A heavy rainstorm occurred in Henan Province, China, between 19 and 21 July, 2021, with a record-breaking 201.9 mm of precipitation in 1 h. To explore the key factors that led to forecasting errors for this extreme rainstorm, as well as the dominant contributor affecting its predictability, we employed the Global/Regional Assimilation and Prediction System-Regional Ensemble Prediction System (GRAPES-REPS) to investigate the impact of the upper tropospheric cold vortex, middle-low vortex, and low-level jet on predictability and forecasting errors. The results showed that heavy rainfall was influenced by the following stable atmospheric circulation systems: subtropical highs, continental highs, and Typhoon In-Fa. Severe convection was caused by abundant water vapor, orographic uplift, and mesoscale vortices. Multiscale weather systems contributed to maintaining extreme rainfall in Henan for a long duration. The prediction ability of the optimal member of GRAPES-REPS was attributed to effective prediction of the intensity and evolution characteristics of the upper tropospheric cold vortex, middle-low vortex, and low-level jet. Conversely, the prediction deviation of unstable and dynamic conditions in the lower level of the worst member led to a decline in the forecast quality of rainfall intensity and its rainfall area. This indicates that heavy rainfall was strongly related to the short-wave throughput, upper tropospheric cold vortex, vortex, and boundary layer jet. Moreover, we observed severe uncertainty in GRAPES-REPS forecasts for rainfall caused by strong convection, whereas the predictability of rainfall caused by topography was high. Compared with the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System, GRAPES-REPS exhibits a better forecast ability for heavy rainfall, with some ensemble members able to better predict extreme precipitation.

Data are the backbone of science. This paper describes the construction of a complex database for social-ecological analysis in Mongolia. Funded through the National Science Foundation (NSF) Dynamics of Coupled Natural and Human (CNH) Systems program, the Mongolian Rangelands and Resilience (MOR2) project focused on Mongolian pastoral systems, community adaptive capacity, and vulnerability to climate change. We examine the development of a complex, multi-disciplinary research database of data collected over a three-year period, both in the field and from other sources. This data set captures multiple types of data: ecological, hydrological and social science surveys; remotely-sensed data, participatory mapping, local documents, and scholarly literature. The content, structure, and organization of the database, development of data protocols and issues related to data access, sharing and long-term storage are described. We conclude with recommendations for long-term data management and curation from large multidisciplinary research projects.

Accurate and timely large-scale paddy rice maps with remote sensing are essential for crop monitoring and management and are used for assessing its impacts on food security, water resource management, and transmission of zoonotic infectious diseases. Optical image-based paddy rice mapping studies employed the unique spectral feature during the flooding/transplanting period of paddy rice. However, the lack of high-quality observations during the flooding/transplanting stage caused by rain and clouds and spectral similarity between paddy rice and natural wetlands often introduce errors in paddy rice identification, especially in paddy rice and wetland coexistent areas. In this study, we used a knowledge-based algorithm and time series observation from optical images (Sentinel-2 and Landsat 7/8) and microwave images (Sentinel-1) to address these issues. The final 10-m paddy rice map had user’s accuracy, producer’s accuracy, F1-score, and overall accuracy of 0.91 ± 0.004, 0.74 ± 0.010, 0.82, and 0.98 ± 0.001 (± value is the standard error), respectively. Over half (62.0%) of the paddy rice pixels had a confidence level of 1 (detected by both optical images and microwave images), while 38.0% had a confidence level of 0.5 (detected by either optical images or microwave images). The estimated paddy rice area in northeast China for 2020 was 60.83 ± 0.86 × 10³ km². Provincial and municipal rice areas in our data set agreed well with other existing paddy rice data sets and the Agricultural Statistical Yearbooks. These findings indicate that knowledge-based paddy rice mapping algorithms and a combination of optical and microwave images hold great potential for timely and frequently accurate paddy rice mapping in large-scale complex landscapes.

