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Bai et al. ([3]) in their article «Impact of climate change on water transfer scale of inter-basin water diversion project» present a systematic approach for runoff prediction, impact analysis, and risk assessment under climate change. Elnashar and Elyamany ([8], [9]) in the paper «Managing Risks of Climate Change on Irrigation Water in Arid Regions», propose a systematic approach to identify, analyse, and respond to the risks of climate change on irrigation water in arid regions using a Risk Management process. In the paper «Complex policy mixes are needed to cope with agricultural water demands under climate change» by Martinez-Valderrama et al. ([23]) the authors argue that the forecast of the water gap (difference between agricultural water use and annual supply of water resources) will continue to widen, affecting the water security of a large part of the world's population. Elnashar and Elyamany ([8], [9]) in the paper «Adapting Irrigation Strategies to Mitigate Climate Change Impacts: A Value Engineering Approach» accept the fact that climate change will increase water demand and decrease crop yields. [Extracted from the article]

Drought is one of the most critical meteorological hazards that has a devastating effect on natural habitats, ecosystems, and many economic and social sectors. Due to these severe impacts of drought events, many studies have focused on drought monitoring, prediction, and risk analysis to aid drought preparedness plans and mitigation measures. This paper presents a new drought composite index (DCI) to measure the intensity and frequency of the hazard component of meteorological drought. The DCI considers multiple indicators, such as precipitation, temperature, evapotranspiration, wind speed, relative humidity, and sunshine duration. The DCI is validated through an application to the Brazilian semiarid context. Based on the expert's opinion, we define the weight of each indicator using the analytical hierarchy process. The results were compared with two other drought indices to provide an initial validation because the other indices are not exclusively meteorological indices. The DCI estimates for January 2019 show that the semiarid central region is the most critical. The findings also indicate that the drought intensity is growing in this region. [ABSTRACT FROM AUTHOR]

In our original paper (Nat Hazards 76:327-346, . doi:), we analyzed hydrological droughts based on the minimum values of the streamflow drought index at different time scales using L-moments. In this paper, we highlight the importance of the magnitude and type of selected drought index for regional frequency analysis and discuss the validity of results only for station clusters considered in response to the commentary by Bahmani and Khorsandi (Nat Hazards, . doi:). [ABSTRACT FROM AUTHOR]

This bibliographic article on Drought and Water Level examined the relationship between organizations, nations, institutions, authors, references, and publishers. It examined 742 papers from Web of Science at the Nanjing University of Information Science and Technology's. The total annual publication volume of articles was increased steadily from 2012 to 2021, with China and the United States ranking first and second in terms of publication volume and citations but in quality Switzerland and England were top-level. Institutional-partnership analyses indicated disparities in network density and connections, with the Chinese Academy of Sciences (2012) receiving the highest citations and degrees. The document co-citation analysis (DCA) network was created to improve understanding of the frequency and amplitude of bursts of various publications in separate clusters. The most cited work was J Hydrol (2012), with 302 citations. The analytical tool from CiteSpace collected high-frequency keywords and performed co-occurrence, grouping, and emerging word recognition. Gorges Dam is the most crowded cluster, followed by drought stress. The greatest burst duration and most significant phrase is reservoir (2019), followed by "water quality," which has a 5 year burst period. Estuaries perform important functions such as water purification and coastal. "Reservoir, water quality, restoration, phytoplankton, temperature, wetland, time series, diversity and carbon dioxide" are the most important terms, while "climate change, drought, water level, impact, growth, variability, response, dynamics, management and model" are the most frequently used keywords. In terms of citations, references, and academic influence, Zhang Q. (2012), the R Core team (2014), and Jappen E. (2015) were the top three contributors. Cook, ER (2013), and Allen, R.G. (2019) ranked first and second in terms of frequency, respectively. In this review work, significant information gaps were discovered in the areas of microbiological dynamics, environmental variables, fen peat incubation, lake water, drought risk reduction, biological ecology, lake acidification, salinity variations, and attribution. Future researchers should focus on these and similar topics, while Chinese and USA authors should concentrate on article quality rather than publishing numbers. [ABSTRACT FROM AUTHOR]

The projection pursuit model is an important tool for processing high-dimensional nonnormal and nonlinear data, and it has a wide range of applications. In this paper, clustering analysis and information entropy theory are simultaneously introduced into the projection pursuit model. The K-means clustering method is used to cluster high-dimensional data, and information entropy is used to measure the overall dispersion and local aggregation of projection data. A projection pursuit model based on clustering and information entropy is proposed. The new model has both the classification advantages of clustering analysis and the evaluation advantages of the projection pursuit model. In the case test, different cases are tested by using the Shapiro-Wilk test, Kolmogorov-Smirnov test, Epps-Pulley test, etc. The results show that in most cases, the new model is better than the original model, and the advantage is clearer when the data dimension is higher. Finally, the new model is applied to the agricultural drought risk assessment of Qiqihar, a major grain-producing area in China that is prone to drought. The regional agricultural drought risk shows a downward trend over time. Spatially, the risk of the central and southern regions is low, while the risk of the northern and western regions is high. Regional ability to resist disasters is the main reason for the spatial and temporal differences. This paper extends projection pursuit model theory, analyzes the characteristics of the spatiotemporal variation in regional agricultural drought risk, and provides a reference for identifying the hidden dangers of drought disasters and disaster reduction. [ABSTRACT FROM AUTHOR]

This study aims to quantify meteorological–hydrological drought propagations and examine the potential impacts by climatic variability, LULC change (LULC), and human regulations. An integrated observation-modeling framework quantifies drought propagation intervals and assesses mechanisms influencing hydrological droughts. Meteorological droughts are characterized using the Standardized Precipitation Evapotranspiration Index (SPEI), and hydrological droughts are assessed through the Standardized Streamflow Index (SSI) across diverse climatic zones. Cross-correlation analysis between SPEI and SSI time series identifies the lag time associated with the highest correlation as the drought propagation interval. Mechanisms are investigated via a coupled empirical-process modeling framework incorporating the Soil and Water Assessment Tool (SWAT). Discrepancies between simulated and observed SSI time series help quantify the extent of human regulation impacts on hydrological drought characteristics and propagation. The Yellow River Basin (YRB), divided into six subzones based on climate characteristics, is selected as the case study. Key findings include: (1) Meteorological droughts were extremely severe across most YRB during the 1990s, while the 2000s showed some mitigation primarily due to precipitation increases. (2) Hydrological droughts and propagation times from meteorology to hydrology demonstrated substantial spatiotemporal variability. In general, summer propagation times were shorter than other seasons. (3) Propagation times were shorter in arid regions with cropland or built-up land cover versus grassland and woodland, while the reverse held for humid regions. (4) Human regulations prolonged propagation times, likely due to reservoir regulations designed to overcome water deficits. While the YRB is the focus of this paper, the methodologies and findings are applicable to other regions worldwide to enhance drought forecasting and water resource management. In various hydrological and climatic contexts worldwide. [ABSTRACT FROM AUTHOR]

