Analysis Of Spatial Variability Research Articles

Analysis of depth spatial variations of accelerograms recorded from the Center for Engineering Strong Motion Data: Numerical Simulation for Multipoint Ground Motions

Analysis of depth spatial variations of accelerograms recorded from the Center for Engineering Strong Motion Data: Numerical Simulation for Multipoint Ground Motions

Soil characterization, CBR modeling, and spatial variability analysis for road subgrade: a case study of Danchuwa – Jajere Road, Yobe State, Nigeria

Road construction projects require a thorough understanding of soil properties to ensure the stability and longevity of the infrastructure. This study investigates soil properties along a proposed 34 km road alignment in Yobe State, Nigeria, to characterize soil variability for road construction and develop a predictive model for California Bearing Ratio (CBR). Of the 34 soil samples analyzed, 30 were classified as A-3(1) and four as A-1(1) according to the AASHTO system. Geotechnical testing, including particle size distribution (grading percentages: gravel 0.02%–75.34%, sand 15.5%–90.88%, fines 8.92%–34.84%), Atterberg limits (liquid limits 17%–33%, plastic limits 14%–27%, plasticity index <12%), specific gravity (2.01 to 2.73), compaction (maximum dry density 1.83–2.19 Mg m−3, optimum moisture content 7.29%–14.42%), and CBR tests (values ranging from 5%–62%), were conducted. Correlation analyses revealed strong positive relationships between maximum dry density (r = 0.82) and specific gravity (r = 0.89) with CBR values. Cluster analysis segmented the samples into four distinct groups: Cluster 0 (11 samples), Cluster 1 (9 samples), Cluster 2 (5 samples), and Cluster 3 (9 samples). A linear regression model predicted CBR using maximum dry density and specific gravity (mean squared error = 9.82, R2 = 0.92). Based on CBR criteria, 8 out of 34 samples (CBR 20%–53%) satisfied subbase requirements, while none met the recommended minimum CBR of 80% for base course materials. This study enhances road construction planning through soil variability analysis, effective soil categorization via cluster analysis, and a reliable CBR prediction model. While on-site materials are unsuitable for subgrade and subbase layers, alternative materials or ground improvement techniques are recommended for the base course layer to enhance bearing capacity.

Enhancing life cycle assessment for reversible ground-coupled heat pump systems through dynamic analysis

Ground-coupled heat pump (GCHP) systems can provide comfortable indoor environments, but also inevitably contribute to greenhouse gas emissions and other impacts on human health, ecosystems, and resources. Life cycle assessment (LCA) methodology has been widely adopted to estimate the environmental impacts associated with GCHP systems, with operational electricity consumption being the largest contributor among most categories. Given the data-intensive nature of LCA, operational electricity consumption should be prioritised to refine the accuracy of LCA results. Previous studies, however, have relied on static COP (Coefficient of Performance) and annual average data to obtain the environmental impacts and have disregarded the effect of long-term performance degradation caused by building thermal load imbalances. In this paper, to bridge this research gap, a dynamic operational environmental impact assessment (DOEIA) method was proposed to improve the precision of LCA results by incorporating higher temporal resolution data of electricity mix and real-time performance modelling of the reversible GCHP system. Impacts of different temporal resolutions (i.e. monthly, trimestral, and annual) on the accuracy of LCA results was examined and the operational performance degradation resulting from imbalanced building heating and cooling loads was considered. Results demonstrate that while performance degradation effects are evenly distributed across impact categories, gaps due to temporal resolutions vary significantly between different categories. Subsequent analysis of spatial variations in the latter further emphasises the importance of accounting for higher temporal resolutions. Finally, life cycle impact assessment (LCIA) results for reversible GCHP systems in different locations were obtained with the support of DOEIA method and the results underscore the necessity of a cleaner energy mix. The proposed DOEIA method can be applied to other energy systems for comparative analyses. It is replicable in other countries and regions and therefore is expected to provide methodological guidance for future decision-makers.

