Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India

Human adaptation to heat in the context of climate change: A conceptual framework

Climate change is causing serious damage to natural and social systems, as well as having an impact on human health. Among the direct effects of climate change is the rise in global surface temperatures and the increase in the frequency, duration, intensity and severity of heat waves. In addition, understanding of the adaptation process of the exposed population remains limited, posing a challenge in accurately estimating heat-related morbidity and mortality. In this context, this study seeks to establish a conceptual framework that would make it easier to understand and organise knowledge about human adaptation to heat and the factors that may influence this process. An inductive approach based on grounded theory was used, through the analysis of case studies connecting concepts. The proposed conceptual framework is made up of five components (climate change, vulnerability, health risks of heat, axes of inequality and health outcomes), three heat-adaptation domains (physiological, cultural and political), two levels (individual and social), and the pre-existing before a heat event. The application of this conceptual framework facilitates the assistance of decision-makers in planning and implementing effective adaptation measures. Recognizing the importance of addressing heat adaptation as a health problem that calls for political solutions and social changes. Accordingly, this requires a multidisciplinary approach that would foster the participation and collaboration of multiple actors for the purpose of proposing effective measures to address the health impact of the rise in temperature.

Probabilistic post-processing of short to medium range temperature forecasts: Implications for heatwave prediction in India

Accurate and reliable air temperature forecasts are necessary for predicting and responding to thermal disasters such as heat strokes. Forecasts from Numerical Weather Prediction (NWP) models contain biases which require post-processing. Studies assessing the skill of probabilistic post-processing techniques (PPTs) on temperature forecasts in India are lacking. This study aims to evaluate probabilistic post-processing approaches such as Nonhomogeneous Gaussian Regression (NGR) and Bayesian Model Averaging (BMA) for improving daily temperature forecasts from two NWP models, namely, the European Centre for Medium Range Weather Forecasts (ECMWF) and the Global Ensemble Forecast System (GEFS), across the Indian subcontinent. Apart from that, the effect of probabilistic PPT on heatwave prediction skills across India is also evaluated. Results show that probabilistic PPT comprehensively outperform traditional approaches in forecasting temperatures across India at all lead times. In the Himalayan regions where the forecast skill of raw forecasts is low, the probabilistic techniques are not able to produce skillful forecasts even though they perform much better than traditional techniques. The NGR method is found to be the best performing PPT across the Indian region. Post-processing Tmax forecasts using the NGR approach was found to considerably improve the heatwave prediction skill across highly heatwave prone regions in India. The outcomes of this study will be helpful in setting up improved heatwave prediction and early warning systems in India.

The severity of dry and hot climate extremes and their related impacts on vegetation in Madagascar

Madagascar is a low-income country, highly vulnerable to natural disasters affecting the small-scale subsistence farming system. Recently, climate change and environmental degradation have contributed to an intensification of food insecurity. We aim to monitor the link between dry and hot extremes on vegetation conditions, separated or concurrently, using satellite data, such as LST, ET, ET0, and FAPAR products from SEVIRI/MSG disseminated by LSASAF-EUMETSAT. The analysis was made for a long record from 2004 to 2021, focusing on the extreme seasons of 2020 and 2021. Results highlight the higher impact of combined dry and hot events when compared with isolated events, with a strong response of vegetation in the southern part of Madagascar. Results point to the added value of using the recent data records from geostationary satellites with high temporal resolution and updated in near real-time, to early detect, monitor, and characterize the impact of climate extremes on vegetation dynamics.

Future climate-driven drought events across Lake Urmia, Iran

Climate change has increased the vulnerability of arid and semi-arid regions to recurrent and prolonged meteorological droughts. In light of this, our study has sought to assess the nature of future meteorological drought in Lake Urmia basin, Iran, within the context of future climate projections. To achieve this, data from 54 general circulation models (GCMs) was calibrated against both in situ and Global Precipitation Climatology Centre datasets. These GCMs were then employed to project drought conditions expected over 2016-2046 under RCP2.6 and RCP8.5 as the most optimistic and pessimistic scenarios, respectively. To provide a comprehensive analysis, these RCPs were combined with two different time scale Standardized Precipitation Index (SPI), leading to eight different scenarios. The SPI was calculated over two temporal scales for the past (1985-2015) and future (2016-2046), including the medium-term (SPI-6) and long-term (SPI-18) index. Results showed that while precipitation is expected to increase by up to 34%, parts of the basin are projected to face severe and prolonged droughts under both RCPs. The most severe drought event is expected to occur around 2045-2046 under the most pessimistic RCP8.5 scenario. Severe droughts with low frequency are also anticipated to increase under other scenarios. By characterizing meteorological drought conditions for Lake Urmia basin under future climate conditions, our findings call for urgent action for adaptation strategies to mitigate the future adverse effects of drought in this region and other regions facing similar challenges. Overall, this study provides valuable insight into the impacts of climate change on future droughts that can adversely influence water resources in arid and semi-arid regions.

Spatio-temporal compounding of connected extreme events: Projection and hotspot identification

In general, the impact of two different connected extreme events is noticed on the same duration and spatial area. However, the connected extreme events can have footprint over different temporal and spatial scales. Thus, this article analyses the connected extreme events over India using the spatio-temporal compounding technique to understand the impact at different temporal and spatial scales. This approach is applied to analyse the historical and future connected extreme events. In the present study, coincident heat waves and droughts (Event C1), coincident heat waves and extreme precipitation (Event C2) are considered as connected extreme events. The future events are investigated using the suitable global climate models (GCMs) projections under three climate change scenarios (Shared Socioeconomic Pathways (SSP) 2-4.5, SSP3-7.0, and SSP5-8.5). The suitable GCMs are identified with the help of compromise programming. Subsequently, the hotspot regions are identified applying the Regional Climate Change Index (RCCI) method. The outcomes from the study suggest that with increasing temporal compounding, the mean duration of extreme events also increases. Highest increase in mean duration is observed for Event C1 over PI (Peninsular India), WCI (West Central India), and some parts of CNI (Central Northeast India) regions. The regions with high magnitude of duration have low magnitude of occurrence. The duration of Event C1 is likely to increase with respect to climate change scenarios and temporal compounding, especially in the PI region and some parts of WCI. However, there is insignificant change in the duration of Event C2. The PI region identified as the most vulnerable region followed by WCI and HR regions. The highest percentage of area under the emerging hotspot category is noticed under SSP5-8.5 climate change scenario.

Socio-ecological Challenges and Adaptation Strategies of Farmers Towards Changing Climate in Vindhyan highlands, India

The Indian agriculture is highly vulnerable to climate change which adversely affects crop production and livelihood of farmers. The effect is more intensified in Vindhyan highlands, where prolong drought and high rate of poverty exist in depredation environment. In this context, present study conducted to explore farmers' perception, attitude about climate change and adaptive capabilities. We undertook an interview schedules survey through structured questionnaire in Duddhi block of Sonbhadra district. A total of 400 households were selected in which 347 (86.75%) male and 53 (13.25%) female respondents have participated. The perception of farmers on climate change is presented in SI (severity index) which varies from 36% to 68.63%. The statement 'rainy season decreases' ranked first with SI (68.63%) followed by 'temperature increases' SI (66.06%). This investigation identified 17 types of adaptive practices which are frequently performed in the Vindhyan highlands. Based on the WAI (Weighted Average Index) score, crop diversification (2.0), cultivation of drought-adapted crop varieties (1.99), changing plantation calendar (1.95) were the most adopted practices. Multiple regression analysis between the socio-economic status of farmers and the adaptation practices, recorded a significant positive relationship with age (P < 0.01), family size (P < 0.05), education (P < 0.01), caste categories (P < 0.05) and livestock ownership (P < 0.01) of farmer. This study will be helpful in developing drought resilience farming practices for sustaining the livelihood of farmers and inform policy making.

