Self-calibrating Palmer Drought Severity Index Research Articles

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.

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.

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.

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.

A multi-centennial drought reconstruction from tree-rings reveals a growing threat to Christmas Island’s water resources

Small islands that depend on limited freshwater resources are at significant risk from seasonal drought, which poses a major threat to both their ecosystems and communities. Christmas Island, located in the eastern Indian Ocean, presents an example for which severe drought conditions during the wet season not only affects its freshwater resources but also biodiversity on the island, including the migration pattern of the iconic red crab species. However, short-term instrumental climate records on this island make it hard to quantify drought variability and assess its associated risks. Tree growth is affected by drought via reduced soil moisture, and hydroclimate reconstruction from tree-ring chronologies can therefore provide longer-term information on historical variability of dry and wet periods. Here, we reconstructed the wet season (December-May) self-calibrating Palmer Drought Severity Index (scPDSI) for Christmas Island using 64 remote tree-ring chronologies from Asia, Australia, and New Zealand. scPDSI was reconstructed using the Point-to-Point Regression (PPR) method and compared with regional marine coral proxies for independent verification. The remote tree-ring chronologies explained more than 66 percent of scPDSI variance (R-squared) over the calibration period. The trees identified as significant predictors in the regression model were primarily located in areas affected by the Indo-Pacific climate drivers including the Indian Ocean Dipole (IOD). The reconstructions span 1540 CE to 2000. During the first four centuries of this period, the frequency of extreme (5th percentile) droughts and pluvial events rarely exceeded one event per 13 years. In contrast, the frequency of both extremes experienced an unprecedented increase during the 20th century, and with a notable shift towards dry conditions. These findings highlight a significant shift towards more frequent and severe dry conditions during the wet season on Christmas Island, posing a challenge to water resource management and potentially threatening the island's ecosystem and services to the community.

Exploring the impact of the recent global warming on extreme weather events in Central Asia using the counterfactual climate data ATTRICI v1.1

We study the impact of recent global warming on extreme climatic events in Central Asia (CA) for 1901-2019 by comparing the composite representation of the observational climate with a hypothetical counterfactual one that does not include the long-term global warming trend. The counterfactual climate data are produced based on a simple detrending approach, using the global mean temperature (GMT) as the independent variable and removing the long-term trends from the climate variables of the observational data. This trend elimination is independent of causality, and the day-to-day variability in the counterfactual climate remains preserved. The analysis done in the paper shows that the increase in frequency and magnitude of extreme temperature and precipitation events can be attributed to global warming. Specifically, the probability of experiencing a +7 K temperature anomaly event in CA increases by up to a factor of seven in some areas due to global warming. The analysis reveals a significant increase in heatwave occurrences in Central Asia, with the observational climate dataset GSWP3-W5E5 (later called also factual) showing more frequent and prolonged extreme heat events than hypothetical scenarios without global warming. This trend, evident in the disparity between factual and counterfactual data, underscores the critical impact of recent climatic changes on weather patterns, highlighting the urgent need for robust adaptation and mitigation strategies. Additionally, using the self-calibrated Palmer drought severity index (scPDSI), the sensitivity of dry and wet events to the coupled precipitation and temperature changes is analyzed. The areas under dry and wet conditions are enhanced under the observational climate compared to a counterfactual scenario, especially over the largest deserts in CA. The expansion of the dry regions aligns well with the pattern of desert development observed in CA in recent decades.

Effects of weather and climate on fluctuations of grain prices in southwestern Bohemia, 1725–1824 CE

Abstract. Grain prices in early modern Europe reflected the effects of weather and climate on crop yields and a complex array of societal and socio-economic factors. This study presents a newly developed series of grain prices for Sušice (southwestern Bohemia, Czech Republic) for the period 1725–1824 CE, based on various archival sources. It aims to analyse their relationships with weather and climate, represented by temperature, precipitation, and drought (self-calibrated Palmer Drought Severity Index, scPDSI) reconstructions, as well as particular weather extremes and anomalies reported in documentary evidence. Wheat, rye, barley, and oats series in Sušice showed high mutual correlations. The mean highest prices during the year typically occurred from May to July before the harvest, while prices usually declined afterwards. Wheat, rye, and barley prices were significantly negatively correlated with spring temperatures and positively correlated with scPDSI from winter to summer. This indicates that wetter winters, cooler and wetter springs, and wetter summers contributed to higher prices. The extremely high grain prices in the years 1746, 1771–1772, 1802–1806, and 1816–1817 were separately analysed with respect to weather and climate patterns and other socio-economic and political factors. The results obtained were discussed in relation to data uncertainty, factors influencing grain prices, and the broader European context.

