A meta-analysis highlights globally widespread potassium limitation in terrestrial ecosystems.

Potassium (K+ ) is the most abundant inorganic cation in plant cells, playing a critical role in various plant functions. However, the impacts of K on natural terrestrial ecosystems have been less studied compared with nitrogen (N) and phosphorus (P). Here, we present a global meta-analysis aimed at quantifying the response of aboveground production to K addition. This analysis is based on 144 field K fertilization experiments. We also investigate the influences of climate, soil properties, ecosystem types, and fertilizer regimes on the responses of aboveground production. We find that: K addition significantly increases aboveground production by 12.3% (95% CI: 7.4-17.5%), suggesting a widespread occurrence of K limitation across terrestrial ecosystems; K limitation is more prominent in regions with humid climates, acidic soils, or weathered soils; the effect size of K addition varies among climate zones/regions, and is influenced by multiple factors; and previous N : K and K : P thresholds utilized to detect K limitation in wetlands cannot be applied to other biomes. Our findings emphasize the role of K in limiting terrestrial productivity, which should be integrated into future terrestrial ecosystems models.

Anthropogenic activities dominated tropical forest carbon balance in two contrary ways over the Greater Mekong Subregion in the 21st century.

The tropical forest carbon (C) balance threatened by extensive socio-economic development in the Greater Mekong Subregion (GMS) in Asia is a notable data gap and remains contentious. Here we generated a long-term spatially quantified assessment of changes in forests and C stocks from 1999 to 2019 at a spatial resolution of 30 m, based on multiple streams of state-of-the-art high-resolution satellite imagery and in situ observations. Our results show that (i) about 0.54 million square kilometers (21.0% of the region) experienced forest cover transitions with a net increase in forest cover by 4.3% (0.11 million square kilometers, equivalent to 0.31 petagram of C [Pg C] stocks); (ii) forest losses mainly in Cambodia, Thailand, and in the south of Vietnam, were also counteracted by forest gains in China due mainly to afforestation; and (iii) at the national level during the study period an increase in both C stocks and C sequestration (net C gain of 0.087 Pg C) in China from new plantation, offset anthropogenetic emissions (net C loss of 0.074 Pg C) mainly in Cambodia and Thailand from deforestation. Political, social, and economic factors significantly influenced forest cover change and C sequestration in the GMS, positively in China while negatively in other countries, especially in Cambodia and Thailand. These findings have implications on national strategies for climate change mitigation and adaptation in other hotspots of tropical forests.

Global Revisit Interval Analysis of Landsat-8 -9 and Sentinel-2A -2B Data for Terrestrial Monitoring.

The combination of Landsat-8, Landsat-9, Sentinel-2A and Sentinel-2B data provides a new perspective in remote sensing application for terrestrial monitoring. Jointly, these four sensors together offer global 10-30-m multi-spectral data coverage at a higher temporal revisit frequency. In this study, combinations of four sensors were used to examine the revisit interval by modelled orbit swath information. To investigate different factors that could influence data availability, an analysis was carried out for one year based on daytime surface observations of Landsat-8 and Sentinel-2A -2B. We found that (i) the global median average of revisit intervals for the combination of four sensors was 2.3 days; (ii) the global mean average number of surface observations was 141.4 for the combination of Landsat-8 and Sentinel-2A -2B; (iii) the global mean average cloud-weighted number of observations for the three sensors combined was 81.9. Three different locations were selected to compare with the cloud-weighted number of observations, and the results show an appropriate accuracy. The utility of combining four sensors together and the implication for terrestrial monitoring are discussed.

Applications of land surface model to economic and environmental-friendly optimization of nitrogen fertilization and irrigation.

Land surface models (LSMs) have prominent advantages for exploring the best agricultural practices in terms of both economic and environmental benefits with regard to different climate scenarios. However, their applications to optimizing fertilization and irrigation have not been well discussed because of their relatively underdeveloped crop modules. We used a CLM5-Crop LSM to optimize fertilization and irrigation schedules that follow actual agricultural practices for the cultivation of maize and wheat, as well as to explore the most economic and environmental-friendly inputs of nitrogen fertilizer and irrigation (FI), in the North China Plain (NCP), which is a typical intensive farming area. The model used the indicators of crop yield, farm gross margin (FGM), nitrogen use efficiency (NUE), water use efficiency (WUE), and soil nitrogen leaching. The results showed that the total optimal FI inputs of FGM were the highest (230 ± 75.8 kg N ha-1 and 20 ± 44.7 mm for maize; 137.5 ± 25 kg N ha-1 and 362.5 ± 47.9 mm for wheat), followed by the FIs of yield, NUE, WUE, and soil nitrogen leaching. After multi-objective optimization, the optimal FIs were 230 ± 75.8 kg N ha-1 and 20 ± 44.7 mm for maize, and 137.5 ± 25 kg N ha-1 and 387.5 ± 85.4 mm for wheat. By comparing our model-based diagnostic results with the actual inputs of FIs in the NCP, we found excessive usage of nitrogen fertilizer and irrigation during the current cultivation period of maize and wheat. The scientific collocation of fertilizer and water resources should be seriously considered for economic and environmental benefits. Overall, the optimized inputs of the FIs were in reasonable ranges, as postulated by previous studies. This result hints at the potential applications of LSMs for guiding sustainable agricultural development.

Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015.

Large datasets of carbon dioxide, energy, and water fluxes were measured with the eddy-covariance (EC) technique, such as FLUXNET2015. These datasets are widely used to validate remote-sensing products and benchmark models. One of the major challenges in utilizing EC-flux data is determining the spatial extent to which measurements taken at individual EC towers reflect model-grid or remote sensing pixels. To minimize the potential biases caused by the footprint-to-target area mismatch, it is important to use flux datasets with awareness of the footprint. This study analyze the spatial representativeness of global EC measurements based on the open-source FLUXNET2015 data, using the published flux footprint model (SAFE-f). The calculated annual cumulative footprint climatology (ACFC) was overlaid on land cover and vegetation index maps to create a spatial representativeness dataset of global flux towers. The dataset includes the following components: (1) the ACFC contour (ACFCC) data and areas representing 50%, 60%, 70%, and 80% ACFCC of each site, (2) the proportion of each land cover type weighted by the 80% ACFC (ACFCW), (3) the semivariogram calculated using Normalized Difference Vegetation Index (NDVI) considering the 80% ACFCW, and (4) the sensor location bias (SLB) between the 80% ACFCW and designated areas (e.g. 80% ACFCC and window sizes) proxied by NDVI. Finally, we conducted a comprehensive evaluation of the representativeness of each site from three aspects: (1) the underlying surface cover, (2) the semivariogram, and (3) the SLB between 80% ACFCW and 80% ACFCC, and categorized them into 3 levels. The goal of creating this dataset is to provide data quality guidance for international researchers to effectively utilize the FLUXNET2015 dataset in the future.

High ambient temperatures are associated with urban crime risk in Chicago.

Urban crime (UC) seriously affects the security and stability of the communities and society. However, the effects of external temperatures on the risk of UC are still confusing. We quantitatively estimated the effects of high and low temperatures on UC in Chicago. After controlling for the confounding factors, we found that high temperature has a positive promoting effect on UC, for non-domestic crime, the effect occurs at lag day 0 with a maximum risk of 1.40 (95%CI, 1.34-1.46) compared to a risk of 1 at temperature of -12.3 °C, and decreased as the lag day increased. The effect of low temperature is not significant for UC. Heat waves above the 99th percentile with a duration of 4.5-5.5 days exert a significant positive impact on non-domestic crime of UC. Our findings confirm the adverse promotion effect of high temperature on UC risk, and effective individual behavior guidance and administrative intervention are of great significance for reducing the risk of UC under specific high temperature environment.

Ambient temperatures associated with increased risk of motor vehicle crashes in New York and Chicago.

Motor Vehicle Crashes (MVC) are a major cause of death and disability worldwide. Yet it remains unknown to what extent changes in cold and hot temperatures affect the MVC. Here we quantify the linkage between ambient temperature exposure and the MVC risk of the driver in four cities of the United States. After adjusting for confounding factors, we find both a cold effect and a hot effect on MVC in New York, and a cold effect on MVC in Chicago. A 1 °C decrease in mean daily temperature below a cold threshold of -4.8 °C is associated with an increase in the overall cumulative relative risk of MVC by 11.59% (95% CI: 5.17%-16.43%) over 0-28 lag days for New York. The respective risk increase is 1.58% (95% CI: 0.36%-2.79%) over 0-2 lag days for a 1 °C increase in mean daily temperature above the hot threshold of 26.1 °C for New York. There is no significant heat wave or cold spell effects except for the heat wave effect on the consecutive heat wave days 7-10 in New York. Our study provides evidence of the association between driver exposure to low or high temperatures and the MVC risk. Improved public- administration preventive measures and policymaking are needed to minimize the negative consequences of abnormal temperatures on road safety driving.

Spatially explicit and multiscale ecosystem shift probabilities and risk severity assessments in the greater Mekong subregion over three decades.