Spatial variation, multi-meteorological factors and potential source analysis of air pollutants in Chengdu megacity of Chengdu-Chongqing economic circle

Spatial variation, multi-meteorological factors and potential source analysis of air pollutants in Chengdu megacity of Chengdu-Chongqing economic circle

Analysis of spatial and temporal variation of frost dates in Jilin Province

In order to understand the changing law of frost date (number) in Jilin Province, and to improve the early warning and forecasting ability of frost disaster, Based on the day-by-day minimum temperature data from 47 meteorological observation stations in Jilin region from 1969 to 2020, the spatial and temporal distribution characteristics of frost date (number) were analyzed by using linear propensity ratio, Mann-Kendall method and sliding t-test, as well as REOF. The results show that: the first frost date, the last frost date, and the number of frost-free days follow the general spatial distribution pattern of "early in the northwest and late in the southeast," "late in the southeast and early in the northwest," and "long in the northwest and short in the southeast". The overall trend of frost dates is delayed first frost date, and early final frost date and prolonged frost-free days. The years with abrupt changes in the first frost date, final frost date and number of frost-free days were 2004, 1997 and 1991, respectively. The number of frost-free days in Jilin Province was categorized into 3 regions based on REOF analysis. It can be seen that analyzing the spatio-temporal variation rule of frost can provide theoretical basis for frost prevention and disaster reduction.

Analysis of spatial variability of smog episodes over National Capital Delhi during (2013–2017)

Air pollution is a pressing issue in Delhi, with smog occurrences causing reduced visibility and various respiratory problems. A series of severe SMOG (smoke + fog) episodes between 2013 and 2017 with reduced visibility and exceptionally high PM2.5 concentrations have been reported in Delhi especially around Diwali festival (October–November). The Smog of 2016 is referred as Great Smog of Delhi. This study examined remote sensing data from 2013 to 2017 to investigate smog episodes in Delhi during pre-Diwali, post-Diwali, and Diwali. Satellite-derived parameters viz absorbing aerosol index (AAI), aerosol optical depth (AOD), and ozone monitoring instrument (OMI) along with air pollution data and climatic parameters were used to analyze smog episodes. The results showed that during smog episodes, AOD, AAI and PM2.5 concentrations exceeded permissible limits significantly at all stations across Delhi during the Diwali festival. The ground-based observations at different locations across Delhi and satellite data-derived datasets confirmed the severity of smog episodes. The findings indicate that burning of fire crackers coupled with agriculture stubble burning and subsequent transport of the smoke from North Western states through the Capital had a greater impact on deteriorating air quality in Delhi than local pollution, especially during unfavorable weather conditions associated with high humidity and weaker winds. The outcomes highlight the significance of remotely sensed information in identifying smog episodes and their severity in Delhi. It also underlines the necessity for efficient interventions to control air pollution, particularly amid festivals like Diwali.

Analysis of Spatial and Temporal Variations in Evapotranspiration and Its Driving Factors Based on Multi-Source Remote Sensing Data: A Case Study of the Heihe River Basin

The validation of remotely sensed evapotranspiration (ET) products is important for the development of ET estimation models and the accuracy of the scientific application of the products. In this study, different ET products such as HiTLL, MOD16A2, ETMonitor, and SoGAE were compared using multi-source remote sensing data and ground-based data to evaluate their applicability in the Heihe River Basin (HRB) during 2010–2019. The results of the comparison with the site observations show that ETMonitor provides a more stable and reliable estimation of ET than the other three products. The ET exhibited significant variations over the decade, characterized by a general increase in rates across the HRB. These changes were markedly influenced by variations in land use and topographical features. Specifically, the analysis showed that farmland and forested areas had higher ET rates due to greater vegetation cover and moisture availability, while grasslands and water bodies demonstrated lower ET rates, reflecting their respective land cover characteristics. This study further explored the influence of various factors on ET, including land use changes, NDVI, temperature, and precipitation. It was found that changes in land use, such as increases in agricultural areas or reforestation efforts, directly influenced ET rates. Moreover, meteorological conditions such as temperature and precipitation patterns also played crucial roles, with warmer temperatures and higher precipitation correlating with increased ET. This study highlights the significant impact of land use and climatic factors on spatiotemporal variations in ET within the HRB, underscoring its importance for optimizing water resource management and land use planning in arid regions.

