Time Lag Effect Research Articles

Time-lag effects of flood stimulation on methane emissions in the Dongting Lake floodplain, China

Natural wetlands are the primary sources of CH4 emissions in the natural environment. However, the understanding of CH4 fluxes in floodplain wetlands remains limited. This study employed eddy covariance methods to observe CH4 fluxes over a three-year period in a subtropical wetland floodplain, specifically the Miscanthus sacchariflorus (M. sacchariflorus) ecosystem in Dongting Lake wetland. Our analysis focused on exploring the impact of flooding frequency on CH4 emissions, flood stimulation effect, time-lag effects, and the environmental factors influencing CH4 fluxes. The M. sacchariflorus ecosystem exhibited an annual CH4 emission rate of 14.54 g CH4C m−2 y−1. During the flood period, the average daily CH4 emissions reached 0.155 g CH4C m−2 d−1, contrasting with the pre-flood period's average of 0.014 g CH4C m−2 d−1. Moreover, the time-lag effect of flooding on CH4 emissions was found to be 10 days, representing the period between inundation and a substantial increase in CH4 emissions. Comparatively, in 2021, following three fluctuations in floodwaters, the average CH4 emission intensity during the flood period decreased by 46.2% and 48.9% when compared to the years 2019 and 2020 which both following one fluctuation, respectively. CH4 emissions during flooding are predominantly influenced by water depth (WD), wherein shallow WD corresponds to higher CH4 emissions. This correlation can be attributed to factors such as vegetation type, water-column pressure, and soil oxygen content. Therefore, increasing frequency of inundation and a higher WD hold promise as effective measures for mitigating CH4 emissions in floodplain wetlands.

The non-uniform time-lag and cumulative responses of terrestrial ecosystem water use efficiency to climate change in Lake Victoria Basin, East Africa

Revealing the response mechanisms of carbon–water cycle to maximum and minimum temperatures and daytime and nighttime warming is important for managing fragile ecosystems exposed to severe climate warming and extreme weathers. Taking water use efficiency (WUE) characterizing the coupled carbon–water cycle as the research object, and maximum and minimum temperatures as the indicators of temperature change, this study explored the response of ecosystem WUE to temperature change in Lake Victoria Basin during distinct rainy and dry seasons. The results revealed the time-lag and cumulative multiple effects of antecedent maximum and minimum temperatures on current ecosystem WUE, which marked differences between the rainy and dry seasons. The monthly average ecosystem WUE in the rainy seasons (0.99 g C kg−1 H2O) were higher than those in the dry seasons (0.83 g C kg−1 H2O) during 2001–2018. WUE were more sensitive to temperature change in the dry seasons, with significant negative correlation with minimum temperature and positive correlation with cumulative maximum temperature. In rainy seasons, WUE showed more significant time-lag and cumulative effects, with response to maximum temperature for lagging approximate 3 months, to minimum temperature for lagging 0–2 months, to cumulative maximum temperature for lagging 1 month, and to cumulative minimum temperature lagging 3–4 months, respectively. The results add new evidence for differential non-instantaneous responses of WUE to maximum and minimum temperatures under dry and wet conditions.

Impact of Climate Change on the Major Eco-Hydrological Parameters in the Dongting Lake Basin

Quantifying the impacts of climate change on evapotranspiration (ET) and gross primary production (GPP) in the Dongting Lake Basin is essential for assessing water scarcity and implementing sustainable development strategies. Premised on actual measurements and remote sensing data from 47 stations, the impact of climate change on eco-hydrological parameters in the Dongting Lake Basin was analyzed in the present study using the BESS model (The Breathing Earth System Simulator), ridge regression analysis, stepwise regression model and time-lag analysis. The results reveal that: (1) the Dongting Lake Basin has been warm and arid over the last four decades, with the frequent occurrence of extreme climate events. Vegetation carbon sequestration capacity exhibited a slightly upward trend with 0.0081 g C m−2 d−1/year from 2000–2017. ET changed with rates of −3.309 mm/year, with possible risk conflicts between water demand and supply in the future. (2) The increasing temperature was the main driver of ET enhancement in the Dongting Lake Basin. Meanwhile, both temperature and precipitation were found to be the dominant drivers of GPP enhancement. The effect of temperature on GPP was found to be greater in the areas covered by crops and tree vegetation, and natural vegetation was more strongly influenced by precipitation than radiation. (3) Extreme temperature events have had a significant impact on evapotranspiration (ET) and gross primary production (GPP) in the Dongting Lake Basin. Specifically, the cold index in extreme temperature events was found to significantly affect ET, while the heat index in extreme temperature events significantly affected GPP. Additionally, both ET and GPP were found to respond to extreme precipitation events in the region. The results of the study established that vegetation is highly sensitive to temperature, especially temperature extremes, and that precipitation also has a stressful effect. Increasing temperatures and precipitation within a range benefit vegetation productivity. (4) In the Dongting Lake Basin, we found that different climatic factors produced different time lag effects on GPP and ET by time lag analysis. This study highlights the lag effects of climate factors and extreme climate events on eco-hydrological parameters. We suggest incorporating the effects into simulation models of eco-hydrological parameters. This will lead to a better understanding of the variation of eco-hydrological parameters under climate change.

