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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]

In the open literature, there are numerous studies on the normal and extreme (flood and drought) behavior of wet and dry periods based on the understanding of the standard precipitation index (SPI), which provides a series of categorizations by considering the standard normal (Gaussian) probability distribution function (PDF). The numerical meaning of each categorization assessment is quite lacking in terms of future predictions of wet and dry period duration based on historical records. This paper presents a new approach for calculating possible formations of future wet and dry period durations based on historical records through an effective fractal geometric forecasting approach. The essence of the proposed methodology is based on the number of dry periods (steps) of non-overlapping monthly duration along consecutive broken line paths in the SPI classification for wet and dry period durations. It has been observed that the plot of periods on double logarithmic paper falls along a straight line against the number of such periods, implying a power function, which is the essence of fractal geometry. Extending the empirically derived straight line provides the number of periods that may occur in the future over a range of SPI levels. This methodology is referred to as SPI fractal (SPIF), and the classic SPI classification is converted into SPIF wet and dry polygons, which provide additional information about the drought period number within a valid polygonal area, compared to the classic SPI results. The wet and dry period features of any hydro-meteorology time series are constrained in SPIF polygons. The application of the methodology was carried out on monthly rainfall records on the European side of the Istanbul Florya meteorological station in Turkey. [ABSTRACT FROM AUTHOR]

Drought stress has serious effects on the growth and yield of wheat in both productivity and quality and is an abiotic factor. Traditional methods of crop drought stress monitoring have some deficits. This work has been conducted in order to enhance these conventional methods by proposing a new deep learning approach. This paper has presented a deep learning-based model customized for monitoring drought stress in winter wheat during the critical growth stages. Drought-afflicted winter wheat images were captured at three crucial phases: rising–jointing, heading–flowering, and flowering–maturity. These images are correlated against soil moisture data to construct a comprehensive dataset. DenseNet121 was chosen as the network model since it extracts features from images relating to phenotypes. Several factors, like training methods, learning rate adjustment, and addition of the attention mechanism, are optimized in eight sets of experiments. This provided the final DenseNet-121 model with an average recognition accuracy of 94.67% on the test set, which means that monitoring drought stress during wheat growth's key periods is feasible and effective. [ABSTRACT FROM AUTHOR]

Administrative strategies to cope with drought are steadily changing, from emergency procedures to day-to-day monitoring. More consideration must be paid to long-term and preventive drought control measures in the future. This paper discusses the risk of hydrological drought in the Yellow River Basin. The standardized runoff index (SRI) was used to characterize hydrological drought, and the run theory was used to identify drought states and quantify drought characteristic variables. Based on the drought severity and duration, a drought development plan was proposed and a three-dimensional copula function was constructed to obtain the joint distribution function of three-dimensional drought characteristic variables. A drought risk assessment system based on the loss × probability risk theory was constructed to explore the spatial and temporal characteristics of hydrological drought risk in the Yellow River Basin. Finally, according to the risk assessment results, the risk level was divided into low, medium and high risk, and a Bayesian network was used to explore the probability of hydrological drought. The main results are as follows: (1) From 1960 to 2018, the severity of hydrological drought in the Yellow River Basin increased, the duration lengthened, and the development speed accelerated. (2) The hydrological drought risk in the Yellow River Basin showed an overall upward trend, with the fastest increase in the HJ region of 0.041/10a. The highest annual average drought risk in the TDG region is 0.598. (3) The spatial transmission of hydrological drought risk is divided into three types: constant, enhanced and mitigation types, of which the constant type is the most common. The transmission probabilities of low, medium and high risk of hydrological drought from the HYK region to the low, medium and high risk of hydrological drought in the LJ region are 0.68, 0.66 and 0.78, respectively. [ABSTRACT FROM AUTHOR]

This document discusses the challenges faced by plants in arid and semi-arid regions due to harsh environmental conditions such as limited rainfall, high temperatures, and poor soil nutrition. The document highlights the importance of understanding plant adaptation strategies to extreme environments in order to protect and manage local vegetation and ecosystems. The document presents research papers that cover various aspects of plant adaptation in arid regions, including water uptake mechanisms, physiological and biochemical adaptations, plant-soil microbial interactions, and ecosystem services. The research findings provide insights into how plants in arid regions adapt to environmental changes and maintain ecological balance. However, further research is needed to fully understand the processes and mechanisms of plant adaptation in extreme environments. [Extracted from the article]

Drought monitoring and early detection have improved greatly in recent decades through the development and refinement of numerous indices and indicators. However, a lack of guidance, based on user experience, exists as to which drought-monitoring tools are most appropriate in a given location. This review paper summarizes the results of targeted user engagement and the published literature to improve the understanding of drought across North America and to enhance the utility of drought-monitoring tools. Workshops and surveys were used to assess and make general conclusions about the perceived performance of drought indicators, indices and impact information used for monitoring drought in the five main Köppen climate types (Tropical, Temperate, Continental, Polar Tundra, Dry) found across Canada, Mexico, and the United States. In Tropical, humid Temperate, and southerly Continental climates, droughts are perceived to be more short-term (less than 6 months) in duration rather than long-term (more than 6 months). In Polar Tundra climates, Dry climates, Temperate climates with dry warm seasons, and northerly Continental climates, droughts are perceived to be more long-term than short-term. In general, agricultural and hydrological droughts were considered to be the most important drought types. Drought impacts related to agriculture, water supply, ecosystem, and human health were rated to be of greatest importance. Users identified the most effective indices and indicators for monitoring drought across North America to be the U.S. Drought Monitor (USDM) and Standardized Precipitation Index (SPI) (or another measure of precipitation anomaly), followed by the Normalized Difference Vegetation Index (NDVI) (or another satellite-observed vegetation index), temperature anomalies, crop status, soil moisture, streamflow, reservoir storage, water use (demand), and reported drought impacts. Users also noted the importance of indices that measure evapotranspiration, evaporative demand, and snow water content. Drought indices and indicators were generally thought to perform equally well across seasons in Tropical and colder Continental climates, but their performance was perceived to vary seasonally in Dry, Temperate, Polar Tundra, and warmer Continental climates, with improved performance during warm and wet times of the year. The drought indices and indicators, in general, were not perceived to perform equally well across geographies. This review paper provides guidance on when (time of year) and where (climate zone) the more popular drought indices and indicators should be used. The paper concludes by noting the importance of understanding how drought, its impacts, and its indicators are changing over time as the climate warms and by recommending ways to strengthen the use of indices and indicators in drought decision making. [ABSTRACT FROM AUTHOR]

The frequency of El Niño occurrences in southern Africa surpasses the norm, resulting in erratic weather patterns that significantly impact food security, particularly in Zimbabwe. The effects of these weather patterns posit that El Niño occurrences have contributed to the diminished maize yields. The objective is to give guidelines to policymakers, researchers, and agricultural stakeholders for taking proactive actions to address the immediate and lasting impacts of El Niño and enhance the resilience of the agricultural industry. This brief paper provides prospective strategies for farmers to anticipate and counteract the El Niño-influenced dry season projected for 2023/24 and beyond. The coefficient of determination R2 between yield and ENSO was low; 11 of the 13 El Niño seasons had a negative detrended yield anomaly, indicating the strong association between El Nino's effects and the reduced maize yields in Zimbabwe. The R2 between the Oceanic Nino Index (ONI) and rainfall (43%) and between rainfall and yield (39%) indirectly affects the association between ONI and yield. To safeguard farmers' livelihoods and improve their preparedness for droughts in future agricultural seasons, this paper proposes a set of strategic, tactical, and operational decision-making guidelines that the agriculture industry should follow. The importance of equipping farmers with weather and climate information and guidance on drought and heat stress was underscored, encompassing strategies such as planting resilient crop varieties, choosing resilient livestock, and implementing adequate fire safety measures. [ABSTRACT FROM AUTHOR]

