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• Foldscope is an efficient, low-cost, lightweight, and portable microscope. • Foldscope is an alternative for a conventional compound microscope for monitoring of the pollination process. • Foldscope can be used for field-level real-time in vivo monitoring of the pollination process. An efficient, low-cost, lightweight, and portable paper microscope can be used to monitor real-time in vivo pollination, which is not feasible with a conventional compound microscope. The advantages of paper microscopes (foldscopes) remain unexplored for in vivo field-oriented assessment of pollination. In this study, experiments were carried out to calibrate and validate the capability of foldscopes to investigate in vitro pollen traits and to understand the feasibility of using in real-time in vivo field-level investigations. Comparison of optical images of pollen morphological traits (color, size, and shape) of diverse plant species (including crops) captured using a foldscope and a compound microscope revealed little to no differences. In addition, foldscopes were calibrated to monitor and estimate in vitro pollen viability in wheat, sorghum, sunflower, chickpea, soybean, and periwinkle and to determine in vitro pollen germination in chickpea, maize, and periwinkle. Pollen tube growth was observed by time-lapse imaging of pollen from periwinkle. The foldscope efficiently captured genotypic variation in in vitro pollen germination of twelve chickpea genotypes under drought revealing the possibility of foldscopes as a tool for field level, real-time in vivo monitoring of pollination under drought. [ABSTRACT FROM AUTHOR]

Numerous recent studies have argued that moisture limitation is leading to growth declines and mortality of black spruce (Picea mariana) and white spruce (Picea glauca) in the North American boreal forest. A parallel line of research suggests that increasingly common severe wildfires are altering successional pathways and leading to long‐term replacement of spruce forests with those dominated by paper birch (Betula papyrifera, Betula neoalaskana) and trembling aspen (Populus tremuloides). When both conifers and deciduous trees establish after fire, this biome shift hypothesis implicitly assumes that deciduous species will outcompete the conifers, owing to their more rapid vertical growth and because they might be less sensitive to warm and dry conditions.We established a research site in a white spruce‐paper birch forest on an east‐facing slope in interior Alaska and tested the hypothesis that Alaska paper birch are better adapted to warm and dry conditions than white spruce. Over 6 years (2013–2018), we made hourly measurements of microclimate and xylem sap flux of both species. We also collected increment cores and conducted climate‐growth analyses for both species.During our 6‐year study, growing seasons with low volumetric soil water content (SWC) were those that followed shallow winter snowpacks and had limited summer rainfall, not necessarily those with warm air temperature. Both species were sensitive to moisture limitation. The tree‐ring data revealed significant positive effects of cumulative water year precipitation on radial growth, with a steeper slope for paper birch than for white spruce. Radial growth of both species was also positively related to mean water year air temperature. Sap flux density declined progressively for white spruce over the range of observed SWC and abruptly for paper birch when SWC fell below ~15%.Synthesis. Our results show that, while paper birch might be less sensitive to mild drought than white spruce, it may be more sensitive to severe drought, raising questions about the ability of paper birch to outcompete co‐occurring white spruce in a drier climate. [ABSTRACT FROM AUTHOR]

Awareness of the potential threat posed by drought necessitates the implementation of appropriate procedures to enable effective and systematic actions aimed at mitigating, or at least partially limiting, the impacts of drought events. This paper seeks to analyze the spatial and temporal changes of atmospheric drought in the period 1961–2020 and assesses drought hazards in southwest Bulgaria, which is a region susceptible to periodic water shortages. In this study, the standardized precipitation evaporation index (SPEI), accounting for both precipitation and temperature changes, was used to analyze drought characteristics. The analysis reveals significant temporal changes and spatial differences in drought patterns across southwest Bulgaria. The northeastern part of the region, including the Sofia district, exhibits the lowest risk of drought, while the central part of the region shows a tendency toward moderate and occasional low drought events. Some stations, particularly in the southern part of the region, consistently experienced more severe drought conditions (Blagoevgrad and Sandanski), as indicated by negative SPEI values in different time scales (3, 6, and 12 months). Results indicate an increased frequency of droughts during 1990–2020 compared to 1961–1990, which was driven by climate change and human activities. Across all stations and in both SPEI time scales, the period from the early to mid-1990s was characterized by significant droughts. The study of drought hazards using short-term and long-term SPEI analysis reveals different levels of drought risk and increased hazard from the northern to southern parts of the study area. The share of areas with a high drought hazard exceeds 40% of the territory in the areas with a transitional and continental-Mediterranean climate. Based on the results, the paper highlights the need to integrate drought risk assessments with regional planning to improve agricultural resilience and water resource management in response to anticipated droughts, especially in drought-prone areas such as southwest Bulgaria. [ABSTRACT FROM AUTHOR]

In recent decades, climatic pressures have altered the forested landscape of Bavaria. Widespread loss of trees has unevenly impacted the entire state, of which 37% is covered by forests (5% more than the national average). In 2018 and 2019—due in large part to drought and subsequent insect infestations—more tree-covered areas were lost in Bavaria than in any other German state. Moreover, the annual crown condition survey of Bavaria has revealed a decreasing trend in tree vitality since 1998. We conducted a systematic literature review regarding the remote sensing of forests in Bavaria. In total, 146 scientific articles were published between 2008 and 2023. While 88 studies took place in the Bavarian Forest National Park, only five publications covered the whole of Bavaria. Outside of the national park, the remaining 2.5 million hectares of forest in Bavaria are understudied. The most commonly studied topics were related to bark beetle infestations (24 papers); however, few papers focused on the drivers of infestations. The majority of studies utilized airborne data, while publications utilizing spaceborne data focused on multispectral; other data types were under-utilized- particularly thermal, lidar, and hyperspectral. We recommend future studies to both spatially broaden investigations to the state or national scale and to increase temporal data acquisitions together with contemporaneous in situ data. Especially in understudied topics regarding forest response to climate, catastrophic disturbances, regrowth and species composition, phenological timing, and in the sector of forest management. The utilization of remote sensing data in the forestry sector and the uptake of scientific results among stakeholders remains a challenge compared to other heavily forested European countries. An integral part of the Bavarian economy and the tourism sector, forests are also vital for climate regulation via atmospheric carbon reduction and land surface cooling. Therefore, forest monitoring remains centrally important to attaining more resilient and productive forests. [ABSTRACT FROM AUTHOR]

We describe the bias adjustment and downscaling to a resolution of 1 × 1 km of daily temperature, precipitation and solar radiation of a multimodel ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) GCMs with the local observational data set BhutanClim for Bhutan and an analysis of the results for 1996–2100. The GCMs were selected based on their ability to reproduce local temporal and spatial precipitation patterns, resulting in a total of 14 models for each shared socioeconomic pathway (SSP). The results are shown for five global warming levels (GWLs) 1.5, 2, 3, 4 and 5°C, providing insight into the projected impacts of climate change at different warming levels. The daily temperature and precipitation data are publicly available, which is of great benefit for climate impact studies, such as ecological, hydrological or infrastructure‐related studies. In this paper, we discuss showcase climate indicators. Peak‐over‐threshold climate indicators such as hot days above 30°C are relevant for human health. In the capital city of Thimphu, these are currently rare, at 4 days per year, but might increase to more than 80 for GWL5.0. Precipitation increases in the summer months, but slightly decreases in the drier winter months. Accordingly, the 3‐month SPEI drought index is projected to increase in summer, but strongly decreases in the winter months, mostly pronounced in February. The increasing severity of late‐winter and spring droughts might have a negative impact on vegetation, wildfires and water supply. [ABSTRACT FROM AUTHOR]

