Delayed impact of natural climate solutions

Acknowledgement: This work was supported by the National Basic Research Program of China (2016YFA0602701), the National Natural Science Foundation of China (41975113; 91937302), and the Guangdong Provincial Department of Science and Technology (2019ZT08G090). We appreciate the support from the China Association for Science and Technology Working Group for UN Environment Consultation. The authors declare no conflict of interests.Peer reviewedPostprin

Soil organic carbon sequestration potential of cropland in China

Soil organic carbon (SOC) in cropland is of great importance to the global carbon (C) balance and to agricultural productivity, but it is highly sensitive to human activities such as irrigation and crop rotation. It has been observed that under certain improved management practices, cropland soils can sequestrate additional C beyond their existing SOC level before reaching the C saturation state. Here we use data from worldwide, long-term agricultural experiments to develop two statistical models to determine the saturated SOC level (SOCS) in upland and paddy agroecosystems, respectively. We then use the models to estimate SOC sequestration potential (SOCP) in Chinese croplands. SOCP is the difference between SOCS and existing SOC level (SOCE). We find that the models for both the upland and paddy agroecosystems can reproduce the observed SOCS data from long-term experiments. The SOCE and SOCS stock in Chinese upland and paddy croplands (0-30cm soil) are estimated to be 5.2 and 7.9 Pg C with national average densities of 37.4 and 56.8Mg C ha(-1), respectively. As a result, the total SOC sequestration potential is estimated to be 2.7 Pg C or 19.4Mg C ha(-1) in Chinese cropland. Paddy has a relatively higher SOCE (45.4Mg C ha(-1)) than upland (34.7Mg C ha(-1)) and also a greater SOCP at 26.1Mg C ha(-1) compared with 17.2Mg C ha(-1) in the upland. The SOC varies dramatically among different regions. Northeast China has the highest SOCE and SOCS density, while the Loess Plateau has the greatest SOCP density. The time required to reach SOC saturation in Chinese cropland is highly dependent on management practices applied. Chinese cropland has relatively low SOC density in comparison to the global average but could have great potentials for C sequestration under improved agricultural management strategies

Soil indigenous nutrients increase the resilience of maize yield to climatic warming in China

Climate warming leads to crop yield loss. Although investigations have shown the region-specific effect of climate warming on maize yield in China, the determinants of this region-specific effect are poorly known. Using county-level data from 1980 to 2010 for China, we investigated the dependence of yield change under climate warming on soil indigenous nutrients. Analysis of the data indicated an average decrease of 2.6% in maize yield for 1 degrees C warming. Warming-related yield loss occurred mostly in western China, the North China Plain, and the southwest region of Northeast China. By contrast, climate warming did not decline maize yield in the northern region of Northeast China, south, and southwest China. Summer maize is more sensitive to warming than spring maize. A 1 degrees C warming resulted in an average loss of 3.3% for summer maize and 1.8% for spring maize. The region-specific change in yield can be well quantified by a combination of soil indigenous total nitrogen (STN), available phosphorus (SAP), and available potassium (SAK). Under climate warming, maize yields in regions with high STN generally increased, while the risk of yield reduction appeared in regions with high SAK. Areas that were vulnerable (defined as a yield loss higher than 1% for a 1 degrees C increase) to climate warming accounted for 62%, while areas that showed resilience (defined as a yield increase higher than 1% for a 1 degrees C increase) to climate warming accounted for 27% of the planting area. An increase in nitrogen fertilizer application is expected to reduce the risk of yield reduction in regions with low STN. Our findings highlight soil resilience to climate warming and underline the practice of fertilizer management to mitigate yield loss due to climate warming

