findings can be used to minimize risk by adapting site-specific management strategies such as triggering irrigation during critical risk weeks, selecting the most appropriate hybrid for a given site/location based on soil type, and determining optimum harvest date.

Research areas: Droughts
Trends in the Frequency of Water and Heat Stress in Mid-Latitude North America since 1980 (2022)

The water-energy balance of many mid-latitude watersheds has been changing in recent decades due to global warming. These changes manifest themselves over both long timescales (e.g., hydrologic drought) and short timescales (e.g., agricultural drought) and may be ameliorated or exacerbated by vegetative response. We apply a Budyko framework to assess short-term response to long-term trends in water and heat stress (HS) across mid-latitude North America. Using high-resolution meteorological data and streamflow records, we calculate the frequency of HS every year since 1980 for every gaged watershed with adequate data (n = 1528). We find that HS has become more frequent in most watersheds in the western US, New England, and southeastern Canada. However, we find that HS has become less frequent in the Midwest and the relatively humid eastern US. By assessing the relationship between trends in HS frequency and proximate forcing variables (annual PPT, annual streamflow, minimum and maximum daily temperatures, actual evapotranspiration, and potential evapotranspiration), we find that these trends in HS frequency are primarily driven by meteorological forcings rather than vegetative response. Finally, we contextualize our findings within the Budyko framework, which assumes a landscape in equilibrium with its climate, with the implication that these trends in HS are only likely to be realized after local vegetation has adapted to new meteorological norms.

Research areas: Droughts, Heat