Understanding of past droughts has been mostly shaped by meteorological data, with relatively less known about the human aspects of droughts, their socio‐economic impacts, as well as choices people make in response to droughts in different environmental and socio‐political contexts. The lack of data that systematically record and categorize drought impacts is an important reason for this disparity. In this paper, we present an Irish drought impacts database (IDID) containing 6094 newspaper reports and 11,351 individual impact records for the island of Ireland, covering the period 1733–2019. Relevant articles were identified through systematic searching of the Irish Newspaper Archives, and recorded impacts were categorized using a modified version of the classification scheme employed by the European drought impact inventory (EDII). Drawing on the wealth and diversity of content provided by the newspapers, the IDID database provides information on the documented temporal and geographical extent of drought events, their socio‐economic and political contexts, their consequences, mitigation strategies employed and their change over time. The IDID also facilitates analysis of long‐term patterns in drought incidence, individual impact categories, as well as detailed insight into the impacts of individual drought events over nearly three centuries of Ireland's history. In addition, by allowing an examination of the coherence between meteorological records and identified impacts, it advances our understanding of the influences that contemporary economic, political, environmental and societal events had on the human experience, perception and impact of droughts. This new open‐access database, therefore, provides opportunities for improving understanding of drought vulnerability and is an important step in developing greater capacity to cope with and respond to future droughts on the island of Ireland. [ABSTRACT FROM AUTHOR]

Europe and the Mediterranean are considered climate change hot spots. This is the reason why this paper focuses on the analysis of the trends of essential climate variables in a Mediterranean country, Greece. The analyzed period is 1991–2020, and the dataset used is ERA5-Land (produced by the European Center for Medium-Range Weather Forecasts), which has global coverage and an improved resolution of ~9 × 9 km compared to other datasets. Significant climatic changes across Greece have been put in evidence during the analyzed period. More specifically, the country averaged a 30-year trend of temperature of +1.5 °C, locally exceeding +2 °C, and this increasing trend is positively correlated with the distance of the areas from the coasts. Accordingly, the number of frost days has decreased throughout the country. In terms of rainfall, a major part of Greece has experienced increasing annual rainfall amounts, while 86% of the Greek area has experienced a positive trend of days with heavy rainfall (>20 mm). Finally, a multiple signal of the trend of consecutive dry days was found (statistically non-significant in the major part of Greece). [ABSTRACT FROM AUTHOR]

South African subsistence livestock farmers rely heavily on traditional mitigation and coping mechanisms to deal with the effects of drought. However, these methods have proven ineffective in managing the full impact of drought. Consequently, policymakers are increasingly interested in promoting Index-based Pasture Insurance (IBPI). This paper aims to assess subsistence livestock farmers' preferences for IBPI. A simple random sampling method was used to select 110 subsistence livestock farmers for data collection. A discrete choice experiment—Conditional Logit and Latent Class models and incentivized lottery games were used to elicit preferences for insurance contracts and loss aversion. The findings indicate that subsistence livestock farmers have a favourable attitude toward IBPI contracts that protect against drought-related pasture degradation. The Conditional Logit model shows that farmers prefer transparent contracts that reimburse with feed and vouchers rather than cash. However, they derive negative marginal utility from basis risk and premium. However, the Latent Class model reveals heterogeneous preferences for IBPI among farmers. Farmers are also loss-averse, but loss aversion did not influence their preference for IBPI. Therefore, the primary recommendation for insurance providers is to consider customizing IBPI attributes to increase adoption among subsistence farmers. [ABSTRACT FROM AUTHOR]

Accurately and reliably predicting droughts under multiple models of Global Climate Models (GCMs) is a challenging task. To address this challenge, the Multimodel Ensemble (MME) method has become a valuable tool for merging multiple models and producing more accurate forecasts. This paper aims to enhance drought monitoring modules for the twenty-first century using multiple GCMs. To achieve this goal, the research introduces a new weighing paradigm called the Multimodel Homo-min Pertinence-max Hybrid Weighted Average (MHmPmHWAR) for the accurate aggregation of multiple GCMs. Secondly, the research proposes a new drought index called the Condensed Multimodal Multi-Scalar Standardized Drought Index (CMMSDI). To assess the effectiveness of MHmPmHWAR, the research compared its findings with the Simple Model Average (SMA). In the application, eighteen different GCM models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) were considered at thirty-two grid points of the Tibet Plateau region. Mann–Kendall (MK) test statistics and Steady States Probabilities (SSPs) of Markov chain were used to assess the long-term trend in drought and its classes. The analysis of trends indicated that the number of grid points demonstrating an upward trend was significantly greater than those displaying a downward trend in terms of spatial coverage, at a significance level of 0.05. When examining scenario SSP1-2.6, the probability of moderate wet and normal drought was greater in nearly all temporal scales than other categories. The outcomes of SSP2-4.5 demonstrated that the likelihoods of moderate drought and normal drought were higher than other classifications. Additionally, the results of SSP5-8.5 were comparable to those of SSP2-4.5, underscoring the importance of taking effective actions to alleviate drought impacts in the future. The results demonstrate the effectiveness of the MHmPmHWAR and CMMSDI approaches in predicting droughts under multiple GCMs, which can contribute to effective drought monitoring and management. [ABSTRACT FROM AUTHOR]

Genetic and agronomic advances consistently lead to an annual increase in global barley yield. Since abiotic stresses (physical environmental factors that negatively affect plant growth) reduce barley yield, it is necessary to predict barley resistance. Artificial intelligence and machine learning (ML) models are new and powerful tools for predicting product resilience. Considering the research gap in the use of molecular markers in predicting abiotic stresses, this paper introduces a new approach called GenPhenML that combines molecular markers and phenotypic traits to predict the resistance of barley genotypes to drought and salinity stresses by ML models. GenPhenML uses feature selection algorithms to determine the most important molecular markers. It then identifies the best model that predicts atmospheric resistance with lower MAE, RMSE, and higher R2. The results showed that GenPhenML with a neural network model predicted the salinity stress resistance score with MAE, RMSE and R2 values of 0.1206, 0.0308 and 0.9995, respectively. Also, the NN model predicted drought stress scores with MAE, RMSE and R2 values of 0.0727, 0.0105 and 0.9999, respectively. The GenPhenML approach was also used to classify barley genotypes as resistant and stress-sensitive. The results showed that the accuracy, accuracy and F1 score of the proposed approach for salinity and drought stress classification were higher than 97%. [ABSTRACT FROM AUTHOR]

Temperate forests are particularly exposed to climate change and the associated increase in weather extremes. Droughts, storms, late frosts, floods, heavy snowfalls, or changing climatic conditions such as rising temperatures or more erratic precipitation are having an increasing impact on forests. There is an urgent need to better assess the impacts of climate change and extreme weather events (EWEs) on temperate forests. Remote sensing can be used to map forests at multiple spatial, temporal, and spectral resolutions at low cost. Different approaches to forest change assessment offer promising methods for a broad analysis of the impacts of climate change and EWEs. In this review, we examine the potential of Earth observation for assessing the impacts of climate change and EWEs in temperate forests by reviewing 126 scientific papers published between 1 January 2014 and 31 January 2024. This study provides a comprehensive overview of the sensors utilized, the spatial and temporal resolution of the studies, their spatial distribution, and their thematic focus on the various abiotic drivers and the resulting forest responses. The analysis indicates that multispectral, non-high-resolution timeseries were employed most frequently. A predominant proportion of the studies examine the impact of droughts. In all instances of EWEs, dieback is the most prevailing response, whereas in studies on changing trends, phenology shifts account for the largest share of forest response categories. The detailed analysis of in-depth forest differentiation implies that area-wide studies have so far barely distinguished the effects of different abiotic drivers at the species level. [ABSTRACT FROM AUTHOR]

Inherent biases in numerical simulation models of global climate models (GCMs) reduce the scope for accurately assessing future droughts under multimodel ensemble. The aim of this research is to increase the efficiency of multimodel ensembles. Consequently, this paper introduces a two-way hybrid weighting scheme for ensembling multiple GCMs. The proposed weighting scheme enhances the coherence of the multimodel ensemble of climate model simulations with real observed data and minimizes the impact of non-identical seasonal behavior in climate simulations within the multimodel ensemble. In the application, we consider precipitation data from 18 GCMs of CMIP6 from the Tibet Plateau region. To evaluate the effectiveness of the proposed scheme, we compared the proposed ensemble data with simple model averaged (SMA) ensemble using the Pearson product-moment correlation coefficient (r), root mean squared error (RMSE), and mean absolute error (MAE). Furthermore, we assessed the future characteristics of various drought categories using the steady-state probabilities of a Markov Chain. Small values of errors metircs and high correlation indicate that the proposed hybrid weighting scheme exhibits better performance than the SMA. Under the proposed index, the long term probabilities of various drought classe show variations across different time scales of drought. These results provide perspectives on how the the long term probabilities of various drought categories evolve over time. [ABSTRACT FROM AUTHOR]

