nasa-gesdisc/es-publications-researchareas huggingface dataset:
Climate change and its impact on drought in Eastern Gansu rainfed agricultural area in Northwest China in the last thirty years (2018)

The monthly precipitation and temperature data,soil moisture data and NDVI data from 1981 to 2010 in Eastern Gansu were used to analyze the temperature, precipitation, soil moisture and drought change in this area. The results show that: 1) The climate in Eastern Gansu appeared a significant warming trend, and the temperature increase was extremely significant in spring, summer, autumn and winter. Among them, the spring temperature increase was the largest with the rate of 0.82 C/10a. 2) The annual precipitation has experienced a process of high-low-high in the past 30 years. Among them precipitation in spring continued to decrease, while other seasons showed increase tendency in the 21st century after a decline in the last 10 years of the 20th century. 3) The soil moisture in the whole layer (10-100cm) in spring showed a significant downward trend, especially in the surface layer. During the growth season, the water storage capacity of the whole layer of soil decreased significantly. 4) The frequency and extent of drought events in Eastern Gansu experienced a change of low-high-low process. The lowest period of drought occurred in the 1980s and the highest period occurred in the 1990s. The frequency and extent in the first 10 years of this century declined. 5) The spring drought occurred most frequently and strongest intensity in the past 30a. AVI has a good consistency with CI and soil moisture on the monitoring of drought process, but the volatility is higher.

Research areas: Droughts
Retrospective Predictions of Rice and Other Crop Production in (2022)

Malagasy subsistence farmers, who comprise 70% of the nearly 26 million people in Madagascar, often face food insecurity because of unreliable food production systems and adverse crop conditions. The 20202021 drought in Madagascar, in particular, is associated with an exceptional food crisis, yet we are unaware of peer-reviewed studies that quantitatively link variations in weather and climate to agricultural outcomes for staple crops in Madagascar. In this study, we use historical data to empirically assess the relationship between soil moisture and food production. Specifically, we focus on major staple crops that form the foundation of Malagasy food systems and nutrition, including rice, which accounts for 46% of the average Malagasy caloric intake, as well as cassava, maize, and sweet potato. Available data associated with survey-based crop statistics constrain our analysis to 20102017 across four clusters of Malagasy districts. Strong correlations are observed between remotely sensed soil moisture and rice production, ranging between 0.67 to 0.95 depending on the cluster and choice of crop calendar. Predictions are shown to be statistically significant at the 90% confidence level using bootstrapping techniques, as well as through an out-of-sample prediction framework. Soil moisture also shows skill in predicting cassava, maize, and sweet potato production, but only when the months most vulnerable to water stress are isolated. Additional analyses using more survey data, as well as potentially more-refined crop maps and calendars, will be useful for validating and improving soil-moisture-based predictions of yield.

Research areas: Agriculture, Droughts
Drought and urbanization: The case of the Philippines (2018)

The Philippines is highly vulnerable to drought, resulting in severe impacts on crop productivity, water availability, and food security. This chapter explores water security in the country by examining the effects of drought on risk governance and its social impacts on the 19971998 and 20152016 El Nino episodes in Metro Manila, Iloilo City, and Cebu City. During these periods, widespread dryness occurred in both urban and rural areas, as rainfall was reduced by more than 50%. This decrease in rainfall affects most especially the urban poor as they experience acutely the dwindling supply of potable water, increasing costs of water, and compromised access to hygiene and sanitation services. Consequently, droughts have become a major concern for risk governance in major urban centers. Science-informed and contextually driven local climate adaptation plans (LCAP) seem to be the most appropriate response to mitigate and adapt to the effects of drought brought about by El Nino.

