Thirty-year changes of the coastlines, wetlands, and ecosystem services in the Asia major deltas

Construction and application of coastal ecosystem model coupling multiple human activities

The combined impact of multiple human activities is the primary driver of coastal ecosystem degradation, and comprehending its mechanisms is essential for developing adaptive management strategies. A coastal ecosystem model coupling multiple human activities, including runoff discharge, reclamation, mariculture, and atmospheric deposition, was developed using the Regional Ocean Modeling System (ROMS) coupled with the Carbon, Silicate, and Nitrogen Ecosystem (CoSiNE) model. The model performance was rigorously evaluated in the Bohai Sea as the study area. Through orthogonal experiment, multiple human activity scenarios were designed, and the contribution rates of these activities to marine ecological factors were quantified using variance analysis. The results showed that (1) the simulated results of dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and Chlorophyll-a (Chl-a) in 2020 were compared with observational data, whose average R2 values were 0.839, 0.820, 0.814, respectively, which indicated the simulated results were generally in accordance with observational data. (2) Orthogonal experiment results revealed that the contribution rates of runoff discharge to DIN, DIP, the nitrogen content of phytoplankton and zooplankton all ranked first, reaching 59.73 %, 65.05 %, 66.92 %, and 68.23 % respectively, indicating that runoff discharge is a key factor affecting the Bohai Sea ecosystem. (3) The response of ecological factors to human activities exhibited significant spatial heterogeneity, with the impacts of runoff discharge, reclamation, and mariculture diminishing with increasing distance from the coastline. The proposed model not only facilitates efficient scenario simulation but also demonstrates high accuracy, making it a valuable tool for quantifying the combined impact of multiple human activities and simulating coastal ecosystems under intense anthropogenic pressure.

Decoding carbon pathways of Shanghai megacity through historical land use patterns and urban ecosystem transitions

Urbanization induces significant land use changes that profoundly impact carbon stocks by altering terrestrial ecosystems' carbon storage capacity. This study employs integrated FLUS model and InVEST model alongside land use data to analyze the spatial and temporal dynamics of car-bon stocks in Shanghai from 2010 to 2020. We predict future land use change and carbon stock distribution patterns under various scenarios for Shanghai in 2030. Despite cultivated land representing the largest land use type in terms of area, rapid urbanization has drastically reduced it, largely converting it into construction land. Construction land expands most rapidly under the HUS scenario, prioritizing economic development, and least under the EPS scenario, emphasizing eco-logical protection. Annual carbon stocks declined by 165.06 × 104 Mg from 2010 to 2020, driven by construction land expansion diminishing carbon stocks in cultivated land, woodland, and grassland. Projections for 2030 anticipate carbon stock increases solely under the EPS scenario, contrasting with significant decreases under the NTS and HUS scenarios. This underscores ecological conservation policies' potential to mitigate carbon stock decline, constrain built-up land expansion, and enhance carbon sequestration capacity. Urbanization profoundly influences land use change and carbon stocks, highlighting the critical role of ecological policies in optimizing urban development space and fostering carbon stock growth.

Climate change and urban sprawl: Unveiling the escalating flood risks in river deltas with a deep dive into the GBM river delta

River deltas, such as the Ganges-Brahmaputra-Meghna (GBM) delta, are highly vulnerable to flooding, exacerbated by intense human activities and rapid urban growth. This study explores the evolution of urban flood risks in the GBM delta under the combined impacts of climate change and urban expansion. Unlike traditional assessments that focus on a single flood source, we consider multiple sources-coastal, fluvial, and pluvial. Our findings indicate that future urban expansion will significantly increase flood exposure, with a substantial rise in flood risk from all sources by the end of this century. Climate change is the main driver of increased coastal flood risks, while urban growth primarily amplifies fluvial, and pluvial flood risks. This highlights the urgent need for adaptive urban planning strategies to mitigate future flooding and support sustainable urban development. The extreme high emissions future scenario (SSP5-8.5) shows the largest urban growth and consequent flood risk, emphasizing the necessity for preemptive measures to mitigate future urban flooding. Our study provides crucial insights into flood risk dynamics in delta environments, aiding policymakers and planners in developing resilience strategies against escalating flood threats.

