Drought Patterns Research Articles

Assessment of the spatiotemporal evolution and driving forces of meteorological drought in the North China Plain

AbstractWith the changing trend of global temperature increase, the occurrence of drought events can no longer be ignored. An investigation of the spatiotemporal evolution characteristics of meteorological droughts and their responses to climatic variables can elucidate the mechanisms of the water cycle evolution in changing environments. In the present study, the standardized precipitation evapotranspiration index was used to characterize meteorological drought in the North China Plain (NCP) from 1970 to 2020. The spatiotemporal patterns of meteorological drought were analysed, and the contributions of climatic factors on annual and seasonal time scale drought evolution were determined. The results revealed that meteorological drought showed a decreasing trend (−0.07/10a) in the NCP from 1970 to 2020 and the most severe meteorological drought occurred in winter, with 56.56% of the region experiencing an increased drought trend in this season. Significant trends were identified for mean temperature (Tmean), temperature difference, and wind speed in the drought trend evolution. Precipitation (P) and Tmean were the primary controlling factors of the drought evolution. The strongest effect on drought change were exerted by P in spring (contribution [Con]: 1.57%), summer (Con: 1.79%) and autumn (Con: 2.43%), and the negative contribution of Tmean in winter (Con: −3.64%) was greater than the positive contribution of P (Con: 2.69%). The contribution of climatic variables to drought were increased by regional altitude difference. The results reveal a novel explanatory mechanism of climate change's effects on drought.

Spatio‐temporal risk analysis of agriculture and meteorological droughts in rainfed Potohar, Pakistan, using remote sensing and geospatial techniques

AbstractDroughts of various types are considered as a major threat to rainfed agriculture, because agricultural production is dependent on the spatio‐temporal distribution of rainfall. The rainfed regions of Pakistan, such as Potohar, have experienced several instances of drought since 2000. The most drought‐affected staple crop of the region is wheat, which ultimately affects regional food security. In this study, we examine the agricultural and the meteorological droughts in the Potohar region during 2000–2020, to create a drought risk map. This region comprises four districts, namely Attock, Chakwal, Jhelum and Rawalpindi. First, the agricultural and meteorological drought severity maps were created using rainfall data (meteorological data) and vegetation indices for three different periods (i.e., drought year, moderate drought year and normal year). The agricultural drought patterns and intensity were identified and evaluated using the MODIS products MOD09A1 and MOD11A2, while the meteorological droughts were identified using CHRIPS rainfall data. Afterwards, a combined drought risk map was generated by integrating the agricultural and meteorological adrought severity maps using a weighted overlay analysis. This drought risk map showed that Attock and Rawalpindi were expeiencing slight to no drought conditions, whereas the southwestern and central parts of Chakwal showed moderate drought conditions. Similarly, the western parts of Jhelum faced moderate drought conditions. Thus, the combined drought risk map may be a useful guide for decision makers in the local and provincial government. Using this map, they can identify adaptation practices in the drought‐prone areas of this region for enhancing agricultural productivity.

Drought Characterization in Croatia Using E-OBS Gridded Data

Droughts are among the major natural hazards that are spreading to many parts of the world, with huge multi-dimensional impacts. An extensive analysis of drought phenomenon is presented for continental Croatia based on a meteorological E-OBS gridded dataset (0.25° × 0.25°), within the period of 1950–2022. The drought events were characterized by the Standardized Precipitation Evapotranspiration Index (SPEI), applied to different time-scales (6 and 12 months), in order to describe the subannual and annual variability of drought. The spatiotemporal patterns of drought are obtained through principal component analysis (PCA) and K-means clustering (KMC) applied to the SPEI field. An areal drought evolution analysis and the changes in the frequency of occurrence of the periods under drought conditions were achieved using a kernel occurrence rate estimator (KORE). The modified Mann–Kendall (MMK) test, coupled with the Sen’s slope estimator test, are applied to the SPEI series in order to quantify the drought trends throughout the country. According to the history drought events and considering the different morphoclimatic characteristics of the study area, the results showed that Croatia could be divided into three different and spatially well-defined regions with specific temporal and spatial characteristics of droughts (central northern, eastern and southern regions). A manifest increase is shown in the percentage of area affected by drought, as well as in the yearly drought occurrences rates, in both central northern and eastern regions, and an evident decrease is shown in the southern region for both 6- and 12-month SPEI time-scales. In the observation of the drought’s temporal characteristics, it was found that downward trends expressing increasing drought severities were strongly significant in northern and eastern regions, while a few significant upward trends were seen in the southern region. From this study, it is possible to obtain a broader view of the historical behaviour of droughts in Croatia, with the results providing useful support for drought risk assessment and decision-making processes.

