Palmer Drought Severity Index Research Articles

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.

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.

Anthropogenic Exacerbation in Dry‐Hot Probability and Consequential Record‐Shattering Droughts in the Middle and Lower Reaches of the Yangtze River

AbstractIn the year 2019, the middle and lower reaches of the Yangtze River (MLRYR) experienced an unprecedented summer‐autumn drought (SAD) driven by dry‐hot conditions [high near‐surface air temperatures (T) and low precipitation (P)], causing substantial agricultural and economic losses. However, the influence of anthropogenic climate change (ACC) on these dry‐hot conditions and their impacts on SAD occurrences remains uncertain. Here, both observations and simulations show that an ACC‐driven T increase led to the greater likelihood of dry‐hot conditions from August to November 1901–2020 in MLRYR. Using the self‐calibrating Palmer drought index (scPDSI) to assess SAD severity, we find an increasing likelihood of SAD occurrence (from 33.3% in 1901–2000 to 85.7% in 2001–2020) in MLRYR associated with more frequent dry‐hot conditions. Under a business‐as‐usual scenario, future dry‐hot association is projected to be stronger, with exceptional dry‐hot conditions to increase by +10% per century. ACC‐induced increase in dry‐hot conditions would elevate the likelihood of SAD events like the 2019 event from 1.59% (1961–2020) to 17.82% (2041–2100). Therefore, effective measures are needed in MLRYR to adapt to increasing dry‐hot conditions and associated SAD occurrences under anthropogenic warming.

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.

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.

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 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.

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.

Changes in Global Heatwave Risk and Its Drivers Over One Century

AbstractHeatwaves represent a significant and growing threat to natural ecosystems and socio‐economic structures, making heatwave risk mitigation and prevention an important area of research. In exploring heatwave frequency and intensity from 1901 to 2020, the present study finds a sharp increase in both. The study also finds that the spatial distribution of heatwaves is unequal, the volatility of intensity characteristics has become more prominent over time, and the Gini coefficients of four key heatwave indictors have become larger due to increasing dryness. Although heatwaves occur more frequently in drylands, there is greater cumulative heat in humid areas, resulting in a higher heatwave risk in those areas. The global heatwave risk over the past three decades (1991–2020) has increased nearly five‐fold compared to the early 20th century (1901–1930). Furthermore, GeoDetector analysis indicates that the Palmer drought severity index (PDSI) and downward surface shortwave radiation (Srad) contributing the most in drylands and humid areas (0.29 and 0.41, respectively). The contribution of relative humidity (RH), wind speed (WS), soil moisture (SM), and the normalized difference vegetation index (NDVI) is also significant in humid areas, but is much smaller in drylands. Composite analysis shows that the years with anomalously high heatwave risk correspond to positive anomalies of 500hPa geopotential height and surface pressure. The inhibition of cloud formation due to sinking air and the resulting increase in temperature in the atmosphere may be increasing the risk of heatwave occurrence. This study emphasizes the urgent need to address worsening climate change impacts.

Exacerbated Tree Growth Decline of an Old Platycladus orientalis Forest after Rapid Warming at the Northern Edge of the Taihang Mountain of China

Old trees are irreplaceable conservation resources with numerous ecological and socio-cultural values. While many forests have experienced significant declines under recent climate warming, the risk of growth declines in old trees remains unknown. Here, we tackle this problem by dendrochronological studies of 30 old trees in a Platycladus orientalis forest at the northern boundary of the Taihang Mountain of China. We examined annual growth trajectories of trees at individual level and discovered four severe growth decline events over the last 150 years, including the periods of 1894–1899, 1913–1919, 1964–1967 and 2004–2018. The most recent growth decline event lasted for 15-year and involced 50% to 75% of the old trees. This decline was unprecedented in both its extent and duration. Furthermore, the growth–climate relationship of these old trees has changed since 1990. Before 1990, tree growth was significantly correlated with minimum winter; after 1990, tree growth became significantly correlated with the self-calibrating Palmer Drought Index. These results suggest that warming-induced droughts after 1990 could be the primary driver of the recent growth decline. If climate warming continues and drought stresses intensify, the old trees may face an increased risk of growth decline and even mortality.

