Drought Frequency Research Articles

Impacts of Climate Change: Examining Precipitation, Temperature Anomalies, and Effects on Water Resources in Turkey

Climate change is a significant global issue that affects our everyday lives and water resources. One of the most critical impacts of climate change is on our water resources, which are vital for sustaining life and supporting various ecosystems. Understanding the complex relationship between climate change and water resources is crucial for developing effective mitigation and adaptation strategies. This study aims to investigate the impact of climate change on water resources and to shed light on the importance of addressing this issue. Water is a limited resource, and its availability and quality directly affect human well-being, agriculture, energy production, and ecosystem health. However, climate change alters the patterns and dynamics of precipitation, temperature, and evaporation, leading to significant changes in water availability and distribution. Increasing global temperatures cause glaciers and ice caps to melt, progressively depleting freshwater resources. Moreover, changing precipitation patterns are leading to more frequent and severe droughts in some regions, while others experience increased rainfall and flooding events. The effects of these changes are profound. As water scarcity becomes more widespread, competition for limited resources intensifies, potentially leading to conflicts and social unrest. Given the importance of water resources and the undeniable impact of climate change on their availability and quality, it is imperative that we deepen our understanding of this complex relationship. This study aims to assess the socio-economic and environmental impacts of climate change on water resources. By doing so, it seeks to provide policymakers, scientists, and communities with information that will guide decision-making processes and direct effective climate change adaptation and mitigation strategies. In conclusion, the impact of climate change on water resources cannot be overstated. This study aims to contribute to our understanding of this critical issue by highlighting the urgency of addressing climate change and its consequences on water resources and sustainability. This study examines the effects of changing precipitation patterns and increasing temperatures due to climate change on underground and surface water resources in Turkey. By conducting this research, we hope to pave the way for informed decision-making and encourage collective responsibility to protect our water resources for future generations.

Economic Impact of Droughts in Southern Brazil, a Duration Analysis

Hydrometeorological hazards are currently a cause for great concern worldwide. Droughts are among the most recurrent events, causing significant losses. This article presents a study on the duration of droughts in the southernmost state of Brazil, which has a large agricultural sector and experiences frequent drought events. The approach focuses on the economic recovery time of municipalities affected by the drought in 2020, 2022 and 2023, using the total value of invoices issued within each municipality between companies and from companies to consumers. The Kaplan–Meier estimator and Cox regression models are applied, incorporating covariates such as the size of the municipality, geographic location, and primary economic activity sector. The results show that the longest recovery period is concentrated in small cities, particularly in those where agriculture or livestock is the primary economic activity. The greatest resilience is observed in cities within the metropolitan region, where economic activity is more concentrated in services and industry and where populations are generally larger. The study identifies that after each drought event, at least 75% of municipalities achieve economic recovery within 3 months. These findings support better planning for both drought prevention and impact reduction and they are relevant for the development of economic and social policies.

Advancing Water Security and Agricultural Productivity: A Case Study of Transboundary Cooperation Opportunities in the Kabul River Basin

The Kabul River Basin (KRB) is witnessing frequent flood and drought events that influence food production and distribution. The KRB is one of the world’s poorest regions regarding food security. Food security issues in the KRB include shifts in short-term climate cycles with significant river flow variations that result in inadequate water distribution. Due to the lack of hydro-infrastructure, low irrigation efficiency, and continuing wars, the Afghanistan portion of the KRB has experienced low agricultural land expansion opportunities for food production. This research assesses the relationship between flood mitigation, flow balances, and food production and, cumulatively, assesses the social and economic well-being of the population of the KRB. SWAT modeling and climate change (CCSM4) implications are utilized to assess how these relationships impact the social and economic well-being of the population in the KRB. The intricacies of transboundary exchange and cooperation indicate that the conservation of ~38% of the water volume would nearly double the low flows in the dry season and result in the retention of ~2B m3/y of water for agricultural developmental use. Results show that the peak flood flow routing in reservoirs on the Afghanistan side of the KRB would have a substantial positive impact on agricultural products and, therefore, food security. Water volume conservation has the potential to provide ~44% more arable land with water, allowing a ~51% increase in crop yield, provided that improved irrigation efficiency techniques are utilized.

