Hurst Index Research Articles

Application of digital twin for industrial process control: A case study of gauge-looper-tension optimized control in strip hot rolling

Background During the hot rolling process, the performance of most control systems gradually degrades due to equipment aging and changing process conditions. However, existing gauge-looper-tension control method remain restricted by a lack of intelligent parameter maintenance strategies. Methods To address this challenge and enhance the smart manufacturing capabilities of strip hot rolling, based on the digital twin method, this paper proposes a data-driven optimized control method for the gauge-looper-tension system of strip hot rolling. First, a hot rolling process model is constructed based on a digital twin method to serve as an evaluation and optimization platform. Subsequently, relevant data are collected to calculate the Hurst index for identifying the performance of the controller during the rolling process. Additionally, for controllers with poor Hurst index values, the crayfish optimization algorithm is employed for adjusting controller parameters to maximum the Hurst index. Results A real case of hot rolling steel production was used to validate the proposed data-driven optimized control method. Experimental results demonstrate that the proposed evaluation method can effectively recognize the state of gauge-looper-tension controller. Moreover, after optimizing the controller parameters through crayfish optimization algorithm (COA), the value of Hurst index showed significant improvement. Conclusions The proposed digital twin -based optimized control method effectively enhance the manufacturing capability and maintain strategy of hot rolling steel production. Through the proposed method, the Hurst index of gauge-looper-tension system increasing from 0.574 to 0.862.

Feature analysis of 5G traffic data based on visibility graph

IntroductionAs 5G networks become widespread and their application scenarios expand, massive amounts of traffic data are continuously generated. Properly analyzing this data is crucial for enhancing 5G services.MethodsThis paper uses the visibility graph method to convert 5G traffic data into a visibility graph network, conducting a feature analysis of the 5G traffic data. Using the AfreecaTV dataset as the research object, this paper constructs visibility networks at different scales and observes the evolution of degree distribution with varying data volumes. The paper employs the Hurst index to evaluate the 5G traffic network and uses community detection to study the networks converted from 5G traffic data of different applications.ResultsExperimental results reveal significant differences in node degree distribution and topological structures of 5G traffic data across different application scenarios, such as star structures and multiple subnetwork structures. It is found that the node degree distribution of 5G traffic networks exhibits heterogeneity, reflecting the uneven growth of node degrees during network expansion. The Hurst index analysis discovers that the 5G traffic network retains the long-term dependence and trends of the original data. Through community detection, it is observed that networks converted from 5G traffic data of different applications exhibit diverse community structures, such as high centrality nodes, star-like community structures, modularity, and multilayer characteristics.DiscussionThese findings indicate that 5G traffic networks in different application scenarios exhibit complex and diverse characteristics. The heterogeneity of node degree distribution and differences in topological structures reflect the imbalance in node connection methods during network expansion. The results of the Hurst index show that the 5G traffic network inherits the long-term dependence of the original data, providing a basis for analyzing the dynamic characteristics of the network. The diverse community structures reveal the inherent modularity and hierarchy of the network, which helps to understand the performance and optimization directions of 5G networks in different applications.

Post-fire vegetation dynamic patterns and drivers in Greater Hinggan Mountains: Insights from long-term remote sensing data analysis

Fire has become a major disturbing factor in boreal forests, and giant forest disturbances play a vital role in regulating the climate under global warming. Therefore, it is essential to investigate the spatiotemporal patterns and main drivers of post-fire vegetation recovery for forest ecological research and post-fire recovery management. However, previous studies have focused on the post-fire forest change within the entire fire perimeter, lacking separate analysis and comparison of the burned zone (BZ) and unburned zone (UNBZ). Here, we propose the utilization of Moderate Resolution Imaging Spectroradiometer land cover type and vegetation index data to monitor vegetation dynamics and explore its drivers after the most serious forest fire in the history of P.R. China in the Greater Hinggan Mountains (GHM). The temporal and spatial patterns of vegetation recovery in the BZ/UNBZ in the GHM were analyzed using the Sen & Mann-Kendall method, Hurst index and coefficient of variation, and their driving mechanisms were explored using GeoDetector and geographically weighted regression. The results showed that there were significant differences in the spatial distribution and fluctuation of vegetation between the BZ and UNBZ, and that the BZ exhibited higher productivity and vigor. Vegetation recovery was influenced by different dominant factors and changed over time, in which land surface temperature and precipitation dominated all the time, whereas topographic relief and elevation had a more significant contribution to vegetation recovery in the BZ and UNBZ, respectively. This study provides a scientific basis for the protection and management of vegetation in disturbed forested areas, particularly after fires.