Effects of Surface Area and Water Level Variability on Fisheries Production in Lake Naivasha, Kenya

Lake Naivasha is a freshwater, a Ramsar site and an Important Bird Area. It provides multiple services to the surrounding communities such as Fishing, Tourism and Agriculture. Lake Naivasha has been experiencing a water level variation cycle over the years as a result of it being shallow and experiencing high evaporation. Variation in water levels leads to flooding, and receding which disrupts the Lake ecosystem thus causing direct and indirect effects on fishing. The study was conducted to establish the relationship between Surface area and water level variability and fish yield and revenue in Lake Naivasha. Primary data was collected using a semi-structured questionnaire, and Key informant interviews. Secondary data was collected using Landsat images that provided the temporal and spatial water level variability and documentary analysis between 1989 to 2022. Both descriptive statistics and inferential statistics were used for data analysis. The results show a positive correlation between Surface area Variability and fish yield (r= 0.6261, R2= 0.3920) and fish revenue (r= 0.5219, R2= 0.2724). Surface area variability accounts for 39% of the variation in total fish yield, and 27% of the fish revenue. The study recommends continuous monitoring and surveillance of the lake level, and water quality by the relevant government agencies, as well as watershed management and conservation measures for the sustainability of the Lake’s fishery.

Achieving urban vibrancy through effective city planning: A spatial and temporal perspective

Achieving urban vibrancy is a critical goal for urban planning and policy-making, and the city's physical plan plays an important role in shaping and supporting urban vibrancy. However, the effectiveness of urban planning strategies in optimizing resource allocation and fostering a vibrant city remains unclear. This study aims to contribute to this understanding by exploring the spatial and temporal effects underlying the association between urban vibrancy and urban forms. Using mobile-phone location-request data along with urban nighttime light data as a proxy for urban vibrancy in Shenzhen, China, we conducted a geographically and temporally weighted regression (GTWR) analysis to examine the relationship between land use, street configuration, and urban vibrancy. The results showed that a diverse land use mix and the proportions of land allotted for sports and cultural usage, parks, and greenspace generally have a positive impact on urban vibrancy. We noted the facilitating role of degree centrality and closeness centrality on urban vibrancy, compared to the negative impact of betweenness centrality. Our analysis of local spatial variations in the influence of road networks suggests a potential connectivity threshold for attracting people to specific areas. Finally, we identified a divergence between weekdays and weekends in terms of temporal variations.

Engineering attributes of ground motions from February 2023 Türkiye earthquake sequence

The 2023 Türkiye earthquake sequence includes the 6 February M7.81 mainshock followed approximately 9 h later by an M7.74 event, and many smaller aftershocks including M6.81 and M6.37 events on 6 and 20 February, respectively. These events occurred in a region near the plate boundary of the East Anatolian Fault, in the proximity of which numerous ground motion recording stations had been installed north of the Türkiye–Syria border. As a result, the events were well recorded both near the fault and at rupture distances up to 582 km. We describe the available recordings and component-specific data processing performed with the aim of optimizing usable bandwidth. The resulting database includes 337, 365, 284, and 229 usable three-component recordings from the M7.81, M7.74, M6.81, and M6.37 events, respectively. We also present source, path, and site metadata that were compiled according to uniform protocols. Comparisons to a global ground motion model (GMM) for active tectonic regions and a local, Türkiye-specific model demonstrate the existence of complex path effects that result in relatively poor fits between the GMMs and observed data at large distances (generally R JB &gt; 200 km). Under-predictions at some stations may be influenced by directivity and/or basin effects that affect the ground motions but that are not accounted for directly in the GMMs. We present analysis of spatial variability of several intensity measures. The residual maps produced from these analyses demonstrate that the global GMM overpredicts on the Anatolian block and underpredicts on the Arabian block, which is suggestive of distinct crustal attenuation features.

