Drought caused by long-term water shortage may lead to insufficient soil moisture and a decline in groundwater, affecting plant growth and species turnover, and thereby altering the structure and function of the ecosystem. The vast majority of regions in China are facing severe ecological water shortage pressure, which poses a challenge to the health of the ecosystem and the sustainable development of the economy and society. The standardized ecological water deficit index (SEWDI) adopts the actual water consumption of vegetation to characterize the ecosystem’s water supply, and calculates the ecological water deficit (EWD) as the difference between ecological water consumption (EWC) and ecological water requirements (EWR), thereby enabling dynamic assessment of regional drought stress status. Based on the SEWDI, this study used multi-source remote sensing data to reveal the dynamic changes and driving factors of ecological drought in China from 1982 to 2024, and the following main conclusions were obtained: (1) the changing trends of ecological drought in various regions of China were different, and the drought situation has been particularly severe since 2000. Except for the Huang-Huai-Hai Plain Region (HPR) and the Middle-Lower Yangtze Plain (MYP), SEWDI showed a downward trend in the other regions, indicating that ecological drought in China generally showed an increasing trend. (2) SEWDI had two seasonal mutation points, which occurred in January 2003 (confidence interval: December 2002 to March 2003) and April 2017 (confidence interval: June 2016 to March 2018). (3) The most severe ecological drought event occurred from July 2019 to April 2020, with a duration and intensity of 10 months and 9.15, respectively. The peak of the drought occurred in February 2020 (SEWDI= –1.21). (4) From spring to winter, the mean range of the grid trend feature Zs of SEWDI was –1.12 (in winter) to 0.13 (in summer), suggesting that drought in summer showed a decreasing trend, while drought in spring, autumn and winter showed an increasing trend. (5) Under the combined influence of climate change and human activities, the three optimal variable factors driving changes in ecological drought in China were evapotranspiration, soil moisture and irrigation water. The research results aim to provide a reference for the identification of ecological drought and its driving factors, and to offer a scientific theoretical basis for China’s response to climate change and ecological environment protection.

The continuous accumulation of meteorological water deficit conditions such as insufficient precipitation and strong evapotranspiration can directly lead to crop water stress and ultimately develop into agricultural drought. As the main commodity grain base in China, the North China Plain (NCP) is a region with frequent drought disasters. This study uses 3D spatiotemporal clustering recognition technology to clarify the dynamic evolution process of typical drought events, and reveal the spatiotemporal response characteristics of agricultural drought to meteorological drought from a three-dimensional perspective. The results showed that: (1) the number of the most serious agricultural drought event in the NCP from 1982 to 2024 was No. A141 (2001.03–2002.10), and its severity and impact area were 4370.04 × 103·month·km2 and 486.21 × 103 km2, respectively; (2) the linear trend rate of the area affected by agricultural drought was 0.934 × 103 km2/10a, and reached its maximum value of 475.44 × 103 km2 in February 2020; and (3) a total of 56 meteorological-agricultural drought events were successfully matched within the research area, and the relationships were divided into three main types: “correspondence” (18 events), “inclusion” (12 events), and “intersection” (26 events).

Groundwater drought poses severe threats to water security, ecological sustainability, and agricultural production, particularly under accelerating climate change and intensified human interference. Characterizing the spatial-temporal evolution, propagation mechanisms, and dominant drivers of groundwater drought across diverse geographical regions is critical for targeted water resource management and drought risk mitigation in China. In this study, we systematically investigated the spatial-temporal dynamics of groundwater drought across China's major climate regions, quantified its long-term trend characteristics and spatiotemporal propagation patterns, and further elucidated the natural and remotely-sensed driving mechanisms behind groundwater drought variations. The results revealed significant spatially heterogeneous and temporally varying groundwater drought patterns across China. Most regions exhibited an overall intensifying trend of groundwater drought, while distinct zonal differences existed between northern arid/semi-arid regions and southern humid regions. Based on the Bayesian estimator algorithm, the mutation points of groundwater drought index (GDI) in most subzones were concentrated between 2018 and 2023, with a coverage area of 48.1% during this period. Wavelet transform analysis shows that the factor with the strongest explanatory power for GDI in the combination of three climate factors was precipitation, evapotranspiration, and soil temperature. Furthermore, vegetation exhibited obvious lag responses and cumulative effects relative to groundwater drought (3.18 months). The findings provide a solid theoretical reference and practical support for regional groundwater drought early warning, differentiated water resource regulation, and sustainable groundwater protection across China.