This paper studies the past and future annual droughts in the five agro-ecological zones (AEZs) of Cameroon. Station data and model outputs from the Coupled Model Intercomparison Project Phase 5 were used to compute areal datasets for each AEZ. Six statistical metrics and compromise programming method were used to evaluate and rank the models, respectively. The top three models were used to build multi-model ensemble (MME) and deduce bias-corrected MME data. They were then used to compute the Standardized Precipitation Index (SPI) used as drought indicator. As a result, the performance of the models depends on the AEZ and decreases with the increase in drought intensity. The 1980s was the most marked by severe-extreme droughts and a significant increase in drought intensity was observed in the entire domain during the past period, the years 1974, 1985 and 1988 showing the highest drought intensities. The MME tends to overestimate and underestimate the frequencies and the magnitude of these events, respectively. Bias-corrected MME data improve the results in most cases. As for the period 2071–2099, all the AEZs are likely to experience severe-extreme droughts which are expected to be more frequent before 2083 in the North (AEZs 1 and 2) and after this year in the South (AEZs 3, 4 and 5). It is also expected a slight increasing trend of the mean spatial SPIs showing a slight decrease in drought intensity. The RCPs 8.5 and 2.6 project the lowest and the highest decrease in drought intensity, respectively, while the RCP4.5 shows an average decrease. This study highlights future periods and areas at potential risk of severe-extreme droughts and can guide decision-makers in mitigation and adaptation measures. [ABSTRACT FROM AUTHOR]

Droughts are increasing in frequency and intensity as the global climate changes. Risk assessment for agricultural drought disasters is an important approach for disaster prevention and mitigation. Based on the formation mechanism of natural disaster risk, the index system model of agricultural drought risk assessment in North China was constructed, and the robustness of the established risk assessment model was tested by using the internal test method. Then based on the characteristics of agricultural drought risk formation in North China, the paper proposes corresponding risk prevention recommendations. The conclusions were as follows: when adjusting the indices and methods in the risk assessment model, the range of changes in the agricultural drought risk ranking for each city was mostly 1-2.5 places, and the model was relatively robust. With the exception of Beijing and Tianjin, which had the highest disaster prevention and mitigation capacity, central and northern China had the highest agricultural drought risk, which was previously linked to the high risk and vulnerability of these regions. The agricultural drought risk in northern Henan and southern Shandong is low, mainly due to the low risk. The 34 cities in North China can be divided into 6 agricultural drought risk clusters, which are not dispersed in space and cross-city distribution, and have good aggregation. Cluster 4, located in the northernmost mountainous areas, has the highest risk of drought, which is associated with high environmental vulnerability and low capacity for disaster prevention and mitigation, so agricultural areas should be reduced and agricultural input should be increased. Clusters 2, 5 and 6, located in the central and southern regions, have higher grain acreage and yields due to drought, so agricultural drought risk can be further reduced by increasing agricultural input and improving irrigation guarantee rate. [ABSTRACT FROM AUTHOR]

In recent years, there has been increasing interest in the joint modelling of compound extreme events such as high temperatures and low rainfall. The increase in the frequency of occurrence of these events in many regions has necessitated the development of models for estimating the concurrent probabilities of such compound extreme events. The current study discusses an application of copula models in predicting the concurrent probabilities of compound low rainfall and high-temperature events using data from the Lowveld region of the Limpopo province in South Africa. The second stage discussed two indicators for monitoring compound high temperature and low rainfall events. Empirical results from the study show that elevations ranging from 100–350 m, 350–700 m and 700–1200 m exhibit varying probabilities of experiencing drought, with mild droughts having approximately 64%, 66%, and 65% chances, moderate droughts around 36%, 39%, and 38%, and severe droughts at approximately 16%, 19%, and 18%, respectively. Furthermore, the logistic regression models incorporating the southern oscillation index as a covariate yielded comparable results of copula-based models. The methodology discussed in this paper is robust and can be applied to similar datasets in any regional setting globally. These findings could be useful to disaster management decision makers, helping them formulate effective mitigation strategies and emergency response plans. [ABSTRACT FROM AUTHOR]

The impacts of climate change increasingly show themselves in many forms in our everyday lives such as heatwaves and droughts. Drought is one of the critical events today for increasing drought frequency. This study focuses on meteorological drought because it directly affects other drought types. Hence, this study focuses on how the future drought conditions will vary under climate change effects in the Mediterranean basin (Turkey). In doing so, this study utilizes precipitation data from three General Circulation Models (GCMs) and three Regional Circulation Models (RCMs). The GCMs are CNRM-CM6, GFDL-CM4, and MPI-ESM1, while the RCMs are (RCA4)-CNRM-CM5, (Reg CM4)-GFDL-ESM2M, and (RCA4)-MPI-ESM-MR. Mitigating biases of the climate models, this study utilizes four statistical downscaling methods (SD), linear scaling (LS), local intensity scaling (LOCI), power transformation (PT), and distribution mapping (DM). Here, the study has two purposes. The main aim of the paper here is to compare the performance of SD methods in improving the representation of observed climate variables in climate models. In addition, the study shows how different methods will affect the spatial drought distribution in the area under the SSP2 4.5 and SSP5 8.5 scenarios. Consequently, the study uses the standardized precipitation index (SPI) and Z-score index (ZSI) to quantify future drought conditions and reaches the following results. The study reveals that mild drought conditions are prevalent in the basin for future periods, and drought indices go down to − 0.55. The study also shows that different SD methods affect the results obtained by each climate model diversely. For example, while the LS method causes the most drought conditions on the results based on CNRM-CM5 and CNRM-CM6, the DM method has a similar impact on outcomes based on GFDL-CM4 and GFDL-ESM2M and causes the most drought conditions. [ABSTRACT FROM AUTHOR]

Aim: As climate change increases the frequency and severity of droughts in many regions, conservation during drought is becoming a major challenge for ecologists. Droughts are multidimensional climate events whose impacts may be moderated by changes in temperature, water availability or food availability, or some combination of these. Simultaneously, other stressors such as extensive anthropogenic landscape modification may synergize with drought. Useful observational models for guiding conservation decision‐making during drought require multidimensional, dynamic representations to disentangle possible drought impacts, and consequently, they will require large, highly resolved data sets. In this paper, we develop a two‐stage predictive framework for assessing how drought impacts vary with species, habitats and climate pathways. Location: Central Valley, California, USA. Methods: We used a two‐stage counterfactual analysis combining predictive linear mixed models and N‐mixture models to characterize the multidimensional impacts of drought on 66 bird species. We analysed counts from the eBird participatory science data set between 2010 and 2019 and produced species‐ and habitat‐specific estimates of the impact of drought on relative abundance. Results: We found that while fewer than a quarter (16/66) of species experienced abundance declines during drought, nearly half of all species (27/66) changed their habitat associations during drought. Among species that shifted their habitat associations, the use of natural habitats declined during drought while use of developed habitat and perennial agricultural habitat increased. Main Conclusions: Our findings suggest that birds take advantage of agricultural and developed land with artificial irrigation and heat‐buffering microhabitat structure, such as in orchards or parks, to buffer drought impacts. A working lands approach that promotes biodiversity and mitigates stressors across a human‐induced water gradient will be critical for conserving birds during drought. [ABSTRACT FROM AUTHOR]

Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends). Plain Language Summary: Our colleagues Drs. Paciorek and Wehner have raised concerns about our paper (Zhang et al., 2021, https://doi.org/10.1029/2020gl092293), which showed widespread increases in the duration of drought events over the last five decades in the western United States. They point out that our decision to smooth the data using a moving average inflated the number of weather stations at which the trends toward longer droughts were deemed significant by a statistical test. We agree with them on this point, and we have recomputed all our results using unsmoothed data to determine the impacts. We find that for most stations and regions, trend magnitudes remained largely unchanged, with many stations nearby one another often suggesting similar trends. Finally, we agree with Paciorek and Wehner that there is a lack of statistical methods to test such coherent regional patterns, and we caution that over‐reliance on p‐values limits the power of regional data to identify important climate trends. Key Points: We agree that smoothing to 5‐year moving windows introduced serial correlation into time series of annual statistics of daily rainfall data, inflating the number of weather stations individually showing significant trends (p < 0.05) with the Mann‐Kendall testRecomputation with unsmoothed values produced substantially the same dry intervals trend magnitude and direction at most stations individually and had only minimal impacts on dry interval trends computed for National Ecological Observatory Network domains using the Regional Kendall testNo perfect statistical approach leverages the capacity of coherent regional patterns among spatially correlated weather stations, and an over‐reliance on p‐values as a binary (significant vs. insignificant) determinant of trends limits the power of regional data [ABSTRACT FROM AUTHOR]

