Search Results (464)

The Brazilian savanna, South America’s second-largest biome, is vital to Brazil’s economy but has suffered from environmental degradation due to unregulated agricultural and urban expansion. This study assesses climate change in the biome from 1961 to 2021 using the Köppen climate classification, drought indices, historical trend analyses, and the climatological water balance. Fourteen municipalities across the biome were analyzed. According to the Köppen classification, most municipalities were identified as Aw (tropical with dry winters) and Am (tropical monsoon), with Dourados, MS, and Sapezal, MT, alternating between Am and Aw. The standardized precipitation index (SPI) revealed changes in rainfall distribution. The Mann–Kendall test detected rising air temperatures in 13 of the 14 municipalities, with Sen’s slope ranging from 0.0156 to 0.0605 °C per year. Rainfall decreased in seven municipalities, with decreases from −4.54 to −12.77 mm per year. The climatological water balance supported the observed decrease in precipitation. The results indicated a clear warming trend and declining rainfall in most of the Brazilian savanna, highlighting potential challenges for water availability in the face of ongoing climate change. Full article

Accurate drought monitoring depends on selecting an appropriate cumulative distribution function (CDF) to model the original data, resulting in the standardized drought indices. In the numerous research studies, while rigorous validation was not made by scrutinizing the model assumptions and uncertainties in identifying theoretical drought CDF models, such oversights lead to biased representations of drought evaluation and characteristics. This research compares the parametric theoretical and empirical CDFs for a comprehensive evaluation of standardized Drought Indices. Additionally, it examines the advantages, disadvantages, and limitations of both empirical and theoretical distribution functions in drought assessment. Three drought indices, Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), and Standardized Precipitation Evapotranspiration Index (SPEI), cover meteorological and hydrological droughts. The assessment spans diverse applications, covering different climates and regions: Durham, United Kingdom (SPEI, 1868–2021); Konya, Türkiye (SPI, 1964–2022); and Lüleburgaz, Türkiye (SDI, 1957–2015). The findings reveal that theoretical and empirical CDFs demonstrated notable discrepancies, particularly in long-term hydrological drought assessments, where underestimations reached up to 50%, posing risks of misinformed conclusions that may impact critical drought-related decisions and policymaking. Root Mean Squared Error (RMSE) for SPI3 between empirical and best-fitted CDF was 0.087, and between empirical and Gamma it was 0.152. For SDI, it ranged between 0.09 and 0.143. The Mean Absolute Error (MAE) for SPEI was approximately 0.05 for all timescales. Additionally, it concludes that empirical CDFs provide more reliable and conservative drought assessments and are free from the constraints of model assumptions. Both approaches gave approximately the same drought duration with different intensities regarding drought characteristics. Due to the complex process of drought events and different definitions of drought events, each drought event must be studied separately, considering its effects on different sectors. Full article

The Standardized Precipitation Index (SPI) is widely used to determine drought severity worldwide. However, inconsistencies exist regarding its application in warm, humid tropical climatic zones. Originally developed for temperate regions with a continental climate, the index may not adequately reflect drought conditions in tropical environments where rainfall regimes differ substantially. This study identifies the following two principal reasons why the traditional calculation method fails to characterize drought severity in tropical domains: first, the marked humidity contrast between the consistently humid rainy season and the rest of the year, and second, the diverse drought types in tropical regions, which include both long-term and short-term events. Using data from meteorological stations in Mexico’s humid tropics and comparing them with temperate regions, the study demonstrates significant discrepancies between SPI-based drought classifications and actual precipitation patterns. Our analysis shows that the abundant precipitation during the rainy season causes biases in longer time scales integrated into multivariate drought indices. Considerations are established for adapting the SPI for decision makers who monitor drought in humid tropics, with specific recommendations on time scale limits to avoid biases. This work contributes to more accurate drought monitoring in tropical regions by addressing the unique climatic characteristics of these environments. Full article