This paper presents the composite drought indicator (CDI) that Jordanian, Lebanese, Moroccan, and Tunisian government agencies now produce monthly to support operational drought management decision making, and it describes their iterative co-development processes. The CDI is primarily intended to monitor agricultural and ecological drought on a seasonal time scale. It uses remote sensing and modelled data inputs, and it reflects anomalies in precipitation, vegetation, soil moisture, and evapotranspiration. Following quantitative and qualitative validation assessments, engagements with policymakers, and consideration of agencies' technical and institutional capabilities and constraints, we made changes to CDI input data, modelling procedures, and integration to tailor the system for each national context. We summarize validation results, drought modelling challenges and how we overcame them through CDI improvements, and we describe the monthly CDI production process and outputs. Finally, we synthesize procedural and technical aspects of CDI development and reflect on the constraints we faced as well as trade-offs made to optimize the CDI for operational monitoring to support policy decision-making—including aspects of salience, credibility, and legitimacy—within each national context. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Comments on the paper `Towards an Interpretation of Historical Droughts in Northern Nigeria,' by Aondover Tarhule and Ming-Ko Woo. Confusion regarding the use of standard deviation of the long-term mean rainfall in the interpretation of their data; Probability of rainfall deficit.

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]

This paper investigates the impact of timeline-bound fetal exposure to drought shocks on birth outcomes in rural Sierra Leone. We link repeated cross-section birth record data across 11 years from the Sierra Leone Demographic and Health Surveys to district-level geolocation precipitation data from the University of Delaware weather repository. The methodology uses spatial distribution of precipitation across districts to identify the impacts of extreme droughts on birth outcomes. This study reinforces both harvest and direct gestation as maternal nutrition pathways for the impact of drought shocks on birth outcomes. Results also show that adverse in utero shock impacts are concentrated among poorer households and may be mitigated by antenatal care services. [ABSTRACT FROM AUTHOR]

Regional cities are having their unique water security challenges due to regional urban water contexts, regional socio-economic structure, and climate conditions. Regional urban community's perceptions of water usage are expected to be different from the communities in large metropolitan cities. The city of Townsville is the largest regional city in the northern tropical region of the state of Queensland in Australia, and it is known to have its unique dry tropic climate condition. The city faced a water crisis due to a prolonged drought in 2013–2018. As part of this research, at first, a literature review was conducted to understand what water demand management (WDM) tools worked well during urban water crisis in different parts of the world. This paper then investigates how residential water usage changed with the changes in drought measures in the city of Townsville in the last decade. A minimum per capita residential water requirement is established for the study region to benchmark the effects of tools implemented in the region. The paper investigates the WDM policies implemented in the city of Townsville including when the policies were applied and the impacts and efficacy of these policies before water crisis, during water crisis and after water crisis. The most effective WDM tools identified are water restrictions, public awareness raising and education programmes. The impacts of water restriction policies and the perceptions of local water professionals on their success elements are also studied. The results are compared and the reasons behind the findings are investigated. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibet Plateau is a climate-sensitive region. The characteristics of drought and flood events in this region are significantly different as compared to other areas in the country, which could potentially induce a series of water security, ecological and environmental problems. It is urgent that innovative theories and methods for estimation of drought and flood disasters as well as their adaptive regulations are required. Based on extensive literature review, this paper identifies new situations of the evolution of drought and flood events on the Qinghai-Tibet Plateau, and analyzes the research progress in terms of monitoring and simulation, forecasting and early warning, risk prevention and emergency response. The study found that there were problems such as insufficient integration of multi-source data, low accuracy of forecasting and early warning, unclear driving mechanisms of drought and flood disaster chains, and lack of targeted risk prevention and regulation measures. On this basis, future research priorities are proposed, and the possible research and development paths are elaborated, including the evolution law of drought and flood on the Qinghai-Tibet Plateau, the coincidence characteristics of drought and flood from the perspective of a water resources system, prediction and early warning of drought and flood coupled with numerical simulation and knowledge mining, identification of risk blocking points of drought and flood disaster chain and the adaptive regulations. Hopefully, the paper will provide technical support for preventing flood and drought disasters, water resources protection, ecological restoration and climate change adaptation on the Qinghai-Tibet Plateau. [ABSTRACT FROM AUTHOR]

Persistent droughts are known to induce stress on agricultural crops and forest ecosystems. Severe long-lasting droughts lead to food insecurity. With rising air temperatures and declining precipitation totals, there is often a presumption that droughts are becoming more severe. To understand the complexity of interrelated physical processes on various temporal scales, it is important to analyse trends and mutual relationships between drought-related climatic variables. In this paper, we analyse monthly time series of near-surface air temperature, precipitation totals, potential evapotranspiration, climatic water balance, and two meteorological drought indices: the standardised precipitation index (SPI) and the standardised precipitation-evapotranspiration index (SPEI), at a total of 71 locations located within the western Carpathian Mountains (Slovakia) and the adjacent northern part of the Pannonian Plain. The analysed data cover the time period 1981–2019. Quantile regression was used to determine the slopes of temporal trends in the investigated climate variables. The mutual relationships between precipitation and air temperature were analysed on a seasonal basis. Negative coupling (~ − 5.1%/°C) between precipitation and air temperature was observed in the spring season. Positive coupling between precipitation and air temperature was observed in the remaining part of the year. Spatio-temporal analyses of SPEI revealed intensification of meteorological drought in the spring season that continues into summer at most of the analysed locations. The negative temporal trends in the SPEI suggest that the severity of meteorological drought has increased over the investigated period. [ABSTRACT FROM AUTHOR]

Drought in Afghanistan has many impacts from the interaction between environmental and socio-economic factors in the agriculture and water supply sectors. The complex topography and climate change impacts cause high spatial and temporal variation in the precipitation pattern and pose several challenges in managing water resources. Therefore, this paper aimed to quantify the sustainability of water resources against the progression of drought. Observed monthly precipitation data monitored by 23 stations located across five river basins covering the entire country for the period 1970–2016 were used to demonstrate the potential impacts of drought on water resources sustainability. Based on severe drought estimation, the year 2000 and 2001 were identified as change points. Accordingly, datasets pertaining to before and after the change points were partitioned to analyze the long-term temporal shift of drought. The spatiotemporal variability of sustainability indicators was estimated using reliability resiliency and vulnerability concept. The results indicated a positive trend of precipitation in most of the river basins after the change points leading to an increase in sustainability. However, the major portion in the upstream of the Kabul River basin showed a decrease in sustainability of around 15% due to a reduction in precipitation. As the Kabul city has high population density, it needs immediate attention in effectively planning and managing available water resources. Furthermore, the comprehensive analyses reported in this paper discuss the possible implications of drought risk assessment and its impact on largely varying sustainability both spatially and temporally in Afghanistan. [ABSTRACT FROM AUTHOR]

