ALBERT

Description: The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (doi: 10.5281/zenodo.4027033). Two scientific publications have been published based on some of these data here.

Description: Extreme weather events threaten food security, yet global assessments of impacts caused by crop waterlogging are rare. Here we first develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a farming systems model to discern changes in global crop waterlogging under future climates. Third, we develop avenues for adapting cropping systems to waterlogging contextualised by environment. We find that yield penalties caused by waterlogging increase from 3–11% historically to 10–20% by 2080, with penalties reflecting a trade-off between the duration of waterlogging and the timing of waterlogging relative to crop stage. We document greater potential for waterlogging-tolerant genotypes in environments with longer temperate growing seasons (e.g., UK, France, Russia, China), compared with environments with higher annualised ratios of evapotranspiration to precipitation (e.g., Australia). Under future climates, altering sowing time and adoption of waterlogging-tolerant genotypes reduces yield penalties by 18%, while earlier sowing of winter genotypes alleviates waterlogging by 8%. We highlight the serendipitous outcome wherein waterlogging stress patterns under present conditions are likely to be similar to those in the future, suggesting that adaptations for future climates could be designed using stress patterns realised today.

Description: Earth system modelling (ESM) is essential for understanding past, present and future Earth processes. Deep learning (DL), with the data-driven strength of neural networks, has promise for improving ESM by exploiting information from Big Data. Yet existing hybrid ESMs largely have deep neural networks incorporated only during the initial stage of model development. In this Perspective, we examine progress in hybrid ESM, focusing on the Earth surface system, and propose a framework that integrates neural networks into ESM throughout the modelling lifecycle. In this framework, DL computing systems and ESM-related knowledge repositories are set up in a homogeneous computational environment. DL can infer unknown or missing information, feeding it back into the knowledge repositories, while the ESM-related knowledge can constrain inference results of the DL. By fostering collaboration between ESM-related knowledge and DL systems, adaptive guidance plans can be generated through question-answering mechanisms and recommendation functions. As users interact iteratively, the hybrid system deepens its understanding of their preferences, resulting in increasingly customized, scalable and accurate guidance plans for modelling Earth processes. The advancement of this framework necessitates interdisciplinary collaboration, focusing on explainable DL and maintaining observational data to ensure the reliability of simulations.

Description: Chinese cities are core in the national carbon mitigation and largely affect global decarbonisation initiatives, yet disparities between cities challenge country-wide progress. Low-carbon transition should preferably lead to a convergence of both equity and mitigation targets among cities. Inter-city supply chains that link the production and consumption of cities are a factor in shaping inequality and mitigation but less considered aggregately. Here, we modelled supply chains of 309 Chinese cities for 2012 to quantify carbon footprint inequality, as well as explored a leverage opportunity to achieve an inclusive low-carbon transition. We revealed significant carbon inequalities: the 10 richest cities in China have per capita carbon footprints comparable to the US level, while half of the Chinese cities sit below the global average. Inter-city supply chains in China, which are associated with 80% of carbon emissions, imply substantial carbon leakage risks and also contribute to socioeconomic disparities. However, the significant carbon inequality implies a leveraging opportunity that substantial mitigation can be achieved by 32 super-emitting cities. If the super-emitting cities adopt their differentiated mitigation pathway based on affluence, industrial structure, and role of supply chains, up to 1.4 Gt carbon quota can be created, raising 30% of the projected carbon quota to carbon peak. The additional carbon quota allows the average living standard of the other 60% of Chinese people to reach an upper-middle-income level, highlighting collaborative mechanism at the city level has a great potential to lead to a convergence of both equity and mitigation targets.

Description: Eco-efficiency enhancement is an inherent requirement of green development and an important indicator of high-quality development in general. It aims to achieve the coordinated development of nature, the economy, and society. Therefore, eco-efficiency measurements should focus on not only total factor input, but also process analysis. Based on the “full world” model in ecological economic theory, this study constructed a theoretical framework for a composite economic-environmental-social system that reflects human welfare and sustainability. To this end, using network data envelopment analysis (DEA), this study established a staged eco-efficiency evaluation model that uses economic, environmental, and social factors to measure the overall and staged eco-efficiency of China’s provinces from 2003 to 2016 and analyze its spatiotemporal characteristics. A geographically weighted regression (GWR) model was also used to analyze the influencing factors of eco-efficiency changes and the spatial differentiation in their effect intensity. The findings were as follows: (1) China’s overall eco-efficiency is still at a low level. It varies significantly from region to region, and only three regions are at the frontier of production. The eastern region has the highest eco-efficiency, followed by the central region, and the gap between the central and western regions has gradually narrowed. In terms of staged efficiency, the level of eco-efficiency in the production stage is less than in the environmental governance stage, which is less than that in the social input stage. (2) In terms of the efficiency of each stage, the efficiency level of the production stage showed a downward trend throughout the entire process, and the decline in the central and western regions was more obvious. The social input stage and the environmental governance stage both showed upward trends. The social input stage showed a higher level, and the increase was relatively flat during the period of study. Efficiency continued to rise during the environmental governance stage from 2003 to 2010 and rose overall, but with some fluctuations from 2011 to 2016. (3) Geographically weighted regression showed that the effects of the influencing factors on eco-efficiency had obvious spatial heterogeneity. The factors affecting overall, production stage, and social input eco-efficiency were, in order of effect intensity from high to low, economic growth level, marketization level, and social input level. In terms of environmental governance, social input level had the greatest impact, followed by economic growth; marketization level did not show a significant impact.