Drought stress (DS) is one of the abiotic stresses that plants encounter commonly in nature, which affects their life, reduces agricultural output, and prevents crops from growing in certain areas. To enhance plant tolerance against DS, abundant exogenous substances (ESs) have been attempted and proven to be effective in helping plants relieve DS. Understanding the effect of each ES on alleviation of plant DS and mechanisms involved in the DS relieving process has become a research focus and hotspot that has drawn much attention in the field of botany, agronomy, and ecology. With an extensive and comprehensive review and summary of hundred publications, this paper groups various ESs based on their individual effects on alleviating plant/crop DS with details of the underlying mechanisms involved in the DS-relieving process of: (1) synthesizing more osmotic adjustment substances; (2) improving antioxidant pathways; (3) promoting photosynthesis; (4) improving plant nutritional status; and (5) regulating phytohormones. Moreover, a detailed discussion and perspective are given in terms of how to meet the challenges imposed by erratic and severe droughts in the agrosystem through using promising and effective ESs in the right way and at the right time.

The area surrounding the Po River, known as the Po Valley, provides a central contribution in the economy of Italy and is highly devoted to agriculture. Recently it has been hit by multiple droughts, among which the exceptional event of summer 2022 is considered the worst dry period of the past 200 years. In the near future, the frequency of such exceptional events is predicted to rise; thus, a deep knowledge of the past droughts that hit the area, the variables used to characterize the events, the impacts they caused and the mitigation strategies adopted to deal with dry periods is of the utmost importance for policy definitions and planning. This study maps the scientific literature published from 2000 to February 2024 on the topic of drought in the Po Valley using the Scopus and Web of Science databases. Overall, 44 articles have been identified and grouped in three main classes: event identification and characterization, impact analysis and management strategies. The main gaps found in the collected papers are the lack of evaluations of the impacts of drought events on human health, hydroelectric energy production and tourism. Furthermore, comprehensive drought management and planning in the area is never addressed in the considered articles. The mentioned aspects deserve more attention, especially the development of drought management plans and policies and the evaluation of their effectiveness. [ABSTRACT FROM AUTHOR]

This paper discusses the increasing severity of droughts due to climate change. It emphasises the complexity of defining drought and the diverse perspectives among stakeholders. Lots of stakeholders with unclear responsibilities are involved, which can lead to uncertainty and indecisiveness in addressing the issue. To tackle this, the present paper proposes a methodology to dissect drought systems and reveal the intricate relationships between their components. This approach combines a comprehensive definition of drought with the "Water Resilience Assessment Framework" and an "Actor-Relational Approach", visualised through network analysis. The methodology was applied to a case study situated in the Leie Basin of Flanders, Belgium. By employing this strategy, policymakers and mediators can gain a deeper understanding of drought, identify its root causes, and prioritise necessary changes for more effective drought and water management. [ABSTRACT FROM AUTHOR]

In times of climate change, periods of drought will occur more frequently. This causes challenges for water use, ranging from limitations on the navigability of water courses, limited availability of water for irrigation and drinking water supply, reduced hydropower production, increasing concentrations of pollutants, deteriorating water quality, and ecosystem degradation. Dealing with droughts, however, is a complex puzzle due to the multi-level governance characteristics of international river basins and the need to meet the freshwater demands of all sectors involved. This increases the need to address drought issues in a coordinated way, along all levels of decision making. Thus far, the way this must be executed has been under-researched. This paper addresses this knowledge gap as it aims to provide design principles for good multi-level drought risk governance in international river basins. In order to meet our aim, we first reviewed literature on multi-level and good governance and established an assessment framework. This framework was applied in a case study on drought risk governance in the international Rhine basin. Policy documents were analyzed and key informants interviewed. We found that although the governance practice in the basin meets most of our framework criteria, differences between the international level, the Netherlands, Germany, and Switzerland also occurred. We have synthesized our findings into a list of 10 design principles for good multi-level drought risk governance, which could function as a starting point for the analysis and improvement of other multi-level drought risk governance practices. [ABSTRACT FROM AUTHOR]

Ecological droughts in rivers, as a new type of drought, have been greatly discussed in the past decade. Although various studies have been conducted to identify and evaluate ecological droughts in rivers from different indices, a forecast model for this type of drought is still lacking. In this paper, a numerical weather forecast, a hydrological model, and a generalized Bayesian model are employed to establish a new general framework for the probabilistic forecasting of ecological droughts in rivers, and the Daitou section in China is selected as the study area to examine the performance of the new framework. The results show that the hydrological model can accurately simulate the monthly streamflow with a Nash–Sutcliffe efficiency of 0.91 in the validation period, which means that the model can be used to reconstruct the natural streamflow from the impact of an upstream reservoir. Based on a comparison of ecological drought events from the observed and model-simulated streamflow series, the events from the observed series have a larger deficit volume and a longer duration of ecological droughts after 2014, indicating that human activities may lead to a more severe situation of ecological droughts. Furthermore, due to the uncertainty of precipitation forecasts, a probabilistic precipitation forecast is employed for probabilistic ecological drought forecasting. Compared to the deterministic forecast, the probabilistic ecological drought forecast has better performance, with a Brier score decrease of 0.35 to 0.18 and can provide more information about the risk of ecological droughts. In general, the new probabilistic framework developed in this study can serve as a basis for the development of early-warning systems and countermeasures for ecological droughts. [ABSTRACT FROM AUTHOR]

Agricultural drought events have become more frequent in the Inner Mongolia inland river basin in recent years, and the spatio-temporal evolution characteristics and development rules can be accurately and comprehensively understood using the three-dimensional identification method. In this paper, standardized soil moisture index (SSMI) was used to characterize agricultural drought, and modified Mann–Kendall trend test (MMK) and 3D recognition of drought events were used to analyze the spatio-temporal evolution characteristics of agricultural drought events in this basin and reveal the drought development law. The relationships between drought and temperature (T), precipitation (P), evapotranspiration (E), and humidity (H) were analyzed using a cross-wavelet method. The results are as follows: (1) When the time scale of agricultural drought was short (monthly scale), the alternations of dry and wet were frequent, but the SSMI index of all scales showed a downward trend; (2) The spatial distribution characteristics of drought change trend in four seasons were similar, but the area with a significant downward trend of drought in spring was the largest, and the area of high frequency region was also the largest, and the drought trend was the most obvious; (3) The most serious agricultural drought event occurred from October 2000 to May 2002, and reached its maximum value in September 2001 (drought area and drought severity of 2.26 × 105 km2 and 3.61 × 105 months·km2, respectively), which mainly experienced five processes—drought onset–intensification–decay–re-intensification–termination—and the migration path of the drought center showed the characteristics of southwest–northeast transmission; (4) All the four meteorological factors were correlated with SSMI, and P had a greater impact on SSMI. This article aims to reveal the spatio-temporal evolution of agricultural drought events in the Inner Mongolia inland river basin, and provide a new way to accurately evaluate the spatio-temporal evolution of drought. [ABSTRACT FROM AUTHOR]