As the evaporation capacity of land has increased, soil drought characterized by soil moisture deficiency directly threatens national food security and ecological security, and affects the sustainable development of the social economy. This study, based on the China region, utilized soil moisture datasets based on remote sensing and in-situ site observation data to assess the temporal evolution and spatial pattern of soil drought in China and its various regions from 2000 to 2022. It identified the main segmented locations, mutation types, and spatial trend characteristics of soil drought. The direct or indirect contributions of climatic factors and circulation factors to soil drought were identified. The results showed that: (1) During the study period, the minimum value (–0.81) of the original standardized soil moisture index (SSMI) sequence occurred in August 2004, and one positive breakpoint occurred in March 2010. Soil drought in China presented a changing trend of first aggravation and then deceleration. (2) There were mainly four types of mutations in soil drought: “interrupted decrease”, “interrupted increase”, “decrease to increase”, and “increase to decrease”, and most of the mutation points occurred after 2010. (3) The most severe soil drought occurred in 2009 (SSMI= –0.55), and the drought center was mainly located in the southern Yunnan-Guizhou Plateau Region (YPR) in autumn and winter. (4) The drought in each month and quarter in the Huang-Huai-Hai Plain Region (HPR) showed an increasing trend. (5) Based on cross-wavelet transform, the three best variables for explaining soil drought changes were soil moisture, precipitation, and evapotranspiration. This study reveals the regions in China where soil drought changes are more likely to occur, which can provide a scientific reference for revealing the formation mechanism of soil drought, as well as drought adaptation and response measures.

Study region: Yellow River Basin (YRB) Study focus: This study aims to systematically investigate the spatiotemporal evolution patterns and driving mechanisms of ecological drought in the Yellow River Basin (YRB) from 1982 to 2022. A novel Standardized Ecological Water Deficit Index (SEWDI) integrating vegetation dynamics and hydrological processes is developed. Using multi-source data and an integrated methodology including improved run theory, Copula joint probability models, Modified MannKendall method (MMK) trend analysis, and XGBoost-SHAP machine learning framework, the research quantitatively assesses drought characteristics across multiple time scales and identifies key driving factors. New hydrological insights for the region: A significant basin-wide drying trend (-0.017/10a) with strongest intensification in western upstream regions (-0.043/10a). Distinct westward migration of drought centers, with over 91% of western areas affected in the 2010s. The July 2019-April 2020 event was the most severe on record, peaking in February 2020 with 98.08% of the basin affected, a severity of 9.14, and a 10-month duration. Drought intensification is particularly pronounced in December (Zs = -1.33) and winter (Zs = -1.46), with 97.79% and 94.25% of the basin showing aggravating trends, respectively. Evapotranspiration (ET) emerges as dominant climatic driver, while Atlantic Multidecadal Oscillation (AMO) is primary circulation factor exacerbating drought. These findings provide crucial insights for ecological drought early warning and adaptive water resource management in the YRB.

With the escalation of global warming, the potential for land evaporation in the Yellow River Basin (YRB) has increased remarkably, and the problem of soil drought has become increasingly prominent. This not only restricts the development of the agricultural economy in the basin, but also intensifies the disparity between water availability and demand, and exerts a profound influence on the ecological security of the basin. In light of this, this study focuses on soil moisture in the YRB from 2000 to 2022, and conducts an in-depth analysis of the temporal evolution, spatial patterns, abrupt change characteristics as well as the driving forces of soil drought by calculating the Standardised Soil Moisture Index (SSMI). The results show that: (1) Soil drought in the YRB from 2000 to 2022 exhibited a decreasing trend. A negative abrupt change point occurred in February 2004, indicating a sudden intensification of arid conditions. The soil drought in the Above Longyangxia (AL) region exhibited the most significant decreasing trend in September. (2) The most severe soil drought occurred in 2006, and the drought was most intense in November of that year, when the drought-affected area reaching 86.96%. The severely drought-stricken areas were predominantly concentrated in the central and northern regions of the basin. (3) The SSMI in the second-level sub-regions of the YRB exhibits three types of abrupt changes: increase to decrease, intermittent increase, and decrease to increase, and shows a clear trend from upstream to downstream. (4) The results of cross-wavelet analysis indicate that soil moisture-absolute humidity-soil temperature is the best explanatory combination for soil drought. (5) The slopes of the small- and medium-scale fittings in the AL region are 0.5021 and 0.2584, respectively, and the corresponding mean values of meteorological drought severity are 0.87 and 2.57, respectively. Drought events in the YRB are mainly of medium/short duration and low severity, and soil drought severity tends to increase with the intensification of meteorological drought. This study reveals the spatiotemporal variation patterns of soil drought in the YRB, as well as the responses of climate to soil drought. These findings provide a scientific basis for drought resistance and ecological protection within the basin.

