The Chaohu Lake Basin (CLB) is the main flow area of the Yangtze River–Huaihe River Water Transfer Project in Central China. It is important to quantitatively evaluate the water resources in the CLB and predict their response to future climate change. This study simulated and calibrated the water yield in the CLB from 2000 to 2019 based on InVEST. We also analyzed the influence factor on the water yield and predicted the water yield in future years with CMIP6 data. The results demonstrate that: (1) The InVEST water production module had good applicability in this study region. There was a strong linear relationship between the simulated water yield and the observed surface runoff (y = 1.2363x − 8.6038, R² = 0.868, p < 0.01); (2) The explanatory percentage of interaction between precipitation and land use/land cover for water yield in 2001, 2008, and 2016 reached 71%, 77%, and 85%, respectively, which were the two dominant factors affecting water yield in the CLB; and (3) The average annual water yield in the CLB increased under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 future scenarios with increasing precipitation, increased with 71%, 139.8%, and 159.5% in 2100 compared with 2040, respectively. The overall trend of water production decreased with increases in carbon emission intensity.

This study examined the influence of the Yangtze River-Huaihe River Water Transfer Project (YHWTP) on the transportation accessibility of bulk cargo in 16 cities of Anhui Province based on modern transportation infrastructure. We also discussed the change in the strength of economic linkages affected by the YHWTP for 16 cities within Anhui Province and the Yangtze River Delta using the gravity model. The results demonstrate that: (1) The YHWTP will significantly improve the transportation accessibility of bulk commodities among the 16 cities in Anhui Province, especially the cities along the project route. It will reduce the minimum average transport cost (MATC) and the weighted average transport cost (WATC). (2) The YHWTP has a different influence on the transportation accessibility of the 16 cities, making the location conditions more unequal. (3) The change of spatial distribution of transportation accessibility of the 16 cities caused by YHWTP will be mainly concentrated in the triangular region, formed by Huainan, Anqing, and Wuhu. (4) The YHWTP will improve the total strength of economic linkages (TSEL) of the 16 cities within Anhui Province and Yangtze River Delta (YRD) region, with an increase by 27.62% and 9.04%, respectively. (5) Overall, Hefei will benefit the most from the YHWTP.

Land subsidence (LS) is a major global geo-environmental issue that profoundly affects the suitability and safety of land use planning (LUP). However, existing LUP systems generally neglect the dynamic evolution of LS and lack a systematic framework for assessing conflicts between land use and subsidence. To address this gap, this study develops an integrated evaluation framework that combines SBAS-InSAR, GeoDetector, and a spatial conflict detection model. A total of 166 Sentinel-1A images acquired from 2017 to 2024 were processed using SBAS-InSAR to derive the spatiotemporal characteristics of LS. GeoDetector was subsequently applied to identify the dominant driving factors and their interactions. A sensitivity classification scheme for current land use (CLU) and LUP types with respect to LS hazards was then developed, and a spatial conflict detection model was constructed to delineate conflict zones and quantify conflict intensity. Using Huainan City as a case study, the results show the following: (1) from 2017 to 2024, LS was generally characterized by slight or negligible subsidence, with severe subsidence mainly concentrated in coal mining areas; ongoing and recently suspended mines exhibited pronounced LS, whereas early-closed and unmined areas showed an overall uplift trend. (2) LS in Huainan was primarily driven by soil type, annual rainfall, and mining activities, and two-factor interactions generally exhibited enhancement effects. (3) Compared with CLU, LUP has, to some extent, incorporated LS risk considerations and implemented corresponding mitigation measures, although certain areas still insufficiently account for LS risks. (4) The proposed framework demonstrates strong rationality and applicability in LS monitoring, driving factor identification, and spatial conflict assessment, providing scientific support for LS risk management and land use spatial optimization.