Research areas: Droughts
A New Agricultural Drought Index to Better Detect and Monitor Millet Crop by Remote Sensing in West Africa: Case of Niger (2019)

In this paper, we propose to establish a remote sensing-based agricultural drought indicator named Agricultural Drought Condition Index (ADCI) that will detect agricultural drought linked mainly to millet crops in the agricultural area of Niger. It is obtained by combining four key parameters: Precipitation Condition index (PCI), Evapotranspiration Condition Index (ETCI), Vegetation Condition Index (VCI) and Temperature Condition Index (TCI). PCI is used to reflect precipitation deficit (Du et al., 2013), VCI is used to reflect variations in the health status of vegetation, TCI is used to identify vegetation stress produced by high temperatures or excessive humidity and ETCI is used to reflect crop shortage. ADCI is calculated over the crop growth period (June to October) from 2003 to 2017 in the agricultural area of Niger. Then, a comparison analysis between the ADCI results and the millet crop yield was carried out. Based on these results, ADCI shows a very statistically significant correlation with the millet crop yield throughout the millet growth period (June-October) and it is strongly correlated with the VHI index but provided better drought conditions then VHI. This new index is quite powerful and capable to identify and monitor the agricultural drought related to the cultivation of millet.

Research areas: Droughts, Agriculture
Drought risk assessment in cultivated areas of central asia using MODIS time-series data (2020)

Drought is one of the most damaging environmental hazards and a naturally occurring phenomenon in Central Asia that is accompanied by crucial consequences for the agriculture sector. This research aimed at understanding the nature and extent of drought over the cropland regions of Central Asia with the help of spatiotemporal information from the region. We assessed drought occurrence using the vegetation health index (VHI). An algorithm was developed to reduce the noise of heterogeneous land surfaces by adjusting the vegetation index and brightness temperature. The vegetation condition index (VCI) and temperature condition index (TCI) were calculated using Moderate Resolution Imaging Spectroradiometer (MODIS) products for the growing season (AprilSeptember) from 2000 to 2015. The intense drought years were identified and a drought map (drought probability occurrence) was generated. The findings of this research indicated regional heterogeneity in the cropland areas having experienced droughts, observed through spatiotemporal variations. Some of the rain-fed and irrigated croplands of Kazakhstan demonstrated a higher vulnerability to annual drought occurrences and climate change impacts, while other cropland regions were found to be more resistant to such changes. The development of policy tools is required to support informed decision-making and planning processes to adapt to the occurrence of droughts. This could be achieved by the timely assessment, monitoring, and evaluation of the spatiotemporal distribution trends and variabilities of drought occurrences in this region. The results from this study focus on the spatiotemporal variations in drought to reveal the bigger picture in order to better understand the regional capacity for sustainable land management and agricultural activities within a changing environment.

Research areas: Droughts, Agriculture
Drought disaster monitoring using MODIS derived index for drought years: A space-based information for ecosystems and environmental conservation (2021)

Drought disaster is one of the major factors restricting the development of vegetation across a wide variety of environments. Monitoring the temporal and spatial dynamics of drought episodes in the study area is crucial for environmental and ecosystem conservation. This study assesses drought disaster by utilising space-based data and R programming for drought years 2003, 2007, 2012 and 2019 in the Free State Province, South Africa. Results revealed that the study area witnessed drought events in the year 2003 where March, August, September, October, November and December were more affected by drought disaster events. It was further observed that February and March were affected by extreme drought conditions in the year 2007. In year 2012, January, October, November and December, there exist moderate to severe drought conditions in the study area where some regions were more affected than the other. Finally, year 2019 witnessed variations in drought event distributions across the months with January, October and November witnessing severe to extreme drought conditions from about 0 to 30% drought values. Overall, this study shows that the 16-day Terra-MODIS composite and EVI products are sensitive to stressors associated with drought. The Vegetation Condition Monitoring Index (VCI) based on MODIS is suited for monitoring drought disasters. The technique used in this study revealed the suitability of MODIS data for assessing drought conditions and their potential environmental impacts.