Drought indices are important resources for monitoring and warning of drought impacts. However, regions like New Mexico, which are highly vulnerable to drought, as identified by the United States Drought Monitor (USDM), lack a comprehensive drought monitoring system that integrates multiple agrometeorological variables into a single indicator. The purpose of this study is to create a Combined Drought Indicator for New Mexico (CDI-NM) as an indicator tool for use in monitoring historical drought events and measuring its extent across the New Mexico. The CDI-NM was constructed using four key variables: the Vegetation Condition Index (VCI), temperature, Smoothed Normalized Difference Vegetation Index (SMN), and gridded rainfall data. A quantitative approach was used to assign weights to these variables employing Principal Component Analysis (PCA) to produce the CDI-NM. Unlike conventional indices, CDI-NM assigns weights to each variable based on their statistical contributions, allowing the index to adapt to local spatial and temporal drought dynamics. The performance of CDI-NM was evaluated against gridded rainfall data using the 3-month Standardized Precipitation Index (SPI3) over a 17-year period (2003–2019). The results revealed that CDI-NM reliably detected moderate and severe droughts with a strong correlation (R² > 0.8 and RMSE = 0.10) between both indices for the entire period of analysis. CDI-NM showed negative correlation (r < 0) with crop yield. While promising, the method assumes linear relationships among variables and consistent spatial resolution in the input datasets, which may affect its accuracy under certain local conditions. Based on the results, the CDI-NM stands out as a promising instrument that brings us closer to improved decision-making by stakeholders in the fight against agricultural droughts throughout New Mexico. Full article

Climate change has intensified natural hazards, including droughts, which have caused substantial damage in South Korea. Drought, characterized by prolonged moisture deficiency, is typically assessed using drought indices that reflect meteorological conditions. This study examined the influence of various meteorological and climate indices on drought variability in the Yeongsan and Seomjin River basins. The Standardized Precipitation Index (SPI) was used to represent drought conditions, and its monthly variations were predicted using the Long Short-Term Memory (LSTM) algorithm. To assess model performance, four statistical indicators—Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Nash–Sutcliffe Efficiency (NSE), and the Coefficient of Determination (R²)—were employed. The LSTM model that utilized both precipitation and drought indices as input data showed the best performance, achieving an MSE of 0.2, RMSE of 0.477, NSE of 0.77, and R² of 0.78. Overall predictive performance ranged from 0.298 to 0.347 (MSE), 0.546 to 0.589 (RMSE), 0.578 to 0.628 (NSE), and 0.580 to 0.675 (R²). This study highlights the effectiveness of LSTM in predicting drought conditions and the value of incorporating meteorological and climatic indices. The results can support improved drought hazard assessment and management strategies in South Korea. Full article

The Tarim River Basin is an important grain and cotton base in Xinjiang, China. Indeed, cotton production in this basin accounts for one-third of the total cotton production in China. The Tarim River Basin is characterized also by the presence of forestry activities and fruit plantations. However, frequent long-term droughts have seriously affected local agricultural productivity. In this paper, a new standardized precipitation evapotranspiration index (nSPEI), with an improved drought detection effect, was constructed based on the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). This drought index was subsequently employed as a hazard indicator of disaster-causing factors in the Tarim River Basin. In addition, a drought disaster risk assessment model was constructed using the natural disaster system theory. This model was applied to analyze the hazard of drought-disaster-causing factors, the exposure of disaster-affected bodies, the vulnerability of disaster-bearing environments, drought prevention/mitigation capabilities, and comprehensive drought disaster risks in the Tarim River Basin over the 2001–2021 period. The results demonstrated the applicability of the 12-month nSPEI (nSPEI-12) in the Tarim River Basin. Specifically, the nSPEI-12 values exhibited a decreasing trend, highlighting an aridification trend in the basin. In addition, a 25% increase in the vegetation cover of the Tarim River Basin was observed from 2000 to 2023 and remained unchanged at 4.5%. On the other hand, a decreasing trend of the vegetation cover was found in the remaining parts of the basin. The hazard level of the disaster-causing factors and the exposure of bearing bodies were high in the northeastern and northwestern parts of the Tarim River Basin, respectively. The disaster prevention/mitigation capacity was greater in the northern and southwestern parts, while the vulnerability level of disaster-bearing environments decreased from the northwestern part to the southeastern part. The western and northern parts of the Tarim River Basin exhibited the highest drought risk levels, followed by the northeastern and southeastern parts. Full article