River interlinking alters land-atmosphere feedback and changes the Indian summer monsoon

Massive river interlinking projects are proposed to offset observed increasing droughts and floods in India, the most populated country in the world. These projects involve water transfer from surplus to deficit river basins through reservoirs and canals without an in-depth understanding of the hydro-meteorological consequences. Here, we use causal delineation techniques, a coupled regional climate model, and multiple reanalysis datasets, and show that land-atmosphere feedbacks generate causal pathways between river basins in India. We further find that increased irrigation from the transferred water reduces mean rainfall in September by up to 12% in already water-stressed regions of India. We observe more drying in La Niña years compared to El Niño years. Reduced September precipitation can dry rivers post-monsoon, augmenting water stress across the country and rendering interlinking dysfunctional. Our findings highlight the need for model-guided impact assessment studies of large-scale hydrological projects across the globe.

CMIP6 models informed summer human thermal discomfort conditions in Indian regional hotspot

The frequency and intensity of extreme thermal stress conditions during summer are expected to increase due to climate change. This study examines sixteen models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that have been bias-adjusted using the quantile delta mapping method. These models provide Universal Thermal Climate Index (UTCI) for summer seasons between 1979 and 2010, which are regridded to a similar spatial grid as ERA5-HEAT (available at 0.25° × 0.25° spatial resolution) using bilinear interpolation. The evaluation compares the summertime climatology and trends of the CMIP6 multi-model ensemble (MME) mean UTCI with ERA5 data, focusing on a regional hotspot in northwest India (NWI). The Pattern Correlation Coefficient (between CMIP6 models and ERA5) values exceeding 0.9 were employed to derive the MME mean of UTCI, which was subsequently used to analyze the climatology and trends of UTCI in the CMIP6 models.The spatial climatological mean of CMIP6 MME UTCI demonstrates significant thermal stress over the NWI region, similar to ERA5. Both ERA5 and CMIP6 MME UTCI show a rising trend in thermal stress conditions over NWI. The temporal variation analysis reveals that NWI experiences higher thermal stress during the summer compared to the rest of India. The number of thermal stress days is also increasing in NWI and major Indian cities according to ERA5 and CMIP6 MME. Future climate projections under different scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) indicate an increasing trend in thermal discomfort conditions throughout the twenty-first century. The projected rates of increase are approximately 0.09 °C per decade, 0.26 °C per decade, and 0.56 °C per decade, respectively. Assessing the near (2022-2059) and far (2060-2100) future, all three scenarios suggest a rise in intense heat stress days (UTCI > 38 °C) in NWI. Notably, the CMIP6 models predict that NWI could reach deadly levels of heat stress under the high-emission (SSP5-8.5) scenario. The findings underscore the urgency of addressing climate change and its potential impacts on human well-being and socio-economic sectors.

Climate change will accelerate the high-end risk of compound drought and heatwave events

Compound drought and heatwave (CDHW) events have garnered increased attention due to their significant impacts on agriculture, energy, water resources, and ecosystems. We quantify the projected future shifts in CDHW characteristics (such as frequency, duration, and severity) due to continued anthropogenic warming relative to the baseline recent observed period (1982 to 2019). We combine weekly drought and heatwave information for 26 climate divisions across the globe, employing historical and projected model output from eight Coupled Model Intercomparison Project 6 GCMs and three Shared Socioeconomic Pathways. Statistically significant trends are revealed in the CDHW characteristics for both recent observed and model simulated future period (2020 to 2099). East Africa, North Australia, East North America, Central Asia, Central Europe, and Southeastern South America show the greatest increase in frequency through the late 21st century. The Southern Hemisphere displays a greater projected increase in CDHW occurrence, while the Northern Hemisphere displays a greater increase in CDHW severity. Regional warmings play a significant role in CDHW changes in most regions. These findings have implications for minimizing the impacts of extreme events and developing adaptation and mitigation policies to cope with increased risk on water, energy, and food sectors in critical geographical regions.

Developing a novel framework to re-examine half a century of compound drought and heatwave events in mainland China

Compound drought and heatwave events (CDHEs) are more devastating than single drought or heatwave events and have gained widespread attention. However, previous studies have not investigated the impacts of the precipitation attenuation effect (PAE) (i.e., the effect of previous precipitation on the dryness and wetness of the current system is attenuated) and event merging (EM) (i.e., merging two CDHEs with short intervals into a single event). Moreover, few studies have assessed short-term CDHEs within monthly scales and their variation characteristics under different background temperatures. Here we propose a novel framework for assessing CDHEs on a daily scale and considering the PAE and EM. We applied this framework to mainland China and investigated the spatiotemporal variation of the CDHE indicators (spatial extent (CDHE_spa), frequency (CDHE_fre), duration (CHHE_dur), and severity (CDHE_sev)) from 1968 to 2019. The results suggested that ignoring the PAE and EM led to significant changes in the spatial distribution and magnitude of the CDHE indicators. Daily-scale assessments allowed for monitoring the detailed evolution of CDHEs and facilitated the timely development of mitigation measures. Mainland China experienced frequent CDHEs from 1968 to 2019 (except for the southwestern part of Northwest China (NWC) and the western part of Southwest China (SWC)), whereas, hotspot areas of CDHE_dur and CDHE_sev had a patchy distribution in different geographical subregions. The CDHE indicators were higher in the warmer 1994-2019 period than in the colder 1968-1993 period, but the rate of increase of the indicators was lower or there was a downward trend. Overall, CDHEs in mainland China have been in a state of remarkable continuous strengthening over the past half a century. This study provides a new quantitative analysis approach for CDHEs.

Spatiotemporal variability of extreme temperature indices and their implications over the heterogeneous river basin, India

The current study on spatiotemporal variability of temperature presents a holistic approach for quantifying the joint space-time variability of extreme temperature indices over the physio-climatically heterogeneous Tapi River basin (TRB) using two unsupervised machine learning algorithms, i.e., principal component analysis (PCA) and cluster analysis. The long-term variability in extreme temperature indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), was evaluated for 1951-2016. The magnitude and statistical significance of the temporal trend in extreme temperature indices were estimated using non-parametric Sen's slope estimator and modified Mann Kendall (MMK) tests, respectively. The multivariate assessment of temporal trends using PCA resulted in four principal components (PCs) encapsulating more than 90% variability. The cluster analysis of corresponding PCs resulted in two spatial clusters exhibiting homogeneous spatiotemporal variability. Cluster 1 is characterized by significantly increasing hottest, very hot, and extremely hot days with rising average maximum temperature and intraday temperature variability. On the other hand, cluster 2 showed significantly rising coldest nights, mean minimum, mean temperature, and Tx37 with significantly decreasing intraday and interannual temperature variability, very cold, and extremely cold nights with reducing cold spell durations. The summertime heat stress computation revealed that the Purna sub-catchment of the Tapi basin is more vulnerable to various health issues and decreased work performance (> 10%) for more than 45 days per year. The current study dealing with the associated effects of rising temperature variability on crop yield, human health, and work performance would help policymakers formulate better planning and management strategies to safeguard society and the environment.