Exploring the impacts of climate change and identifying potential adaptation strategies for sustainable rice production in Thailand's Lower Chao Phraya Basin through crop simulation modeling.

The lower Chao Phraya River Basin (CPRB) in Thailand, a major rice-producing area, is grappling with increased water scarcity alongside more frequent floods and droughts, necessitating effective adaptation strategies to sustain agricultural productivity. This study assesses the impacts of climate change on rice yield and irrigation water use, using the DSSAT-CERES-Rice model. Based on these findings, potential genotype- and management-based adaptation strategies were recommended. The model was calibrated and evaluated using the data from field experiments conducted at the Asian Institute of Technology, Thailand during 2017-2018 and 2021-2022. The grain yield and irrigation water use between baseline (2010-2022) and future climate periods (early-century: 2023-2040, mid-century: 2041-2070, and late-century: 2071-2100) were compared. Future climate projections were based on five Global Climate Models (GCMs) from the NEX-GDDP-CMIP6 project under three scenarios (SSP126, SSP245, and SSP585). The model calibration and evaluation demonstrated very good performance statistics, with a d-index of 0.85 during both calibration and evaluation. The model simulations indicated that the maximum and minimum temperatures in the lower CPRB are projected to increase by ~ 2 °C and ~ 4 °C in the late century under SSP245 and SSP585, respectively. Consequently, rice yields are projected to decline by up to 33%, and irrigation water usage to increase by 53% under SSP585 by the late century. Based on the findings, the following major genotype- and management-based adaptation strategies are recommended: (1) Developing heat-tolerant rice cultivars to mitigate yield losses under future climate scenarios, (2) Developing rice cultivars with extended grain-filling duration to enhance both irrigation water use and yield, (3) Shifting the planting date 1-2 weeks earlier (from baseline planting date of 20 July), and shifting fertilizer application date 1-2 weeks earlier (from baseline fertilizer application date of 20 September) for the panicle initiation stage to improve yield, and (4) Optimizing irrigation thresholds (remaining soil water at which to irrigate) to reduce irrigation water use without compromising yield. Overall, the findings highlight the importance of genotype improvement and adaptive management practices in mitigating the adverse effects of climate change on rice production in the lower CPRB.

An Innovative TOPSIS–Mahalanobis Distance Approach to Comprehensive Spatial Prioritization Based on Multi-Dimensional Drought Indicators

This research explores a new methodological framework that blends the TOPSIS (technique for order of preference by similarity to ideal solution) and Mahalanobis Distance methods, allowing for the prioritization of nine major watersheds in China based on the integration of multi-dimensional drought indicators. This integrated approach offers a robust prioritization model by accounting for spatial dependencies between indices, a feature not commonly addressed in traditional multi-criteria decision-making applications in drought studies. This study utilized three drought indices—the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Health Index (VHI), and Palmer Drought Severity Index (PDSI). Over years of significant drought prevalence, three types of droughts occurred simultaneously across various watersheds in multiple years, particularly in 2001, 2002, 2006, and 2009, with respective counts of 16, 17, 19, and 18 concurrent episodes. The weights derived from Shannon’s entropy emphasize the importance of the Potential Drought Severity Index (PDSI) in evaluating drought conditions, with PDSI-D (drought duration) assigned the highest weight of 0.267, closely followed by VHI-D (Vegetation Health Index under drought conditions) at 0.232 and SPEI-F (drought frequency) at 0.183. The results demonstrated considerable spatial variability in drought conditions across the watersheds, with Watersheds 1 and 4 exhibiting the highest drought vulnerability in terms of meteorological and agricultural droughts, while Watersheds 6 and 3 showed significant resilience to hydrological drought after 2012. In particular, the severe meteorological drought conditions at Watershed 1 highlight the urgent need for rainwater harvesting and strict water use policies, and in contrast, the conditions at Watershed 4 show the need for the modernization of irrigation to mitigate agricultural drought impacts. This integrated framework allows for targeted drought management solutions that directly relate to the specific contexts of the watersheds, while being more conducive to planning and prioritizing resource allocations for regions facing the highest drought vulnerability.