Traditional concepts of river health and methods for identifying ecological river disconnection are not applicable to seasonal rivers. Given the substantial negative impact of ecological river disconnection on river ecosystems and the importance of groundwater and soil moisture for the ecology of seasonal rivers in arid and semi-arid regions, developing a comprehensive index for accurately identifying ecological river disconnection in these rivers is crucial. This study introduces a new Standardized Seasonal River Disconnection Index (SSRDI), which is based on a Standardized Index (SCI) and a three-variable Copula function. The SSRDI integrates surface water, groundwater, and soil water information to reveal the true hydrological conditions of seasonal rivers and provides a comprehensive analysis of ecological river disconnection patterns in Tabu River from 2002 to 2020. The findings are as follows: (1) The Gaussian Copula is most suitable for constructing the SSRDI for Tabu River. Optimal distribution functions vary across regions, times, and datasets; therefore, using the best monthly distribution functions to compute SRI, SGDI, and SSMI provides a more scientifically robust mathematical and statistical basis. (2) The SSRDI, which combines surface water, groundwater, and soil water, is more consistent with the actual hydrological conditions of Tabu River compared to the three univariate indices. (3) From 2002 to 2020, the SSRDI for Tabu River shows a continuous declining trend, albeit at a slowing rate. The overall pattern exhibits a cyclical trend of worsening followed by improvement, with June being the month of most severe ecological river disconnection. (4) The seasonal component of the SSRDI from 2002 to 2020 displays cyclical changes, with a mutation in June 2004. The trend component shows a general decline, with two mutations observed in April 2005 and February 2007. This study provides valuable insights for identifying ecological river disconnection of seasonal rivers. The index can be applied to monitoring, forecasting, and mitigating ecological river disconnection in arid and semi-arid river systems, and can more accurately and comprehensively grasp the true health status of seasonal rivers, which is of great significance for the sustainable development of seasonal river ecological environment.

Accurately depicting the spatiotemporal evolution patterns and driving mechanisms of soil drought is of great significance for regional agricultural drought warning and adaptive management of water resources. There are still shortcomings in the existing research in terms of indicator applicability, mutation detection and trend persistence collaborative diagnosis, as well as the quantification of multi-scale meteorological driving factors. In response to the above issues, this study constructs the Standardized Soil Moisture Index (SSMI) based on the principle of soil moisture supply and demand balance, and comprehensively uses BFAST structure mutation detection, autocorrelation correction Mann–Kendall (MMK) trend test, Hurst persistence analysis, and cross-wavelet transform methods to systematically analyze soil drought in the Haihe River Basin (HRB) from 2000 to 2022. Using the FLDAS reanalysis dataset and multi-source meteorological observation data, this study revealed the stage changes, seasonal evolution characteristics, and dominant meteorological driving factors of soil drought in the watershed. Key findings include: (1) the most significant structural breakpoint occurred in May 2005 (confidence interval: March–November 2005); (2) spring exhibited the strongest drying trend (mean Zs = −0.51), while autumn showed the strongest anti-persistence (mean Hurst = 0.41), making it the most vulnerable season for future soil moisture state shifts; (3) evapotranspiration was the dominant meteorological driver, with the highest significant coherence area percentage (SCAP), followed by air humidity, soil moisture, soil temperature, air temperature, and precipitation in descending order of influence.

