Short-term Drought Research Articles

Spatiotemporal analysis of meteorological drought in El Niño years over Oromia region, Ethiopia

Drought is one of the most common natural disasters globally, having major impacts on environmental, economic, and social conditions and Ethiopia is no exception particularly the Oromia region. In order to evaluate and characterize the meteorological droughts associated with El Niño years over the Oromia region, the satellite data CHIRPS was used. The monthly time series data for the period from 1991 to 2020 was used for temporal and spatial analysis of meteorological drought using standardized precipitation index (SPI) across SPI3, SPI6, SPI9 and SPI12 indices using GeoCLIM, GIS, and Python tools. The results of this study show that meteorological droughts during the El Niño years indicated an increment across weak, moderate, and strong El Niño events over the Oromia region. The dryness was visualized with frequency and duration in time-scale across short-term drought indices in time steps. The time-scale temporal meteorological drought indices in three to twelve months show that the drought indices varied in magnitude, duration, and frequency in meteorological droughts. In general, the meteorological drought severity of correlation for the remaining zones analysis between SPI3 and SPI6, SPI6 and SPI12, and SPI9 and SPI12 indices was dominated by an increment of the correlation values over short to long timescales over the study area.

Twenty years of irrigation acclimation is driven by denser canopies and not by plasticity in twig- and needle-level hydraulics in a Pinus sylvestris forest.

Climate change is predicted to increase atmospheric vapor pressure deficit, exacerbating soil drought, and thus enhancing tree evaporative demand and mortality. Yet, few studies have addressed the longer-term drought acclimation strategy of trees, particularly the importance of morphological versus hydraulic plasticity. Using a long-term (20 years) irrigation experiment in a natural forest, we investigated the acclimation of Scots pine (Pinus sylvestris) morpho-anatomical traits (stomatal anatomy and crown density) and hydraulic traits (leaf water potential, vulnerability to cavitation (Ψ50), specific hydraulic conductivity (Ks), and tree water deficit) to prolonged changes in soil moisture. We found that low water availability reduced twig water potential and increased tree water deficit during the growing season. Still, the trees showed limited adjustments in most branch-level hydraulic traits (Ψ50 and Ks) and needle anatomy. In contrast, trees acclimated to prolonged irrigation by increasing their crown density and hence the canopy water demand. This study demonstrates that despite substantial canopy adjustments, P. sylvestris may be vulnerable to extreme droughts because of limited adjustment potential in its hydraulic system. While sparser canopies reduce water demand, such shifts take decades to occur under chronic water deficits and might not mitigate short-term extreme drought events.

Drought offsets the vegetation greenness-induced gross primary productivity from 1982 to 2018 in China

Recent studies have shown that climate stress may cause asynchronous changes between greenness and productivity, but the mechanism how greenness and productivity respond to drought remains unclear. The significant greening of vegetation in China has attracted worldwide attention; nevertheless, it is unclear whether large-scale greening will translate into productivity in the context of recent droughts. In this study, we used the vegetation index dataset to extract long-term trends in vegetation greenness and productivity from 1982 to 2018, and then chose the multi-scale Standardized Precipitation Evapotranspiration Index (SPEI) index to measure how vegetation greenness and productivity change in response to drought. The results show that significant vegetation greening did not cause a simultaneous increase in productivity during 1982–2018. Vegetation greenness and productivity trends were relatively consistent before 2000 and diverged after 2000, especially during the prolonged drought of 2000–2009. The opposite trend between greenness and productivity in humid ecosystems during drought periods mainly caused this trend difference. In humid ecosystems, short-term drought promotes vegetation greening, while long-term drought has no significant impact on greenness. However, the inhibitory effect of drought on productivity deepens with an increase in drought duration. Therefore, prolonged droughts offset vegetation greenness-induced gross primary productivity. We also found that prolonged drought causes a significant decrease in greenness but has little effect on productivity in arid ecosystems, possibly because vegetation productivity is more sensitive to soil water than atmospheric drought. Our results highlight the complexity of vegetation growth in the context of climate change. Furthermore, it is crucial to pay attention to the impact of climate stress on regional carbon sequestration functions, considering the significant carbon sink potential of forest areas in southern China.

