The aim of investigating the coordination of the complex system constituted by resources, environment, ecology, economy and society subsystems (CSR3ES), is to achieve sustainable development by: (1) describing the complicated relationships of the inner-subsystems and inter-systems; (2) designing the calculation method of the coordination degree for CSR3ES; (3) analyzing its developing trends from a macro point of view through comprehensive coordination degree; and (4) to determine the direction of system restoration from a micro point of view based on the inner-subsystem and inter-subsystem coordination degree, and to develop specific improvement strategies. By setting a province-level administrative region as a decision-making unit (DMU), the nationwide coordination of CSR3ES was studied. The main conclusions are: (1) The coordination degree between the subsystems is directly influenced by interactions between the elements. (2) Within the provinces and autonomous regions (PARs) in China, the levels of coordinated development of inter-subsystems are high, while the coordinated development between the elements within a single subsystem level are low. Furthermore, a positive coherent effect is exerted on the comprehensive coordination degree of CSR3ES by the inter-system synergistic effects. (3) In terms of spatial correlation, five comprehensive coordinated development modes of CSR3ES are formed: the northeast regional coordination mode group; the northwest regional uncoordinated mode group; the southeast regional coordination mode group; the central regional random mode group; and the Beijing-Tianjin-Hebei polarization mode.

The current study presents a detailed assessment of risk zones related to karst collapse in Wuhan by analytical hierarchy process (AHP) and logistic regression (LR) models. The results showed that the LR model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.911 compared to 0.812 derived from the AHP model. Both models performed well in identifying high-risk zones with only a 3% discrepancy in area. However, for the medium- and low-risk classes, although the spatial distribution of risk zoning results were similar between two approaches, the spatial extent of the risk areas varied between final models. The reliability of both methods were reduced significantly by excluding the InSAR-based ground subsidence map from the analysis, with the karst collapse presence falling into the high-risk zone being reduced by approximately 14%, and karst collapse absence falling into the karst area being increased by approximately 6.5% on the training samples. To evaluate the practicality of using only results from ground subsidence maps for the risk zonation, the results of AHP and LR are compared with a weighted angular distortion (WAD) method for karst risk zoning in Wuhan. We find that the areas with relatively large subsidence horizontal gradient values within the karst belts are generally spatially consistent with high-risk class areas identified by the AHP- and LR-based approaches. However, the WAD-based approach cannot be used alone as an ideal karst collapse risk assessment model as it does not include geological and natural factors into the risk zonation.

The spatial pattern is a kind of typical structural knowledge that reflects the distribution characteristics of object groups. As an important semantic pattern of road networks, the city center is significant to urban analysis, cartographic generalization and spatial data matching. Previous studies mainly focus on the topological centrality calculation of road network graphs, and pay less attention to the delineation of main centers. Therefore, this study proposes an automatic recognition method of main center pattern in road networks. We firstly extract the main clusters from road nodes by improving the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with fuzzy set theory. Moreover, the center area is generated with road meshes according to the area ratio with the covering discs of the main clusters. This proposed algorithm is applied to the road networks of a monocentric city and polycentric city respectively. The results show that our method is effective for identifying the main center pattern in the road networks. Furthermore, the contrast experiments demonstrate our method’s higher accuracy.

Zhengzhou city (China) experienced relatively significant land deformation following the July 20, 2021, extreme rainstorm (7·20 event). This study jointly utilised Multi-temporal synthetic aperture radar interferometry (MT-InSAR), eXtreme Gradient Boosting (XGBoost), and hydrogeological analysis to quantitatively assess the extent and trends, as well as the causes of land deformation before and after the 7·20 event in Zhengzhou city. The findings detected three major subsidence zones and two uplift zones within the city. The most significant subsidence occurred in the northern part of Zhongmu (− 28 mm/year), the northwest of Xingyang (− 16 mm/year), and the western region of Gongyi (− 6 mm/year). Conversely, a notable uplift was observed in the central city district (13 mm/year) and Xinzheng Airport (12 mm/year). The accuracy assessment of in-situ measurements (GNSS and levelling) yielded an overall root-mean-square error (RMSE) of 2.2 mm/year and an R-square of 0.948. Subsequently, the feature evaluation results based on the XGBoost method suggest that road density and precipitation are the dominant factors affecting land deformation in the entire study area or in the subsidence and uplift zones individually. Nevertheless, the other five factors (groundwater storage, soil type, soil thickness, NDVI, and slope) also act on land deformation individually and are intricately intertwined with each other. Furthermore, hydrogeological analysis from six groundwater wells reveals a synchronous relationship between groundwater level decline and land subsidence. The building load analysis shows a significant correlation between build-up density and subsidence rates, especially for those severe subsidence areas, with the maximum correlation coefficient reaching 0.6312. Finally, the geographic patterns analysis of post-event demonstrated a northeastward trend in land deformation, with a gradual reduction of deformation impact from 2018 to 2022.

Aquifers supporting irrigated agriculture in Henan Plain in China (HNP) are under immense stress due to scarcity of surface water and over-pumping of groundwater. To assist in establishing a crop planting structure in this region that aligns more with the capacity of water resources, we have developed an enhanced methodology for estimating Groundwater Storage change (GWSC). This method integrates Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) inversion, Gravity Recovery and Climate Experiment (and Follow-on)/Global Land Data Assimilation System (GRACE/GLDAS) modelling, and hydraulic head measurements. The time-series comparisons based on well water levels and residual statistics of equivalent water thickness (EWT) validate the reliability of both MT-InSAR- and GRACE/GLDAS-derived GWSC, exhibiting a mean R-square of 0.8288 and an RMSE of 7.5 cm. The corrected and integrated GWSC significantly enhances the equal lon-lat tile of 1/4-degree (∼27 km) from CSR GRACE/GRACE-FO Mascon solution (CSR-M) to within 1 km in aquifers with known prior hydrogeological information. Furthermore, the geographic patterns analysis of GWSC in HNP using standard deviational ellipse (SDE) reveals that the central regions in HNP between the Yellow River and the Huaihe River suffer the most severe regional groundwater depletion. The depletion center has shifted to the northwest by 87 km, showing a trend of easing in the north-south direction and expanding in the east-west direction.