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Sustainability is one of the major challenges in the 21st century for humanity. Spatial zoning of ecosystem services is proposed in this study as a solution to meet the demands for the sustainable use of ecosystem services. This study presented a workflow and performed an exploratory analysis using self-organizing maps (SOM) for visualizing the spatial patterns of the ecosystem service value (ESV) of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). The zoning was performed based on 11 types of ecosystem services, resulting in 11 ecosystem service zones. Each of the zones derived has its unique characteristics in terms of the dominating ecosystem service types, ESV, land use/land cover patterns, and associated human activity levels. It is recommended that reasonable and effective utilization of the ecosystem services in the GBA should be based on its zonal characteristics rather than haphazard exploitations, which can contribute to the sustainable economy and environment of the region. The applicability of SOM for the GBA ecosystem service zoning has been demonstrated in this study. However, it should be stressed that the method and workflow presented in this study should mainly be used for supporting decision-making rather than used for deriving gold-standard zoning maps. [ABSTRACT FROM AUTHOR]

Gridded precipitation data with a high spatiotemporal resolution are of great importance for studies in hydrology, meteorology, and agronomy. Observational data from meteorological stations cannot accurately reflect the spatiotemporal distribution and variations of precipitation over a large area. Meanwhile, radar-derived precipitation data are restricted by low accuracy in areas of complex terrain and satellite-based precipitation data by low spatial resolution. Therefore, hourly precipitation models were employed to merge data from meteorological stations, Radar, and satellites; the models used five machine learning algorithms (XGBoost, gradient boosting decision tree, random forests (RF), LightGBM, and multiple linear regression (MLR)), as well as the CoKriging method. In the north of Guangdong Province, data of four heavy rainfall events in 2018 were processed with geographic data to obtain merged hourly precipitation data. The CoKriging method secured the best prediction of spatial distribution of accumulated precipitation, followed by the tree-based machine learning (ML) algorithms, and significantly, the prediction of MLR deviated from the actual pattern. All machine learning methods showed poor performances for timepoints with little precipitation during the heavy rainfall events. The tree-based ML method showed poor performance at some timepoints when precipitation was over-related to latitude, longitude, and distance from the coast. [ABSTRACT FROM AUTHOR]

• A coordinated development index of urbanization is established. • The level of urbanization coordination in the Greater Bay Area is gradually increasing. • 2015 is the important turning point in the urbanization of the Greater Bay Area. • The distribution of urbanization coordination in the Greater Bay Area is agglomeration. Evaluation of the coordinated development level of urbanization is an essential step in the implementation of sustainable development strategies. In this study, we constructed a comprehensive evaluation indicator system for the coordinated development of urbanization by considering the coupling effect of urbanization on land, population, and economic, social, and ecological aspects. We used this system to evaluate the urbanization coordinated development level of the Guangdong-Hong Kong-Macao Greater Bay Area from 2000 to2020. The spatial distribution and evolution were analyzed and explored at the regional, urban, and county scales. The results showed that (1) the coordination development level of urbanization in the Guangdong-Hong Kong-Macao Greater Bay Area was gradually increasing. It rapidly increased from 2000 to 2015 and entered a period of steady development after 2015. (2) From the perspective of spatial distribution, the level of urbanization coordination in the Greater Bay Area was higher in the eastern part than in the western part and higher in the central part than in the periphery. There was obvious spatial agglomeration in which high coordination areas were concentrated near the Pearl River estuary. (3) At different scales, the level of regional coordination showed slight variations despite the overall positive trend. The results of this study can aid in decision-making regarding the sustainable development of the Guangdong-Hong Kong-Macao Greater Bay Area. [ABSTRACT FROM AUTHOR]