The Anthesis-Silking Interval (ASI) is a crucial indicator of the synchrony of reproductive development in maize, reflecting its sensitivity to adverse environmental conditions such as heat stress and drought. This paper presents an automated method for detecting the maize ASI index using a field high-throughput phenotyping platform. Initially, high temporal-resolution visible-light image sequences of maize plants from the tasseling to silking stage are collected using a field rail-based phenotyping platform. Then, the training results of different sizes of YOLOv8 models on this dataset are compared to select the most suitable base model for the task of detecting maize tassels and ear silks. The chosen model is enhanced by incorporating the SENetv2 and the dual-layer routing attention mechanism BiFormer, named SEBi-YOLOv8. The SEBi-YOLOv8 model, with these combined modules, shows improvements of 2.3% and 8.2% in mAP over the original model, reaching 0.989 and 0.886, respectively. Finally, SEBi-YOLOv8 is used for the dynamic detection of maize tassels and ear silks in maize populations. The experimental results demonstrate the method's high detection accuracy, with a correlation coefficient (R2) of 0.987 and an RMSE of 0.316. Based on these detection results, the ASI indices of different inbred lines are calculated and compared. [ABSTRACT FROM AUTHOR]

Drought is a key factor restricting plant survival, growth and development. The physiological parameters of plants are commonly used to determine the water status, in order to irrigate appropriately and save water. In this study, mulberry (Morus alba L.) and paper mulberry (Broussonetia papyrifera (L.) Vent.) seedlings were used as experimental materials, and four soil moisture treatments were set up for both plant species: 70–75% (CK: the control group, referred to as T0), 55–60% (T1: mild drought), 40–45% (T2: moderate drought), and 25–30% (T3: severe drought). The growth parameter of the plants was measured every two days from the onset of the treatment, the photosynthetic and electrophysiological parameters of the plants were measured every other week for a total of five times. The physiological responses and electrophysiological traits of leaves under different treatment levels were analyzed. The results showed that the photosynthetic and electrophysiological parameters could characterize the response of mulberry growth and development to soil water, and the growth and electrophysiological parameters could characterize the response of paper mulberry growth and development to soil water. Mild drought had no significant effects on the growth and development of mulberry and paper mulberry. [ABSTRACT FROM AUTHOR]

Extreme water incidents point out a value conflict surrounding the resource. While drought and floods echo the inadequate land and resource uses, the increase in social inequalities exposes the practical, physiological, and social consequences. The multiple value action throughout the water cycle also narrowed disputes to those that neglect its vital importance, and the constraints imposed to the services, such as low tariffs, and lack of local engagement, make sustainable water systems more difficult. This article develops a systematic literature review to understand the academic motivations surrounding water value and gaps in its systematic approach. A sample with 84 papers is created by an interactive keyword selection and its general characteristics are presented. A dynamic reading technique extracts data and classifies the papers according to 14 research motivations, where the water multifunctionality and the user value stand out. The bibliographic coupling analysis identifies a cluster of 16 papers related to integration and connected to planning, decision, and management. There is a lack of contribution with a systemic approach to water resources by way of integrating actors and values, such as including local contexts. [ABSTRACT FROM AUTHOR]

Managed Aquifer Recharge (MAR) and water banking are of increasing importance to water resources management. MAR can be used to buffer against drought and changing or variable climate, as well as provide water to meet demand growth, by making use of excess surface water supplies and recycled waters. Along with hydrologic and geologic considerations, economic and policy analyses are essential to a complete analysis of MAR and water banking opportunities. The papers included in this Special Issue fill a gap in the literature by revealing the range of economic and policy considerations relevant to the development and implementation of MAR programs. They illustrate novel techniques that can be used to select MAR locations and the importance and economic viability of MAR in semi-arid to arid environments. The studies explain how MAR can be utilized to meet municipal and agricultural water demands in water-scarce regions, as well as assist in the reuse of wastewater. Some papers demonstrate how stakeholder engagement, ranging from consideration of alternatives to monitoring, and multi-disciplinary analyses to support decision-making are of high value to development and implementation of MAR programs. The approaches discussed in this collection of papers, along with the complementary and necessary hydrologic and geologic analyses, provide important inputs to water resource managers. [ABSTRACT FROM AUTHOR]

Traditional univariate drought indices may not be sufficient to reflect comprehensive information on drought. Therefore, this paper proposes a new composite drought index that can comprehensively characterize meteorological and hydrological drought. In this study, the new drought index was established by combining the standardized precipitation index (SPI) and the standardized baseflow index (SBI) for the Jiaojiang River Basin (JRB) using the copula function. The prediction model was established by training random forests on past data, and the driving force behind the combined drought index was explored through the LIME algorithm. The results show that the established composite drought index combines the advantages of SPI and SBI in drought forecasting. The monthly and annual droughts in the JRB showed an increasing trend from 1991 to 2020, but the temporal characteristics of the changes in each subregion were different. The accuracies of the trained random forest model for heavy drought in Baizhiao (BZA) and Shaduan (SD) stations were 83% and 88%, respectively. Furthermore, the Local Interpretable Model-Agnostic Explanations (LIME) interpretation identified the essential precipitation, baseflow, and evapotranspiration features that affect drought. This study provides reliable and valid multivariate indicators for drought monitoring and can be applied to drought prediction in other regions. [ABSTRACT FROM AUTHOR]

In this paper, an approach is proposed for assessing the performance of experts in the group from two perspectives: (1) individual consistencies and (2) deviations from the group decision. The quality of performance of the experts is based on combining the standard and rough analytic hierarchy process (AHP) with the technique for order of preference by similarity to the ideal solution (TOPSIS). The statistical method CRITIC is used to derive weights for the TOPSIS method before the experts are assessed based on demonstrated consistency and deviations from the group. Common performance indicators, such as consistency ratio, Euclidean distance, compatibility, and Spearman's correlation coefficient, are proposed for re-grouping experts before making the final decisions. A genetic algorithm enables the efficient solving of this complex clustering problem. Implementing the described approach and method can be useful in comparable assessment frameworks. A critical aspect is conducting a thorough pre-assessment of the competence of potential decision makers, often referred to as experts who may not consistently exhibit apparent expertise. The competence of decision makers (which does not have to be associated with compatibility) is evidenced by selected consistency parameters, and in a way, a pre-assessment of their competence follows Plato's 'government of the wise' principle. In the presented study, the compatibility of individuals in the group with the collective position (group decision) is measured by parameters related to their compatibility with the group solution and statistical deviation while ranking decision elements. The proposed multi-model-based approach stands out for its resilience in conducting thorough pre-assessment of the quality (competence) of potential decision makers, often regarded as experts who might not consistently display evident expertise. The wetland study area in Serbia is used as an example application, where seven measures for reducing the risk of drought were evaluated by twelve experts coming from different sectors and with different backgrounds and expertise. [ABSTRACT FROM AUTHOR]

As climate change intensifies, drought has become a major global engineering and environmental challenge. In critical areas such as agricultural production, accurate drought monitoring is vital for the sustainable development of regional agriculture. Currently, despite extensive use of traditional meteorological stations and remote sensing methods, these approaches have proven to be inadequate in capturing the full extent of drought information and adequately reflecting spatial characteristics. Therefore, to improve the accuracy of drought forecasts and achieve predictions across extensive areas, this paper employs deep learning models, specifically introducing an attention-weighted long short-term memory network model (AW-LSTM), constructs a composite drought monitoring index (CDMI) and validates the model. Results show that: (1) The AW-LSTM model significantly outperforms traditional long short-term memory (LSTM), support vector machine (SVM) and artificial neural network (ANN) models in drought monitoring, offering not only better applicability in meteorological and agricultural drought monitoring but also the ability to accurately predict drought events one month in advance compared to machine learning models, providing a new method for precise and comprehensive regional drought assessment. (2) The Huang-Huai-Hai Plain has shown significant regional variations in drought conditions across different years and months, with the drought situation gradually worsening in the northern part of Hebei Province, Beijing, Tianjin, the southern part of Huai North and the central part of Henan Province from 2001 to 2022, while drought conditions in the northern part of Huai North, southern Shandong Province, western Henan Province and southwestern Hebei Province have been alleviated. (3) During the sowing (June) and harvesting (September) periods for summer maize, the likelihood of drought occurrences is higher, necessitating flexible adjustments to agricultural production strategies to adapt to varying drought conditions. [ABSTRACT FROM AUTHOR]