Parameter estimation and singularity of laws on the path space for SDEs driven by Rosenblatt processes

In this paper, we study parameter identification for solutions to (possibly non-linear) SDEs driven by additive Rosenblatt process and singularity of the induced laws on the path space. We propose a joint estimator for the drift parameter, diffusion intensity, and Hurst index that can be computed from discrete-time observations with a bounded time horizon and we prove its strong consistency under in-fill asymptotics with a fixed time horizon. As a consequence of this strong consistency, singularity of measures generated by the solutions with different drifts is shown. This results in the invalidity of a Girsanov-type theorem for Rosenblatt processes.

Analysis of differences in fossil fuel consumption in the world based on the fractal time series and complex network

Fossil fuels remain indispensable energy resources despite their non-renewable nature. Understanding the patterns of global fossil fuel consumption is essential for energy security and policy-making. This study employs complex network theory and fractal time series analysis to explore the underlying dynamics and patterns of fossil fuel consumption globally, with a focus on coal, oil, and gas consumption.The study applies the Hurst index to raw fossil fuel consumption data to identify fractal characteristics. Additionally, the visibility graph method is used to convert time series data into complex networks, allowing further analysis of consumption patterns. The study examines fossil fuel consumption in 38 countries to assess global trends and differences. The analysis reveals that global fossil fuel consumption follows a fractal time series pattern, with Hurst index values exceeding 0.9, indicating long-term memory characteristics. The application of the visibility graph method demonstrates variations in the Hurst index of degree distribution, enabling the differentiation of consumption patterns across regions. The method also uncovers distinct features of coal, oil, and gas consumption when viewed from a network perspective. The findings suggest that fossil fuel consumption has predictable long-term patterns, which are crucial for assessing future energy demands. The study highlights the importance of legislative measures to safeguard fossil fuel resources, especially for countries like China, where energy security and international competitiveness are paramount. Understanding these consumption patterns could guide future energy policies aimed at managing non-renewable resources more effectively.

Spatial and Temporal Patterns of Vegetation Water Use Efficiency and Its Response to Climatic and Environmental Factors in Gansu Province

Vegetation water use efficiency (WUE) is a crucial indicator for elucidating the interconnections between the carbon and water cycles of ecosystems and for discerning the response of vegetation ecosystems to climate change. Gansu Province in northwestern China is facing significant ecological water-related challenges. However, the response of vegetation WUE to climate environmental factors in this region remains unclear. In this study, the MODIS vegetation gross primary productivity (GPP) and evapotranspiration (ET) datasets were used to calculate the vegetation WUE in Gansu Province and, combined with meteorological data, Theil–Sen median trend analysis and partial correlation analysis were used to determine the spatial and temporal characteristics of vegetation WUE in this region and its response to climate environmental factors. Finally, the random forest model was used to rank the importance of climate environmental factors. The results indicate the following: (1) The average values of vegetation WUE, GPP, and ET in Gansu Province from 2000 to 2020 were 1.46 gC·mm−1·m−2, 510.22 gC·m−2, and 343.68 mm, respectively, and their spatial distribution was high in the southeast and low in the northwest, which was closely related to the distribution of vegetation in the region. (2) Over the past 20 years, the vegetation WUE in this region showed a slowly decreasing trend in general, with a decrease rate of 16.57%. There were significant differences in the WUE of different vegetation types, among which forest WUE was the highest and grassland WUE the lowest. (3) The trend prediction of WUE in Gansu Province was performed by using the rescaled extreme difference method, and the Hurst index was 0.45, which means that the vegetation WUE in this region is expected to increase in the future. (4) In general, precipitation was the main factor influencing the change in vegetation WUE in Gansu Province, followed by vapor pressure deficit (VPD), temperature, and soil moisture. This study provides strategy support for the coupling process of vegetation ecosystems and the sustainable development of agriculture and animal husbandry in Gansu Province and has scientific reference value for promoting and planning the sustainable development of vegetation in arid and semi-arid areas.