Research areas: Droughts
Bidirectional drought-related canopy dynamics across pantropical (2022)

Droughts cause extreme anomalies in tropical forest growth, but the direction and magnitude of tropical forests in response to droughts are still widely debated. Here, we used four satellite‑based canopy growth proxies (CGPs), including three optical and one passive microwave, and in situ fluxes observations from eddy covariance (EC) measurements for a retrospective investigation of the impacts of historical droughts on tropical forest growth from a statistical point of view. Results indicate two opposite directions in drought‑related canopy dynamics across pantropical forests. The canopy of tropical forests with higher CGPs is more vulnerable to drought stress and recovers faster in the post‑drought recovery period. In contrast, the canopy of tropical forests with lower CGPs increases during the drought period and declines in the subsequent recovery period, which is beyond general expectation. In situ measurements from eddy‑covariance flux towers showed that forests with higher gross primary production and latent heat flux decreased photosynthesis and evapotranspiration during the drought period but increased photosynthesis and evapotranspiration faster during the post‑drought recovery period, supporting the findings from satellite observations. Our statistical analysis against climatic factors predicts that higher‑CGPs tress with probably taller and bigger canopies are more responsive to shortage of water availability caused by drought; while lower‑CGPs tress with shorter and smaller canopies are more responsive to sunlight availability and tend to increase their canopy leaves and enhance photosynthesis in sunnier days during the drought period. Our results highlight the differences in tropical forests in responding to drought stress, which are worth incorporated in Earth system models for time‑series evaluations.

Research areas: Droughts
Decadal variability modulates trends in concurrent heat and drought over global croplands (2021)

Abstract
            Extreme heat and drought often reduce the yields of important food crops around the world, putting stress on regional and global food security. The probability of concurrently hot and dry conditions, which can have compounding impacts on crops, has already increased in many regions of the globe. The evolution of these trends in coming decades could have important impacts on global food security. However, regional variation and the influence of natural climate variability on these trends remains an important gap in understanding future climate risk to crops. In this study, we examine trends in concurrent hot-and-dry extremes over global maize and wheat croplands since 1950. We find that the mean extent of cropland in a joint hot-and-dry extreme increased by 2% over 19502009, and this trend has accelerated substantially since the mid-2000s, notably in the tropics. While joint hot-and-dry seasons affected at most 1%2% of global cropland per year during the mid-20th century, they regularly exceeded this extent after about 1980, affecting up to 5% of global crop area. These results suggest that the global climate is transitioning from one in which concurrent heat and drought occur rarely to one in which they occur over an important fraction of croplands every year. While these long-term global trends are primarily attributable to anthropogenic climate change, we find they have been suppressed by decadal climate variability in some regions, especially ones with chronic food insecurity. Potential reversals in these tendencies of decadal variability would accelerate exposure of croplands to concurrent heat and drought in coming decades. We conclude by highlighting the need for research and adaptive interventions around multivariate hazards to global crops across timescales.

Research areas: Droughts, Agriculture
Spatiotemporal patterns of water consumption and irrigation requirements of wheat-maize in the Huang-Huai-Hai Plain, China and options of their reduction (2022)

Severe water shortages threaten the sustainability of agriculture in the Huang-Huai-Hai Plain (3HP), China. This study investigated spatiotemporal variations in crop water consumption (evapotranspiration, ET) and irrigation water requirements (IWR) under existing winter wheatsummer maize (WWSM) cropping system in the 3HP, and tested alternatives of their reduction that may require less irrigation, i.e., wheatmaizewheatfallow (WMWF), wheatmaizefallowmaize (WMFM), wheatmaizefallowspring maize (WMFSM), and fallowspring maizefallowspring maize (FSMFSM). The results showed that the annual ET indicated no significant change from 2001 to 2018, which decreased from southeast to northwest and ranged from 700 mm to 900 mm. In the winter wheat (WW) season, ET increased significantly at the junction of Hebei and Shandong provinces, while it decreased in western Hebei during the summer maize (SM) period. Moreover, ET for WW decreased from dry to normal and wet years (433, 413, and 373, mm), while it increased in SM season (377, 392, and 396, mm). IWR showed great interannual variability. Anhui, central and southern Henan, and southwestern Shandong presented annual IWR below 200 mm, with 100300 mm in the WW period, and  300 (0)  100 mm in the SM period (negative IWR means rainfall surplus and no real irrigation). The north of the 3HP required considerable irrigation in the WW season, especially in western Hebei and northwestern Shandong (over 300 mm in dry years and 200 mm in wet years), with IWR ranging from  100100 mm in the SM period. The 18-year average IWR of WWSM, WMWF, WMFM, WMFSM, and FSMFSM was 344, 321, 211, 240, and 119 mm, respectively, from 2001 to 2018 (Luancheng as example, same below). Reducing irrigation by 32%, 44%, and 64%, WMFSM, WMFM and FSMFSM showed the greatest potential for reducing IWR, but food security should also be considered when adjustments are made.

