Search Results (263)

This study assesses future agricultural drought risk in the Ganjiang River Basin under climate change and land use change. A coupled analysis framework was established using the SWAT hydrological model, the CMIP6 climate models (SSP1-2.6, SSP2-4.5, SSP5-8.5), and the PLUS land use simulation model. Key methods included the Standardized Soil Moisture Index (SSMI), travel time theory for drought event identification and duration analysis, Mann–Kendall trend test, and the Pettitt change-point test to examine soil moisture dynamics from 2027 to 2100. The results indicate that the CMIP6 ensemble performs excellently in temperature simulations, with a correlation coefficient of R² = 0.89 and a root mean square error of RMSE = 1.2 °C, compared to the observational data. The MMM-Best model also performs well in precipitation simulations, with R² = 0.82 and RMSE = 15.3 mm, compared to observational data. Land use changes between 2000 and 2020 showed a decrease in forestland (−3.2%), grassland (−2.8%), and construction land (−1.5%), with an increase in water (4.8%) and unused land (2.7%). Under all emission scenarios, the SSMI values fluctuate with standard deviations of 0.85 (SSP1-2.6), 1.12 (SSP2-4.5), and 1.34 (SSP5-8.5), with the strongest drought intensity observed under SSP5-8.5 (minimum SSMI = −2.8). Drought events exhibited spatial and temporal heterogeneity across scenarios, with drought-affected areas ranging from 25% (SSP1-2.6) to 45% (SSP5-8.5) of the basin. Notably, abrupt changes in soil moisture under SSP5-8.5 occurred earlier (2045–2050) due to intensified land use change, indicating strong human influence on hydrological cycles. This study integrated the CMIP6 climate projections with high-resolution human activity data to advance drought risk assessment methods. It established a framework for assessing agricultural drought risk at the regional scale that comprehensively considers climate and human influences, providing targeted guidance for the formulation of adaptive water resource and land management strategies. Full article

The paper presents the results of investigations into the relationship between selected water quality parameters and hydrological streamflow drought in a small river situated in the Mazovian Lowlands in Poland. As hydrological streamflow drought periods become more frequent in Poland, investigations about the relationship between flow and water quality parameters can be an essential contribution to a better understanding of the impact of low flow on the status of water rivers. Data from a three-year study of a small lowland river along with significant agricultural land management was used to analyze the connection between low flows and specific water quality indicators. The separation of low-flow data from water discharge records was achieved using two criteria: Q90% (the discharge value from a flow duration curve) and a minimum low-flow duration of 10 days. During these periods, the concentration of water quality indicators was determined based on collected water samples. In total, 30 samples were gathered and examined for pH, suspended sediments, dissolved substances, hardness, ammonium, nitrates, nitrites, phosphates, total phosphorus, chloride, sulfate, calcium, magnesium, and water temperature during sampling. The study’s main aim was to describe the relation between hydrological streamflow droughts and chosen water quality parameters. The analysis results demonstrate an inverse statistically significant relationship between concentration and low-flow values for total hardness and sulfate. In contrast, there was a direct relationship between nutrient indicators, suspended sediment concentration, and river hydrological streamflow drought. Statistical tests were applied to compare the datasets between years, revealing statistical differences only for nutrient indicators. Full article

Drought is one of the most complex natural hazards, characterized by its slow onset, persistent nature, diverse sectoral impacts (e.g., agriculture, water resources, ecosystems), and dependence on meteorological, hydrological, and socioeconomic factors. Over the years, significant scientific effort has been devoted to developing methodologies that address its multifaceted nature, reflecting the interdisciplinary challenges of drought analysis. However, previous reviews have typically focused on individual methods, while this study presents a unified, multidisciplinary framework that integrates multiple drought analysis methods and links them to key factors guiding method selection. To address this gap, five widely used methods—index-based, remote sensing, threshold-level methods (TLM), impact-based methods, and the storyline approach—are critically evaluated from a multidisciplinary perspective. In addition, the study examines spatial and temporal trends in scientific publications, illustrating how the application of these methods has evolved over time and across regions. The primary objective of this review is twofold: (1) to provide a holistic, state-of-the-art synthesis of these methods, their applications, and their limitations; and (2) to evaluate and prioritize the critical decision-making factors, including drought type, data type/availability, study scale, and management objectives that influence method selection. By bridging this gap, the paper offers a conceptual decision-support framework for selecting context-appropriate drought analysis methods. However, challenges remain, including the vast diversity of methods beyond the scope of this review and the limited consideration of less influential factors such as user expertise, computational resources, and policy context. The paper concludes with insights and recommendations for optimizing method selection under varying circumstances, aiming to support both drought research and effective policy implementation. Full article