Mitigating the negative effects of droughts on alpine grassland productivity in the northern Xizang Plateau via degradation-combating actions

The Qinghai-Xizang Plateau, hosting the world’s largest alpine pastures, plays a pivotal environmental role in Asia. These ecosystems face alterations driven by both climate change and anthropogenic activities, resulting in the widespread consideration of grassland degradation. From 2000 to 2020, the northern Xizang Plateau experienced a pronounced escalation in drought conditions, particularly following a management policy aimed at combating grassland degradation, via restricting livestock numbers and implementing fencing enclosures, initiated in 2009. Specially, following the enforcement of this policy (2010–2020), there was a substantial 13.5 % reduction in soil moisture levels compared to the pre-implementation period (2000–2009), accompanied by a 42.8 % increase in drought severity (measured by the Palmer Drought Severity Index, PDSI) and an 11.6 % rise in drought duration. Under these challenging drought conditions, we identified negative trends of potential grassland Net Primary Productivity (NPP) which in absence of anthropogenic influence, as simulated by terrestrial ecosystem model (TEM). Contrary to this simulation, satellite time series observations revealed a widespread increase in actual grassland productivity. Remarkably, despite the challenging drought conditions between 2010 and 2020, we observed a substantial increase of 4.8 % in actual NPP (measured by light use efficiency model), 2.7 % in Normalized Difference Vegetation Index (NDVI), 10 % in solar-induced chlorophyll fluorescence (SIF), and 7.95 % in Gross Primary Productivity (GPP), respectively. Much of this enhancement in grassland productivity occurred in areas where livestock populations had markedly declined. Notably, the percentage of NPP increase mainly caused by degradation-combating actions doubled from 24.8 % during 2000–2009 to 49.8 % during 2010–2020 across the northern Xizang Plateau. In conclusion, our findings highlight the potential of large-scale degradation-combating actions to mitigate the negative impacts of climate change and contribute to a greener Earth.

Exploring Relationships Between Drought Indices and Ecological Drought Impacts Using Machine Learning and Explainable AI

Rangeland ecosystems in the United States hold great ecological, economic, and cultural value. However, the increasing frequency and severity of droughts pose potential threats to these ecosystems. This study used remotely sensed data, machine learning, and explainable artificial intelligence (XAI) to explore relationships between drought indices and vegetation health in the Cheyenne River Basin, USA. The study employed XGBoost Regressor and Extra Trees Regressor models in conjunction with SHapley Additive exPlanations (SHAP) to identify the most influential drought indices and environmental variables for predicting Normalized Difference Vegetation Index (NDVI), thereby uncovering indicators of vegetation stress in the basin. The XGBoost regressor was moderately successful at predicting NDVI, making the model suitable for subsequent XAI analysis using SHAP. SHAP results revealed that the Palmer Drought Severity Index (PDSI), the 90-day Standardized Precipitation Index (SPI), and snow water equivalent (SWE) were the most influential predictors of NDVI, indicating their strong association with changes in vegetation health in the Cheyenne River Basin. This study demonstrates the feasibility and value of applying XAI methods to investigate both the strength and directionality of ecological drought indicators—an approach that has been underutilized in drought research. These insights can inform future drought research, improve monitoring efforts, and help anticipate ecological drought impacts in the region.

The optimal applications of scPDSI and SPEI in characterizing meteorological drought, agricultural drought and terrestrial water availability on a global scale

The Palmer Drought Severity Index (scPDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are two of the most commonly used drought indices. However, scPDSI and SPEI at a specific scale are often used interchangeably to characterize meteorological drought, agricultural drought, or terrestrial water availability, leading to potential inaccuracies in research outcomes. This study thus presents a global-scale assessment of the applications of scPDSI and SPEI at various timescales (SPEIs) in these contexts. Our findings indicate that scPDSI is more suitable for monitoring agricultural drought than meteorological drought, and highlight the effectiveness of SPEI at one month scale (SPEI01) for meteorological drought. Additionally, SPEI at nine months scale (SPEI09) is more appropriate for agricultural drought. Regarding their relationship with vegetation water stress, scPDSI and SPEI09 are more closely associated with root-zone soil moisture, while SPEI01 is most closely linked to vapor pressure deficit. Furthermore, we evaluate the capability of scPDSI and SPEI in representing terrestrial water availability by analyzing the responses of diverse vegetation indicators to them, including the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Solar-Induced Chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP). All four vegetation indicators show the highest sensitivity of negative response to SPEI01 in cold climate regions, suggesting SPEI01 is most applicable in these regions. In drylands, vegetation indicators exhibit higher sensitivity of positive responses to SPEI at six months scale (SPEI06) and SPEI09, indicating SPEI06 and SPEI09 effectively characterize water availability in such areas. These findings enhance the understanding of scPDSI and SPEI, providing clearer guidelines for their global-scale applications in meteorological drought, agricultural drought, and terrestrial water availability.