Application of Fashion Element Trend Prediction Model Integrating AM and EfficientNet-b7 Models in Art Design

The poor application effect of traditional sports training methods and the difficulty of recording data due to the time and space constraints of sports make it difficult for trainers to improve their learning outcomes. Based on this, the study proposes to apply human posture recognition in sports teaching design, and use VGGNet-19 as a feature extractor and OpenCV open source software to capture posture movements, and introduce the concepts of joint angle and movement similarity to design a sports assessment system for physical education based on the geometric spatial feature variability analysis of posture based on limb angle information. The testing outcomes demonstrate that the study’s improved gesture recognition algorithm has a recognition rate of more than 90% on gesture movements, and the maximum recognition error value (0.010) is smaller than that of the dynamic time-regularised gesture algorithm (0.014) and the convolutional neural network algorithm (0.017). The assessment system is also better able to improve students’ professional performance and satisfaction, with its average professional score and satisfaction reaching 86 and 92%, which is significantly better than other comparative algorithms. The method is effective in providing trainers with data-based training scenarios and helping them to improve their learning in sport.

Evaluating trends in groundwater quality of coastal alluvial aquifers of Eastern India for sustainable groundwater management.

Groundwater is a precious natural element which ensures global water, food, and environmental security in the twenty-first century. Systematic monitoring, sustainable utilization, preservation and remediation are critical aspects of efficient groundwater resource management. This study deals with the analysis of spatial variability and trend in groundwater chemistry as well as identification of possible contamination sources in a coastal alluvial basin of eastern India. Pre-monsoon season data of 14 groundwater-quality variables measured in 'leaky confined' and 'confined' aquifers were analyzed for ten years (2012-2021). Mann-Kendall (M-K) test with the Sen's Slope Estimator, Spearman Rank Order Correlation (SROC) and Innovative Trend Analysis (ITA) tests were employed to assess decadal (2012-2021) trends. The analysis of the results indicated that the 'critical' water-quality parameters exceeding the acceptable limits for drinking are TDS, EC, TH, pH, Mg2+, Na+, K+, Fe2+, HCO3-, Cl- and NO3-. Weak negative correlations between rainfall and groundwater elevation for both the aquifers reveal poor rainfall recharge into the aquifers. Therefore, a reduction in groundwater abstraction and augmentation of groundwater recharge is recommended. Trend analysis results indicated that the concentrations of TH, Mg2+ and Fe2+ exhibit significant increasing trends in the 'leaky confined aquifer'. In contrast, significant rising trends in TH, Mg2+, Na+, Fe2+, HCO3- and NO3- concentrations are identified in the 'confined aquifer'. Further, the SROC test could not detect the trends in groundwater quality in most blocks and for many parameters. On the other hand, the ITA test revealed significant trends in most of the parameters of the two aquifers in almost all the blocks. Trend magnitudes of the groundwater-quality parameters based on the Sen's Slope Estimator and the ITA test vary from -63.7 to 58.65mg/L/year for TDS, -14 to 39.07mg/L/year for TH, -1.49 to 4.83mg/L/year for Mg2+, -7.14 to 22.96mg/L/year for Na+, -0.32 to 0.44mg/L/year for Fe2+, -8.33 to 20.75mg/L/year for HCO3-, -26.52 to 31.01mg/L/year for Cl- and 1.29 to 3.76mg/L/year for NO3- over the study area. The results of M-K and ITA tests were found in agreement in all the blocks for both the aquifers. Groundwater contamination in both the aquifers can be attributed to weathering, geogenic processes, mineral dissolution, seawater intrusion, poor recharge pattern and injudicious anthropogenic activities. It is strongly recommended that concerned authorities urgently formulate efficient strategies for managing groundwater quality in the 'leaky confined' and 'confined' aquifers which serve as vital sources of drinking and irrigationwater suppliesin the study area.

