Hexin Lai

Under the extensive influence of the monsoon climate, droughts in the Yangtze River Basin (YRB) occur frequently and pose a serious threat to grain security. To better understand the evolution and drivers of soil drought, this study employed remote sensing-based soil moisture and atmospheric circulation data from 2000 to 2022. It assessed the spatiotemporal characteristics of soil drought across the YRB and its sub-basins, identified the main mutation points and types, and quantified the relative contributions of climatic and circulation factors. The results show that: (1) the most severe soil drought month occurred in August 2022 (Standardized Soil Moisture Index SSMI = –1.69), with two major mutation points in May 2011 (“decrease to increase”) and June 2019 (“increase to decrease”); (2) drought mutations were mainly categorized as “interrupted decrease” (9 sub-basins) and “increase to decrease” (1 sub-basin), most occurring after 2010; (3) the year 2022 experienced the most severe annual drought (SSMI = –0.94), with extreme drought covering 39.36% of the basin in August; (4) precipitation (PC) was the dominant climatic factor influencing drought (percentage area of significant coherence PASC = 15.48%), while the Interannual Pacific Oscillation (IPO), Pacific Decadal Oscillation (PDO), and Dipole Mode Index (DMI) all showed significant remote-correlation effects, with mean Shapley additive explanations (SHAP) values of 0.138, 0.111, and 0.090, respectively. This study clarifies the spatiotemporal patterns and drivers of soil drought in the YRB, providing a scientific basis for improved drought monitoring and agricultural risk management.

Terrestrial water storage includes soil water storage, groundwater storage, surface water storage, snow water equivalent, plant canopy water storage, biological water storage, etc., which can comprehensively reflect the total change in water volume during processes such as precipitation, evapotranspiration, runoff, and human water use in the basin hydrological cycle. The Gravity Recovery and Climate Experiment (GRACE) satellite provides a powerful tool and a new approach for observing changes in terrestrial water storage and groundwater storage. The North China Plain (NCP) is a major agricultural region in the northern arid area of China, and long-term overexploitation of groundwater has led to increasingly prominent ecological vulnerability issues. This study uses GRACE and Global Land Data Assimilation System (GLDAS) hydrological model data to assess the spatiotemporal patterns of groundwater drought in the NCP and its various sub-regions from 2003 to 2022, identify the locations, occurrence probabilities, and confidence intervals of seasonal and trend mutation points, quantify the complex interactive effects of multiple climate factors on groundwater drought, and reveal the propagation time from groundwater drought to agricultural drought. The results show that: (1) from 2003 to 2022, the linear tendency rate of groundwater drought index (GDI) was −0.035 per 10 years, indicating that groundwater drought showed a gradually worsening trend during the study period; (2) on an annual scale, the most severe groundwater drought occurred in 2021 (GDI = −1.59). In that year, the monthly average GDI in the NCP ranged from −0.58 to −2.78, and the groundwater drought was most severe in July (GDI = −2.02); (3) based on partial wavelet coherence, the best univariate, bivariate for groundwater drought were soil moisture (PASC = 19.13%); and (4) in Beijing, Tianjin and Hebei, the propagation time was mainly concentrated in 1–5 months, with average lag times of 2.87, 3.20, and 2.92 months, respectively. This study can not only reduce and mitigate the harm of groundwater drought to agricultural production, social life, and ecosystems by monitoring changes in groundwater storage, but also provide a reference for the quantitative identification of the dominant factors of groundwater drought.

Ecological drought in terrestrial systems is a vegetation-functional degradation phenomenon triggered by the long-term imbalance between ecosystem water supply and demand. This process involves nonlinear coupling of multiple climatic factors, ultimately forming a compound ecological stress mechanism characterized by spatiotemporal heterogeneity. Based on meteorological and remote sensing datasets from 1982 to 2022, this study identified the spatial distribution and temporal variability of ecological drought in China, elucidated the dynamic evolution and return periods of typical drought events, unveiled the scale-dependent effects of climatic factors under both univariate dominance and multivariate coupling, as well as deciphered the response mechanisms of ecological drought to meteorological drought. The results demonstrated that (1) terrestrial ecological drought in China exhibited a pronounced intensification trend during the study period, with the standardized ecological water deficit index (SEWDI) reaching its minimum value of −1.21 in February 2020. Notably, the Alpine Vegetation Region (AVR) displayed the most significant deterioration in ecological drought severity (−0.032/10a). (2) A seasonal abrupt change in SEWDI was detected in January 2003 (probability: 99.42%), while the trend component revealed two mutation points in January 2003 (probability: 96.35%) and November 2017 (probability: 43.67%). (3) The drought event with the maximum severity (6.28) occurred from September 2019 to April 2020, exhibiting a return period exceeding the 10-year return level. (4) The mean values of gridded trend eigenvalues ranged from −1.06 in winter to 0.19 in summer; 87.01% of the area exhibited aggravated ecological drought in winter, with the peak period (88.51%) occurring in January. (5) Evapotranspiration (ET) was identified as the dominant univariate driver, contributing a percentage of significant power (POSP) of 18.75%. Under multivariate driving factors, the synergistic effects of ET, soil moisture (SM), and air humidity (AH) exhibited the strongest explanatory power (POSP = 19.21%). (6) The response of ecological drought to meteorological drought exhibited regional asynchrony, with the maximum correlation coefficient averaging 0.48 and lag times spanning 1–6 months. Through systematic analysis of ecological drought dynamics and driving mechanisms, a dynamic assessment framework was constructed. These outcomes strengthen the scientific basis for regional drought risk early-warning systems and spatially tailored adaptive management strategies.