Research areas: Droughts, Agriculture
Relative effect of anthropogenic warming and natural climate variability to changes in Compound drought and heatwaves (2021)

Compound drought and heatwave (CDHW) events can be influenced by large scale teleconnections and anthropogenic warming, leading to severe socio-economic impacts across various climate regions. In this study, the relative influence of six different teleconnection patterns and anthropogenic global warming on the global CDHW occurrences is quantified systematically using the instrumental data period, 19822016. The results from the study suggest a substantial increase in the CDHW events (15 events per year) across various parts of the globe at the beginning of 21st century (20002016). A Bayesian approach is implemented to identify the most vulnerable climate regions based on the degree of susceptibility of heatwaves (DSHW) towards drought. As such, top ten most vulnerable regions are selected based on the DSHW magnitude, and a partial correlation analysis is performed to select the natural and anthropogenic drivers of CDHW in those regions, separately. A logistic regression model is then used to determine significant changes in the odds of CDHW due to changes in the selected drivers that suggest a significantly positive, and multiplicative effect of anthropogenic global warming in the top ten most vulnerable climate regions. Finally, the same logistic regression model, integrated with an analytical framework, is applied to determine the relative influence of anthropogenic global warming on the changes in odds of CDHW for the future, 1.5 C and 2 C warming limits. The results suggest that relative to the 2 C global warming, constraining to the 1.5 C global warming limit may conduce about 17-fold reduction in the odds of CDHW in the most vulnerable climate region, East Asia, 58-fold reduction in Western North America, Northern Australia, Central North America, Central Europe, South Asia, and the Mediterranean region, and 34-fold reduction in Northeastern Brazil, Eastern North America, and West Asia.

Research areas: Droughts, Heat, Atmospheric/Ocean Indicators
Long-lead predictions of warm season droughts in South Korea using North Atlantic SST (2020)

Abstract Understanding and predicting warm season (MayOctober) droughts is critically important in South Korea for agricultural productivity and water resource management. Using a 6-month standardized precipitation index ending in October (SPI6_Oct), we investigate the interannual variability of warm season droughts and the related large-scale atmospheric circulations for the most recent 20-yr period (19952014). Cyclonic (anticyclonic) circulations to the east of Japan (in the North Pacific) tend to induce warm season droughts (wetness) by suppressing (enhancing) moist water transport from the south of the Korean Peninsula. These circulation patterns to the east of Japan are linked to a barotropic Rossby wavelike teleconnection pattern from the North Atlantic to East Asia, which is found to be responsible for the interannual variability of SPI6_Oct. This teleconnection pattern is highly correlated with the difference in sea surface temperature (SST) between the Norwegian Sea and the Barents Sea (referred to as NA_dipole) in JanuaryMarch (r = 0.68), which modulates the snow depth over the Ural Mountains in spring and the sea ice concentration over the Barents Sea during the entire warm season. Two drought prediction models, an empirical model and a hybrid machine learning model, are developed and tested for their predictive skills for South Korea. An empirical prediction model using NA_dipole as one of the predictors is found to accurately capture the interannual variability of SPI6_Oct (r2 = 53%). NA_dipole is found to improve the predictive skills of the hybrid machine learning drought prediction model, especially for longer lead times. Our results emphasize the significant role of North Atlantic SST anomalies in warm season medium-range droughts in South Korea.

