This paper reviews urban heat (UrHT) challenges following the SBAR (situation, background, assessment and recommendation) framework. The results indicate that heatwaves become more frequent, lasting and intense, especially after 1990s. Above 1960s level, heatwaves across China doubled in both magnitude and frequency by 2018. Jianghuai and Southern China underwent the largest magnitude and most widespread increases. Under 1.5 °C warming limit, the average heatwave days and duration across China will increase by 10.8 days and 3.9 days. Drought–heatwave co–occurrence is increasingly frequent at 7–11%/decade (from 1961 to 2018) and the co–occurrence leads to more intense heatwaves. UHIs are a common issue for almost all Chinese cities and UHIs have been aggravating annually. Daytime UHIs peak in summer, indicating the synergies with heatwaves. The synergies are prominent in southeastern cities for strong summer daytime UHIs in eastern cities and intense heatwaves in southern regions. UrHTs have not been recognised and there are no dedicated/mandatory plans. Mega–challenges of climate change, rapid urbanisation, carbon– and labour–intensive economic growth and demographic changes can potentially lock China into UrHT challenges. Addressing UrHT challenges is urgent in China not only for environmental, ecosystem, social and health consequences, but also for economic impacts relevant to labour, capital, and goods or services. Efforts are suggested in technical improvement, policy formulation, social participation, economic investment and co-benefit approach recognition. Overall, this paper provides a comprehensive understanding of heat–related challenges in China and can guide the creation of cool cities and communities in practice. 

Beating the urban heat: Situation, background, impacts and the way forward in China

Remote sensing multi-decadal time-series provide important information for analysing long-term environmental change. The Advanced Very High Resolution Radiometer (AVHRR) has been providing data since the early 1980s. Normalised Difference Vegetation Index (NDVI) time-series derived thereof can be used for monitoring vegetation conditions. This study presents the novel TIMELINE NDVI product, which provides a consistent set of daily, 10-day, and monthly NDVI composites at a 1 km spatial resolution based on AVHRR data for Europe and North Africa, currently spanning the period from 1981 to 2018. After investigating temporal and spatial data availability within the TIMELINE monthly NDVI composite product, seasonal NDVI trends have been derived thereof for the period 1989–2018 to assess long-term vegetation change in Europe and northern Africa. The trend analysis reveals distinct patterns with varying NDVI trends for spring, summer and autumn for different regions in Europe. Integrating the entire growing season, the result shows positive NDVI trends for large areas within Europe that confirm and reinforce previous research. The analyses show that the TIMELINE NDVI product allows long-term vegetation dynamics to be monitored at 1 km resolution on a pan-European scale and the detection of specific regional and seasonal patterns.

Seasonal Vegetation Trends for Europe over 30 Years from a Novel Normalised Difference Vegetation Index (NDVI) Time-Series—The TIMELINE NDVI Product

Accurately understanding the intra-annual variation in runoff evolution attribution is essential for basin-scale water resources management. In this study, the runoff in the Yellow River source area was divided into two intra-annual time scales: flood season, non-flood season and spring, summer, autumn and winter, and the sensitivity and attribution differences of runoff changes in the Yellow River source area at different intra-annual time scales were quantitatively assessed based on the time-varying Budyko framework. Results show that the runoff in the Yellow River source area decreases during the flood season and spring, summer and autumn, and increases during the non-flood season and winter from 1960 to 2020, with an insignificant decreasing trend in annual runoff. Flood season and autumn runoff changes are the main reasons for the reduction in annual runoff. Runoff in the Yellow River source area is most sensitive to the underlying surface such as vegetation and soil freeze–thaw change, and in terms of climate change, non-flood season and autumn runoff is more sensitive to changes in precipitation, while flood season and summer runoff is more sensitive to changes in potential evaporation. The underlying surface change is the dominant factor for annual runoff change. And for the intra-annual runoff change, the non-flood season, spring and winter runoff in the Yellow River source area is dominated by the underlying surface change, and flood season, summer and autumn runoff is dominated by the climate change. These findings can provide theoretical support for the scientific response to environmental change and the enhancement of water conservation capacity in the Yellow River source area. 

Intra-annual variation in the attribution of runoff evolution in the Yellow River source area

Response of runoff to vegetation change in typical basin of the Loess Plateau

The Source Region of the Yellow River Basin (SRYRB), China To improve daily runoff prediction accuracy in data-scarce areas, this study focuses on incorporating multiple grid-based data (precipitation, EVI, soil moisture (SM)) to drive the CNN-LSTM hybrid model. The spatial features of precipitation and underlying surface of the basin can be extracted by CNN, while the temporal features of the input data series can be captured by the LSTM. The hybrid model is compared with the single models (CNN, LSTM), and hybrid model performances under different driven data are also investigated. Driven by the in-situ precipitation, grid-based precipitation (GPM) and SM data, the CNN-LSTM hybrid model achieved the best prediction result with NSE of 0.834, outperforming the single LSTM model (NSE=0.510) and the CNN model (NSE=0.612). It indicates that the hybrid model captures the spatiotemporal change features of precipitation and underlying surface of the basin. When using only GPM and SM data as input, the hybrid model achieved comparable result with NSE of 0.827. It implies that GPM could serve as a good alternative of in-situ precipitation and SM could provide additional value to improve prediction. This study highlights the value of using multiple grid-based data to drive the hybrid model, which provides new insights into runoff prediction in data-scarce regions. 