Spatial Heterogeneity in the Response of Winter Wheat Yield to Meteorological Dryness/Wetness Variations in Henan Province, China

Knowledge of the responses of winter wheat yield to meteorological dryness/wetness variations is crucial for reducing yield losses in Henan province, China’s largest winter wheat production region, under the background of climate change. Data on climate, yield and atmospheric circulation indices were collected from 1987 to 2017, and monthly self-calibrating Palmer drought severity index (sc-PDSI) values were calculated during the winter wheat growing season. The main results were as follows: (1) Henan could be partitioned into four sub-regions, namely, western, central-western, central-northern and eastern regions, based on the evolution characteristics of the time series of winter wheat yield in 17 cities during the period of 1988–2017. Among them, winter wheat yield was high and stable in the central-northern and eastern regions, with a remarkable increasing trend (p < 0.05). (2) The sc-PDSI in February had significantly positive impacts on climate-driven winter wheat yield in the western and central-western regions (p < 0.05), while the sc-PDSI in December and the sc-PDSI in May had significantly negative impacts on climate-driven winter wheat yield in the central-northern and eastern regions, respectively (p < 0.05). (3) There were time-lag relationships between the sc-PDSI for a specific month and the atmospheric circulation indices in the four sub-regions. Furthermore, we constructed multifactorial models based on selected atmospheric circulation indices, and they had the ability to simulate the sc-PDSI for a specific month in the four sub-regions. These findings will provide scientific references for meteorological dryness/wetness monitoring and risk assessments of winter wheat production.

Dryland hydroclimatic response to large tropical volcanic eruptions during the last millennium

Drylands are highly vulnerable to climate change due to their fragile ecosystems and limited ability to adapt. In contrast to the global drying after tropical volcanic eruptions shown previously, we demonstrate that large tropical volcanic eruptions can induce significant two-year hydroclimatic wetting over drylands by employing the last millennium simulations. During this wetting period, which extends from the first to the third boreal winter after the eruption, several hydroclimatic indicators, such as self-calibrating Palmer Drought Severity Index based on the Penman-Monteith equation for potential evapotranspiration (scPDSIpm), standard precipitation evapotranspiration index (SPEI), aridity index (AI), top-10cm soil moisture (SM10cm), and leaf area index (LAI), show significant positive anomalies over most drylands. The primary contribution to the wetting response is the potential evapotranspiration (PET) reduction resulting from dryland surface cooling and reduced solar radiation, as well as a weak contribution from increased precipitation. The latter is due to the wind convergence into drylands caused by slower tropical cooling compared to drylands. The wetting response of drylands to volcanic eruptions also demonstrates some benefits over the global hydrological slowdown resulting from stratospheric aerosol injection, which replicates the cooling effects of volcanic eruptions to address global warming.

A comparative study of various drought indices at different timescales and over different record lengths in the arid area of northwest China.

Although many drought indices have been developed to monitor drought conditions, their applicability differs across climatic regions, which results in different characteristics of drought index assessments at different timescales and over different record lengths. Therefore, the applicability of precipitation-based and precipitation-evapotranspiration/temperature-based drought indices was examined in this study using the Generalized Extreme Value Index (GEVI), Homogeneity Index (HI) of precipitation and temperature, K index (K), precipitation anomaly percentage (Pa), Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), and China-Z Index (CZI) over different record lengths and at a timescale range of 1-24months. In addition, the results that were obtained using these indices were compared with the Self-Calibrating Palmer Drought Severity Index (scPDSI). The stability, accuracy, and consistency of the different drought indices were evaluated using precipitation and evapotranspiration/temperature data (1-24months) collected from different climatic regions in the arid area of northwest China in the 1961-2017 period. The results indicated that the Pa, CZI, K, and SPEI were more stable; the SPI, SPEI, and HI were more accurate; and the GEVI, SPI, HI, and SPEI were more consistent with the scPDSI. In addition, the results indicated that it is more appropriate to select a long record length (> 35 years) to monitor drought when sufficient data are available. However, defining constant drought classes may not be appropriate for all drought timescales. In fact, precipitation and evapotranspiration data from different timescales had different optimal distribution functions. The drought indices also demonstrated that they were applicable to a temperate arid area in the study region. In addition, the HI and SPEI better captured the precipitation and evapotranspiration/temperature characteristics, while the CZI, K, and Pa overestimated or underestimated the frequency of different drought classes at different timescales to some extent in the study region. The results of this study suggest that greater priority should be given to the precipitation-evapotranspiration-based indices. In addition, it is suggested to change the drought index class thresholds in future related studies on drought event recognition at different timescales to ensure more accurate drought monitoring.