Hydrological Drought Severity in Different Return Periods in Rivers of Ardabil Province, Iran

Hydrological drought (HD) characterization with different return periods is essential to appropriately design the best water management practices. In particular, characterizing the interactive relations of discharge, drought, and return periods using a novel triple diagram can deepen the interpretation of regional droughts, which have not been adequately considered, especially in semi-arid areas. Considering the critical role of HD in water exploitation and management in Iran, this study was therefore conducted to analyze the HD in different return periods in rivers of the Ardabil Province (area = 17,953 km2). To this end, the streamflow drought index (SDI) was computed using DrinC software at 1-, 3-, and 6-month time scales for 25 hydrometric stations during 1981–2014. Then, the drought severity was evaluated by CumFreq software in different return periods (2, 5, 10, 25, 50, and 100 years). Finally, the relationship between discharge, SDI, and return periods was analyzed using triple diagram models. The results revealed that the drought events had mild (−1 ≤ SDI < 0) and moderate (−1.5 ≤ SDI < −1) severity for most study stations in the study area. The mean values of SDI in the 1-, 3-, and 6-month time scales were 1.08, 0.80, and 0.55, respectively. At all study time scales, the drought severity in both rivers with low and high flows increased with increasing return periods. In such a way, the maximum drought severity has been found for rivers with high flow at a 100-year return period. The current results can be considered a screening tool for the distinctive conservation and directive management of watershed resources.

Hotspots, co-occurrence, and shifts of compound and cascading extreme climate events in Eurasian drylands

Eurasian drylands are the regions that are most vulnerable to climate change. Climate extremes have caused enormous or even devastating impacts on ecosystems and the social economy in this region, and the compound climate extremes (com_CEs, two or more extreme events occurring simultaneously) and cascading climate extremes (cas_CEs, two or more extreme events occurring successively) have exacerbated these problems. However, little is known about the occurrence patterns of com_CEs and cas_CEs in the Eurasian drylands. Based on the ERA5 reanalysis data range from 1979 to 2020, we improved the methodology for the extraction of co-occurrence events and identified high-frequency types, their hotspots, and occurrence rhythms (seasonally and annually) in Eurasian drylands. Our results showed that com_CEs and cas_CEs have high similarities in the types and spatial hotspots of extreme events; however, the former has a wider geographical and spatial distribution, and the latter has a longer duration. Specifically, co-occurring drought and heatwave events (DH) frequently appear in South Asia and western mid-latitude regions during summer, while in the winter, high latitude regions should be alert to the co-occurrence of drought and low-temperature events (DT). Central Asia and the Mongolian Plateau regions are prone to frequent drought and wind events (DW), and wind and high precipitation events (WP) in the spring and autumn. We have noticed that mid-latitude may suffer from extreme events that have never occurred before, such as com_DH being scattered sporadically in the first two decades and suddenly surging in West Asia and East Asia after the year 2000, and com_DT migrating from high-latitude areas such as the Arctic Ocean coast to mid-latitudes. Our results contribute to understanding hotspots of co-occurring CEs in Eurasian drylands, where more efforts will be needed in the future, especially in mid-latitudes which may suffer extreme climate events that have never occurred before.

Climate warming and summer monsoon breaks drive compound dry and hot extremes in India

Considering the severe impacts of compound dry and hot extremes, we examine the primary drivers of CDHEs during the summer monsoon in India. Using ERA5 reanalysis, we show that most of the CDHEs in India occur during the droughts caused by the summer monsoon rainfall deficit. Despite a decline in the frequency of summer monsoon droughts in recent decades, increased CDHEs are mainly driven by warming and dry spells during the summer monsoon particularly in the Northeast, central northeast, and west central regions. A strong land-atmospheric coupling during droughts in the summer monsoon season leads to frequent CDHEs in the Northwest and southern peninsular regions. Furthermore, regional variations in land-atmospheric coupling cause substantial differences in the CDHE occurrence in different parts of the country. Summer monsoon rainfall variability and increased warming can pose a greater risk of compound dry and hot extremes with severe impacts on various sectors in India.

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CDHEs have increased in the observed period despite a decline in droughts in India

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CDHEs are largely driven by the rise in dry and warm spells during the monsoon season

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Regional variations in CDHEs are linked with the changes in land-atmospheric coupling

Drought-heatwave nexus in Brazil and related impacts on health and fires: A comprehensive review

Climate change is drastically altering the frequency, duration, and severity of compound drought-heatwave (CDHW) episodes, which present a new challenge in environmental and socioeconomic sectors. These threats are of particular importance in low-income regions with growing populations, fragile infrastructure, and threatened ecosystems. This review synthesizes emerging progress in the understanding of CDHW patterns in Brazil while providing insights about the impacts on fire occurrence and public health. Evidence is mounting that heatwaves are becoming increasingly linked with droughts in northeastern and southeastern Brazil, the Amazonia, and the Pantanal. In those regions, recent studies have begun to build a better understanding of the physical mechanisms behind CDHW events, such as the soil moisture-atmosphere coupling, promoted by exceptional atmospheric blocking conditions. Results hint at a synergy between CDHW events and high fire activity in the country over the last decades, with the most recent example being the catastrophic 2020 fires in the Pantanal. Moreover, we show that HWs were responsible for increasing mortality and preterm births during record-breaking droughts in southeastern Brazil. This work paves the way for a more in-depth understanding on CDHW events and their impacts, which is crucial to enhance the adaptive capacity of different Brazilian sectors.

Effect of rainfall variability on tree phenology in moist tropical deciduous forests

Plants in their life cycle go through a series of life processes. These phenological changes are influenced by different climatic conditions. Abiotic factors like temperature, precipitation, and photoperiodism affect the onset and offset of particular phenophase in the plant periodic cycle. In this study, we tested the influence of precipitation on the forest phenology at two sites of Dudhwa National Park (DNP), Uttar Pradesh and Simlipal National Park (SNP), Odisha, India. DNP and SNP receive an annual average rainfall of 1093.5 mm and 1500 mm, respectively, of which most rainfall (~ 90%) occurs during June-September. Normalized Difference Vegetation Index (NDVI) was measured for 2 years 2015 and 2018, with 2015 being a drought year and 2018 being a normal rainfall year. NDVI was analyzed at different temporal scales of months, season, and years using the t test (Welch's two-tailed) and General Linear Mixed Model (GLMM). Effect of drought (2015) and normal (2018) rainfall year was not significant at both the sites, whereas season, year*season interaction, season*rainfall interaction, and year*season*rainfall interaction were found significant at DNP (P < 0.05, ICC = 0.68, marginal R² = 0.81; conditional R² = 0.94). At SNP, rainfall, year, season, and their interaction were non-significant, whereas several months showed a significant effect on the NDVI values for both sites. Winter and monsoon season in DNP, and post-monsoon season in SNP, showed a significant effect on the NDVI patterns. Thus, the effect of precipitation stress in the deciduous forests was evident at small intervals of observation. Tree phenology compensated for differences when observed from a higher temporal scale of a year. There existed a mechanism in trees to tide over adverse conditions and maintain the phenology over longer intervals of time. The resilience and vulnerability of such forest ecosystems against abiotic factors and extreme events would be instrumental in climate change adaptation strategies. Tree phenology can be used as an indicator of forest health and resilience.

Reproductive-Stage Heat Stress in Cereals: Impact, Plant Responses and Strategies for Tolerance Improvement

Reproductive-stage heat stress (RSHS) poses a major constraint to cereal crop production by damaging main plant reproductive structures and hampering reproductive processes, including pollen and stigma viability, pollination, fertilization, grain setting and grain filling. Despite this well-recognized fact, research on crop heat stress (HS) is relatively recent compared to other abiotic stresses, such as drought and salinity, and in particular, RSHS studies in cereals are considerably few in comparison with seedling-stage and vegetative-stage-centered studies. Meanwhile, climate change-exacerbated HS, independently or synergistically with drought, will have huge implications on crop performance and future global food security. Fortunately, due to their sedentary nature, crop plants have evolved complex and diverse transient and long-term mechanisms to perceive, transduce, respond and adapt to HS at the molecular, cell, physiological and whole plant levels. Therefore, uncovering the molecular and physiological mechanisms governing plant response and tolerance to RSHS facilitates the designing of effective strategies to improve HS tolerance in cereal crops. In this review, we update our understanding of several aspects of RSHS in cereals, particularly impacts on physiological processes and yield; HS signal perception and transduction; and transcriptional regulation by heat shock factors and heat stress-responsive genes. We also discuss the epigenetic, post-translational modification and HS memory mechanisms modulating plant HS tolerance. Moreover, we offer a critical set of strategies (encompassing genomics and plant breeding, transgenesis, omics and agronomy) that could accelerate the development of RSHS-resilient cereal crop cultivars. We underline that a judicious combination of all of these strategies offers the best foot forward in RSHS tolerance improvement in cereals. Further, we highlight critical shortcomings to RSHS tolerance investigations in cereals and propositions for their circumvention, as well as some knowledge gaps, which should guide future research priorities. Overall, our review furthers our understanding of HS tolerance in plants and supports the rational designing of RSHS-tolerant cereal crop cultivars for the warming climate.