Unveiling the time-lag effects of digital transformation on financing constraints in Chinese listed enterprises: a study of automation and network technology

Unveiling the time-lag effects of digital transformation on financing constraints in Chinese listed enterprises: a study of automation and network technology

Shifts bidirectional dependency between vegetation greening and soil moisture over the past four decades in China

Soil moisture (SM) has changed significantly over the past 40 years in China, while NDVI has varied dramatically, leading to increasing regional conflict between vegetation growth and water resource use. Quantifying the bidirectional dependency between SM and NDVI is essential for understanding the balance between land vegetation and water resources. However, few studies have reported their mutual feedback and spatiotemporal bidirectional dependency. This paper aims to reveal the bidirectional dependency between SM and NDVI using Granger causality test to show spatiotemporal tendency coupling patterns through trend coupling analysis, wavelet transform, and lag correlation. The Results indicated that a coupling relationship existed between SM and NDVI over most of China. The unidirectional Granger effect between SM on NDVI was 58 %, the unidirectional Granger effect of NDVI on SM was 26 %, and the bidirectional Granger relationship between SM and NDVI was 16 %. The Granger relationship is different for different soil layers or land cover types. SM and NDVI increased together in 36 % of the land cover areas, but SM increased and NDVI decreased in 12 %, and the SM decreased and NDVI increased in 27 %. The trend coupling between SM and NDVI has spatial heterogeneity. There is no change rule of coupling relationship with drought variation, but SM and NDVI increased together with more overlapping ecological restoration projects. SM decreased with the increase of NDVI from 1982 to 2010 but has reversed since 2011. NDVI and SM co-increased significantly with the implementation of ecological restoration projects during 2011–2022. The coupling relationship has a time lag effect of 1–3 months, and the time lag of NDVI to SM of deep soil layers mainly occurred in Southern China. This study illustrated the coupling framework and feedback analysis between SM and vegetation greening, which is helpful for the scientific implementing ecological restoration projects and the management of ecosystem carbon and water cycles.

Coupling mechanism between vegetation and multi-depth soil moisture in arid–semiarid area: Shift of dominant role from vegetation to soil moisture

Soil moisture (SM) is an essential water source for vegetation, while vegetation also exerts importance on SM during succession. It is crucial to investigate the coupling mechanism between SM and Normalized Difference Vegetation Index (NDVI), which can drive terrestrial carbon sinks while maintaining water sustainability. The Granger causality was used to analyze the unidirectional and bidirectional coupling relationship of SM and NDVI in the 0–289 cm soil layers. The windowed cross correlation was utilized to quantify the bidirectional time lag effect. The findings indicate that the vegetation improved significantly (92.47%) in 2001–2021 in the arid–semiarid area, while the SM in all four layers showed a decreasing trend (>50%). In most areas, NDVI was bidirectional dependent on SM. Vegetation dominated the loss of shallow SM (<100 cm) through root uptake and transpiration, whereas deep SM (>100 cm) in turn dominated the vegetation growth, structure and spatial pattern. At the spatial scale, the top layer of SM in the arid area demonstrated less sensitivity to NDVI due to coupling with the atmosphere. Instead, the influence of NDVI on SM in the semiarid area was dominant, so more attentiveness to the consumption of SM by vegetation is needed. The bidirectional legacy effect could be up to 6 months, attributed to water transport distance, vegetation root systems and physiological mechanisms. The correlation reversed when the lag time was extended or the soil layer was deepened, which means that the greening of vegetation will intensify deep soil drying. Differences in functional traits among plants lead to varying responses. The changes of deep SM need to focus on when implementing artificial meadow construction. This study provides a theoretical basis for optimizing ecological engineering projects, which is essential for restoring global drylands ecosystems.