Spatiotemporal characteristics of meteorological drought events in 34 major global river basins during 1901–2021

Meteorological drought is a crucial driver of various types of droughts; thus, identifying the spatiotemporal characteristics of meteorological drought at the basin scale has implications for ecological and water resource security. However, differences in drought characteristics between river basins have not been clearly elucidated. In this study, we identify and compare meteorological drought events in 34 major river basins worldwide using a three-dimensional drought-clustering algorithm based on the standardised precipitation evapotranspiration index on a 12-month scale from 1901 to 2021. Despite synchronous increases in precipitation and potential evapotranspiration (PET), with precipitation increasing by more than three times the PET, 47 % (16/34) of the basins showed a tendency towards drought in over half their basin areas. Drought events occurred frequently, with more than half identified as short-term droughts (lasting less than or equal to three months). Small basins had a larger drought impact area, with major drought events often originating from the basin boundaries and migrating towards the basin centre. Meteorological droughts were driven by changes in sea surface temperature (SST), especially the El Niño Southern Oscillation (ENSO) or other climate indices. Anomalies in SST and atmospheric circulation caused by ENSO events may have led to altered climate patterns in different basins, resulting in drought events. These results provide important insights into the characteristics and mechanisms of meteorological droughts in different river basins worldwide.

Detecting multidecadal variation of short-term drought risk by combining frequency analysis and Fourier transformation methods: A case study in the Yangtze River Basin

Due to the steady increase in global temperature, both long-term (seasonal or longer) and short-term (monthly to seasonal) drought events have become more frequent around the world (especially for short-term drought events). However, methods for detecting changes in short-term drought risk currently remain inadequate. We proposed a statistical method to quantify the risk of only short-term droughts by combining frequency analysis and Fourier transformation with phase-randomization. The effectiveness of the proposed method was demonstrated using the long-term data (1949–2018) of the largest river basin in China, i.e., Yangtze River Basin (YRB) where drought risk is high and short-term drought risks have rarely been evaluated. The mean short-term drought risk was 71.7 ± 11.9 % (mean ± one standard deviation). Regarding the temporal trend, short-term drought risk for 65.4 % of grids increased over time (0.23 ± 0.15 %·yr −1), of which 90.0 % were statistically significant (p < 0.05). Such spatiotemporal variability is possibly resulted from the joint influence of the Western Pacific Subtropical High, the Atlantic Multidecadal Oscillation and the Pacific Decadal Oscillation. This study proposed a new method to quantify the risk of short-term droughts that enable us to better manage short-term droughts. Our finding can help regional governments give more attention to short-term droughts and promote the rational allocation of regional drought preparedness resources in the YRB.

Elevated tolerance of both short-term and continuous drought stress during reproductive stages by exogenous application of hydrogen peroxide on soybean

