Spatial Correlation Patterns Research Articles

A geographic-semantic context-aware urban commuting flow prediction model using graph neural network

Urban commuting flow prediction is crucial for urban planning, transportation optimization, and supply chain management. Traditional models and machine learning solutions often fall short in capturing the complex dynamics of urban mobility, overlooking factors like spatial correlation and human mobility patterns. Recent deep learning advancements have shown promise by integrating spatial correlation through incorporating geographical adjacency. However, this approach may not fully capture spatial correlation, particularly the semantic adjacency effect of zones connected via public transportation lines, such as metro networks—a novel dimension overlooked in prior research. The metro station serves as a major transit hub, attracting passengers from various origins and destinations and correlating the flow between the connected zones and other zones within the metro network. Devising an optimized model to encapsulate these spatial correlations is another crucial area of research where scientists are actively seeking the most effective solutions. We propose a novel sequential hybrid approach leveraging graph-based learning, focusing on the underexplored impact of metro networks on commuting flow prediction. Our model integrates two types of adjacency—geographic and semantic—using Graph Convolutional Networks (GCNs) to embed geographic correlations among proximate zones and Graph Attention Networks (GATs) to incorporate semantic adjacency defined by metro line connectivity. Furthermore, our approach innovatively incorporates features directly from online maps, eliminating reliance on third-party resources and enhancing scalability and efficiency. We validate our model using real-world datasets from Montreal, Canada, comprising travel surveys and GPS trajectory types, totaling nearly 900,000 trip records. Through evaluation using established performance metrics such as MAE, RMSE, MAPE, and CPC, we demonstrate significant improvement in predictive accuracy compared to existing state-of-the-art models. Our research has practical implications for urban planning and infrastructure development, aiding policymakers and urban planners in analyzing the impact of new urban infrastructure on commuting flow dynamics and facilitating informed decisions regarding transportation and infrastructure planning.

Temporal variability of spatial patterns of correlations between summer rainfall and the Oceanic Niño Index in the Pampean region

The Argentinean Pampean region is essential for global food security, known for its extensive production of soybeans, corn, and wheat. The November to January (NDJ) trimester is critical for rainfed summer crops, as precipitation during this period directly affects soil moisture and crop yields. The El Niño-Southern Oscillation (ENSO), particularly its Oceanic Niño Index (ONI), plays a crucial role in influencing precipitation patterns in this region. This study investigates the spatial correlation between the ONI and NDJ precipitation from 1990 to 2021 sing a 20-year sliding window approach. We conducted Pearson correlation and cluster analyses to identify regions with consistent ONI-precipitation relationships. Our findings reveal notable temporal variability, with a general decrease in correlation strength since the window 1995–2014. This decline is likely driven by changes in ENSO dynamics and the influence of other climate variability modes, highlighting the need to consider factors modulating the ONI-precipitation relationship. Notably, the addition of the strong 2015 El Niño, which exhibited inconsistent precipitation behaviour compared to previous strong El Niño events such as 1997, contributed to this weakening. While the 1997 El Niño brought widespread positive rainfall anomalies, the 2015 event was marked by weaker and even negative precipitation anomalies in part of the region. This difference mirrors similar patterns reported in other parts of South America, such as Ecuador and Coastal Peru, and may be linked to a poleward shift in the jet streams during the 2015/2016 El Niño. The results underscore the complexity of ENSO's impact on regional climate and highlight the need for adaptive agricultural planning. By enhancing the understanding of ONI-precipitation dynamics, this study aims to improve long-term climate predictions and support sustainable agricultural practices in the Pampean region.

Memory of shear flow in soft jammed materials.