Prominent Increase in Air Temperatures on Two Small Mediterranean Islands, Lastovo and Lošinj, Since 1998 and Its Effect on the Frequency of Extreme Droughts

The article analyzes the development of air temperatures and precipitation on two remote islands in the Adriatic Sea from 1961 to 2023, examining annual and monthly time scales. Lastovo Island is located in the southern Adriatic, and Lošinj Island is situated 277 km north, and both exhibit a sharp rise in air temperatures since 1998, though precipitation series show no significant trends of an increase or decrease. Using the New Drought Index (NDI) method, this study calculated drought intensities for the period 1961–2023. The analyses conducted in this study undoubtedly indicate a rising frequency and intensity of droughts, with severe droughts doubling and extreme droughts increasing fourfold in the recent period (1998–2023) compared to the previous one (1961–1997). The most pronounced increase in severe and extreme droughts occurs specifically from June to August. This trend is likely applicable to many small Mediterranean Islands, which number over 10,000 and have a permanent population of more than 1.6 million people, with numbers significantly rising during the tourist season. The increased water demand for agriculture and daily use, combined with increased drought risk, not only exacerbates the potential for forest fires but also threatens social structures and ecological conditions. This is particularly critical as the combination of drier conditions and increased fire risk poses a significant challenge, endangering natural landscapes and valuable historical sites that are integral to the islands’ identity and heritage. This study’s findings indicate a dangerous trend likely to persist and worsen with continued increases in air temperatures in the Mediterranean region.

Simulated sensitivity of the Amazon rainforest to extreme drought

Abstract The Amazon rainforest is highly biodiverse and the largest intact, tropical forest in the world. Undisturbed tropical forests act as a carbon sink by taking up about 15% of anthropogenic carbon emissions per year. In the past decades, a declining trend in the carbon sink capacity in the Amazon rainforest has been observed due to increased carbon losses and tree mortality. The causes are disputed, but increasing temperatures and more frequent severe droughts are potentially major drivers. We employ a novel modelling framework and hypothesize that previously rare, extreme droughts in the Amazon, such as the ones in 2005 and 2010, constitute the main cause behind the decline of the net carbon sink in aboveground biomass. Our dynamic vegetation model simulates process-based plant hydraulics and drought-induced mortality, and accounts for the diversity of strategies in plant responses to drought based on observed hydraulic vulnerability curves. The simulated impact of the 2005 drought event temporarily turned the annual Amazon net carbon sink to a carbon source of about 0.25 MgC ha-1. In contrast to other dynamic vegetation models our model simulated an increasing trend in carbon losses and a declining trend in the Amazon carbon sink over the past 25 years (net sink rate of -0.018 Mg C ha-1 year-1 or -0.22 Mg C ha-1 per decade) which corresponds well with long-term forest monitoring data (net sink rate of -0.016 Mg C ha-1 year-1). We show that this trend is entirely attributable to drought-induced forest mortality during extreme years. The simulations show a threshold-like behaviour between drought intensity and biomass loss, which is due to xylem vulnerability, indicating the potentially high sensitivity of Amazon forests to extreme drought. Further increases in the severity and frequency of droughts might thus lead to greater carbon release and tree mortality than previously assumed.

Investigating the impacts of climate variations and armed conflict on drought and vegetation cover in Northeast Syria (2000–2023)

Over the last decades, The Northeast part of Syria (NES) has been significantly affected by multiple drought events, which are exacerbated by armed conflict and climate variations. In this study, the spatiotemporal effects of climate fluctuations on drought episodes and agricultural areas in NES from 2000 to 2023 were examined by utilizing diverse meteorological parameters combined with the normalized difference vegetation index. The relationships between the change in climatic variables and vegetation cover alterations were determined by performing different statistical methods, such as the Pearson correlation coefficient and Mann-Kendall trend analysis. The results indicated a significant decrease in the agricultural area, especially in recent years, accompanied by a notable increase in the precipitation levels. Moreover, there has been a substantial increase in temperatures, particularly in the minimum temperatures. The results also indicate that drought severity and frequency have increased since the armed conflict despite the area receiving higher precipitation amounts, highlighting the role and impacts of violence. Therefore, we recommend further research on how different vegetation species have been affected by climate change and armed conflict, defining specific growing seasons for each vegetation species, and creating land use land cover maps to understand the spatial alteration of these types better.