The Yangtze River Basin (YRB), serving as a crucial ecological shield and economic lifeline in China, has witnessed a gradual increase in the frequency of drought disasters, posing a significant ecological security challenge to the region. Addressing the typical characteristics of “high temperature-high evapotranspiration” droughts in the YRB, this study employs the Standardized Vegetation Water-deficit Index (SVWI) (1982–2022) in the basin, which overcomes the limitations of traditional greenness indices in distinguishing between water stress and abiotic stress. The aim is to precisely identify typical drought events, capture trend mutations, and quantify the statistical associations and relative importance of meteorological and teleconnection factors with vegetation drought. The results show that: (1) from 1982 to 2022, drought with the highest intensity (6.73) in the YRB occurred from August 2019 to June 2020; (2) the grid-based trend characteristic value Zs was –0.06 in summer and –0.86 in winter; (3) the most severe vegetation drought occurred in January 2014 (SVWI=–1.33). The mutation point of SVWI in the YRB appeared in December 2002, showing a monotonically decreasing trend; (4) there are 4 main mutation types of vegetation drought in the YRB, including interrupted decline, increase to decrease, decrease to increase, and monotonic decrease, with most mutation points occurring around 2002; (5) among circulation factors, the Atlantic Multidecadal Oscillation (AMO), Sunspot Index (SSI), and El Niño-Southern Oscillation (ENSO) have the most significant teleconnection driving effects on vegetation drought; and (6) the trivariate combination of evapotranspiration-soil moisture-precipitation is the optimal multi-factor combination for explaining the dynamic changes of vegetation drought. The results help to clarify the evolutionary characteristics of vegetation drought and dominant driving factors, and provide a scientific basis for the adaptive management of ecosystems and the prevention of drought risks.

Soil drought impact on irrigation areas is not merely a single reduction in crop yields, but rather a chain reaction that occurs from multiple dimensions including crop growth, water resource allocation, soil environment, operation of irrigation area projects, agricultural economy and ecosystems. The changing trend and mutation characteristics of soil drought are unclear in the People's Victory Canal Irrigation District (PVCID). The Standardized Soil Moisture Index (SSMI) and the breaks for additive seasons and trend (BFAST) decomposition algorithm were adopted, combined with the eXtreme Gradient Boosting (XGBoost) model, to explore spatio-temporal evolution characteristics, driving factors and response to meteorological drought of soil drought. During the research period, the area percentage of SSMI showing a downward trend was 97.30%. The most severe soil drought occurred in 2019. In addition, the optimal trivariate combination is precipitation, evapotranspiration, and air temperature. This study has clarified the spatio-temporal evolution laws and driving mechanisms of soil drought in the PVCID, providing an important theoretical basis for the early warning, prevention and control of soil drought and the adaptive management of the ecosystem.

The North China Plain (NCP) is China’s vital grain-producing core and a typical water-scarce region threatened by frequent soil drought under global climate warming and intensive human disturbances. In this study, the 0.1° monthly FLDAS datasets spanning 1982–2024 were adopted to construct the Standardized Soil Moisture Index (SSMI). We systematically analyzed the spatiotemporal variations, seasonal differentiation, extreme drought dynamics and long-term trend persistence of soil drought across the whole NCP and its five sub-regions. The results revealed that: (1) The most serious drought event occurred from July 2002 to November 2002, with September being the driest month and an average SSMI value of –1.33 in the NCP. (2) Anti-persistence dominated the drought situation in most areas, implying that the current trend is likely to reverse in the future. (3) Wavelet coherence analysis indicated strong coupling interactions among soil moisture, soil temperature and air temperature, which dominate drought evolution, while precipitation and evapotranspiration exert stable regulatory effects across monthly, interannual and interdecadal scales. (4) Soil drought severity rises synchronously with the intensification of meteorological drought, with distinct spatial heterogeneity in drought response among sub-regions. The findings provide a theoretical support for agricultural drought early warning, differentiated water resource allocation and regional drought disaster prevention and mitigation.

