Drought Severity Index Research Articles

An Innovative TOPSIS–Mahalanobis Distance Approach to Comprehensive Spatial Prioritization Based on Multi-Dimensional Drought Indicators

This research explores a new methodological framework that blends the TOPSIS (technique for order of preference by similarity to ideal solution) and Mahalanobis Distance methods, allowing for the prioritization of nine major watersheds in China based on the integration of multi-dimensional drought indicators. This integrated approach offers a robust prioritization model by accounting for spatial dependencies between indices, a feature not commonly addressed in traditional multi-criteria decision-making applications in drought studies. This study utilized three drought indices—the Standardized Precipitation Evapotranspiration Index (SPEI), Vegetation Health Index (VHI), and Palmer Drought Severity Index (PDSI). Over years of significant drought prevalence, three types of droughts occurred simultaneously across various watersheds in multiple years, particularly in 2001, 2002, 2006, and 2009, with respective counts of 16, 17, 19, and 18 concurrent episodes. The weights derived from Shannon’s entropy emphasize the importance of the Potential Drought Severity Index (PDSI) in evaluating drought conditions, with PDSI-D (drought duration) assigned the highest weight of 0.267, closely followed by VHI-D (Vegetation Health Index under drought conditions) at 0.232 and SPEI-F (drought frequency) at 0.183. The results demonstrated considerable spatial variability in drought conditions across the watersheds, with Watersheds 1 and 4 exhibiting the highest drought vulnerability in terms of meteorological and agricultural droughts, while Watersheds 6 and 3 showed significant resilience to hydrological drought after 2012. In particular, the severe meteorological drought conditions at Watershed 1 highlight the urgent need for rainwater harvesting and strict water use policies, and in contrast, the conditions at Watershed 4 show the need for the modernization of irrigation to mitigate agricultural drought impacts. This integrated framework allows for targeted drought management solutions that directly relate to the specific contexts of the watersheds, while being more conducive to planning and prioritizing resource allocations for regions facing the highest drought vulnerability.

Assessment of Soil Moisture in Vegetation Regions of Mu Us Sandy Land Using Several Aridity Indicators

Drought, a significant calamity in the natural domain, has extensive worldwide repercussions. Drought, primarily characterized by reduced soil moisture (SM), presents a significant risk to both the world environment and human existence. Various drought indicators have been suggested to accurately represent the changing pattern of SM. The study examines various indices related to the Drought Severity Index (DSI), Evaporation Stress Index(ESI), Vegetation Supply Water Index(VSWI), Temperature-Vegetation Dryness Index(TVDI), Temperature Vegetation Precipitation Dryness Index(TVPDI), Vegetation Health Index(VHI), and Temperature Condition Index (TCI). An evaluation was conducted to assess the effectiveness of seven drought indicators, such as DSI, ESI, TVPDI, VSWI, etc., in capturing the changes in SM in Mu Us Sandy Land. The research results indicated that DSI and ESI had the highest accuracy, while TVDI and VSWI showed relatively lower accuracy. However, their smaller fluctuations in the time series demonstrated stronger adaptability to different regions. Additionally, the delayed impact of aridity indices on soil moisture, variable attributes, temperature, and vegetation coverage in sandy land and grassland areas with low, medium, and high coverage all contributed to the effectiveness of the four aridity indices (DSI, ESI, VSWI, and TVPDI) in capturing the dynamics of soil moisture. The primary element that affects the effectiveness of TVDI is the divergence of the relationship curve between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), which is a kind of deterioration. This paper presents a very efficient approach for monitoring soil moisture dynamics in dry and semi-arid regions. It also analyzes the patterns of soil moisture changes, offering valuable scientific insights for environmental monitoring and ecological enhancement.

Tree-ring maximum latewood density reveals unprecedented warming and long-term summer temperature in the upper Indus Basin, northern Pakistan