Increased Compound Droughts and Heatwaves in a Double Pack in Central Asia

Compound droughts and heatwaves (CDHWs) are likely to cause more severe natural disasters than a single extreme event, and they have been exacerbated by rapid global warming. Based on high-resolution grid data, this study combines the daily-scale ERA5-Land dataset and the monthly-scale SPEI dataset with multiple indicators to analyze CDHWs. We calculated and analyzed the temporal and spatial modal distribution of CDHWs in Central Asia from 1981 to 2018, and in this paper, we discuss the sequence relationship between drought events, heatwave events, and CDHWs. The results show that the number of CDHWs in the study region have increased over time and expanded in terms of area, especially in eastern and southwestern Central Asia. The tsum (total frequency of CDHWs) was 0.5 times higher than the total heatwave frequency and it increased at a rate of 0.17/yr. The maximum duration of tmax (maximum duration of CDHWs in days) was 17 days. Furthermore, the occurrence rate of tmax was 96.67%, and the AH (CDHWs’ accumulated heat) had a rate of 97.78%, which, upon examination of the spatial trend pattern, accounted for the largest increase in terms of area. We also found that the TAH (CDHWs’ average temperature anomalies, SPEI < −0.5) shows obvious seasonality, with the increases in winter and spring being significantly greater than the increases in summer and autumn. The intensity of the CDHWs was stronger than that of a single extreme event, the temperature anomaly was higher than the average of 0.4–0.8 °C, and there was a north–south spatial pattern across the study region. In eastern and northwestern Central Asia, the AH and heatwaves (SPEI < −0.5) increased by 15–30 times per year on average. During the transition from the base period to the reference period, CDHWs increased by 25%, and the number of dry days prior to the CDHWs decreased by 7.35 days. The conclusion of our study can provide a theoretical basis for coping with climate change in arid zones.

Amplified risk of compound heat stress-dry spells in Urban India

Compound warm-dry spells over land, which is expected to occur more frequently and expected to cover a much larger spatial extent in a warming climate, result from the simultaneous or successive occurrence of extreme heatwaves, low precipitation, and synoptic conditions, e.g., low surface wind speeds. While changing patterns of weather and climate extremes cannot be ameliorated, effective mitigation requires an understanding of the multivariate nature of interacting drivers that influence the occurrence frequency and predictability of these extremes. However, risk assessments are often focused on univariate statistics, incorporating either extreme temperature or low precipitation; or at the most bivariate statistics considering concurrence of temperature versus precipitation, without accounting for synoptic conditions influencing their joint dependency. Based on station-based daily meteorological records from 23 urban and peri-urban locations of India, covering the 1970-2018 period, this study identifies four distinct regions that show temporal clustering of the timing of heatwaves. Further, combining joint probability distributions of interacting drivers, this analysis explored compound warm-dry potentials that result from the co-occurrence of warmer temperature, scarcer precipitation, and synoptic wind patterns. The results reveal 50-year severe heat stress solely based on the temperature at each location tends to be more frequent and is expected to become 5 to 17-year compound warm-dry events considering interdependence between attributes. Notably, considering dependence among drivers, a median 6-fold amplification (ranging from 3 to 10-fold) in compound warm-dry spell frequency is apparent relative to the expected annual number of a local (univariate) 50-year severe heatwave episode, indicating warming-induced desiccation is already underway over most of the urbanized areas of the country.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00382-022-06324-y.

Impact of monsoon teleconnections on regional rainfall and vegetation dynamics in Haryana, India

Our study has investigated the impact of El Niño-Southern Oscillation (ENSO) on spatio-temporal dynamics of Indian summer monsoon rainfall (ISMR) as well as vegetation for a period of 1980 to 2019 at regional scale in Haryana, India. The gridded rainfall datasets of India Meteorological Department (IMD) were examined on monthly and seasonal scale using various statistical methods like mean climatology, coefficient of variation, slope of linear, Sen's slope, Mann-Kendall Z statistic, and hierarchical cluster analysis. The influence of ENSO on spatial distribution of ISMR was observed, where we found increasing and decreasing rainfall patterns during La Niña and El Niño years, respectively. We attempted to establish a link between ISMR and various teleconnections using time series of the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory, and statistically significant and positive correlation was observed with the Southern Oscillation Index (SOI), whereas significantly negative correlations were observed with SST of Niño 3, Niño 3.4, and Niño 4 regions. The gridded datasets of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis V5 (ERA5) were used to evaluate the influence of ENSO on atmospheric dynamics using lower and upper tropospheric wind circulation (850 hPa and200 hpa), vertically integrated moisture transport (VIMT), and surface moisture flux (SMF). We have used satellite-based normalised difference vegetation index (NDVI) datasets of the Global Inventory Monitoring and Modeling System (GIMMS) to investigate the impact of ENSO on vegetation dynamics of Haryana and found that NDVI values were higher and lower in case of La Niña and El Niño years, respectively.

Heatwaves in South Asia: Characterization, Consequences on Human Health, and Adaptation Strategies

South Asia, with more than one-fifth of the world’s population, is highly vulnerable to heatwaves and associated health consequences. The population experiences considerably higher residential vulnerability due to limited infrastructural capacities, economic resources, and health and environmental quality deficiencies. However, a limited number of studies are available from the region to account for the health effects of heatwaves. Therefore, this study has conducted a comprehensive review to characterize heatwaves across South Asian countries. The review explicitly identifies the population’s vulnerability to heatwaves during recent years and heatwave management policies in the region. The literature review suggests increased heat-related deaths in most South Asian countries, with few exceptions. In addition, the analysis of historical temperature records identified an upward trend in annual average temperature across the South Asian countries. The study highlights various heatwave definitions that have been used in the region to facilitate comparative evidence. The review of policies identified that only a few South Asian countries have functional heatwave management plans and majorly lack community and residential preparedness for heatwaves. Therefore, this study identifies potential community- and residential-based adaptation strategies to mitigate heat discomfort. As prospective solutions, the study recommends adaptation strategies such as blue–green spaces, indoor passive cooling, infrastructural adjustments, heat action plans, etc. However, such adaptation measures require a holistic amalgamation of different stakeholders to fabricate heatwave-resilient cities.

Population exposure to compound extreme events in India under different emission and population scenarios

It is well understood that India is largely exposed to different climate extremes including floods, droughts, heat waves, among others. However, the exposure of co-occurrence of these events is still unknown. The present analysis, first study of its kind, provides the projected changeability of five different compound extremes under three different emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). These changes are combined with population projection under SSP2, SSP3, and SSP5 scenarios to examine the total exposure in terms of number of persons exposed during 2021-2060 (T1) and 2061-2100 (T2). Here, the outputs from thirteen GCMs are used under CMIP6 experiment. The findings from the study show that all the compound extremes are expected to increase in future under all the emission scenarios being greater in case of SSP5-8.5. The population exposure is highest (2.51- to 4.96-fold as compared to historical) under SSP3-7.0 scenario (2021-2100 i.e., T1 and T2) in case of coincident heat waves and droughts compound extreme. The total exposure in Central Northeast India is projected to be the highest while Hilly Regions are likely to have the lowest exposure in future. The increase in the exposure is mainly contributed from climate change, population growth and their interaction depending on different kinds of compound extremes. The findings would help in devising sustainable policy strategies to climate mitigation and adaptation.