The global production of soybean, among other drought-susceptible crops, is reportedly affected by drought periods, putting more pressure on food production worldwide. Drought alters plants’ morphology, physiology and biochemistry. As a response to drought, reactive oxygen species (ROS) concentrations are elevated, causing cellular damage. However, lower concentrations of ROS were reported to have an alleviating role through up-regulating various defensive mechanisms on different levels in drought-stressed plants. This experiment was set up in a controlled environment to monitor the effects of exogenous spray of different (0, 1, 5 and 10 mM) concentrations of H2O2 on two soybean genotypes, i.e., Speeda (drought-tolerant), and Coraline (drought-susceptible) under severe drought stress conditions (induced by polyethylene glycol) during flowering stage. Furthermore, each treatment was further divided into two groups, the first group was kept under drought, whereas drought was terminated in the second group at the end of the flowering stage, and the plants were allowed to recover. After 3 days of application, drought stress significantly decreased chlorophyll-a and chlorophyll-b, total carotenoids, stomatal conductance, both optimal and actual photochemical efficiency of PSII (Fv/Fm and Df/Fm, respectively), relative water content, specific leaf area, shoot length and dry weight, and pod number and fresh weight, but significantly increased the leaf concentration of both proline and total soluble sugars, the root length, volume and dry weight of both genotypes. The foliar application of 1 mM and 5 mM H2O2 on Speeda and Coraline, respectively enhanced most of the decreased traits measurably, whereas the 10 mM concentration did not. The group of treatments where drought was maintained after flowering failed to produce pods, regardless of H2O2 application and concentration, and gradually deteriorated and died 16 and 19 days after drought application on Coraline and Speeda, respectively. Overall, Speeda showed better performance under drought conditions. Low concentrations of foliar H2O2 could help the experimented soybean genotypes better overcome the influence of severe drought during even sensitive stages, such as flowering. Furthermore, our findings suggest that chlorophyll fluorescence and the cellular content of proline and soluble sugars in the leaves can provide clear information on the influence of both drought imposition and H2O2 application on soybean plants.

Spatial patterns and indices for heat waves and droughts over Europe using a decomposition of extremal dependency

Abstract. We present a method for the analysis and compact description of large-scale multivariate weather extremes. Spatial patterns of extreme events are identified using the tail pairwise dependence matrix (TPDM) proposed by Cooley and Thibaud (2019). We also introduce the cross-TPDM to identify patterns of common extremes in two variables. An extremal pattern index (EPI) is developed to provide a pattern-based aggregation of temperature. A heat wave definition based on EPI is able to detect the most important heat waves over Europe. As an extension for considering simultaneous extremes in two variables, we propose the threshold-based EPI (TEPI) that captures the compound character of spatial extremes. We investigate daily temperature maxima and precipitation deficits at different accumulation times and find evidence that preceding precipitation deficits have a significant influence on the development of heat waves and that heat waves often co-occur with short-term drought conditions. We exemplarily show for the European heat waves of 2003 and 2010 that TEPI is suitable for describing the large-scale compound character of heat waves.

Short-term drought Index forecasting for hot and semi-humid climate Regions: A novel empirical Fourier decomposition-based ensemble Deep-Random vector functional link strategy

The development of advanced technologies based on computer aid models in the domain of crops and agriculture productively is a modern advancement. Machine learning (ML) based forecasting of the short-term drought indicators (e.g., Standardized Precipitation Evapotranspiration Index (SPEI)) based on individual signals is a complex process that involves several factors including data quality and availability, inherent signal complexity, non-stationarity of climate, and uncertainty in ML models. Recent achievements in the field of Fourier-based signal processing integrated with advanced deep learning approaches have made it possible to produce very accurate intelligent frameworks for multi-temporal drought indicators and fill this gap. In this research, a new ultramodern complementary intelligent framework comprised of the SelectKbest feature selection (FS), Empirical Fourier Decomposition (EFD), and deep ensemble random vector functional link (Deep RVFL) was developed for multi-temporal monthly forecasting of short-term drought indicators for three and six months (SPEI3 and SPEI6) for two different very hot and semi-humid climate zones of Iran.. For this purpose, the most influential time lags associated with each drought indicator were indicated using the SelectKbest FS in each zone. Afterwards, the individual SPEI signals were decomposed by the EFD technique imposing the most important lagged components to feed the ML approaches. Here, a new hybrid architecture deep learning model, namely a convolutional neural network coupled with a bidirectional gated recurrent unit (CNN-Bi-GRU) designed for multi-temporal drought forecasting in the next one-and three- months. Two advanced approaches, introducing the convolutional neural network coupled with bidirectional recurrent neural network (CNN-Bi-RNN), and Random vector function link (RVFL) were adopted to validate the main model (EFD-DeepRVFL) in complementary and standalone counterpart forms. The robustness of all the models was examined using several metrics such as coefficient of determination (R2), root mean square error (RMSE), reliability, and squared Chi-square Distance (SquD). The comprehensive assessment of the outcomes of hybrid schemes revealed that EFD-DeepRVFL owing to superior performance (R2|SPEI3(t + 1) = 0.953, R2|SPEI3(t + 3) = 0.837, R2|SPEI6(t + 1) = 0.962, and R2|SPEI6(t + 3) = 0.887) at Ahvaz station and (R2|SPEI3(t + 1) = 0.964, R2|SPEI3(t + 3) = 0.863, R2|SPEI6(t + 1) = 0.935, and R2|SPEI6(t + 3) = 0.839) at Kermanshah station outperformed the EFD-RVFL and CNN-Bi-RNN, respectively. The developed expert system provides early warning of drought conditions, as a decision-making tool, crop yield prediction, and water resources risk assessment.