Meteorological drought can gradually propagate through land surface hydrological processes and further develop into agricultural drought, resulting in clear cascading propagation and time-lag responses between different drought types. In this study, the Pearl River Basin (PRB) was selected as the study area. A three-dimensional spatiotemporal clustering method was applied to identify drought events, and a spatiotemporal matching approach was further used to analyze drought propagation relationships and dynamic evolution characteristics between different drought types. The results showed that: (1) a total of 419 meteorological drought events and 154 agricultural drought events were identified in the PRB during 1982-2024, and agricultural drought generally showed longer duration, higher severity, and broader spatial extent than meteorological drought; (2) the most severe meteorological drought event occurred from August 2009 to April 2010, with a total severity of 204.05 & times; 10 degrees center dot month center dot km2, whereas the most severe agricultural drought event occurred from August 2022 to April 2024, with a total severity of 845.50 & times; 10 degrees center dot month center dot km2; (3) 57 meteorological-agricultural drought-event pairs were successfully identified. Among them, one-to-one pairs accounted for the highest proportion (59.65%), while complex propagation patterns such as many-to-many relationships were also observed, indicating strong nonlinear characteristics in drought propagation. These findings reveal the propagation characteristics from meteorological drought to agricultural drought in the PRB from a three-dimensional spatiotemporal perspective, and provide a scientific basis for regional agricultural drought early warning and basin-scale water resources management.

Drought propagation is a key process linking meteorological anomalies to agricultural impacts within the hydrological cycle. Under climate warming, the superimposition of long-term increasing temperature trends ("Press") and short-term extreme drought events ("Pulse") fundamentally alters the propagation dynamics from meteorological drought to agricultural drought. Therefore, it is important to elucidate the driving mechanisms and impact patterns of this superposition effect on the drought propagation process. Using multi-source hydrometeorological datasets, we developed a Copula-based "Press-Pulse" framework to quantify meteorologicalto-agricultural drought propagation (MTAD) in the Yellow River Basin during 1961-2021, with a focus on propagation probabilities, propagation thresholds, and temperature-regulation effects,with a focus on propagation probabilities, propagation thresholds, and the temperature-regulation effects on MTAD. The results show that: (1) Propagation exhibits strong spatiotemporal heterogeneity, peaking in early summer (June) with basinaveraged probabilities exceeding 0.6 and response areas covering similar to 23% of the basin, before attenuating to similar to 0.3-0.4 by August due to precipitation replenishment. (2) The superimposition of high-temperature 'Press' significantly amplifies this risk, increasing agricultural drought probabilities by 10-25% and systematically deepening the triggering SPEI thresholds (e.g., from - 0.5 to -1.0), particularly in the water-limited middle reaches. (3) Identification of a critical Press Tipping Point reveals a distinct spatial divergence: while an intensified temperature press exacerbates drought susceptibility across the semi-arid Loess Plateau by accelerating soil moisture depletion, it conversely exerts a localized buffering effect in the upstream high-altitude regions, where the press-induced snowmelt recharge offsets pulse (precipitation) deficits. Overall, warming systematically lowers the barriers for drought propagation, underscoring the necessity of incorporating temperature-dependent dynamic thresholds into drought early warning and adaptation strategies.

Under the national strategy of ecological protection and high-quality development in the Yellow River Basin, Ordos, a major coal production base in China contributing 17.1

Climate change and human activities have led to serious challenges and threats to the global water environment. Protecting suitable benthic niches and clarifying the drivers of niche changes can effectively regulate the intensity of human activities and cope with the impacts of climate change. This study took the sampling data of Qinhuangdao in 2023 as the basis, and used the dominance model to select dominant taxa. Then, it calculated the niche breadth and overlap of the dominant benthic macroinvertebrate using niche models. What's more, it combined canonical correlation analysis to analyze the correlation between environmental factors and benthic macroinvertebrate density and biomass. Finally, partial correlation analysis was used to identify key driving factors. The results showed that 13 dominant taxa were selected for the dominance model, Unio douglasiae and Orthetrum coerulescens had the greatest dominance. For water quality physical metrics, all dominant taxa had the highest mean niche breadth along the conductivity gradient (2.51) and the greatest mean niche overlap along the turbidity gradient (4.82). For water chemistry indicators, the niche breadth of dominant taxa was highest along the biochemical oxygen demand gradient and lowest along the hexavalent chromium gradient. Key drivers of niche breadth and overlap spatial differentiation for dominant taxa were chemical oxygen demand for Cr (COD_Cr), biochemical oxygen demand (BOD) and water temperature (WT). High concentrations of COD-Cr and BOD can change the competition and food chain structure among benthic macroinvertebrates, thus affecting the niche breadth and overlap of benthic communities. WT has a direct impact on the physiological and ecological processes of benthic macroinvertebrates. In estuaries and sandy beaches, the key drivers of benthic niche may be organic carbon and chlorophyll α. This study provides a scientific basis for benthic conservation and ecological restoration in Qinhuangdao, and a reference and guidance for similar benthic macroinvertebrates around the globe to cope with climate change, regulate human activities, and enhance biodiversity.