This paper evaluates a unique, transdisciplinary participatory research and knowledge exchange methodology developed in the Drought Risk and You (DRY) project and offers it as a transferable framework for others engaging stakeholders and systemic connections with environmental risk. Drought in the UK is a complex, diffuse and hidden risk, involving multiple stakeholders and systemic connections across diverse sectors. Historically, drought risk management has been underpinned by specialist science and technology implemented by statutory stakeholders. This paper critically evaluates the social learning from a longitudinal research process that involved co-working with seven river catchment-based, multi-stakeholder groups. The DRY project was a creative experiment in bringing drought science and stories into the same space, aiming to reveal different knowledges—specialist science, practical sector-level insight, and local knowledge—as a new evidence base to support better decision-making in UK drought risk management. An evaluative multi-method research methodology was overlaid on this process, using surveys, within meeting reflective evaluations, and summative semi-structured narrative interviews. This paper reflects on participant experiences of the 'open' scientific modelling development, 'storying' approaches, and their iterative interaction. It outlines the enablers, inhibitors and required support for this engagement process, which aimed to facilitate integration of different forms of knowledge as evidence, with social and sustainability learning among diverse stakeholders at its core. The process offered opportunity for valuable experiential learning as researchers of the nuanced impacts of intersecting factors on participatory place-based methods. It showed that similar approaches to science-narrative dialogic processes can play out locally to integrate aspects of social and sustainability learning in different ways. This sustainability learning provided a valuable platform for creative multi-stakeholder scenario-ing possible drought futures for increased local climate resilience. It then proposes a transferable research framework that promotes participatory, place-based, narrative-science knowledge exchange for building local capital for managing systemic environmental risk. [ABSTRACT FROM AUTHOR]

Introduces a series of papers related to mathematical techniques, modeling, numerical simulations, atmospheric and oceanic processes and field experiments conducted to gain insight into the weather and climate system. Application of the "splitting methods" of numerical modeling to a high resolution model of the Indian Ocean; Numerical experiments designed to determine the fidelity of predictions of major features of the Indian monsoon season; Analysis of the 2002 Colorado drought.

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]

Shandong Province, the main grain-producing area in China, has ranked first in China in terms of total agricultural output value for many years. However, droughts with high frequency and long duration have been hindering local agricultural production. This paper aims to assess the risk of drought disasters in Shandong Province. Firstly, based on the natural disaster system theory, an agricultural drought disaster risk assessment model is developed. This model is applied to assess the agricultural drought hazard, exposure, vulnerability, emergency response and recovery capability, and agricultural drought disaster risk from 2012 to 2020. Secondly, risk uncertainty is analyzed through the evolution of risk over the past years. Finally, the accuracy of the risk assessment is verified through agricultural drought-related losses. The results show that: (1) The risk assessment results are in good agreement with the actual drought losses. (2) From the spatial scale, the high-risk areas of agricultural drought disasters were mainly located in the western part of Shandong Province. High-hazard areas of drought were located in eastern Shandong Province, and areas with high agricultural exposure and vulnerability were concentrated in the western part of the province, and the emergency response and recovery capacity of Rizhao and Zaozhuang was low. (3) From the time scale, there was high uncertainty of agricultural drought disaster risk in Dongying, Qingdao, and Heze in 2012–2020, all of which had reached a high-risk level of agricultural drought disaster several times. The agricultural exposure in Dongying, the agricultural vulnerability in Heze, and the emergency response and recovery capacity in Heze and Linyi all showed an increasing trend. The interannual variation characteristics and spatial zoning of agricultural drought risk are explored, and it is instructive for risk decision-makers to better develop drought response measures and improve drought resilience. [ABSTRACT FROM AUTHOR]

MiRNAs have received much attention over the last 2 decades as one of the major regulators of drought response in the plant. However, there has been little systematic discussion of the identified drought-responsive miRNAs. This paper is an overview of conducted studies until March 1, 2020, to identify frequent drought-responsive miRNAs (DRMs), their expression patterns, and functional roles. The present study organized 5628 records of detected DRMs from 40 flowering plants. Based on the available data, 72% of these DRMs are detected by deep sequencing techniques. Oryza Sativa was uncovered as the most frequently studied species under drought stress. Likewise, leaf tissue was detected as the most examined tissue. The present study noticed dissimilar expression patterns of DRMs in the various plant species which could be exemplified in Oryza sativa and Triticum aestivum that their DRMs were more promoted and inhibited under drought stress, respectively. Results of this study proposed miR166, miR169, miR172, miR396, and miR399 as frequently down-regulated DRMs while miR160 was suggested as an up-regulated DRM in many instances. Based on the reported data, it would seem to suggest a reverses expression patterns for some DRMs between drought tolerant and sensitive varieties (like miR156, miR159, miR395, and miR393) or shoot and root tissues (like miR156, miR171, and miR812). According to the previous functional studies of miRNAs, miR156, miR529, miR1119, miR159, miR172, miR319, miR394, and miR396 were categorized as positive regulators while mir166, miR160, and miR1916 were classified as negative regulators of drought stress tolerance induction. The outcome of various experiments led to the conclusion that although miRNAs are numerous with dynamic activity, a systematical study could uncover their discipline in the regulation of environmental stresses. [ABSTRACT FROM AUTHOR]

Due to its potential geo-political and environmental implications, climate migration is an increasing concern to the international community. However, while there is considerable attention devoted to migration in response to sea-level rise, there is a limited understanding of human mobility due to freshwater and inland hydroclimatic changes. Hence, the aim of this paper is to examine the existing evidence on migration as an adaptation strategy due to freshwater and inland hydroclimatic changes. A meta-review of papers published between 2014 and 2019 yielded 67 publications, the majority of which focus on a handful of countries in the Global South. Droughts, floods, extreme heat, and changes in seasonal precipitation patterns were singled out as the most common hazards triggering migration. Importantly, most of the papers discuss mobility as part of a portfolio of responses. Motivations to migrate at the household level range from survival to searching for better economic opportunities. The outcomes of migration are mixed — spanning from higher incomes to difficulties in finding employment after moving and struggles with a higher cost of living. While remittances can be beneficial, migration does not always have a positive outcome for those who are left behind. Furthermore, this meta-review shows that migration, even when desired, is not an option for some of the most vulnerable households. These multifaceted results suggest that, while climate mobility is certainly happening due to freshwater and inland hydroclimatic changes, studies reviewing it are limited and substantial gaps remain in terms of geographical coverage, implementation assessments, and outcomes evaluation. We argue that these gaps need to be filled to inform climate and migration policies that increasingly need to be intertwined rather than shaped in isolation from each other. [ABSTRACT FROM AUTHOR]