Research on Rapid Detection Method of Cavitation State of Water jet Propulsion Pump Based on Multi-source and Multi-feature of Acoustic Emission Signal and Bayesian Network

Abstract Cavitation prevents the conversion of internal dynamic and pressure energy in a water jet propulsion pump and causes erosion of flow passage components. Acoustic emission is a non-destructive test method for instability signals. Based on the incipient mechanism and development process of cavitation, three acoustic emission signal feature parameters (ring counting rate, energy release rate, Hurst index) are proposed to define the cavitation state utilizing the waveform pattern and signal self-similarity. The multi-source and multi-feature cavitation identification model of acoustic emission signals is established through cavitation simulation, multi-monitoring point acoustic emission signal testing, and feature parameter extraction, and a large number of probabilistic trainings are carried out using Bayesian network, resulting in a fast cavitation state identification method based on acoustic emission signals. The research results have important practical engineering application significance for the prediction of cavitation resistance, real-time dynamic monitoring of cavitation phenomenon and prevention of cavitation damage.

Spatial-temporal dynamics and influencing factors of city level carbon emission of mainland China

Urban areas are major sources of carbon emissions, making it crucial to understand their emission characteristics for effective carbon reduction and sustainable development. Using carbon emission data from mainland China (2001–2021), we analyzed the spatio-temporal dynamics and future trends of city-level emissions and explored influencing factors using machine learning methods. Results indicate significant fluctuations in carbon emissions, with over 40 % of mainland Chinese cities experiencing a doubling in total emissions. Geospatially, cities in South Coast (SC), East Coast (EC), Northeast (NE), and NorthCoast (NC) show stronger intensity and increasing trends in carbon emissions compared to other regions, with over 80 % of cities in these regions experiencing high or higher increases. Additionally, a continued rise in carbon emissions was detected in most Chinese cities, with an average Hurst index of 0.64, indicating persistent trends. Using the XGBoost method, factors such as population density, built-up area, urban green coverage rate, and GDP were found to strongly correlate with urban carbon emissions, exhibiting significant spatial heterogeneity. This research uncovers the characteristics and influencing factors of urban-scale carbon emissions, offering valuable insights for policymakers to tailor carbon reduction strategies to the specific needs and conditions of various urban areas.

Detecting rough volatility: a filtering approach

In this paper, we focus on filtering and parameter estimation in stochastic volatility models when observations arise from high-frequency data. We are particularly interested in rough volatility models where spot volatility is driven by fractional Brownian motion with Hurst index H < 1 2 . Since volatility is not directly observable, we rely on particle filtering techniques for statistical inference regarding the current level of volatility and the parameters governing its dynamics. In order to obtain numerically efficient and recursive algorithms, we use the fact that a fractional Brownian motion can be represented through a superposition of Markovian semimartingales (Ornstein-Uhlenbeck processes). We analyze the performance of our approach on simulated data and we compare it to similar studies in the literature. The paper concludes with an empirical case study, where we apply our methodology to high-frequency data of a liquid stock.

Spatiotemporal Variations in MODIS EVI and MODIS LAI and the Responses to Meteorological Drought across Different Slope Conditions in Karst Mountain Regions

Based on monthly MODIS EVI and LAI data from 2001 to 2020, combined with the Standardized Precipitation Evapotranspiration Index (SPEI), this study employs Theil–Sen trend analysis, Mann–Kendall (MK) test, Hurst index analysis, and correlation analysis to comparatively analyze the overall vegetation trends, spatial distribution characteristics, and future trends of different vegetation types in Guizhou Province under varying slope conditions. The study also explores the response of vegetation to SPEI at different time scales across different slopes. The results indicate the following: (1) From 2001 to 2020, the average values of EVI (0.34%/a) and LAI (1.4%/a) during the growing season exhibited an increasing trend, with the improved vegetation areas primarily concentrated in the western region of Guizhou, while the degradation areas were mainly located in the central and eastern regions. (2) Under different slope conditions, EVI generally showed slight improvement, while LAI exhibited significant improvement, with dry-lands experiencing the largest changes. Future trends indicate continuous improvement, but the proportion of vegetation improvement area decreases with increasing slope. When the slope is less than 5°, the proportion of vegetation improvement area is the highest. (3) The positive correlation between EVI, LAI, and SPEI at different time scales is stronger than the negative correlation, with the strongest correlations observed when the slope is less than 5°. When the slope exceeds 35°, the relationship between vegetation and drought response is almost unaffected by the slope. These findings provide a scientific basis for vegetation growth monitoring and the study of climate change and vegetation interactions in Guizhou Province.