Simple model to incorporate statistical noise based on a modified hodgkin-huxley approach for external electrical field driven neural responses

Noise activity is known to affect neural networks, enhance the system response to weak external signals, and lead to stochastic resonance phenomenon that can effectively amplify signals in nonlinear systems. In most treatments, channel noise has been modeled based on multi-state Markov descriptions or the use stochastic differential equation models. Here we probe a computationally simple approach based on a minor modification of the traditional Hodgkin-Huxley approach to embed noise in neural response. Results obtained from numerous simulations with different excitation frequencies and noise amplitudes for the action potential firing show very good agreement with output obtained from well-established models. Furthermore, results from the Mann–Whitney U Test reveal a statistically insignificant difference. The distribution of the time interval between successive potential spikes obtained from this simple approach compared very well with the results of complicated Fox and Lu type methods at much reduced computational cost. This present method could also possibly be applied to the analysis of spatial variations and/or differences in characteristics of random incident electromagnetic signals.

Analysis of spatial and temporal variations of NDVI and its driving factors in the corridor of Lan-Xin railway

Introduction: Vegetation is the main factor of ecological stability, but the construction of largescale corridor projects will bring some damage to vegetation.Methods: Based on the NDVI, temperature and precipitation data of the growing season from 1990–2021 (June to September), this paper uses the maximum synthesis method, trend analysis method, correlation analysis method and coefficient of variation method to study the spatial-temporal changes and driving factors of NDVI in the past 32 years, taking the 30 km belt buffer zone along the Lan-Xin Railway as the research area. The influence range of railway construction on NDVI in different climatic conditions is analyzed.Results: The results showed that: (1) From 1990–2021, the average annual temperature of the growing season in the study area showed an increasing trend, the average annual precipitation showed a decreasing trend, and the average annual NDVI showed an increasing trend; NDVI was positively correlated with temperature in 54.04%, and positively correlated with precipitation in 62.88%. Precipitation had a greater effect on NDVI than temperature. (2) Under the same climatic conditions, the NDVI variability coefficient in the study area fluctuates significantly within 3,000–21,000 m along the railway line, stabilizing beyond 21,000 m; the impact range of railway construction on NDVI gradually increases with increasing precipitation within the same temperature range, and increases gradually with rising temperatures within the same precipitation range. (3) During the growing season (June to September) in the study area, when the temperature is below 25°C, NDVI increases initially with increasing precipitation before decreasing, showing an overall upward trend; when the temperature is above 25°C, NDVI initially increases with increasing precipitation before decreasing. High temperature and high precipitation will inhibit vegetation growth and reduce NDVI. The NDVI of the core area before the railway construction is larger than that of the background area, while the NDVI of the core area after the railway construction is smaller than that of the background area.Discussion: In short, the construction of Lan-Xin Railway has a certain influence on NDVI along the line. When studying the impact of large-scale engineering projects on vegetation in the future, higher resolution imagery can be used along with the incorporation of human activities' influence. These factors will enable more accurate and comprehensive research.