Drought has emerged as a major challenge to global food and water security, and is particularly pronounced for Algeria, which frequently grapples with water shortages. This paper sought to monitor and assess the temporal and spatial distribution of drought severity across northern Algeria (excluding the Sahara) during the growing season from 2011 to 2022, while exploring the relationship between the normalized difference vegetation index (NDVI) anomaly and climate variables (rainfall and temperature). Temporal NDVI data from the Terra moderate resolution imaging spectroradiometer (MODIS) satellite covering the period 2000–2022 and climate data from the European Reanalysis 5th Generation (ERA5) datasets collected during the period 1990–2022 were used. The results showed that a considerable portion of northern Algeria has suffered from droughts of varying degrees of severity during the study period. The years 2022, 2021, 2016, and 2018 were the hardest hit, with 76%, 71%, 66%, and 60% of the area, respectively, experiencing drought conditions. While the relationship between the NDVI anomaly and the climatic factors showed variability across the different years, the steady decrease in vegetation health indicated by the NDVI anomaly corroborates the observed increase in drought intensity during the study period. We conclude that the MODIS-NDVI product offers a cost-efficient approach to monitor drought in data-scarce regions like Algeria, presenting a viable alternative to conventional climate-based drought indices, while serving as an initial step towards formulating drought mitigation plans. [ABSTRACT FROM AUTHOR]

Cities around the world are facing increased sensitivity to drought effects. Climate-change-induced drought affects not only the natural hydrology of the broad macroclimate but also those in the urban microclimates. The increasing frequency and duration of droughts are creating challenges for urban water utilities to convey water through distribution systems to customers reliably and consistently. This has led many urban areas like San Francisco, California, to search for unique alternative water supply projects to help bolster the drought resilience of the coupled human and natural water system. This paper focuses on applying the features of resilience (i.e., plan, adapt, absorb, and recover) through a drought resilience matrix to water supply alternatives to analyze how the addition of these projects would increase the overall water system's drought resilience. San Francisco, California, was used as the case study to test the use of this matrix. Three portfolios (modifying existing supply, recycling, and desalination, as well as local approaches) were created and tested in the matrix. Each portfolio is composed of various alternative water supply projects that the San Francisco Public Utilities Commission (SFPUC) is considering for implementation. Results concluded that the local approaches portfolio provided the most drought resilience, with the recycling and desalination portfolio providing the least resilience. The study approach and the presented findings will provide guidance to water utility professionals in supply planning to enhance drought resilience. [ABSTRACT FROM AUTHOR]

The Canadian Prairies are associated with high natural hydroclimatic variability including the frequent periodic occurrence of droughts and pluvials. These extremes carry various risks including significant damage to the economy, environment and society. The well-documented level of damage necessitates further risk assessment and planned reductions to vulnerability, particularly in light of a warming climate. A logical starting point involves awareness and information about the changing characteristics of such climate extremes. We focus on the compound occurrence of droughts and pluvials as the risks from this type of event are magnified compared to the hydroclimatic extremes in isolation. Compound droughts and pluvials (CDP) are drought and pluvial events that occur in close succession in time or in close proximity in area. Also, research on CDP is limited even for the worldwide literature. Therefore, the purposes of this paper are to synthesize recent literature concerning the risks of CDP, and to provide examples of past occurrences, with a focus on the Canadian Prairies. Since literature from the Prairies is limited, global work is also reviewed. That literature indicates increasing concern and interest in CDP. Relationships between drought and pluvials are also characterized using the SPEI Global Monitor for the Prairies, emphasizing the recent past. Research mostly considers drought and pluvials as separate events in the Prairies, but is integrated here to characterize the relationships of these extremes. The spatiotemporal patterns showed that several of the extreme to record pluvials were found to be closely associated with extreme droughts in the Prairies. The intensities of the extremes and their dry to wet boundaries were described. This is the first research to explore the concept of and to provide examples of CDP for the Prairies and for Canada. Examples of CDP provide insights into the regional hydroclimatic variability. Furthermore, most literature on future projections strongly suggests that this variability is likely to increase, mainly driven by anthropogenic climate change. Therefore, improved methods to characterize and to quantify CDP are required. These findings suggest means of decreasing vulnerability and associated damages. Although the study area is the Canadian Prairies, the work is relevant to other regions that are becoming more vulnerable to increasing risks of and vulnerabilities to such compound extremes. [ABSTRACT FROM AUTHOR]

Atriplex canescens is widely cultivated as drought and salt-tolerant fodder in arid regions of Northwest China, which is used for photoremediation of degraded land and soil and water conservation. To explore the growth performance of A. canescens when exposed to drought and salt stress, seedlings were treated with a range of drought stress (WC1: 75 ± 3.6%, WC2: 49 ± 2.9% and WC3: 27 ± 2.5% of soil water content) and the corresponding drought stress with additional sodium salt supplementation (NaCl:Na2SO4 = 1:1 with the total concentration of Na+ set to 150 mM). The findings of this paper indicated that moderate sodium salt could stimulate the growth of A. canescens and effectively alleviate the deleterious impact of drought stress by increasing the turgor potential (ψt) and relative water content (RWC) and decreasing the leaf water osmotic potential (ψs). Furthermore, the photosynthetic capacity was improved and the negative effects of drought stress on photosystem II (PSII) were mitigated. The extra 150 mM sodium salt also markedly increased the contribution of Na+ to ψs and the contribution of betaine to ψs. In summary, these results indicate that A. canescens can adapt to drought stress by accumulating enough Na+ for osmotic adjustment (OA). Additionally, this paper is aimed to provide a fundamental basis for the utilization and cultivation of A. canescens as a favored pasture crop in the Qaidam basin, thus increasing the ecological and environmental benefits for arid regions worldwide. [ABSTRACT FROM AUTHOR]

Accurately identifying and predicting droughts can provide local managers with a basis for decision-making. The Xinyang region is prone to droughts and floods, which have a large impact on local agriculture and socio-economics. This paper employs precipitation data from the Xinyang region to provide a scientific basis for drought and flood control measures in this region. The data are first treated with standardized precipitation indices (SPIs) on three-month, six-month, and nine-month time scales. Subsequently, a Morlet wavelet analysis is performed for each of the three time scales analyzed for the SPI. The results show multiple time scales of drought and flood disasters in the Xinyang region. The cycles of drought and flood disasters in the Xinyang region show different fluctuations on different SPI scales. The SPI time series reflect a strong fluctuation period of 17a for drought and flood disasters in the Xinyang region. An analysis of the variance of the wavelet coefficients showed that the first main cycle of drought and flood disasters in the Xinyang region is 7a, and the second and third sub-cycles are 4a and 13a, respectively. We conclude that floods are more frequent than droughts in Xinyang and are more likely to occur from 2017 to 2021, with a subsequent shift to droughts. Local managers should put drought prevention measures in place to deal with droughts after 2021. [ABSTRACT FROM AUTHOR]