Climatic events are expected to significantly impact global agricultural production, with China being particularly vulnerable. Research in China emphasizes the urgent need for sustainable agricultural practices that address climate change, implement effective management strategies to mitigate the impacts of climatic events, and ensure food security. Therefore, this study examines the impact of climatic events on agricultural production optimization in Northeast China. To complete this objective, this study uses Method-of-Moments Quantile Regression (MM-QR) and data from 2003 to 2020. The main findings reveal that climatic factors, such as the Standardized Precipitation Index (SPI) and High-Temperature Days (HTDs), have a more pronounced effect on agricultural outcomes at higher production levels, particularly for larger producers. In addition, machinery power (TPAM) enhances productivity. Its role is more focused on risk mitigation than on expanding production. Insurance payouts (AIPE) increase grain production capacity at higher quantiles, while fertilizer use (FEU) has diminishing returns on capacity but encourages planting. Granger causality tests further demonstrate that management factors—such as machinery, irrigation, and insurance—play a more significant role in shaping agricultural outcomes than extreme climatic events. To improve agricultural sustainability in the context of climate change, policy recommendations include promoting climate-resilient crops, investing in smart irrigation systems, expanding affordable agricultural insurance, and encouraging sustainable fertilizer use through incentives and training. These strategies can help mitigate climate risks, enhance productivity, and reduce the environmental impact of agricultural activities. 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

Drought significantly reduces global agricultural productivity and destabilizes ecosystems. As the primary grain-producing region and a key ecological buffer zone in China, Northeast China is experiencing intensifying drought stress. However, the regional-scale characteristics of refined drought and the impact mechanisms on different types of vegetation in the Northeast are rarely investigated. In this study, we analyzed the spatial and temporal characteristics of drought over 30-, 60-, 90-, 180-, 270-, and 360-day time scales in Northeast China using the Standardized Precipitation Index (SPI) based on high-precision daily precipitation data simulated by CLM3.5 from 2008 to 2023. Additionally, we used the MODIS Normalized Difference Vegetation Index (NDVI) to elucidate the response of vegetation to drought across different land use types. The results showed that SPI-30 was the most sensitive for drought detection, and there was a clear trend of drought aggravation in the northern part of the Northeast region. The strongest correlation between vegetation and drought was found in September. A significant lag in the response of vegetation to drought was observed in May, June, July, and August, with the best correlation observed at a one-month lag. In addition, the degree of response to drought varies among different types of vegetation. Grasslands are the most sensitive to drought, while woodlands and wetlands have a weaker response. This study provides a reference for assessing the dynamics of refined climates at different spatial and temporal scales and offers actionable insights for ecosystem management in climate-sensitive agricultural regions. 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

Meteorological droughts are a complex and recurring phenomenon in Mexico, posing significant challenges for water availability, ecosystems, and socio-economic activities. Furthermore, several worldwide studies highlight that the impacts of droughts may intensify due to the potential effects of climate change. Using projections from global climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study evaluates future trends in drought frequency and severity across the Mexican hydrological regions. We applied the Standardized Precipitation Index (SPI) to assess meteorological drought indicators under two Shared Socio-economic Pathway (SSP) scenarios (SSP2-4.5 and SSP5-8.5) for the periods 2040–2069 and 2070–2099. Climate models show high variability in projected precipitation changes between the reference and future periods. The SSP5-8.5 scenario indicates the greatest decrease, with reductions of at least 5 to 10%, and even larger declines projected for hydrological regions along the Pacific and Gulf of Mexico coasts, as well as the Yucatán Peninsula. Changes in drought indicators vary depending on the time horizon and scenario considered. For instance, projections for the period 2070–2099 under the high-emission scenario SSP5-8.5 suggest more frequent (three to four events) and prolonged (15 to 18 months) droughts in central and southern hydrological regions. These insights highlight the urgency of strengthening water management policies and adaptive strategies to mitigate the anticipated impacts of climate change on Mexico’s water resources. Full article

This study focuses on Qilu Lake to study how to mitigate the impacts of seasonal droughts and provide technical support for drought resistance decision-making in low-latitude plateau lake basins. Using the Standardized Precipitation Index (SPI), the Vegetation Condition Index (VCI), and the Temperature Condition Index (TCI) as bases, in this study, the applicability of the vegetation health index (VHI) within the basin is investigated, and the optimal weight distribution between the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI) in the VHI is determined. The VHI is then applied to analyze the correlation between drought frequency and severity within the basin. The results indicate that the method is most effective in assessing agricultural drought in the Qilu Lake Basin when the VCI and TCI are weighted at a 4:6 ratio, optimizing the VHI’s evaluative performance. The drought limit water levels of lakes are further divided into short- and long-term drought limit water levels. The short-term drought limit water level is divided into the drought warning water level and the drought emergency water level. The drought warning water level (corresponding to moderate drought conditions, with a frequency of P = 75%) ranges from 1794.53 m to 1795.11 m, while the drought emergency water level (corresponding to extreme drought conditions, with a frequency of P = 95%) ranges from 1793.94 m to 1794.31 m. These levels are set to meet the emergency water demand during droughts in the basin. The long-term drought limit water levels are calculated by accumulating the water deficits of various sectors within the watershed under different agricultural drought conditions, based on the short-term drought limit water levels. By setting the drought limit water level using this method, as well as considering the original water regulation capacity of the lake resources, when the watershed experiences drought, the scheduling method based on this drought limit water level can better alleviate the water supply pressure on various sectors in the local area. Full article