The spatiotemporal continuity characteristics of drought are the basis for analyzing its spatial migration and evolution, which is significant for mitigation and early warning of drought. The aim of this paper is to identify meteorological and vegetation drought events in China from 1982 to 2022, reveal the dynamic changes of typical drought events, and elucidate the propagation characteristics of meteorological and vegetation drought. The results showed that (1) based on a three-dimensional spatiotemporal clustering algorithm, China experienced 138 meteorological drought events and 76 vegetation drought events; (2) the severity of the meteorological drought event No. M138 (2022.03–2022.11) reached 667.58 × 10⁴ km²·month, and the severity of the vegetation drought event No. V68 (2019.06–2020.04) reached 572.89 × 10⁴ km²·month; (3) a total of 40 meteorological-vegetation drought event pairs had been identified, which was divided into three main types: “single”, “simple”, and “complexity”; and (4) in the typical drought event pair No. P-34, the area difference was 16.45 × 10⁴ km², and the severity difference was 3.89 × 10⁴ km². The research results can provide a new perspective for identifying the dynamic changes and propagation characteristics of drought events from a three-dimensional perspective, which is of great significance for predicting vegetation drought and protecting the ecological environment.

During the dry season, river flow gradually diminishes, and surface water flow dries up. Therefore, the investigation of runoff during the dry season is of great practical significance for rational water allocation and water resource management. Based on hydrological station data from the Xijiang River Basin (XRB) from 1961 to 2020, this study examines the trend and periodic characteristics of dry-season runoff, identifies fluctuation and variability in dry-season runoff, and investigates the main circulation factor combinations influencing dynamic changes in dry-season runoff. The results indicate the following: (1) the characteristics of dry-season runoff variations are basically consistent across sub-basins in the XRB during the study period, with the minimum (21.96 × 10⁸ m³) and maximum (54.67 × 10⁸ m³) average monthly runoff occurring in February and October, respectively; (2) interannual-scale dry-season runoff exhibits periodicity of 3.53 years and 7.5 years; (3) using the Bayesian estimator of abrupt seasonal and trend change algorithm (BEAST), a seasonal abrupt point with a probability of 20.5% occurs in 1983, and the confidence interval for this abrupt point is from 1980 to 1986; (4) based on the cross wavelet approach, solar sunspots are identified as the primary circulation factor contributing to dry-season runoff in the XRB, exhibiting a significant 8–14 years resonance cycle of negative correlation with runoff during the high-energy phase from 1972 to 2006. These findings offer a new perspective on understanding the evolution of dry-season runoff and circulation factor variations, which are crucial for accurate prediction, early warning, and rational allocation of water resources during the dry season.

Drought is a complex and recurrent natural disaster that can have devastating impacts on economies, societies, and ecosystems around the world. In light of climate change, the frequency, duration, and severity of drought events worldwide have increased, and extreme drought events have caused more severe and irreversible damage to terrestrial ecosystems. Therefore, estimating the resilience of different vegetation to drought events and vegetation’s response to damage is crucial to ensuring ecological security and guiding ecological restoration. Based on meteorological and remote-sensing datasets from 1982 to 2022, the spatial distribution characteristics and temporal variability of vegetation were identified in the Yellow River Basin (YRB), the dynamic changes and recurrence periods of typical drought events were clarified, and the driving effects of different drought types on vegetation were revealed. The results indicated that (1) during the research period, the standardized vegetation water-deficit index (SVWI) showed a downward trend in the YRB, with a 99.52% probability of abrupt seasonal changes in the SVWI occurring in January 2003; (2) the characteristic values of the grid trend Zs were −1.46 and 0.20 in winter and summer, respectively, indicating a significant downward trend in the winter SVWI; (3) the drought with the highest severity (6.48) occurred from September 1998 to February 1999, with a recurrence period of 8.54 years; and (4) the growth of vegetation was closely related to drought, and as the duration of drought increased, the sensitivity of vegetation to drought events gradually weakened. The research results provide a new perspective for identifying vegetation’s dynamic changes and responses to drought, which is of great significance in revealing the adaptability and potential influencing factors of vegetation in relation to climate.

The vegetation drought phenomenon will reduce the amount of water available to the vegetation system, change the ecological and hydrological cycles of plants, and affect the aquatic and terrestrial ecosystem in various forms. Therefore, research on the dynamic variation and driving mechanism of vegetation drought will help us recognize and predict the response of vegetation under drought stress conditions, implement appropriate policy measures to deal with the drought crisis, and provide technical support for implementing vegetation protection and alleviating the increasing risk of vegetation drought. However, the dynamic variation of vegetation drought and its dynamic propagation mechanism are still undefined across China. In this study, the spatio-temporal evolutions and pixel-scaled trends of vegetation drought were analyzed during the period between 1999 and 2020. Additionally, the propagation features were investigated between vegetation drought and meteorological drought. Finally, the relationships between vegetation drought and atmospheric teleconnection were explicitly clarified using multivariate cross wavelet transform technology. The results highlighted five key findings: (1) the vegetation drought presented an overall decreasing trend across China in 1999–2020; (2) the most serious vegetation drought occurred in the year 2000, with the average vegetation condition index (VCI) values ranging from 0.36 to 0.46; (3) vegetation droughts were alleviating at the pixel scale for each season; (4) the propagation time from meteorological drought to vegetation drought was shorter in summer (1.26 months) and longer in winter (2.26 months); and (5) the three-factors combination of Pacific North American (PNA), El Niño-Southern Oscillation (ENSO), and Trans Polar Index (TPI) can satisfactorily explain the variations of vegetation drought. This study sheds new viewpoints into the identification of vegetation drought variation across China, which can also be applied in other areas.