Modeling of Time Geographical Kernel Density Function under Network Constraints

Time geography considers that the probability of moving objects distributed in an accessible transportation network is not always uniform, and therefore the probability density function applied to quantitative time geography analysis needs to consider the actual network constraints. Existing methods construct a kernel density function under network constraints based on the principle of least effort and consider that each point of the shortest path between anchor points has the same density value. This, however, ignores the attenuation effect with the distance to the anchor point according to the first law of geography. For this reason, this article studies the kernel function framework based on the unity of the principle of least effort and the first law of geography, and it establishes a mechanism for fusing the extended traditional model with the attenuation model with the distance to the anchor point, thereby forming a kernel density function of time geography under network constraints that can approximate the theoretical prototype of the Brownian bridge and providing a theoretical basis for reducing the uncertainty of the density estimation of the transportation network space. Finally, the empirical comparison with taxi trajectory data shows that the proposed model is effective

Modeling of Time Geographical Kernel Density Function under Network Constraints

Time geography considers that the probability of moving objects distributed in an accessible transportation network is not always uniform, and therefore the probability density function applied to quantitative time geography analysis needs to consider the actual network constraints. Existing methods construct a kernel density function under network constraints based on the principle of least effort and consider that each point of the shortest path between anchor points has the same density value. This, however, ignores the attenuation effect with the distance to the anchor point according to the first law of geography. For this reason, this article studies the kernel function framework based on the unity of the principle of least effort and the first law of geography, and it establishes a mechanism for fusing the extended traditional model with the attenuation model with the distance to the anchor point, thereby forming a kernel density function of time geography under network constraints that can approximate the theoretical prototype of the Brownian bridge and providing a theoretical basis for reducing the uncertainty of the density estimation of the transportation network space. Finally, the empirical comparison with taxi trajectory data shows that the proposed model is effective

Identifying eco-functional zones on the Chinese Loess Plateau using ecosystem service bundles

Optimizing the function of ecosystem services (ESs) is vital for implementing regional ecological management strategies. In this study, we used multi-source data and integrated modelling methods to assess the spatiotemporal variations in eight typical ESs on the Chinese Loess Plateau from 2000 to 2015, including grain production, raw material provision, water conservation, carbon storage service, soil conservation, oxygen production, recreation, and net primary productivity (NPP) services. Then, we divided the ecosystem service bundles (ESBs) according to relationships among the eight ESs, obtaining four types of eco-functional areas at the county (city or banner or district) level based on the spatial clustering of similarities in different ES types. We also identified and assessed the contributions of influencing factors to these eco-functional areas using principal component analysis (PCA) across spatiotemporal scales. We found that the spatiotemporal variations in different ESs were noticeable, with an overall increase in grain production and soil conservation services, no significant change in carbon storage service, and overall decreases in raw material provision, water conservation, oxygen production, recreation, and NPP services. From 2000 to 2015, the number of significant synergistic ES pairs decreased, while that of significant trade-off pairs increased. To the changes of ESBs in the eco-functional areas, the results indicated that the indirect loss of these ESs from forest and grassland due to urban expansion should be reduced in ecological development area (ESB 2) and multi ecological functional area (ESB 3). Meanwhile, crop planting structures and planting densities should be adjusted to reduce ES trade-offs associated with water conservation service in grain-producing area (ESB 4). Lastly, ESB-based eco-functional zoning can be used to improve ecological restoration management strategies and optimize ecological compensation schemes in ecologically fragile area (ESB 1)

Measuring of the COVID-19 Based on Time-Geography

At the end of 2019, the COVID-19 pandemic began to emerge on a global scale, including China, and left deep traces on all societies. The spread of this virus shows remarkable temporal and spatial characteristics. Therefore, analyzing and visualizing the characteristics of the COVID-19 pandemic are relevant to the current pressing need and have realistic significance. In this article, we constructed a new model based on time-geography to analyze the movement pattern of COVID-19 in Hebei Province. The results show that as time changed COVID-19 presented an obvious dynamic distribution in space. It gradually migrated from the southwest region of Hebei Province to the northeast region. The factors affecting the moving patterns may be the migration and flow of population between and within the province, the economic development level and the development of road traffic of each city. It can be divided into three stages in terms of time. The first stage is the gradual spread of the epidemic, the second is the full spread of the epidemic, and the third is the time and again of the epidemic. Finally, we can verify the accuracy of the model through the standard deviation ellipse and location entropy