Assessment of drought variability in the Marrakech-Safi region (Morocco) at different time scales using GIS and remote sensing

Abstract In the semi-arid Marrakech-Safi (MS) region of southwest Morocco, climate change has amplified drought occurrences, posing significant threats to water resources and agriculture. A comprehensive understanding of drought patterns is imperative to manage these risks and enhance resilience effectively. Precipitation from 18 pluviometer stations was employed to analyze meteorological drought using the standardized precipitation index (SPI) from 1980 to 2018. Additionally, agricultural drought is quantified using three remote sensing-driven indices: vegetation condition index (VCI), temperature conditions index (TCI), and vegetation health index (VHI) from 2000 to 2018. These indices are correlated with SPI to evaluate their performance and gauge vegetation sensitivity to meteorological drought. The Mann–Kendall test assesses trends in drought events and their severity. The results demonstrated that SPI, VCI, TCI, and VHI experienced alternating dry and wet periods with an overall upward trend, especially in mountainous areas, plateau zones, and Haouz plain of the MS region. The correlation analysis establishes a significant relationship between remote sensing-based indices and SPI-6, with mean correlation coefficients exceeding 0.6. The findings underscore the importance of considering multiple time scales to comprehensively assess climate's impact on vegetation. Seasonal drought trends analysis indicates no significant negative trends in winter and spring but positive trends in autumn.

Meteorological and Hydrological Drought Risks under Future Climate and Land-Use-Change Scenarios in the Yellow River Basin

The primary innovation of this study lies in the development of an integrated modeling framework that combines downscaled climate projections, land-use-change simulations, and copula-based risk analysis. This framework allows for the assessment of localized sub-seasonal and seasonal drought hazards under future scenarios. The BCC-CSM1-1 climate model projections from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset are utilized to represent the future climate for 2025–2060 under RCP 4.5 and 8.5 scenarios. The CA-Markov model is employed to predict future land-use-change distributions. The climate–land use–drought modeling nexus enables the generation of refined spatio-temporal projections of meteorological and hydrological drought risks in the Yellow River Basin (YRB) in the future period of 2025–2060. The results highlight the increased vulnerability of the upper YRB to sub-seasonal meteorological droughts, as well as the heightened sub-seasonal hydrological drought risks in the Loess Plateau. Furthermore, downstream areas experience escalated seasonal hydrological drought exposure due to urbanization. By providing actionable insights into localized future drought patterns, this integrated assessment approach advances preparedness and climate adaptation strategies. The findings of the study enhance our understanding of potential changes in this integral system under the combined pressures of global climate change and land use shifts.

Assessing the Spatiotemporal Patterns and Impacts of Droughts in the Orinoco River Basin Using Earth Observations Data and Surface Observations

