Editorial: Regional coastal deoxygenation and related ecological and biogeochemical modifications in a warming climate

Coastal ecosystems play tremendous roles in socio-economic development, but their functions are degrading due to human activities [...]This work was funded by the Science and Technology Committee of Shanghai Municipal (No. 21ZR1421400), the National Science Foundation of China (No. 41706015), and by MCIN/AEI/and by “ERDF A way of making Europe” (PID2021-123352OB-C31). We acknowledge financial support to CESAM by FCT/MCTES (UIDP/50017/2020+UIDB/50017/2020+LA/P/0094/2020), through national funds. The present research was carried out in the framework of the AEI accreditation `Maria de Maeztu Centre of Excellence'' given to IMEDEA (CSIC-UIB) (CEX2021-001198).With funding from the Spanish government through the ‘María de Maeztu Unit of Excelence’ accreditation (CEX2021-001198)Peer reviewe

Ensuring water resource security in China; the need for advances in evidence based policy to support sustainable management.

China currently faces a water resource sustainability problem which is likely to worsen into the future. The Chinese government is attempting to address this problem through legislative action, but faces severe challenges in delivering its high ambitions. The key challenges revolve around the need to balance water availability with the need to feed a growing population under a changing climate and its ambitions for increased economic development. This is further complicated by the complex and multi-layered government departments, often with overlapping jurisdictions, which are not always aligned in their policy implementation and delivery mechanisms. There remain opportunities for China to make further progress and this paper reports on the outcomes of a science-to-policy roundtable meeting involving scientists and policy-makers in China. It identifies, in an holistic manner, new opportunities for additional considerations for policy implementation, continued and new research requirements to ensure evidence-based policies are designed and implemented and identifies the needs and opportunities to effectively monitor their effectiveness. Other countries around the world can benefit from assessing this case study in China

Influences of Climate Change and Human Activities on NDVI Changes in China

The spatiotemporal evolution of vegetation and its influencing factors can be used to explore the relationships among vegetation, climate change, and human activities, which are of great importance for guiding scientific management of regional ecological environments. In recent years, remote sensing technology has been widely used in dynamic monitoring of vegetation. In this study, the normalized difference vegetation index (NDVI) and standardized precipitation–evapotranspiration index (SPEI) from 1998 to 2017 were used to study the spatiotemporal variation of NDVI in China. The influences of climate change and human activities on NDVI variation were investigated based on the Mann–Kendall test, correlation analysis, and other methods. The results show that the growth rate of NDVI in China was 0.003 year−1. Regions with improved and degraded vegetation accounted for 71.02% and 22.97% of the national territorial area, respectively. The SPEI decreased in 60.08% of the area and exhibited an insignificant drought trend overall. Human activities affected the vegetation cover in the directions of both destruction and restoration. As the elevation and slope increased, the correlation between NDVI and SPEI gradually increased, whereas the impact of human activities on vegetation decreased. Further studies should focus on vegetation changes in the Continental Basin, Southwest Rivers, and Liaohe River Basin

Features of architectural landscape fragmentation in traditional villages in Western Hunan, China

Abstract With rapid industrialization and urbanization in China, inadequate preservation of traditional architecture coupled with natural deterioration have led to the fragmentation of architectural landscapes. Drawing from ecological fragmentation research in landscape ecology, we consider the cultural landscape as our research object, viewing buildings as landscape patches, and determine a system for measuring architectural landscape fragmentation in traditional villages. The study shows the degree of landscape fragmentation can reveal the characteristics of traditional villages and the process of regional modernization. The results are as follows: (1) From the perspective of landscape diversity, the study area was rich in landscape types in all dimensions, and the relative evenness index was high, signifying evident or severe fragmentation. (2) The index of landscape heterogeneity in the dimensions of building quality, height, and landscape appearance is low in the study area, with mild levels of landscape fragmentation caused by heterogeneity in the aforementioned dimensions. (3) Mild fragmentation suggests the integrity and homogeneity of architectural landscape types, reflecting a lagging level of economic development, whereas high fragmentation signifies rapid economic development, leading to a substantial deterioration in the integrity and homogeneity of architectural landscape types. Therefore, efforts to preserve and develop traditional villages should not solely aim for low fragmentation as it could potentially constrain sustainable development

Evaluation of nonidentical versus identical twin approaches for observation impact assessments: an ensemble-Kalman-filter-based ocean assimilation application for the Gulf of Mexico

Assessments of ocean data assimilation (DA) systems and observing system design experiments typically rely on identical or nonidentical twin experiments. The identical twin approach has been recognized as yielding biased impact assessments in atmospheric predictions, but these shortcomings are not sufficiently appreciated for oceanic DA applications. Here we present the first direct comparison of the nonidentical and identical twin approaches in an ocean DA application. We assess the assimilation impact for both approaches in a DA system for the Gulf of Mexico that uses the ensemble Kalman filter. Our comparisons show that, despite a reasonable error growth rate in both approaches, the identical twin produces a biased skill assessment, overestimating the improvement from assimilating sea surface height and sea surface temperature observations while underestimating the value of assimilating temperature and salinity profiles. Such biases can lead to an undervaluation of some observing assets (in this case profilers) and thus a misguided distribution of observing system investments