Pakistan is among the countries that are highly vulnerable to climate change. The country has experienced severe floods and droughts during recent decades. The agricultural sector in Pakistan is adversely affected by climate change. This systematic review paper set out to analyze the existing literature on adaptation measures at the farm level toward climate change in Pakistan. Adopting a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, a total of 62 articles were identified from the Web of Science and Scopus databases. The review paper indicates that the main adaptation strategies adopted by farmers are as follows: changing cropping practices, changing farm management techniques, advanced land use management practices, and nonagriculture livelihood options. Further, this review shows the factors influencing the farmer's adaptation measures to climate change. Influencing factors were examined and classified into three groups: demographic, socioeconomic, and resources and institutional. Barriers hindering farmers' adaptive capacity were identified as lack of access to information and knowledge, lack of access to extension services, lack of access to credit facility, and lack of farm resources. [ABSTRACT FROM AUTHOR]

In response to the challenges posed by climate change, including extreme weather events, such as heavy rainfall and droughts, the agricultural sector is increasingly seeking solutions for the efficient use of resources, particularly water. Pivotal aspects of smart agriculture include the establishment of weather-independent systems and the implementation of precise monitoring and control of plant growth and environmental conditions. Hydroponic cultivation techniques have emerged as transformative solutions with the potential to reduce water consumption for cultivation and offer a sheltered environment for crops, protecting them from the unpredictable impacts of climate change. However, a significant challenge lies in the frequent need for human intervention to ensure the efficiency and effectiveness of these systems. This paper introduces a novel system with a modular architecture, offering the ability to incorporate new functionalities without necessitating a complete system redesign. The autonomous hydroponic greenhouse, designed and implemented in this study, maintains stable environmental parameters to create an ideal environment for cultivating tomato plants. Actuators, receiving commands from a cloud application situated at the network's edge, automatically regulate environmental conditions. Decision-making within this application is facilitated by a PID control algorithm, ensuring precision in control commands transmitted through the MQTT protocol and the NGSI-LD message format. The system transitioned from a single virtual machine in the public cloud to edge computing, specifically on a Raspberry Pi 3, to address latency concerns. In this study, we analyzed various delay aspects and network latency to better understand their significance in delays. This transition resulted in a significant reduction in communication latency and a reduction in total service delay, enhancing the system's real-time responsiveness. The utilization of LoRa communication technology connects IoT devices to a gateway, typically located at the main farm building, addressing the challenge of limited Internet connectivity in remote greenhouse locations. Monitoring data are made accessible to end-users through a smartphone app, offering real-time insights into the greenhouse environment. Furthermore, end-users have the capability to modify system parameters manually and remotely when necessary. This approach not only provides a robust solution to climate-induced challenges but also enhances the efficiency and intelligence of agricultural practices. The transition to digitization poses a significant challenge for farmers. Our proposed system not only represents a step forward toward sustainable and precise agriculture but also serves as a practical demonstrator, providing farmers with a key tool during this crucial digital transition. The demonstrator enables farmers to optimize crop growth and resource management, concretely showcasing the benefits of smart and precise agriculture. [ABSTRACT FROM AUTHOR]

Hydrological droughts may be referred to as sustained and regionally extensive water shortages as reflected in streamflows that are noticeable and gauged worldwide. Hydrological droughts are largely analyzed using the truncation level approach to represent the desired flow condition such as the median, mean, or any other flow quantile of an annual, monthly, or weekly flow sequence. The quantification of hydrologic droughts is accomplished through indices, such as the standardized streamflow index (SSI) in tandem with the standardized precipitation index (SPI) commonly used in meteorological droughts. The runs of deficits in the SSI sequence below the truncation level are treated as drought episodes, and thus, the theory of runs forms an essential tool for analysis. The parameters of significance from the modeling perspective of hydrological droughts (or tantamount to streamflow droughts in this paper) are the longest duration and the largest magnitude over a desired return period of T-year (or month or week) of the streamflow sequences. It is to be stressed that the magnitude component of the hydrological drought is of paramount importance for the design and operation of water resource storage systems such as reservoirs. The time scales chosen for the hydrologic drought analysis range from daily to annual, but for most applications, a monthly scale is deemed appropriate. For modeling the aforesaid parameters, several methodologies are in vogue, i.e., the empirical fitting of the historical drought sequences through a known probability density function (pdf), extreme number theorem, Markov chain analysis, log-linear, copulas, entropy-based analyses, and machine learning (ML)-based methods such as artificial neural networks (ANN), wavelet transform (WT), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS), and hybrid methods involving entropy, copulas, and machine learning-based methods. The forecasting of the hydrologic drought is rigorously conducted through machine learning-based methodologies. However, the traditional stochastic methods such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), copulas, and entropy-based methods are still popular. New techniques for flow simulation are based on copula and entropy-based concepts and machine learning methodologies such as ANN, WT, SVM, etc. The simulated flows could be used for deriving drought parameters in consonance with traditional Monte Carlo methods of data generation. Efforts are underway to use hydrologic drought models for reservoir sizing across rivers. The ML methods whilst combined in the hybrid form hold promise in drought forecasting for better management of existing water resources during the drought periods. Data mining and pre-processing techniques are expected to play a significant role in hydrologic drought modeling and forecasting in future. [ABSTRACT FROM AUTHOR]

Sand prevention and control are the main tasks of desertification control. The MU Us Sandy Land (MUSL), one of China's four main deserts, frequently experiences droughts and has a very fragile biological environment. Climate change is the main factor leading to drought, and it may result in more serious drought situations in the future. The Temperature Vegetation Dryness Index (TVDI) was established using land surface temperature and normalized difference vegetation index data. In this paper, we investigate spatial and temporal change characteristics, future change trends, and the time-lag effect of TVDI on climate factors at different scales in MUSL from 2001 to 2020 using Sen + Mann–Kendall trend analysis, Hurstexponent, partial correlation analysis, and lag analysis methods. The results show that (1) the overall drought shows a spatial characteristic of gradually alleviating from west to east (TVDI = 0.6). A significant drying trend dominated 38.5% of the pixels in the fall (Z = 1.99), and a highly significant drying trend dominated the rest of the three seasons (Z average = 2.95) and the whole year (Z = 3.47). (2) In the future, dry autumn, winter, and the whole year will be dominated by continuous drying, and spring and summer will mainly change from dry to wet. The main relationships between winter TVDI and temperature (−0.06) and precipitation (−0.07) were negative, while evapotranspiration (0.18) showed a positive correlation. The six land use types in spring, summer, fall, and the whole year were primarily non-significantly positively correlated with temperature and evapotranspiration. (3) At the seasonal scale, the sensitive factors in spring and autumn were opposite, with spring TVDI responding quickly to precipitation (0.3 months) and being less sensitive to temperature (1.8 months) and evapotranspiration (2 months). At the interannual scale, desert land TVDI was most sensitive to precipitation (2.6 months) and least responsive to temperature (3 months). [ABSTRACT FROM AUTHOR]

Devising preparedness plans, including means for efficient monitoring and timely identification of drought events, is essential for decision making on drought mitigation and water management. Drought is considered to be among the major natural hazards faced by human society, with significant impacts on environment, society, agriculture and economy stemming from its consequences. The performance of the procedure to estimate drought length was found to be satisfactory up to the truncation level of Q75, whereas the estimation of drought magnitude was rated as good. Sharma and Panu presents a procedure for the estimation of drought magnitude (M), which then forms the basis for estimating the drought duration or length (L). [Extracted from the article]