The stable carbon isotope composition of cellulose (δ¹³C_cell) in fossil wood is valuable for reconstructing past climatic and ecological changes, on seasonal to decadal timescales. However, extracting cellulose from fossil wood is challenging, leading to a lack of δ¹³C_cell data over deep time; moreover, there is a debate about whether the stable carbon isotope composition of whole wood (δ¹³C_wood) can reliably reflect past paleoclimatic or palaeoecological conditions. Here, we present an improved method for extracting cellulose from fossil wood. We initially used conventional methods to extract cellulose from a fossil wood sample a drill core from the Yuncheng Basin, near the Chinese Loess Plateau; however, we were unsuccessful. Subsequently, we successfully extracted cellulose and recovered 94% of the cellulose after modifying the conventional procedure. This involved increasing the reaction time during lignin removal, reducing the concentration of NaOH solution during hemicellulose removal, and employing multiple centrifugation steps for sample separation instead of a single step. We examined the relationship between δ¹³C_cell and δ¹³C_wood values (n = 136), and the results revealed a positive correlation between them (R² = 0.51, P < 0.001). This indicates that δ¹³C_wood is a dependable proxy for qualitative paleoclimatic reconstruction. However, the apparent enrichment factor ε between δ¹³C_cell and δ¹³C_wood values varied between samples, highlighting the need for caution when using records of δ¹³C_wood for quantitative paleoenvironmental reconstruction.

Urban expansion has far-reaching implications for economy, environment, and socio-cultural aspects of a city. Therefore, it is essential to have a thorough understanding of the complex dynamics and driving factors behind urban expansion in order to make informed decisions that promote the long-term sustainability of a city. Currently, cellular automata (CA) and agent-based modeling (ABM) have been widely employed to simulate urban land growth. However, existing research lacks a comprehensive consideration of the influence of individual agent attributes and land population capacity on site selection decisions. Consequently, we propose a novel approach that incorporates fine-scale population data into the site-selection decision simulation process, allowing for a granular depiction of individual decision attributes. Moreover, the site selection process integrates assessment criteria, including population capacity and neighborhood development status. Furthermore, to address the issue of fragmented simulated residential land use outcomes, population redistribution is iteratively conducted. Additionally, by integrating extended reinforcement learning mechanisms, the site selection process of residential multi-agent systems experiences a significant improvement in overall simulation accuracy. The proposed model was applied to simulate urban expansion in Shenzhen, Guangdong province, China. The results demonstrated that this model effectively enhances the behavioral decision-making capabilities of intelligent agents, thereby providing insights into the mechanisms underlying urban expansion. These findings hold considerable significance for making informed urban planning decisions and advancing the goal of sustainable urban development.

Clarifying the pore structure characteristics of shale reservoirs, which are low porosity, low permeability and high heterogeneity, is an essential prerequisite for the efficient development of shale oil and gas. Fractal theory is especially suited for characterizing the complex pore structures of shales. This work compares the pore structure characteristics between marine shales from the Longmaxi Formation and continental shales from the Shahejie Formation through low-temperature nitrogen adsorption, nuclear magnetic resonance, and scanning electron microscopy. Different fractal scaling models are adopted to determine the fractal dimensions and lacunarities of shales by low-temperature nitrogen adsorption data and scanning electron microscopy images. In addition, the mineral compositions from X-ray diffraction are analyzed to elucidate the mechanisms by which mineral content influences fractal dimensions. Finally, the correlations between total organic carbon content and microscopic structure are discussed. These results indicate that the pore size of marine shale is smaller than that of continental shale. Additionally, the fractal dimensions of marine shales are greater than that of continental shales, suggesting a more complex pore structure. The more quartz and clay content lead to greater complexity in pore space, resulting in higher fractal dimensions. The illite/smectite mixed layer shows a strong positive correlation with fractal dimensions for marine shales, whereas this correlation is less pronounced for continental shales. The presence of microfractures in organic matter leads to a reduction for the pore surface fractal dimension in continental shales.