Droughts impact the water cycle, ecological balance, and socio-economic development in various regions around the world. The Orinoco River Basin is a region highly susceptible to droughts. The basin supports diverse ecosystems and supplies valuable resources to local communities. We assess the spatiotemporal patterns and impacts of droughts in the basin using remote sensing data and surface observations. We use monthly precipitation (P), air temperature near the surface (T2M), enhanced vegetation index (EVI) derived from Earth observations, and average daily flow (Q) data to quantify drought characteristics and impacts. We also investigated the association between drought and global warming by correlating the drought intensity and the percentage of dry area with sea surface temperature (SST) anomalies in the Pacific (Niño 3.4 index), Atlantic (North Atlantic Index [NATL]), and South Atlantic Index [SATL]) oceans. We evaluate the modulating effect of droughts on the hydrological regime of the most relevant tributaries by calculating the trend and significance of the regional standardized precipitation index (SPI) and percentage area affected by dry conditions. El Niño events worsen the region’s drought conditions (SPI vs. Niño 3.4 index, r = −0.221), while Atlantic SST variability has less influence on the basin’s precipitation regime (SPI vs. NATL and SATL, r = 0.117 and −0.045, respectively). We also found that long-term surface warming trends aggravate drought conditions (SPI vs. T2M anomalies, r = −0.473), but vegetation greenness increases despite high surface temperatures (SPI vs. EVI anomalies, r = 0.284). We emphasize the irregular spatial-temporal patterns of droughts in the region and their profound effects on the ecological flow of rivers during prolonged hydrological droughts. This approach provides crucial insights into potential implications for water availability, agricultural productivity, and overall ecosystem health. Our study underlines the urgent need for adaptive management strategies to mitigate the adverse effects of droughts on ecosystems and human populations. The insights derived from our study have practical implications for developing strategies to address the impacts of droughts and ensure the protection of this ecologically significant region.

Comparative assessment of drought indices for evaluating drought patterns in Peninsular Malaysia

Abstract Drought has been the main environmental issue in Peninsular Malaysia. Hence, this study undertook a thorough evaluation of drought assessment methodologies and focused on the temporal analysis of multiple drought indices, namely, the standardised precipitation index (SPI), deciles index (DI), percent of normal precipitation index (PNPI), rainfall anomaly index (RAI) and Z-score index (ZSI) – across timescales of one-, six- and 12-month durations. This assessment incorporates the average moving range (AMR), Mann–Kendall (MK) test and Sen's slope estimator in temporal analysis and the results showed that shorter timescales lead to higher fluctuation in AMR values, indicating short-term droughts are best assessed using drought indices of shorter timescale. It was found that most drought indices exhibited a similar trend and trend magnitude in all timescales. SPI is utilised as the standard model for the accuracy evaluation of drought indices using root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The results showed that ZSI has the highest accuracy of all indices. The novelty of this study lies in evaluating the accuracy and temporal characteristics of precipitation-based drought indices in tropical areas, particularly in Peninsular Malaysia.

Comparative analyses of SPI and SPEI as drought characterization tools in Massili watershed, central Burkina Faso

Assessment of hydrological drought indices in drought prone areas provides useful information for accurate and sustainable water resources management. However, meteorological drought estimation in ungauged basin remains less studied. The main objective of this study was to compare the ability of Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) to reproduce historiological drought within Massili basin. To this end, monthly historical rainfall and temperature time series spanning from 1960 to 2021 have been collected from the national agency of meteorology. R programming language has been applied to display Boxplots in order to identified the temporal dispersion of the rainfall indices. Then the Spearman correlation was applied to illustrate the relationships between the SPEI and the SPI. The results shows that both SPEI and SPI exhibit consistent behavior in observed drought within the Massili watershed. Dry conditions prevailed during the period 1980–1990, 1990–2000 and 2000-2010. Severe wet conditions prevailed in years 1984, 1985,1998 and 2002. In all time scale the SPEI and SPI are characterized by high correlation. The Spearman correlation coefficients value is above 0.7 with the highest correlation value detected between SPEI-24 and SPI-24 (0.97). This study may contribute to better understand the drought patterns within the basin for water resources planning perspectives.

