Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 10⁶ Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 10⁶ Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA.

Accurate precipitation estimation is crucial for hydrological and meteorological applications. IMERG-Late, a widely used global satellite-based precipitation product, exhibits biases in China due to complex topography and climate variability. This study proposes a U-Net-based deep learning framework for calibrating IMERG-Late precipitation estimates using CMPA as the ground truth. The model effectively learns spatial and temporal patterns to reduce systematic errors and improve precipitation consistency. Comparative experiments demonstrate that U-Net outperforms traditional interpolation, statistical correction, and other deep learning methods in enhancing precipitation accuracy. While the approach significantly improves satellite-based precipitation estimates, challenges such as computational costs and generalization in extreme weather events remain. Future work will explore hybrid deep learning-physical model approaches and Transformer-based architectures to further enhance precipitation calibration.

In this study, a two-phase flow numerical wave tank model based on the viscous flow theory was applied to conduct computational research on the interaction between waves and submerged horizontal cylinders. The research objective is to reveal the hydrodynamic characteristics of nonlinear loads on submerged horizontal cylinders with a focus on vortex effects. The influence of the sharp and round corners of cross-sections on the wave forces on cylinders was summarized. The reasons for the characteristics of the wave forces were explained by analyzing the flow field distribution around the cylinder and decomposing the wave forces into inertial and drag forces. This study found that under the various incident wave amplitudes, the section corner and aspect ratio have significant impacts on each frequency component of the horizontal and vertical wave forces. The distribution of the vorticity field shows that the vortex effects lead to the differences between the loads on the cylinder under different cross-sectional corners and aspect ratios. The characteristics of inertial forces and drag forces on the cylinders were given by comparing and analyzing the cases with different sectional sharp and round corners. The inertia and drag coefficients were obtained by solving Morison’s equation. Under various Kc and Re numbers, the maximum values of the inertia and drag coefficients obtained are significantly different from those for submerged cylinders under oscillatory flow action.

Various global precipitation datasets have been used in precipitation-related fields such as hydrology, meteorology, climatology, and ecology to achieve different research objectives. Error analysis is an integral part before applying them to operational fields. However, the growing number of precipitation products and the absence of comprehensive error comparison research jointly impede users in distinguishing product-specific error patterns and constrain developers from enhancing precipitation estimation accuracy. To address this issue, we performed error analysis and comparison of thirteen global precipitation products—categorized as delayed time (DT), near real-time (NRT), and real-time (RT) types—across mainland China. Results revealed that GSMaP-Gauge (Gauge-adjusted Global Satellite Mapping of Precipitation) performed best in terms of detection indicators, while MGP (Multi-source merged global precipitation product) performed best in estimating precipitation accuracy. However, IMERG-Final (Integrated Multisatellite Retrievals for Global Precipitation Measurement Final Run) proved ineffective in reducing the overestimations of both storm and light precipitation events in regions of complex topography. Furthermore, two DT products (i.e., ERA5 (Fifth generation of ECMWF atmospheric reanalyses of the global climate) and MGP) overestimated the frequency of light precipitation events, with relative rainfall occurrence biases exceeding 80%. This bias is attributable to both false detections and the misclassification of high intensity rainfall as light precipitation. Although GSMaP-NOW (based exclusively on passive microwave data) detected precipitation more effectively than the infrared-only PDIRNow (Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)—Dynamic Infrared Rain Rate (Now)), it achieved lower accuracy. This discrepancy reflects the tradeoff between the higher precipitation sensitivity of passive microwave observations and their sparse temporal sampling, compared with the continuous coverage provided by infrared data. Finally, our findings indicated that current evaluation approaches do not reliably determine the optimal precipitation product, since product superiority is contingent upon the selected error metric. This underscores the urgent need to develop theoretically grounded and operationally reliable methods for selecting optimal precipitation products to support data users in deriving robust and reliable conclusions in hydrology, meteorology, and ecology.