Conducting suitability assessment for sugarcane cultivation is of great significance for optimizing the sugarcane cultivation structure and industrial layout. In this paper, based on the requirements of sugarcane growth and development on climate, terrain, and other environmental conditions, as well as the influence of natural disasters, a total of 11 specific indicators in terms of climate factor, terrain factor, and disaster factor were selected to construct a sugarcane cultivation suitability assessment system based on the analytic hierarchy process (AHP). Then, using Guangxi Province, China, as an example, a suitability assessment for sugarcane cultivation was conducted using multi-source data on climate, terrain, and hazards over the past 30 years. The results showed that among 11 indicators, including annual average temperature, elevation had the largest contribution rate, followed by precipitation during the period of ≥20 °C, slope, and the autumn drought frequency. From the spatial distribution, 37% of the provincial regions were suitable for sugarcane cultivation, mainly distributed in Chongzuo City, Nanning City, Qinzhou City, and Beihai City. In total, 44% of the provincial regions were moderately suitable for sugarcane cultivation, mainly distributed in Hezhou City, Laibin City, and Liuzhou City. Additionally, only 19% of the provincial regions were unsuitable for sugarcane cultivation, mainly distributed in Baise City, Hechi City, and Guilin City, with the terrain factor being the main influencing factor of sugarcane suitability assessment. In order to make reasonable use of land resources and increase sugarcane yield, it is suggested that sugarcane cultivation areas should be adjusted to the central and southern regions such as Chongzuo City, Nanning City, Beihai City, and Qinzhou City, and other industries should be developed in the northern regions which are not suitable for sugarcane cultivation. [ABSTRACT FROM AUTHOR]

Climate change is a product of the Anthropocene, and the human–nature system in which we live. Effective climate change adaptation requires that we acknowledge this complexity. Theoretical literature on sustainability transitions has highlighted this and called for deeper acknowledgment of systems complexity in our research practices. Are we heeding these calls for 'systems' research? We used hydrohazards (floods and droughts) as an example research area to explore this question. We first distilled existing challenges for complex human–nature systems into six central concepts: Uncertainty, multiple spatial scales, multiple time scales, multimethod approaches, human–nature dimensions, and interactions. We then performed a systematic assessment of 737 articles to examine patterns in what methods are used and how these cover the complexity concepts. In general, results showed that many papers do not reference any of the complexity concepts, and no existing approach addresses all six. We used the detailed results to guide advancement from theoretical calls for action to specific next steps. Future research priorities include the development of methods for consideration of multiple hazards; for the study of interactions, particularly in linking the short- to medium-term time scales; to reduce data-intensivity; and to better integrate bottom–up and top–down approaches in a way that connects local context with higher-level decision-making. Overall this paper serves to build a shared conceptualisation of human–nature system complexity, map current practice, and navigate a complexity-smart trajectory for future research. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) run at regional resolution or at a continental scale. Therefore, these results cannot be used directly for local temperatures and precipitation prediction. Downscaling techniques are required to calibrate GCMs. Statistical downscaling models (SDSM) are the most widely used for bias correction of GCMs. However, few studies have compared SDSM with multi-layer perceptron artificial neural networks and in most of these studies, results indicate that SDSM outperform other approaches. This paper investigates an alternative architecture of neural networks, namely the long-short-term memory (LSTM), to forecast two critical climate variables, namely temperature and precipitation, with an application to five climate gauging stations in the Lake Chad Basin. Lake Chad is a data scarce area which has been impacted by severe drought, where water resources have been influenced by climate change and recent agricultural expansion. SDSM was used as the benchmark in this paper for temperature and precipitation downscaling for monthly time–scales weather prediction, using grid resolution GCM output at a 5 degrees latitude × 5 degrees longitude global grid. Three performance indicators were used in this study, namely the root mean square error (RMSE), to measure the sensitivity of the model to outliers, the mean absolute percentage error (MAPE), to estimate the overall performance of the predictions, as well as the Nash Sutcliffe Efficiency (NSE), which is a standard measure used in the field of climate forecasting. Results on the validation set for SDSM and test set for LSTM indicated that LSTM produced better accuracy on average compared to SDSM. For precipitation forecasting, the average RMSE and MAPE for LSTM were 33.21 mm and 24.82% respectively, while the average RMSE and MAPE for SDSM were 53.32 mm and 34.62% respectively. In terms of three year ahead minimum temperature forecasts, LSTM presents an average RMSE of 4.96 degree celsius and an average MAPE of 27.16%, while SDSM presents an average RMSE of 8.58 degree celsius and an average MAPE of 12.83%. For maximum temperatures forecast, LSTM presents an average RMSE of 4.27 degree celsius and an average MAPE of 11.09 percent, while SDSM presents an average RMSE of 9.93 degree celsius and an average RMSE of 12.07%. Given the results, LSTM may be a suitable alternative approach to downscale global climate simulation models' output, to improve water management and long-term temperature and precipitations forecasting at local level. [ABSTRACT FROM AUTHOR]

From 19 July 2022 to 31 August 2022, a rare persistent drought and heat event occurred in the middle of the Yangtze River basin (MYRB). Normalized difference vegetation Index (NDVI) over 25% of the area decreased more than 0.05 compared with the climatology, causing extremely agricultural drought disaster and economic losses to China. Previous studies have shown that the occurrence of compound drought and heat events (CDHEs) in the MYRB was associated with intra-seasonal oscillations (ISOs) from different latitudes. Nevertheless, what was the role of ISOs at different latitudes in the formation of the CDHE? To address this question, this paper designed a numerical simulation experiment of partial lateral forcing to investigate the changes in meteorological elements by removing the signals of ISOs on different lateral boundaries. We found that the wave series formed in the upper troposphere at 200 hPa played a significant role in the occurrence of the CDHE in the northern part of the MYRB in this progress. It was found that the ISO component of the northern boundary caused the mean temperature to rise by 2.4 °C and aggravated the drought in 53.7% of the region. On the other hand, the anticyclone anomaly in the lower troposphere at 800 hPa had a continuous impact on the southern and eastern boundaries. It was found that the ISO component of these two boundaries can increase the average temperature by 1.93 °C in the MYRB and intensify the drought in 49.7% of the area. In the developing period of the CDHE, the South Asian high and the Western North Pacific subtropical high were coupled with each other and jointly controlled the MYRB, so that the significant positive geopotential height anomaly stayed above the MYRB for a long time, which was conducive to the development of local subsidence. The results of this paper will help to better understand the formation mechanism of CDHEs in the MYRB and assist meteorologists to prevent and forecast the occurrence of CDHEs in advance. [ABSTRACT FROM AUTHOR]

Global climate changes, particularly extreme weather events, can directly or indirectly affect freshwater availability and food production, and cause disease outbreaks, floods and droughts. Using the advanced research version of the weather research and forecasting model (ARWv3) and a hydrostatic wind speed change equation, Liu et al. [[5]] assessed the effects of four CPSs on a 10 m wind speed simulation over mainland China in the summer of 2003. It was found that the IAP climate model creditably reproduced the spatial patterns of the first three dominant modes of summer rainfall in Thailand, and that the correlation between the observed rainfall anomalies and the Niño 3.4 index could be reproduced through the use of the IAP model. Advanced climate simulations and observations can improve the accuracy of climate change predictions and long-term trends, which can mitigate the impacts of climate events on social and economic development, as well as human lives. [Extracted from the article]