Understanding long-term temperature variability in the Upper Indus Basin (UIB), northern Pakistan, and its driving mechanisms is challenging due to the scarcity of long observational records and available literature. In this study, we reconstructed a 651-year (1370–2020 CE) warm-season (March–September) temperature record using the tree-ring maximum latewood density (MXD) of blue pine (Pinus wallichiana). The reconstruction explains 57 % of the variance in actual temperature during the common calibration period (1972–2020 CE). Our analysis identified ten high-temperature periods (temperature > 20.9 °C) and nineteen low-temperature periods (temperature < 19.8 °C), with the coldest years being 1392, 1707, 1817, and 1837, and the warmest years being 2013, 2016, 2017, and 2018. Spatial correlation analysis reveals a significant positive correlation with field temperature, predominantly in neighboring regions, and a significant negative correlation with relative humidity and precipitation. Multi-taper spectral analysis shows inter-annual (1.9, 2.5, 2.7, 5.5 years) and interdecadal (11, 18, 23, 25, 40, 71 years) cycles, suggesting a potential linkage with the Atlantic Multidecadal Oscillation (AMO). The negative linkage between our reconstruction and the region's standardized Palmer Drought Severity Index (scPDSI) indicates that continued temperature increase could result in severe drought in northern Pakistan in the near future. During the 20th century, the UIB experienced two distinct warming phases: 1948–1970 CE and 1994–2020 CE. The warming rate during 1994–2020 CE was 0.5 °C higher, indicating unprecedented recent warming. The reconstructed temperature record also demonstrates a large-scale spatiotemporal signal and a strong connection with most recorded volcanic eruptions. These findings enhance our understanding of long-term temperature variability in the region, highlighting the significance of MXD in reconstructing past temperature patterns.

Drought Characterization Using Multiple Indices over the Abbay Basin, Ethiopia

Analyzing agricultural and hydrological drought at different timescales is essential for designing adaptation strategies. This study aimed to assess agricultural and hydrological drought in the Abbay Basin of Ethiopia by using multiple indices, namely the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), and drought severity index (DSI). Climate extremes were assessed over the Abbay Basin between 1981 and 2022. The results indicate that the years 1982 and 2014 were the most drought-prone, while the year 1988 was the wettest year in the Abbay Basin. The results revealed the presence of extremely dry and severely dry conditions, potentially impacting agricultural output in the region. Agricultural drought was identified during the main crop seasons (June to September). The VCI results indicated the presence of extremely wet and severely wet conditions. In 2012, 65% of the area was affected by extreme drought conditions, while nearly half of the Basin experienced extreme drought in 2013 and 2022. The DSI results indicated the occurrence of agricultural drought, although the spatial coverage of extreme dry conditions was lower than that of the other indices. In 2003, 78.49% of the Basin experienced moderate drought conditions, whereas severe drought affected 20% of the region. In 2010, about 90% of the Basin experienced moderate drought. This study provides valuable insights for agricultural communities, enabling them to mitigate the impact of drought on crop yields by utilizing different adaptation strategies. An adequate knowledge of agricultural and hydrological drought is essential for policymakers to assess the potential effects of drought on socioeconomic activities and to recognize the significance of implementing climate change adaptation measures.

Assessing the impact of natural hazards on U.S. cotton yields: a random forest analysis of drought, wildfires, and hurricanes (2012-2023)

Abstract. The cotton industry is essential in the world. This paper investigates the impacts of natural hazards, particularly drought, wildfires, and hurricanes, on cotton yield (LB/Acre) in 14 major cotton-producing states in the United States from 2012 to 2023. The paper uses a random forest model with drought severity and cover index (DSCI), number of wildfire alerts, and number of hurricanes and their interactions as influencing factors on cotton yield. The results shows that wildfires were most likely to affect cotton yield among the three major categories of natural hazards, and hurricanes did not significantly affect cotton yield. Cotton yield decreases significantly in years with severe drought and high wildfire activity. In addition, the study emphasizes the compounding impact of these hazards, suggesting that the interaction between drought and wildfires exacerbates the effects on cotton yields. Overall, cotton growers and local authorities need substantial mitigation and adaptation strategies for wildfires and droughts.

Multisensor Integrated Drought Severity Index (IDSI) for assessing agricultural drought in Odisha, India

Recurrent droughts in India have severely impacted the economy and the quality of life. The agricultural drought from June to October 2023 in Odisha (the Kharif season), India, highlighted the urgent need for precise monitoring and assessment due to its significant effects on crop yield and food security. This study develops and validates the Multisensor Integrated Drought Severity Index (IDSI) to accurately assess agricultural drought severity using multiple remote sensing indices, including optical, thermal, and microwave sensors. Ten indices were computed and combined using the Analytic Hierarchy Process (AHP) to assign weights, aiming to establish a new agricultural drought index that can monitor severity, identify critical indices, and assess uncertainties in affected areas. Validation results from ROC-AUC indicate that the IDSI model achieved a precision exceeding 85% using empirical weights. The study area's mapping shows that approximately 8.91% experience extreme drought conditions, with significant impacts in specific districts of Odisha. This comprehensive tool provides critical insights for policymakers and farmers, enhancing global drought preparedness and response strategies through its adaptable methodology.