Vulnerability assessment of agricultural production systems to drought stresses using robustness measures

Intensification of droughts in agricultural areas threaten global food security. The impacts of drought stresses vary widely across a region, not only due to climate variability but also due to heterogeneous soil and groundwater buffering capacities which protect against droughts. An innovative drought vulnerability index was developed by reconciling the negative effects of drought stresses against the robustness offered by hydrologic buffers. Indicators for climate stresses, soil and groundwater buffering capacities were defined using physical principles and integrated using a multi-criteria decision making (MCDM) framework. The framework was applied to delineate drought vulnerability of agricultural production systems and evaluate current cropping choices across the High Plains region of the US that is underlain by the Ogallala Aquifer. Current crop growth choices appeared to be compatible with the intrinsic drought vulnerabilities with cotton and sorghum grown in higher vulnerability areas and corn and soybean produced in areas with lower vulnerability. Nearly 50% of the aquifer region fell in the transition zone exhibiting medium to high vulnerabilities warranting the need for better water management to adapt to a changing climate.

Will advances in functional renal magnetic resonance imaging translate to the nephrology clinic?

Characterizing structural and tissue abnormalities of the kidney is fundamental to understanding kidney disease. Functional multi-parametric renal magnetic resonance imaging (MRI) is a noninvasive imaging strategy whereby several sequences are employed within a single session to quantify renal perfusion, tissue oxygenation, fibrosis, inflammation, and oedema without using ionizing radiation. In this review, we discuss evidence surrounding its use in several clinical settings including acute kidney injury, chronic kidney disease, hypertension, polycystic kidney disease and around renal transplantation. Kidney size on MRI is already a validated measure for making therapeutic decisions in the setting of polycystic kidney disease. Functional MRI sequences, T1 mapping and apparent diffusion coefficient, can non-invasively quantify interstitial fibrosis and so may have a near-future role in the nephrology clinic to stratify the risk of progressive chronic kidney disease or transplant dysfunction. Beyond this, multi-parametric MRI may be used diagnostically, for example differentiating inflammatory versus ischaemic causes of renal dysfunction, but this remains to be proven. Changes in MRI properties of kidney parenchyma may be useful surrogate markers to use as end points in clinical trials to assess if drugs prevent renal fibrosis or alter kidney perfusion. Large, multi-centre studies of functional renal MRI are ongoing which aim to provide definitive answers as to its role in the management of patients with renal dysfunction.

Quantification of Resilience Considering Different Migration Biographies: A Case Study of Pune, India

Urbanization proceeds globally and is often driven by migration. Simultaneously, cities face severe exposure to environmental hazards such as floods and heatwaves posing threats to millions of urban households. Consequently, fostering urban households’ resilience is imperative, yet often impeded by the lack of its accurate assessment. We developed a structural equation model to quantify households’ resilience, considering their assets, housing, and health properties. Based on a household survey (n = 1872), we calculate the resilience of households in Pune, India with and without migration biography and compare different sub-groups. We further analyze how households are exposed to and affected by floods and heatwaves. Our results show that not migration as such but the type of migration, particularly, the residence zone at the migration destination (formal urban or slum) and migration origin (urban or rural) provide insights into households’ resilience and affectedness by extreme weather events. While on average, migrants in our study have higher resilience than non-migrants, the sub-group of rural migrants living in slums score significantly lower than the respective non-migrant cohort. Further characteristics of the migration biography such as migration distance, time since arrival at the destination, and the reasons for migration contribute to households’ resilience. Consequently, the opposing generalized notions in literature of migrants either as the least resilient group or as high performers, need to be overcome as our study shows that within one city, migrants are found both at the top and the bottom of the resilience range. Thus, we recommend that policymakers include migrants’ biographies when assessing their resilience and when designing resilience improvement interventions to help the least resilient migrant groups more effectively.

A Comprehensive Evaluation of Near-Real-Time and Research Products of IMERG Precipitation over India for the Southwest Monsoon Period

Precipitation is one of the integral components of the global hydrological cycle. Accurate estimation of precipitation is vital for numerous applications ranging from hydrology to climatology. Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, the Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product was released. The IMERG provides global precipitation estimates at finer spatiotemporal resolution (e.g., 0.1°/half-hourly) and has shown to be better than other contemporary multi-satellite precipitation products over most parts of the globe. In this study, near-real-time and research products of IMERG have been extensively evaluated against a daily rain-gauge-based precipitation dataset over India for the southwest monsoon period. In addition, the current version 6 of the IMERG research product or Final Run (IMERG-F V6) has been compared with its predecessor, version 5, and error characteristics of IMERG-F V6 for pre-GPM and GPM periods have been assessed. The spatial distributions of different error metrics over the country show that both near-real-time IMERG products (e.g., Early and Late Runs) have similar error characteristics in precipitation estimation. However, near-real-time products have larger errors than IMERG-F V6, as expected. Bias in all-India daily mean rainfall in the near-real-time IMERG products is about 3–4 times larger than research product. Both V5 and V6 IMERG-F estimates show similar error characteristics in daily precipitation estimation over the country. Similarly, both near-real-time and research products show similar characteristics in the detection of rainy days. However, IMERG-F V6 exhibits better performance in precipitation estimation and detection of rainy days during the GPM period (2014–2017) than the pre-GPM period (2010–2013). The contribution of different rainfall intensity intervals to total monsoon rainfall is captured well by the IMERG estimates. Furthermore, results reveal that IMERG estimates under-detect and overestimate light rainfall intensity of 2.5–7.5 mm day−1, which needs to be improved in the next release. The results of this study would be beneficial for end-users to integrate this multi-satellite product in any specific application.

Disentangling increasing compound extremes at regional scale during Indian summer monsoon

Compound extremes exhibit greater adverse impacts than their univariate counterparts. Studies have reported changes in frequency and the spatial extent of extremes in India; however, investigation of compound extremes is in the infancy state. This study investigates the historical variation of compound dry and hot extremes (CDHE) and compound wet and cold extremes (CWCE) during the Indian summer monsoon period from 1951 to 2019 using monthly data. Results are analyzed for 10 identified homogeneous regions for India. Our results unravelled that CDHE (CWCE) frequency has increased (decreased) by 1-3 events per decade for the recent period (1977-2019) relative to the base period (1951-1976). Overall, the increasing (decreasing) pattern of CDHE (CWCE) is high across North-central India, Western India, North-eastern India and South-eastern coastlines. Our findings help in identification of the parts of the country affected by frequent and widespread CDHE during the recent period, which is alarming. More detailed assessments are required to disentangle the complex physical process of compound extremes to improve risk management options.

Biochemical and Molecular Effects Induced by Triacontanol in Acquired Tolerance of Rice to Drought Stress

To assess the effect of triacontanol (TRIA) on rice plants grown under normal or drought conditions, rice seeds were presoaked in TRIA (35 ppm) for two hours. After 20 days of sowing, rice seedlings developed from TRIA-treated or untreated seeds were subjected to drought stress. After 10 days of plant exposure to drought stress, data of major growth attributes and the content of photosynthetic pigments were recorded. Moreover, the effect of drought stress on stomatal conductance and the photochemical efficiency of PSII (Fv/Fm) were followed. The data obtained indicated that the species of rice (Oryza sativa L.) cultivar Giza 177 under investigation was sensitive to drought stress where there were significant decreases in the fresh and dry weights of shoots and roots and in stomatal conductance, as well as in the content of chlorophyll a, chlorophyll b, and carotenoids. Seed priming with TRIA enhanced both growth and acquired plant tolerance to drought stress. Thus, TRIA via the enhancement of stomatal conductance through the regulation of stomatal closure, the rate of water loss, ABA metabolism, the accumulation of osmolytes, and the regulation of aquaporins genes improved the water status of plants grown under water scarcity. Moreover, TRIA via increasing the content of free amino acids and sugars under drought stress may increase the chance of plant tissues to retain more water under scarcity conditions.