Among the various methods for estimating reservoir volumes, the Gould probability matrix (GPM) method has been touted as a powerful method for estimating reservoir volumes. The other methods in vogue are the Behavior analysis (BA) with the latest induction of the Drought magnitude (DM) method. A comparison of the above methods in terms of ease, efficiency, and relative merits from each other is currently lacking in the literature. This paper compares the above three methods with a detailed analysis of the GPM method using the monthly flows from 16 Canadian rivers at the draft ratios of 75 and 50% with the probability of failure of 2.5, 5 and 10%. The results reported in this paper indicate that fifteen zones are sufficient in the GPM method to yield the reservoir capacity for the Canadian rivers while requiring no standardization of the data, similar to the BA method. In the DM method, standardized monthly flow sequences in combination with a scaling parameter Φ yielded effective drought length, which, when multiplied by drought intensity and the average of 12 monthly standard deviations, resulted in the appropriate values of reservoir capacity. The results of this paper affirm that the GPM method offers little special merit in obtaining reservoir capacity in view of the rigor of computational efforts and uncertainty in the correction factors for significantly autocorrelated (dependent) annual flows. The DM method was found to be comparable to the BA method, though it requires standardization of the monthly flow data. The study suggests that all three methods result in comparable estimates of reservoir capacity for nearly independent annual flows with a slight edge to the Behavior analysis (BA) method. [ABSTRACT FROM AUTHOR]

The paper explores the occurrence of minimal precipitation extremes at specific meteorological stations in the southeastern region of Serbia. With climate change leading to increased instances of droughts, these natural phenomena have garnered heightened interest due to their negative impacts on society, environment, and the economy. Employing the SPI-12 index in the southeastern part of Serbia from 1946 to 2021, the study sheds light on the vulnerability of this natural phenomenon in the observed stations. Understanding historical manifestation of these events helps water resource managers, farmers and policymakers manage these risks in the southeastern region of Serbia. [ABSTRACT FROM AUTHOR]

Drought is a complex natural hazard that affects various regions of the world, causing significant economic and environmental losses. Accurate and timely monitoring and forecasting of drought conditions are essential for mitigating their impacts and enhancing resilience. Satellite-based drought indices have the advantage of providing spatially continuous and consistent information on drought severity and extent. A new drought product was developed from the thermal infrared observations of the Fengyun (FY) series of satellites. We proposed a data fusion algorithm to combine multiple FY satellites, including FY-2F, FY-2G, and FY-4A, to create a long time series of a land surface temperature (LST) data set without systematic bias. An FY drought index (FYDI) is then derived by coupling the long-term LST data set with the surface–atmospheric energy exchange model at 4 km spatial resolution over China from 2013 to present. The performance and reliability of the new FYDI product are evaluated in this study by comparing it with the Meteorological-drought Composite Index (MCI), one of the authoritative drought monitoring indices used in the Chinese meteorological services. The main objectives of this paper are: (1) to evaluate the performance of the FYDI in capturing the spatiotemporal patterns of drought events over China; (2) to quantitively analyze the consistency between the FYDI and MCI products; and (3) to explore the advantages and limitations of the FYDI for drought monitoring and assessment. The preliminary results show that the FYDI product has good agreement with the MCI, indicating that the FYDI can effectively identify the occurrence, duration, severity, and frequency of drought events over China. These two products have a strong correlation in terms of drought detection, with a correlation coefficient of approximately 0.7. The FYDI was found to be particularly effective in the regions where ground observation is scarce, with the capability of reflecting the spatial heterogeneity and variability of drought patterns more clearly. Overall, the FYDI can be a useful measure for operational drought monitoring and early warning, complementing the existing ground-based MCI drought indices. [ABSTRACT FROM AUTHOR]

This paper examines the effect of rainfall decline on water resources in each sub-basin (Bozoum: 8100 km2 and Bossangoa: 22,800 km2) and at the outlet of Batangafo (43,650 km2) over the 1951–1995 period, due to a lack of measurements since 1996. Annual, monthly, and daily series of rainfall and discharges were subjected to statistical tests (rainfall and flow indices, SPI, search for ruptures/breaks, depletion coefficient, and potential groundwater discharge) to present and discuss the rainfall variability impact on the water resources of the whole basin. The average rainfall per sub-basin decreases from the west to the east according to the Ouham river direction: 1423 mm at Bozoum, 1439 mm at Bossangoa, and 1393 mm at Batangafo, the main outlet. The SPI approach provides evidence of a moderate to normal drought in the whole basin in the 1980s, mainly compared to the 1970s. Thus, deficient breaks in the rainfall series of the Ouham Basin at Batangafo were noticed in 1967 (Bossangoa and Batangafo) and 1969 (Bozoum). A declining rainfall of −5% on average tended to have the highest impact on the runoff deficit, from about −30 to −43%. The deficit seems more important from west to east, and is also high over the groundwater in each outlet (−33% at Bozoum, −29% at Bossangoa, and −31% at Batangafo) in the 1986–1995 period, despite rainfall recovery in 1991 having generated a flow increase in 1995 at Bossangoa as well as at Batangafo. At the same time, Chari/Logone at Ndjamena recorded critical discharges in both 1987 (313 m3/s) and 1990 (390 m3/s) before they increased, such as on the Ouham. These results demonstrate the decline in water resources in the Ouham River, and their direct impact on the water level of the Chari River and Lake Chad in the targeted period. [ABSTRACT FROM AUTHOR]

Evidence shows how food system activities, from production to consumption, underpin food security. However, studies exploring climate impacts on food security in northern Ghana have overly focused on production systems, neglecting post-production activities that loom large in food security. This paper addresses the research need to comprehensively analyze how climate change and weather variabilities affect post-production activities and exacerbate food insecurity risks in northern Ghana. It analyzes data on climate hazards, impacts, and food system vulnerabilities using questionnaires and participatory engagement with farming households in northern Ghana. Results show that climate-induced food insecurity risks in northern Ghana are not just products of persistent exposure to climate hazards and their impacts on food production in the region. Instead, risks are inextricably connected to the vulnerability contexts within which food is harvested, processed, stored, and marketed. Specifically, the results reveal that climate hazard events such as floods, extreme temperatures, and droughts damage stored grain, disrupt food supply to the market, and cause seasonal volatilities in food prices. However, these impacts are not solely externally generated circumstances. The food system is highly vulnerable; most households lack access to threshing and grinding machines, warehouse storage, post-harvest management information, and transportation services. These underlying characteristics of the post-food production system of northern Ghana, which is ultimately quite remote from climate change and weather variabilities, exacerbate household-level food insecurity risks. [ABSTRACT FROM AUTHOR]

Common purslane (Portulaca oleracea) is a halophyte, consumed not only as an edible vegetable but also as a traditional medicine. This plant can adapt to extreme salinity and drought conditions and their effects on plant growth, yield and quality were elucidated, but the effects on seed germination was still unclear. Seed germination is an important stage for establishing P. oleracea seedlings that contribute to plant yield and productivity. Thus, the objective of the present study was to examine the individual effects of salinity and drought stress at different levels on the characteristics of seed germination, which the seeds suffered from −0.22, −0.45, −0.89, and −1.78 MPa osmotic stresses induced by polyethylene glycol (PEG), or from 50, 100, 200, and 400 mM NaCl stresses with equivalent osmotic pressures. The seeds sown in petri dishes containing filter paper layers that were flooded with NaCl or PEG-6000 solutions for the treatments and germination parameters were determined daily for 15 days. Data showed that germination percentage (GP), germination rate (GR), germination energy (GE), and mean germination time (MGT) were significantly decreased with increasing levels of salt and osmotic stresses, suggesting that the salinity and drought stress reduced the germination capability of seeds. Moreover, the seeds maintained germination with PEG osmotic pressure above −0.22 MPa, but not with NaCl concentration greater than 50 mM that induced a similar osmotic pressure, suggesting that the ion toxicity effect on the seed germination might be higher than the osmotic effects. [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]