Agricultural sectors worldwide are under direct threat from climate change conditions. In Iran, agricultural production has decreased due to droughts originating in an increase in annual maximum temperatures—with the corresponding increase in crop respiration and evapotranspiration—and a decrease in accumulated precipitation. Based on a static computable general equilibrium approach, this paper reports implication for the Iranian economy from the effects of climate change on agriculture––as modeled through three scenarios relying on assumptions about the magnitude of continued reduction in total agricultural production. Reductions of 6%, 12%, and 18% in total agricultural production reasonably cover the range of impacts that climate change is expected to impose on the Iranian agricultural sector––under the assumption that no behavioral adaptations or policy interventions are in place. Our simulations suggest that effects on the Iranian economy imply a reduction in GDP ranging between 3.7 and 6.3%. In addition, 5–17% of labor moves away from the agriculture sector––this labor relocation occurs due to declining agriculture incomes. Findings illustrate that climate change will reduce households' consumption and income in all economic sectors, particularly among rural households. We suggest that policies in Iran should focus on improving cultivation methods to save water resources and alleviate the expected effects of climate change. The current study's outcomes are helpful for policymakers, especially in countries with water scarcity. [ABSTRACT FROM AUTHOR]

Jilin Province is one of the important grain-producing regions in China, the frequent drought and waterlogging events in this region have seriously impacted local agricultural production, therefore, it is particularly necessary to explore the spatiotemporal variations in drought and waterlogging and how they effect on maize yields. In this paper, we use the daily meteorological data recorded at 27 meteorological stations in Jilin Province from 1961 to 2020 to calculate the modified crop water deficit index (mCWDI), which were based on the daily crop coefficient (Kc) corresponding to different growth stages for each station. The spatiotemporal evolution processes of drought and waterlogging during the growing season in Jilin Province were analysed by the linear regression model, and the impacts of drought and waterlogging conditions on maize yields in Jilin Province under different growing seasons were quantified by means of the correlation analysis and multiple regression analysis methods. The results showed that the effective precipitation during the whole reproductive period showed a spatial distribution pattern of decreasing from southeast to northwest, with precipitation totals ranging from 335.02 to 677.38 mm, while the spatial distribution of the water demand showed the opposite trend. The south-eastern region of Jilin Province was in a state of water surplus, while the precipitation in other areas could not meet the water requirements of maize, resulting in decreasing drought frequency trends from northwest to southeast and from the early stage to the developmental stage of maize; in addition, increasing trends were observed in the middle and late reproductive stages of maize. The waterlogging frequency in the south-eastern region showed the spatial distribution characteristics of being higher in the early and late reproductive periods and lower in the development period of maize, and the growth rate of the maize-waterlogging frequency was higher in central Jilin Province than in other areas. Moreover, drought showed a more significant negative correlation with the maize yield in Jilin Province, while waterlogging showed a positive correlation. The relative importance results show that drought has a greater impact on maize yields than waterlogging, and the impacts of drought and waterlogging events on maize yields are mainly concentrated in the middle and late growth periods. The findings could inform the development of contingency plans for farmers to minimize crop losses and ensure food security in the region. [ABSTRACT FROM AUTHOR]

The Dingwu Great Famine (DGF) is considered to be the worst drought in China over the past 300 years. How the central government took steps to cope with the catastrophe during this period and ultimately survived deserves in-depth study. This paper collects the official records during the DGF and uses textual analysis and statistical methods to explore the responses carried by the central government. We found that 21 measures in 7 categories had been taken in response to this extreme drought, with political, economic, and cultural responses being the most commonly used. The governmental responses during DGF can be divided into three phases and had a significant “mismatch” (6 months lagging behind) compared with the meteorological process, which may be related to the higher social robustness in the early period. The 7 categories can be further clustered into 3 types based on their time-series performance. The first type includes economic, material, and political responses, which were aimed at responding to the drought impacts and reducing social losses more quickly and played the role of a “quick effect drug” for society. The second type includes agricultural and engineering, and population responses, which mitigated the drought impacts through “after-effects” measures and played the role of “slow-release drug”. The third type is the cultural response, which acted like a “placebo”. It did not help to mitigate the drought but alleviated the dissatisfaction of the refugees by shaping the spiritual objects (rain gods), thus maintaining social stability. [ABSTRACT FROM AUTHOR]

The use of remote sensing for monitoring and managing droughts is examined in this review study. Drought has a significant impact on how water resources are managed and agricultural production is produced, and remote sensing is a vital technique for assessing and monitoring the severity of drought. A number of remote sensing data sources are discussed in the paper; including precipitation, groundwater and surface water storage, soil moisture, land surface temperature, evaporation, and agricultural indicators. With the use of these data sources, drought indices and indicators that measure the severity and spatiotemporal fluctuations of the drought may be developed. The novel approach of this review study emphasizes the benefits of using remote sensing to gain a full understanding of drought dynamics and to accurately capture fine-scale fluctuations in drought conditions. However, the study also highlights certain limitations, including issues related to data accessibility, data interpretation, and validation difficulties. It emphasizes the significance of using remote sensing to promote the developing policies and strategies to enhance drought resilience and adaptation. The importance of continuous research, technical development, and stakeholder cooperation in order to fully realize remote sensing's promise for tackling the complex problems associated with drought and promoting sustainable water resource management. [ABSTRACT FROM AUTHOR]

Climate change is a major concern, as it profoundly affects many facets of our lives. It has brought about several issues, including declining water supply, reduced agricultural yields, increased drought occurrences, and increased heat waves. Amidst these challenges, the influence of short-term drought events on plant growth and irrigation schedule emerges as a critical concern. However, despite these evident consequences, a nuanced understanding of the intricate relationship between the severity and duration of short-period drought/deficit events still needs to be explored. This paper analyses fortnightly water deficit periods over different regions of India, which would be more relevant to re-scheduling the irrigation events than monthly or longer duration in preventing crops from reaching the permanent wilting point. Hence, this work considers analyzing 15-day Standardized Precipitation Index (SPI) trends and drought characteristics using conventional methods and Innovative Trend Analysis (ITA) techniques. The analysis uses gridded rainfall data from the India Meteorological Department (IMD), which has a spatial resolution of 0.250-E and 0.250-N. The data spans the period from 1970 to 2021. The ITA and Mann Kendall (MK) displayed nearly identical areas of increasing and decreasing trends, but ITA effectively identified significant trends. While MK and Modified Mann Kendall (MMK) could only indicate significant trends for 12.94%, 9.57%, and 9.9% of grid points for SPI, drought severity, and duration, respectively, ITA was able to identify significant trends at 44.31%, 10.9%, and 10.1% on an annual scale. The ITA method effectively identified the significant trends and magnitudes of fortnightly SPI and drought characteristics. The Tropical monsoon (Am), Tropical savannah (Aw), Arid desert hot (BWh), Arid steppe hot (BSh), and Temperate dry winter warm summer (Cwb) climatic zones have shown a significant increase in annual drought severity. Similarly, a significant increase in monsoon drought severity is observed across several states, including Gujarat and Mizoram, impacting diverse geographic extents. The present study can help policymakers and water resource managers decide on water allocation, irrigation, and crop management practices. [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]