Cessation of flow in yield stress fluids results in a stress relaxation process that eventually leads to a finite residual stress. Both the rate of stress relaxation and the magnitude of the residual stresses systematically depend on the preceding flow conditions. To assess the microscopic origin of this memory effect, we combine experiments with large-scale computer simulations, exploring the behavior of jammed suspensions of soft repulsive particles. A spatiotemporal analysis of particle motion reveals that memory formation during flow is primarily governed by the emergence of domains of spatially correlated nonaffine displacements. These domains imprint the configuration of stress imbalances that drive dynamics upon flow cessation, as evidenced by a striking equivalence of the spatial correlation patterns in particle displacements observed during flow and upon flow cessation. Additional contributions to stress relaxation result from the particle packing that reorganizes to minimize the resistance to flow by decreasing the number of locally stiffer configurations. Regaining rigidity upon flow cessation drives further relaxation and effectively sets the magnitude of the residual stress. Our findings highlight that flow in yield stress fluids can be seen as a training process during which the material stores information of the flowing state through the development of domains of correlated particle displacements and the reorganization of particle packings optimized to sustain the flow. This encoded memory can then be retrieved in flow cessation experiments.

Historical drivers and future streamflow variations of the Kura River in the Western Transcaucasia region of Georgia: Analysis of tree-ring chronologies from 1720 to 2021 CE

In this paper, we conduct a dendrochronological study in the Western Transcaucasia region of Georgia to elucidate the relationship between tree growth and climatic variables (temperature, precipitation, and streamflow) in order to reconstruct the paleohydrology of the Kura River and to assess the impacts of climate change on water resources. We analyzed tree-ring chronologies from 1720 to 2021 CE, and analysis of July–August streamflow revealed five wet phases (1739–1753, 1764–1807, 1811–1831, 1962–1988 and 2000–2019 CE ranging in duration from 11 to 49 years) when streamflow exceeded the long-term mean, and four dry phases (1720–1738, 1832–1881, 1936–1961 and 1989–1999 CE) when streamflow was below average. The most extreme wet and dry years were 1771 and 1851 CE, respectively. Spatial correlation patterns of Kura River reconstructed runoff against sea level pressure (SLP) are negative in the Arctic and positive in mid- to high-latitude Eurasia. The Arctic Oscillation (AO)/North Atlantic Oscillation (NAO), and solar activity are important drivers of hydroclimatic conditions in the Mediterranean region, by modifying the location of high-pressure areas and large-scale atmospheric circulations. Comparison of our Kura streamflow reconstructions and data in the Old World Drought Atlas (OWDA) reveals a moderately positive correlation (0.40) throughout the study period, being strongest over the Southern Caucasus and Eastern Turkey. This validates the drought signals and emphasizes the importance of using multi-proxy approaches, supported by spatially resolved data, to enhance understanding of regional hydroclimatic variability and drought patterns. We additionally conducted future streamflow scenarios, most notably under the high-emission SSP585 scenario, in line with global trends in CMIP6 projections. Our streamflow results indicate a trend towards increasing drought severity, which highlights the need for urgent adaptation and mitigation strategies in water resource management in this region. Ultimately, this study provides valuable information on historical hydroclimatic conditions in the Kura River Basin, which help to develop strategies to mitigate the hydrologic impacts of climate change in the Western Transcaucasia Caucasus region. We further highlight the importance of regional and global teleconnections for understanding regional hydrological dynamics.

Study on the spatial distribution pattern of correlation between surface deformation and reservoir water level in the Three Gorges Reservoir area

Study on the spatial distribution pattern of correlation between surface deformation and reservoir water level in the Three Gorges Reservoir area

Examining relations between sea-level anomalies in the Niño 3.4 region and terrestrial hydroclimatic conditions in China