Concurrent trend turnings of drought severity across Afro-Eurasian continent since 1950

Rapidly intensifying global land drought poses severe threats to human societies, economies, and ecosystems. While previous studies have primarily investigated long-term drought trends, the frequency and concurrence of trend turnings have been largely neglected. In this study, we address this gap by employing the Running Slope Difference (RSD)-t-test to quantify trend turning frequency in Afro-Eurasian drought severity. Based on Palmer Drought Severity Index (PDSI), our analysis indicates that the PDSI trend in most parts of the Afro-Eurasian continent has experienced two turnings since 1950, although, the types of trend turnings vary regionally. The concurrence of these PDSI trending turnings is further investigated. Around 1985, a dipole pattern emerged - Eastern Europe experiences a drying trend turning, accompanied by decreased P-E and intensified drought, while Sahel exhibits a wetting trend turning, with increased P-E and mitigated drought. Around 2000, a tripole pattern is observed in Eastern Eurasia: The Russian Far East and South Asia experienced a drying trend turning, with reduced P-E and intensified drought, while Northeast Asia exhibited a further wetting trend, characterized by increased P-E and mitigated drought. We further investigate the influence of large-scale circulation changes. The enhanced Northern Hemisphere warming trend before and after 1985 contributes to increased land surface high pressure and an amplified meridional temperature gradient, favoring cross-equatorial water vapor transport. This mechanism potentially drives the dipole pattern of trend turning observed around 1985. Additionally, the North Pacific Ocean Sea Surface Temperature (SST) exhibited an enhanced North Pacific Gyre Oscillation (NPGO) pattern around 2000, which induced a tripole atmospheric circulation pattern over East Asia, corresponding to the observed tripole pattern of PDSI trend turnings. The identified dipole and tripole patterns of drought trend turnings, and their potential links to large-scale atmospheric circulation changes, provide insights into the complex dynamics of land drought variability across Afro-Eurasian.

Evaluation of the spatial responses in vegetation phenology to drought and the analysis of their driving factors in China

The warming and drying trend accompanying climate change challenges global ecosystem stability. Vegetation phenology, which can serve as a sensitive indicator of climate change, is crucial in understanding ecosystem carbon cycling and climate-carbon cycle feedback. Therefore, assessing the phenological responses to drought is essential for addressing climate change. In this study, vegetation phenology data [including the start and end of season (SOS, EOS) and length of growing season (LOS)] and the Palmer drought severity index (PDSI) were employed to analyze the impacts of drought on plant phenology in China by maximum Pearson correlation coefficients and partial least squares regression. The findings showed that drought significantly affected the timing of phenology, delaying senescence in approximately 62% of China and extending the growing season in about 53% of the country, indicating the critical role of water availability in vegetation biomass. Preseason nocturnal warming was found to advance SOS, delay EOS, and extend LOS across China, with significant effects observed in approximately 60% of the country. Meanwhile, daytime warming delayed SOS, delayed EOS and extended LOS in 50∼60% of the regions. Moreover, preseason precipitation is conducive to advanced SOS, delayed EOS and extended LOS in northern China and areas susceptible to drought. It is suggested that vegetation management should be strengthened to mitigate the impact of climate change in temperate and drought-prone regions in China since climate warming will lead to frequent droughts.

A Coupled Model for Forecasting Spatiotemporal Variability of Regional Drought in the Mu Us Sandy Land Using a Meta-Heuristic Algorithm