How Severe Was the 2022 Flash Drought in the Yangtze River Basin?

Flash droughts, characterized by their rapid onset and severe impacts, have critical implications for the ecological environment and water resource security. However, inconsistent definitions of flash droughts have hindered scientific assessments of drought severity, limiting efforts in disaster prevention and mitigation. In this study, we propose a new method for explicitly characterizing flash drought events, with particular emphasis on the process of soil moisture recovery. The temporal and spatial evolution of flash droughts over the Yangtze River Basin was analyzed, and the severity of the extreme flash drought in 2022 was assessed by comparing its characteristics and impacts with those of three typical dry years. Additionally, the driving factors of the 2022 flash drought were evaluated from multiple perspectives. Results indicate that the new identification method for flash droughts is reasonable and reliable. In recent years, the frequency and duration of flash droughts have significantly increased, with the Dongting Lake and Poyang Lake basins being particularly affected. Spring and summer were identified as peak seasons for flash droughts, with the middle reaches most affected in spring, while summer droughts tend to impact the entire basin. Compared to 2006, 2011, and 2013, the flash drought in 2022 affected the largest area, with the highest number of grids experiencing two flash drought events and a development rate exceeding 15%. Moreover, the summer heat in 2022 was more extreme than in the other three years, extending from spring to fall, especially during July–August. Its evolution was driven by the Western Pacific Subtropical High, which suppressed precipitation and elevated temperatures. The divergence of water vapor flux intensified water shortages, while anomalies in latent and sensible heat fluxes increased surface evaporation and heat transfer, further disturbing the regional water cycle. This study provides valuable insights for flash drought monitoring and early warning in the context of a changing climate.

Assessing systematic biases in farmers’ local weather change perceptions

Scientific data concerning climate change are critical for designing mitigation and adaptation strategies. Equally important is how stakeholders perceive climate change because perceptions influence decision-making. In this paper, we employ spatially-delineated primary surveys to evaluate weather perception biases among corn and soybean farmers located on western frontier of the U.S. Corn Belt where substantial loss of grassland has been documented. We characterize farmers’ perception biases by measuring the gap between survey-based perception reports for three distinct weather indicators (i.e., temperature, precipitation and drought) and corresponding meteorological evidence. About 70% farmers in our sample misperceive past weather changes. Three-fourths of these misperceiving farmers over-estimate local temperatures and drought frequency and 40% of them under-estimate precipitation trends relative to past records. We further find evidence that farmers’ weather change perceptions are systematically biased in a manner that would justify past land use decisions. Particularly, higher cropping incidence on previously protected grasslands effected more farmers to under-perceive drier conditions and over-perceive wetter conditions. Our investigation of perception biases across distinct weather indicators with a reference to past economic decisions enriches the understanding of climate change perceptions and related policies.

An Abrupt Decline in Global Terrestrial Water Storage and Its Relationship with Sea Level Change

AbstractAs observed by the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow On (GRACE-FO) missions, global terrestrial water storage (TWS), excluding ice sheets and glaciers, declined rapidly between May 2014 and March 2016. By 2023, it had not yet recovered, with the upper end of its range remaining 1 cm equivalent height of water below the upper end of the earlier range. Beginning with a record-setting drought in northeastern South America, a series of droughts on five continents helped to prevent global TWS from rebounding. While back-to-back El Niño events are largely responsible for the South American drought and others in the 2014–2016 timeframe, the possibility exists that global warming has contributed to a net drying of the land since then, through enhanced evapotranspiration and increasing frequency and intensity of drought. Corollary to the decline in global TWS since 2015 has been a rise in barystatic sea level (i.e., global mean ocean mass). However, we find no evidence that it is anything other than a coincidence that, also in 2015, two estimates of barystatic sea level change, one from GRACE/FO and the other from a combination of satellite altimetry and Argo float ocean temperature measurements, began to diverge. Herein, we discuss both the mechanisms that account for the abrupt decline in terrestrial water storage and the possible explanations for the divergence of the barystatic sea level change estimates.

