Spatio-temporal coupling analysis and tipping points detection of China's coastal integrated land-human activity-ocean system.

The coastal zone is typically highly developed and its ocean environment is vastly exposed to the onshore activities. Land-based pollution, as the "metabolite" of terrestrial human activities, significantly impacts the ocean environment. Although numerous studies have investigated these effects, few have quantified the interactions among land-human activity-ocean across both spatial and temporal scales. In this study, we have developed a land-human activity-ocean systemic framework integrating the coupling coordination degree model and tipping point to quantify the spatiotemporal dynamic interaction mechanism among the land-based pollution, human activities, and ocean environment in China from 2001 to 2020. Our findings revealed that the overall coupling coordination degree of the China's coastal zone increased by 36.9 % over last two decades. Furthermore, the effect of human activities on China's coastal environment remained within acceptable thresholds, as no universal tipping points for coastal pollution or ocean environment has been found over the 20-year period. Notably, the lag time for algal blooms, the key indicator of ocean environment health, was found to be 0-3 years in response to the land economic development and 0-4 years in response to land-based pollution. Based on the differences in spatiotemporal interactions among land-human activity-ocean system, we employed cluster analysis to categorize China's coastal provinces into four types and to develop appropriate management measures. Quantifying the interaction mechanism within the land-human activity-ocean system could aid decision-makers in creating sustainable coastal development strategies. This enables efficient use of land and ocean resources, supports coastal conservation and restoration efforts, and fosters effective management recommendations to enhance coastal sustainability and resilience.

Dynamics of land cover changes and carbon emissions driven by large dams in China.

The recent surge in dam construction has sparked debates regarding their contribution to carbon neutrality and food security, focusing on trade-offs between production benefits and ecological drawbacks. However, how dams affect carbon emissions and land cover changes, including their spatial differentiations, remains unclear. We quantified spatiotemporal variations in carbon emissions and storage of 137 large dams in China from 1992 to 2020, resulting from land cover change in potentially affected areas. We observed a lesser increase in carbon emissions and a more pronounced increase in carbon storage driven by forest conservation and regeneration within dam-affected areas compared to unaffected areas. Additionally, we noticed an increased grain yield in nearby areas potentially due to increased water availability. Our findings highlight the importance of considering land cover change when assessing carbon neutrality or grain yield at regional and national scales. This study provides useful insights into optimizing dam locations to mitigate future carbon emissions effectively.

Sustainably developing global blue carbon for climate change mitigation and economic benefits through international cooperation.

Blue carbon is the carbon storage in vegetated coastal ecosystems such as mangroves, salt marshes, and seagrass. It is gaining global attention as its role in climate change mitigation and local welfare growth. However, a global assessment on the long-term spatiotemporal sustainable development status of blue carbon has not been conducted, and the relations among blue carbon ecosystems, driving forces for climate change mitigation, and socioeconomic interventions for development capacity on a global scale are still unclear. Here, we constructed a blue carbon development index (BCDI), comprising three subsystems: driving force, resource endowment, and development capacity, to assess the sustainable development level of 136 coastal countries' blue carbon over 24 consecutive years and explore the relationship among subsystems. We further propose a cooperation model to explore the feasibility of global blue carbon cooperation and quantify benefit allocation to specific countries. The results showed an upward trend in BCDI scores with variations in regional performance over the past two decades, and we found a positive correlation between development capacity and blue carbon resource endowment. Based on the scenario simulations of global cooperation, we found that coastal countries could improve the global average BCDI score, add 2.96 Mt of annual carbon sequestration, and generate $136.34 million in 2030 under Global Deep Cooperation scenario compared with the Business-As-Usual scenario.

Optimize the spatial distribution of crop water consumption based on a cellular automata model: A case study of the middle Heihe River basin, China.

Globally, agriculture is by far the largest water consuming sector and in areas where water is scarce, the spatial optimization of crop water consumption used to improve irrigation benefits becomes critical for regional water management. The spatial heterogeneity of environmental parameters brings great challenge to spatial optimization. Therefore, cellular automaton (CA), crop suitability (CS), spatial distributed crop water consumption model and optimization model were integrated and applied on the middle reaches of Heihe River basin, northwest of China. The cellular automata based Water Consumption Optimization (CA-WCSO) model is not only a spatial dynamic optimization model for crop water consumption, but also a decision support tool that reflects the interaction between water consumption at field level and management regulations at regional level. Six optimization paths: i) forward progressive (FP), ii) forward interlacing (F-IL), iii) forward interpolation (F-IP), iv) reverse progressive (R-P), v) reverse interlacing (R-IL) and vi) reverse interpolation (R-IP) of crop water consumption for the baseline year and the planning year were applied on the study site. Results for baseline year (2015) demonstrate that the six optimization paths can slightly reduce the water consumption (>1.4%) but significantly improve the irrigation benefits of the region by 20.56%. Using CA-WCSO model, decision makers can modify model's constraints and select appropriate optimization path to get the optimized crop planting patterns and make future regional water allocation plans.