Ecosystem functioning and related risks could become compromised by climate change and severely affect livestock in different ways. Based on four climate indices (i.e., SPI, SPEI, PDSI and SEDI), livestock determinants and biogeochemical proxies, we analysed the temporal and geographical extent of terrestrial ecosystem shift probabilities and drought-wetness risk severity at multiple scales (i.e., land cover, climate and elevation) in the greater Mekong subregion (GMS) during the period 1981-2020 by using different cartographic techniques. The results indicated that in the GMS area, approximately 3.8% experienced the highest ecosystem shift probability, 4% was exposed to a high risk of drought and wetness, and only approximately 55% experienced a low risk of drought and/or wetness stress. Cambodia and Thailand experienced the highest ecosystem shift probability ratio and drought-wetness risk severity compared to other GMS countries. Woody savanna and urban land covers; temperate-fully humid-cold summer and tropical rainfall fully humid climate zones; and elevations -47-200 m and ≥2500 m showed common characteristics leading to a very high ecosystem shift probability and experienced high drought-wetness risk severity. This study provides useful information that may exert to a strong control and improved future terrestrial in the context of changes in climate and biogeophysical aspects at the regional and country scales.

A new approach combining a simplified FLEXPART model and a Bayesian-RAT method for forecasting PM10 and PM2.5.

In this study, we evaluated atmospheric particulate matter (PM) concentration predictions at a regional scale using a simplified Lagrangian particle dispersion modeling system and the Bayesian and multiplicative ratio correction optimization (Bayesian-RAT) method to improve the mixing ratio forecast of PM10 and PM2.5. We first examined the forecast performance of the LPD (i.e., the simplified FLEXPART model combined with the Bayesian-RAT method) by comparing the model predictions with the PM concentration observations from 95 observation stations in Xingtai city and its surrounding areas. The first 2 months (i.e., Oct. and Nov. 2017) of the study period represented the typical spin-up time period, and the analysis period was December 2017. The LPD forecast system was much better (correlation coefficient: R=0.64 vs. 0.48 and 0.67 vs. 0.50 for PM10 and PM2.5, respectively; root mean square error: RMSE = 74.98 vs. 105.96 µg/m3 for PM10 and 54.89 vs. 72.81 µg/m3 for PM2.5) than the pre-calibration results. We also compared the LPD forecasting model with other models (WRF-Chem and Camx) using data from monitoring stations in Xingtai, China, and the LPD forecasting model had higher accuracy than the other models. In particular, the RMSE scores for hourly PM10 concentrations were reduced by 36.51% and 42.21% compared to WRF-Chem and to Camx, respectively. The PM2.5 forecast results, as in the case of PM10, showed a better performance when applying the LPD model to the data from the monitoring stations. The RMSE was reduced by 26.44% and 18.47% relative to the WRF-Chem and Camx, respectively. The results confirm that there is much advantage of the LPD forecast system for predicting PM and may be for other pollutants.

Understanding of coupled terrestrial carbon, nitrogen and water dynamics-an overview.

Coupled terrestrial carbon (C), nitrogen (N) and hydrological processes play a crucial role in the climate system, providing both positive and negative feedbacks to climate change. In this review we summarize published research results to gain an increased understanding of the dynamics between vegetation and atmosphere processes. A variety of methods, including monitoring (e.g., eddy covariance flux tower, remote sensing, etc.) and modeling (i.e., ecosystem, hydrology and atmospheric inversion modeling) the terrestrial carbon and water budgeting, are evaluated and compared. We highlight two major research areas where additional research could be focused: (i) Conceptually, the hydrological and biogeochemical processes are closely linked, however, the coupling processes between terrestrial C, N and hydrological processes are far from well understood; and (ii) there are significant uncertainties in estimates of the components of the C balance, especially at landscape and regional scales. To address these two questions, a synthetic research framework is needed which includes both bottom-up and top-down approaches integrating scalable (footprint and ecosystem) models and a spatially nested hierarchy of observations which include multispectral remote sensing, inventories, existing regional clusters of eddy-covariance flux towers and CO(2) mixing ratio towers and chambers.

A regional data assimilation system for estimating CO surface flux from atmospheric mixing ratio observations-a case study of Xuzhou, China.

Carbon monoxide (CO) emission inventory data are crucial for air quality control. However, the emission inventories are labor-intensive and time-consuming and generally have large uncertainties. In this study, we developed a new regional data assimilation system (TracersTracker) for estimating the surface CO emission flux from continuous mixing ratio observations using the proper orthogonal decomposition (POD)-based four-dimensional variational (4D-VAR) data assimilation method (POD-4DVar) and a coupled regional model (Weather Research and Forecasting model (WRF) with the Models-3 Community Multi-scale Air Quality (CMAQ) model). This system was applied to estimate CO emissions in Xuzhou city, China. An experiment was conducted with the continuous hourly surface CO mixing ratio observations from 21 monitoring towers in January and July of 2016. The experimental results of the system were examined and compared with the continuous surface CO observations (a priori emission). We found that the retrieved CO emission fluxes were higher than the a priori emission and were mainly distributed in urban and industrial areas, which were 104% higher in January (winter) and 44% higher in July (summer).

