中華人民共和国自然資源部

This research aims to explore the response of sea surface wave fields across mesoscale eddies under the influence of tropical cyclones (TCs) in the South China Sea, utilizing numerical simulations from the Finite-Volume Community Ocean Model (FVCOM) one-way coupling with the Simulating WAves Nearshore (SWAN) model. Four types of vertical coordinates were tested in FVCOM to improve the representation of upper-ocean variability under rapidly changing depths. The high-resolution hindcast sea surface temperature data was used for validation, revealing that the general vertical coordinate in FVCOM exhibited the most accurate results, showing a correlation (COR) of 0.90 and a root mean square error (RMSE) reaching 0.29 °C. Wave simulations were conducted using the SWAN model, which incorporated wave breaking influenced by depth and complex nonlinear interactions between waves. Based on 51 mesoscale eddies detected during five TCs from 2022 to 2024, the results reveal distinct modulation patterns. Regression analysis indicates a significant coupling among wind, waves, currents, and Eddy Kinetic Energy (EKE) within the eddies. While significant wave height (SWH) increases with prolonged TC forcing duration, TC translation speed shows no significant impact on SWH or EKE, suggesting that internal geostrophic adjustment plays a dominant role in eddy dynamics. Structurally, intense TC forcing was observed to disrupt pre-existing eddies and trigger the ageostrophic cold eddies characterized by high Rossby numbers (> 0.5) and low Richardson numbers (< 0.25). Furthermore, the modulation of TC-induced waves is identified as a spatial process involving steepening-induced dissipation at the periphery and refraction-induced redistribution at the edge. The warm eddy periphery acts as a low-pass filter, dissipating high-frequency energy early due to opposing currents. Combined with refractive effects, this mechanism leads to wave energy convergence in cold eddies versus divergence in warm eddies.

To overcome the critical bottleneck of long-term soil thermal imbalance in conventional Ground Source Heat Pump (GSHP) applications, this study proposes a novel, universally applicable operational framework based on a Photovoltaic/Thermal Heat Pump and GSHP (PVTHP-GSHP) hybrid system. The core novelty of this work lies in the development of a unified Multi-Source Complementary Strategy (MSCS), which enables the system to flexibly switch between soil heat replenishment and dissipation modes, ensuring adaptability across diverse climates. Dynamic simulations were conducted in TRNSYS for five representative cities-Shenyang, Yinchuan, Shanghai, Kunming, and Guangzhou-covering China's five major climate zones over a 20-year lifecycle. Performance evaluation focused on soil temperature evolution, system energy consumption, Coefficient of Performance (COP), and economic feasibility. Results demonstrate that the proposed strategy significantly mitigates soil thermal degradation. In heating-dominated regions, the absolute value of the Thermal Imbalance Ratio (TIR) decreased by up to 23%, while in cooling-dominated regions, the average reduction was around 20%. Lifecycle energy consumption decreased by an average of 40%, and system COP improved by 25-54% compared with conventional GSHP systems. Economic assessment reveals payback periods ranging from 4.4 to 10 years, with Kunming achieving the optimal return. Ultimately, this research fills the knowledge gap regarding cross-regional system adaptability, offering a robust theoretical basis and a promising pathway for large-scale, clean, and efficient building energy applications in China.