Extreme drought impacts have been underestimated in grasslands and shrublands globally.

Climate change is increasing the frequency and severity of short-term (~1 y) drought events-the most common duration of drought-globally. Yet the impact of this intensification of drought on ecosystem functioning remains poorly resolved. This is due in part to the widely disparate approaches ecologists have employed to study drought, variation in the severity and duration of drought studied, and differences among ecosystems in vegetation, edaphic and climatic attributes that can mediate drought impacts. To overcome these problems and better identify the factors that modulate drought responses, we used a coordinated distributed experiment to quantify the impact of short-term drought on grassland and shrubland ecosystems. With a standardized approach, we imposed ~a single year of drought at 100 sites on six continents. Here we show that loss of a foundational ecosystem function-aboveground net primary production (ANPP)-was 60% greater at sites that experienced statistically extreme drought (1-in-100-y event) vs. those sites where drought was nominal (historically more common) in magnitude (35% vs. 21%, respectively). This reduction in a key carbon cycle process with a single year of extreme drought greatly exceeds previously reported losses for grasslands and shrublands. Our global experiment also revealed high variability in drought response but that relative reductions in ANPP were greater in drier ecosystems and those with fewer plant species. Overall, our results demonstrate with unprecedented rigor that the global impacts of projected increases in drought severity have been significantly underestimated and that drier and less diverse sites are likely to be most vulnerable to extreme drought.

Millions more Egyptians will be exposed to drought by 2100 under the goals of the Paris climate agreement

The Paris climate agreement, one of the most promising global agreements on climate change, aims to limit global warming to 2.0 °C by 2100, with an even more ambitious goal of limiting it to 1.5 °C above preindustrial levels. Achieving these goals would result in the least severe impacts of climate change. This study assessed the future exposure of the Egyptian population to various drought events. An ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) models, aligned with the goals of the Paris Agreement (SSP1-1.9 and SSP1-2.6), was used to simulate future changes. The Standard Precipitation Evapotranspiration Index (SPEI) was employed to calculate drought conditions in two timeframes: early (2020–2059) and late (2060–2100). The findings revealed that even under these low-emission scenarios, an additional 33 million Egyptians would experience an increased frequency of droughts. However, the magnitude of this increase would be less for longer and more severe droughts. By 2100, the number is expected to remain almost the same for SSP1-1.9, while it would rise to 44 million for SSP1-2.6. Furthermore, the frequency of droughts would increase by 3.90 % and 2.65 % in the two futures for SSP1-1.9 and by 4.83 % in the late future for SSP1-2.6. These climate change signals were found to be robust. Overall, the study suggests that short-term droughts are likely to increase more significantly compared to longer-term droughts. The future climate projections presented in this study can provide valuable insights for developing strategies to monitor, adapt to, and mitigate the impacts of climate change in Egypt.