Assessing the Spatiotemporal Variation and Impact Factors of Net Primary Productivity in China.

In this study, the net primary productivity (NPP) in China from 2001 to 2012 was estimated based on the Carnegie-Ames-Stanford Approach (CASA) model using Moderate Resolution Imaging Spectroradiometer (MODIS) and meteorological datasets, and the accuracy was verified by a ChinaFLUX dataset. It was found that the spatiotemporal variations in NPP present a downward trend with the increase of latitude and longitude. Moreover, the influence of climate change on the evolution of NPP shows that NPP has had different impact factors in different regions and periods over the 12 years. The eastern region has shown the largest increase in gross regional product (GRP) and a significant fluctuation in NPP over the 12 years. Meanwhile, NPP in the eastern and central regions is significantly positively correlated with annual solar radiation, while NPP in these two regions is significantly negatively correlated with the growth rate of GRP. It is concluded that both the development of the economy and climate change have influenced NPP evolution in China. In addition, NPP has shown a steadily rising trend over the 12 years as a result of the great importance attributed to ecological issues when developing the economy.

A comprehensive estimate of recent carbon sinks in China using both top-down and bottom-up approaches.

Atmospheric inversions use measurements of atmospheric CO2 gradients to constrain regional surface fluxes. Current inversions indicate a net terrestrial CO2 sink in China between 0.16 and 0.35 PgC/yr. The uncertainty of these estimates is as large as the mean because the atmospheric network historically contained only one high altitude station in China. Here, we revisit the calculation of the terrestrial CO2 flux in China, excluding emissions from fossil fuel burning and cement production, by using two inversions with three new CO2 monitoring stations in China as well as aircraft observations over Asia. We estimate a net terrestrial CO2 uptake of 0.39-0.51 PgC/yr with a mean of 0.45 PgC/yr in 2006-2009. After considering the lateral transport of carbon in air and water and international trade, the annual mean carbon sink is adjusted to 0.35 PgC/yr. To evaluate this top-down estimate, we constructed an independent bottom-up estimate based on ecosystem data, and giving a net land sink of 0.33 PgC/yr. This demonstrates closure between the top-down and bottom-up estimates. Both top-down and bottom-up estimates give a higher carbon sink than previous estimates made for the 1980s and 1990s, suggesting a trend towards increased uptake by land ecosystems in China.

[Review of dynamic global vegetation models (DGVMs)].

Dynamic global vegetation model (DGVM) is an important and efficient tool for study on the terrestrial carbon circle processes and vegetation dynamics. This paper reviewed the development history of DGVMs, introduced the basic structure of DGVMs, and the outlines of several world-widely used DGVMs, including CLM-DGVM, LPJ, IBIS and SEIB. The shortages of the description of dynamic vegetation mechanisms in the current DGVMs were proposed, including plant functional types (PFT) scheme, vegetation competition, disturbance, and phenology. Then the future research directions of DGVMs were pointed out, i. e. improving the PFT scheme, refining the vegetation dynamic mechanism, and implementing a model inter-comparison project.

Diurnal, seasonal and interannual variability of carbon isotope discrimination at the canopy level in response to environmental factors in a boreal forest ecosystem.

Accurate estimation of temporal and spatial variations in photosynthetic discrimination of 13C is critical to carbon cycle research. In this study, a combined ecosystem-boundary layer isotope model, which was satisfactorily validated against intensive campaign data, was used to explore the temporal variability of carbon discrimination in response to environmental driving factors in a boreal ecosystem in the vicinity of Fraserdale Tower, Ontario, Canada (49 degrees 52'30''N, 81 degrees 34'12''W). A 14 year (1990-1996 and 1998-2004) hourly CO2 concentration and meteorological record measured on this tower was used for this purpose. The 14 year mean yearly diurnal amplitude of canopy-level discrimination Delta(canopy) was computed to be 2.8 +/- 0.5 per thousand, and the overall diurnal cycle showed that the greatest Delta(canopy) values occurred at dawn and dusk, while the minima generally appeared in mid-afternoon. The average annual Delta(canopy) varied from 18.3 to 19.7 per thousand with the 14 year average of 19 +/- 0.4 per thousand. The overall seasonality of Delta(canopy) showed a gradually increasing trend from leaf emergence in May-September and with a slight decrease at the end of the growing season in October. Delta(canopy) was negatively correlated to vapour pressure deficit and air temperature across hourly to decadal timescales. A strong climatic control on stomatal regulation of ecosystem isotope discrimination was found in this study.