Compound and cascading drought impacts do not happen by chance: A proposal to quantify their relationships

The understanding of compounding and cascading impacts is becoming increasingly central to the risk reduction debate as hazard consequences are strongly coupled. Still, studies on their quantification and visualization are limited. This restricts the establishment of impact-based early warning systems. Here, a novel method for quantifying drought compound impacts and their cascading paths is presented by integrating network analysis and data mining tools. The 2018/19 drought in Germany is used as a case study. Network graphs are employed to display impact co-occurrences and cascades of agriculture, livestock, forestry, industry, and recreation impacts. Furthermore, sequential pattern mining is used to predict the next impact that is likely to take place. A synthesis of the identified relationships is presented using accessible visual formats. Results show that simultaneous and cascading drought impacts may not happen by chance but follow a pattern. Indeed, statistically significant co-occurrence associations outnumbered randomly distributed ones (91.1% versus 8.9%). With regard to the cascading paths, cross-validation results show that within three attempts, the next impact class was accurately predicted in 72.9% of the cases. Crop losses were usually followed up by a shortage of feed for livestock and consequent early slaughtering of animals. This implies that in order to limit drought impacts, there is a need to consider their compounding and cascading effects. Hence, researchers need to move from the analysis of single impacts to the understanding of how multi-sectoral impacts are connected with each other. The methodology proposed here paves towards this direction. The visualization tools used can help to increase awareness of the possible impact interactions and dependency, improving drought managers' decision-making ability. Moreover, the obtained results can serve as the basis for inferring impact causal relationships.

Global Observations and CMIP6 Simulations of Compound Extremes of Monthly Temperature and Precipitation

Compound climate extremes, such as events with concurrent temperature and precipitation extremes, have significant impacts on the health of humans and ecosystems. This paper aims to analyze temporal and spatial characteristics of compound extremes of monthly temperature and precipitation, evaluate the performance of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models in simulating compound extremes, and investigate their future changes under Shared Socioeconomic Pathways (SSPs). The results show a significant increase in the frequency of compound warm extremes (warm/dry and warm/wet) but a decrease in compound cold extremes (cold/dry and cold/wet) during 1985-2014 relative to 1955-1984. The observed upward trends of compound warm extremes over China are much higher than those worldwide during the period of interest. A multi-model ensemble (MME) of CMIP6 models performs well in simulating temporal changes of warm/wet extremes, and temporal correlation coefficients between MME and observations are above 0.86. Under future scenarios, CMIP6 simulations show substantial rises in compound warm extremes and declines in compound cold extremes. Globally, the average frequency of warm/wet extremes over a 30-yr period is projected to increase for 2070-2099 relative to 1985-2014 by 18.53, 34.15, 48.79, and 59.60 under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. Inter-model uncertainties for the frequencies of compound warm extremes are considerably higher than those of compound cold extremes. The projected uncertainties in the global occurrences of warm/wet extremes are 3.82 times those of warm/dry extremes during 2070-2099 and especially high for the Amazon and the Tibetan Plateau.

Changes in the Compound Drought and Extreme Heat Occurrence in the 1961–2018 Period at the European Scale

Compound extreme weather events can cause large economic damage and endanger human lives. Therefore, identification of changes in such compound event frequency and magnitude is important information that could be useful for decision makers and practitioners in water management and agriculture sector. This is especially the case for dry hazards that can be significantly influenced by the increasing air temperature and can have significant impact on water availability and consumption as well as on agricultural production. This study investigated changes in the compound occurrence of drought and extreme heat at the European scale using Uncertainties in Ensembles of Regional Reanalyses (UERRA) regional reanalysis data for the 1961–2018 period. The effective drought index (EDI) and the air temperature percentile threshold were used for the identification of the compound events at the catchment scale where entire Europe was divided into more than 4000 catchments. The results revealed multiple hotspots of compound drought and extreme heat events such as parts of Western Europe, Italy, Balkan Peninsula and Northern and Eastern Europe. At the continental scale, no uniform trend pattern could be detected. However, multiple areas with either positive or negative changes were identified. A positive change was characteristic for parts of Western Europe, Italy, Balkan Peninsula, etc. In these cases, the trend was mostly driven by the decreasing total precipitation trend and was not directly affected by the increasing air temperature trend. Areas with negative changes include parts of Northern and Eastern Europe and British Isles. In these cases, the detected trend was mostly driven by an increasing total precipitation trend. However, local drivers could be different.

Using GRACE satellite observations for separating meteorological variability from anthropogenic impacts on water availability

Gravity Recovery and Climate Experiment (GRACE) observations provide information on Total Water Storage Anomaly (TWSA) which is a key variable for drought monitoring and assessment. The so-called Total Water Storage Deficit Index (TWSDI) based on GRACE data has been widely used for characterizing drought events. Here we show that the commonly used TWSDI approach often exhibits significant inconsistencies with meteorological conditions, primarily upon presence of a trend in observations due to anthropogenic water use. In this study, we propose a modified version of TWSDI (termed, MTWSDI) that decomposes the anthropogenic and climatic-driven components of GRACE observations. We applied our approach for drought monitoring over the Ganges–Brahmaputra in India and Markazi basins in Iran. Results show that the newly developed MTWSDI exhibits consistency with meteorological drought indices in both basins. We also propose a deficit-based method for drought monitoring and recovery assessment using GRACE observations, providing useful information about volume of deficit, and minimum and average time for drought recovery. According to the deficit thresholds, water deficits caused by anthropogenic impacts every year in the Ganges–Brahmaputra basin and Markazi basins is almost equal to an abnormally dry condition and a moderate drought condition, receptively. It indicates that unsustainable human water use have led to a form of perpetual and accelerated anthropogenic drought in these basins. Continuation of this trend would deplete the basin and cause significant socio-economic challenges.

Divergent Sensitivities of Spaceborne Solar-Induced Chlorophyll Fluorescence to Drought among Different Seasons and Regions

As a newly emerging satellite form of data, solar-induced chlorophyll fluorescence (SIF) provides a direct measurement of photosynthetic activity. The potential of SIF for drought assessment in different grassland ecosystems is not yet clear. In this study, the correlations between spaceborne SIF and nine drought indices were evaluated. Standardized precipitation evapotranspiration index (SPEI) at a 1, 3, 6, 9, 12 month scale, Palmer drought severity index (PDSI), soil moisture, temperature condition index (TCI), and vapor pressure deficit (VPD) were evaluated. The relationships between different grassland types and different seasons were compared, and the driving forces affecting the sensitivity of SIF to drought were explored. We found that the correlations between SIF and drought indices were different for temperate grasslands and alpine grasslands. The correlation coefficients between SIF and soil moisture were the highest (the mean value was 0.72 for temperate grasslands and 0.69 for alpine grasslands), followed by SPEI and PDSI at a three month scale, and the correlation coefficient between SIF and TCI was the lowest (the mean value was 0.38 for both temperate and alpine grasslands). Spaceborne SIF is more effective for drought monitoring during the peak period of the growing season (July and August). Temperature and radiation are important factors affecting the sensitivity of SIF to drought. The results from this study demonstrated the importance of SIF in drought monitoring especially for temperate grasslands in the peak growing season.