Multivariate Drought Risk Analysis for the Weihe River: Comparison between Parametric and Nonparametric Copula Methods

This study analyzed the multivariate drought risks for the Wei River basin by characterizing the interdependence between the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). Both parametric and nonparametric copulas were adopted to quantify the dependence between the SPI and SPEI. The results indicated that the Gaussian copula demonstrated the best fit in most cases, while the nonparametric copula method showed superiority over the parametric models at only one out of eighteen meteorological stations. The joint return periods (TOR, TAND, and TKendall) were computed through copula modeling, providing valuable insights into the co-occurrence of extreme drought events. For the SPI and SPEI with a 50-year return period, the TOR values range from 25.5 to 37.9 years, the TAND values fluctuate between 73.4 and 1233 years, and the TKendall values range from 60.61 to 574.71 years, indicating a high correlation between the SPI and SPEI in the study area. The spatial analysis revealed varying patterns across the basin with some regions more prone to experiencing simultaneous drought conditions characterized by both the SPI and SPEI. Furthermore, our results indicated that the SPEI exhibited more severity in drought characterization than the SPI due to its consideration of temperature effects. The disparities in the spatial features of the SPI and SPEI underscore the importance of incorporating multiple meteorological factors for a comprehensive drought risk analysis. This research contributes to a better understanding of the drought patterns and their joint risks in the Wei River basin, offering valuable information for drought preparedness and water resource management.

Drought variations in the southeastern Tibetan Plateau region since 1704 and their link to the Atlantic Multidecadal Oscillation

Understanding the long-term history of drought in the Tibetan Plateau region is important to improve understanding of drought frequency in response to future global warming. Although the Tibetan Plateau has become generally wetter recently, the southeastern part of the Tibetan Plateau has become drier since the 2000s. To place this drying trend in a historical context, we conducted tree-ring studies for Abies spectabilis and Tsuga dumosa at three sites in the southeastern Tibetan Plateau, and developed tree-ring width (TRW), earlywood width (EWW), and latewood width (LWW) chronologies to permit local drought reconstruction. Based on correlation between these tree-ring parameters and instrumental meteorological data, the EWW chronologies were identified as suitable variables for reconstructing the average April–July self-calibrating Palmer Drought Index (scPDSI). The reconstruction accounted for 45.86% of the variance in the instrumental record and allowed us to extend the drought record back to 1704 CE. Based on this new dataset, the driest interval was from 1907 to 1919 CE during which time agricultural production fell by about 70% according to written historical records, leading to severe famine. Our findings suggest that this early twentieth century drought was in phase with the previously known drought over the northern Daxing'an Mountains. However, the southeastern Tibetan Plateau drought occurred one decade earlier than the known widespread drought event in North China during the 1920s–1930s. We show that this spatial drought pattern may have been modulated by the Atlantic Multidecadal Oscillation.

Copula-Based Joint Drought Index Using Precipitation, NDVI, and Runoff and Its Application in the Yangtze River Basin, China

Drought monitoring ensures the Yangtze River Basin’s social economy and agricultural production. Developing a comprehensive index with high monitoring precision is essential to enhance the accuracy of drought management strategies. This study proposes the standardized comprehensive drought index (SCDI) using a novel approach that utilizes the joint distribution of C-vine copula to effectively combine three critical drought factors: precipitation, NDVI, and runoff. The study analyzes the reliability and effectiveness of the SCDI in detecting drought events through quantitative indicators and assesses its applicability in the Yangtze River Basin. The findings are as follows: (1) The SCDI is a highly reliable and applicable drought index. Compared to traditional indices like the SPI, VCI, and SRI, it has a consistency rate of over 67% and can detect drought events in more sensitive months by over 51%. It has a low false negative rate of only 2% and a false positive rate of 0%, making it highly accurate. The SCDI is also applicable to all the third-level sub-basins of the Yangtze River Basin, making it a valuable tool for regional drought monitoring. (2) The time lag effect of the NDVI can affect the sensitivity of the SCDI. When the NDVI time series data are shifted forward by one month, the sensitivity of the SCDI in detecting agricultural drought improves from 47.8% to 53%. (3) The SDCI can assist in monitoring drought patterns in the Yangtze River Basin. From 2001 to 2018, the basin saw fluctuations in drought intensity, with the worst in December 2008. The western region had less frequent but more intense and prolonged droughts, while the eastern part had more frequent yet less severe droughts.