As a key driver of global climate variability, El Niño–Southern Oscillation (ENSO) significantly impacts the regional hydrological cycle. Similar to the recognized ENSO monitoring index (i.e., sea surface temperature anomaly), a potential advancement in the usage of sea level anomaly (SLA) in the equatorial Pacific is expected to enhance our knowledge of ENSO and its influence on regional terrestrial hydroclimatic conditions (THCs). In this study, we employed wavelet coherence and multi-channel singular spectral analysis to investigate the ability of satellite altimetry-based SLA, and its two major components (i.e., steric SLA (SSL) and non-steric SLA (NSSL)) averaged over Niño 3.4 region, to identify ENSO-related signals. Subsequently, we explored their spatial association with THCs at interannual scales in China using a cross-correlation method. We found that the time series of sea surface temperature anomaly index (SSTaI), SLA index (SLAI), and SSL index (SSLI) displayed high consistency. SSTaI was found to present higher correlations to SLAI (0.91) and SSLI (0.88), compared to NSSL index (NSSLI; 0.40). Furthermore, the high (low) wavelet coherence and in-phase (orthogonal) relationship between SSTaI and SLAI/SSLI (NSSLI) was also revealed. This indicated that SLAI and SSLI may be good complementary indices to SSTaI in observing ENSO-related signals. Similar to the temporal association of SSTaI and SLAI/SSLI, their spatial correlation pattern with THCs also displayed consistency. The time lag characterizing the relationship between SSTaI/SLAI/SSLI and THCs was also consistent, despite some regional differences. In summary, our results indicated that the combined analyses of the time lag determined between SSTaI/SLAI/SSLI and THCs can eventually complement the time lag analysis based solely on SSTaI. This, in turn, provides a more comprehensive understanding of ENSO’s influence on THCs in China.

Interdecadal variability of winter and spring Eurasian snow depth and its impact on Eastern China precipitation

Empirical orthogonal function (EOF) and correlation analyses were employed to investigate the winter and spring snow depth in Eurasia and its relationship with Eastern China precipitation based on the observed and reanalyzed data from 1980 to 2016. The results show that the winter and spring snow cover in Eurasia not only highlights a decreasing trend due to global warming (the first EOF mode, its variance accounted for 24.4% and 22.6% of the total variance) but also exhibits notable interdecadal variation (the second EOF mode, its variance accounted for 10.2% and 11.5% of the total variance). The second EOF mode of winter snow depth in Eurasia is characterized by a west-east dipole pattern. It was observed that the spatial correlation pattern between the EOF2 of Eurasian snow depth and summer precipitation in China closely resembles the meridional quadrupole structure of the third EOF mode of summer precipitation in China. This pattern is characterized by excessive rainfall in Northeast China and the lower-middle reaches of the Yangtze River, and less rainfall over the Yellow River basin and southern China. The EOF mode of spring snow depth not only reflects the declining trend but also regulates precipitation in Eastern China. The possible mechanisms by which snow depth causes changes in soil moisture and subsequently affects atmospheric circulation are then explored from the perspective of the hydrological effects of snow cover. Decreased (Increased) snow depth in Eurasia during the winter and spring directly leads to diminished (increased) soil moisture while increasing (decreasing) net radiation and sensible heat flux at the surface. The meridional distribution of surface temperature also exhibits a dipole pattern, leading to enhanced subtropical westerly jet in the upper troposphere. The Eurasian snow cover anomalies pattern triggered an anomalous mid-latitude Eurasian wave train, which strengthened significantly in the Western Siberian Plain. It then splits into two branches, one continuing to propagate eastward at high latitudes and the other shifting towards East Asia, thereby impacting precipitation in Eastern China. This work indicates that the second EOF mode of Eurasian snow cover can impact the precipitation variability in Eastern China during the same period and in summer on an interdecadal scale.

Interpretable multi-graph convolution network integrating spatio-temporal attention and dynamic combination for wind power forecasting

Graph neural networks (GNN) can effectively improve wind power forecasting accuracy due to their ability to capture spatial correlations between wind farms. However, conventional GNN-based forecasting models constructing the graphs from only one specific perspective fail to characterize the spatial correlation patterns comprehensively and accurately. Furthermore, their black-box structure leads to deficient interpretability of the forecasting results, which hampers the quantification of forecasting uncertainty and reduces the credibility of applying these forecasting models in practice. To address the above problems, an interpretable ultra-short-term wind power forecasting method is proposed based on multi-graph convolution network integrating spatio-temporal attention and dynamic graph combination. Four undirected weight graphs are constructed to form a multi-graph scheme by considering various geographical and statistical aspects, and then graph convolution network (GCN) and temporal convolution network (TCN) are used to enhance the forecasting performance by leveraging information in both space and time dimensions. Meanwhile, it consists of temporal attention that deeply mines the temporal dependencies and spatial attention that interprets the influence of wind farms on the forecasts. Additionally, to boost the adaptability and generalization capability of the proposed method, an improved reinforcement learning is elaborately designed to combine the results of different graphs by dynamically optimizing their weights to obtain the final forecasts. Experiment results suggest that the proposed method exhibits superior forecasting accuracy and reasonable interpretability compared with other benchmark methods.

Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation.

Soft robotic glove controlled by a brain-computer interface (BCI) have demonstrated effectiveness in hand rehabilitation for stroke patients. Current systems rely on static visual representations for patients to perform motor imagination (MI) tasks, resulting in lower BCI performance. Therefore, this study innovatively used MI and high-frequency steady-state visual evoked potential (SSVEP) to construct a friendly and natural hybrid BCI paradigm. Specifically, the stimulation interface sequentially presented decomposed action pictures of the left and right hands gripping a ball, with the pictures flashing at specific stimulation frequencies (left: 34 Hz, right: 35 Hz). Integrating soft robotic glove as feedback, we established a comprehensive "peripheral - central - peripheral" hand rehabilitation system to facilitate the hand rehabilitation of patients. Filter bank common spatial pattern (FBCSP) and filter bank canonical correlation analysis (FBCCA) algorithms were used to identify MI and SSVEP signals, respectively. Additionally, we proposed a novel fusion algorithm to decide the final output of the system. The feasibility of the proposed system was validated through online experiments involving 12 healthy subjects and 9 stroke patients, achieving accuracy rates of 95.83 ± 6.83% and 63.33 ± 10.38, respectively. The accuracy of MI and SSVEP in 12 healthy subjects reached 81.67 ± 15.63% and 95.14 ± 7.47%, both lower than the accuracy after fusion, these results confirmed the effectiveness of the proposed algorithm. The accuracy rate was more than 50% in both healthy subjects and patients, confirming the effectiveness of the proposed system.

Multi-Height and Heterogeneous Sensor Fusion Discriminant with LSTM for Weak Fire Signal Detection in Large Spaces with High Ceilings

Fire is a significant cause of fatalities and property loss. In tall spaces, early smoke dispersion is hindered by thermal barriers, and initial flames with limited smoke production may be obscured by ground-level structures. Consequently, smoke, temperature, and other fire sensor signals are weakened, leading to delays in fire detection by sensor networks. This paper proposes a multi-height and heterogeneous fusion discriminant model with a multilayered LSTM structure for the robust detection of weak fire signals in such challenging situations. The model employs three LSTM structures with cross inputs in the first layer and an input-weighted LSTM structure in the second layer to capture the temporal and cross-correlation features of smoke concentration, temperature, and plume velocity sensor data. The third LSTM layer further aggregates these features to extract the spatial correlation patterns among different heights. The experimental results demonstrate that the proposed algorithm can effectively expedite alarm response during sparse smoke conditions and mitigate false alarms caused by weak signals.

A site-resolved two-dimensional quantum simulator with hundreds of trapped ions.

A large qubit capacity and an individual readout capability are two crucial requirements for large-scale quantum computing and simulation1. As one of the leading physical platforms for quantum information processing, the ion trap has achieved a quantum simulation of tens of ions with site-resolved readout in a one-dimensional Paul trap2-4 and of hundreds of ions with global observables in a two-dimensional (2D) Penning trap5,6. However, integrating these two features into a single system is still very challenging. Here we report the stable trapping of 512 ions in a 2D Wigner crystal and the sideband cooling of their transverse motion. We demonstrate the quantum simulation of long-range quantum Ising models with tunable coupling strengths and patterns, with or without frustration, using 300 ions. Enabled by the site resolution in the single-shot measurement, we observe rich spatial correlation patterns in the quasi-adiabatically prepared ground states, which allows us to verify quantum simulation results by comparingthe measured two-spin correlations with the calculated collective phonon modes and with classical simulated annealing. We further probe the quench dynamics of the Ising model in a transverse field to demonstrate quantum sampling tasks. Our work paves the way for simulating classically intractable quantum dynamics and for running noisy intermediate-scale quantum algorithms7,8 using 2D ion trap quantum simulators.

