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As one of the four major bay areas in the world, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a highly integrated mega urban agglomeration and its unparalleled urbanization has induced prominent land contradictions between humans and nature, which hinders its sustainability and has become the primary concern in this region. In this paper, we probed the historical characteristics of land use and land cover change (LUCC) in the GBA from 2005 to 2015, and forecasted its future land use pattern for 2030, 2050, and 2070, using a cellular automata–Markov (CA–Markov) model, under three typical tailored scenarios, i.e., urban development (UD), cropland protection (CP), and ecology security (ES), for land use optimization. The major findings are as follows: (1) The encroachments of build-up land on the other land uses under rapid urbanization accounted for the leading forces of LUCCs in the past decade. Accordingly, the urban sprawl was up to 1441.73 km2 (23.47%), with cropland, forest land, and water areas reduced by 570.77 km2 (4.38%), 526.05 km2 (1.76%), and 429.89 km2 (10.88%), respectively. (2) Based on the validated CA–Markov model, significant differences are found in future land use patterns under multiple scenarios, with the discrepancy magnified over time and driven by different orientations. (3) Through comprehensive comparisons and tradeoffs, the ES scenario mode seems optimal for the GBA in the next decades, which optimizes the balance between socio-economic development and ecological protection. These results serve as an early warning for future land problems and can be applied to land use management and policy formulation to promote the sustainable development of the GBA. [ABSTRACT FROM AUTHOR]

Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks tend to become stuck in local optima, and their prediction accuracy fluctuates significantly, thus posing restrictions to their accuracy and stability in the inversion process. Studies have found that metaheuristic optimization algorithms can significantly improve these shortcomings by optimizing the initial parameters (weights and biases) of BP neural networks. In this paper, the adaptive nonlinear weight coefficient, the path search strategy "Levy flight" and the dynamic crossover mechanism are introduced to optimize the three main steps of the Artificial Ecosystem Optimization (AEO) algorithm to overcome the algorithm's limitation in solving complex problems, improve its global search capability, and thereby improve its performance in optimizing BP neural networks. Relying on Google Earth Engine and Google Colaboratory (Colab), a model for the inversion of Chl-a concentration in the coastal waters of Hong Kong was built to verify the performance of the improved AEO algorithm in optimizing BP neural networks, and the improved AEO algorithm proposed herein was compared with 17 different metaheuristic optimization algorithms. The results show that the Chl-a concentration inversion model based on a BP neural network optimized using the improved AEO algorithm is significantly superior to other models in terms of prediction accuracy and stability, and the results obtained via the model through inversion with respect to Chl-a concentration in the coastal waters of Hong Kong during heavy precipitation events and red tides are highly consistent with the measured values of Chl-a concentration in both time and space domains. These conclusions can provide a new method for Chl-a concentration monitoring and water quality management for coastal waters. [ABSTRACT FROM AUTHOR]

In order to accomplish the calibration and validation (Cal/Val) of altimeters, the Wanshan calibration site (WSCS) has been used as a calibration site for satellite altimeters since its completion in August 2019. In this paper, we introduced the WSCS and the dedicated equipment including permanent GNSS reference stations (PGSs), acoustic tide gauges (ATGs), and dedicated GNSS buoys (DGB), etc. placed on Zhi'wan, Wai'ling'ding, Dan'gan, and Miao'Wan islands of the WSCS. The PGSs data of Zhi'wan and Wai'ling'ding islands were processed and analyzed using the GAMIT/GLOBK (Version 10.7) and Hector (Version 1.9) software to define the datum for Cal/Val of altimeters in WSCS. The DGB was used to transfer the datum from the PGSs to the ATGs of Zhi'wan, Wai'ling'ding, and Dan'gan islands. Separately, the tidal and mean sea surface (MSS) corrections are needed in the Cal/Val of altimeters. We evaluated the global/regional tide models of FES2014, HAMTIDE12, DTU16, NAO99jb, GOT4.10, and EOT20 using the three in situ tide gauge data of WSCS and Hong Kong tide gauge data (No. B329) derived from the Global Sea Level Observing System. The HAMTIDE12 tide model was chosen to be the most accurate one to maintain the tidal difference between the locations of the ATGs and the altimeter footprints. To establish the sea surface connections between the ATGs and the altimeter footprints, a GPS towing body and a highly accurate ship-based SSH measurement system (HASMS) were used to measure the sea surface of this area in 2018 and 2022, respectively. The global/regional mean sea surface (MSS) models of DTU 2021, EGM 2008 (mean dynamic topography minus by CLS_MDT_2018), and CLS2015 were accurately evaluated using the in situ measured data and HY-2A altimeter, and the CLS2015 MSS model was used for Cal/Val of altimeters in WSCS. The data collected by the equipment of WSCS, related auxiliary models mentioned above, and the sea level data of the hydrological station placed on Dan'gan island were used to accomplish the Cal/Val of HY-2B, HY-2C, Jason-3, and Sentinel-3A (S3A) altimeters. The bias of HY-2B (Pass No. 375) was −16.7 ± 45.2 mm, with a drift of 0.5 mm/year. The HY-2C biases were −18.9 ± 48.0 mm with drifts of 0.0 mm/year and −5.6 ± 49.3 mm with −0.3 mm/year drifts for Pass No. 170 and 185, respectively. The Jason-3 bias was −4.1 ± 78.7 mm for Pass No. 153 and −25.8 ± 85.5 mm for Pass No. 012 after it has changed its orbits since April 2022, respectively. The biases of S3A were determined to be −16.5 ± 46.3 mm with a drift of −0.6 mm/year and −9.8 ± 30.1 mm with a drift of 0.5 mm/year for Pass No. 260 and 309, respectively. The calibration results show that the WSCS can commercialize the satellite altimeter calibration. We also discussed the calibration potential for a wide swath satellite altimeter of WSCS. [ABSTRACT FROM AUTHOR]