Drought, a highly detrimental natural disaster, poses significant threats to both human populations, wildlife, and vegetation. Traditional methods of monitoring soil moisture levels rely on ground-based measurements from meteorological stations. However, these stations often lack comprehensive coverage in certain agricultural areas, necessitating the use of alternative methods such as satellite remote sensing. This technique provides a reliable means of measuring soil moisture, a critical factor in effective agricultural management. This paper investigates variations in soil moisture and drought using data from the Cyclone Global Navigation Satellite System (CYGNSS) and the Soil Moisture Active and Passive (SMAP) system. To evaluate the accuracy of these data products, we compared both datasets with the Global Land Data Assimilation System (GLDAS) NOAH model from 2018 to 2019. Our findings reveal a strong correlation between the datasets and the model, with Pearson correlation coefficients (r) and Root Mean Square Errors (RMSE) of approximately r = 0.98 and RMSE = 0.03 for SMAP, and r = 0.97 and RMSE = 0.02 for CYGNSS, respectively. We further compared these measurement datasets with drought indicators such as the Standardized Precipitation Index over three months (SPI3), the Normalized Difference Vegetation Index (NDVI), and Total Water Storage (TWS). The correlation coefficients between SMAP and the three indicators (NDVI, SPI3, and TWS) were 0.93, 0.84, and 0.047, respectively, while the coefficients between CYGNSS and the same indicators were 0.86, 0.78, and 0.56, respectively. All the variables also exhibited significant p-values. Despite minor differences, the results demonstrate excellent agreement. Our findings underscore the sensitivity of space-based sensors to drought conditions, highlighting their effectiveness as tools for detecting and monitoring drought (e.g. agricultural drought), particularly in the short term. [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]

Drought is a natural phenomenon that occurred in Central Europe in the past, but it is becoming a more serious problem due to the changes in its occurrence and intensity related to climate change. The main aim of this paper was to determine changes in air temperature and precipitation amounts in Slovakia from 1931 to 2020 and to identify changes in drought occurrence and intensity using the Standardised Precipitation and Evapotranspiration Index (SPEI). Cluster analysis was used to determine regions with similar drought occurrences. For each of the identified five clusters, drought characteristics were determined and compared between two reference periods 1961–1990 and 1991–2020. While a decrease in the number of months with SPEI-1 below − 1 was observed in autumn, spring and summer months showed a drying tendency. Overall, we can say that we observed a shift in drought occurrence from autumn and winter months to spring and summer months. This is an important finding for the agriculture or forestry because it affects work management and planning. The clearest tendency in drought events was observed in the western part of Slovakia covering areas with agriculturally intensive land use. Besides prolonging drought events, there was also a higher accumulated deficit for each event. The increase in drought intensity was not spatially consistent over the cluster area, mostly reaching a slight decline of about − 0.1 to − 0.3, which means slight intensification of drought periods in 1991–2020. [ABSTRACT FROM AUTHOR]

The frequency of extreme events is increasing on a global scale due to various factors, accounting for natural disasters such as landslides, floods, and droughts. The Heilongjiang province, which is agriculturally important for China, has suffered from several extreme rainfall events in the past as a high-latitude plain region. Analysis of historical rainfall characteristics of the Heilongjiang region helps to understand and predict the behavior of extreme rainfall events and provides a reference for reducing agricultural economic losses. Precipitation data from 1974 to 2017 is selected to estimate whether rainfall series in Heilongjiang obey the Gumbel-Logistic Model. We compared the Gumbel-Logistic Model based on the data length of 5 years, 10 years, and 20 years with the original 44-year data to determine the appropriate data partition length and explore the reasons for errors. Combining the empirical frequency and KS test, this paper concludes that the proposed model is appropriate for the representation of the joint distribution of monthly extreme rainfall and annual rainfall. The proposed model improves the rainfall assessment of these two positively correlated variables in Heilongjiang. Univariate and bivariate return periods and correlation coefficients are derived as the basis for assessing the risk of extreme rainfall. The proposed model obtained from the 5-year data length has the largest error, while the distribution curve is progressively smoother as the data length increases. [ABSTRACT FROM AUTHOR]

The detection and the functioning of the mid-summer drought (MSD) represent valuable information due to its socio-economic implications for the Caribbean. Current methods using local-scale rainfall to define the MSD have some limitations. This paper presents a novel approach to detect MSD from regional-scale weather types (WT). Long sequences of a typical summertime anticyclonic WT allow the detection of the onset and demise dates of the MSD for most years in 1950–2021. The MSD defined with WT (MSD-WT) begins rather abruptly on June 13th and concludes on August 21st, on average. While the interannual variations of the MSD-WT onset are relatively weak, long-term trends since 1950 indicate progressive earlier onsets, longer durations, and weakening intensities of the MSD-WT. Our results do not show the anticipated westward shift of the North Atlantic subtropical high pressure typically related to MSD. Instead, they indicate a halt of its eastward shift close to the MSD-WT onset. The absence of significant correlations between MSD-WT onset and either the Tropical North Atlantic or the Eastern Tropical Pacific sea surface temperature (SST) in May–June also suggests a mostly intrinsic atmospheric mechanism locked to the annual cycle, thus limiting the seasonal predictability of the MSD-WT onset. Furthermore, our results indicate a lag between the timing of increased subsidence, acceleration of the Caribbean low-level jet (CLLJ), and intensification of the deep convection over Central America and Mexico. This leads us to develop a novel hypothesis putting forward the monsoon circulation over Meso-America as a potential precursor of the increased subsidence over the Caribbean basin and the synchronous acceleration of CLLJ. The MSD-WT demise is more gradual and corresponds to a decrease in regional-scale mean sea level pressure and a weakening of the CLLJ. MSD-WT demise dates exhibit also relatively larger interannual variations but, unlike its onset, do not show any significant long-term trend. Anomalously warm (cold) Tropical North Atlantic and cold (warm) East Pacific SST from early June are correlated with an earlier (later) MSD-WT demise than usual. MSD-WT demise seems to involve regional-scale air-sea couplings and exhibits thus more seasonal predictability. Finally, despite a consistent response to the CLLJ, distinct processes seem to produce the relatively dry conditions observed over the Pacific coast of Central America and the Caribbean basin during the MSD. [ABSTRACT FROM AUTHOR]

The north-western part of Bangladesh especially the Barind region is considered as a drought-prone area. The objective of this paper is to find out the adaptation measures practised by farmers to cope with climate change and agricultural drought in two drought-prone villages of the north-western Bangladesh. The primary data for this study were collected from a structured questionnaire survey conducted on 130 households selected from 192 through simple random sampling. The present study finds that agriculture of this area is recurrently disrupted by frequent drought events. Moreover, the climate change will increase both frequency and magnitude of extreme drought events in this region. This paper identifies that the impacts of drought on agriculture are difficulties in irrigation, disruption in cropping pattern, depletion of ground water table, problem in fish cultivation and so on. On the other hand, analyses unveil that respondents practised a range of adjustment strategies to combat adverse impacts of drought such as adoptions of drought-tolerant crop varieties, rainwater harvesting, mango and jujube intercropping with rice, kitchen gardening, weed control and reducing water loss, constructing water control structures, irrigation and cultivation of fast-growing fish species. Therefore, the present study recommends proper drought early warning system, reserving surface water, managing supplemental irrigation, tree plantation, re-excavation of canals and traditional ponds, credit support to the farmers, preparedness and awareness rising to ensure the future sustainable agricultural development in the study areas. [ABSTRACT FROM AUTHOR]