Severe soil erosion on the Loess Plateau has led to a reduction in the area of agricultural land as well as an increase in the risk of flooding in the lower reaches of the Yellow River. Ten Kongdui (Mongolian for “Kongdui”, meaning “Great Mountain Gully”) is located in the upper reaches of the arsenic sandstone hilly and gully area. It is located in the heart of the Kubuqi sandstorm area. This area is one of the sandy and coarse sand production areas in the middle reaches of the Yellow River. It is also the main sand source area of the Inner Mongolia section of the Yellow River. The Ten Major Kongdui Xiliugou Basin is located in the upper and middle reaches of the Yellow River in the coarse sand-producing area. The gullies are deep and steep, with exposed arsenic sandstone. The chain reaction of heavy rain, flood and sediment is intense, making it a key channel for coarse sand from the Yellow River to flow into the river. To effectively address soil erosion in this area, curb the expansion of pyrite sandstone gully erosion and reduce the amount of sediment flowing into the Yellow River, it is proposed to establish an integrated engineering system of “soil and water conservation - sediment interception” within the basin. Through the measure of check dam local sediment storage will be achieved, the ecosystem functions will be restored, and the healthy life of the Yellow River will be maintained. Using distributed hydrologic modeling to explore the effects of a sand detention project in the Xiliugou watershed on watershed runoff and sand transport, the SWAT model was calibrated (1990–1999) and validated (2000–2020) using observed runoff and sediment data at Longtouguai Station, the simulated runoff and sand transport at Longtouguai Hydrological Station were found to fit well with the measured values through model simulation. The linear fitting coefficient R2 exceeds 0.6, it is considered that the linear relationship between the simulated values and the measured values is reasonable, which indicates that the reservoir model in SWAT model can be used for check dam simulation, and the water and sand impacts of water and sand reduction of the new check dam project on the Xiliugou watershed are analyzed through the results of the SWAT model calculations and the impacts of further calculations on the channel siltation of the Inner Mongolia section of the Yellow River are further calculated. The results show that: 1, the construction of check dams can affect the runoff volume of the basin to a certain extent, and intercepts part of the runoff, the average annual water reduction of the newly built 79 check dams is 2.44 × 106 m3. 2, it has a great influence on the sand transport in the basin, and the effect of sand reduction is obvious, the average annual sand reduction of the newly built 79 check dams is 4.09 × 105 t. 3, Reduces sand content in the Yellow River and enhances flushing of existing sediment in the Nei Mongol section of the river, and reduces water demand for sediment transport. The results of this study provide reference for promoting the construction of water sand replacement project in Xiliugou Basin and the high-quality development of the Yellow River Basin.

Agricultural drought poses significant challenges to food production and ecosystem sustainability. The evolution of drought and its recurrence period feature are important for drought mitigation and risk management. This study aims to evaluate agricultural drought using the Standardized Soil Moisture Index (SSMI) based on Global Land Data Assimilation System (GLDAS) products, and extract drought variables using a three-dimensional identification method in Northwestern China. Then, the spatiotemporal dynamics and recurrence characteristics of agricultural droughts were evaluated. The results showed that: (1) Study area experienced alternating phases of dry and wet, with intensification in the 1960s and 1990s, and significant humidification post-2000. All four seasons show a wetting trend, while spring exhibited notable humidification trends. (2) Drought intensity and frequency displayed regional variability, agricultural drought in the west part of the study area were high in frequency but low in intensity, while the opposite was true in the east. (3) Agricultural drought exhibited cyclical behavior with dominant periods of 3.5, 6.6, and 13.5 years, reflecting interannual and interdecadal fluctuations. (4) Multivariate joint recurrence periods highlighted significant correlations among drought characteristics, emphasizing the risk of underestimation when considering single variables. These results offer valuable insights for water resources allocation and drought mitigation.