Multiscale Spatiotemporal Dynamics of Drought within the Yellow River Basin (YRB): An Examination of Regional Variability and Trends

Drought, as a recurring extreme climatic event, inflicts diverse impacts on ecological systems, agricultural productivity, water resources, and socio-economic progress globally. Discerning the drought patterns within the evolving environmental landscape of the Yellow River Basin (YRB) is imperative for enhancing regional drought management and fostering ecological conservation alongside high-quality development. This study utilizes meteorological drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) and the self-calibrating Palmer Drought Severity Index (scPDSI), for a detailed spatiotemporal analysis of drought conditions. It examines the effectiveness of these indices in the basin’s drought monitoring, offering a comprehensive insight into the area’s drought spatiotemporal dynamics. The findings demonstrate the following: (1) SPEI values exhibit distinct fluctuation patterns at varying temporal scales, with more pronounced fluctuations at shorter scales. Drought years identified via the 12-month SPEI time scale include 1965, 1966, 1969, 1972, 1986, 1997, 1999, 2001, and 2006. (2) A modified Mann–Kendall (MMK) trend test analysis of the scPDSI time series reveals a worrying trend of intensifying drought conditions within the basin. (3) Correlation analysis between SPEI and scPDSI across different time scales yields correlation coefficients of 0.35, 0.54, 0.69, 0.76, and 0.62, highlighting the most substantial correlation at an annual scale. Spatial correlation analysis conducted between SPEI and scPDSI across various scales reveals that, within diverse temporal ranges, the correlation peaks at a 12-month time scale, with subsequent prominence observed at 6 and 24 months. This observed pattern accentuates the applicability of scPDSI in the monitoring of medium- to long-term drought phenomena.

Radial growth responses of Larix gmelinii to drought events in dry and wet areas of northern temperate forests

Drought stress caused by global climate warming affects tree growth in both dry and wet areas. However, the differences in tree growth responses to drought in dry and wet areas are poorly understood. Here, we collected 93 tree cores to analyze the differences in the radial growth responses of larch (Larix gmelinii) under climate change and tree growth resilience under drought events in the Altai Mountains (dry area) and Changbai Mountains (wet area). The results showed that larch growth in the Altai Mountains was significantly positively correlated with the self-calibrating Palmer Drought Severity Index (sc-PDSI) in all months and precipitation in the previous growth season and May, whereas it was significantly (p < 0.05) negatively correlated with temperature in May and the previous June to August. In the Changbai Mountains, larch growth was significantly positively correlated with May maximum and mean temperature, and significantly positively and negatively correlated with precipitation in April and May，respectively (p < 0.05). The mean resistance (recovery) of larch growth to drought in wet areas were significantly stronger (weaker) than that in dry areas (p < 0.05). Moreover, strengthening the drought frequency led to a significant (p < 0.05) decline in larch resistance in dry areas. Therefore, warming-induced increases in drought stress will aggravate negative impacts on the radial growth of larch forests in temperate dry areas but not in wet areas in the future.

Aging Mongolian pine plantations face high risks of drought-induced growth decline: evidence from both individual tree and forest stand measurements

Discerning vulnerability differences among different aged trees to drought-driven growth decline or to mortality is critical to implement age-specific countermeasures for forest management in water-limited areas. An important species for afforestation in dry environments of northern China, Mongolian pine (Pinus sylvestris var. mongolica Litv.) has recently exhibited growth decline and dieback on many sites, particularly pronounced in old-growth plantations. However, changes in response to drought stress by this species with age as well as the underlying mechanisms are poorly understood. In this study, tree-ring data and remotely sensed vegetation data were combined to investigate variations in growth at individual tree and stand scales for young (9 − 13 years) and aging (35 − 52 years) plantations of Mongolian pine in a water-limited area of northern China. A recent decline in tree-ring width in the older plantation also had lower values in satellited-derived normalized difference vegetation indices and normalized difference water indices relative to the younger plantations. In addition, all measured growth-related metrics were strongly correlated with the self-calibrating Palmer drought severity index during the growing season in the older plantation. Sensitivity of growth to drought of the older plantation might be attributed to more severe hydraulic limitations, as reflected by their lower sapwood- and leaf-specific hydraulic conductivities. Our study presents a comprehensive view on changes of growth with age by integrating multiple methods and provides an explanation from the perspective of plant hydraulics for growth decline with age. The results indicate that old-growth Mongolian pine plantations in water-limited environments may face increased growth declines under the context of climate warming and drying.

