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.

Research progress in parameterizing irrigation and fertilization in land surface model.

Under the context of global climate change and growing population, irrigation and fertilization have become important ways to ensure food production, with consequences on water cycling, energy flow, and materials cycling in terrestrial ecosystems. In the land surface model (LSM), coupling irrigation and fertilization schemes are of great importance for clearly understanding the land-atmosphere interactions to ensure food security. We reviewed the expression methods of three key parameters, namely, the applied method, usage, and time in the parameterization process of irrigation and fertilization (nitrogen fertilizer) in LSM. We found that the ways to irrigate and ferti-lize in LSM are different from the ways used in actual practice due to the limitation of the high resolution of spatio-temporal data, which makes it difficult to understand the actual influences of irrigation and fertilization on grain yield, environment, and local climate. Finally, we proposed future works: 1) taking the differences of crop water demand into account and making the different irrigation thresholds for different crops to properly evaluate the total and intensity of water consumption of different crops; 2) using the field records and the regional grid data of fertilization and irrigation developed in recent years to develop parameterized schemes that are more in line with actual agricultural operations, which can accurately reveal their economic, ecological, and climatic effects; 3) developing fertilization diagnosis scheme considering crop type, phenological stage, and soil basic fertility as the supplementary scheme in LSM, to improve the applicability and simulation accuracy of LSM in the areas without nitrogen fertilizer data.

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.

First report of Candida auris in Guangdong, China: clinical and microbiological characteristics of 7 episodes of candidemia.

Candida auris is an emerging multidrug-resistant fungal pathogen worldwide. To date, it has not been reported in Guangdong, China. For the first time, we reported 7 cases of C. auris candidemia from two hospitals in Guangdong. The clinical and microbiological characteristics of these cases were investigated carefully. Two geographic clades, i.e. III and I, were found popular in different hospitals by whole genome sequencing analyses. All C. auris isolates from bloodstream were resistant to fluconazole, 5 of which belonged to Clade III harbouring VF125AL mutation in the ERG11 gene. The isolates with Clade I presented Y132F mutation in the ERG11 gene as well as resistance to amphotericin B. All isolates exhibited strong biofilm-forming capacity and non-aggregative phenotype. The mean time from admission to onset of C. auris candidemia was 39.4 days (range: 12 - 80 days). Despite performing appropriate therapeutic regimen, 42.9% (3/7) of patients experienced occurrences of C. auris candidemia and colonization after the first positive bloodstream. C. auris colonization was still observed after the first C. auris candidemia for 81 days in some patient. Microbiologic eradication from bloodstream was achieved in 85.7% (6/7) of patients at discharge. In conclusion, this study offers a crucial insight into unravelling the multiple origins of C. auris in Guangdong, highlighting great challenges in clinical prevention and control.

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.