The evolution of ensemble learning has recently offered a new approach to model complex systems. Inspired by the success of such methods, this paper introduces a new ensemble approach that integrates capabilities of two top state-of-the-art machine learning (ML) methods, namely random forests (RF) and genetic programming (GP), to model and forecast meteorological drought onset and severity. The new method, called gene-random forest (GeRF), follows the same steps of a standard GP systems, but with differences in the generation of initial population of potential solutions. The GeRF was tested to model and predict standardized precipitation evapotranspiration indices (SPEI-3 and SPEI-6) at two meteorology stations in Ankara province, Turkey. We have compared its efficiency to those of a classic autoregressive model as well as standalone RF, GP, and a hybrid ML model, called Bat-ELM, achieving results meaningfully superior to the benchmarks, particularly in the testing data. [ABSTRACT FROM AUTHOR]

Ecological environment is the essential material basis of human survival and connects regional economy with socially sustainable development. However, climate changes characterized by global climate warming have caused a series of ecological environmental problems in recent years. Few studies have discussed various climate factors affecting the ecological environment, and the spatial non-stationary effects of different climate factors on the ecological environment are still unclear. Dynamically monitoring ecological environment changes in fragile areas and identifying its climate-driving mechanism are essential for ecological protection and environmental repair. Taking Zoige Plateau as a case, this paper simulated the eco-environmental quality during 1987–2020 using remote sensing data, utilized Geodetector method to identify the contributions of various climate drivers to ecological environment quality, and then adopted the Geographically Weighted Regression model to explore the spatial non-stationary impacts of climate factors on ecological environment quality. The results showed that the ecological quality in the middle regions of the Zoige Plateau was slightly better than in the surrounding marginal areas. For the whole area of Zoige Plateau, the average ecological environment quality index was 54.92, 53.99, 56.17, 57.88, 63.44, 56.93, 59.43, and 59.76 in 1987, 1992, 1997, 2001, 2006, 2013, 2016 and 2020, respectively, which indicated that eco-environmental quality witnessed several fluctuations during the study period but showed a generally increasing trend. Among five climate factors, the temperature was the dominant climate factor affecting the ecological environment quality (q value: 0.11–0.19), sunshine duration (0.03–0.17), wind speed (0.03–0.11), and precipitation (0.03–0.08) were the main climate drivers, while the explanatory power of relative humidity to ecological environment quality was relatively small. Such various climate factors impacting the ecological environment quality demonstrated distinct spatial non-stationary and the range of driving impact varied with time. Temperature, sunshine duration, wind speed, and relative humidity promoted ecological environment quality in most regions (regression coefficients > 0), while precipitation mainly had a negative inhibitory impact (regression coefficients < 0). Meanwhile, the greater impacts of these five climate factors were concentrated in high-elevation regions of the south and west or the northern areas. The appropriate enhancement of climate warming and air humidity was beneficial to the improvement of the ecological environment, but the excessive precipitation would result in landslides and exhibit inhibition of vegetation growth. Therefore, selecting cold-tolerant herbs and shrubs, and strengthening climate monitoring and early warning systems (such as drought and excessive precipitation) are essential for ecological restoration. [ABSTRACT FROM AUTHOR]

The frequency of extreme weather events has increased in the latest years in Europe. The recent consecutive droughts caused severe damage in many sectors and underlined the demand for adaptation. There is a need for a better understanding of the response of ecosystems to climate change and the consequences for key ecosystem services, such as water supply and carbon sequestration, at a local and regional scale. This paper aims to support decision-making for climate adaptation in a low-mountainous region of central Germany. We analysed the temperature and precipitation trends and drought conditions. The response of two key services (surface water provision and carbon sequestration) to droughts is estimated using an ecosystem service model. The spatially averaged water yield, net ecosystem productivity (NEP), and soil moisture are assessed and compared for the five worst droughts with long-term averages to identify the vulnerable areas and ecosystems. The temperature increased on seasonal and annual scales, while precipitation decreased in some areas in summer and increased in winter and annually. The standardised precipitation-evapotranspiration index (SPEI) showed worsening drought conditions, especially after the late 1980s. Droughts caused a reduction of water yield by 54%, NEP by 18%, and upper zone soil moisture by 13%. The impacts varied spatially, with the central region being worst affected, while the southern part was relatively more resilient. Adaptation is urgently needed to reduce drought risks and enhance climate resilience. Adaptive measures can include amending crop rotations, introducing more drought-tolerant varieties, upgrading agriculture and food industry technology, increasing mixed forests, and reducing non-native tree species. [ABSTRACT FROM AUTHOR]

This paper describes the existence of high salinity in an inland aquifer system of a limited extent located in the granitic terrain of south India. Salinity levels observed in 55 water samples collected pre- and post-monsoon are discussed. Limited repeat sampling during the two seasons shows no significant change in hydrochemistry, probably due to the prevailing drought conditions during the study period (2017–18). As per the salinity classification based on total dissolved solids (TDS), only one sample indicates freshwater while ~ 40% of the samples are slightly saline, and the remaining have moderate salinity. All the high TDS samples are located over a limited area, and most show the water type as Ca–Mg–Na–Cl–SO4. Hydrochemical data indicate a gradual increase in salinity from west to east. Mainly two distinct processes (1) reverse ion exchange and (2) evaporation are likely to have influenced the groundwater chemistry. Variation in water chemistry between granite and gneiss formations indicates the influence of geology on hydrochemistry. Insignificant changes in hydrochemistry for the limited repeat sampling during pre and post-monsoon periods show a meagre recharge due to failed monsoon. However, depleted stable isotope values with reference to high TDS suggest faster recharge conditions in the granitic region than in the gneissic region. Accumulation of high salinity in the groundwater is restricted to the granitic area. We postulate that the presence of Hornblende Biotite Gneiss along the southern parts of the granitic region may be acting as a boundary for accumulating saline groundwater in a limited area in this region. However, it needs confirmation through further detailed geological and geophysical studies. [ABSTRACT FROM AUTHOR]

Hydrological drought is one of the most important natural phenomena affecting the various aspects of life on Earth, especially with the increasing impact of climate change around the world. Drought forecasting is one of the important strategies in preparing treatments to reduce the effects of drought on life, especially in water resource management. The process of forecasting hydrological drought is one of the complex issues in hydrology, especially in the semi-arid regions. In the current paper, a hybrid model was developed to predict hydrological drought, using two machine learning models, namely the K-Nearest Neighbor (KNN) and the K-means clusters. The results of the stream drought index for the (3, 6, 9, 12) months-time scale were used to predict the hydrological drought of the study area. The flow data of the Great Zab River in Iraq was used as the case study. The results showed the efficiency of the proposed hybrid model in predicting hydrological drought compared to using the KNN model. The results showed that the results of the hybrid model are better than the results of the KNN model by 85%. Thus, the proposed hybrid model has overcome the difficulty of predicting hydrological drought in semi-arid regions. [ABSTRACT FROM AUTHOR]