Analysis of Indigenious Vegetation Diversity in Urban Area of Bekasi Regency

The primary role of vegetation is to provide structural support for the surrounding ecosystem, while also contributing to the visual appeal of the road environment. The presence of Green Open Space (GOS) as a venue for sustaining the urban environment is inherently essential, owing to its substantial capacity to enhance both the qualitative and quantitative aspects of urban living. This study aimed to analyze the indigenous vegetation well-suited for developing GOS along road routes within the urban center of Bekasi Regency. The study utilizes a multi-faceted approach, including field surveys, and historical data comparison, to offer a comprehensive analysis of spatial and temporal variations in vegetation diversity. The analysis of vegetation diversity involved the quantification of canopy density, dominance frequency, important/historical value, function in landscape design, and habitat profile analysis of vertical and horizontal structures. These measurements collectively provided an assessment of the state of local vegetation that is being inventoried. The vegetation in Bekasi Regency, within both GOS and urban residential areas, generally meets satisfactory standards in supporting ecological functions. However, in specific residential locations, there remains a lack of diversity in local and rare vegetation. In summary, the study underscores the pressing demand for comprehensive conservation approaches that effectively harmonize development initiatives with the protection of indigenous biodiversity. The results of this study offer a pivotal asset for policymakers and environmental planners in crafting sustainable development strategies for Bekasi Regency.

Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics.

Spatially resolved transcriptomics (SRT) has emerged as a powerful tool for investigating gene expression in spatial contexts, providing insights into the molecular mechanisms underlying organ development and disease pathology. However, the expression sparsity poses a computational challenge to integrate other modalities (e.g. histological images and spatial locations) that are simultaneously captured in SRT datasets for spatial clustering and variation analyses. In this study, to meet such a challenge, we propose multi-modal domain adaption for spatial transcriptomics (stMDA), a novel multi-modal unsupervised domain adaptation method, which integrates gene expression and other modalities to reveal the spatial functional landscape. Specifically, stMDA first learns the modality-specific representations from spatial multi-modal data using multiple neural network architectures and then aligns the spatial distributions across modal representations to integrate these multi-modal representations, thus facilitating the integration of global and spatially local information and improving the consistency of clustering assignments. Our results demonstrate that stMDA outperforms existing methods in identifying spatial domains across diverse platforms and species. Furthermore, stMDA excels in identifying spatially variable genes with high prognostic potential in cancer tissues. In conclusion, stMDA as a new tool of multi-modal data integration provides a powerful and flexible framework for analyzing SRT datasets, thereby advancing our understanding of intricate biological systems.

Spatial variability and source analysis of soil heavy metals: A case study of the key planting area of special agricultural products in Cangxi County, China.

Heavy metal pollution in farmland soil represents a considerable risk to ecosystems and human health, constituting a global concern. Focusing on a key area for the cultivation of special agricultural products in Cangxi County, we collected 228 surface soil samples. We analyzed the concentration, spatial distribution, and pollution levels of six heavy metals (Cr, Cu, Pb, Ni, Zn, and Hg) in the soil. Moreover, we investigated the sources and contribution rates of these heavy metals using Principal Component Analysis/Absolute Principal Component Scores (PCA/APCS) and Positive Matrix Factorization (PMF) models. Our findings indicate that none of the six metals exceeded the pollution thresholds for farmland soils. However, the mean concentrations of Cr and Ni surpassed the background levels of Sichuan Province. A moderate spatial correlation existed between Pb and Ni, attributable to both natural and anthropogenic factors, whereas Zn, Cu, Hg, and Cr displayed a strong spatial correlation, mainly due to natural factors. The spatial patterns of Cr, Cu, Zn, Pb, and Ni were similar, with higher concentrations in the northern and eastern regions and lower concentrations centrally. Hg's spatial distribution differed, exhibiting a broader range of lower values. The single pollution index evaluation showed that Cr and Ni were low pollution, and the other elements were no pollution. The average value of comprehensive pollution index is 0.994, and the degree of pollution is close to light pollution. Predominantly, higher pollution levels in the northern and eastern regions, lower around reservoirs. The PCA/APCS model identified two main pollution sources: agricultural traffic mixed source (65.2%) and natural parent source (17.2%). The PMF model delineated three sources: agricultural activities (32.59%), transportation (30.64%), and natural parent sources (36.77%). Comparatively, the PMF model proved more accurate and reliable, yielding findings more aligned with the study area's actual conditions.