Description: As the forerunner and policy test field of the sustainable development, the sustainable development pilot zones are an important strategy for China to explore the mechanism and model of the coordinated development of human and land in different regional units. However, the impact of sustainable development pilot zones, especially on the improvement of environmental efficiency, needs to be assessed. In this paper, 187 prefecture-level cities in China were taken as samples (22 sustainable development pilot zones and 165 nonpilot ones). Firstly, the environmental efficiency of 187 prefecture-level cities between 2006 and 2016 was measured by data envelopment analysis (DEA). Then, the effect of construction of sustainable development pilot zones on environmental efficiency was assessed using the difference-in-difference (DID) model. The assessment results were further verified by propensity score matching with difference-in-difference (PSM-DID). In addition, the impact mechanism of construction of the sustainable development pilot zones on the environmental efficiency was discussed. Results show that the environmental efficiency of sustainable development pilot zones is 27.7∼31.7% greater than that of nonsustainable one, which is mainly attributed to the environmental regulation and industrial structure adjustment.

Description: Boosted by impressive technological innovation and cost reductions, renewable energy in a growing number of countries is now primarily considered for its social and economic benefits. Among the renewable energy promotion actions at the global level, photovoltaic poverty alleviation (PVPA) program in China is very unique since the targeted users are villagers in poverty regions. Under this condition, it would be quite necessary to understand how are the PVPA program carrying out. In this study, three poverty villages are selected in the northwestern part of China to conduct the social impact analysis. An evaluation system including four categories and thirteen indicators was established. Site investigation and questionnaire interview was carried out to collect required information. Our findings reveal that the poor families in the three counties can increase their income by around 3000 RMB per year with the implementation of PVPA program. The final social impact indicator of Yanchi County, Dingbian County and Guazhou County are 2.61, 2.09 and 2.15 respectively. Villagers’ living standards in the three counties are prominently improved because of the solar power supply. The factors that hinder the development of PVPA projects are the lack of investment funds, poor quality of solar panels, low public awareness, high abandon rate of photovoltaic, etc. Recommendations for improving the sustainable development of PVPA program based on the findings are also proposed.

Description: The Yangtze River Delta (YRD) region frequently experiences ozone pollution events during the summer and autumn seasons. High-concentration events are typically related to synoptic weather patterns, which impact the transport and photochemical production of ozone at multiple scales, ranging from the local to regional scale. To better understand the regional ozone pollution problem, studies on ozone source attribution are needed, especially regarding the contributions of sources at different vertical heights based on tagging the region or time periods. Between September 3 and 8, 2020, an episode of ozone concentration anomaly high was observed in Hefei through ground-based stations and ozone Lidar. The mechanism behind this event was uncovered through synoptic weather pattern analysis and using the Weather Research and Forecasting Chemistry model (WRF-Chem). Because an approaching typhoon caused variable wind direction, the O3-rich air masses (ORMs) arising from the YRD region were transported to Hefei via the nocturnal residual layer and descended to the ground through horizontal advection and vertical mixing processes the next day. Based on geographic source tagging, the anthropogenic NOx emissions (ANEs) from local and regional sources were the main contributors to the heavy ozone pollution over Hefei on September 6. Furthermore, the intra-regional transported ozone from southern Jiangsu (SJS), southern Anhui (SAH), and Zhejiang (ZJ) in the YRD was the main driving factor of the surface and upper atmosphere ozone pollution. Based on time period tagging, The ozone generated due to ANEs from September 3 to 5 significantly contributed to this episode. It is important to pay attention to the impact of ANEs on September 5 on the surface peak ozone concentration the following day (i.e., September 6). Our findings provide significant insights into the regional ozone transport mechanism in the YRD and optimization of measures to prevent and control heavy ozone pollution on spatiotemporal scales.

Description: The isotopic and hydrochemical signatures measured from various waters in a catchment have been widely used to separate flow components, identify recharge sources, and integrate such tracers into hydrological models to help model evaluation. However, the reliability of using auxiliary data for strengthening hydrological functioning heavily depends on if the sampling is representative of hydro-chemical dynamic behaviors in a catchment. Herein, we illustrate the necessary sampling resolution or frequency to facilitate our understanding and illustrating the complex hydrological functioning in two catchments respectively characterizing the high aquifer heterogeneity in karst landform and the multi-recharge sources in the cryosphere environment. Our comprehensive analysis and modeling show that the sampling intervals of stable isotope should be shorter than hours for aiding hydrological models in capturing the sharp rise and decline of hydrograph in the cockpit karst catchment of southwest China. The daily sampling of stable isotope and chemistry can identify turning points of dominant recharge sources (rainwater, melt water from glacier and snowpack, and shallow and deep groundwater) and their contributions to streamflow in the glacierized catchment of the Tibet Plateau in China.

Description: This paper demonstrates the effect of geographical location on Cyclone Global Navigation Satellite System (CYGNSS) observables for the first time. It is found that after controlling the wind speed, the observables vary with geographic location regularly. Interestingly, the SNR observations are somewhat correlated with marine gravity, with a correlation coefficient of about 0.6. In addition to marine gravity, other factors such as temperature, salinity, and seawater density, may also affect sea surface roughness. There are so many possible factors involved that it is difficult to eliminate the influence of each factor individually. Thus, an improved method for CYGNSS wind speed retrieval that takes geographical differences into account is proposed. The sea surface is divided into different regions for independent wind speed retrieval, and the training set is resampled to account for high wind speeds. To balance the accuracy of high and low wind speeds retrieval, the retrievals of the random training samples and the resampling samples are fused. Compared with the conventional method, in the range of 0–20 m/s, the improved method reduces the root mean square error (RMSE) of retrieved wind speeds by 11.8%; while in the range of 20–30 m/s, the RMSE decreased by 49.7%.