A century of observations reveals increasing likelihood of continental-scale compound dry-hot extremes

Using over a century of ground-based observations over the contiguous United States, we show that the frequency of compound dry and hot extremes has increased substantially in the past decades, with an alarming increase in very rare dry-hot extremes. Our results indicate that the area affected by concurrent extremes has also increased significantly. Further, we explore homogeneity (i.e., connectedness) of dry-hot extremes across space. We show that dry-hot extremes have homogeneously enlarged over the past 122 years, pointing to spatial propagation of extreme dryness and heat and increased probability of continental-scale compound extremes. Last, we show an interesting shift between the main driver of dry-hot extremes over time. While meteorological drought was the main driver of dry-hot events in the 1930s, the observed warming trend has become the dominant driver in recent decades. Our results provide a deeper understanding of spatiotemporal variation of compound dry-hot extremes.

Developing a Remote Sensing-Based Combined Drought Indicator Approach for Agricultural Drought Monitoring over Marathwada, India

The increasing drought severities and consequent devastating impacts on society over the Indian semi-arid regions demand better drought monitoring and early warning systems. Operational agricultural drought assessment methods in India mainly depend on a single input parameter such as precipitation and are based on a sparsely located in-situ measurements, which limits monitoring precision. The overarching objective of this study is to address this need through the development of an integrated agro-climatological drought monitoring approach, i.e., combined drought indicator for Marathwada (CDI_M), situated in the central part of Maharashtra, India. In this study, satellite and model-based input parameters (i.e., standardized precipitation index (SPI-3), land surface temperature (LST), soil moisture (SM), and normalized difference vegetation index (NDVI)) were analyzed at a monthly scale from 2001 to 2018. Two quantitative methods were tested to combine the input parameters for developing the CDI_M. These methods included an expert judgment-based weight of each parameter (Method-I) and principle component analysis (PCA)-based weighting approach (Method-II). Secondary data for major types of crop yields in Marathwada were utilized to assess the CDI_M results for the study period. CDI_M maps depict moderate to extreme drought cases in the historic drought years of 2002, 2009, and 2015–2016. This study found a significant increase in drought intensities (p ≤ 0.05) and drought frequency over the years 2001–2018, especially in the Latur, Jalna, and Parbhani districts. In comparison to Method-I (r ≥ 0.4), PCA-based (Method-II) CDI_M showed a higher correlation (r ≥ 0.60) with crop yields in both harvesting seasons (Kharif and Rabi). In particular, crop yields during the drier years showed a greater association (r > 6.5) with CDI_M over Marathwada. Hence, the present study illustrated the effectiveness of CDI_M to monitor agricultural drought in India and provide improved information to support agricultural drought management practices.

Climatological Drought Forecasting Using Bias Corrected CMIP6 Climate Data: A Case Study for India

This study forecasts and assesses drought situations in various regions of India (the Araveli region, the Bundelkhand region, and the Kansabati river basin) based on seven simulated climates in the near future (2015–2044). The self-calibrating Palmer Drought Severity Index (scPDSI) was used based on its fairness in identifying drought conditions that account for the temperature as well. Gridded temperature and rainfall data of spatial resolution of 1 km were used to bias correct the multi-model ensemble mean of the Global Climatic Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) project. Equidistant quantile-based mapping was adopted to remove the bias in the rainfall and temperature data, which were corrected on a monthly scale. The outcome of the forecast suggests multiple severe-to-extreme drought events of appreciable durations, mostly after the 2030s, under most climate scenarios in all the three study areas. The severe-to-extreme drought duration was found to last at least 20 to 30 months in the near future in all three study areas. A high-resolution drought index was developed and proven to be a key to assessing the drought situation.

Quantifying likelihoods of extreme occurrences causing maize yield reduction at the global scale

A variety of weather and climate extremes (e.g., droughts, heatwaves) can lead to negative impacts on crop yields and food security. It is thus important to understand likelihoods of extreme occurrences causing crop yield reduction for enhanced resilience of the food system. Here, we investigate the likelihood of occurrences of dry, hot, and compound dry-hot conditions causing crop yield reduction for ten maize-producing countries based on climate observations and country-level maize yields for the period 1961-2016. The likelihood of occurrences of different extremes causing maize yield reduction is quantified using a multivariate statistical model. Results show that the multivariate model performs well in quantifying the likelihood of extreme occurrences (i.e., dry, hot and compound dry-hot conditions) causing maize yield reduction. Overall, the likelihood of occurrences of the above three conditions leading to yield reduction varies among ten maize-producing countries and that of compound dry-hot condition is the highest for most countries, which is shown to be closely related to the precipitation-temperature dependence of each country. Moreover, the likelihood of compound dry-hot occurrences becomes higher as the severity of crop yield reduction increases. These findings highlight significant impacts of compound dry-hot conditions on maize yield reduction and provide valuable information for formulating effective agricultural measures under global warming.

Oxytocin Response Following Playful Mother-Child Interaction in Survivors of the Great East Japan Earthquake

BACKGROUND

Children who are exposed to natural disasters are at greater risk of developing mental and behavior problems. Prior studies have suggested that positive parenting practices could prevent child mental and behavior problems among those who were exposed to natural disasters. Parent-child interaction increases oxytocin level in parents and infants; however, studies assessing the change in oxytocin level after positive parent-child interaction and its effect on child behavior problems among preadolescents who were exposed to natural disasters are lacking. This study investigated whether playful interaction stimulated oxytocin levels in 34 mother-child dyads who experienced the 2011 Great East Japan Earthquake in Kesennuma City in Miyagi Prefecture, Japan, and the effect of the maternal oxytocin changes on child behavior problems.

METHODS

Participants were recruited in 2012 after the Great East Japan Earthquake. Annual surveys were conducted from 2012 to 2017. Salivary oxytocin level was assessed before and after the playful interaction in 2015. Behavior problems were evaluated by caregivers, using the Child Behavior Checklist (CBCL) in 2017. Fixed effect regression analyses were conducted to determine the effect of playful mother-child interaction on oxytocin level by comparing the change in the 10 min after the interaction with the change in the 10 min before the interaction. We also examined the effect of maternal oxytocin changes before and after the playful interaction on the onset of child behavior problems in 2017.

RESULTS

A significant increase in maternal oxytocin level was detected following playful interaction, especially among mothers of first-born boys (2.63 pg/mg protein. 95% CI: 0.45, 4.81). Maternal psychological distress and trauma were also negatively associated with an increase of oxytocin levels. The increase in maternal oxytocin level was significantly associated with lower externalizing problem score of children 2 years later.

CONCLUSION

Our results might suggest a rational for potential preventive intervention for child behavior problems through playful mother-child interaction after natural disasters.

Probabilistic evaluation of the impact of compound dry-hot events on global maize yields

Weather and climate extremes, such as droughts and hot extremes, may result in marked damages to crop yields and threaten regional and global food security. Understanding the relationship between climate extremes and crop yields is of critical importance for food security under a changing climate. The objective of this study is to investigate the probabilistic variability of maize yields with respect to compound dry-hot events, which has been shown to be more stressful to crops compared with individual dry or hot events. A multivariate model is first constructed to model the joint behavior of the dry condition, hot condition, and crop yields. The response of crop yields under different dry, hot, and compound dry-hot conditions at national and global scales is then investigated based on the conditional distribution. For the major maize producing countries (top 5), the probability of maize yield reduction could increase by from 0.07 to 0.31 (from 0.04 to 0.31) when the individual extreme drought (extreme hot) conditions changed to compound dry-hot conditions. The probabilistic evaluation of compound dry-hot events' impacts on maize yields is expected to provide useful insights for the mitigation of compound events and their impacts under a changing climate.