Background: The drought condition is responsible for considerable losses in soybean production, which in turn may result in billionaire losses. After drought perception, plants activate a cascade of protecting genes against water deficit (WD), many of which are responsive to abscisic acid, the most important phytohormone to plants' adaptation. This work aimed to recover abscisic acid (ABA)-responsive differentially expressed genes (DEG) from an RNA-Seq, carried out from leaves and roots of drought-sensitive (BR16) and tolerant (Embrapa 48) soybean cultivars under mild (MiWD), moderate (MoWD), and severe (SWD) water-deficit treatments. Enriched ABA-responsive pathways important to drought tolerance in soybean were also identified. Results: In drought-sensitive and tolerant soybean cultivars, approximately 75% of genes were identified as ABA-responsive by containing more than two ABRE (ABA-responsive elements) in the promoter region. Most of these genes were positively regulated. Roots were the tissue with more ABA-responsive genes and pathways triggered in response to WD in both cultivars, although, on the tolerant cultivar, these pathways were higher expressed. The most important enriched ABA pathways observed in the roots of both cultivars were involved in sugar and sulfur amino acid biosynthesis, osmoregulation, and crosstalk among ABA and ethylene, jasmonate, auxin, and cytokinin. Other pathways enriched were involved in phytoalexin production, ROS homeostasis, and membrane stability by glycerolipid and glycerophospholipid production. ABA-responsive genes were also ordered based on their expression profile in tissue and cultivar, and nine confidence groups could be observed. More than 80% of these clustered genes showed the same regulation profile under MiWd, MoWD, and SWD treatments. Activation of ABA biosynthesis under water deficit was validated by RT-qPCR by increasing the expression level of NCED3, an important enzyme in this pathway, and GOLS, a known ABA-responsive gene. Conclusions: A robust catalog of ABA-responsive genes was made available in this work. Considering ABA's role in drought-response mechanisms, the genes in the groups pointed out in this study would be reliable candidates to be used in strategies to develop soybean lines more tolerant to drought. This paper, presented for the first time, ABA-responsive genes and ABA-enriched pathways in contrasting soybean cultivars for drought tolerance. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for drought forecasting based on combining Long Short-Term Memory (LSTM) and Multi-Resolution Analysis Wavelet Transform (MRA-WT), called MRA-WT-LSTM. In fact, Deep Learning (DL) methods provided an outstanding performance in several forecasting fields, especially drought. LSTM has proved a high ability in dealing with time-series drought indices compared to the others existing methods. Therefore, in this study LSTM was used to provide long-term drought forecasts. However, the non-stationarity of the drought indices is a major challenge that needs to be taken into consideration. In this regard, MRA-WT was applied to analyze the non-stationary time-series drought indices. Experiments were carried out in the Sarab region, Iran in the period between 1988–2016 in order to predict the standardized precipitation Evaporation index (SPEI). Drought is the most natural hazards in Sarab region that affect the socioeconomic development. The input data include the station data (i.e. rainfall, temperature (mean, minimum, and maximum), humidity, pressure, and Evaporation) and Normalized Difference Vegetation Index (NDVI) derived from Landsat images. Through the experiment, the proposed methodology is evaluated compared to three ML methods (i.e. Artificial Neural Network (ANN), Support Vector Regression (SVR), Random Forest Regression (RFR)) and LSTM. The different models were compared in terms of statistical metrics such as the coefficient of determination ( R 2 ), Root-Mean-Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Results revealed the effectiveness of MRA-WT-LSTM for drought forecasting with R 2 up to 0,93 and RMSE reached 0,02 for the different time lags. [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]

The risk analysis of flood and drought disasters and the study of their influencing factors enhance our understanding of the temporal and spatial variation law of disasters and help identify the main factors affecting disasters. This paper uses the provincial administrative region of China as the research area. The proportion of the disaster area represents the degree of the disaster. The statistical distribution of the proportions was optimized from 10 alternative distributions based on a KS test, and the disaster risk was analyzed. Thirty-five indicators were selected from nature, agriculture and the social economy as alternative factors. The main factors affecting flood and drought disasters were selected by Pearson, Spearman and Kendall correlation coefficient test. The results demonstrated that the distribution of floods and drought is right-skewed, and the gamma distribution is the best statistical distribution for fitting disasters. In terms of time, the risk of flood and drought disasters in all regions showed a downward trend. Economic development and the enhancement of the ability to resist disasters were the main reasons for the change in disasters. Spatially, the areas with high drought risk were mainly distributed in Northeast and North China, and the areas with high flood risk were mainly distributed in the south, especially in Hubei, Hunan, Jiangxi and Anhui. The distribution of floods and drought disasters was consistent with the distribution characteristics of precipitation and water resources in China. Among the natural factors, precipitation was the main factor causing changes in floods and drought disasters. Among the agricultural and socioeconomic factors, the indicators reflecting the disaster resistance ability and regional economic development level were closely related to flood and drought disasters. The research results have reference significance for disaster classification, disaster formation mechanisms and flood and drought resistance. [ABSTRACT FROM AUTHOR]

An important and under-quantified facet of the risks associated with human-induced climate change emerges through extreme weather. In this paper, we present an initial attempt to quantify recent costs related to extreme weather due to human interference in the climate system, focusing on economic costs arising from droughts and floods in New Zealand during the decade 2007–2017. We calculate these using previously collected information about the damages and losses associated with past floods and droughts, and estimates of the "fraction of attributable risk" that characterizes each event. The estimates we obtain are not comprehensive, and almost certainly represent an underestimate of the full economic costs of climate change, notably chronic costs associated with long-term trends. However, the paper shows the potential for developing a new stream of information that is relevant to a range of stakeholders and research communities, especially those with an interest in the aggregation of the costs of climate change or the identification of specific costs associated with potential liability. [ABSTRACT FROM AUTHOR]

Drought forecasting can effectively reduce the risk of drought. We proposed a hybrid model based on deep learning methods that integrates an autoregressive integrated moving average (ARIMA) model and a long short-term memory (LSTM) model to improve the accuracy of short-term drought prediction. Taking China as an example, this paper compares and analyzes the prediction accuracy of six drought prediction models, namely, ARIMA, support vector regression (SVR), LSTM, ARIMA-SVR, least square-SVR (LS-SVR), and ARIMA-LSTM, for standardized precipitation evapotranspiration index (SPEI). The performance of all the models was compared using measures of persistence, such as the Nash-Sutcliffe efficiency (NSE). The results show that all three hybrid models (ARIMA-SVR, LS-SVR, and ARIMA-LSTM) had higher prediction accuracy than the single model, for a given lead time, at different scales. The NSEs of the hybrid models for the predicted SPEI1 are 0.043, 0.168, and 0.368, respectively, and the NSEs of SPEI24 is 0.781, 0.543, and 0.93, respectively. This finding indicates that when the lead time remains unchanged, the hybrid model has high prediction accuracy for SPEI on long time scales and low prediction accuracy for SPEI on short time scales, and the prediction accuracy of the model with a 1-month lead time is higher than that of the model with a 2-month lead time. In addition, the ARIMA-LSTM model has the highest prediction accuracy at the 6-, 12-, and 24-month scales, indicating that the model is more suitable for the forecasting of long-term drought in China. [ABSTRACT FROM AUTHOR]