Path-breaking transformative change is needed in human-environment relations to move towards more sustainable development trajectories at local, national and global scales. Crises may trigger transformative change and learning in the short and in the long term. However, in particular, a short-term response to crises may also be reactive, strengthening established unsustainable practices and further perpetuating vulnerability and inequality rather than supporting transformative change towards a more sustainable path. To understand the nature and response to a crisis in the context of sustainability transformations, this paper elaborates on the following aspects of a crisis: What are the characteristics of a crisis? What and who shapes the narrative(s) of a crisis? What and who shapes the nature of the response to a crisis? Do responses to crises trigger higher levels of learning? Conceptual synthesis is complemented with an exploratory comparative analysis of the Cape Town water crisis and of the COVID-19 pandemic in South Africa. To this end the paper analyzes the interplay between mobilizing individual, collective and relational agency and navigating and transforming power relations to challenge and profit from already weakened unsustainable structures. This approach proves to be promising to understand the role of crises in catalysing and supporting transformative learning to eventually replace unsustainable structures. ● During and immediately after crises, it is important to identify opportunities for policy change to address persistent governance failures. ● To support transformative change towards sustainability, governments typically should adopt a network governance style and act more as a convenor for deliberative processes in the later phase of the response to a crisis. ● Formation of innovation platforms bringing together actors from different levels and different roles (e.g. pioneering innovators, investors, scientists, policymakers, regulators) could support the scaling up of local initiatives and innovative approaches that have been developed during crises. [ABSTRACT FROM AUTHOR]

Agro-pastoral ecotone of northern China is the prominent area for agricultural production, but it is also the most typical ecological fragile area with frequent drought disasters. Taking Yulin City at Shaanxi Province in China as the case area, the paper aims to investigate the spatio-temporal changes of agricultural vulnerability to drought in China's agro-pastoral ecotone in the period 2000 to 2020. The results show that: 1) the agricultural vulnerability to drought in Yulin City has shifted from high vulnerability in the period 2000–2010 to low vulnerability in the period 2011–2020. 2) There exist obvious spatio-temporal differences of the agricultural vulnerability to drought in Yulin City during the research period. 3) Four sensitive events and 14 resilient events were identified in the research and the crops of Yulin had become more resilient to drought. Finally, the paper put forward with policy implications to make adaptive strategies of agriculture to climate change in China's agro-pastoral ecotone in the future, e.g., carrying out agricultural zoning based on agricultural production conditions, intensifying the construction of disaster prevention and relief system, and integrating with modern agricultural technology to develop new type agriculture. [ABSTRACT FROM AUTHOR]

Food security is a key pillar of environmental security yet remains one of the world's greatest challenges. Its obverse, food insecurity, negatively impacts health and well-being, drives mass migration, and undermines national security and global sustainable development. Ensuring food security is a delicate balance of myriad concerns within the atmospheric and Earth sciences, agronomy and agriculture engineering, social sciences, economics, monitoring, and policymaking. A Food Security Presidential Session at the American Meteorological Society's (AMS) 2022 Annual Meeting brought together experts across disciplines to tackle issues at the nexus of weather, climate, and food security. The starkest takeaway was the realization that, despite its importance and clear roles for the atmospheric and climate sciences, food security has not been a focus for the AMS community. The aim of this paper is to build on the perspectives shared by this expert panel and to identify overlapping issues and key points of intersection between the food-security community and AMS. We examine 1) the interactions between weather, climate, and the food system and how they influence food security; 2) the time and spatial scales of food security decision support that match weather and climate phenomena; 3) the role of both providers and users of information as well as decision-makers in improving research to operations for food security; and 4) the opportunities for the AMS community to address food security. We conclude that, moving forward, the AMS community is well-positioned to scale up its engagement across the global food system to address existing scientific needs and technology gaps to improve global food security. SIGNIFICANCE STATEMENT: We examine how members of the AMS community can help ensure global food security, whether they are engaged in the physical and social sciences or the realms of policy and society. Inspired by the messages of panelists at a Presidential Forum on Food Security at the 2022 AMS Annual Meeting, we gather their perspectives and those of experts working in the various relevant fields and find that there are roles for everyone across the AMS—from providing forecasts of the hydrometeorological variables used in food security outlooks to converting data to knowledge and engaging with stakeholders and decision-makers. We make concrete suggestions to ensure the AMS and its members are fully engaged in feeding the world. [ABSTRACT FROM AUTHOR]

Water stress (drought and waterlogging) leads to an imbalance in plant water distribution, disrupts cell homeostasis, and severely inhibits plant growth. Melatonin is a growth hormone that plants synthesise and has been shown to resist adversity in many plants. This review discusses the biosynthesis and metabolism of melatonin, as well as the changes in plant morphology and physiological mechanisms caused by the molecular defence process. Melatonin induces the expression of related genes in the process of plant photosynthesis under stress and protects the structural integrity of chloroplasts. Exogenous melatonin can maintain the dynamic balance of root ion exchange under waterlogging stress. Melatonin can repair mitochondria and alleviate damage caused by reactive oxygen species and reactive nitrogen species; and has a wide range of uses in the regulation of stress-specific genes and the activation of antioxidant enzyme genes. Melatonin improves the stability of membrane lipids in plant cells and maintains osmotic balance by regulating water channels. There is crosstalk between melatonin and other hormones, which jointly improve the ability of the root system to absorb water and breathe and promote plant growth. Briefly, as a multifunctional molecule, melatonin improves the tolerance of plants under water stress and promotes plant growth and development. Melatonin, as an exogenous hormone, has been widely used in improving plant stress resistance. This paper systematically describes how melatonin improves the ability of plants to resist water stress (drought and waterlogging) in the processes of photosynthesis, antioxidation and osmosis at the physiological and genetic levels. Melatonin can also improve plant stress resistance with other hormone crosstalk, which has high application value. [ABSTRACT FROM AUTHOR]

Climate change is expected to have long-term impacts on drought and wildfire risks in Oregon as summers continue to become warmer and drier. This paper investigates the projected changes in drought characteristics and drought propagation in the Umatilla River Basin in northeastern Oregon for mid-century (2030–2059) and late-century (2070–2099) climate scenarios. Drought characteristics for projected climates were determined using downscaled CMIP5 climate datasets from ten climate models and Soil and Water Assessment Tool to simulate effects on hydrologic processes. Short-term (three months) drought characteristics (frequency, duration, and severity) were analyzed using four drought indices, including the Standardized Precipitation Index (SPI-3), Standardized Precipitation-Evapotranspiration Index (SPEI-3), Standardized Streamflow Index (SSI-3), and the Standardized Soil Moisture Index (SSMI-3). Results indicate that short-term meteorological droughts are projected to become more prevalent, with up to a 20% increase in the frequency of SPI-3 drought events. Short-term hydrological droughts are projected to become more frequent (average increase of 11% in frequency of SSI-3 drought events), more severe, and longer in duration (average increase of 8% for short-term droughts). Similarly, short-term agricultural droughts are projected to become more frequent (average increase of 28% in frequency of SSMI-3 drought events) but slightly shorter in duration (average decrease of 4%) in the future. Historically, drought propagation time from meteorological to hydrological drought is shorter than from meteorological to agricultural drought in most sub-basins. For the projected climate scenarios, the decrease in drought propagation time will likely stress the timing and capacity of water supply in the basin for irrigation and other uses. [ABSTRACT FROM AUTHOR]