Spatial and Temporal Changes in Habitat Quality and Driving Forces in Typical Loess Hill and Gully Areas：A Case Study of the Zuli River Basin

Loess hills and gully areas are one of the important ecological barriers in China, and the study of the spatial and temporal changes of its habitat quality and its driving force is of great significance to guaranteeing the ecological security of China and safeguarding the national ecological rights and interests. Taking the Zuli River Basin as an example, the spatiotemporal distribution of the remote-sensing ecological index （RSEI） from 2000 to 2020 was systematically investigated using the Google Earth Engine platform and Landsat remote-sensing data. Combined with the coefficient of variation CV, the Theil-Sen Median slope estimation, the Mann-Kendall test of significance, and the Hurst index, the spatial and temporal changes of habitat quality in the study area were analyzed over a period of 20 years, and the effects of six major driving factors on the spatial distribution of RSEI were investigated using the geodetector method. The results of the study showed that： ① From 2000 to 2020, the value of the RSEI showed a downward and then upward trend, with an average annual increase of 0.084 5·（10 a）-1. ② During the 20-year period, the habitat quality improvement area accounted for 92.06%, of which the significant improvement area accounted for 28.49%, and the improvement area was mainly in Huining County, whereas the habitat degradation area only accounted for 7.82%. The trend of future ecological conditions showed that 74.98% of the areas would show a trend of continuous improvement or future improvement, but there would still be a potential risk of ecological degradation in 23.48% of the areas in the future. ③ Climate factors such as precipitation were the key factors affecting the habitat quality in the Zu Li River Basin； the interaction between factors had a higher explanatory power than that of any single factor on the habitat quality, among which the interaction between the precipitation factor and the elevation factor had the strongest explanatory power. The interaction between the terracing factor and the environmental factor significantly increased the explanatory power of the spatial variance, which indicated that terracing played an important role in improving habitat quality. The results of this study can provide a scientific basis for the management and sustainable development of the ecological environment in the loess hills and gullies.

Analysis of the Influence of Driving Factors on Vegetation Changes Based on the Optimal-Parameter-Based Geographical Detector Model in the Yima Mining Area

The growth of vegetation directly maintains the ecological security of coal mining areas. It is of great significance to monitor the dynamic changes in vegetation in mining areas and study the driving factors of vegetation spatial division. This study focuses on the Yima mining area in Henan Province. Utilizing MODIS and multi-dimensional explanatory variable data, the Theil–Sen Median + Mann–Kendall trend analysis, variation index, Hurst index, and optimal-parameter-based geographical detector model (OPGD) are employed to analyze the spatiotemporal changes and future trends in the EVI (enhanced vegetation index) from 2000 to 2020. This study further investigates the underlying factors that contribute to the spatial variation in vegetation. The results indicate the following: (1) During the period studied, the Yima mining area was primarily characterized by a moderate-to-low vegetation cover. The area exhibited significant spatial variation, with a notable pattern of “western improvement and eastern degradation”. This pattern indicated that the areas that experienced improvement greatly outnumbered the areas that underwent degradation. Moreover, there was an inclination towards a deterioration in vegetation in the future. (2) Based on the optimal parameter geographic detector, it was found that 2 km was the optimal spatial scale for the analysis of the driving factors of vegetation change in this area. The optimal parameter combination was determined by employing five spatial data discretization methods and selecting an interval classification range of 5–10. This approach effectively addresses the subjective bias in spatial scales and data discretization, leading to enhanced accuracy in vegetation change analysis and the identification of its driving factors. (3) The spatial heterogeneity of vegetation is influenced by various factors, such as topography, socio-economic conditions, climate, etc. Among these factors, population density and mean annual temperature were the primary driving forces in the study area, with Q &gt; 0.29 and elevation being the strongest explanatory factor (Q = 0.326). The interaction between temperature and night light was the most powerful explanation (Q = 0.541), and the average Q value of the interaction between the average annual temperature and other driving factors was 0.478, which was the strongest cofactor among the interactions. The interactions between any two factors enhanced their impact on the vegetation’s spatial changes, and each driving factor had its suitable range for affecting vegetative growth within this region. This research provides scientific support for conserving vegetation and restoring the ecological system.