The prediction skill for precipitation anomalies in late spring and summer months—a significant component of extreme climate events—has remained stubbornly low for years. This paper presents a new idea that utilizes information on boreal spring land surface temperature/subsurface temperature (LST/SUBT) anomalies over the Tibetan Plateau (TP) to improve prediction of subsequent summer droughts/floods over several regions over the world, East Asia and North America in particular. The work was performed in the framework of the GEWEX/LS4P Phase I (LS4P-I) experiment, which focused on whether the TP LST/SUBT provides an additional source for subseasonal-to-seasonal (S2S) predictability. The summer 2003, when there were severe drought/flood over the southern/northern part of the Yangtze River basin, respectively, has been selected as the focus case. With the newly developed LST/SUBT initialization method, the observed surface temperature anomaly over the TP has been partially produced by the LS4P-I model ensemble mean, and 8 hotspot regions in the world were identified where June precipitation is significantly associated with anomalies of May TP land temperature. Consideration of the TP LST/SUBT effect has produced about 25–50% of observed precipitation anomalies in most hotspot regions. The multiple models have shown more consistency in the hotspot regions along the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train. The mechanisms for the LST/SUBT effect on the 2003 drought over the southern part of the Yangtze River Basin are discussed. For comparison, the global SST effect has also been tested and 6 regions with significant SST effects were identified in the 2003 case, explaining about 25–50% of precipitation anomalies over most of these regions. This study suggests that the TP LST/SUBT effect is a first-order source of S2S precipitation predictability, and hence it is comparable to that of the SST effect. With the completion of the LS4P-I, the LS4P-II has been launched and the LS4P-II protocol is briefly presented. [ABSTRACT FROM AUTHOR]

The low-lying Vietnamese Mekong Delta (VMD) is a key agricultural production landscape increasingly threatened by anthropogenic stresses and climate change. Among the different threats, droughts caused by extreme events, climate change and upstream developments, affect the delta the most. This paper explores the relationship between the intensity, duration, and frequency of meteorological droughts and hydrological droughts using a range of indices. We used monthly rainfall and stream flow data for the period 1992–2021 to calculate the Standardized Precipitation Index (SPI), the Reconnaissance Drought Index (RDI), and the streamflow drought Index (SDI) for different time intervals. We found no observed time lag, and a strong correlation coefficient between upstream hydrological and downstream meteorological drought events assessed over long-term scales (i.e., 12-months). This is true for all downstream sites, except Ben Tre City. Hydrological drought events onset lagged 5–6-, 6-, and 3–4-month behind meteorological droughts at mid- and shorter assessment time scales (9-, 6-, 3-month). The average correlation coefficients between hydrological indices and meteorological indices at 9–3-month time scales ranged from moderate to weak. These findings shed light and advance the understanding of the progression of meteorological to hydrological droughts in the VMD. Our results aid the regional understanding of drought onset and the causative mechanisms at work, which is important for both medium- and long-term drought forecasting and adaptation planning. [ABSTRACT FROM AUTHOR]

This paper focuses on the cold damage and drought cross-stress in maize in Northeast China. The WOFOST model based on parameter localization was used to simulate the growth and development process of maize using daily meteorological data from 110 stations in the research area from 1981 to 2020. The experiment determined that the grouting index and the number of drought days were the indicators for identifying the low-temperature and drought cross-stress in maize, as well as the impact assessment indicators for the fluctuation percentage of dry matter weight in storage organs. It also achieved a quantitative assessment of the impact of cross-stress of low-temperature and drought between 1981 and 2020 and typical years. The results indicated that the WOFOST model can effectively simulate the impact of low-temperature and drought on maize growth, and the historical occurrence of cold damage identified by using the grouting index and drought days as indicators of the low-temperature and drought cross-stress in maize is basically in line with the actual situation. Compared with the average temperature from May to September and the regional cold damage index of > 105 °C supplemented by the meteorological industry standard "Technical Specification for Assessment of Cold Damage to Spring Maize in Northern China", as well as the identification results of the "Drought Grade of Spring Maize in Northern China", the average identification accuracy of low-temperature drought cross-stress in Northeast China based on the WOFOST model is 82.0%, 76.4% of stations have an accuracy of 80.0% or above, and only 4.5% of stations have an accuracy of less than 50.0%. Under the combined influence of low temperature and drought cross-stress, 88.9% of the years showed a reduction in maize production. The evaluation results reflect the historical production reality of maize in Northeast China and are consistent with existing research results. [ABSTRACT FROM AUTHOR]

The paper studies whether a drought in 1788 affected the outbreak of peasant revolts during the French Revolution. I construct a community-level data set with information on local drought severity and peasant uprisings in 1789. Communities with severe drought conditions more often experienced peasant revolts against the feudal system. Then, I investigate a mechanism through which drought may have affected peasant revolts. Those more affected by the drought had higher demand for institutional change as expressed in the lists of grievances. The results provide evidence on specific ways in which the drought of 1788 impacted the French Revolution, a milestone in the democratization of Western Europe. [ABSTRACT FROM AUTHOR]

Accurate and timely information on evapotranspiration (ET0) is essential for multiple agricultural applications, including irrigation scheduling, studying crop-specific water loss at different growth stages, predicting crop yields, and forecasting drought conditions. This study aims to examine the spatiotemporal patterns of ET0 and facilitate the monitoring of crop water demand, optimize irrigation water usage, and enhance agricultural advisory services. This paper estimates regional-level daily ET0 gridded data with a spatial resolution of 12.5 km by integrating observed weather data, IMD GFS-T1534 reanalysis data, and INSAT-3D satellite-based insolation product using the standard FAO56 Penman–Monteith empirical equation. The estimated monthly mean of ET0 across India ranged from 10 to 400 mm. ET0 exhibited an increasing trend from January to May, reaching its maximum values in May. In June, ET0 significantly decreased as the monsoon arrived, coinciding with the movement of rainfall patterns. The month of December exhibited the lowest ET0 values. The estimated daily gridded ET0 was compared with station-based ET0, resulting in daily correlation coefficient R2 and daily maximum absolute percentage errors ranging from 0.34 to 0.90 and 10% to 27% respectively. However, these errors decreased to a large extent when considering multiday accumulated values. A comparison was conducted between the GLDAS model ET0 and the station-estimated values, revealing an overestimation of ET0 by the GLDAS model. Additionally, significant variations were observed among the meteorological subdivisions. This highlights the necessity for proper calibration of the GLDAS model ET0 or its effective agricultural application. [ABSTRACT FROM AUTHOR]