Inadequate water quality can mean that water is unsuitable for a variety of human uses, thus exacerbating freshwater scarcity. Previous large-scale water scarcity assessments mostly focused on the availability of sufficient freshwater quantity for providing supplies, but neglected the quality constraints on water usability. Here we report a comprehensive nationwide water scarcity assessment in China, which explicitly includes quality requirements for human water uses. We highlight the necessity of incorporating water scarcity assessment at multiple temporal and geographic scales. Our results show that inadequate water quality exacerbates China’s water scarcity, which is unevenly distributed across the country. North China often suffers water scarcity throughout the year, whereas South China, despite sufficient quantities, experiences seasonal water scarcity due to inadequate quality. Over half of the population are affected by water scarcity, pointing to an urgent need for improving freshwater quantity and quality management to cope with water scarcity.

Heatwaves are consecutive hot days with devastating impacts on human health and the environment. These events may evolve across both space and time, characterizing a spatiotemporally contiguous propagation pattern that has not been fully understood. Here, we track the spatiotemporally contiguous heatwaves in both reanalysis datasets and model simulations and examine their moving patterns (i.e., moving distance, speed, and direction) in different continents and periods. Substantial changes in contiguous heatwaves have been identified from 1979 to 2020, with longer persistence, longer traveling distance, and slower propagation. These changes have been amplified since 1997, probably due to the weakening of eddy kinetic energy, zonal wind, and anthropogenic forcing. The results suggest that longer-lived, longer-traveling, and slower-moving contiguous heatwaves will cause more devastating impacts on human health and the environment in the future if greenhouse gas emissions keep rising and no effective measures are taken immediately. Our findings provide important implications for the adaption and mitigation of globally connected extreme heatwaves.

Linear cultural heritage (LCH) is a precious treasure of human beings, but its spatial distribution and development modes in the context of tourism value are not clear. This study constructs the resource (R) –natural environment (N) –industrial economy (I) evaluation system through the Delphi method, analytical hierarchy process and entropy method to examine tourism value from the perspective of the whole heritage. This system is verified to the case of a World Heritage Site, the 1800 km-long Beijing–Hangzhou Grand Canal in China. Combined with the situation of 22 cities along the canal, the standard values of each evaluation variable are determined. Besides total tourism value, R–N–I coordination is also calculated through a three-dimensional model. They are regarded as a classification basis in the grouping analysis method. According to the results, a resource is the most important factor, while natural environment and industrial economy are external supports for LCH tourism. Tourism value along Beijing–Hangzhou Grand Canal is divided into ‘high-value imbalance type’, ‘key breakthrough type’, ‘comprehensive strengthening type’ and ‘low-value restriction type’. The findings offer theoretical construct and policy recommendations for the development and sustainability of LCH tourism.

Fine-grained prediction of urban population is of great practical significance in many domains that require temporally and spatially detailed population information. However, fine-grained population modeling has been challenging because the urban population is highly dynamic and its mobility pattern is complex in space and time. In this study, we propose a method to predict the population at a large spatiotemporal scale in a city. This method models the temporal dependency of population by estimating the future inflow population with the current inflow pattern and models the spatial correlation of population using an artificial neural network. With a large dataset of mobile phone locations, the model’s prediction error is low and only increases gradually as the temporal prediction granularity increases, and this model is adaptive to sudden changes in population caused by special events.