Investigating Whether the Ensemble Average of Multi-Global-Climate-Models Can Necessarily Better Project Seasonal Drought Conditions in China

Global drought patterns are substantially impacted by climate change, with far-reaching implications for socioeconomic and ecological systems. Existing global climate models (GCMs) are unable to accurately project precipitation and drought characteristics, particularly in countries or regions with complex topography and significant seasonal variability, such as China. Consequently, the purpose of this study is to assess the efficacy of GCMs, and their multi-model ensemble mean, as well as to investigate the seasonal drought characteristics in China using precipitation data from CMIP6 under various “possible future” scenarios. This study selected five GCMs with historical (1961–2014) and future (2015–2100) periods, namely CNRM-CM6-1, GFDL-ESM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and NorESM2-MM, as well as their ensemble mean ENS-CGMMN. Based on the China Daily Precipitation Analysis Product (CPAP) as the reference precipitation, the performance of these models is evaluated using the DISO index and the quantile mapping (QM) method for calibration, as well as seasonal-scale drought using the standardized precipitation index (SPI) and spatiotemporal variability analysis methods. In comparison to other climate models and the ensemble mean, the calibrated MPI-ESM1-2-HR model can more precisely describe the actual precipitation conditions at the seasonal scale. Under four scenarios, China’s climate will shift from arid to moist in the future period (2015–2100) (SSP126, SSP245, SSP370, and SSP585). Autumn and summer will see a considerable increase in China’s moisture levels. During the autumn, winter, and spring, the moisture will generally increase in the northern subregions of China, including the Qinghai-Tibet Plateau (QTP), Xinjiang (XJ), Northwest (NW), Northeast (NE), and North China (NC). Dryness will decrease in southern subregions, such as the Southwest (SW) and South China (SC). In contrast to these three seasons, summer in XJ exhibits a distinct trend of aridity, especially in the SSP245 scenario, whereas the NE, NC, and SC exhibit a distinct trend of moisture. To be more specific, the aridity changes in subregions during various seasons under different future climate scenarios vary significantly. This study’s findings can provide significant support for future research on climate change and drought, which can help improve the accuracy of future climate projections and serve as a reference for drought risk management and policy formulation.

Assessing the Spatial–Temporal Pattern of Spring Maize Drought in Northeast China Using an Optimised Remote Sensing Index

Northeast China plays a pivotal role in producing commodity grains. The precipitation and temperature distribution during the growth season is impacted by geographical and climate factors, rendering the region vulnerable to drought. However, relying on a single index does not reflect the severity and extent of drought in different regions. This research utilised the random forest (RF) model for screening remote sensing indices. Relative soil moisture (RSM) was employed to compare seven commonly used indices: the temperature vegetation dryness index (TVDI), vegetation supply water index (VSWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), multi-band drought index (MBDI), and normalised difference drought index (NDDI). The effectiveness of these indices for monitoring drought during different developmental stages of spring maize was evaluated. Trend rates were employed to investigate the temporal changes in drought patterns of spring maize from 2003 to 2020, and the Sen + Mann–Kendall test was used to analyse spatial variations. The results showed the following: (1) The seven remote sensing indices could accurately track drought during critical growth stages with the TVDI demonstrating higher applicability than the other six indices. (2) The application periods of two TVDIs with different parameters differed for the drought monitoring of spring maize in different developmental periods. The consistency and accuracy of the normalised difference vegetation index (NDVI)-based TVDI (TVDIN) were 5.77% and 34.62% higher than those of the enhanced vegetation index (EVI)-based TVDI (TVDIE), respectively, in the early stage. In contrast, the TVDIE exhibited 13.46% higher consistency than the TVDIN in the middle stage, and the accuracy was the same. During the later stage, the TVDIE showed significantly higher consistency and accuracy than the TVDIN with consistency increases of 9.61% and 38.64%, respectively. (3) The drought trend in northeast China increased from 2003 to 2020, exhibiting severe spring drought and a weakening of the drought in summer. The southern, southwestern, and northwestern parts of northeast China showed an upward drought trend; the drought-affected areas accounted for 37.91% of the study area. This paper identified the most suitable remote sensing indices for monitoring drought in different developmental stages of spring maize. The results provide a comprehensive understanding of the spatial–temporal patterns of drought during the past 18 years. These findings can be used to develop a dynamic agricultural drought monitoring model to ensure food security.