A Climate History of Early Dutch Settlement at Cape Town, 1652–62

This article uses historical and climatological methods to recontextualise the first decade of Dutch settlement at the Cape, 1652–62. It draws on weather data contained in the Journal of Jan van Riebeeck, natural proxies in the region, climate reanalysis and the global context of a particularly severe period during the Little Ice Age to reconstruct climatic conditions at monthly and seasonal scales for the decade under review. This reconstruction shows that the Dutch settled at a time of declining moisture at the Cape: relatively wet summers gave way to dry winters, while short-term droughts and deluges were regular occurrences. It then integrates this climatic context into an understanding of the history of this period. In so doing, it argues that adverse climatic conditions helped to undermine early Dutch designs on intensive agriculture, accelerated many free burghers’ initial transition towards pastoralism, shaped the timing and volume of the cattle trade between settlers and indigenous populations and exacerbated tensions that led to the first Khoikhoi–Dutch war (1659–60). It may also have enhanced the transmission of and deaths from disease, though the evidence in this instance is less conclusive. In short, a climate-historical perspective shows that adverse climatic and environmental factors critically affected many of the core themes that are already associated with the history of early Dutch settlement at the Cape.

Combined effects of mixing ratios and tree size: how do mixed forests respond to climate and drought events?

Although the relationship between biodiversity and ecosystem functionality (BEF) has been studied comprehensively, how the mixing ratio of tree species in mixed forests affects the response of trees to climate and drought remains an unexplored and rather unknown question. Hence, we established tree-ring chronologies for Pinus tabuliformis Carr. (P) and Quercus variabilis Blume. (Q) mixed forests with different mixing ratios. In the temperate region of China, we investigated three mixing ratios: 90% P and 10% Q (P9Q1), 60% P and 40% Q (P6Q4), and 20% P and 80% Q (P2Q8). We collected tree ring samples using three tree size categories: dominant, intermediate, and suppressed trees. We explored the climate sensitivity of these trees and their drought tolerance indices-resilience (Rs), resistance (Rt), and recovery (Rc) under two drought conditions: short-term drought (1993 drought) and long-term drought (1999-2015 drought). P6Q4 made P. tabuliformis more sensitive to the Palmer drought severity index (PDSI) from the previous year than the other two ratios. The effect of the mixing ratio on drought response was insignificant under short-term drought in both tree species. Rt, Rc, and Rs of P. tabuliformis decreased with an increasing Q. variabilis:P. tabuliformis ratio in long-term drought. Rt, Rc, and Rs of Q. variabilis were the highest in P6Q4. The sensitivity of trees to PDSI varied among classes and was influenced by the mixing ratio. Dominant trees were most sensitive to PDSI in P6Q4 and P2Q8, whereas intermediate and suppressed trees were more sensitive to PDSI in P9Q1. The impact of tree size on drought tolerance indices varied according to drought type and mixing ratio. These findings showed that the mixing ratio has a confounding effect on the drought sensitivity of temperate tree species. Differences in hydrological niches allow Q. variabilis to benefit from mixing with P. tabuliformis. Mixing with optimal proportion of P. tabuliformis maximizes the drought resilience of Q. variabilis. Additionally, weakly competitive species (P. tabuliformis) do not benefit from mixed forests during prolonged water deficits. This result complements previous arguments that species mixing reduces the biological vulnerability of individuals. This study emphasizes the importance of species selection based on the biological and physiological characteristics of tree species in the afforestation of mixed forests. It highlights the critical role of species mixing ratios in the resistance of mixed forest ecosystems to climate change, which may provide a reference for sustainable forest management.

Growth and non-structural carbohydrates response patterns of Eucommia ulmoides under salt and drought stress.