Evaluation of fraternal versus identical twin approaches for observation impact assessments: An EnKF-based ocean assimilation application for the Gulf of Mexico

Abstract. Assessments of ocean data assimilation (DA) systems and observing system design experiments typically rely on identical or fraternal twin experiments. The identical twin approach has been recognized as yielding biased impact assessments in atmospheric predictions but these shortcomings are not sufficiently appreciated for oceanic DA applications. Here we present the first direct comparison of the fraternal and identical twin approach in an ocean DA application. We assess the assimilation impact for both approaches in a DA system for the Gulf of Mexico that uses the Ensemble Kalman Filter. Our comparisons show that, despite a reasonable error growth rate in both approaches, the identical twin produces a biased skill assessment overestimating the improvement from assimilating sea surface height and sea surface temperature observations while underestimating the value of assimilating temperature and salinity profiles. Such biases can lead to an undervaluation of some observing assets (in this case profilers) and thus misguided distribution of observing system investments

Hybrid machine learning hydrological model for flood forecast purpose

Machine learning-based data-driven models have achieved great success since their invention. Nowadays, the artificial neural network (ANN)-based machine learning methods have made great progress than ever before, such as the deep learning and reinforcement learning, etc. In this study, we coupled the ANN with the K-nearest neighbor method to propose a novel hybrid machine learning (HML) hydrological model for flood forecast purpose. The advantage of the proposed model over traditional neural network models is that it can predict discharge continuously without accuracy loss owed to its specially designed model structure. In order to overcome the local minimum issue of the traditional neural network training, a genetic algorithm and Levenberg–Marquardt-based multi-objective training method was also proposed. Real-world applications of the HML hydrological model indicated its satisfactory performance and reliable stability, which enlightened the possibility of further applications of the HML hydrological model in flood forecast problems

DataSheet3_Typing characteristics of metabolism-related genes in osteoporosis.CSV

Objective: Osteoporosis is a common musculoskeletal disease. Fractures caused by osteoporosis place a huge burden on global healthcare. At present, the mechanism of metabolic-related etiological heterogeneity of osteoporosis has not been explored, and no research has been conducted to analyze the metabolic-related phenotype of osteoporosis. This study aimed to identify different types of osteoporosis metabolic correlates associated with underlying pathogenesis by machine learning.Methods: In this study, the gene expression profiles GSE56814 and GSE56815 of osteoporosis patients were downloaded from the GEO database, and unsupervised clustering analysis was used to identify osteoporosis metabolic gene subtypes and machine learning to screen osteoporosis metabolism-related characteristic genes. Meanwhile, multi-omics enrichment was performed using the online Proteomaps tool, and the results were validated using external datasets GSE35959 and GSE7429. Finally, the immune and stromal cell types of the signature genes were inferred by the xCell method.Results: Based on unsupervised cluster analysis, osteoporosis metabolic genotyping can be divided into three distinct subtypes: lipid and steroid metabolism subtypes, glycolysis-related subtypes, and polysaccharide subtypes. In addition, machine learning SVM identified 10 potentially metabolically related genes, GPR31, GATM, DDB2, ARMCX1, RPS6, BTBD3, ADAMTSL4, COQ6, B3GNT2, and CD9.Conclusion: Based on the clustering analysis of gene expression in patients with osteoporosis and machine learning, we identified different metabolism-related subtypes and characteristic genes of osteoporosis, which will help to provide new ideas for the metabolism-related pathogenesis of osteoporosis and provide a new direction for follow-up research.</p

DataSheet4_Typing characteristics of metabolism-related genes in osteoporosis.CSV

Objective: Osteoporosis is a common musculoskeletal disease. Fractures caused by osteoporosis place a huge burden on global healthcare. At present, the mechanism of metabolic-related etiological heterogeneity of osteoporosis has not been explored, and no research has been conducted to analyze the metabolic-related phenotype of osteoporosis. This study aimed to identify different types of osteoporosis metabolic correlates associated with underlying pathogenesis by machine learning.Methods: In this study, the gene expression profiles GSE56814 and GSE56815 of osteoporosis patients were downloaded from the GEO database, and unsupervised clustering analysis was used to identify osteoporosis metabolic gene subtypes and machine learning to screen osteoporosis metabolism-related characteristic genes. Meanwhile, multi-omics enrichment was performed using the online Proteomaps tool, and the results were validated using external datasets GSE35959 and GSE7429. Finally, the immune and stromal cell types of the signature genes were inferred by the xCell method.Results: Based on unsupervised cluster analysis, osteoporosis metabolic genotyping can be divided into three distinct subtypes: lipid and steroid metabolism subtypes, glycolysis-related subtypes, and polysaccharide subtypes. In addition, machine learning SVM identified 10 potentially metabolically related genes, GPR31, GATM, DDB2, ARMCX1, RPS6, BTBD3, ADAMTSL4, COQ6, B3GNT2, and CD9.Conclusion: Based on the clustering analysis of gene expression in patients with osteoporosis and machine learning, we identified different metabolism-related subtypes and characteristic genes of osteoporosis, which will help to provide new ideas for the metabolism-related pathogenesis of osteoporosis and provide a new direction for follow-up research.</p