Evolution characteristics and attribution analysis of runoff in the coal mining area in northwest China: A case study of the Kuye River Basin

ABSTRACT The Kuye River Basin is the main coal-producing area in northwest China, and it is also a typical arid and semi-arid area. Under the combined influence of climate change and human activities, the runoff of the basin decreases noticeably. The Mann–Kendall trend test, the Hurst index, cumulative anomaly, and wavelet analysis were used to analyze the runoff evolution characteristics of the Kuye River, and the contributions of climate change and human activities to the runoff reduction were quantitatively separated by the slope-changing ratio of the cumulative quantity. The results showed that the runoff decreased significantly from 1970 to 2020, and there was an obvious abrupt change in 1996, while the change trends in precipitation and evaporation were not significant. The contributions of climate change and human activities to runoff reduction were 22.71 and 77.29%, respectively, and human activities have become the dominant factor in runoff variations. Coal mining and the implementation of soil and water conservation measures are considered to be the main human activities contributing to the reduction in runoff. In particular, mining activities reduce groundwater recharge to rivers, which provides a profound understanding of the effect of human activities in mining areas on runoff variation.

Spatial and temporal dynamics of livestock grazing intensity in the Selinco region: Towards sustainable grassland management

Grazing pressure in the Selinco region exhibits significant complexity and variability. To optimize sustainable grassland management in the region, understanding the spatial and temporal dynamics of grazing intensity is crucial. Previous studies have shown the substantial potential of machine learning algorithms in accurately modeling grazing intensity. Consequently, this study predicted grazing intensity for the period 2001–2020 on a 500 × 500 m grid, using variables such as climate, topography, vegetation, anthropogenic disturbances, water sources, and roads as inputs to the machine learning model, with livestock density at the township level in suitable grazing areas as the output variable. Subsequently, the spatial and temporal dynamics of grazing intensity from 2001 to 2020 were synthesized and analyzed. Of these, the Support Vector Machine Regression (SVMR) model exhibited the best fit, with a mean absolute error (MAE) of just 0.068 SU/km2 and a coefficient of determination (R2) of 0.98. The results indicate that grazing intensity shows a spatial pattern of being high in the southeast and low in the northwest, with significant regional differences. The average annual trend declined by −0.006/10a, indicating a continuous downward trend; however, a substantial increase is anticipated in the future, as suggested by a mean Hurst index value of 0.39. Additionally, the grazing pattern in the northern region, including the Qiangtang Nature Reserve, remains relatively stable. However, it is worth noting that the southeastern region exhibited a significant exacerbation of grazing patterns during 2015–2020, necessitating focused attention. These findings are crucial for making informed decisions regarding grassland-livestock balance management and can aid in optimizing resource allocation and ecological conservation strategies. Future research will utilize higher resolution geographic data to enhance the accuracy and efficiency of grazing intensity models.

Spatiotemporal Patterns of Vegetation Evolution in a Deep Coal Mining Subsidence Area: A Remote Sensing Study of Liangbei, China

This study aims to provide a comprehensive analysis of the impacts of high-intensity coal mining on vegetation in Liangbei Town, a typical deep coal mining area in central of China. Using Landsat remote sensing data from 2000 to 2023, processed by the Google Earth Engine (GEE) platform, the study calculates the Normalized Difference Vegetation Index (NDVI). Temporal and spatial distribution patterns of vegetation were assessed using LandTrendr algorithm, Sen’s slope estimation, the Mann–Kendall test, the coefficient of variation, and the Hurst index. Vegetation growth dynamics were further analyzed through transfer matrix and intensity analysis frameworks. Driving factors influencing vegetation trends were evaluated using local climate data and surface deformation variables from SAR imagery. Temporal Dimension: From 2000 to 2023, the annual NDVI in Liangbei Township showed an upward trend with a growth rate of 0.0894 (10a)−1, peaking at 0.51 in 2020. Spatial Dimension: The NDVI distribution in Liangbei Township displayed a pattern of being lower in the center and higher around the edges, with values concentrated between 0.4 and 0.51, covering 50.34% of the total area. Trend of Change: Between 2000 and 2023, 83.28% of the area in Liangbei Township experienced significant improvement in the NDVI, with vegetation growth trends shifting primarily from slight to significant improvement, encompassing a total area of 10.98 km². This shift exhibited a marked tendency. Driving Factors: Deep mining in Liangbei Township is concentrated in the eastern part, with SAR imagery indicating a maximum surface subsidence of 0.26 m. As surface subsidence increases, the NDVI significantly decreases. The findings suggest that in the future, 91.13% of the vegetation in Liangbei Township will display an antipersistent change trend. The study offers critical insights into the interaction between mining activities and vegetation cover can serve as a reference for environmental evolution and management in similar mining areas.