This study investigates meteorological drought characteristics across three climatic zones in Benin using the SPEI (Standardized Precipitation Evapotranspiration Index) and SPI (Standardized Precipitation Index). A comprehensive statistical approach was employed, including the Mann–Kendall trend test, drought duration and intensity analysis, Pearson correlation, cross-wavelet transform, and the Standardized Relative Air Humidity Index (SRHI), to assess drought patterns and trends. The findings indicate increasing consistency between SPI and SPEI trends at longer timescales, though significant regional variations persist. In Zone 1 (northern Benin), SPI exhibited an increasing trend across all timescales, whereas SPEI demonstrated a decreasing trend at shorter timescales. In contrast, in Zones 2 (central Benin) and 3 (south Benin), both indices generally displayed a decreasing trend, except at the one-month scale. An analysis of drought duration and intensity revealed that, at shorter timescales (SPI and SPEI at 1- and 3-month intervals), the longest droughts occurred in Zones 1 and 3, while the most intense events were recorded in Zone 2. At longer timescales (SPI and SPEI at 6- and 12-month intervals), Zone 2 experienced the longest droughts, whereas Zone 3 exhibited the highest intensities. These findings illustrate the need for monitoring strategies tailored to a given area’s characteristics. Despite these insights, data uncertainties and regional differences present challenges for drought investigation. Future studies should incorporate more datasets and investigate different drought indices to improve decision-making and improve strategies for safeguarding Benin’s agricultural sector, ecosystems, and food supply. Full article

In the context of global climate warming, compound drought and heatwave events (CDHEs) have exhibited a pronounced escalation in frequency since the Second Industrial Revolution, incurring substantial socioeconomic losses. This study investigates the spatiotemporal variations of CDHEs in semi-arid and semi-humid regions of northern China based on daily Standardized Precipitation Index (SPI) and maximum temperature (Tmax) datasets. The results show that compared to the 1980s, the occurrence frequency of CDHEs during the 2010s exhibited an increasing trend increase by 20–50 times in the southern region and 10–30 times in the northern region, while some watersheds in the central part of the study area show a decreasing trend. From the 1980s to the 2010s, the percentage of area affected by CDHE with a duration exceeding 11 days/year has risen from 28.3% to 56.7%, reflecting a pronounced upward trend in CDHE duration. Spatiotemporal patterns revealed significant interdecadal disparities in both the frequency and duration of CDHEs, which are primarily determined by heatwave events pattern and the synchronicity of heatwave and drought events. However, drought intensity exhibits comparatively weaker influence. Due to the decrease in the proportion of short–duration heatwaves, the short–duration CDHEs (1–2 days) in all levels exhibited a declining trend in their proportions. Furthermore, the delayed occurrence of drought events resulted in the peak occurrence of CDHEs has gradually shifted June to July–August. Full article

This study presents a comprehensive analysis of hydrological alterations and environmental flow components in the Beht River basin in northwest Morocco, using a coupled approach involving the Soil and Water Assessment Tool (SWAT) for hydrological modeling, the Indicators of Hydrologic Alteration (IHA) for flow regime assessment, and the Standardized Precipitation Index (SPI) for drought characterization. The SWAT model, run on a daily time step, showed satisfactory performance in terms of statistical criteria for both calibration and validation periods, despite encountering limitations, and proved its ability to simulate and reproduce the hydrological behavior of the basin. Using the IHA, we investigated changes in the hydrological regime over two distinct periods, revealing significant hydrological alteration. The SPI analysis supported these findings by highlighting the variable impacts of dry and wet periods on the hydrological regime, thus validating the observed changes in river flow indicators. As a preliminary step toward establishing environmental flows in the Beht River, this study provides foundational insights into the temporal evolution of its hydrology. These findings offer a valuable basis for better water resource management and conservation in the region. Full article