Global Drought-Wetness Conditions Monitoring Based on Multi-Source Remote Sensing Data

Drought is a common hydrometeorological phenomenon and a pervasive global hazard. To monitor global drought-wetness conditions comprehensively and promptly, this research proposed a spatial distance drought index (SDDI) which was constructed by four drought variables based on multisource remote sensing (RS) data, including the normalized difference vegetation index (NDVI), land surface temperature (LST), soil moisture (SM), and precipitation (P), using the spatial distance model (SDM). The results showed that the consistent area of SDDI with the 1-month and 3-month standardized precipitation-evapotranspiration index (SPEI1 and SPEI3), and the self-calibrating Palmer drought severity index (scPSDI) accounted for 85.5%, 87.3%, and 85.1% of the global land surface area, respectively, indicating that the index can be used to monitor global drought-wetness conditions. Over the past two decades (2001–2020), a discernible spatial distribution pattern has emerged in global drought-wetness conditions. This pattern was characterized by the extreme drought mainly distributed deep within the continent, surrounded by expanding moderate drought, mild drought, and no drought areas. On the annual scale, the global drought-wetness conditions exhibited an upward trend, while on the seasonal and monthly scales, it fluctuated steadily within a certain cycle. Through this research, we found that the sensitive areas of drought-wetness conditions were mainly found on the east coast of Australia, the Indus Basin of the Indian Peninsula, the Victoria and Katanga Plateau areas of Africa, the Mississippi River Basin of North America, the eastern part of the Brazilian Plateau and the Pampas Plateau of South America.

Enhancing spatial resolution of drought monitoring through a novel random forest-based GRACE drought index: a case study in Central Yunnan

The original coarse spatial resolution of Gravity Recovery and Climate Experiment (GRACE) data limits its application in small and medium-scale areas. The study proposes a novel random forest-based GRACE drought index (NRFGDI) to enhance the spatial resolution of drought monitoring, which is applied to central Yunnan. We first employed the random forest (RF) algorithm to improve the spatial resolution of GRACE-derived terrestrial water storage anomaly (TWSA) to 0.1°, then established the NRFGDI based on the downscaled TWSA. Subsequently, the NRFGDI was verified using traditional drought indexes, including the standardized precipitation evapotranspiration index (SPEI) and the self-calibrating Palmer drought severity index (sc-PDSI), along with the 2009/2010 Bulletin of Flood and Drought Disasters in China. The NRFGDI not only captures drought characteristics, but also offers finer spatial resolution. The study provides an effective means for medium to small-scale drought monitoring, thereby making valuable contributions to decision-making processes concerning droughts in central Yunnan.

Resilience and response: Unveiling the impacts of extreme droughts on forests through integrated dendrochronological and remote sensing analyses

Extreme droughts are anticipated to have detrimental impacts on forest ecosystems, especially in water-limited regions, due to the influence of climate change. However, considerable uncertainty remains regarding the patterns in species-specific responses to extreme droughts. Here, we conducted a study integrating dendrochronology and remote sensing methods to investigate the mosaic-distributed maple-oak (native) natural forests and poplar plantations (introduced) in the Horqin Sandy Land, Northeast China. We assessed the impacts of extreme droughts on tree performances by measuring interannual variations in radial growth and vegetation index. The results showed that precipitation and self-calibrated palmer drought severity index (scPDSI) are the major factors influencing tree-ring width index (RWI) and normalized difference vegetation index (NDVI). The severe droughts between 2000 and 2004 resulted in reduced RWI in the three studied tree species as well as led to NDVI reductions in both the maple-oak natural forests and the poplar plantations. The RWI reached the nadir during the 2000–2004 severe droughts and remained at low levels two years after the severe drought, creating a legacy effect. In contrast to the lack of significant correlation between RWI and scPDSI, NDVI exhibited a significant positive correlation with scPDSI indicating the greater sensitivity of canopy performance to droughts than radial growth. Furthermore, interspecific differences in RWI and NDVI responses were observed, with the fast-growing poplar species experiencing a more significant RWI decrease and more negative NDVI anomaly during severe droughts than native species, highlighting the species-specific trade-offs between drought resilience and growth rate. This study emphasizes the importance of combining tree-level radial growth with landscape-scale canopy remote sensing to understand forest resilience and response. Our study improves our understanding of forest responses to extreme drought and highlights species differences in climate responses, offering crucial insights for optimizing species selection in sustainable afforestation and forest management in water-limited regions under the influence of climate change.