There are several standardized meteorological, hydrological, agricultural, and environmental indices for drought classification into a set of dry categories all of which are derived from the basic concept of standardized precipitation index (SPI). Almost all these indices are based on crisp (bivalent) logic where boundary limits between neighboring categories are numbers without transboundary inclusiveness. The SPI procedure depends on the standard normal (Gaussian) probability distribution function (PDF) with zero mean and standard deviation equal to one. This paper proposed fuzzification of the SPI limits among categories for the inclusion of more than one category with different membership degrees. This method is referred to as the fuzzy SPI (FSPI) procedure, which provides drought tracing possibility and categorization. At the end for a numerical value, one can defuzzify the fuzzy result through defuzzification methods. FSPI provides preliminary warning system in terms of two or more fuzzy rule propositions and categories. Thus, one can know the logical alternatives of the drought behavior of a given hydro-meteorology variable. The application of the FSPI is presented for New Jersey State wise and Istanbul annual precipitation records. [ABSTRACT FROM AUTHOR]

Significance: A paper recently published in AIDS and Behavior on 20 February 2024, authored by Trickey et al.1, argues that drought potentially increases HIV (Human Immunodeficiency Virus) transmission in sub-Saharan Africa. They established a positive correlation between drought, poverty, sexual behaviour, and HIV contraction. The study's methodology is problematic, and does not consider key confounding factors, utilises data which is subject to biases, and utilises inappropriate means to define drought. We do not dispute the possibility that links exist between these variables, but assert that the data and methods used in this study are insufficient to support such a claim. [ABSTRACT FROM AUTHOR]

The future risk for droughts and water shortages calls for substantial efforts by authorities to adapt at local levels. Understanding their perception of drought hazards, risk and vulnerability can help to identify drivers of and barriers to drought risk planning and management in a changing climate at the local level. This paper presents a novel interdisciplinary drought case study in Sweden that integrates soft data from a nationwide survey among more than 100 local practitioners and hard data based on hydrological measurements to provide a holistic assessment of the links between drought severity and the perceived levels of drought severity, impacts, preparedness, and management for two consecutive drought events. The paper highlights challenges for drought risk planning and management in a changing climate at the local level and elaborates on how improved understanding of local practitioners to plan for climate change adaptation can be achieved. [ABSTRACT FROM AUTHOR]

In Senegal, agriculture is an important sector underpinning the socioeconomic fabric of the populace. Notably, the agricultural production in this region exhibits heightened sensitivity to climatic perturbations, particularly droughts and heat waves. This study aims to determine the resilience of different agronomic interventions for farmers practicing mixed farming that produce both crops (i.e., groundnut (Arachis hypogaea L.) and pearl millet (Pennisetum glaucum (L.) R. Br.)) and raise animals in the Groundnut Basin in Senegal, which holds historical and socioeconomic significance. To understand the current situation regarding demographics, economics, consumption behavior, and farm operations for smallholder farmers, data were comprehensively collected from government and nongovernment organizations (NGO) reports, scientific papers, organization databases, and surveys. Additionally, the Agricultural Production Systems sIMulator (APSIM) was used to understand how combinations of three planting dates, three plant densities, and six urea nitrogen (N) fertilizer rates affected the yield of pearl millet, which were used as the alternative scenarios to the baseline in the farm modeling and analyses. All the collected and generated data were used as inputs into the Farm Simulation Model (FARMSIM) to generate economic, nutritional, and risk data associated with mixed farming systems. The generated data were then used to determine the resilience of the alternative scenarios against the baseline. Initially, a multi‐objective optimization was employed to meet nutritional needs while maintaining a healthy diet at the lowest cost. Then, the scenarios that met the population's nutritional requirements were evaluated based on four economic indicators: net cash farm income (NCFI), ending cash reserves (EC), net present value (NPV), and internal rate of return (IRR). Lastly, those that passed the economic feasibility test were ranked based on risk criteria certainty equivalent (CE) and risk premium (RP). The analyses found N fertilizer rates of 0, 20, and 100 kg N ha−1 were generally economically not feasible. Additionally, medium (early‐July to late‐August) and late (late‐July to mid‐September) planting dates generally performed better than early (early‐June to late‐July) planting dates, while plant densities of 3.3 and 6.6 pL m−2 performed better than 1.1. The robust resilience approach introduced in this study is easily transferable to other regions. [ABSTRACT FROM AUTHOR]

Meteorological hazards, such as floods, can develop rapidly and are usually a local phenomenon. On the other hand, drought is a phenomenon arising over longer periods. Moreover, it occurs in areas that are remarkably diverse in terms of surface area. Drought has a massive impact on agriculture, socio-economic activities, and the natural environment. However, determining the losses associated with the phenomenon of drought and its identification is complicated. The aim of this paper is to identify and quantify droughts using climatic indices, which include the combined climatologic deviation index, groundwater drought index, water storage deficit index and multivariate standardized drought index. Based on the research, it was concluded that the CCDI, GGDI, WSDI, and MSDI indicators can be a useful tool, on the basis of which it was possible to analyze drought periods. These periods were not related to changes and loss of groundwater, but resulted from low rainfall and snowfall. [ABSTRACT FROM AUTHOR]

Accurate, timely, and accessible meteorological and soil moisture measurements are essential for a number of applications including weather forecasting, agricultural decision-making, and flood and drought prediction. Such data are becoming increasingly available globally, but the large number of networks and various data reporting formats often make utilization of such data difficult. The TexMesonet is a "network of networks" developed within the state of Texas to collect, process, and make public data collected from more than 1700 monitoring stations throughout the state. This paper describes the TexMesonet, with special attention paid to monitoring sites installed and managed by the Texas Water Development Board. It also provides a case study exemplifying how these data may be used and gives recommendations for future data applications. [ABSTRACT FROM AUTHOR]

The paper is focused on the following research objectives: (1) characterization of the elements of physical-geographic similarity of the Oltenia Plain, (2) recording the evolution over time of temperature and precipitation parameters from the five meteorological stations located in the surveyed area (3) analysis of the index of desertification (De Martonne), and (4) investigating the distribution of vegetation (CLC), which may bring changes in the analysis of interval 1990-2018. The extreme meteorological phenomenon –desertification - for the Oltenia Plain was analysed in terms of time and space, through the calculation and statistical interpretation of the Martonne Index. In Oltenia Plain, there is an obvious altitudinal zoning of vegetation as result of the fact that the altitude increases from the south (about 18-50 m in the Danube Meadow) to the north (about 200 m in the Getic Piedmont), which means a decrease in temperatures in highlands, and, to a certain extent, an increase in the amounts of average annual precipitation in the meadow areas. The purpose of this research was the analysis of the vegetation's territorial distribution, taking into account the values, the “De Martonne” desertification? Index, the information presented in books and scientific articles, but also the Corine Land Cover statistical database (CLC 1990/2012- 2018). [ABSTRACT FROM AUTHOR]

Academic research on mining and climate change and associated impacts on water resources particularly for the developing world has been limitedly explored. Climate change is expected to make water insecurity in rural areas more severe as weather patterns become unfavourable. The extent to which the mining sector is able to reduce its impact on water resources and adapt to climate change will have implications for host communities. This paper explores the relationship between climate change, mining development and water security and how this places rural communities in a position of risk from mining development and climate change for water security. This paper focuses on the Fuleni and Somkhele rural communities located within the uMkhanyakude District Municipality in Northern KwaZulu-Natal, a climate change-induced water scarce area. Despite drought, mining operations continue. Semi-structured interviews were used to collect data from key social actors in the Fuleni community (i.e. residents opposing mining development) and with Somkhele residents (already burdening with mining operations). Additionally for Somkhele, a questionnaire was used to ascertain 424 household views on the impacts of climate variability and mining impacts on livelihoods and water. Results indicated an interplay between climate change, mining impacts and water (and food) security. Development must be implemented in an integrated and holistic manner that contributes to sustainable development and does not impact on water resources. Technological innovations related to water and energy and inter-sectoral collaborations must be prioritised between mining, the government, and civil society, to achieve water security. [ABSTRACT FROM AUTHOR]