Prediction and assessment of meteorological drought in southwest China using long short-term memory model

Abstract Drought prediction is crucial for mitigating risks and designing measures to alleviate its impact. Machine learning models have been widely applied in the field of drought prediction in recent years. This study concentrated on predicting meteorological droughts in southwest China, a region prone to frequent and severe droughts, particularly in areas with sparse meteorological station coverage. The long short-term memory (LSTM) predictive model, which is a deep learning model, was constructed by calculating standardized precipitation evapotranspiration index (SPEI) values based on 144 weather station observations from 1980 to 2020. The 5-fold cross-validation method was used for the hyperparameter optimization of the model. The LSTM model underwent comprehensive assessment and validation through multiple methods. This included the use of several accuracy assessment indicators and a comparison of results. The comparison covered different drought characteristics among the LSTM predictive model, the benchmark random forest (RF) predictive model, the historical drought situations, and the calculated SPEI values based on observations from 144 weather stations. The results showed that the training results of the LSTM predictive model basically agreed with the SPEI values calculated from weather station observations. The model-predicted variation trend of SPEI values for 2020 was similar to the variation in SPEI values calculated based on weather station observations. On the test set, the coefficient of determination (R 2), the root mean square error, the explained variance score, the Nash–Sutcliffe efficiency, and the Kling–Gupta efficiency were 0.757, 0.210, 0.802, 0.761, and 0.212, respectively. The total consistency rate of the drought grade was 59.26%. The spatial correlation distribution of SPEI values between LSTM model prediction and calculation from meteorological stations in 2020 was more than 0.5 for most regions. The correlation coefficients exceeded 0.6 in western Tibet and Chengdu Plains. Compared to the RF model, the LSTM model excelled in all five performance evaluation metrics and demonstrated a higher overall consistency rate for drought categories. The Kruskal–Wallis test for both the LSTM and RF models all indicated no significant difference in the distributions between the predicted and observed data. Scatter plots revealed that the prediction accuracy for both models in 2020 was suboptimal, with the SPEI showing a comparatively narrow range of values. Nonetheless, the LSTM model significantly outperformed the RF model in terms of prediction accuracy. In summary, the LSTM model demonstrated good overall performance, accuracy, and applicability. It has the potential to enhance dynamic drought prediction in regions with complex terrain, diverse climatic factors, and sparse weather station networks.

Geospatial monitoring and analysis of agricultural drought to identify hotspots and risk assessment for Senegal

Agricultural drought, characterized by insufficient soil moisture crucial for crop growth, poses significant challenges to food security and economic sustainability, particularly in water-scarce regions like Senegal. This study addresses this issue by developing a comprehensive geospatial monitoring system for agricultural drought using the Regional Hydrologic Extremes Assessment System (RHEAS). This system, with a high-resolution of 0.05°, effectively simulates daily soil moisture and generates the Soil Moisture Deficit Index (SMDI)-based agricultural drought monitoring. The SMDI derived from the RHEAS has effectively captured historical droughts in Senegal over the recent 30 years period from 1993 to 2022. The SMDI, also provides a comprehensive understanding of regional variations in drought severity (S), duration (D), and frequency (F), through S-D-F analysis to identify key drought hotspots across Senegal. Findings reveal a distinct north-south gradient in drought conditions, with the northern and central Senegal experiencing more frequent and severe droughts. The study highlights that Senegal experiences frequent short-duration droughts with high severity, resulting in extensive spatial impact. Additionally, increasing trends in drought severity and duration suggest evolving climate change effects. These findings emphasize the urgent need for sustainable interventions to mitigate drought impacts on agricultural productivity. Specifically, the study identifies recurrent and intense drought hotspots affecting yields of staple crops like maize and rice, as well as cash crops like peanuts. The developed high-resolution drought monitoring system for Senegal not only identifies hotspots but can also enables prioritizing sustainable approaches and adaptive strategies, ultimately sustaining agricultural productivity and resilience in Senegal's drought-prone regions.