Salinity and droughts are severe abiotic stress factors that limit plant growth and development. However, the differences and similarities of non-structural carbohydrates (NSCs) responses patterns of trees under the two stress conditions remain unclear. We determined and compared the growth, physiology, and NSCs response patterns and tested the relationships between growth and NSCs concentrations (or pool size) of Eucommia ulmoides seedlings planted in field under drought and salt stress with different intensities and durations. We found that drought and salt stress can inhibit the growth of E. ulmoides, and E. ulmoides tended to enhance its stress resistance by increasing proline concentration and leaf thickness or density but decreasing investment in belowground biomass in short-term stress. During short-term drought and salt stress, the aboveground organs showed different NSCs response characteristics, while belowground organs showed similar change characteristics: the starch (ST) and NSCs concentrations in the coarse roots decreased, while the ST and soluble sugar (SS) concentrations in the fine roots increased to enhance stress resistance and maintain water absorption function. As salt and drought stress prolonged, the belowground organs represented different NSCs response patterns: the concentrations of ST and SS in fine roots decreased as salt stress prolonged; while ST in fine roots could still be converted into SS to maintain water absorption as drought prolonged, resulting in an increase of SS and a decrease of ST. Significant positive relationships were found between growth and the SS and total NSCs concentrations in leaves and branches, however, no significant correlations were found between growth and below-ground organs. Moreover, relationships between growth and NSCs pool size across organs could be contrast. Our results provide important insights into the mechanisms of carbon balance and carbon starvation and the relationship between tree growth and carbon storage under stress, which were of great significance in guiding for the management of artificial forest ecosystem under the context of global change.

The effect of plant-associated rhizobacteria on the resistance of white mustard to soil drought

The paper discusses the results of a vegetation experiment with Plant Growth-Promoting Rhizobacteria (PGPR), namely such products as Agrofil, Mizorin, Flavobakterin, and Ekstrasol, used as organic fertilizers for white mustard of the Rapsodiya/Rhapsody variety (k-4278) grown in normal soil moisture and drought. The study aims to determine the role of plant-associated PGPR strains in physiological processes and productivity of white mustard grown in normal moisture and soil drought during the critical stages of plant development. The growth stimulators showed to increase plant productivity under normal moisture facilitating growth and an increase in the number of leaves and seeds. Insufficient soil moisture disrupts water balance in plants. The seeds treated with PGPR were more physiologically stable and retained more water under short-term drought facilitating sufficient seed yield. Mizorin and Flavobakterin proved to be most effective under normal moisture, while the seeds inoculated with Agrofil performed best—both biologically and economically—in drought.

Stem Hydraulic Conductance, Leaf Photosynthesis, and Carbon Metabolism Responses of Cotton to Short-Term Drought and Rewatering

Water stress can trigger acclimation responses and damage plants. The aim of this study was to evaluate the integrative responses of cotton hydraulic conductance, leaf photosynthesis, and carbon metabolism to short-term drought and subsequent rewatering. A water-controlled pot experiment was conducted in 2020, with soil water drying continuing for one day (D1), two days (D2), and three days (D3) after it reached 40% ± 5% of the soil water holding capacity at the blooming stage of cotton, and the soil was then rewatered to the soil water holding capacity. We investigated how the stem hydraulic conductance, gas exchange, and biochemical traits of cotton were affected by imposed drought stress and subsequent rewatering. The hydraulic characteristics of cotton in the D2 and D3 treatments evolved with damage, complete closure of stomatal conductance, and complete deterioration of photosynthesis, in addition to severe floating changes in the carbon metabolism affected by drought. The leaves’ functional characteristics after rewatering cannot be completely recovered to full-irrigation levels, and the recovery extent was strongly linked to the duration. Consequently, it is considered desirable to maintain normal physiological activity during the cotton reproductive period, and the drought episode can be sustained for 1 day in a long-term perspective when the soil water content is depleted to 40% ± 5% of the soil water holding capacity. These results can provide in-depth ideas for better understanding the hydraulic and physiological responses of cotton to drought episodes and rewatering, and they can help drought-affected cotton to cope with future climate change.