Vegetation plays a vital role in terrestrial ecosystems, and droughts driven by rising temperatures pose significant threats to vegetation health. This study investigates the evolution of vegetation drought from 2010 to 2024 and introduces a deep-learning-based forecasting model for analyzing regional spatial and temporal variations in drought. Extensive time-series remote-sensing data were utilized, and we integrated the Temperature–Vegetation Dryness Index (TVDI), Drought Severity Index (DSI), Evaporation Stress Index (ESI), and the Temperature–Vegetation–Precipitation Dryness Index (TVPDI) to develop a comprehensive methodology for extracting regional vegetation drought characteristics. To mitigate the effects of regional drought non-stationarity on predictive accuracy, we propose a coupling-enhancement strategy that combines the Whale Optimization Algorithm (WOA) with the Informer model, enabling more precise forecasting of long-term regional drought variations. Unlike conventional deep-learning models, this approach introduces rapid convergence and global search capabilities, utilizing a sparse self-attention mechanism that improves performance while reducing model complexity. The results demonstrate that: (1) compared to the traditional Transformer model, test accuracy is improved by 43%; (2) the WOA–Informer model efficiently handles multi-objective forecasting for extended time series, achieving MAE (Mean Absolute Error) ≤ 0.05, MSE (Mean Squared Error) ≤ 0.001, MSPE (Mean Squared Percentage Error) ≤ 0.01, and MAPE (Mean Absolute Percentage Error) ≤ 5%. This research provides advanced predictive tools and precise model support for long-term vegetation restoration efforts.

Analyzing the Spatiotemporal Dynamics of Drought in Shaanxi Province

Drought, as a natural disaster with wide-ranging impacts and long duration, has an adverse effect on the global economy and ecosystems. In this paper, four remote sensing drought indices, namely the Crop Water Stress Index (CWSI), Vegetation Supply Water Index (VSWI), Temperature Vegetation Dryness Index (TVDI), and Normalized Difference Water Index (NDWI), are selected for drought analysis. The correlation analysis is carried out with the self-calibrated Palmer Drought Severity Index (sc-PDSI), and based on the optimal index (CWSI), the spatiotemporal characteristics of drought in Shaanxi Province from 2001 to 2021 were studied by SEN trend analysis, Mann–Kendall test, and a center of gravity migration model. The results show that (1) the CWSI performs best in drought monitoring in Shaanxi Province and is suitable for drought studies in this region. (2) Drought in Shaanxi Province shows a decreasing trend from 2001 to 2021; the main manifestation of this phenomenon is the decrease in the occurrence of severe drought, with severe drought covering less than 10% of the area in 2010 and subsequent years. The most severely affected regions in the province are the northern Loess Plateau region and Guanzhong Plain region. In terms of the overall trend, only 0.21% of the area shows an increase in drought, primarily concentrated in the Guanzhong Plain region and the outskirts of the Qinling–Bashan mountainous region. (3) Drought conditions are generally improving, with the droughts’ center of gravity moving northeastward at a rate of 3.31 km per year. The results of this paper can provide a theoretical basis and a practical reference for drought control and decision-making in Shaanxi Province.

A new Multivariate Drought Severity Index to identify short-term hydrological signals: case study of the Amazon River basin

The Earth's climate is changing rapidly and unexpectedly, causing more frequent, longer and more severe droughts, with lasting impacts on plants, ecosystems, communities and people. Consequently, this is leading to an increased importance of monitoring the climate and water storage trends in different regions. This information on a global scale is already commonly derived using satellite-based geodetic techniques such as the Global Positioning System (GPS) and the Gravity Recovery and Climate Experiment (GRACE). The use of both techniques has significant advantages, especially in regions where changes in the hydrosphere are notable, such as the Amazon basin, where 25 GPS stations were lately classified as benchmarks for hydrogeodesy. We show that the vertical displacements obtained from GPS and GRACE have good spatio-temporal agreement with the Standardized Precipitation and Standardized Precipitation Evapotranspiration indices, abbreviated respectively as SPI and SPEI, for all these stations. Drought severity index (DSI) estimated separately from GPS-observed and GRACE-derived vertical displacements on a station-by-station basis is capable to identify dry and wet events previously reported for the Amazon basin. However, due to the weaknesses of both techniques, such as technique-related systematic errors or coarse spatial resolution, a few extreme hydrological events may not be properly captured by GPS-DSI and/or GRACE-DSI. To take full advantage of both techniques and overcome their weaknesses, we introduce a completely new methodology to combine individual GPS-DSI and GRACE-DSI indices. As a novelty, both indices are estimated using short-term changes (<9 months) of monthly vertical displacements observed by GPS permanent stations and those derived by GRACE for GPS locations. Then, to capture and detect drought events that either both geodetic techniques metrics missed or incorrectly depicted, the Multivariate Drought Severity Index (MDSI) is estimated through the concept of Frank copulas. We demonstrate that the MDSI captures more hydroclimatic events reported in previous studies, which are not identified by individual series of GPS-DSI or GRACE-DSI indices, and is temporally consistent with Standardized Streamflow Index (SSI) based on the in-situ river discharge changes.