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO2 emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a "core–subcore–periphery" emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers. [ABSTRACT FROM AUTHOR]

Tracking long-term tidal flat dynamics is crucial for coastal restoration decision making. Accurately capturing the loss and gain of tidal flats due to human-induced disturbances is challenging in the micro-tidal areas. In this study, we developed an automated method for mapping the annual tidal flat changes in the micro-tidal areas under intense human activities, by integrating spectral harmonization, time series segmentation from dual spectral indices, and the tide-independent hierarchical classification strategy. Our method has two key novelties. First, we adopt flexible temporal segments for each pixel based on the dual full-time series spectral indices, instead of solely using a fixed period window, to help obtain more reliable inundation frequency features. Second, a tide-independent hierarchical classification strategy based on the inundation features and the Otsu algorithm capture the tidal flat changes well. Our method performed well in Guangdong, Hong Kong, and Macao (GHKM), a typical area with micro-tidal range and intense human activities, with overall accuracies of 89% and 92% for conversion types and turning years, respectively. The tidal flats in GHKM decreased by 24% from 1986 to 2021, resulting from the loss of 504.45 km2, partially offset by an accretion of 179.88 km2. Further, 70.9% of the total loss was in the Great Bay Area, concentrated in 1991–1998 and 2001–2016. The historical trajectories of tidal flat loss were driven by various policies implemented by the national, provincial, and local governments. Our method is promising for extension to other micro-tidal areas to provide more scientific support for coastal resource management and restoration. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting is critical for areas such as agriculture, water resource management, urban drainage systems, transport and disaster preparedness. In recent years, methods such as convolutional recurrent neural networks (ConvRNN) in deep learning techniques have been used to solve this task. Despite the effective improvement in forecasting quality, there are still problems with blurred and distorted prediction images, as well as difficulties in effectively forecasting high echo regions. To solve the above problems, this article presents a spatio-temporal long–short-term memory network model in view of multi-scale context fusion and attention mechanisms. This method fully extracts the short-term context information of different scales of radar image through the multi-scale context fusion module. The attention module broadens the time perception domain of the prediction unit so that the model perceives more historical time dynamics. Using the Hong Kong region weather radar data as a sample, the results of the experimental comparative analysis show that the spatio-temporal long and short-term memory network in view of multi-scale context fusion and attention mechanism achieves better prediction performance. Our model is effective in improving both image quality and meteorological assessment metrics with higher accuracy and more details. [ABSTRACT FROM AUTHOR]

The loss of life and property in economically developed areas due to geological hazards caused by intense ground subsidence is incalculable. As one of the fastest growing areas in the Guangdong-Hong Kong-Macao Greater Bay Area, the study of ground subsidence in Nansha will help to provide a scientific basis for urban planning and improve the capacity of monitoring and prevention of ground subsidence. The combination of coastal soft soil foundation and urbanization conditions creates a certain risk of land subsidence. We chose Nansha District, the geographical center of the Greater Bay Area, as the study area to analyze its surface subsidence characteristics in recent years. The 20-view Sentinel-1A data and SBAS-InSAR technique were used to monitor the ground subsidence in Nansha from 2017 to 2023. The main rate of ground subsidence in Nansha ranges from −19.4 to 7.7 mm/yr and is distributed in the urban area, along the rivers, in the construction area, and in the reclamation area. As of 4 May 2023, the average ground settlement in Nansha is 10.05 mm and the maximum settlement can be up to 142.45 mm. The 6-year total settlement at all four settlement intensities is greater than 60 mm, with the highest value exceeding 110 mm. The cumulative settlement increases with time, but inverse settlement and no settlement also occur at points where settlement is severe. For settlement caused by soft soil consolidation, it is recommended that drainage pipes be installed to accelerate drainage as a means of stabilizing settlement. For settlement caused by groundwater extraction and additional loads on the road surface, it is recommended to rationally extract groundwater and reinforce the foundation of the road surface with severe settlement. [ABSTRACT FROM AUTHOR]