Natural and socio-environmental factors contribute to the transmissibility of COVID-19: evidence from an improved SEIR model.

COVID-19 has ravaged Brazil, and its spread showed spatial heterogeneity. Changes in the environment have been implicated as potential factors involved in COVID-19 transmission. However, considerable research efforts have not elucidated the risk of environmental factors on COVID-19 transmission from the perspective of infectious disease dynamics. The aim of this study is to model the influence of the environment on COVID-19 transmission and to analyze how the socio-ecological factors affecting the probability of virus transmission in 10 states dramatically shifted during the early stages of the epidemic in Brazil. First, this study used a Pearson correlation to analyze the interconnection between COVID-19 morbidity and socio-ecological factors and identified factors with significant correlations as the dominant factors affecting COVID-19 transmission. Then, the time-lag effect of dominant factors on the morbidity of COVID-19 was investigated by constructing a distributed lag nonlinear model and standard two-stage meta-analytic model, and the results were considered in the improved SEIR model. Lastly, a machine learning method was introduced to explore the nonlinear relationship between the environmental propagation probability and socio-ecological factors. By analyzing the impact of environmental factors on virus transmission, it can be found that population mobility directly caused by human activities had a greater impact on virus transmission than temperature and humidity. The heterogeneity of meteorological factors can be accounted for by the diverse climate patterns in Brazil. The improved SEIR model was adopted to explore the interconnection of COVID-19 transmission and the environment, which revealed a new strategy to probe the causal links between them.

Effect of national COVID-19 control measures on water quality in China using an improved dual difference-in-differences method

The impact of COVID-19 on the natural environment, particularly the aquatic environment, is a major concern. However, there is a lack of national-level research on the impact of COVID-19 on the aquatic environment. This study analysed the impacts of COVID-19 on water quality using an improved dual spatial-temporal Difference-in-Differences Method (DDiD) models in cities with and without formal lockdown policies all over the China. The study found that the effectiveness of national COVID-19 control measures varied spatially for different types of pollutants. The lockdown measure resulted in a 0.052-point decline in TN concentration and a 0.056-point decline in CODMn concentration, while the non-formal lockdown measures led to a 0.2-point increase in TN concentration and a 0.016-point decline in CODMn. Spatial-temporal analysis revealed that COVID-19 and control measures immediately reduced TN concentration. The decrease effect of non-formal lockdown measures was lower than that of the lockdown measure and caused an opposite overall effect of TN concentration change. The impact of COVID-19 and control measures on CODMn concentration had a time lag effect, as it maintained or slightly increased local CODMn concentration once the lockdown measure was implemented, and then decreased after 4 weeks. Heterogeneity analysis showed that the reduction effect on TN was more significant in cities that rely more on industrial activities, while the increase effect on CODMn was more significant in cities that rely more on general and domestic services industries, revealing the diversity of water pollution sources. Overall, the impact of the COVID-19 pandemic on water quality is not as significant as that on the atmospheric environment due to the diversity of pollution sources. The study partially proves that China has achieved excellent point source control for water pollution, but there is little potential reduction of water pollution at point sources, especially industrial sources. The best way to improve the aquatic environment is still to enhance its capacity by increasing water flow and storage ability and reducing the amount of discharge from every pollution source by improving production levels and pollution treatment ratios, especially for nonpoint sources.