Drought limits vegetation carbon sequestration by affecting photosynthetic capacity of semi-arid ecosystems on the Loess Plateau

Drought is the driver for ecosystem production in semi-arid areas. However, the response mechanism of ecosystem productivity to drought remains largely unknown. In particular, it is still unclear whether drought limits the production via photosynthetic capacity or phenological process. Herein, we assess the effects of maximum seasonal photosynthesis, growing season length, and climate on the annual gross primary productivity (GPP) in vegetation areas of the Loess Plateau using multi-source remote sensing and climate data from 2001 to 2021. We found that maximum seasonal photosynthesis rather than growing season length dominates annual GPP, with above 90 % of the study area showing significant and positive correlation. GPP and maximum seasonal photosynthesis were positively correlated with self-calibrating Palmer Drought Severity Index (scPDSI), standardized precipitation and evapotranspiration index (SPEI) in >95 % of the study area. Structural equation model demonstrated that both drought indices contributed to the annual GPP by promoting the maximum seasonal photosynthesis. Total annual precipitation had a positive and significant effect on two drought indices, whereas the effects of temperature and radiation were not significant. Evidence from wood formation data also confirmed that low precipitation inhibited long-term carbon sequestration by decreasing the maximum growth rate in forests. Our findings suggest that drought limits ecosystem carbon sequestration by inhibiting vegetation photosynthetic capacity rather than phenology, providing a support for assessing the future dynamics of the terrestrial carbon cycle and guiding landscape management in semi-arid ecosystems.

Drought-heatwave compound events are stronger in drylands

Climate change is exacerbating the occurrence of compound droughts and heatwaves (CDHWs), which pose a serious threat to human health and socio-economic development. Using daily maximum temperature (Tmax) and monthly self-calibrating Palmer drought severity index (sc-PDSI) dataset, the evolution patterns of CDHWs and compound wet-heatwave events, the dominant drivers and the relative contributions of droughts and heatwaves in the drylands and humid areas from 1961 to 2020 were compared and analyzed. The results show that the CDHWs are stronger in drylands than in humid areas, the growth rate of CDHWs in drylands was almost twice that of the humid areas, the CDHWs are greater than the multi-year average intensity of compound wet-heatwave events by up to 2.4 times. Moreover, CDHWs has increased significantly from the past period (1961–1990) to the recent warm period (1991–2020), and the heatwave threshold has increased by about 5 °C. In most drylands, the contribution of heatwaves to CDHWs dominates, whereas in humid areas, the droughts contribution to CDHWs does. The compounding effects of droughts and heatwaves may exacerbate the severity of CDHWs regionally and are most pronounced in drylands, taking into account optimal lags. The study findings could provide scientific and technological support to actively address global climate change risks.

Asthma incidence can be influenced by climate change in Italy: findings from the GEIRD study—a climatological and epidemiological assessment

An association between climatic conditions and asthma incidence has been widely assumed. However, it is unclear whether climatic variations have a fingerprint on asthma dynamics over long time intervals. The aim of this study is to detect a possible correlation of the Summer North Atlantic Oscillation (S-NAO) index and the self-calibrated palmer drought severity index (scPDSI) with asthma incidence over the period from 1957 to 2006 in Italy. To this aim, an analysis of non-stationary and non-linear signals was performed on the time series of the Italian databases on respiratory health (ISAYA and GEIRD) including 36,255 individuals overall, S-NAO, and scPDSI indices to search for characteristic periodicities. The ISAYA (Italian Study on Asthma in Young Adults) and GEIRD (Gene Environment Interactions in Respiratory Diseases) studies collected information on respiratory health in general population samples, born between 1925 and 1989 and aged 20–84 years at the time of the interview, from 13 Italian centres. We found that annual asthma total incidence shared the same periodicity throughout the 1957–2006 time interval. Asthma incidence turned out to be correlated with the dynamics of the scPDSI, modulated by the S-NAO, sharing the same averaged 6 year-periodicity. Since climate patterns appear to influence asthma incidence, future studies aimed at elucidating the complex relationships between climate and asthma incidence are warranted.