Incorporating multiple grid-based data in CNN-LSTM hybrid model for daily runoff prediction in the source region of the Yellow River Basin

Natural disasters play significant destructive roles in agricultural production. Agriculture has substantially altered the biogeochemical silica cycle via the harvest of the grain and straw of silica-rich crops. China is a developing agricultural country that experiences frequent natural disasters. Although the spatiotemporal changes in the occurrence of natural disasters are well known for individual disaster events, the study of the effects of multiple disasters on cereal crop productivity and the resultant silica harvest is still relatively new. To make the connection between natural disasters and the silica biogeochemical cycle, we compiled a dataset of natural disasters and crop production and silicon contents in cereal crops (rice, wheat, and maize) in China using province-level data from 1988 to 2016. Our results show that the area affected by natural disasters declined significantly after 2000, and changes in the area affected by natural disasters varied at the province level. From 1988 to 2016, the total silica losses from grain and straw harvests due to natural disasters were 7.14 and 53.10 million tons, respectively. Half of the silica loss in more than half of the provinces was caused by drought. Our study suggests that drought prevention will increase the size of the silica sink and thereby increase the size of the carbon sink in China's agriculture.

Natural disasters and their impacts on the silica losses from agriculture in China from 1988 to 2016

Identifying the individual contributions of factors impacting the water resources in Beijing plays crucial roles in planning water management for resolving water shortage problems and for the sustainable utilization of water resources. In this study, four statistical methods, the Mann-Kendall test, double mass curve method, modified slope changing ratio of accumulative quantity method, and generalized linear model, were employed to detect the individual contributions of climate change and human activities on the variation of runoff in Beijing from 1961 to 2017. Our results showed that the annual runoff declined significantly during the entire study period. The mean temperature increased significantly, while the annual precipitation did not change significantly, over the entire study period. An abrupt change was detected in 1977 with a change in runoff. During the period of change, climate change contributed 20.0% to the increase in runoff, while human activities contributed 120% to the decline in runoff from 1978 to 2017. Furthermore, we partitioned the contributions of human activities into eight social and economic factors. These factors explained 55.29% of the total contributions of human activities. The economic factors, the changes in the arable land area and accumulative construction area, and the change in urbanization rate contributed 15.94%, 18.37%, and 6.05% to the total variation in runoff, respectively. Finally, the South-to-North Water Transfer Project contributed 5.19% of the change in runoff. Our study provides the individual contributions of the specific impact factors to runoff in Beijing and may help policy-makers to devise targeted water resource management plans.

Separating variance in the runoff in Beijing's river system under climate change and human activities

Climate change is an urgent global issue and anthropogenic influence on the climate system is clear. Climate models project a world with significant increases in the frequency and intensity of climate extremes and natural disasters. Although climate change impacts are not always negative, adverse impacts of climate change will alter the normal conditions of the physical environments, ecosystems, and social environments, and thus threaten the societal sustainable developments and human well-beings. The risks of climate change on human health, ecosystems, economics, agriculture, fishery and forestry have been alerted. However, we still lack a thorough understanding of climate change induced disasters as well as impacts and risks of these disasters. To better understand the climate change impacts, a special issue focusing on Climate Change Adaptation and Disaster Risk Assessments incorporates the progresses on global change, regional climate modeling and future projections in the light of Representative Concentration Pathways (RCPs). It focuses on extreme events and natural disasters, and their social and economic impacts as well as advances in predictability over time and space. This special issue covers three aspects of climate change impacts, i.e., natural environments, ecosystems, and social environments.

Climate change adaptation and disaster risk assessments: A preface

More than four hundred million people suffer from respiratory diseases each year. Respiratory diseases are associated with a large disease burden. Heatwaves and cold spells, the two most common extreme weather events, have been shown to have crucial negative effects on the prevalence of respiratory diseases. However, impacts of extreme weather on the prevalence of respiratory diseases has been largely overlooked in western China, where more intense and frequent extreme temperature events have been occurring over the past decades. This research gap will obtain an attribution bias in the effects of extreme weather events on the prevalence of respiratory diseases. Therefore, in this study, we analyzed the impact of heatwaves and cold spells on the morbidity of respiratory diseases using a distributed lag nonlinear model with daily disease cases from 2013 to 2016 in Lanzhou, one of the largest cities in western China. A reverse U-shaped relationship depicted the relationship between temperature and the morbidity of respiratory diseases. The highest relative risk was found at 2.6 °C by 1.15 (95% confidence interval: 1.09–1.21). Furthermore, we found a significant decrease in the relative risk for heatwaves and a significant increase in the relative risk of cold spells when the temperature exceeded the corresponding threshold by 1 °C. Heatwaves and cold spells play harvest effects on the morbidity of respiratory diseases. Our study suggest that the relative risk of respiratory diseases will increase as the climate warms in the future, and thus a preventive system is needed for individuals and medical policy-makers.

Impact of heatwaves and cold spells on the morbidity of respiratory diseases: A case study in Lanzhou, China

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Natural disasters and their impacts on the silica losses from agriculture in China from 1988 to 2016

Separating variance in the runoff in Beijing's river system under climate change and human activities

Climate change adaptation and disaster risk assessments: A preface

Impact of heatwaves and cold spells on the morbidity of respiratory diseases: A case study in Lanzhou, China