Time-Lag Response of Landslide to Reservoir Water Level Fluctuations during the Storage Period: A Case Study of Baihetan Reservoir

Fluctuations in reservoir water levels exert a strong triggering effect on landslides along reservoir banks, constituting a long-term concern in the safe operation of hydroelectric projects and in the prevention and management of geological disasters. While existing research has investigated the impact of periodic water level changes on the deformation of reservoir bank landslides, observation and detection of such deformation are challenging, with noticeable gaps in understanding how these deformations respond to water level changes during the water impoundment period. To address this, our study targets the Baihetan Reservoir, leveraging 567 ascending and descending LiCSAR data and LiCSBAS (the small-baseline subset within LiCSAR) technology to construct a time series of ground deformations in the study area from 2019 to 2023. The TLCC (Time Lag Cross Correlation) model was employed to examine the time-lag response pattern of reservoir bank landslide deformations to reservoir water level changes during the impoundment period. Our findings indicate a clear time-lag response in reservoir bank landslide deformations to water level changes during the impoundment process. The rise in water levels emerged as a primary factor influencing the instability of reservoir bank landslides. During the half-year impoundment period of the Baihetan Reservoir, a time lag of 5–7 days was observed between landslide deformations and increases in water levels, with landslides on the eastern and western banks exhibiting differing time-lag response patterns. Our study illuminates the time-lag effect between water level changes during reservoir impoundment and reservoir bank landslide deformation monitoring. By proposing a quantitative analysis methodology utilizing LiCSBAS technology and the TLCC model, our findings can inform decision-making in the field of disaster prevention and reduction in reservoir engineering.

Transition to organic farming negatively affects bat activity.

The effectiveness of organic farming on biodiversity has been widely documented especially for plants, arthropods and birds; however, the effects of the transition period required to become an organic farm on wildlife remain poorly understood.We assessed the effects of organic farming on insectivorous bats in citrus orchards in the Republic of Cyprus employing two matched designs (conventional vs. 3-year organic-transitional and conventional vs. organic-certified) and a third unmatched design (3-year organic-transitional vs. organic-certified). We specifically investigated whether the transition period prior to full organic certification influenced bat activity with a special focus on any moderation effects from surrounding semi-natural areas.The activity of three (Pipistrellus kuhlii, Hypsugo savii and Miniopterus schreibersii) of four bat species was significantly lower in farms undergoing the transitional period than in conventional farms, and P. kuhlii and H. savii were significantly less active in organic transitional farming systems that in organic-certified ones. Furthermore, the activity of the most dominant species (P. kuhlii) was significantly higher on organic than transitional and conventional citrus orchards, thus suggesting a time-lag effect. Landscape complexity measured as the amount of semi-natural areas did not moderate the effects of farming system for any study species. Synthesis and application. The transition to organic farming had persistent detrimental effects on bats and potentially on the pest suppression services they provide. Future agri-environmental policy should consider the transition period and implement measures to mitigate any negative effects on biodiversity, alongside promoting asynchronous transition of nearby farms. Our findings further highlight the crucial need to consider the time since transition to organic farming when assessing potential benefits of organic management on biodiversity.

Effect of digital transformation on innovation performance in China: corporate social responsibility as a moderator

Introduction: In the digital economy, digital transformation (DT) is a deliberate decision to improve organizational procedures, alter production processes, introduce precision marketing, and more, ultimately impacting how well businesses innovate. This is why the current article investigates the effect of DT and the firm’s innovation performance and the boundary condition of corporate social responsibility (CRS).Method: This study proposed a conceptual research model for the effect of DT on innovation performance and discussed the boundary condition of CRS. We collected China’s listed A-share firms’ data to examine the proposed hypotheses statistically. After Hausman test, the current study adopted fixed-effect regression, examined the heterogeneity issues resulting from different industry classifications, and robustness test for the correctness of the results.Results and Implications: The following main conclusions are drawn: 1) DT can significantly enhance product innovation performance; 2) DT can significantly improve process innovation performance; 3) There is a time lag effect on the innovation performance (both product and process innovation performance) of the previous period on the innovation performance of the current period; 4) CSR positively moderates the role of DT on innovation performance; and 5) The impact of DT is heterogeneous across industries and patent. This study not only enriched the literature on DT and innovation performance but also provided the guidelines to promote digital transformation at the firm level.