West African rainfall is an important part of the global climate system that influences the Atlantic thermohaline circulation, hurricane activities, and dust transport. The water cycle is linked to the monsoon and its interannual to decadal variations. Over the past decades, West Africa has seen major climate variability with extended droughts that had negative effects during the 1970s and 1980s. Indeed, when it is too scarce, rain causes shortages, reduces agricultural yields, and leads to migrations. On the other hand, when it is too abundant, it causes catastrophic floods and poses threats to populations, water resources as well as natural and farmlands. In this paper, drought is considered as part of climate-related hazards and one of the main hydrometeorological extreme events occurring in West Africa. The exposure to drought has made the region more vulnerable. Thus, two sites, namely the Niger river basin and the Bandama watershed (Côte d'Ivoire), are studied in this paper to review and analyze the weather and climate extreme events that affect vast areas of West Africa. Grounded in remote sensing, statistical, and socio-anthropological approaches, this work first reviews drought as observed from space; then assesses rainfall and evapotranspiration between 1970 and 2013 as indicators of risks of water resources scarcity in the hydro-system of the Bandama river in Côte d'Ivoire. The results reveal that the West African region is highly vulnerable to this hydrometeorological extreme event with heavy impacts on people and the economy due to a large dependency on rainfed agriculture. Thus, planning and management of drought require a change of paradigm. In addition, more comprehensive studies on hydrometeorological extreme events are necessary and policies must be better designed to significantly improve the tackling of droughts with better mitigation strategies. [ABSTRACT FROM AUTHOR]

The water table depth (WTD) in peatlands determines the soil carbon decomposition rate and influences vegetation growth, hence the above‐ground carbon assimilation. Here, we used satellite‐observed Solar‐Induced chlorophyll Fluorescence (SIF) as a proxy of Gross Primary Production (GPP) to investigate water‐related vegetation stress over northern peatlands. A linear model with interaction effects was used to relate short‐ and long‐term anomalies in SIF with WTD anomalies and the absolute WTD. Most locations showed the occurrence of drought and waterlogging stress though regions with exclusively waterlogging or drought stress were also detected. As a spatial median, minimal water‐related vegetation stress was found for a WTD of −0.22 m (short‐term) and −0.20 m (long‐term) (±0.01 m, 95% confidence interval of statistical uncertainty). The stress response observed with SIF is supported by an analysis of in situ GPP data. Our findings provide insight into how changes in WTD of northern peatlands could affect GPP under climate change. Plain Language Summary: Water table depth is an important variable influencing the carbon cycle and vegetation growth in northern peatlands. In this paper, the impact of changing wetness conditions on vegetation growth over peatlands was studied through satellite measurements of solar‐induced fluorescence (SIF), which is a radiation signal emitted by vegetation during photosynthesis. Previous studies over ecosystems on mineral soil, that is, not over peatland, suggested a response of SIF to drought conditions. In our study, it was shown that peatland vegetation experiences moisture‐related growth stress under both very wet and very dry conditions, which might reduce the photosynthesis efficiency and the ability to capture and store CO2. Stress due to drought conditions was detected for peatlands in the south of the Western Siberian Lowlands and the Boreal Plains. Stress due to prolonged wet conditions occurred for example, in the north of the Western Siberian Lowlands and the north of the Hudson Bay Lowlands. Key Points: Spaceborne Solar‐Induced Fluorescence (SIF) data was used to analyze soil moisture‐related vegetation stress regimes in northern peatlandsFor most locations, waterlogging as well as drought stress regimes occurred and alternated depending on peatland water level dynamicsThe SIF‐based stress response observations are supported by in situ data of Gross Primary Production [ABSTRACT FROM AUTHOR]

Recent research has revealed that the dynamics of autumn phenology play a decisive role in the inter-annual changes in the carbon cycle. However, to date, the shifts in autumn phenology (EGS) and the elements that govern it have not garnered unanimous acknowledgment. This paper focuses on the Yellow River Basin (YRB) ecosystem and systematically analyzes the dynamic characteristics of EGS and its multiple controls across the entire region and biomes from 1982 to 2015 based on the long-term GIMMS NDVI3g dataset. The results demonstrated that a trend toward a significant delay in EGS (p < 0.05) was detected and this delay was consistently observed across all biomes. By using the geographical detector model, the association between EGS and several main driving factors was quantified. The spring phenology (SGS) had the largest explanatory power among the interannual variations of EGS across the YRB, followed by preseason temperature. For different vegetation types, SGS and preseason precipitation were the dominant driving factors for the EGS in woody plants and grasslands, respectively, whereas the explanatory power for each driving factor on cultivated land was very weak. Furthermore, the EGS was controlled by drought at different timescales and the dominant timescales were concentrated in 1–3 accumulated months. Grasslands were more significantly influenced by drought than woody plants at the biome level. These findings validate the significance of SGS on the EGS in the YRB as well as highlight that both drought and SGS should be considered in autumn fall phenology models for improving the prediction accuracy under future climate change scenarios. [ABSTRACT FROM AUTHOR]

Predicting drought severity is essential for drought management and early warning systems. Although numerous physical model-based and data-driven methods have been put forward for drought prediction, their abilities are largely constrained by data requirements and modeling complexity. There remains a challenging task to efficiently predict categorial drought, especially for the U.S. Drought Monitor (USDM). Aiming at this issue, multiple Markov chains for USDM-based categorial drought prediction are successfully proposed and evaluated in this paper. In particular, this study concentrated on how the Markov order, step size, and training set length affected prediction accuracy (PA). According to experiments from 2000 to 2021, it was found that the 1-step and first-order Markov models had the best accuracy in predicting droughts up to 4 weeks ahead. The PA steadily dropped with increasing step size, and the average accuracy at monthly scale was 88%. In terms of seasonal variability, summer (July-August) had the lowest PA while winter had the highest (January-February). In comparison with the western region, the PA in the eastern United States is 25% higher. Moreover, the length of the training set had an obvious impact on the PA of the model. The PA in 1-step prediction was 87% and 78% under 20- and 5-yr training sets, respectively. The results of the study showed that Markov models predicted categorical drought with high accuracy in the short term and showed different performances on time and space scales. Proper use of Markov models would help disaster managers and policy makers to put mitigation policies and measures into practice. [ABSTRACT FROM AUTHOR]

Water scarcity is a growing problem in many regions worldwide. According to the United Nations, around one-fifth of the world's population lives in areas where water is scarce. Another one-quarter of the world's population has to face water supply cuts, mainly because this proportion of the population lacks the necessary infrastructure to acquire water from rivers and aquifers (UN, 2005). Water is a resource that is essential to human survival and is also present in all productive processes in the economy. Therefore, we are challenged to adequately manage water to ensure the population's well-being and to achieve socioeconomic development. Specifically, this paper analyzes the situation present in the summer of 2022 at Riudecanyes (a village in Catalonia, Spain), where a drought problem exists. We propose applying the conflicting claims problem theory to give possible solutions to distribute the water. We propose to use this theory to describe the distribution of the available irrigation hours in 2022, considering the demand made by the farmers in the previous year, when there was regular irrigation. [ABSTRACT FROM AUTHOR]

Drought stress remains one of the most detrimental environmental constraints that hampers plant growth and development resulting in reduced yield and leading to economic losses. Studies have highlighted the beneficial role of carbon-based nanomaterials (NMs) such as multiwalled carbon nanotubes (MWNTs), single-walled carbon nanotubes (SWNTs), graphene, fullerene, and metal-based nanoparticles (NPs) (Ag, Au, Cu, Fe2O3, TiO2, and ZnO) in plants under unfavorable conditions such as drought. NPs help plants cope with drought by improving plant growth indices and enhancing biomass. It improves water and nutrient uptake and utilization. It helps retain water by altering the cell walls and regulating stomatal closure. The photosynthetic parameters in NP-treated plants reportedly improved with the increase in pigment content and rate of photosynthesis. Due to NP exposure, the activation of enzymatic and nonenzymatic antioxidants has reportedly improved. These antioxidants play a significant role in the defense system against stress. Studies have reported the accumulation of osmolytes and secondary metabolites. Osmolytes scavenge reactive oxygen species, which can cause oxidative stress in plants. Secondary metabolites are involved in the water retention process, thus improving plant coping strategies with stress. The deleterious effects of drought stress are alleviated by reducing malondialdehyde resulting from lipid peroxidation. Reactive oxygen species accumulation is also controlled with NP treatment. Furthermore, NPs have been reported to regulate the expression of drought-responsive genes and the biosynthesis of phytohormones such as abscisic acid, auxin, gibberellin, and cytokinin, which help plants defend against drought stress. This study reviewed 72 journal articles from 192 Google Scholar, ScienceDirect, and PubMed papers. In this review, we have discussed the impact of NP treatment on morphological, physio-biochemical, and molecular responses in monocot and dicot plants under drought conditions with an emphasis on NP uptake, transportation, and localization. [ABSTRACT FROM AUTHOR]