Efficient water management is critical for sustainable dryland agriculture, especially under increasing water scarcity and climate variability. Shanxi Province, a typical dryland region in northern China characterized by pronounced climatic variability and limited soil water availability, faces severe challenges due to uneven precipitation and restricted water resources. This study aimed to evaluate the spatiotemporal dynamics of soil water resources and their coupling with crop water demand under different hydrological year types. Using daily meteorological data from 27 stations (1963–2023), we identified dry, normal, and wet years through frequency analysis. Soil water resources were assessed under rainfed conditions, and water deficits of major crops—including millet, soybean, sorghum, winter wheat, maize, and potato—were quantified during key reproductive stages. Results showed a statistically significant declining trend in seasonal precipitation during both summer and winter cropping periods (p < 0.05), which corresponds with the observed intensification of crop water stress over recent decades. Notably, more than 86% of daily rainfall events were less than 5 mm, indicating low effective rainfall. Soil water availability closely followed precipitation distribution, with higher values in the south and west. Crop-specific analysis revealed that winter wheat and sorghum had the largest water deficits in dry years, necessitating timely supplemental irrigation. Even in wet years, water regulation strategies were required to improve water use efficiency and mitigate future drought risks. This study provides a practical framework for soil water–crop demand assessment and supports precision irrigation planning in dryland farming. The findings contribute to improving agricultural water use efficiency in semi-arid regions and offer valuable insights for adapting to climate-induced water challenges. Full article

Danube River represents a critical axis of ecological and economic importance for the countries along its course. From this perspective, this paper aims to assess the most significant characteristics of the river and of its main tributaries, as well as its impact on the environmental sustainability and socio-economic development. Navigation and the economic contribution of the Danube River are the key issues of this work, emphasizing its importance as an international transport artery that facilitates trade and tourism, and develops the energy industry through hydropower plants. The study includes an analysis of the volume of goods transported from 2019 to 2023, as well as an analysis of the goods traffic in the busiest port on the Danube. Furthermore, climate change affects the hydrological regime of the Danube, as well as the ecosystems, economy, and energy security of the riparian countries. Main impacts include changes in the hydrological regime, increased frequency of droughts and floods, reduced water quality, deterioration of biodiversity, and disruption of the economic activities dependent on the river, such as navigation, agriculture, and hydropower production. Thus, hydrological risks and challenges are investigated, focusing on the extreme events of the last two decades and the awareness of their repercussions. In this context, the national and international institutions responsible for monitoring and managing the Danube are presented, and their role in promoting a sustainable river policy is explored. Methods and technologies are shown to be essential tools for monitoring and prediction studies. The Danube includes an extensive network of hydrometric stations that help to prevent and manage the most significant risks. Finally, a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of the development of the hydrological studies was conducted, highlighting the potential of the river. Full article

The failure of traditional drought indices to capture the dynamic supply–demand imbalance in socio-hydrological systems hinders proactive water management and necessitates novel assessment frameworks. The reservoir drought warning water level, serving as a dynamic threshold indicating supply–demand imbalance, provides a critical basis for drought early warning. From a socio-hydrological drought perspective, this study develops a framework for determining staged and graded soft partition thresholds for reservoir drought warning water levels, encompassing three key stages: water stress analysis, phase classification, and threshold determination. First, water demands for the ecological, agricultural, and domestic sectors were quantified based on hydrological analysis and official operational rules. Second, an optimized KPCA-Fisher model delineated the intra-annual supply–demand dynamics into distinct periods. Thirdly, the soft partition thresholds were formulated by coupling these multi-sectoral demands with water deficit rates using a triangular membership function. Applied to the Xianan Reservoir, the framework yielded distinct drought warning thresholds for the identified main flood, critical demand, and dry seasons. Validation against historical droughts (2019 and 2022) confirmed that these soft thresholds more accurately tracked the drought evolution process compared to traditional hard partitions. Furthermore, a sensitivity analysis identified the ecological water demand methodology as a key factor influencing the thresholds, particularly during the critical demand period. The proposed framework for determining staged and graded reservoir drought warning water levels better reflects the complexity of socio-hydrological systems and provides a scientific basis for refined reservoir drought early warnings and management under changing environments. Full article