Assessment of Meteorological Drought in a Changing Environment: An Example in the Upper Yangtze River

AbstractRecent studies have suggested that drought projections using Palmer drought severity index (PDSI) and standardized evapotranspiration precipitation index (SPEI) may overestimate drought severity. This overestimation occurs because the potential evapotranspiration (PET) calculations fail to consider the interactive effects of vegetation responses such as increased leaf area index (LAI) and constrained stomatal conductance, which are influenced by elevated atmospheric CO2 concentrations ([CO2]). To address this issue, our study replaced the traditional Penman‐Monteith (PM) equation with a recently proposed PET equation that includes the effects of changing [CO2] and LAI to assess droughts at monthly scale in the Upper Yangtze River basin, which experiences the vegetation greening. The findings indicated a consistent increasing trend in drought conditions with minimal discrepancy between the two equations over the historical period (1986–2017). This consistency arises because the water‐saving effects of increased [CO2] and the greening effects of rising LAI largely counterbalance each other. However, for the future period (2018–2100), projections using PM equation predicted an intensification of drought conditions. In contrast, the improved SPEI indicated no significant drought variations, and the improved PDSI suggested a wetting trend. This divergence can be attributed to the water‐saving effects increasingly outweighing the greening effects, as PET shows a decreasing sensitivity to LAI with LAI increasing, but maintains a near‐constant sensitivity to elevated [CO2]. Consequently, the indices based on PM equation tend to overestimate future drought severity. Overall, this study demonstrates that the new PET estimation method is more capable of responding to the changing environment.

Combined Drought Index Using High-Resolution Hydrological Models and Explainable Artificial Intelligence Techniques in Türkiye

We developed a combined drought index to better monitor agricultural drought events. To develop the index, different combinations of the temperature condition index, precipitation condition index, vegetation condition index, soil moisture condition index, gross primary productivity, and normalized difference water index were used to obtain a single drought severity index. To obtain more effective results, a mesoscale hydrologic model was used to obtain soil moisture values. The SHapley Additive exPlanations (SHAP) algorithm was used to calculate the weights for the combined index. To provide input to the SHAP model, crop yield was predicted using a machine learning model, with the training set yielding a correlation coefficient (R) of 0.8, while the test set values were calculated to be 0.68. The representativeness of the new index in drought situations was compared with established indices, including the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Self-Calibrated Palmer Drought Severity Index (scPDSI). The index showed the highest correlation with an R-value of 0.82, followed by the SPEI with 0.7 and scPDSI with 0.48. This study contributes a different perspective for effective detection of agricultural drought events. The integration of an increased volume of data from remote sensing systems with technological advances could facilitate the development of significantly more efficient agricultural drought monitoring systems.

Multi-scale drought variability over West Africa and the associated large-scale circulation patterns

This study addresses the critical research gap in understanding regional drought dynamics in West Africa (WA) using advanced multivariate wavelet coherence (MWC) techniques, revealing critical insights into the complex interplay between large-scale climate indices and regional drought patterns. Utilizing the Standardized Precipitation Evapotranspiration Index (SPEI) and self-calibrating Palmer Drought Severity Index (scPDSI), we examined drought characteristics across WA from 1950 to 2018. Our analysis revealed significant drying trends across all seasons, with the Arid zone experiencing the most severe and prolonged drought (158 months) from August 1981 to September 1994, affecting 79.77% of the area. The entire WA region faced its most extensive drought during this period, impacting over 60% of the study area. MWC analysis demonstrated enhanced drought predictability when combining climate indices, particularly the Southern Oscillation Index with the Atlantic Multidecadal Oscillation, yielding high Average Wavelet Coherence values of 0.52 (SPEI) and 0.53 (scPDSI), with substantial Percentage Area of Significant Coherence of 40.19% and 43.68%, respectively. These findings highlight the crucial role of oceanic-atmospheric interactions in modulating drought conditions across WA. Our results provide valuable insights for improving drought forecasting and management strategies in the region, emphasizing the need for integrated approaches that consider multiple climate indices and their combined effects on drought dynamics.