Context: A significant opportunity remains to further increase the supply of premium sheepmeat products to Australia's customers, which requires a more rapid rebuilding of the national sheep flock. To help meet this challenge, developing relevant information for sheep producers to assist them to achieve a profitable flock-rebuilding outcome is viewed as highly desirable. Aims: (1) Develop flock-rebuild scenarios that track inventory, cashflow, profit and loss, and the overall balance sheet over a 5-year projection. (2) Model the impact on flock-rebuilding pathways of exposure to variation in supplementary-feed costs, purchase of stock and price received for sale animals and flock structure. (3) Model variation in benefits and costs for a range of production zones, enterprise types and flock-age profiles. Methods: Fourteen flock-rebuilding pathways were assessed for up to nine flock types, using a spreadsheet model that tracked inventory, cashflow, profit and loss, and the overall balance sheet over 5 years. Key results: The top four pathways for profitability and capacity to rapidly rebuild flock numbers include retention of more older ewes, purchasing young ewes, purchasing older ewes (mostly 5–6-year olds) in Merino and Merino-cross flock types only and joining ewe lambs in Maternal and Cleanskin flock types only. These pathways were not sensitive to variation in the cost of supplementary feed, stock prices or flock structure, although joining Merino ewe lambs became one of the top four pathways when feed costs were lower. Conclusions and implications: The well established practices of retaining ewes for longer and purchasing ewes, especially young ewes, have the most potential to both rapidly and profitably rebuild flock numbers. However, joining ewe lambs, particularly in Maternal and Cleanskin flocks, can also profitably contribute to rapid flock rebuilding. While reducing reproductive wastage or increasing reproductive potential were mostly profitable, they could not rapidly rebuild flock numbers. In contrast, accelerated lambing systems can rapidly rebuild flock numbers, but are only marginally profitable. The outlook for Australia's sheepmeat industry is very positive, with a significant opportunity available to further increase the supply of premium sheepmeat products to customers, by more rapidly rebuilding the national flock. This paper presents the findings of a study focussed on providing the confidence and material to sheep producers to choose optimal pathways to rebuild their flocks. At least 6 of the 14 alternatives investigated were deemed suitable for commercial use and not sensitive to variations in supplementary-feed costs, stock-purchasing and -sale prices. [ABSTRACT FROM AUTHOR]

Water scarcity is already set to be one of the main issues of the 21st century, because of competing needs between civil, industrial, and agricultural use. Agriculture is currently the largest user of water, but its share is bound to decrease as societies develop and clearly it needs to become more water efficient. Improving water use efficiency (WUE) at the plant level is important, but translating this at the farm/landscape level presents considerable challenges. As we move up from the scale of cells, organs, and plants to more integrated scales such as plots, fields, farm systems, and landscapes, other factors such as trade-offs need to be considered to try to improve WUE. These include choices of crop variety/species, farm management practices, landscape design, infrastructure development, and ecosystem functions, where human decisions matter. This review is a cross-disciplinary attempt to analyse approaches to addressing WUE at these different scales, including definitions of the metrics of analysis and consideration of trade-offs. The equations we present in this perspectives paper use similar metrics across scales to make them easier to connect and are developed to highlight which levers, at different scales, can improve WUE. We also refer to models operating at these different scales to assess WUE. While our entry point is plants and crops, we scale up the analysis of WUE to farm systems and landscapes. [ABSTRACT FROM AUTHOR]

There is a need to support applied, community‐relevant hydrologic research within changing climate, population, and socioeconomic conditions to better inform water policy and management. We hypothesized that providing a rural agricultural community in a semiarid valley with the necessary monitoring tool to meet local water management needs would increase adaptive capacity within the context of long‐term drought. Through a community science approach, researchers installed a telemetry monitoring system at participating acequia irrigation diversions that remotely sent water data to a web interface every 15 min. Two surveys distributed before and after web interface access targeted seven adaptive capacity indicators. After the first season of improved data accessibility, the following adaptive capacity indicators significantly increased: information diversity, cognitive social capital, leadership, and proactivity. Rather than focusing on the water savings aspect of real‐time monitoring, this paper summarizes a novel water resources study that assessed the social impact of real‐time irrigation water delivery monitoring on adaptive capacity. This study suggests that bridging the gap between community need and hydrologic research through community science, sociologic analysis, and stakeholder engagement provides significant benefits for communities facing water management challenges, further supporting problem‐driven water resources research. [ABSTRACT FROM AUTHOR]

Tree rings have been central to the understanding of variability of flow of the Colorado River. Spurred by steadily declining flows after the 1920s, early tree‐ring research drew attention to the importance of climate variability to water supply by identifying episodes in the past that were even drier. Application of modern statistical methods to tree‐ring data later yielded a reconstruction of annual flows at Lees Ferry back to the early 1500s that highlighted the unprecedented wetness of the base period for the 1922 Colorado River Compact. That reconstruction served as the framework for a collection of papers in a 1995 special issue of Water Resources Bulletin on coping with severe sustained drought on the Colorado River. This retrospective paper reviews historical aspects of the dendrohydrology of the Colorado River, and the updates since 1995. A constantly expanding tree‐ring network has been subjected to an array of new statistical approaches to reconstruction. Climate change and increasing demand for water have meanwhile driven increased interest in the processing and presentation of reconstructions for optimal use in water resources planning and management. While highlighting the robustness of main findings of earlier studies, recent research yields improved estimates of magnitudes of flow anomalies, extends annual flows to more than 1200 years, and underscores unmatched drought duration in the medieval period. [ABSTRACT FROM AUTHOR]

Melatonin is a highly conserved and ubiquitous molecule that operates upstream of a broad array of receptors in animal systems. Since melatonin was discovered in plants in 1995, hundreds of papers have been published revealing its role in plant growth, development, and adaptive responses to the environment. This paper summarizes the current state of knowledge of melatonin's involvement in regulating plant ion homeostasis and abiotic stress tolerance. The major topics covered here are: (i) melatonin's control of H+-ATPase activity and its implication for plant adaptive responses to various abiotic stresses; (ii) regulation of the reactive oxygen species (ROS)–Ca2+ hub by melatonin and its role in stress signaling; and (iii) melatonin's regulation of ionic homeostasis via hormonal cross-talk. We also show that the properties of the melatonin molecule allow its direct scavenging of ROS, thus preventing negative effects of ROS-induced activation of ion channels. The above 'desensitization' may play a critical role in preventing stress-induced K+ loss from the cytosol as well as maintaining basic levels of cytosolic Ca2+ required for optimal cell operation. Future studies should focus on revealing the molecular identity of transporters that could be directly regulated by melatonin and providing a bioinformatic analysis of evolutionary aspects of melatonin sensing and signaling. [ABSTRACT FROM AUTHOR]

Interactions across meteorological drought, agricultural drought and irrigation enlarge complexities of simulating and forecasting crop yield under droughts. Besides, contradictory relationships amid multiple objectives considering respective critical thresholds aggravate difficulties in agricultural water resources optimization. To address the above problems, this paper developed a framework of random forest based multi-objective multi-critical control programming with fuzzy boundary intervals (RFMOMCCPFBI) model for agricultural water resources optimization. The MOMCCP is established based on the MOP approach, and it improves upon the MOP in giving lower and upper thresholds of each objective, promising achievement of subjectivities of managers. Moreover, the model enhances the advantages of MCCP in reflecting multiple variations and uncertainties of thresholds by introducing fuzzy boundary intervals. In addition, the crop yield is simulated by random forest approach, which improves calculation efficiency as well as reflect influences of multiple interactive drought and irrigation on crop yield. The RFMOMCCPFBI model is used to Yingke irrigation district to allocate water resources to multiple crops at different growth stages to verify its applications. The results disclose that frequencies of meteorological drought are around 27%, 19% at five growth stages, higher than agricultural drought in future year 2022 to year 2047. Meteorological drought has many times of abrupt changes without significance. Agricultural water-saving potential increases about 20% considering demands of acceptable thresholds of objectives. Water saving potential enlarges with increase of tolerance levels of objective deviations. The model can provide alternatives for managers based on their preferences on objectives. [ABSTRACT FROM AUTHOR]