Spatio-Temporal Changes and Driving Mechanisms of Vegetation Net Primary Productivity in Xinjiang, China from 2001 to 2022

Net primary productivity (NPP), a key indicator of terrestrial ecosystem quality and function, represents the amount of organic matter produced by vegetation per unit area and time. This study utilizes the MOD17A3 NPP dataset (2001–2022) to analyze the spatio-temporal dynamics of NPP in Xinjiang and projects future trends using Theil-Sen trend analysis, the Mann–-Kendall test, and the Hurst Index. By integrating meteorological data, this study employs partial correlation analysis, the Miami model, and residual analysis to explore the driving mechanisms behind NPP changes influenced by climatic factors and human activities. The results indicate that: (1) The average NPP in Xinjiang has increased over the years, displaying a spatial pattern with higher values in the north and west. Regions with increasing NPP outnumber those with declining trends, while 75.18% of the area shows un-certain future trends. (2) Precipitation exhibits a stronger positive correlation with NPP compared to temperature. (3) Climate change accounts for 28.34% of the variation in NPP, while human activities account for 71.66%, making the latter the dominant driving factor. This study aids in monitoring ecological degradation risks in arid regions of China and provides a scientific basis for developing rational coping strategies and ecological restoration initiatives.

The Pore Structure Multifractal Evolution of Vibration-Affected Tectonic Coal and the Gas Diffusion Response Characteristics

Vibrations caused by downhole operations often induce coal and gas outburst accidents in tectonic zone coal seams. To clarify how vibration affects the pore structure, gas desorption, and diffusion capacity of tectonic coal, isothermal adsorption-desorption experiments under different vibration frequencies were carried out. In this study, high-pressure mercury intrusion experiments and low-pressure liquid nitrogen adsorption experiments were conducted to determine the pore structures of tectonic coal before and after vibration. The pore distribution of vibration-affected tectonic coal, including local concentration, heterogeneity, and connectivity, was analyzed using multifractal theory. Further, a correlation analysis was performed between the desorption diffusion characteristic parameters and the pore fractal characteristic parameters to derive the intrinsic relationship between the pore fractal evolution characteristics and the desorption diffusion characteristics. The results showed that the vibration increased the pore volume of the tectonic coal, and the pore volume increased as the vibration frequency increased in the 50 Hz range. The pore structure of the vibration-affected tectonic coal showed multifractal characteristics, and the multifractal parameters affected the gas desorption and diffusion capacity by reflecting the density, uniformity, and connectivity of the pore distribution in the coal. The increases in the desorption amount (Q), initial desorption velocity (V0), initial diffusion coefficient (D0), and initial effective diffusion coefficient (De) of the tectonic coal due to vibration indicated that the gas desorption and diffusion capacity of the tectonic coal were improved at the initial desorption stage. Q, V0, D0, and De had significant positive correlations with pore volume and the Hurst index, and V0, D0, and De had negative correlations with the Hausdorff dimension. To a certain extent, vibration reduced the local density regarding the pore distribution in the coal. As a result, the pore size distribution was more uniform, and the pore connectivity was improved, thereby enhancing the gas desorption and diffusion capacity of the coal.

Multifractal characterization of meteorological to agricultural drought propagation over India

Agricultural drought affects the regional food security and thus understanding how meteorological drought propagates to agricultural drought is crucial. This study examines the temporal scaling trends of meteorological and agricultural drought data over 34 Indian meteorological sub-divisions from 1981 to 2020. A maximum Pearson's correlation coefficient (MPCC) derived between multiscale Standardised Precipitation Index (SPI) and monthly Standardised Soil Moisture Index (SSMI) time series was used to assess the seasonal as well as annual drought propagation time (DPT). The multifractal characteristics of the SPI time series at a time scale chosen from propagation analysis as well as the SSMI-1 time series were further examined using Multifractal Detrended Fluctuation Analysis (MF-DFA). Results reveal longer average annual DPT in arid and semi-arid regions like Saurashtra and Kutch (~ 6 months), Madhya Maharashtra (~ 5 months), and Western Rajasthan (~ 6 months), whereas, humid regions like Arunachal Pradesh, Assam and Meghalaya, and Kerala exhibit shorter DPT (~ 2 months). The Hurst Index values greater/less than 0.5 indicates the existence of long/short-term persistence (LTP/STP) in the SPI and SSMI time series. The results of our study highlights the inherent connection among drought propagation time, multifractality, and regional climate variations, and offers insights to enhance drought prediction systems in India.