Compound Droughts and Heat Waves in China

Droughts and heat waves both are natural extreme climate events occurring in most parts of the world. To understand the spatio-temporal characteristics of droughts and heat waves in China, we examine changes in droughts, heat waves, and the compound of both during 1961–2017 based on high resolution gridded monthly sc_PDSI and daily temperature data. Results show that North China and Northwest China are the two regions that experience the most frequent droughts, while Central China is the least drought-affected region. Significant drought decreasing trends were mostly observed Qinghai, Xinjiang, and Tibet provinces, while the belt region between Yunnan and Heilongjiang provinces experienced significant drought increasing trends. Heat waves occur more frequently than droughts, and the increase of heat wave occurrence is also more obvious. The increasing of heat wave occurrence since the 2000s has been unprecedented. The compound droughts and heat waves were mild from the 1960s to 1980s, and began to increase in 1990s. Furthermore, the significant increasing trends of the percentage of compound droughts and heat waves to droughts are observed in entire China, and more than 90% drought occurrences are accompanied by one or more heat waves in the 2010s. The results highlight the increased percentage of compound droughts and heat waves and call for improved efforts on assessing the impact of compound extremes, especially in an era of changing climate.

Variations of compound precipitation and temperature extremes in China during 1961-2014

Concurrent precipitation and temperature extremes usually have significant impacts on the society, economy and ecosystem. Changes in precipitation or temperature extremes in China have been extensively studied; however, less attention has been paid to their concurrence (or compound extremes) to date. This study explores the historical variations of compound extremes including dry/warm, dry/cold, wet/warm, and wet/cold combinations based on monthly precipitation and temperature observations during summer and winter from 1961 to 2014 over China. Results show that there is a significant increase in the frequency of compound dry/warm and wet/warm extremes while a decrease in compound dry/cold and wet/cold extremes for the period 1988-2014 relative to 1961-1987. In addition, statistically significant increase in the spatial extent exists in compound dry/warm and wet/warm extremes, while the areas affected by the compound dry/cold and wet/cold extremes present a decrease across China, which is shown to be partly related to the large-scale circulation patterns. The results of this study could improve our understanding of changes of compound precipitation and temperature extremes from a multivariate perspective.

Spatial and Temporal Variations of Compound Droughts and Hot Extremes in China

Droughts and hot extremes may lead to tremendous impacts on the ecosystem and different sectors of the society. A variety of studies have been conducted on the variability of the individual drought or hot extreme in China. However, the evaluation of compound droughts and hot extremes, which may induce even larger impacts than the individual drought or hot extreme, is still lacking. The aim of this study is to investigate changes in the frequency and spatial extent of compound droughts and hot extremes during summer in China using monthly precipitation and daily temperature data from 1953 to 2012. Results show that a high frequency of compound droughts and hot extremes mostly occur in the regions stretching from northeast to southwest of China. There is an overall increase in the frequency of co-occurrence of droughts and hot extremes across most parts of China with distinct regional patterns. In addition, an increasing trend in the areas covered by compound extremes has been observed, especially after the 1990s. At regional scales, the increase of the frequency and spatial extent of compound extremes has been shown to be most profound in North China (NC), South China (SC), and Southwest China (SWC), while the decrease of compound extremes was found in Central China (CC). These results show the variability of compound droughts and hot extremes and could provide useful insights into the mitigation efforts of extreme events in China.

Meteorological Drought Monitoring in Northeastern China Using Multiple Indices

Drought monitoring is one of the significant issues of water resources assessment. Multiple drought indices (DIs), including Percent of Normal (PN), Standardized Precipitation Index (SPI), statistical Z-Score, and Effective Drought Index (EDI) at 18 different timesteps were employed to evaluate the drought condition in Wuyuer River Basin (WRB), Northeast China. Daily precipitation data of 50 years (1960–2010) from three meteorological stations were used in this study. We found DIs with intermediate time steps (7 to 18 months) to have the highest predictive values for identifying droughts. And DIs exhibited a better similarity in the 12-month timestep. Among all the DIs, EDI exhibited the best correlation with other DIs for various timesteps. When further comparing with historical droughts, Z-Score, SPI, and EDI were found more sensitive to multi-monthly cumulative precipitation changes (r2 > 0.55) with respect to monthly precipitation changes (r2 ≤ 0.10), while EDI was more preferable when only monthly precipitation data were available. These results indicated that various indices for different timesteps should be investigated in drought monitoring in WRB, especially the intermediate timesteps should be considered.

Proteomics unravel the regulating role of salicylic acid in soybean under yield limiting drought stress

Drought is a major concern for sustainable yield under changing environment. Soybean, an economically important oil and protein crop, is prone to drought resulting in yield instability. Salicylic acid (SA), a multifaceted growth hormone, modulates a series of parallel processes to confer drought tolerance thereby relieving yield limitations. The present study was performed in soybean plants treated with SA (0.5 mM) through seed pretreatment under drought regimes: severe stress (50% RWC) and moderate stress (75% RWC), and rehydration. Differential leaf proteome profiling with morphological, physiological and antioxidative metabolism studies were performed at two developmental stages (vegetative and flowering). This explained the tolerance attribution to soybean throughout the development attaining yield stability. Abundance of proteins involved in photosynthesis and ATP synthesis generated energy driving metabolic processes towards plant growth, development and stress acclimation. Carbon (C) metabolism proteins involved in growth, osmoregulation and C partition relieved drought-induced C impairment under SA. Defensive mechanisms against redox imbalance and protein misfolding and degradation under stress were enhanced as depicted by the abundance of proteins involved in redox balance and protein synthesis, assembly and degradation at vegetative stage. Redox signaling in chloroplast and its interplay with SA signaling triggered different defense responses as shown through thioredoxin protein abundance. Amino acid metabolism proteins abundance resulted in increased osmoprotectants accumulation like proline at initial stage which contributed later towards N (nitrogen) remobilization to developing sink. At later stage, abundance of these proteins maintained redox homeostasis and N remobilization for improved sink strength. The redox homeostasis was supported by the increased antioxidative metabolism in SA treated plants. The downregulation of proteins at flowering also contributed towards N remobilization. Yield potential was improved by SA under drought through acclimation with enhanced N and C remobilization to sink as demonstrated by increased yield parameters like seed number and weight per plant, thousand seed weight and harvest index. The potential of SA in conferring drought tolerance to plants to maintain sustainable yield possess future research interests.

Assessment of ecosystem resilience to hydroclimatic disturbances in India

Recent studies have shown an increasing trend in hydroclimatic disturbances like droughts, which are anticipated to become more frequent and intense under global warming and climate change. Droughts adversely affect the vegetation growth and crop yield, which enhances the risks to food security for a country like India with over 1.2 billion people to feed. Here, we compared the response of terrestrial net primary productivity (NPP) to hydroclimatic disturbances in India at different scales (i.e., at river basins, land covers, and climate types) to examine the ecosystems' resilience to such adverse conditions. The ecosystem water use efficiency (WUE_e : NPP/Evapotranspiration) is an effective indicator of ecosystem productivity, linking carbon (C) and water cycles. We found a significant difference (p < .05) in WUE_e across India at different scales. The ecosystem resilience analysis indicated that most of the river basins were not resilient enough to hydroclimatic disturbances. Drastic reduction in WUE_e under dry conditions was observed for some basins, which highlighted the cross-biome incapability to withstand such conditions. The ecosystem resilience at land cover and climate type scale did not completely relate to the basin-scale ecosystem resilience, which indicated that ecosystem resilience at basin scale is controlled by some other ecohydrological processes. Our results facilitate the identification of the most sensitive regions in the country for ecosystem management and climate policy making, and highlight the need for taking sufficient adaptation measures to ensure sustainability of ecosystems.