Remote Sensing-Based Drought Monitoring in Iran’s Sistan and Balouchestan Province

Drought is a natural phenomenon that has adverse effects on agriculture, the economy, and human well-being. The primary objective of this research was to comprehensively understand the drought conditions in Sistan and Balouchestan Province from 2002 to 2017 from two perspectives: vegetation cover and hydrology. To achieve this goal, the study utilized MODIS satellite data in the first part to monitor vegetation cover as an indicator of agricultural drought. In the second part, GRACE satellite data were employed to analyze changes in groundwater resources as an indicator of hydrological drought. To assess vegetation drought, four indices were used: Vegetation Health Index (VHI), Vegetation Drought Index (VDI), Visible Infrared Drought Index (VSDI), and Temperature Vegetation Drought Index (TVDI). To validate vegetation drought indices, they were compared with Global Land Data Assimilation System (GLDAS) precipitation data. The vegetation indices showed a strong, statistically significant correlation with GLDAS precipitation data in most regions of the province. Among all indices, the VHI showed the highest correlation with precipitation (moderate (0.3–0.7) in 51.7% and strong (≥0.7) in 45.82% of lands). The output of vegetation indices revealed that the study province has experienced widespread drought in recent years. The results showed that the southern and central regions of the province have faced more severe drought classes. In the second part of this research, hydrological drought monitoring was conducted in fifty third-order sub-basins located within the study province using the Total Water Storage (TWS) deficit, Drought Severity, and Total Storage Deficit Index (TSDI Index). Annual average calculations of the TWS deficit over the period from April 2012 to 2016 indicated a substantial depletion of groundwater reserves in the province, amounting to a cumulative loss of 12.2 km3 Analysis results indicate that drought severity continuously increased in all study basins until the end of the study period. Studies have shown that all the studied basins are facing severe and prolonged water scarcity. Among the 50 studied basins, the Rahmatabad basin, located in the semi-arid northern regions of the province, has experienced the most severe drought. This basin has experienced five drought events, particularly one lasting 89 consecutive months and causing a reduction of more than 665.99 km3. of water in month 1, placing it in a critical condition. On the other hand, the Niskoofan Chabahar basin, located in the tropical southern part of the province near the Sea of Oman, has experienced the lowest reduction in water volume with 10 drought events and a decrease of approximately 111.214 km3. in month 1. However, even this basin has not been spared from prolonged droughts. Analysis of drought index graphs across different severity classes confirmed that all watersheds experienced drought conditions, particularly in the later years of this period. Data analysis revealed a severe water crisis in the province. Urgent and coordinated actions are needed to address this challenge. Transitioning to drought-resistant crops, enhancing irrigation efficiency, and securing water rights are essential steps towards a sustainable future.

The collapse of the Ming Dynasty actually began with the Wanli megadrought: Insights from a hydroclimate reconstruction based on tree-ring δ18O over the past 460 years

Climate change has played a pivotal role in shaping Chinese history, especially during the Ming Dynasty. Previous studies have focused primarily on the Chongzhen megadrought, which is widely considered as the primary climatic perturbation behind the demise of the Ming Dynasty. However, relatively little is known about other severe drought events and their potential impact on the dynasty collapse. Additionally, the characteristics of an exceptional climatic anomaly termed the “Late Ming Weak Monsoon Period” are unclear. In this study, we reconstructed the historical variations of Palmer Drought Severity Index for July–September based on tree-ring stable oxygen isotopes (δ18O) from 1556 CE to 2015 CE in southwest Chinese Loess Plateau. Our study reveals a significant weakening of the Asian summer monsoon between 1561 CE and 1661 CE, consistent with the Late Ming Weak Monsoon Period, and unravels its structural characteristics in detail. Our reconstruction also captures a distinct humidification trend over northwest China since the early 2000s. Notably, in addition to the well-known Chongzhen megadrought, our study records the severe Wanli megdrought (1585–1590 CE) during the late Ming Dynasty, an event that rarely featured in earlier studies, exhibiting comparable duration and severity to the Chongzhen megadrought. Further analysis indicates that Wanli megadrought may have served as an early trigger for the collapse of the Ming Dynasty. Furthermore, our analysis implicates the El Niño–Southern Oscillation as a contributing factor in both the Wanli and Chongzhen megadroughts, and thus to the ultimate collapse of the Ming Dynasty by affecting the Asian summer monsoon intensity.