Impact of climate extreme events and their causality on maize yield in South Africa

Extreme climate events can have a significant negative impact on maize productivity, resulting in food scarcity and socioeconomic losses. Thus, quantifying their effect is needed for developing future adaptation and mitigation strategies, especially for countries relying on maize as a staple crop, such as South Africa. While several studies have analyzed the impact of climate extremes on maize yields in South Africa, little is known on the quantitative contribution of combined extreme events to maize yield variability and the causality link of extreme events. This study uses existing stress indices to investigate temporal and spatial patterns of heatwaves, drought, and extreme precipitation during maize growing season between 1986/87 and 2015/16 for South Africa provinces and at national level and quantifies their contribution to yield variability. A causal discovery algorithm was applied to investigate the causal relationship among extreme events. At the province and national levels, heatwaves and extreme precipitation showed no significant trend. However, drought severity increased in several provinces. The modified Combined Stress Index (CSIm) model showed that the maize yield nationwide was associated with drought events (explaining 25% of maize yield variability). Heatwaves has significant influence on maize yield variability (35%) in Free State. In North West province, the maize yield variability (46%) was sensitive to the combination of drought and extreme precipitation. The causal analysis suggests that the occurrence of heatwaves intensified drought, while a causal link between heatwaves and extreme precipitation was not detected. The presented findings provide a deeper insight into the sensitivity of yield data to climate extremes and serve as a basis for future studies on maize yield anomalies.

Extreme Droughts and Their Relationship with the Interdecadal Pacific Oscillation in the Peruvian Altiplano Region over the Last 100 Years

The Peruvian Altiplano Region (RAP) is a high plateau area surrounded by the Western and Eastern Andes mountain ranges. This study examines the relationship between extreme droughts in the region and the interdecadal Pacific Oscillation (IPO) over the past century. Previous research has shown that precipitation patterns in the region follow a decreasing trend, with systematic increases in precipitation on the western slope and decreases in the eastern, southern, and central parts. The temporal and spatial variability of precipitation in the Altiplano region is influenced by the easterly moisture flux and the interaction between the El Niño Southern Oscillation (ENSO) and below-average values. The study utilizes water level data for Lake Titicaca and IPO data from 1914 to 2015. The analysis employs wavelet transform and empirical orthogonal function (EOF) techniques to identify the relationship between water levels and IPO. The results indicate multidecadal variability in water levels associated with El Niño/La Niña events and the IPO. The negative phase of the IPO aligns with extreme drought periods, suggesting a connection between the IPO climate index and drought events. The EOF analysis shows a moderate positive correlation between water levels and IPO. The findings highlight the importance of considering IPO and its interaction with ENSO in understanding drought patterns in the Altiplano region. However, other atmospheric conditions also influence precipitation in the region. The study contributes to a better understanding of the factors affecting water levels and droughts in the Peruvian Altiplano, with implications for water resource management in the region.