Drought prediction using artificial intelligence models based on climate data and soil moisture

Drought is deemed a major natural disaster that can lead to severe economic and social implications. Drought indices are utilized worldwide for drought management and monitoring. However, as a result of the inherent complexity of drought phenomena and hydroclimatic condition differences, no universal drought index is available for effectively monitoring drought across the world. Therefore, this study aimed to develop a new meteorological drought index to describe and forecast drought based on various artificial intelligence (AI) models: decision tree (DT), generalized linear model (GLM), support vector machine, artificial neural network, deep learning, and random forest. A comparative assessment was conducted between the developed AI-based indices and nine conventional drought indices based on their correlations with multiple drought indicators. Historical records of five drought indicators, namely runoff, along with deep, lower, root, and upper soil moisture, were utilized to evaluate the models’ performance. Different combinations of climatic datasets from Alice Springs, Australia, were utilized to develop and train the AI models. The results demonstrated that the rainfall anomaly drought index was the best conventional drought index, scoring the highest correlation (0.718) with the upper soil moisture. The highest correlation between the new and conventional indices was found between the DT-based index and the rainfall anomaly index at a value of 0.97, whereas the lowest correlation was 0.57 between the GLM and the Palmer drought severity index. The GLM-based index achieved the best performance according to its high correlations with conventional drought indicators, e.g., a correlation coefficient of 0.78 with the upper soil moisture. Overall, the developed AI-based drought indices outperformed the conventional indices, hence contributing effectively to more accurate drought forecasting and monitoring. The findings emphasized that AI can be a promising and reliable prediction approach for achieving better drought assessment and mitigation.

Increasing drought frequency in the central Zagros Mountains of western Iran over the past two centuries

The Zagros region in western Iran has experienced prolonged drought, significantly impacting water resource and forest ecosystems over the past few centuries. Understanding historical prolonged drought is crucial for making precise forecasts of shifts in regional drought in the Zagros region. Due to the lack of comprehensive historical records, the use of proxy records is considered a valuable tool for reconstructing past drought variations. We aimed to construct a tree-ring chronology of Juniper (Juniperus polycarpus) in the central Zagros region to comprehend its growth response to climate variables. We cored 25 J. polycarpus trees from the Keygooran forest reserve in western Iran and developed the tree ring chronology (1802–2022) using dplR. The relationships between tree growth and climate variables of monthly mean temperature, precipitation, PDSI (Palmer Drought Severity Index), and SPEI (Standardized Precipitation Evapotranspiration Index) were examined. The analysis revealed a strong positive correlation between tree growth and SPEI March-September of 1990–2018 on a 48-month timescale with a 2-year lag (r = 0.61, p < 0.05) which was included in the transfer model. Consequently, the SPEI-48 March-September was reconstructed for the period 1802–2022. On this reconstruction, several extremely dry years in 1832, 1867, and 1876 and extremely wet years in 1807, 1814, 1838, 1850, 1907, and 1908 years were identified, some of them aligning with historical records in Iran. Furthermore, there was a relationship between SPEI-48 March-September and teleconnection events suggesting that certain drought occurrences in the area were associated with the El Niño-Southern Oscillation (ENSO). Consequently, this reconstruction can serve as a reliable proxy for large-scale drought variability in the Zagros region of western Iran. Moreover, our research underscores the utility of SPEI in reconstructing the history of semi-arid climate conditions.

A multi-sensor drought index for improved agricultural drought monitoring and risk assessment in the heterogeneous landscapes of the China–Pakistan Economic Corridor (CPEC)

Droughts cause significant economic damage worldwide. Evaluating their impacts on crop yield and water resources can help mitigate these losses. Using single variables such as precipitation, temperature, the soil moisture condition index (SMCI) and the vegetation condition index (VCI) to estimate drought impacts does not provide sufficient information on these complex conditions. Therefore, this study uses station-based and remote-sensing-based data to develop new composite drought indexes (CDIs), including the principal component analysis drought index (PSDI) and the gradient boosting method drought index (GBMDI). The first dataset includes historical observations of the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), and the self-calibrated Palmer drought severity index (SC-PDSI) at the 1-, 3-, 6-, and 12-month timescales. The second dataset consists of remote-sensing-based data including the VCI, SMCI, temperature condition index (TCI), and precipitation condition index (PCI). We validated the results of PSDI and GBMDI by comparing them with historical drought events, in-situ drought indices, and annual winter wheat crop yield data from 2003 to 2022 using a regression model. Our temporal analysis revealed extreme to severe drought events during1990s and 2010s. GBMDI typically aligned with actual drought events and exhibited stronger correlations with in-situ drought indices than PSDI. We observed that drought intensity in winter were more severe than in summer. GBMDI was the most effective method, followed by PSDI, for assessing drought impacts on winter wheat yields. Thus, the proposed integrated monitoring framework and indexes offered a valuable and innovative approach to addressing the complexities of agricultural drought, particularly in evaluating its effects.