Spatial distribution patterns and intraspecific and interspecific spatial associations of Quercus myrsinifolia population in Shennongjia, China

Quercus myrsinifolia is one of the dominant species in the evergreen broad-leaf forest on the southern slope of Shennongjia. The study of spatial distribution pattern and spatial correlation of Q. myrsinifolia population will help to understand population development and potential ecological processes, as well as the structure and biodiversity maintenance mechanism of evergreen broad-leaf forests at the northern edge of the subtropics. Based on forest dynamic monitoring data from one 1 hm2 permanent plot on the southern slope of Shennongjia, we employed pair correlation functions g(r) and marked correlation functions to analyze the diameter structure of the Q. myrsinifolia population, spatial distribution patterns at different diameter classes, and intraspecific and interspecific spatial associations. The results showed that diameter structure of Q. myrsinifolia population exhibited an inverted 'J'-shaped distribution, suggesting a healthy regeneration status and belonging to a growing population type. The spatial distribution showed a decreasing trend in aggregation with increasing diameter. Positive correlations among individuals strengthened with closer diameter classes, while weakening with larger diameter differences. Interspecific spatial associations showed an increasing correlation of Q. myrsinifolia with understory dominant species with increasing spatial scales, but no correlation was observed with canopy-dominant species. Our results suggested that the spatial pattern of Q. myrsinifolia populations on the southern slope of Shennongjia was mainly influenced by habitat filtering, seed dispersal limitation, and intraspecific and interspecific competition. Furthermore, the adaptive strategies of Q. myrsinifolia varied when they coexisted with different species.

Landscape Ecological Risk Assessment of Saihanba under the Change in Forest Landscape Pattern

Examining the Saihanba Mechanical Forest Farm, this study utilized Landsat remote sensing data from 1987, 1997, 2001, 2013, and 2020 to interpret land use from the Support Vector Machine (SVM) method, and to decipher evolving land use patterns over the last four decades. Grounded in landscape ecology theory, an innovative evaluation index for landscape ecological risk was introduced, leading to the delineation of 382 ecological risk evaluation units. Employing landscape pattern indices and a method of spatial autocorrelation, we analyzed the spatial and temporal distribution characteristics and spatial correlation patterns of landscape ecological risk across five distinct periods. Geostatistical approaches were used to explore the driving factors of landscape risk. The results indicate that since 1987, there have been significant changes in land use types, especially in forest landscapes, their proportion increasing from 23.19% to 74.55%. In 1987, the proportion of high-risk areas was 72.30%, but in 2020, high-risk areas had significantly decreased and clustered in specific locations. The landscape ecological risks in each period of the study area showed a positive spatial correlation and tended to gather in space. After comprehensive exploration using a geographic detector, we found that landscape type, temperature, and vegetation coverage are the main risk factors. Among them, landscape type has the greatest impact on the landscape and works together with slope, aspect, and precipitation. In forest farm management, only the adaptation and adjustment of single factors are often paid attention to, while the compound effects of multiple factors are ignored. The results of this study bring important reference value to the operation and development of forest farms.

Analysis of the Evolution Pattern and Regional Conservation of Cultural Heritage from the Perspective of Urban Sustainable Transformation: The Case of Xiamen, China

This study focuses on the cultural heritage of Xiamen and uses the spatial analysis method of ArcGIS to analyze the spatiotemporal evolution and distribution characteristics of cultural heritage. Through analysis, we aim to optimize the structure of cultural heritage protection areas and tourism routes in urban planning by analyzing the spatial structure characteristics of Xiamen’s cultural heritage types in various historical periods. The results show that in terms of the time dimension, the existing cultural heritage in Xiamen was mainly built between 1840 and 1949, and the center of cultural heritage shows an overall trend of a “north-south direction”. In terms of spatial dimension, the existing cultural height in Xiamen shows a clustering distribution pattern, with clusters located in the Gulangyu and Xiagang areas of Siming District. According to the period, there is a clear spatial correlation pattern, showing a spatial clustering trend of “strong first and then weak”.