Spatial and temporal changes in shorebird habitats under different land use scenarios along the Yellow and Bohai Sea coasts in China

The effect of coastal wetland loss on shorebird habitat in recent years has been widely reported in previous studies. Various coastal wetland conservation and restoration measures have been implemented or will soon be implemented in China. The extent to which these measures will affect the area and structure of coastal wetland habitat in the future remains unclear. Here, we predicted changes in habitat area and structure for 39 common shorebird species along the coasts of the Yellow and Bohai Seas using a cellular automata-Markov (CA-Markov) land use scenario model and a maximum entropy species distribution model, along with terrain factors (slope, aspect, and digital evaluation model) and climate factors (temperature and precipitation) from the Data Centre for Resources and Environmental Sciences at the Chinese Academy of Sciences, land cover maps interpreted using the human-computer interactive method, and citizen science data of shorebird occurrences derived from eBird, Global Biodiversity Information Facility, and Bird Report. We found that shorebird habitat was most abundant along the coasts of Bohai Bay, Laizhou Bay, and Yancheng. The area of habitat decreased and became increasingly fragmented between 2000 and 2020 for more than half of the 39 species. Under the future business-as-usual scenario, the area of shorebird habitat decreased from 2020 to 2050, and the remaining habitat became increasingly fragmented. Under the ecological protection (EP) scenario, habitat loss was mitigated, and habitat connectivity was improved. The area of habitat was lower in 2050 under the EP scenario than in 2000 for most species, especially threatened species, suggesting that the area of habitat will not return to year-2000 levels under the EP scenario. These results emphasize the need to protect remaining shorebird habitats and implement ecological conservation measures to ensure the long-term preservation of coastal wetlands.

STIRUnet: SwinTransformer and inverted residual convolution embedding in unet for Sea-Land segmentation

Extraction of coastline from optical remote sensing images is of paramount importance for coastal zone management, erosion monitoring, and intelligent ocean construction. However, nearshore marine environment complexity presents a challenge when capturing small-scale and detailed information regarding coastlines. Furthermore, the presence of numerous tidal flats, suspended sediments, and coastal biological communities exacerbates the reduction in segmentation accuracy, which is particularly noticeable in medium-high-resolution remote sensing image segmentation tasks. Most previous related studies, based primarily on convolutional neural networks (CNNs) or traditional feature extraction methods, faced challenges in detailed pixel-level refinement and lacked comprehensive understanding of the studied images. Therefore, we proposed a new U-shaped deep learning model (STIRUnet) that combines the excellent global modeling ability of SwinTransformer with an improved CNN using an inverted residual module. The proposed method has the capability of global supervised feature learning and layer-by-layer feature extraction, and we conducted sea-land segmentation experiments using GF-HNCD and BSD remote sensing image datasets to validate the performance of the proposed model. The results indicate the following: 1) suspended sediments and coastal biological communities are major contributors to coastline blurring, and 2) the recovery of minute features (e.g., narrow watercourses and microscale artificial structures) effectively enhances edge details and leads to more realistic segmentation outcomes. The findings of this study are highly important in relation of accurate extraction of sea-land information in complex marine environments, and they offer novel insights regarding mixed-pixel identification.