The paper analyzes the national DK-model hydrological information and prediction (HIP) system and HIP portal viewed as a 'digital twin' and how the introduction of real-time dynamic updating of the DK-model HIP simulations can make room for plug-in submodels with real-time boundary conditions made available from an HIP portal. The possible feedback to a national real-time risk knowledge base during extreme events (flooding and drought) is also discussed. Under climate change conditions, Denmark is likely to experience more rain in winter, more evapotranspiration in summer, intensified cloudbursts, drought, and sea level rise. These challenges were addressed as part of the Joint Governmental Digitalization Strategy 2016–2020 for better use and sharing of public data about the terrain, water, and climate to support climate adaptation, water management, and disaster risk reduction. This initiative included the development of a new web-based data portal (HIP portal) developed by the Danish Agency for Data Supply and Infrastructure (SDFI). GEUS delivered 5 terabytes of hydrological model data to the portal, with robust calibration methods and hybrid machine learning (ML) being key parts of the deliverables. This paper discusses the challenges and potentials of further developing the HIP digital twin with 'plug-in digital twins' for local river basins, including feedback to the national level. [ABSTRACT FROM AUTHOR]

The uncertainties raised by climate change in the Mediterranean region justify a detailed study on rainfall regime over a long time scale. This study is not just about reconstructing and characterizing climate variability, but also identifying the hydrometeorological episodes of greater severity and low frequency. These phenomena, such as floods and droughts, affect the availability of water resources, generate casualties and have significant impacts on human activity. In this context of global warming, knowledge of past extreme hydrometeorological events has gained importance over time. Moreover, the ability to classify these past extreme events in order to compare them with current ones strengthens research capabilities. For this reason, this paper proposes a multivariable classification system for flood episodes, both historical and recent. The classification considers the physical conditions of a flood, its damage to structures and its social impacts. The proposed methodology has been applied to the AMICME flood database for the Spanish Mediterranean catchment areas from CE 1035 to 2022. This paper shows the application of this methodology for six flood episodes from different centuries and locations. It has been possible to display all the indexed information for the different flood cases of every episode through cartographic summary representations. Results prove the adaptability of the classification system to different hydrographical and historical contexts. [ABSTRACT FROM AUTHOR]

Meteorological drought (MDr) causes considerable economic losses in many countries, including in relation to agriculture. To examine the diversity of seasonal meteorological drought in Poland, the study uses monthly precipitation sums in the period from March to November, collected from 74 ground-based meteorological stations in 1951–2020. The paper defines meteorological drought on the basis of the standardized precipitation index in three seasons (SPI-3) and differentiates three degrees of drought intensity: extreme, severe and moderate. The study also calculates the size of Poland's area affected by meteorological drought (AAMDr) and determines the relationship between AAMDr and SPI-3. MDr in Poland occurred more frequently in spring and autumn (every 4–5 years) than in summer (every 7 years). In the areas affected by extreme drought, precipitation was below the average, mainly ≤50% of the climatic norm, and air temperature values were below or above the average of the climatic norm, mainly ranging from −1.0 to 1.0 °C. A significant negative correlation between AAMDr and SPI-3 indices was found. The obtained results could be useful for managing climatic risk and developing regional and local agriculture adaptation plans aimed at mitigating the effects of climate change. [ABSTRACT FROM AUTHOR]

Climate and other environmental factors continue to play important contributions on the livelihoods of communities all over the world. Their influence during historical periods and the roles they played remain under-reported. The main objective of this review is to investigate the climatological conditions during the time of the invasion of early European settlers in Southern Africa in the 19th and early 20th centuries. It establishes the possible relationships between climate variability and historical conflicts and wars, famines, disease pandemics, and the migration of African people to towns in search of sustainable and predictable livelihoods away from unreliable agriculture. A qualitative analysis of published peer reviewed literature in the form of reports, papers, and books was used in this review. At least 60 literature items were reviewed in this paper. There is a relationship between climate variability and the historical events of the 19th and early 20th centuries. Tribal conflicts and most of the wars between the settlers and the African people for land coincided with periods of droughts. Drought were key causes of famines, instabilities, and land degradation in the region. This study highlights the influence of environmental conditions on socio-economic conditions as the world enters an era of climate change and urbanization in developing countries, particularly in Africa. It shows that the hardships caused by environmental conditions have the potential to destabilize societies. [ABSTRACT FROM AUTHOR]

Drought is the most important natural disaster affecting crop growth and development. Crop rhizosphere microorganisms can affect crop growth and development, enhance the effective utilization of nutrients, and resist adversity and hazards. In this paper, six spring wheat varieties were used as research material in the dry farming area of the western foot of the Greater Khingan Mountains, and two kinds of water control treatments were carried out: dry shed rain prevention (DT) and regulated water replenishment (CK). Phenotypic traits, including physiological and biochemical indices, drought resistance gene expression, soil enzyme activity, soil nutrient content, and the responses of potential functional bacteria and fungi under drought stress, were systematically analyzed. The results showed that compared with the control (CK), the leaf wilting, drooping, and yellowing of six spring wheat varieties were enhanced under drought (DT) treatment. The plant height, fresh weight (FW), dry weight (DW), net photosynthetic rate (Pn) and stomatal conductance (Gs), soil total nitrogen (TN), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), microbial biomass phosphorus (MBP), organic carbon (SOC), and soil alkaline phosphatase (S-ALP) contents were significantly decreased, among which, FW, Gs and MBC decreased by more than 7.84%, 17.43% and 11.31%, respectively. By contrast, the soil total phosphorus (TP), total potassium (TK), and soil catalase (S-CAT) contents were significantly increased (p < 0.05). TaWdreb2 and TaBADHb genes were highly expressed in T.D40, T.L36, and T.L33 and were expressed at low levels in T.N2, T.B12, and T.F5. Among them, the relative expression of the TaWdreb2 gene in T.L36 was significantly increased by 2.683 times compared with CK. Soil TN and TP are the most sensitive to drought stress and can be used as the characteristic values of drought stress. Based on this, a drought-tolerant variety (T.L36) and a drought-sensitive variety (T.B12) were selected to further analyze the changes in rhizosphere microorganisms. Drought treatment and cultivar differences significantly affected the composition of the rhizosphere microbial community. Drought caused a decrease in the complexity of the rhizosphere microbial network, and the structure of bacteria was more complex than that of fungi. The Shannon index and network modular number of bacteria in these varieties (T.L36) increased, with rich small-world network properties. Actinobacteria, Chloroflexi, Firmicutes, Basidiomycota, and Ascomycota were the dominant bacteria under drought treatment. The beneficial bacteria Bacillus, Penicillium, and Blastococcus were enriched in the rhizosphere of T.L36. Brevibacillus and Glycomyce were enriched in the rhizosphere of T.B12. In general, drought can inhibit the growth and development of spring wheat, and spring wheat can resist drought hazards by regulating the expression of drought-related genes, regulating physiological metabolites, and enriching beneficial microorganisms. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) and tree-ring parameters are commonly used indicators in the research on forest ecology and responses to climate change. This paper compiles and analyzes the literature on vegetation index research on the basis of tree-ring information in the past 20 years and provides an overview of the relationship between tree-ring parameters and NDVI, as well as NDVI reconstruction. The research on the vegetation index based on tree-ring data is mainly concentrated in the middle and high latitudes, and relatively few studies are concentrated in the low latitudes. The tree-ring parameters have a strong correlation with the NDVI in the summer. In terms of tree-ring reconstruction NDVI, Sabina przewalskii is the tree with the longest reconstruction sequence so far, and the tree-ring width is the main proxy index. In addition, combining tree rings with the NDVI is useful for assessing forest decline, quantifying the forest response to drought, and monitoring forest productivity. In the future, it is necessary to consider a variety of environmental factors to find the optimal model construction parameters and carry out research on the climate response of forest tree growth and the reconstruction of the historical sequence of the vegetation index at large spatial scales. [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]