Portraying passenger travel patterns for Beijing public transit system with user profiling method

Purpose The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network. Design/methodology/approach To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week. Findings In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system. Originality/value This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map

Detecting the spatial organization patterns of urban networks with multiple traffic flows from the perspective of complex networks and traffic behavior will help to optimize the urban spatial structure and thereby promote the sustainable development of the city. However, there are notable differences in regional spatial patterns among the different modes of transportation. Based on the road, railway, and air frequency data, this article investigates the spatial distribution and accessibility patterns of multiple transportation flows in the Yangtze River Economic Belt. Next, we use the TCD (Transportation Cluster Detection) community discovery algorithm and integrate it with the Baidu Maps API to obtain real-time time cost data to construct a community detection model of a multiple traffic flow network. We integrate the geographical network and topological network to perform feature extraction and rule mining on the spatial organization model of the urban network in the Yangtze River Economic Belt. The results show that: (1) The multiple traffic flow network of the Yangtze River Economic Belt has significant spatial differentiation. The spatial differentiation of aviation and railway networks is mainly concentrated between regions and within provinces, while the imbalance of highway networks is manifested as an imbalance within regions and between provinces. (2) The accessibility pattern of the highway network in the Yangtze River Economic Belt presents a “core–edge” spatial pattern. The accessibility pattern of the railway network generally presents a spatial pattern of “strong in the east and weak in the west”. Compared with sparse road and railway networks, the accessibility pattern of the aviation network shows a spatial pattern of “time and space compression in western cities”. (3) A total of 24 communities were identified through the TCD algorithm, mainly encompassing six major “urban economic communities” located in Guizhou, Yunnan, Anhui, Sichuan–Chongqing, Hubei–Hunan–Jiangxi, and Jiangsu–Zhejiang–Shanghai. (4) The urban network space organization model of the Yangtze River Economic Belt can be roughly divided into three models: the “single-core” model, with Guizhou, Kunming, and Hefei as the core, the “dual-core” model, constructed by Chengdu–Chongqing, and the “multi-core” model, constructed by Changsha–Wuhan–Nanchang and Shanghai–Nanjing–Hangzhou. This model of urban network spatial organization holds indicative significance in revealing the spatial correlation pattern among prefecture-level city units.

Analysis of the spatial correlation pattern of logistics carbon emission efficiency and its influencing factors: the case of China.

As logistics carbon emission efficiency is an essential industry linking regions, investigating this issue from a spatial correlation perspective is practically significant. Utilizing data from 282 prefecture-level cities spanning 2006 to 2019, we used a super slacks-based measure model, a modified gravity model, motif analysis, the Infomap algorithm, and an exponential random graph model to analyze the spatial correlation patterns and influencing factors of logistics carbon emission efficiency. The following conclusions were drawn. (1) The spatial correlation of logistics carbon emission efficiency during the study period exhibited a core-edge pattern, with the central region emerging as a high-correlation hub. (2) The scale of the spatial association network community of carbon emission efficiency in the logistics industry changed constantly, and the stability of the network community structure gradually increased. From a microstructural perspective, the dispersed-mode structure was a pivotal element in the formation of the spatial correlation network of logistics carbon emission efficiency. (3) Node interaction tendencies were a critical force driving network formation. Financial investment, government concern, international openness, population density, and innovation ability were conducive to the formation of spatial correlations of logistics carbon emission efficiency.

Spatial-temporal differentiation pattern and influencing factors of land economic density at the township scale in Zhejiang Province.