Effects of short-term drought, nitrogen application and their interactions on the composition and functional genes of soil microbial communities in alfalfa grassland on the Loess Plateau

Drought and nitrogen addition are important factors influencing soil microorganisms and changes in the soil environment. In the future, droughts will become more frequent, shorter, and more severe. However, little is known about the soil organic carbon components, enzyme activity, and composition, structure, and functional genes of soil microbial communities under short-term drought and nitrogen application conditions. In this study, we used metagenomics sequencing technology to explore the changes in the composition and functional genes of soil microbial communities under short-term drought, nitrogen application, and their interactions in the artificial grasslands of the Loess Plateau. The results indicated that (I) short-term drought, nitrogen application, and their interactions all increased the particulate organic carbon (POC) content. (II) Short-term drought increased the contents of soil microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN), as well as soil enzyme activity. (III) Short-term drought significantly increased the bacterial alpha diversity, whereas the interactions of short-term drought and nitrogen application enhanced the fungal alpha diversity. (IV) The interactions of short-term drought and nitrogen application inhibited Fusarium to prevent plant diseases. (V) Short-term drought enriched the relative abundance of genes related to carbon cycling and amino acid metabolism, while nitrogen application reduced genes related to carbon cycling but enriched genes related to glycan biosynthesis and metabolism. These results clearly showed that, short-term drought altered the composition and functional genes of soil microbial communities. Our research suggests that in the event of frequent short-term droughts in the future, nitrogen addition can be considered to maintain the diversity of soil microbial communities and sustain soil carbon and nitrogen cycling.

Identifying and characterizing short-term drought with rapid onset based on the SAPEI in the Yangtze River basin

Study regionThe Yangtze River basin (YRB) Study focusThis study presented a methodology for identifying short-term drought events, with a particular emphasis on rapid onset. The standardized antecedent precipitation evapotranspiration index (SAPEI) was adopted to detect drought at the grid level within a time series dataset, and a clustering algorithm was employed to identify drought events with three-dimensional structures, followed by a comprehensive analysis of their spatiotemporal characteristics and the factors contributing to spatial heterogeneity. New hydrological insights for the regionsThe proposed method effectively captures the rapid intensification phase and the drought development processes. Short-term drought with rapid onset was predominantly observed in the northwestern region and the middle to lower reaches of the YRB from 1960 to 2014, exhibiting mild and moderate drought conditions. This spatial variability was related to the interactions among temperature, potential evapotranspiration, precipitation and wind. Furthermore, a total of 246 three-dimensional drought events were identified in the YRB. Among these events, centroid no-transfer events were prevalent, and the primary migration direction for centroid transfer events was from southeast to northwest. Additionally, short-term drought events displayed a characteristic evolution pattern, with the affected area remaining relatively stable while drought intensity exhibited a significant decrease which indicated the drought rapidly developed into more severe drought condition. This study can provide a reference for early warning and monitoring of short-term drought with rapid onset.

Simulating the Effects of Drought Stress Timing and the Amount Irrigation on Cotton Yield Using the CSM-CROPGRO-Cotton Model