Damage from climate change is widespread throughout the world. This change has brought about calamities, the most prevalent of which is the emergence of numerous droughts which are increasingly threatening human lives. In this paper, we studied the spatial and temporal variations of drought under the effect of climate change in the Huaibei Plain, which is a very important agricultural zone in China. Drought has attracted increasing attention in research due to its heavy impact on agriculture, the environment, livelihood, and food security. The SPEI (Standardized Precipitation Evapotranspiration Index) has been used in this study to express and identify drought events in the Huaibei Plain due to climate change. A general circulation model (GCM), HadGEM2-AO, which was the most appropriate for the study area's precipitation simulation, and three Representative Concentration Pathways (RCP), RCP 2.6, RCP 4.5, and RCP 8.5, were used to analyze and compare the drought effect for the baseline (1985–2017) and the future climate scenarios (2025–2090). At 3 and 6 months, the SPEI successfully detects agricultural drought in temporal and spatial variation. However, according to the analysis, more severe agricultural drought events are foreseen in the future than in the baseline because of climate change. SPEI performed better than SPI in detecting drought in the baseline and simulated data due to increased evapotranspiration. Between the SPEI-3 and SPEI-6, the Pearson coefficient correlation reveals a positive association. The Mann-Kendall test was used to cover the two studied periods in order to establish the drought trend. Both decreasing and increasing trends, in different timescales, were detected by Sen's Slope in the baseline and future periods with all RCPs. [ABSTRACT FROM AUTHOR]

Understanding spatiotemporal patterns of climate variability and trends is essential to designing context-specific adaptation and mitigation interventions for climate change impacts. To this end, this paper examines the 1983–2016 rainfall and temperature variability and trends using merged satellite-gauge station data in the UNESCO designated Cultural Landscapes of Konso, Ethiopia. We have employed a non-parametric Mann-Kendall test to analyze rainfall and temperature trends, whereas the coefficient of variation (CV) has been used for variability analysis. The distribution and severity of meteorological droughts were mapped using the Standardized Precipitation Index (SPI). Our findings revealed an increase in annual and summer (Kiremt) season rainfall by 3.16 mm and 0.42 mm per year, respectively. However, rainfall in the spring (Belg) season decreased by 1.12 mm per year. Our findings also showed a substantial spatiotemporal variability in drought events due to variations in rainfall and topography. Consequently, the observed increase in frequency and the spatial extent of drought pose potential threats to rainfed farming, which gradually exposes smallholders to food insecurity and socioeconomic vulnerability. Therefore, agroecological-based local adaptation and mitigation interventions to climate variability are needed to enhance the resilience of smallholder farmers and ensure the continuity of the Konso cultural landscapes. [ABSTRACT FROM AUTHOR]

The agriculture sector is highly vulnerable to natural disasters and climate change, leading to severe impacts on food security, economic stability, and rural livelihoods. The use of geospatial information and technology has been recognized as a valuable tool to help farmers reduce the adverse impacts of natural disasters on agriculture. Remote sensing and GIS are gaining traction as ways to improve agricultural disaster response due to recent advancements in spatial resolution, accessibility, and affordability. This paper presents a comprehensive overview of the FAIR agricultural disaster services. It holistically introduces the current status, case studies, technologies, and challenges, and it provides a big picture of exploring geospatial applications for agricultural disaster "from farm to space". The review begins with an overview of the governments and organizations worldwide. We present the major international and national initiatives relevant to the agricultural disaster context. The second part of this review illustrates recent research on remote sensing-based agricultural disaster monitoring, with a special focus on drought and flood events. Traditional, integrative, and machine learning-based methods are highlighted in this section. We then examine the role of spatial data infrastructure and research on agricultural disaster services and systems. The generic lifecycle of agricultural disasters is briefly introduced. Eventually, we discuss the grand challenges and emerging opportunities that range from analysis-ready data to decision-ready services, providing guidance on the foreseeable future. [ABSTRACT FROM AUTHOR]

For an effective reservoir operation during drought, the variations of both water supply and water demand which depend on hydrological and meteorological conditions need to be dealt with. This paper aimed to consider these variations in the Aharchay basin (Iran) by coupling a hedging rule (HR)-based reservoir operation model (HRROM) with a climate-based irrigation scheduling model (CBISM) at the farm level. Through the HRROM, optimal long-term decisions for Sattarkhan reservoir were made by considering the probable streamflow scenarios in the system. Given the variable agricultural demands (VAD) in the CBISM, the irrigation water was optimally allocated to the crops using several evapotranspiration (ET) scenarios. The CBISM employs three sub-models including linear programming (LP), nonlinear programming (NLP), and particle swarm optimization (PSO) to maximize the total income of the Aharchay agricultural network as a function of the climate factors and the supplied water. To this end, the daily weather and discharge data from 1990 to 2015 were used in this study. The standardized precipitation-evapotranspiration index (SPEI) and the streamflow drought index (SDI) were used to detect the meteorological and hydrological droughts, respectively. The SPEI was calculated based on the high-resolution-gridded datasets of the Climatic Research Unit (CRU). The findings demonstrated that the HRROM-CBISM generally managed to increase the time-based (αt) and volume-based (αv) reliability indices by 20% and 44%, respectively, compared with the conventional standard operation policy (SOP). For more investigations, the three major droughts of 2000–2002, 2004–2006, and 2008–2014 were separately analyzed. The average values of αt, αv, and vulnerability (V) for SOP were 0.33, 0.51, and 0.48, respectively. With the HRROM-CBISM, these values were about 0.5, 0.55, and 0.45, respectively. Among these indices, αt had the highest variations, while αv had the lowest variations in both the SOP and HRROM-CBISM approaches. The average water shortage for the mentioned droughts was significantly decreased from 89 (SOP) to 75 MCM (HRROM-CBISM). [ABSTRACT FROM AUTHOR]

Drought poses a serious threat to agricultural production and food security in the context of global climate change. Few studies have explored the response mechanism and lag time of agricultural drought to meteorological drought from the perspective of cultivated land types. This paper analyzes the spatiotemporal evolution patterns and hysteresis relationship of meteorological and agricultural droughts in the middle and lower reaches of the Yangtze River in China. Here, the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products and surface temperature products were selected to calculate the Temperature Vegetation Dryness Index (TVDI) from 2010 to 2015. Furthermore, we obtained the Standardized Precipitation Evapotranspiration Index (SPEI) and the Palmer Drought Severity Index (PDSI) for the same period. Based on these indices, we analyzed the correlation and the hysteresis relationship between agricultural and meteorological drought in rainfed and irrigated arable land. The results showed that, (1) compared with SPEI, the high spatial resolution PDSI data were deemed more suitable for the subsequent accurate and scientific analysis of the relationship between meteorological and agricultural droughts. (2) When meteorological drought occurs, irrigated arable land is the first to experience agricultural drought, and then alleviates when the drought is most severe in rainfed arable land, indicating that irrigated arable land is more sensitive to drought events when exposed to the same degree of drought risk. However, rainfed arable land is actually more susceptible to agricultural drought due to the intervention of irrigation measures. (3) According to the cross-wavelet transform analysis, agricultural droughts significantly lag behind meteorological droughts by about 33 days during the development process of drought events. (4) The spatial distribution of the correlation coefficient between the PDSI and TVDI shows that the area with negative correlations of rainfed croplands and the area with positive correlations of irrigated croplands account for 77.55% and 68.04% of cropland areas, respectively. This study clarifies and distinguishes the details of the meteorological-to-agricultural drought relationship in rainfed and irrigated arable land, noting that an accurate lag time can provide useful guidance for drought monitoring management and irrigation project planning in the middle and lower reaches of the Yangtze River. [ABSTRACT FROM AUTHOR]