This study investigates the capabilities of a non-hydrostatic global, variable-resolution model in simulating tropical cyclone precipitation, with historically significant Typhoon Fitow (1323) as a case study. Employing three grid settings (24 km, 60−10 km, 60−3 km) and two microphysical parameterization schemes (WSM6 and Thompson), the study investigates the influence of grid resolution and microphysical parameterization on precipitation simulation. The simulated precipitation intensity and spatial distribution of high-resolution grids exhibit better agreement with the observations compared to the coarse-resolution grids. Specifically, the 60−3 km grid setting shows the greatest improvement in spatial correlation with observed precipitation data compared to the 24 km grid. Through the analysis of the thermal dynamic field, the high-resolution grid configuration more effectively simulates indicators for strong convective weather events, such as convective available potential energy (CAPE), helicity, and nonadiabatic heating. Analysis of TRMM satellite observations reveals that the high-resolution grid simulation results more accurately capture the distribution characteristics of hydrometeor mixing ratio compared to the coarse-resolution grids. Differences in hydrometeor content within convective clouds are more pronounced across grid resolutions than in stratiform clouds, even with the same parameterization scheme. Additionally, at the same resolution, the disparity in ice-phase particle content between the two schemes is much greater than the disparity in liquid-phase particle content. It is also noteworthy that the WSM6 scheme delivers superior performance compared to the Thompson scheme. In summary, this study demonstrates that refining model resolution has a more significant impact on precipitation intensity than the selection of physical parameterization scheme. The Model for Prediction Across Scales (MPAS), using a high-resolution variable-resolution grid, can be effectively used for typhoon precipitation simulation research.

In recent years, fine-scale gridded population data has been widely adopted for assessing and monitoring the Sustainable Development Goals (SDGs). However, the existing population disaggregation techniques struggle to generate precise population grids for small areas with scarce data. To address this, we have introduced a novel, lightweight population gridding technique that integrates dasymetric mapping and point-based surface modeling, titled three-weight surface modeling. This method comprises three weights, each offering a unique perspective on population spatial heterogeneity. The first weight, termed building-volume weight, is equivalent to the preliminary results of assigning population based on building volume data. The second weight, termed POI-center weight, comprises POI (Point of Interest) categories and aggregation patterns, aiming to articulate high-density population centers. It is computed using the neighborhood accumulation rule of Spearman’s correlation coefficients between POIs and population size. The third weight, termed POI-distance weight, represents varying decay rates of population with distance from high-density centers. This three-weight surface model facilitates dynamic adjustment of parameters to refine the building-volume weight according to the remaining POI-related weights, thereby generating a more precise population surface. Our analysis of the census population and the disaggregation outcomes from 544 villages in three counties of southern Guizhou Province, China (namely, Huishui, Luodian, and Pingtang) revealed that the three-weight surface model using local parameter groups outperformed individual dasymetric mapping or point-based surface modeling in terms of accuracy. Also, the 10 m population grid generated by this local parameter model (LPTW-POP) presented greater resolution and fewer errors (RMSE of 1109, MAE of 422, and MRE of 0.2630) compared to commonly use gridded population datasets like LandScan, WorldPop, and GHS-POP.

Seismic hazards pose a major threat to life safety, social development, and the economy. Traditional seismic vulnerability and risk assessments, such as field survey methods, may not be suitable for densely built-up urban areas due to the limited availability of comprehensive data and potential subjectivity in judgment. To overcome these limitations, an integrated method for seismic vulnerability and risk assessment based on multimodal remote sensing data, support vector machine (SVM) and GIScience methods was proposed and applied to the central urban area of Jinan City, Shandong Province, China. First, an area with representative buildings was selected for field survey research, and an attribute information base established. Then, the SVM method was used to establish the susceptibility proxies, which were applied to the whole study area after accuracy evaluation. Finally, the spatial distribution of seismic vulnerability and risk under different seismic intensity scenarios (from VI to X) was analyzed in GIScience. The results show that the average building vulnerability index in the central urban area of Jinan City is 0.53, indicating that the overall seismic performance of buildings is at a moderate level. Under the seismic intensity scenario of VIII, the buildings in the Starting area and New urban district of Jinan would mostly suffer ‘Moderate’ damage, while Old urban areas, with more seismic-resistant buildings, would experience only ‘Slight’ damage. This study aims to offer an efficient and accurate method for assessing seismic vulnerability in mid to large-sized cities characterized by concentrated population densities and rapid urbanization, as well as provide a valuable reference for efforts in urban renewal, seismic mitigation, and land planning, particularly in cities and regions of developing countries. Additionally, it contributes to the realization of Sustainable Development Goal 11, which seeks to make cities and human settlements inclusive, safe, resilient, and sustainable.