In the complex planting area with scattered parcels, combining the parcel vector data with remote sensing images to extract the winter wheat planting information can make up for the deficiency of the classification from remote sensing images simply. It is a feasible direction for precision agricultural subsidies, but it is difficult to collect large-scale parcel data and obtain high spatial resolution or time-series remote sensing images in mass production. It is a beneficial exploration of making use of existing parcel data generated by the ground survey and medium-resolution remote sensing images with suitable time and spatial resolution to extract winter wheat planting areas for large-scale precision agricultural subsidies. Therefore, this paper proposes a new algorithm to extract winter wheat planting areas based on ownership parcel data and medium-resolution remote sensing images for improving classification accuracy. Initially, the segmentation of the image is carried out. To this end, the parcel data is used to generate the region of interest (ROI) of each parcel. Second, the homogeneity of each ROI is detected by its statistical indices (mean value and standard deviation). Third, the parallelepiped classifier and rule-based feature extraction classification methods are utilized to conduct the homogeneous and nonhomogeneous ROIs. Finally, two classification results are combined as the final classification result. The new algorithm was applied to a complex planting area of 103.60km² in central China based on the ownership parcel data and Gaofen-1 PMS and WFV remote sensing images in this paper. The experimental results show that the new algorithm can effectively extract winter wheat planting area, eliminate the problem of salt-and-pepper noise, and obtain high-precision classification results (kappa=0.9279, overall accuracy=96.41, users accuracy=99.16, producers accuracy=93.39, commission errors=0.84, and omission errors=6.61) when the size of ownership parcels matches the spatial resolution of remote sensing images.

This paper proposes an improved algorithm for simulating the surface flow dynamics based on the flow-path network model. This algorithm utilizes the parallel-multi-point method to extract the critical points and the D8 algorithm to retrieve the drainage networks from the regular-grid digital elevation model (DEM) for constructing a drainage-constrained triangulated irregular network (TIN). Then, it combines the flow directions of triangular facets over TIN with resampled flow source points to track flow lines to generate the flow path network (FPN) based on the flow-path network model. On this basis, the proposed algorithm employs three terrain parameters (slope length factor, topographic wetness index and flow path curvature) to improve the classical Manning equation based on the analytic hierarchy process (AHP) to enhance the accuracy of the flow velocity calculation. The topographic wetness index and flow path curvature are derived by the flow-path-network-triangular-facet-network (FPN_TFN) algorithm, a new flow-path-network-topographic-wetness-index (FPN_TWI) algorithm and the flow-path-network-flow-path-curvature (FPN_C) algorithm, respectively. Finally, the velocity estimation function and surface flow discharge simulation function are parallelized by the Compute Unified Device Architecture (CUDA) to enhance its computational efficiency. The outcomes are compared with the algorithm before improvement (TIN_based algorithm) and the SWAT model. The results demonstrate that the speedup ratio reaches 15.7 compared to the TIN_based algorithm. The Nash coefficient increases by 6.49 %, the correlation coefficient decreases slightly, and the balance coefficient increases by 19.08 %. Compared with the SWAT model, the Nash coefficient and correlation coefficient increase by 97.56 % and 4.60 %, respectively. The balance coefficient is close to 1 and outperforms the compared algorithms.

The topographic wetness index (TWI) is a common factor for describing the control of the relative soil moisture spatial distribution. However, the application accuracy of the TWI remains unfavorable. Thus, this study aimed to enhance the precision of the TWI by combining the triangular facet network (TFN) with flow-path network (FPN) from a digital elevation model (DEM). First, the aspect and slope were derived from the vector TFN. Then, the FPN was designed to estimate the flow accumulation. Finally, the TWI was obtained by combining the slope and specific catchment area (SCA) which was calculated by integrating the flow accumulation with the aspect. Experiments were conducted in two respects: (1) to quantitatively assess the error in the vector method under the DEMs of 5 m, 10 m, 20 m, 30 m, 60 m, and 90 m constructed by twelve mathematical models (three groups of parameters each for four surface types: ellipsoid, inverse ellipsoid, saddle, and plane); (2) to qualitatively assess the error under two real-world DEMs of the small Black Brook watershed (BBW) and the mountainous plateau area. The experimental results demonstrated that the TWI value derived from the proposed method was generally closer to the theoretical value with the lowest root mean standard error (RMSE) and mean absolute error (MAE) than the results of other commonly used algorithms. The results of Kruskal-Wallis test demonstrated that the vector method exhibits the best accuracy and stability in TWI calculation under the study scenario. The proposed method can maintain consistent results on different surfaces at various resolutions. Moreover, the spatial distribution of the TWI calculated from the vector method has advantages.