Based on the land economic density of 892 town units, the spatial pattern of the land economic density in Zhejiang Province is analyzed using the coefficient of variation, spatial classification, and spatial correlation methods, and the influencing factors are analyzed using a spatial regression model. The results are as follows: (1) The coefficients of variation were 2.6 and 3.1 in 2014 and 2019, respectively, indicating that the degree of imbalance of the town's industrial economy at the county level increased. (2) The distribution of the high-level agglomeration areas was characterized by one core area and two sub-core areas. The main core area was located at the junction of Hangzhou City, Shaoxing City, and Jiaxing City, and the two sub-core areas were located in Yuyao City and the main urban area of Ningbo City. In addition, several small-scale agglomeration areas composed of medium and high-level units were distributed in Wenzhou City. (3) The high-value agglomeration and low-value agglomeration distribution in the spatial correlation patterns was identified using the spatial auto-correlation method. The hot spots and sub-hot spots were distributed in Northern Zhejiang, and the cold spots formed a large-scale agglomeration in Quzhou City, Lishui City, Taizhou City, and several other cities in Southern Zhejiang. (4) Compared with the county scale, the spatial scope of the high-level areas in Northern Zhejiang shrunk significantly at the township scale, and the high-level agglomeration areas along the southeast coast changed into a cluster of several townships. (5) According to the geographically weighted regression (GWR) model, the importance of influencing factors is as follows: population density > regional area > industrial output value per capita > total population > proportion of secondary and tertiary personnel > total employees.

Short-term forecast large-scale error characteristics and their relationship with precipitation forecast skill under two rainfall regimes

Although precipitation forecast skill has improved significantly in recent decades, both location and intensity of precipitation forecast based on kilometric-scale numerical weather prediction (NWP) models still contain errors. The importance of large-scale error in NWP has been recognized, but characteristics of large-scale error under different rainfall regimes and the influence of large-scale error on precipitation forecast skill remain unclear. In this regard, we focused on the Yangtze-Huaihe River Basin (YHRB) and used an operational forecast dataset covering the summer seasons of 2018 and 2019 obtained from the Anhui Meteorology Observatory to investigate large-scale error characteristics and their relationship with precipitation errors under two rainfall regimes.Large-scale error characteristics were measured by root-mean-square errors (RMSE), relative RMSE (RRMSE), and large-scale spatial pattern correlation (LSCC). The large-scale RMSEs were highly correlated with the rainfall intensity and area, while the large-scale RRMSE and LSCC can be used to describe precipitation forecast skill for rainfall events under the two rainfall regimes. In general, the precipitation forecast for strongly forced rainfall events performed better than that for weakly forced rainfall events. Under strong forcing conditions, the majority of rainfall events were concentrated in the intervals of smaller precipitation errors with smaller RRMSEs and higher LSCCs, and the precipitation forecast skill was weakly related to the large-scale RRMSE, LSCC and the initial large-scale variance contribution (LVC). Under weak forcing conditions, most of the rainfall events had a larger precipitation error and the frequency of location error was higher than that of intensity error. The low precipitation forecast skill and large forecast uncertainties for weakly forced rainfall events were strongly related to the large RRMSEs and low LSCCs in the geopotential height in the middle-upper troposphere and meridional wind component (V) in the lower troposphere. In particular, a strong negative correlation was found between the initial LVC and the large-scale RRMSE under weak forcing. This suggests that large-scale information could play a more important role in precipitation forecast for weakly forced rainfall events, and could potentially be used to indicate the magnitude of large-scale forecast errors in short-term forecasts under weak forcing conditions.

Implementing generative adversarial network (GAN) as a data-driven multi-site stochastic weather generator for flood frequency estimation

Precipitation is a key driving factor of hydrologic modeling for impact studies. However, there are challenges due to limited long-term data availability and complex parameterizations of existing stochastic weather generators (SWGs) due to spatiotemporal uncertainty. We introduced state-of-the-art Generative Adversarial Network (GAN) as a data-driven multi-site SWG and synthesized extensive hourly precipitation over 30 years at 14 stations. These samples were then fed into an hourly-calibrated SWAT model for streamflow generation. Results showed that the well-trained GAN improved rainfall data by accurately representing spatiotemporal distribution of raw data rather than simply replicating its statistical characteristics. GAN also helped display authentic spatial correlation patterns of extreme rainfall events well. We concluded that GAN offers a superior spatiotemporal distribution of raw data compared to conventional methods, thus enhancing the reliability of flood frequency evaluations.