Spatiotemporal Variation Characteristics and Driving Factors of Vegetation NPP in the Ulansuhai Nur Basin from 2001 to 2020

Vegetation net primary productivity （NPP） represents the ability of plants to fix ecosystem carbon, which is a key indicator to determine the health status and sustainable development of ecosystems. Its spatial and temporal changes and driving factors play an important role in revealing the status of vegetation restoration and guiding ecological restoration. Based on MODIS17A3 NPP data, land use, and meteorological data from 2001 to 2020, the temporal and spatial variation characteristics and driving factors of vegetation NPP in the Ulansuhai Nur Basin of Inner Mongolia were explored by using the methods of coefficient of variation, Theil-Sen Median trend analysis, Mann-Kendall significance test, Hurst index, and Geodetector. The results showed that： ① From 2001 to 2020, the vegetation NPP in the Ulansuhai Nur Basin showed a fluctuating upward trend, with an average value （in terms of C） of 141.03 g·（m2·a）-1 and an average increase rate of 2.33 g·（m2·a）-1. The vegetation NPP had obvious spatial differentiation, which was characterized by high in the southwest and low in the northeast and high in Hetao Plain and low in sandy land and mountainous areas. ② NPP mainly showed an increasing trend, and the area proportions of increasing, decreasing, and unchanged areas were approximately 80%, 3%, and 17%, respectively. The average coefficient of variation of vegetation NPP was 0.149, which mainly showed low fluctuation change, and the area accounted for approximately 51%. The future change trend of NPP was mainly characterized by anti-persistence, with an area ratio of approximately 75%. ③ Land use, altitude, maximum temperature, and slope were the dominant driving factors of variation NPP change in the Ulansuhai Nur Basin, and the q values were all above 0.200. The interaction between altitude and relative humidity had the greatest explanatory power for the spatial differentiation of vegetation NPP in the Ulansuhai Nur Basin. There were significant differences in the explanatory power of land use and all factors except nighttime light to the spatial differentiation of vegetation NPP in the Ulansuhai Nur Basin. According to the research results, in the future, we should strengthen the ecosystem management of the Ulansuhai Nur Basin； continue to implement strict ecological protection and restoration policies； and comprehensively consider factors such as climate, topography, and human activities to carry out comprehensive ecological management according to local conditions to improve the quality of ecosystem services.

Spatiotemporal Variation Characteristics and Driving Factors of Cultivated Land NPP in the Shandong Area Around the Bohai Sea

The Bohai Rim Plain is an important grain-producing area in China. The cultivated land resources have great potential for production, but there are many influencing factors. Understanding the spatiotemporal change characteristics and driving factors of the net primary productivity （NPP） of cultivated land vegetation in this region is of great significance to improve the regional cultivated land production conditions, excavate and enhance the production capacity of cultivated land, and ensure national food security. In this study, Theil-Sen Median trend analysis, Mann-Kendall significance test, Hurst index, and other methods were used to explore the spatiotemporal change characteristics, stability, and sustainability of regional cultivated land NPP. The influence of driving factors on the spatial heterogeneity of cultivated land NPP was analyzed by using optical parameters-based geographical detectors. The results showed that： ① From 2001 to 2019, due to the expansion of construction land during industrialization and urbanization, the cumulative decrease in the area of cultivated land in the Shandong area around the Bohai Sea was 2 004.51 km2. ② During the selected research period, the interannual variation of average cultivated land NPP showed a fluctuating and increasing trend. In terms of spatial distribution, the NPP of cultivated land was bounded by the Dongying District, with the spatial heterogeneity in the north being significantly lower than that in the south. ③ The area with increasing NPP of cultivated land accounted for 88.06% of the total area of cultivated land, mainly with low and medium fluctuations. The NPP of cultivated land will maintain an overall sustained trend of increase across the region in the future. ④ The average annual relative humidity study area had the strongest explanatory power for the spatial variability of NPP in cropland, with a q-value of 0.26, followed by surface soil salinity and subsoil salinity, with q-values greater than 0.2. The interactions between the different drivers all showed either nonlinear enhancement or bifactorial enhancement. The results of this study will help to reveal the characteristics of the changes in cultivated land production capacity and its driving forces in the Bohai Sea region and also provide a theoretical basis for the ecological protection and sustainable development of the region.