Surface subsidence induced by underground coal mining is a primary driver of ecological degradation. The traditional Remote Sensing Ecological Index (RSEI), however, struggles to capture surface deformation constraints and vegetation response lags. To address this, we developed a Mining Deformation–Ecology Coupling Index (MDECI). This index integrates Interferometric Synthetic Aperture Radar (InSAR)-monitored surface stability with multi-spectral indicators via Principal Component Analysis (PCA). We applied this method to the Datong Coalfield, China, using 231 Sentinel-1A SAR scenes and 8 Landsat images (2017–2024) to validate the effectiveness of the index. Meanwhile, we systematically analyzed non-linear response mechanisms, the Ecological Turning Point (ETP), and spatial clustering characteristics. The results demonstrate the following: (1) InSAR and MDECI effectively identified patterns of surface subsidence and ecological decline. Subsidence centers expanded to a maximum of −2085 mm, causing the mean MDECI in these areas to drop to 0.185 (<−1800 mm). This represents a 57.4% decrease relative to the regional average (0.434). (2) MDECI outperformed traditional models with a stable Average Correlation Coefficient (ACC) (0.63–0.75) and high cross-correlation coefficients with RSEI (0.906) and the Mine-specific Eco-environment Index (MSEEI) (0.931). During the 2018 drought, MDECI maintained a robust ACC of 0.628 while RSEI dropped to 0.482. (3) Multi-scale analysis revealed a unimodal MDECI response with an ETP at −100 mm. Initial ‘micro-disturbance gain’ (0.371 to 0.471) is followed by a progressive decline to a minimum of 0.185 under severe deformation. (4) Local Indicators of Spatial Association (LISA) spatial clustering characterized the distribution patterns of ecological damage and localised high-maintenance areas. High–Low damaged areas accounted for 5.09%, while High–High high-maintenance areas reached 9.00%. The scale of High–High areas was approximately 1.77 times that of the damaged areas. The MDECI addresses the deficiencies of traditional indices in high-disturbance areas and isolates the impact of mining on the ecology, providing a quantitative basis for risk identification and differentiated restoration.

This study introduces a novel uninsulated microchannel photovoltaic/thermal (PV/T) panel integrated with a solar direct-expansion heat pump system. During dynamic operation, the system effectively regulates evaporation/collector temperature to a couple of solar and air heat sources. A dedicated experimental platform is established. This study first quantifies the impact of uncontrollable environmental factors-solar irradiance, ambient temperature, and wind speed on the heat-source contribution ratio. Thereafter, to adapt for its variability, the expansion-valve opening and compressor speed are adaptively optimized to enhance overall system performance. Results reveal that a 484.6 W/m2 reduction in solar irradiance decreases the solar contribution ratio by 30.10% and lowers the PV/T evaporator's heat transfer and performance coefficient (COPpv/t) by 52.58% and 53.79%, respectively. Ambient temperature and wind speed have minor effects; a 2 degrees C rise increases the air contribution ratio by 2.31%. Meanwhile, a 2.8 m/s increase in wind speed raises the air contribution ratio by 7.53%. Under low solar irradiance, reducing the expansion valve opening boosts the air contribution ratio by 19.71%, and raises the evaporator's heat transfer rate and heat pump performance coefficient (COPhp) by 9.07% and 4.84%. Conversely, under high solar irradiance, increasing compressor speed further enhances the solar contribution ratio by 7.00%, increases the evaporator's heat transfer by 40.85%, reduces photovoltaic panel temperature, and improves overall system efficiency by 18.31%. This study provides experimental evidence and theoretical support for optimizing heat source regulation and efficiency in direct-expansion microchannel PV/T heat pump systems.

Ionospheric effects constitute a key error source limiting the accuracy of surface deformation monitoring using L-band interferometric synthetic aperture radar (InSAR). Efficient identification of interferometric pairs affected by ionospheric disturbances is therefore essential for large-scale and high-throughput automated InSAR processing. To address this issue, a parameterized ionospheric detection method based on azimuth offsets derived from sub-aperture images is proposed. The proposed method integrates random-sampling pixel offset tracking (RS-POT) with piecewise Gaussian fitting to enable rapid and robust detection of ionospheric disturbances. Experimental validation was conducted using 50 interferometric pairs acquired by the LuTan-1 (LT-1) satellite, China’s first dual-satellite L-band SAR mission, covering high-, mid-, and low-latitude regions with varying ionospheric conditions. The results demonstrate that the proposed method can reliably identify ionospheric disturbances under diverse conditions while maintaining high computational efficiency. The proposed framework provides an effective solution for determining whether ionospheric correction is required, thereby improving the efficiency of automated interferometric processing workflows.