Satellite-based precipitation products can be a better alternative of rain gauges for hydro-meteorological studies in data-poor regions. This study aimed to evaluate how regional and seasonal precipitation and drought patterns had changed in the Ganga–Brahmaputra Basin between 1983 and 2020 with PERSIANN-CDR precipitation data. The spatial pattern of winter drought, monsoon drought, and Standardized Precipitation Index (SPI) calculated for different time scales were evaluated using principal component analysis. Ganga–Brahmaputra is one of the most populated river basins that flows through different geographical regions. Rain gauges are heterogeneously distributed in the basin due to its complex orography, highlighting the significance of gridded precipitation products over gauge observations for climate studies. Annual and monthly precipitation trends between 1983 and 2020 were evaluated using the original and modified Mann–Kendall trend test, and annual precipitation in the basin was found to be declining at a rate of 5.8 mm/year. An increasing trend was observed in pre-monsoon rainfall, whereas precipitation exhibited a decreasing trend for other months. Results of the Pettitt test showed precipitation time series was inhomogeneous and changepoint occurred around 2000. Decreasing trends of SPI indicated increasing frequency and intensity of drought events. Winter drought showed a clear spatial pattern in the basin; however, SPIs calculated for different time scales and monsoon drought had complex spatial patterns. This study demonstrates the applicability of satellite-based PERSIANN-CDR precipitation data in climate research in the Ganga–Brahmaputra Basin.

Ecological environment evaluation is of great significance to achieve the Sustainable Development Goals (SDGs) and promote the harmonious development of economy, society, and environment. To evaluate environmental SDGs, single environmental indicators have been analyzed at national or large regional scale in some literature, while the urban integrated environment is ignored. Therefore, it is necessary to systematically and quantically evaluate the sustainability of ecological environment integrating the water, soil, and air environment at the urban scale. This study aims to construct the Integrated Perception Ecological Environment Indicator (IPEEI) based on the Driver-Pressure-State-Impact-Response (DPSIR) framework to solve the above-mentioned problems. The IPEEI model was proposed based on the three-level association mechanism of the Domain-Theme-Element, and the DPSIR framework conforming to the relevant standards for indicator determination. Moreover, the multi-dimensional, multi-thematic, and multi-urban quantitative evaluations were conducted using the entropy weight method, and the comprehensive evaluation grades by the Jenks natural breaks classification method of the geospatial analysis. Nine cities in the Wuhan Metropolitan Area were selected as the experimental areas. The results were consistent with the Ecological Index and local government’s planning and measures, which demonstrated that IPEEI can be effectively verified and applied for the evaluation of urban ecological environment sustainability.

Due to the incomprehensive and inconsistent description of spatial and temporal information for city data observed by sensors in various fields, it is a great challenge to share the massive, multi-source and heterogeneous interdisciplinary instant point observation data resources. In this paper, a spatio-temporal enhanced metadata model for point observation data sharing was proposed. The proposed Data Meta-Model (DMM) focused on the spatio-temporal characteristics and formulated a ten-tuple information description structure to provide a unified and spatio-temporal enhanced description of the point observation data. To verify the feasibility of the point observation data sharing based on DMM, a prototype system was established, and the performance improvement of Sensor Observation Service (SOS) for the instant access and insertion of point observation data was realized through the proposed MongoSOS, which is a Not Only SQL (NoSQL) SOS based on the MongoDB database and has the capability of distributed storage. For example, the response time of the access and insertion for navigation and positioning data can be realized at the millisecond level. Case studies were conducted, including the gas concentrations monitoring for the gas leak emergency response and the smart city public vehicle monitoring based on BeiDou Navigation Satellite System (BDS) used for recording the dynamic observation information. The results demonstrated the versatility and extensibility of the DMM, and the spatio-temporal enhanced sharing for interdisciplinary instant point observations in smart cities.

The accurate prediction of sea surface temperature (SST) is highly important for climate change research and the management of marine ecosystems. Traditional numerical models rely on complex physical processes and precise initial conditions, resulting in high computational costs and limited generalizability. Although deep learning methods can improve the physical consistency and interpretability of ocean processes by incorporating physical constraints, their performance may degrade under anomalous or extreme SST conditions, where rigid constraints limit the model’s adaptability to complex variations. In contrast, incorporating motion-aware mechanisms enables models to flexibly capture dynamic patterns from data, thus increasing their responsiveness to nonstationary processes. Therefore, we propose a novel SST prediction model that integrates optical flow guidance with spatiotemporal awareness, aiming to enhance the modeling of motion and spatiotemporal features in SST evolution. The proposed model consists of three key modulesa spatiotemporal information extraction module (SIEM), a motion trend extraction module (MTEM), and a spatiotemporal feature fusion module (SFFM). First, the SIEM, composed of multiple layers of SwinLSTM, captures spatial and temporal dependencies in the SST time series. The MTEM then estimates the optical flow to extract motion information from the SST data. Finally, the motion information is used to dynamically adjust the spatiotemporal features, which are then fused in the SFFM. To evaluate the performance of the proposed model, we conducted SST prediction experiments at both daily (1–10 days) and weekly (1–10 weeks) scales over the South China Sea (SCS) and the Pacific Ocean, and compared the results with several existing models. The experimental results demonstrate that our model outperforms existing methods across multiple evaluation metrics, with superior prediction accuracy and robustness.

Sea surface temperature (SST) predictions are critical for marine climate research and ecosystem management. To address the deficiencies of the existing data-driven models in regard to physical consistency constraints and multiscale feature modeling, this paper proposes a deep learning model that fuses physical constraints and frequency domain features for the SST prediction. This model employs the fast fourier transform (FFT) to process spatio-temporal features. At the same time, it incorporates a constraint integration module (CIM), which embeds the physical conservation properties of the advection-diffusion equation (ADE) into the prediction process, thereby enhancing the modeling capability for the dynamic evolution patterns of SST. The experimental results on both daily and weekly scales demonstrate that the proposed PCFNet achieves consistently higher prediction accuracy compared with existing SST forecasting models. In particular, it provides more stable short-term predictions and effectively mitigates multi-step error accumulation. These results further confirm the effectiveness of integrating frequency-domain structure modeling and cross-scale interaction mechanisms. Overall, this study offers a promising and interpretable framework for SST prediction by combining physical priors with data-driven learning.