Evaluation of historical and future coastal wetland change in the Yellow and Bohai Seas using satellite images and a land use model

Predicting the future distribution of coastal wetlands and characterizing changes in the area of wetlands between historical and future periods are important for the formulation of wetland conservation and management plans. Here, we used a cellular automata-Markov model and satellite images to simulate the future distribution of coastal wetlands under the business-as-usual scenario (BAU) and ecological protection scenario (EP) along the Yellow and Bohai Seas in China; we also explored historical (from 1990 to 2020) and future (from 2020 to 2050) changes in wetlands and the factors driving these changes. We found that the area of tidal flats gradually decreased because of increases in the area of saltpans, and the aquaculture area increased because of land reclamation and the invasion of Spartina alterniflora; most of the tidal flat area was fragmented into multiple small patches. If the current rate of degradation continues (BAU), the area of tidal flats will decrease by 21.25%, and the area of saltpans and aquaculture will increase by 13.83% and 21.25%, respectively. By contrast, under EP, the area of tidal flats will increase by 13.81%, and this increase will mainly stem from the conversion of areas with S. alterniflora (174.49 km2, 33.22%) to aquaculture areas (155.17 km2, 29.54%). Clear differences between historical and future periods were observed among Liaohe Estuary, Bohai Bay, Laizhou Bay, and the Yancheng-Nantong coasts. Land reclamation is the main factor inducing changes in the area of tidal flats, saltpans, and aquaculture in Liaohe Estuary, Bohai Bay, and Laizhou Bay. Land reclamation and the S. alterniflora invasion both affect the distribution of wetlands along the Yancheng-Nantong coasts.

Effects of production-living-ecological space changes on the ecosystem service value of the Yangtze River Delta urban agglomeration in China

In the process of urbanization, exploring the relationship between production-living-ecological space (PLES) and ecosystem service value (ESV) is a major scientific issue in promoting regional sustainable development. The Yangtze River Delta (YRD) urban agglomeration is an ideal study area, which has the highest urbanization rate in China. Based on Landsat TM/ETM imaging data from 2005, 2010, 2015, and 2018, this study established a land use classification system of PLES. The spatial and temporal characteristics of PLES and ESV were analyzed, and the response of ESV to changes in PLES was investigated based on the elasticity formula. The results showed that from 2005 to 2018, production space and ecological space were the main types of PLES and exhibited an imbalance in transformation. Production space was the main transfer type, and living space significantly expanded. Moreover, from 2005 to 2018, the ESV of the YRD urban agglomeration showed an increasing and then decreasing trend. ESV presented a "high in the southwest and low in the northeast" spatial pattern. Furthermore, ESV was sensitive to changes in PLES, showing a trend of ecological space > production space > living space. However, the sensitivity of ESV to changes in PLES varied according to urbanization level.

Rapid and large changes in coastal wetland structure in China's four major river deltas

Coastal wetlands provide essential ecosystem goods and services but are extremely vulnerable to sea-level rise, extreme climate, and human activities, especially the coastal wetlands in large river deltas, which are regarded as "natural recorders" of changes in estuarine environments. In addition to the area (loss or gain) and quality (degradation or improvement) of coastal wetlands, the information on coastal wetland structure (e.g., patch size and number) are also major metrics for coastal restoration and biodiversity protection, but remain very limited in China's four major river deltas. In this study, we quantified the spatial-temporal dynamics of total area (TA) and patch number (PN) of coastal wetlands with different sizes in the four deltas and the protected areas (PAs) and assessed the effects of major driving factors during 1984-2020. We also investigated the effectiveness of PAs through the comparison of TA and PN of coastal wetlands before and after the years in which PAs were listed as Ramsar Sites. We found both TA and PN experienced substantial losses in the Liaohe River Delta and Yellow River Delta but recent recoveries in the Yangtze River Delta. The coastal wetlands had a relatively stable and variable trend in TA but had a continually increasing trend in PN in the Pearl River Delta. Furthermore, reduced coastal reclamation, ecological restoration projects, and rapid expansion of invasive plants had great impacts on the coastal wetland structure in various ways. We also found that PAs were effective in halting the decreasing trends in coastal wetland areas and slowing the expansion of reclamation, but the success of PAs is being counteracted by soaring exotic plant invasions. Our findings provide vital information for the government and the public to address increasing challenges of coastal restoration, management, and sustainability in large river deltas.