Influences of Talent Cultivation and Utilization on the National Human Resource Development System Performance: An International Study Using a Two-Stage Data Envelopment Analysis Model

To enhance national competitiveness, countries are committed to building a National Human Resource Development (NHRD) system to develop talents. However, studies have rarely investigated the internal development process of the NHRD system and the performance at the system and sub-system levels. Thus, this study constructed the performance evaluation model of the NHRD system from a two-stage process efficiency perspective that first cultivates talents and then uses the talents produced to create value. In addition, considering the problem of international talent flow and the time-lag effect, the bad output and the time-lag between inputs and outputs were incorporated into the model. The subjects included 60 countries, including Argentina, China, and OECD member countries. The results reveal that countries that excel at nurturing talents do not necessarily have the ability to effectively use talents to create value. Only having high-efficiency talent cultivation cannot strengthen competitiveness. Sensitivity analysis was also conducted to identify the input that affects talent cultivation and utilization efficiency, which could be used as a reference for competitiveness and NHRD performance improvement.

Comigration and interactions between two species of rice planthopper (Laodelphax striatellus and Sogatella furcifera) and natural enemies in eastern Asia.

Natural enemies are important in pest control. However, control by natural enemies is hindered by the migration of rice planthoppers. Therefore, comigration and interactions between Laodelphax striatellus (Fallén) and Sogatella furcifera (Horváth) and five predator species, Chrysoperla sinica Tjeder, Harmonia axyridis (Pallas), Episyrphus balteatus, Syrphus corollae (Fab.) and Chrysopa pallens (Rambur) in eastern Asia were investigated. From 2012 to 2021, the migration patterns of two rice planthoppers and five natural enemy species were monitored by suction trapping on Beihuang Island, Shandong Province, China. Both planthoppers and the five natural enemies regularly comigrated from late April to late October each year. There were significant interannual and seasonal differences in the numbers of two rice planthoppers migrating across this island. Simulated seasonal migration trajectories indicated different source areas for the two rice planthoppers, which mainly originated in northeast, north and east China. The biomass of planthoppers was significantly positively correlated with that of the ladybug H. axyridis in all migration periods, and significant differences in the ratio of rice planthoppers to natural enemies among months. A time-lag effect between seasons was obtained when natural enemies and pests comigrated. Migration was coordinated between rice planthoppers and natural enemies in East Asia. When natural enemies and rice planthoppers comigrated, time lags between seasons were observed. The unique insights into the migration patterns will help to increase understanding of the occurrence of rice planthoppers in eastern Asia and provide an important theoretical basis for regional monitoring and management of rice planthoppers. © 2023 Society of Chemical Industry.

Heating efficiency enhanced by combination of phase change materials and activated carbon for dry floor heating system

Phase change materials (PCMs) have diverse applications in energy storage, buildings, cooling, and heat exchangers. In this study, n-docosane and activated carbon (AC) were combined using the vacuum impregnation method to form an aggregate, which was then injected into a Poly vinyl chloride container and applied to a dry floor heating system. Thermal storage, gravimetric, conductivity, and specific heat analyses were performed to evaluate the thermal performance of n-docosane and AC-based heat-storage aggregate (D-HSA). The dynamic heat transfer performance and power consumption of the dry floor heating system after D-HSA application were then evaluated. Pure n-docosane and D-HSA showed latent heat capacities of 242.7 J/g and 235.1 J/g and 92.10 J/g and 92.66 J/g during heating and cooling. In terms of thermal durability, D-HSA was more stable than pure n-docosane. Thermal conductivity analysis revealed that D-HSA had a high thermal conductivity (approximately 597 %) in the liquid state. In the field-based dynamic heat transfer analysis experiment, the PCM dry floor heating system filled with D-HSA had the advantages of both dry and wet floor heating systems, namely, a fast surface temperature increase rate and the time-lag effect, respectively. Therefore, the PCM dry floor heating system can compensate for the disadvantages of the dry and wet floor heating systems and can be considered an efficient heating system in terms of energy savings.

Does climate vulnerability promote green investment under energy supply restriction?

This research investigates the impact of climate vulnerability on green investment using panel data of 107 countries over the period 1995–2019. We find that climate vulnerability reduces green investment in both climate change mitigation and climate change adaptation technologies. This finding holds up under a series of robustness tests and after taking into consideration the time lag effect, cross-sectional dependence, and endogenous problems. We further present that it is socioeconomic-related climate vulnerability that hinders green investment, whereas physical vulnerability does the opposite. Analyses of moderating channels show that the negative impact of climate vulnerability on green investment is more pronounced in countries with lower levels of adaptation readiness, economic development, and technical innovation, and higher levels of energy supply restriction.