Understanding the dynamics of terrestrial carbon cycling is imperative for mitigating climate change. However, conventional analyses of Net Ecosystem Productivity (NEP) often treat extreme events as mere fluctuations, obscuring the mechanistic linkage between short-term disturbances and long-term ecosystem stability. To address this gap, we developed a novel analytical framework integrating three-dimensional event identification, long-term trend mutation detection, and machine learning-based attribution to analyze NEP in the Yellow River Basin (YRB) from 1982 to 2022. We found that: (1) The YRB’s overall greening trend conceals a complex reality of widespread structural changes, with “accelerated growth” patterns coexisting alongside alarming “increase-to-decrease” reversals, revealing significant underlying risks. (2) Our three-dimensional analysis, validated with independent data, identified 58 extreme carbon source events and established them as the direct trigger for the most frequent long-term trend mutations. (3) Water availability is the absolute dominant factor, and its quantified critical threshold (e.g., < 189 mm annual precipitation) provide a unified mechanistic framework that explains the basin’s spatial vulnerabilities, trend reversals, and extreme events. By pioneering an event-based, three-dimensional perspective, our study offers a new paradigm for assessing ecosystem resilience. The established linkages among short-term events, long-term mutations, and their hydro-climatic thresholds provide critical insights for developing proactive ecological risk management strategies.

The carbon cycle in terrestrial ecosystems is a crucial component of the global carbon cycle, and drought is increasingly recognized as a significant stressor impacting their carbon sink function. Net ecosystem productivity (NEP), which is a key indicator of carbon sink capacity, is closely related to vegetation Net Primary Productivity (NPP), derived using the Carnegie-Ames-Stanford Approach (CASA) model. However, there is limited research on desert grassland ecosystems, which offer unique insights due to their long-term data series. The relationship between NEP and drought is complex and can vary depending on the intensity, duration, and frequency of drought events. NEP is an indicator of carbon exchange between ecosystems and the atmosphere, and it is closely related to vegetation productivity and soil respiration. Drought is known to negatively affect vegetation growth, reducing its ability to sequester carbon, thus decreasing NEP. Prolonged drought conditions can lead to a decrease in vegetation NPP, which in turn affects the overall carbon balance of ecosystems. This study employs the improved CASA model, using remote sensing, climate, and land use data to estimate vegetation NPP in desert grasslands and then calculate NEP. The Standardized Precipitation Evapotranspiration Index (SPEI), based on precipitation and evapotranspiration data, was used to assess the wetness and dryness of the desert grassland ecosystem, allowing for an investigation of the relationship between vegetation productivity and drought. The results show that (1) from 1982 to 2022, the distribution pattern of NEP in the Inner Mongolia desert grassland ecosystem showed a gradual increase from southwest to northeast, with a multi-year average value of 29.41 gCm⁻2. The carbon sink area (NEP > 0) accounted for 67.99%, and the overall regional growth rate was 0.2364 gcm−2yr−1, In addition, the area with increasing NEP accounted for 35.40% of the total area (p < 0.05); (2) using the SPEI to characterize drought changes in the Inner Mongolia desert grassland ecosystems, the region as a whole was mainly affected by light drought. Spatially, the cumulative effect was primarily driven by short-term drought (1–2 months), covering 54.5% of the total area, with a relatively fast response rate; (3) analyzing the driving factors of NEP using the Geographical detector, the results showed that annual average precipitation had the greatest influence on NEP in the Inner Mongolian desert grassland ecosystem. Interaction analysis revealed that the combined effect of most factors was stronger than the effect of a single factor, and the interaction of two factors had a higher explanatory power for NEP. This study demonstrates that NEP in the desert grassland ecosystem has increased significantly from 1982 to 2022, and that drought, as characterized by the SPEI, has a clear influence on vegetation productivity, particularly in areas experiencing short-term drought. Future research could focus on extending this analysis to other desert ecosystems and incorporating additional environmental variables to further refine the understanding of carbon dynamics under drought conditions. This research is significant for improving our understanding of carbon cycling in desert grasslands, which are sensitive to climate variability and drought. The insights gained can help inform strategies for mitigating climate change and enhancing carbon sequestration in arid regions.