This study evaluates the impacts of Solar Radiation Management (SRM) on precipitation-related climate extremes in Southeast Asia. Using simulations from the Geoengineering Large Ensemble (GLENS), we assess spatial anomalies and differences in extreme precipitation indices—number of wet days (RR1), very heavy precipitation days (R20mm), maximum 5-day precipitation (Rx5day), consecutive dry days (CDD), and consecutive wet days (CWD)—relative to historical (1980–2009) and Representative Concentration Pathway 8.5 (RCP8.5) baselines. The results reveal that SRM induces highly heterogeneous precipitation responses across the region. While SRM increases rainfall frequency in parts of Indonesia, it reduces the number of wet days and lengthens dry spells over Vietnam, Thailand, and the Philippines. Spatial variations are also observed in changes to heavy precipitation days and multi-day rainfall events, with potential implications for flood and drought risks. These findings highlight the complex trade-offs in hydrological responses under SRM deployment, with important considerations for agriculture, water resource management, and climate adaptation strategies in Southeast Asia. Full article

The Mediterranean region is currently experiencing the effects of a climate crisis, marked by an increase in the frequency and intensity of drought events. Climate variability has led to prolonged periods of drought, even in areas not traditionally classified as arid. These events have significant impacts on water resources, agricultural productivity, and socioeconomic systems. This study investigates the evolution of meteorological, hydrological, and socioeconomic droughts using the Standardized Precipitation Index (SPI) at time scales of 3, 12, and 24 months in a Mediterranean region identified as particularly vulnerable to climate change. Observational data from local meteorological stations were used for the 1991–2020 baseline period. Future climate projections were derived from the MPI-ESM model under the RCP 4.5 and RCP 8.5 scenarios, extending to the year 2080. Data were aggregated on a 0.50° × 0.50° spatial grid and bias-corrected using linear scaling. The Kolmogorov–Smirnov test was applied to assess the statistical compatibility between observed and projected precipitation data. Results indicate a substantial decline in annual precipitation, with reductions of up to 20% under the RCP 8.5 scenario for the period 2051–2080, compared to the reference period. The frequency of severe and extreme drought events is projected to increase by 30–50% in several grid meshes, especially during summer. Conversely, altered weather patterns in other areas may increase the likelihood of flood events. This study identifies the grid meshes most vulnerable to drought, highlighting the urgent need for adaptive water management strategies to ensure agricultural sustainability and reduce the socioeconomic impacts of climate-induced drought. Full article

Droughts caused by runoff are an important environmental issue in the context of global climate change, with profound impacts on ecosystems, agriculture and water resource management. To assess the impact of future climate change on the hydrological response of watersheds, this study combines the SWAT (Soil and Water Assessment Tool) and MODFLOW (MODular groundwater FLOW model) models to predict future changes in runoff and hydrological drought in watersheds using data from two scenarios under 15 CMIP6 climate models. The results show that: (1) The R² and NSE values of monthly runoff at the Caizuzi station in the Naoli River basin are greater than 0.60 in different periods; (2) the ensemble of climate models after screening can effectively improve the accuracy of runoff simulation and reduce the prediction uncertainty of a single climate model; (3) under different scenarios, the temperature generally increases, the precipitation increases and evapotranspiration increased under the SSP2-4.5 scenario and decreased under the SSP5-8.5 scenario; (4) runoff showed an increasing trend under the SSP2-4.5 scenario and the opposite trend under the SSP5-8.5 scenario; (5) the frequency of winter runoff droughts decreased in the future period, while the frequency of spring and summer droughts increased, with the change trend being more pronounced under the SSP5-8.5 scenario; (6) compared with the baseline period (1965–2014), under the SSP2-4.5 and SSP5-8.5 scenarios, the average annual temperature in the watershed increased by 1.89 °C and 3.22 °C, respectively, and the annual precipitation increased by 32% and 36.19%, respectively, but the summer and autumn runoff decreased; and (7) The SRI-3 model analysis indicates that hydrological droughts will significantly intensify under both future emission scenarios. Under the SSP5-8.5 scenario, droughts will worsen earlier and the abrupt change will occur earlier, while under the SSP2-4.5 scenario, although the abrupt change will occur later, the drought intensity will be higher. The critical drought transition periods are 2030–2047 (SSP5-8.5) and 2045–2055 (SSP2-4.5). This study provides important scientific basis for adaptive water resources management and drought mitigation strategies in cold-region watersheds under future climate scenarios. Full article