Drought stress disrupts the molecular-level water balance in plants, and severe water deficiency can be fatal for cotton plants. However, mild water deficits or short-term drought stress may enhance crop resilience, increasing yields. The present study aims to determine the optimal watering time and irrigation amount to induce drought tolerance in cotton seedlings during drought training. Specifically, the investigation focuses on identifying the ideal day for watering and the corresponding irrigation volume that effectively triggers the transition of cotton plants into a state of enhanced resistance to drought stress during the seedling stage. In this study, the CSM-CROPGRO-Cotton model was utilized, and our objectives were to (i) evaluate the predictive capability of CSM-CROPGRO-Cotton for yield estimation in field experiments in Xinjiang and (ii) simulate and assess the range of time during the seedling stage when cotton plants can withstand drought stress without reducing yields, identifying irrigation strategies that induce drought training while maintaining yield under mild water deficiency. The model was validated using yield data from field experiments conducted in 2023. The validation criteria included a normalized root mean square error (nRMSE)&gt;10% and a coefficient of determination (r2)&gt;85% for yield; for the leaf area index (LAI), the criterion was (r2)&gt;90%, with a degree of agreement of (d)&gt;75%. The results demonstrated the accuracy of the CSM-CROPGRO-Cotton model in predicting cotton yield. Based on the validated CSM-CROPGRO-Cotton model, this study employed the LINUX crop model batch-processing technique to efficiently simulate 357 different irrigation strategies by adjusting the amount of “first irrigation” and timing. The findings revealed that in the irrigation scheme for cotton during the seedling stage, when the amount of first irrigation was in the lower range of 10 mm to 15 mm, the cotton plants underwent drought training during the early growth stage, and their yields did not exhibit drastic fluctuations due to reduced amounts of first irrigation. The suitable period for first irrigation for drought training was from 25 June to 6 July, and the amount of first irrigation could save approximately 57.14% in irrigation water. This implies that subjecting cotton plants to a certain level of drought training can enhance their stress tolerance and increase yields. This finding holds great significance for cotton cultivation in drought-prone regions.

Biochar Enhances the Resistance of Legumes and Soil Microbes to Extreme Short-Term Drought.

Short-term drought events occur more frequently and more intensively under global climate change. Biochar amendment has been documented to ameliorate the negative effects of water deficits on plant performance. Moreover, biochar can alter the soil microbial community, soil properties and soil metabolome, resulting in changes in soil functioning. We aim to reveal the extent of biochar addition on soil nutrients and the soil microbial community structure and how this improves the tolerance of legume crops (peanuts) to short-term extreme drought. We measured plant performances under different contents of biochar, set as a gradient of 2%, 3% and 4%, after an extreme experimental drought. In addition, we investigated how soil bacteria and fungi respond to biochar additions and how the soil metabolome changes in response to biochar amendments, with combined growth experiments, high-throughput sequencing and soil omics. The results indicated that biochar increased nitrites and available phosphorus. Biochar was found to influence the soil bacterial community structure more intensively than the soil fungal community. Additionally, the fungal community showed a higher randomness under biochar addition when experiencing short-term extreme drought compared to the bacterial community. Soil bacteria may be more strongly related to soil nutrient cycling in peanut agricultural systems. Although the soil metabolome has been documented to be influenced by biochar addition independent of soil moisture, we found more differential metabolites with a higher biochar content. We suggest that biochar enhances the resistance of plants and soil microbes to short-term extreme drought by indirectly modifying soil functioning probably due to direct changes in soil moisture and soil pH.

Drought priming at seedling stage improves photosynthetic performance and yield of potato exposed to a short-term drought stress

Potato (Solanum tuberosum L.) is an important food and vegetable crop worldwide. In recent years, the arid environment resulting from climate change has caused a sharp decline in potato yield. To clarify the effect of drought priming at the seedling stage on the tolerance of potato plants to drought stress during tuber expansion, we conducted a pot experiment to investigate the physiological response of the plants generated from seed potatoes of the variety ‘Favorita’ to varied water supply conditions: normal water supply at the seedling stage (control), normal water supply at the seedling stage and drought stress at the mid-tuber-expansion stage (non-primed), and drought priming at the seedling stage plus drought stress at the mid-tuber-expansion stage (primed). Drought priming resulted in an increase in the number of small vascular bundles in potato plants compared to non-primed plants. It also altered the shape and density of stomata, enhancing water use efficiency and reducing whole-plant transpiration. The primed plants maintained the basal stem cambium for a longer time under drought stress, which gained an extended differentiation ability to generate a greater number of small vascular bundles compared to non-primed plants. Drought priming increased the amount and rate of dry matter translocation, and so reduced the adverse effects on tubers of potato under drought stress. Therefore, drought priming at the seedling stage improved the photosynthetic performance and yield, and probably enhanced the drought tolerance of potato.