Decadal trends and climatic influences on flash droughts and flash floods in Indian cities

Flash droughts/floods are extreme weather phenomena that are expected to become increasingly frequent and severe with the changing climate. Flash droughts result from a rapid decline in soil moisture, while flash floods occur due to a high extreme rainfall intensity over a short duration. This study analyzes the ERA5 reanalysis data (hourly temperature, soil moisture, and precipitation) from 1992 to 2022 to assess flash drought/flood attribute variations across fourteen Indian cities. Flash drought events are identified based on specific conditions using the obtained Soil Moisture Index (SMI) values. At the same time, we propose a novel approach to attribute flash floods by setting thresholds for precipitation and soil moisture.This study examines the frequency and trends of flash drought and flood events across India's various Köppen-Geiger climatic zones from 1992 to 2022. Jaipur and Dehradun show a statistically significant decrease in flash drought events with magnitudes of −0.0833 events/year and −0.0769 events/year, respectively. Conversely, Hyderabad exhibits a highly significant increase in flash flood events with a magnitude of 1.1851 events/year. Similarly, Bengaluru, Varanasi, and Vishakhapatnam also show substantial increases in flash flood events. These findings underscore the impact of climate change on flash droughts/floods, highlighting the necessity for sustainable strategies.

Differentiated growth of the most widely planted conifer in response to extreme droughts across semi-arid regions in Northern China

Global warming is leading to more frequent and intense drought events, exerting unprecedented pressure on forest growth. Although post-drought recovery in plantation growth has been studied enormously, the variation of planted populations across the whole distribution range of a species is not well understood. In this study, the growth suitability of the most widely planted conifer species in dry lands of northern China, Mongolian Scots pine (Pinus sylvestris var. mongolica), was estimated, based on tree-ring data sampled in 289 plots of afforestation across most of its planting range. According to changes in the hydrothermal condition during the last forty years (1980–2018), the distribution range was divided into three parts: warming, warming-wetting, and non-significant trends (NT) regions. Two severe droughts in 2000 and 2007 were identified using the Standardized Precipitation Evapotranspiration Index (SPEI). Then, the resistance (Rt), recovery (Rc), and resilience (Rs) growth indices were calculated and compared among these three parts. The growth of planted Mongolian Scots pine has overall significantly increased from 1980 to 2018 (p < 0.001). The pine plantations in warming regions were sensitive to annual precipitation, while warming-wetting regions were more influenced by the growing season climate. Pines reverted to pre-drought growth levels within 3 years after the drought event in 2000 but failed in 2007, and populations in the warming region showed significantly lower post-drought recovery. Furthermore, high pre-drought growth levels were linked to low Rs in both drought events (p < 0.001). Post-drought precipitation enhanced the Rc of plantations, compensating for the growth reduction caused by droughts. Soil moisture during the drought events enhanced Rt and Rs. The Rt and Rs indices decreased significantly with the age of plantations (p < 0.05). Overall, our results revealed that pre-drought growth and post-drought precipitation were key factors influencing the resilience of planted Mongolian Scots pine. Besides, the plantations experiencing wetting trends revealed favorable drought resilience and stable radial growth, which indicated promising potential for planted Mongolian Scots pine in those areas. Controlling the age of plantations seems to be crucial for maintaining drought resilience in large-scale plantations and maximizing ecological benefits.

Identification of thresholds and key drivers on Water Use Efficiency in different maize ecoregions in Yellow River Basin of China