Drought is a complex and interconnected natural phenomenon, involving multiple drought types that mutually influence each other. To capture this complexity, various composite drought indices have been developed using diverse methodologies. Traditionally, Principal Component Analysis (PCA) has served as the primary method for extracting index weights, predominantly capturing linear relationships among variables. This study proposes an innovative approach by employing Independent Component Analysis (ICA) to develop an ICA-based Composite Drought Index (ICDI), capable of addressing both linear and nonlinear interdependencies. Three drought indices—representing meteorological, hydrological, and agricultural droughts—were integrated. Specifically, the Standardized Precipitation Index (SPI) was adopted as the meteorological drought indicator, whereas the Standardized Reservoir Supply Index (SRSI) was utilized to represent both hydrological (SRSI(H)) and agricultural (SRSI(A)) droughts. The ICDI was derived by extracting optimal weights for each drought index through ICA, leveraging the optimization of non-Gaussianity. Furthermore, constraints (referred to as ICDI-C) were introduced to ensure all index weights were positive and normalized to unity. These constraints prevented negative weight assignments, thereby enhancing the physical interpretability and ensuring that no single drought index disproportionately dominated the composite. To rigorously assess the performance of ICDI, a PCA-based Composite Drought Index (PCDI) was developed for comparative analysis. The evaluation was carried out through three distinct performance metrics: difference, model, and alarm performance. The difference performance, calculated by subtracting composite index values from individual drought indices, indicated that PCDI and ICDI-C outperformed ICDI, exhibiting comparable overall performance. Notably, ICDI-C demonstrated a superior preservation of SRSI(H) values, yielding difference values closest to zero. Model performance metrics (Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and correlation) highlighted ICDI’s comparatively inferior performance, characterized by lower correlations and higher RMSE and MAE. Conversely, PCDI and ICDI-C exhibited similar performance across these metrics, though ICDI-C showed notably higher correlation with SRSI(H). Alarm performance evaluation (False Alarm Ratio (FAR), Probability of Detection (POD), and Accuracy (ACC)) further confirmed ICDI’s weakest reliability, with notably high FAR (up to 0.82), low POD (down to 0.13), and low ACC (down to 0.46). PCDI and ICDI-C demonstrated similar results, although PCDI slightly outperformed ICDI-C as meteorological and agricultural drought indicators, whereas ICDI-C excelled notably in hydrological drought detection (SRSI(H)). The results underscore that ICDI-C is particularly adept at capturing hydrological drought characteristics, rendering it especially valuable for water resource management—a critical consideration given the significance of hydrological indices such as SRSI(H) in reservoir management contexts. However, ICDI and ICDI-C exhibited limitations in accurately capturing meteorological (SPI(6)) and agricultural droughts (SRSI(A)) relative to PCDI. Thus, while the ICA-based composite drought index presents a promising alternative, further refinement and testing are recommended to broaden its applicability across diverse drought conditions and management contexts. Full article

Sustaining instream flows is becoming increasingly critical due to the combined pressure of climate change and intensive reservoir operations in multi-dam watersheds. This study evaluates instream flow sustainability in the Seomjin River basin by integrating the SWAT and K-WEAP models with CMIP6-based climate scenarios. Two contrasting dam operation strategies—firm and deficit supply—were assessed over multiple temporal scales, including hydrological seasons and agricultural activity. Sustainability was quantified using the Sustainability Index (SI), which integrates reliability, resilience, and vulnerability. The probabilistic assessment revealed that the relative performance of the two strategies varied depending on the season and flow conditions. The firm supply generally exhibited higher sustainability under drought and low-demand periods, effectively reducing the probability of unsustainable outcomes. In contrast, the deficit supply often achieved higher sustainability under wet conditions or peak agricultural demand, although it was occasionally linked to extremely low SI values. These findings underscore the importance of season-specific, risk-informed dam operation planning over reliance on a single strategy and emphasize the need for flexible management frameworks capable of responding to diverse hydrological futures. Full article