Should time-lag and time-accumulation effects of climate be considered in attribution of vegetation dynamics? Case study of China's temperate grassland region.

Although the time-lag and time-accumulation effects (TLTAEs) of climatic factors on vegetation growth have been investigated extensively, the uncertainties caused by disregarding TLTAEs in the attribution analysis of long-term changes in vegetation remain unclear. This hinders our understanding of the associated changes in ecosystems and the effects of climate change. In this study, using multiple methods, we evaluate the biases of attribution analyses of vegetation dynamics caused by the non-consideration of TLTAEs in the temperate grassland region (TGR) of China from 2000 to 2019. Based on the datasets of the normalized difference vegetation index (NDVI), temperature (TMP), precipitation (PRE), and solar radiation (SR), the temporal reaction patterns of vegetation are analyzed, and the relationships among these variables under two scenarios (considering and disregarding TLTAEs) are compared. The results indicate that most areas of the TGR show a greening trend. A time-lag or time-accumulation effect of the three climatic variables is observed in most areas with significant spatial differences. The lagged times of the vegetation response to PRE are particularly prominent, with an average of 2.12 months in the TGR. When the TLTAE is considered, the areas where changes in the NDVI are affected by climatic factors expanded significantly, whereas the explanatory power of climate change on NDVI change increased by an average of 9.3% in the TGR; these improvements are more prominent in relatively arid areas. This study highlights the importance of including TLTAEs in the attribution of vegetation dynamics and the assessment of climatic effects on ecosystems.

Sensitivity of Vegetation Productivity to Extreme Droughts across the Yunnan Plateau, China

Extreme drought has negative impacts on the health of vegetation and the stability of ecosystems. In this study, the CASA model was employed to estimate the net primary productivity of vegetation over the Yunnan Plateau. The time-lag effects on vegetation were observed within a 0–6 month period of extreme droughts using the Pearson correlation coefficient. The resistance of vegetation during extreme droughts was quantified, and the recovery capability of vegetation following these events was analyzed using the ARIMA model. Moreover, the study investigated the response of vegetation to extreme droughts across diverse altitudinal gradients. The results showed that: (1) This round of extreme drought led to a decrease in the NPP of vegetation in the Yunnan Plateau. (2) Vegetation exhibits a 1–3-month lag period in response to extreme drought, with forests showing slower responses than grasslands and shrubs and higher resistance to the drought. Except for agricultural vegetation, most other vegetation types are able to recover their productivity within a year. (3) Vegetation above 3000 m is less susceptible to the impacts of extreme drought. With increasing elevation, forests exhibit an earlier lag period in response to extreme drought and an increase in resistance, but lower elevation vegetation demonstrates better recovery from extreme drought events. Shrub vegetation shows the highest resistance at elevations between 3000–4000 m, and shrubs at middle to high elevations have better recovery capacity than those at low elevations. Grassland vegetation exhibits increased resistance to extreme drought with higher elevation and shows better recovery. Agricultural vegetation demonstrates higher resistance at middle to high elevations, with no significant elevation differences in recovery capacity. Extreme drought events not only have a lag effect on the vegetation ecosystem, but also affect its stability and resilience to future drought events. To adapt to climate change, future research should emphasize the role of small-scale climate in vegetation’s response to drought.

Assessing the interrelation between NDVI and climate dependent variables by using granger causality test and vector auto-regressive neural network model