Identifying the constrains on water use efficiency (WUE) of crops along a wet-to-dry gradient is important due to irrigation water scarcity, as well as the increasing drought risk under climate change in China. This study coupled five high-resolution climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6) with the Decision Support System for Agrotechnology Transfer (DSSAT)-CERES-Maize model to quantify drought risk and the drivers affecting WUE in five major maize ecoregions of the Yellow River Basin (YRB) under three future scenarios (SSP126, SSP370, and SSP585) for both the historical baseline (1985-2014) and three future periods: 2021-2040 (2030s), 2041-2070 (2050s), and 2071-2100 (2080s). And a bias correction method was implemented for the crop model to analyze optimal WUE thresholds for maize across varying dry-wet gradients. The results indicated that future drought risk will likely persist in the YRB under all scenarios, but with regional differences in drought severity and frequency. The southwestern region (V) experienced the highest frequency of drought (62.50%-SSP126), while the northwestern region (III) exhibited the lowest frequency (33.00%-SSP585) in 2030s, and 83.30% of areas in the southwestern (V) showed significant wetting in the 2080s under SSP126. The bias-corrected CERES-Maize model effectively simulated crop yield and evapotranspiration (ET), resulting in an average reduction of 4.00% and 9.73% in normalized root mean square error (nRMSE) respectively. Distinct WUE thresholds ranging from 1.96 to 8.41 kg ha−1 mm-1 were observed across various scenarios-periods in the maize regions, mostly under slight and moderate dry/wet conditions. Notably, all SSP585 scenarios demonstrated a decrease in WUE thresholds compared to the baseline. Across all scenarios and periods, WUE was mainly driven by yield in the eastern regions (I and II) but by ET in the western regions (III, IV, and V). These findings suggest that regions experiencing varying degrees of drought severity should undergo differentiated management and optimization of agricultural practices to improve WUE under future climate scenarios.

Unveiling the paleosalinity constraints on southern peri-Pannonian Lower Miocene lacustrine systems in Serbia and Bosnia and Herzegovina: Lopare (Dinaride Lake System) versus Toplica basin (Serbian Lake System)

Early Neogene saline lakes were widely developed across Central Paratethys, particularly across its „Dinaride-Anatolide“ landbridge (area of Dinarides, Balkans). The constraints on Lopare (western part of Jadar block; eastern Bosnia and Herzegovina) and Toplica (Jastrebac Mt., central Serbia) Neogene paleolakes provide a better understanding of deep-time climate evolution. The study focused on early Neogene paleolacustrine configuration, paleogeography, and the factors affecting a considerable paleosalinity increase. The study integrated available records on the predominantly Lower Miocene sedimentary sequence by analyzing and interpreting several paleosalinity markers (inorganic geochemical proxies, mineralogical data). The constraints on paleosalinity markers are afterward coupled with rather complex tectonic inferences. The two wellbores drilled in the Lopare and Toplica basins expose Neogene sections from which the critical paleosalinity markers are extracted (drillhole depths up to 350 and 1000 m, respectively). The mineral searlesite (NaBSi2O5(OH)2), and high B/Ga, markers of alkaline and arid paleoenvironmental lacustrine conditions, suggested intense evaporation (frequent drought periods). The evaporation was associated with paleoclimatic conditions during the Miocene Climatic Optimum. The Early Neogene arid climate and frequent evaporation episodes resulted in significant salinization of the Lower Miocene Lopare lacustrine sequence. The salinity in the Lopare sequence changed its character between brackish and saline, as evidenced by S/TOC and Ca/(Ca + Fe) ratios (TOC—Total Organic Carbon). At the same time, the salinity in the early Toplica basin was significantly lower and characterized by freshwater and brackish environments. The salinity variations between Toplica and Lopare were further exposed by Fe/(Ca + Mg), (Al + Fe)/(Ca + Mg), and C-value, yielding the change in the (paleo)water column. These markers further indicated the influence of warm climate conditions, consistent with the Miocene paleoclimatic record. Regarding paleogeography and Early Neogene tectonics, the results showed that before the Middle Miocene Badenian transgression, the Paratethyan Sea had no interference with the investigated intramontane lacustrine basins.