Drought characteristics and their impacts on vegetation net primary productivity in the subtropical China

Drought is one of the main factors limiting forest productivity, and thus greatly affects the carbon sink capacity of forests. Here we first chose two drought indices including standardized precipitation evapotranspiration index (SPEI) and self-calibrating Palmer drought severity index (scPDSI) to reflect and analyze the spatiotemporal patterns of drought in the subtropical China. Then, the validated CASA (Carnegie-Amer-Stanford Approach) model was applied to estimate forest net primary productivity (NPP) and further quantify the contributions of drought events and their characteristics on forest NPP. The results showed that drought events during 2000–2015 have resulted in a mean decline of forest NPP of 7.2%. Moderate or severe drought events reduced NPP more significantly than extremely severe drought events. In addition, there was 1–2 years of lagging in the NPP responses to drought, and the lagging time varied with forest types. Our study suggests that forest managers and local governments should pay more attention to the places with moderate and severe drought events, and take measures to avoid NPP decline within the 2 years after drought. Our study also provides data support for further identifying the contribution of drought to ecosystem carbon fluxes in the subtropical China.

Extreme events in the multi-proxy South Pacific drought atlas

Droughts are a natural occurrence in many small Pacific Islands and can have severe impacts on local populations and environments. The El Niño-Southern Oscillation (ENSO) is a well-known driver of drought in the South Pacific, but our understanding of extreme ENSO events and their influence on island hydroclimate is limited by the short instrumental record and the infrequency of ENSO extremes. To address this gap, we developed the South Pacific Drought Atlas (SPaDA), a multi-proxy, spatially resolved reconstruction of the November–April Standardised Precipitation Evapotranspiration Index for the southwest Pacific islands. The reconstruction covers the period from 1640 to 1998 CE and is based on nested principal components regression. It replicates historical droughts linked to ENSO events with global influence, compares well to previously published ENSO reconstructions, and is independently verified against fossil coral records from the Pacific. To identify anomalous hydroclimatic states in the SPaDA that may indicate the occurrence of an extreme event, we used an Isolation Forest, an unsupervised machine learning algorithm. Extreme El Niño events characterised by very strong southwest Pacific drought anomalies and a zonal SPCZ orientation are shown to have occurred throughout the reconstruction interval, providing a valuable baseline to compare to climate model projections. By identifying the spatial patterns of drought resulting from extreme events, we can better understand the impacts these events may have on individual Pacific Islands in the future.

Agricultural drought severity in NE Italy: Variability, bias, and future scenarios

This study investigated the variability of agricultural drought severity, as depicted by vegetation indices, and the bias in identifying drought events when considering a stationary vs nonstationary climate reference. The work leveraged gridded climate data (NCEP CFSv2, CHIRPS 1981–2022), soil properties (OpenLandMap), satellite imagery (Sentinel2/Landsat, 2000–2022), and future climate projections (NEX-GDDP, 2050) together with local knowledge of selected farms, to augment drought monitoring techniques and identify potential issues for agriculture. For the study domain, significant differences were observed when comparing drought characteristics using stationary and nonstationary drought indexes, with biases being not ubiquitous in either space or time of year. When developing sustainable drought mitigation and adaptation strategies, decision-makers should carefully address this uncertainty to avoid a possible underestimation of drought magnitude. Results showed a drought increase (∼50%) by the mid and late twenty-first century. Projection of future climate highlighted an even more significant impact (∼80%) with a wide variability of risk across the domain. As drought impact was also related to soil organic carbon (SOC), our results suggest that improving SOC content could be a sustainable strategy for enhancing soil drought resilience, especially in areas commonly characterized by low concentrations of organic carbon and nutrients. The analysis highlighted that drought impacts were also modulated by investment in irrigation infrastructure and irrigation efficiency. Researchers and land managers could apply the proposed analysis design to address historical, current and future indicators of vegetation conditions within irrigated regions. By providing spatio-temporal information on the patterns of drought impacts and their bias, this study supports identifying priority regions for targeted drought risk reduction and adaptation options, including water resources and soil management sustainability criteria, to move towards more resilient agricultural systems.