Climate of the Pyrenees: Extremes indices and long-term trends

We utilized an extensive, multisource, cross-border dataset of daily meteorological observations from over 1500 stations in the Pyrenees, spanning from the mid-20th century to 2020, to examine the spatial and temporal climate patterns. Our focus was on 17 indices related to extreme precipitation and temperature events across the mountain range. The original data underwent rigorous quality control and homogenization processes, employing a comprehensive workflow that included spatial modeling based on environmental predictors. This process yielded two main outcomes: 1) a high-resolution gridded dataset (1 km2) of daily precipitation, maximum and minimum temperature from 1981 to 2020, allowing for a detailed analysis of spatial variations; and 2) an evaluation of long-term annual and seasonal trends from 1959 to 2020, using selection of high-quality data series that were homogenized to preserve their temporal structure and coherence. The findings revealed a clear elevation-related pattern in temperature indices (with the exception of tropical nights, which were predominantly observed on the Mediterranean side) and a distinct north-south latitudinal disparity in precipitation, turning longitudinal when focusing on extreme precipitation events. Overall, there was a notable and significant warming trend of 0.2 to 0.4 °C per decade, and a non-significant change of precipitation, with the exception of the southern and Mediterranean regions, where there was a notable decrease, approximately −3 % per decade, observed on an annual basis.

Analysis of spatial and seasonal variations of Haemaphysalis longicornis population based on field survey collected under different habitats and years

AbstractTicks are vectors of disease‐causing pathogens and are found on domestic and wild animals; thus, they are also vectors of significant human diseases. For this reason, pre‐emptive measures to prevent tick‐borne diseases are necessary in the form of exploring their major habitats, population increase period, and factors affecting their population growth, all of which indicate the purpose of this study. In the study, a variation of Haemaphysalis longicornis, a major vector of fever‐causing conditions, was statistically analyzed to identify the spatial and climatic factors affecting the time‐dependent variations of its population. The survey occurred in different habitats (grassland, mixed forest, deciduous forest, and coniferous forest) in South Korea. In addition, we fitted a phenology model by using a probability function to find the peak occurrence time annually. As a result, the numbers of adults and nymphs were found to be related to temperature and relative humidity and their population peaked at the end of May in all habitats except deciduous forests. This study is expected to provide information on habitat types, times, and climate patterns that require attention to help control H. longicornis populations.

The standardized precipitation evapotranspiration index based on cumulative effect attenuation

The drought index is an important indicator that reflects the degree of regional drought and plays an important role in drought disaster monitoring, prediction and evaluation. According to the process definition of the standard precipitation evapotranspiration index (SPEI), the influence of cumulative effect attenuation was considered and aggregated at different time scales k, the linear attenuation coefficient was defined, and attenuation parameters were used to adjust the degree of attenuation. A value closer to the current month was assigned a larger weight, and a value further from the current month was assigned a smaller weight. Thus, the cumulative water surplus or deficit was recalculated, and the standard precipitation evapotranspiration index based on the cumulative effect attenuation (CASPEI) was defined and applied to the drought analysis of Heilongjiang Province, which has the highest grain output in China. The comparative analysis showed that the CASPEI dataset was more consistent with the log-logistic distribution, and its sensitivity in identifying regional drought events was greater than that of the SPEI, which effectively corrected the original index's errors in identifying the drought levels of some stations and was more consistent with regional historical drought events. The temporal and spatial variation analysis revealed that drought occurrence is seasonal: summer is the season with the highest frequency of severe drought and extreme drought, and the area experiencing extreme drought is greater than that during other seasons. The areas with moderate drought in spring and autumn were relatively large. With the increase in time scale, the area of extreme drought decreased, and the area of severe drought increased. The area of moderate drought increased, while the area of light drought decreased. The CASPEI can accurately judge the extent to which drought occurs to respond to drought and effectively reduce or prevent large losses in people and the economy caused by drought.