Highly Efficient Water and Nitrogen Application Strategies for Maintaining Summer Maize Yield in the North China Plain During Future Drought Years

ABSTRACTFuture frequent droughts threaten summer maize production in the North China Plain (NCP). A proper combination of irrigation and nitrogen (N) application can improve water and N use efficiency while maintaining summer maize yield. However, the optimal irrigation and N application strategies (OINASs) for summer maize during future drought years in the NCP require further exploration. This study applied the DSSAT‐CERES‐Maize model to investigate OINASs for summer maize for all drought years during 2021–2050 under three shared socioeconomic pathways (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5). The performance of the OINASs was subsequently evaluated against no irrigation and N application (CK) condition and a conventional irrigation and N application strategy (CINAS). The results highlight the following: (1) For all drought years under the three SSP scenarios, the base fertilizer rate should be 60 kg/hm2, after that the irrigation and N application are required during the jointing and heading periods. Under the SSP1‐2.6 scenario, the average values of irrigation and N application during each earlier period are 35.5 mm and 22 kg/hm2. Under the SSP2‐4.5 and SSP5‐8.5 scenarios, the average values are (34.5 mm, 23 kg/hm2) and (47.5 mm, 18 kg/hm2). (2) Under all SSP scenarios, the optimal irrigation amounts and N application rates are much lower than those under the CINAS. After applying OINASs for summer maize, an average of 1.16–1.22 billion kg of N and 2.98–5.19 billion m3 of freshwater will be saved per future drought year in the NCP. (3) Under all SSP scenarios, the summer maize yields under the OINASs are slightly and significantly greater than those under the CINAS and CK conditions. Moreover, both water and N use efficiencies improved under the OINASs compared with those under the CINAS, with more significant improvements in N use efficiency. The OINASs provide a practical way to ensure food security and environmental sustainability.

Physiological and enzyme dynamics of tomato under drought stress

Climate change leads to an increase in the frequency and severity of droughts, which have a negative impact on agriculture by altering plant growth and lowering water availability, placing food production systems at risk of sustainability. In order to improve drought resistance and ensure food security in the face of growing water shortages, this research was conducted to determine the physiological and enzymatic responses of tomato plants to drought stress. This study was conducted at Tamil Nadu Agricultural University, Coimbatore, involving 3 hybrids, viz., H1: EC169966 × LE118, H2: EC177824 × LE118 and H3: Arka Ashish × LE 27, along with their parents, P1: EC177824, P2: LE27, P3: EC169966, P4: LE118 and P5: Arka Ashish, under 50 % and 100 % field capacity conditions in factorial completely randomized design (FCRD) with 3 replications. Results indicated a significant change in physiological and enzymactic activities. Here, the parent P2 and hybrid H2 showed superior tolerance, with higher relative water content, proline, leaf water potential, membrane and chlorophyll stability index. Added to that, the response of enzyme activities including peroxidase, nitrate reductase, catalase, polyphenol oxidase and superoxide dismutase was found to be increase notably in drought-tolerant hybrids and parents, which correlated strongly with physiological markers of drought resistance. These modifications highlight the capacity of some genotypes to preserve photosynthetic efficiency and cellular integrity in water-limited environments. The results highlight the significance of choosing and developing drought-tolerant cultivars in order to maintain agricultural production in areas vulnerable to drought and address issues related to global food security.

Underestimation of meteorological drought intensity due to lengthening of the drought season with climate change

Abstract Meteorological drought may lead to water shortages, which has negative impacts on water-dependent sectors. Whilst there is a wealth of studies on changing drought intensity or frequency due to climate change, much less is known regarding potential shifts in the timing of drought. The purpose of this study is to analyse the timing of the drought season in the Netherlands and climatic changes therein, with a special focus on the onset of the drought season. Based on an analysis of meteorological observations in the Netherlands over the period 1965-2023, we conclude that the Dutch meteorological drought season has extended forward in time. On average, the drought season starts 16 days earlier in the period 1994-2023 compared to 1965-1993. This is mostly the result of an increase in potential evapotranspiration, while the amount of precipitation does not show a clear change at the start of the growing season. Using three climate model ensembles, we show that a forced climate change signal exists, but that natural variability also plays a role. Following this assessment of trends in meteorological variables, we analyse the consequences for the operational monitoring of meteorological drought. In the Netherlands, this is done by means of the `precipitation deficit’-indicator, based on a fixed-in-time starting point (1 April) of the drought season. The combination of this fixed starting point and the observed earlier onset of the drought season, means that in some years the indicator underestimates drought intensity, and that climatic trends are underestimated. We therefore advocate for an update of the operational drought indicator, such that meteorological drought occurring before 1 April will not be missed.