Afforestation resists the drought disturbance on soil carbon sink in subtropical agricultural ecosystems

Southwest China (SWC), which belongs to the subtropical climatic zone and has been the largest terrestrial carbon sink in China over the past 30 years, has been frequently disturbed by flash drought events since the 21st century. Due to its intensive land use changes during past decades, SWC has also become a global critical area of vegetation restoration. Drought disturbances and land use changes potentially affect local soil organic carbon (SOC), but how SOC responds to land-use and cover-change (LUCC) under drought disturbance in SWC is still unknown. Here, we explored the responses of SOC to vegetation cover changes when drought occurred with different frequencies in SWC. Based on multisource data synthesis, the drought conditions of SWC from 2001 to 2018 are appraised by referring to multiple drought indices, and the SOC content of the 0–10 cm soil layer in SWC is also estimated. Certain areas were disturbed by droughts more than 6 times during the last two decades, while there was an average 20.02% increase in SOC in the SWC subtropical region. Among the different subtropical ecosystems, the average SOC content increased from 25.81 g kg -1 to 33.35 g kg -1 in agricultural land, 30.4 g kg -1 to 34.97 g kg -1 in forest, and 26.97 g kg -1 to 32.89 g kg -1 in grassland. The results also indicated that drought has a significant impact on subtropical forests and agricultural land compared to grasslands. Additionally, mainly due to the self-adjustment abilities of forest ecosystems and human interference on agricultural lands, the SOC stock in subtropical forest responses is more susceptible to drought disturbance, but its capability to resist drought is stronger than that in agricultural land. Superimposing the influence of LUCC, afforestation in abandoned farmland has a positive influence on soil carbon accumulation and can greatly mitigate the adverse effects of extreme climate events on SOC accumulation under special drought patterns and conditions in the SWC subtropical zone. Moreover, the droughts that occur in SWC are considered to be a short-term moderate temperature rise that has a promoting effect on SOC sinks in afforestation areas. Our findings contribute to understanding the implications of extreme climate events and anthropogenic activities on the terrestrial carbon cycle in subtropical agricultural ecosystems.

The performance of SPEI integrated remote sensing data for monitoring agricultural drought in the North China Plain

Agricultural drought is significantly affecting sustainable crop production. One of climate-based drought indices, the Standardized Precipitation Evapotranspiration Index (SPEI) calculated by the difference between precipitation and potential evapotranspiration (PET), is being more and more widely employed to characterize the spatio-temporal pattern of agricultural drought. However, substantial uncertainties exist in the current PET methods due to unrealistic configurations of the heterogeneity of crop surface characteristics or complicated parameterization scheme. In this study, standard Penman–Monteith (PM) model is modified and derived by meteorological factors and leaf area index (LAI) and albedo data based on remote sensing. Then, the modified SPEI was calculated by the monthly difference between precipitation and PM model integrated remote sensing data (referred as PMRS) over the North China Plain (NCP) between 1982 and 2016. Comparing with the FAO56 reference evapotranspiration (referred as PMRC) and a simplified version of standard PM over open-water surface (referred as PMOW), this study examines how well the PMRS-SPEI captures drought evolution and impacts on crop growth and yield over the NCP in order to evaluate its performance in agricultural drought monitoring. The comparison of SPEIs based on three evapotranspiration equations with soil water moisture in root depth shows that a 6-month lag can be used as the optimum time scale over the NCP. Additionally, the correlation of PMRS-SPEI performs better than PMRC-SPEI and PMOW-SPEI. For typical drought events, PMRS-SPEI6 shows remarkable smaller (larger) severe drought values between April and June (August and October) and a shorter (longer) recovery period in the growing season for wheat (maize). Comparing to PMRC-SPEI6 and PMOW-SPEI6, the PMRS-SPEI6 shows better performance when examining the Vegetation Health Index and Composite Index of Crop Yield Reduction. Additionally, climate-induced crop yield correlates with PMRS-SPEI6 better than PMOW-SPEI6 and PMRC-SPEI6, particularly in the emergence and filling stages for winter wheat, and silking stage for summer maize. These results demonstrate that integrating remotely sensed data can enhance the agricultural drought monitoring accuracy of SPEI.