This study evaluates the performance of CMIP6 models in simulating drought characteristics in the Mekong region, including drought duration, intensity, and severity, using the SPI and SPEI indices. The results show that CMIP6 models are capable of accurately reproducing past drought conditions, with a high agreement between model data and actual data from ERA5. This study projects that future droughts will become more prolonged and severe which could lead to long-term agricultural and hydrological droughts tending to increase. In the SSP585 scenario, drought intensity will increase sharply in the southern and central regions by the end of the century. The SSP245 and SSP585 climate scenarios have distinct differences in drought trends, with SSP245 showing a strong drought trend, while SSP585 indicates a potential increase in precipitation. The SPEI indices show a clear improvement in wet conditions, with the highest drought variability in zone 2 and stable trends across scenarios. Ecosystems influence drought impacts and management needs. These results highlight the importance of accurately assessing drought characteristics to develop effective water resource and agricultural management measures, especially in the context of climate change. However, this study also points out some limitations, including the imperfect accuracy in future projections and the use of only SPI and SPEI indices without combining them with other indices which may reduce the comprehensiveness of drought impact assessment. This requires future studies to improve and expand to overcome the above limitations, thereby enhancing the reliability of drought forecasts and water resource management strategies. Full article

Drought as an extreme weather phenomenon has recently become more frequent with significant impacts on water resources, such as reduced infiltration and surface runoff. To assess the vulnerability of water resources to drought, the Standardized Drought Vulnerability Index (SDVI) was applied to the hydrological basin of Lake Karla in Thessaly, Central Greece. The Lake Karla basin has a semi-arid climate and is an agricultural basin in which water-demanding crops are cultivated. The SDVI is a composite index that integrates all types of droughts and, with its holistic approach, can be used as a monitoring tool to provide knowledge for the delineation of vulnerable areas. Full article

Water scarcity in Chile, particularly in the Mediterranean region, has been exacerbated by prolonged drought and climate change. Rainwater harvesting systems (RHS) have emerged as viable solutions for addressing water shortages, particularly for agricultural irrigation and aquifer recharge. This study evaluated the implementation and efficiency of RHS in rural areas of the Biobío Region, Chile, through the design and construction of two pilot systems in Arauco and Florida. These systems were assessed based on their water collection capacity, storage efficiency, and monitoring of water level variations in wells after rainwater incorporation, using depth probes to quantify stored volumes. The hydrological design incorporated site-specific precipitation analyses, runoff coefficients, and catchment area dimensions, estimating annual precipitation of 861 mm/year for Arauco and 611 mm/year for Florida. The RHS Arauco collected and stored 40 m³ of rainwater in a flexible tank, while RHS Florida stored 10 m³ in a polyethylene tank, demonstrating the effectiveness of the system. Additionally, we analyzed the economic feasibility and quality of harvested rainwater, ensuring its suitability for agricultural use according to Chilean regulations. The cost-effectiveness analysis indicated that the cost of stored water was $263.51 USD/m³ for Arauco and $841.07 USD/m³ for Florida, highlighting larger systems are more cost-effective owing to economies of scale. The Net Present Value (NPV) was calculated using a discount rate of 6% and a useful life of 10 years, yielding CLP $9,564,745 ($10,812.7 USD) for the Florida and CLP $2,216,616 ($2505.8 USD) for the Arauco site. The results indicate that both projects are financially viable and highly profitable, offering rapid payback periods and sustainable long-term benefits. RHS significantly contributes to water availability during the dry season, reducing dependence on conventional water sources and enhancing agricultural sustainability. Based on the evaluation of the cost–benefit, water availability, and infrastructure adaptability, we infer the feasibility of large-scale implementation at locations with similar characteristics. These findings support the role of RHS in sustainable water resource management and strengthening rural resilience to climate variability, highlighting their potential as an adaptation strategy to climate change in water-scarce Mediterranean regions. Full article

In this study, we propose an enhanced methodology for assessing drought conditions through the systematic categorization of Condition Monitoring Observer Reports (CMORs) from Missouri between 2018 and 2024. Our approach introduces a novel classification framework to categorize drought impacts—meteorological, agricultural, hydrological, and socioeconomic—and aligns the analysis with established United States Drought Monitor (USDM) severity classifications. To complement this framework, we incorporate the New Drought Index (NDI), a recently developed quantitative metric that integrates atmospheric anomalies. Brief consideration is also given to atmospheric blocking patterns, which influence drought development. Advanced text processing techniques are employed to bridge qualitative and quantitative insights. The findings underscore the importance of integrating observer insights, atmospheric processes, and advanced indices to refine drought monitoring, inform climate adaptation strategies, and support proactive resource management. Full article