Research areas: Droughts
Integrated meteorological drought monitoring framework using multi-sensor and multi-temporal earth observation datasets and machine learning algorithms: A case … (2021)

Devising strategies for drought planning and risk mitigation in predominantly small holder agriculture regions like South Asia requires improved understanding of spatio-temporal characteristics of droughts at finer spatial scales. In this study, an integrated framework is developed to generate high-resolution gridded precipitation products from publicly available coarse scale data, and use the downscaled products for analysis of meteorological droughts. Data from a region in Central India is used to develop and test the framework. The Standard Precipitation Index (SPI) is used to characterize meteorological drought. The proposed framework incorporates: (i) a Random Forest machine learning algorithm to downscale CHIRPS (5 km) gridded long term rainfall data to a higher resolution gridded product (1 km) using high resolution multi-sensor and multi-temporal earth observation data of land surface characteristics (vegetation, temperature, and topography) as cofactors; (ii) Computation of 3-month SPI (SPI-3) for the downscaled gridded rainfall; (iii) Run theory applied to downscaled SPI grid to determine drought characteristics - duration, severity, intensity, frequency, and onset and cessation of droughts, and (iv) Principal Components Analysis (PCA) to combine effects of multiple drought characteristics into a single composite Effective Meteorological Drought Index (EMDI), to identify sub-regional drought patterns. The downscaled gridded precipitation product (1 km) was validated using: (i) original CHIRPS (5 km) data, and (ii) measured rainfall data from 65 rain gauge stations in the study area, with respect to conservation of both statistical properties and spatial structure. Comparisons were also made with drought characteristics obtained from Standardized Evapotranspiration Index (SPEI) derived from MODIS evapotranspiration data. The downscaled product captures spatio-temporal variability of droughts at finer village and sub-village scales, compared to original CHIRPS (5 km) data for which each pixel encompassed multiple villages. Spatial distribution of composite index EMDI delineates the study area into two characteristic drought risk regions. In one region, droughts are of longer duration, but are less frequent, with lower severity and intensity. In the second region droughts are of shorter duration but of higher severity, intensity and frequency. The integrated framework developed in this study for high resolution spatio-temporal analysis of droughts, starting from coarse scale precipitation data, is practical and sufficiently general to adopt in other regions to support local drought risk planning and targeting mitigation decisions and actions.

Research areas: Droughts
El Nino drought increased canopy turnover in Amazon forests (2018)

Summary Amazon droughts, including the 20152016 El Nino, may reduce forest net primary productivity and increase canopy tree mortality, thereby altering both the short and the longterm net forest carbon balance. Given the broad extent of drought impacts, inventory plots or eddy flux towers may not capture regional variability in forest response to drought. We used multitemporal airborne Lidar data and field measurements of coarse woody debris to estimate patterns of canopy turnover and associated carbon losses in intact and fragmented forests in the central Brazilian Amazon between 20132014 and 20142016. Average annualized canopy turnover rates increased by 65% during the drought period in both intact and fragmented forests. The average size and height of turnover events was similar for both time intervals, in contrast to expectations that the 20152016 El Nino drought would disproportionally affect large trees. Lidarbiomass relationships between canopy turnover and field measurements of coarse woody debris were modest (R2 0.3), given similar coarse woody debris production and Lidarderived changes in canopy volume from single tree and multiple branch fall events. Our findings suggest that El Nino conditions accelerated canopy turnover in central Amazon forests, increasing coarse woody debris production by 62% to 1.22 Mg C ha1 yr1 in drought years . 

Research areas: Droughts, Habitat Conversion/Fragmentation