Changes in ecosystem structure and function can be revealed by examining the prevailing patterns in vegetation growth and the forces that shape those patterns. The mechanism of ecosystem behaviour may be better understood if the trend of vegetation change and its sensitivity to climatic variation are well understood. The interaction of vegetation and climatic factors (it's driving variables) is non-linear in behaviour and affected by time lag and time accumulation. Jharkhand has a typical plateau in eastern India, having a mixed climate (arid and semi-arid), taken as the study area, and the spatiotemporal distributions of the normalized difference vegetation index (NDVI) were explored with interaction driving factors. This study investigated the time-lag and time-accumulation effects of the NDVI response to climate factors Evapotranspiration (ET), Land Surface Temperature (LST), Potential Evapotranspiration (PET), Precipitation (PREC), and Soil Moisture (SM) and identified the primary controlling factors that affect the vegetation dynamics. The observations indicate that the correlation between NDVI and summer LST (- 0.838) was discovered to be greater than the correlation of NDVI with SM (r = 0.90) and PREC (r = 0.751), showing NDVI as more sensitive to LST when comparing to SM, and PREC, while PET exhibits the significant positive correlation (r = −0.751) with the NDVI in autumn during the studied duration. Higher NDVI values were seen during the monsoon (0.54 ± 0.12), which is correlated with a decrease in the monsoon LST (25.8 ± 0.20), followed by the winter (0.47 ± 0.13), summer (0.33 ± 0.18), and fall (0.37 ± 0.04). Vegetation growth is influenced by both the time-lag and time-accumulation effects of temperature and the time-accumulation impact of precipitation. Regarding the climate-vegetation response mechanism, the application of the Granger Causality (GC) Test and GC-based Vector Auto-Regressive Neural Network (VARNN) Model test reveals that the 0–2 month optimum time lag effect is prevalent in the study area. In addition, the LST and SM have a more prominent stimulating influence on plant growth in the study region than precipitation. The above findings highlight the need to effectively monitor vegetation dynamics under environmental changes by considering the temporal impacts of vegetation response to climate when the current climate models research vegetation-climate interactions.

Constructing GRACE-Based 1 km Resolution Groundwater Storage Anomalies in Arid Regions Using an Improved Machine Learning Downscaling Method: A Case Study in Alxa League, China

Using the Gravity Recovery and Climate Experiment (GRACE) satellite to monitor groundwater storage (GWS) anomalies (GWSAs) at the local scale is difficult due to the low spatial resolution of GRACE. Many attempts have been made to downscale GRACE-based GWSAs to a finer resolution using statistical downscaling approaches. However, the time-lag effect of GWSAs relative to environmental variables and optimal model parameters is always ignored, making it challenging to achieve good spatial downscaling, especially for arid regions with longer groundwater infiltration paths. In this paper, we present a novel spatial downscaling method for constructing GRACE-based 1 km-resolution GWSAs by using the back propagation neural network (BPNN) and considering the time-lag effect and the number of hidden neurons in the model. The method was validated in Alxa League, China. The results show that a good simulation performance was achieved by adopting varying lag times (from 0 to 4 months) for the environmental variables and 14 hidden neurons for all the networks, with a mean correlation coefficient (CC) of 0.81 and a mean root-mean-square error (RMSE) of 0.70 cm for each month from April 2002 to December 2020. The downscaled GWSAs were highly consistent with the original data in terms of long-term temporal variations (the decline rate of the GWSAs was about −0.40 ± 0.01 cm/year) and spatial distribution. This study provides a feasible approach for downscaling GRACE data to 1 km resolution in arid regions, thereby assisting with the sustainable management and conservation of groundwater resources at different scales.

Forecasting per Capita Energy Consumption in China Using a Spatial Discrete Grey Prediction Model

To overcome the limitations of the present grey models in spatial data analysis, a spatial weight matrix is incorporated into the grey discrete model to create the SDGM(1,1,m) model, and the L1-SDGM(1,1,m) model is proposed, considering the time lag effect to realize the simultaneous forecasting of spatial data. The validation of the SDGM(1,1,m) and L1-SDGM(1,1,m) models is achieved, and finally, the per capita energy consumption levels (PCECs) of 30 provinces in China from 2020 to 2025 is predicted using SDGM(1,1,m) with a metabolic mechanism. We draw the following conclusions. First, the SDGM(1,1,m) and L1-SDGM(1,1,m) models established in this paper are reasonable and improve forecasting accuracy while supporting interactive regional forecasting. Second, although SDGM(1,1,m) resembles the DGM(1,n) model, their modeling conditions and targets are different. Third, the SDGM(1,1,m) and L1-SDGM(1,1,m) models can be used to effectively analyze the spatial spillover effects within the selected modeling interval while achieving accurate predictions; notably, from 2010 to 2017, the PCECs of Inner Mongolia and Qinghai were most affected by spatial factors, while the PCECs of Jilin, Jiangxi, and other provinces were influenced little by spatial factors. Fourth, predictions indicate that the PCECs of most Chinese provinces will increase under the current grey conditions, while the PCECs of provinces such as Beijing are expected to decrease.