Reconstructing a Fine Resolution Landscape of Annual Gross Primary Product (1895–2013) with Tree-Ring Indices

Low carbon management and policies should refer to local long-term inter-annual carbon uptake. However, most previous research has only focused on the quantity and spatial distribution of gross primary product (GPP) for the past 50 years because most satellite launches, the main GPP data source, were no earlier than 1980. We identified a close relationship between the tree-ring index (TRI) and vegetation carbon dioxide uptake (as measured by GPP) and then developed a nested TRI-GPP model to reconstruct spatially explicit GPP values since 1895 from seven tree-ring chronologies. The model performance in both phases was acceptable: We chose general regression neural network regression and random forest regression in Phase 1 (1895–1937) and Phase 2 (1938–1985). With the simulated and real GPP maps, we observed that the GPP for grassland and overall GPP were increasing. The GPP landscape patterns were stable, but in recent years, the GPP’s increasing rate surpassed any other period in the past 130 years. The main local climate driver was the Palmer Drought Severity Index (PDSI), and GPP had a significant positive correlation with PDSI in the growing season (June, July, and August). With the GPP maps derived from the nested TRI-GPP model, we can create fine-scale GPP maps to understand vegetation change and carbon uptake over the past century.

The effects of climate change on Pinus tabulaeformis radial growth in the Xiaowutai Mountains, northern China

Clarifying the climate change effects on the radial growth of trees has implications for sustainable forest management, especially under global warming. To investigate tree growth responses to regional climate change of Xiaowutai Mountain, four Chinese pine (Pinus tabulaeformis) ring-width index chronologies were established at different elevations (1290–1600 m). Species growth trends were estimated using climate change projections derived from global climate models. The results show: (1) the four ring-width chronologies exhibited strong statistical characteristics, making them suitable for dendroclimatology studies. Radial growth-climate relationships were highly consistent, showing a negative correlation with previous September temperatures and current May–June temperatures, as well as a positively correlated with precipitation and Palmer Drought Severity Index during the corresponding period; and (2) climate change scenarios revealed that temperature will gradually increase on the Xiaowutai Mountain, and only a slight variation in precipitation is expected. Chinese pine radial growth may show a decline under future climate change.

Anthropogenic climate change has reduced drought recovery probabilities across the western US

During drought, resource managers want to know when the drought will end to make informed management decisions. However, as anthropogenic climate change has intensified drought conditions, we hypothesize it has affected drought recovery. Here, we leverage monthly self-calibrating Palmer Drought Severity Index data across the western US derived from observations and climate models, and quantify the probability of drought recovery following severe drought. We find that the probability of drought recovery is ~25–50% lower in recent decades (2000–2021) than in the historical record (1901–1980), with at least one-third of the reduced recovery probability attributable to anthropogenic climate change. Climate model ensembles show reduced recovery probabilities in the contemporary era (2000–2040), primarily due to increased evaporative demand in non-winter months, resulting in an additional 1–4 months for droughts to recover compared with the historical record. These findings suggest climate change is slowing drought recovery, with ramifications for water management decisions and drought planning.

Remote-Sensed Determination of Spatiotemporal Properties of Drought and Assessment of Influencing Factors in Ordos, China

Ordos drought impacts are complex; the Geodetector model is able to explore the interaction between impact factors. Based on the drought severity index (DSI), this study explored the spatio-temporal dynamics and changing trends of drought, and analyzed the driving factors of DSI spatial differentiation by using the Geodetector model. The results show that: the evapotranspiration (ET) and normalized difference vegetation index (NDVI) in Ordos showed a significant increasing trend (p &lt; 0.05). The increasing rates were ET (4.291 mm yr−1) and NDVI (0.004 yr−1). In addition, the interannual variation of the DSI also showed a significant increase, with a trend change rate of 0.089. The spatial pattern of ET and the NDVI was low in the southwest and high in the northeast, and the spatial pattern of potential evapotranspiration (PET) was high in the southwest and low in the northeast, while the distribution of the DSI was dry in the west and wet in the east. The spatial differentiation of the DSI was mainly affected by five factors: air temperature, precipitation, land use type, soil type, and the digital elevation model (DEM), with q exceeding 0.15, which were the main driving factors of drought in the Loess Plateau. Under the interaction of multiple factors, the four combinations of temperature and the DEM, precipitation and the DEM, sunshine duration and the DEM, and relative humidity and the DEM jointly drive drought, in which precipitation (0.156) ∩ DEM (0.248) has the strongest influence on drought occurrence, and q reaches 0.389. This study directly informs specific drought management strategies or ecological conservation efforts in the region.