A total of 634 annual soil respiration observations was assembled from 352 published studies across China’s forest, including geographical location (latitude, longitude and elevation) and climate factors (MAT and MAP).
Abstract A large literature exists on mechanisms driving soil production of the greenhouse gases CO 2 and CH 4 . Although it is common knowledge that measurements obtained through field studies vs. laboratory incubations can diverge because of the vastly different conditions of these environments, few studies have systematically examined these patterns. These data are used to parameterize and benchmark ecosystem- to global-scale models, which are then susceptible to the biases of the source data. Here, we examine how greenhouse gas measurements may be influenced by whether the measurement/incubation was conducted in the field vs. laboratory, focusing on CO 2 and CH 4 measurements. We use Q 10 of greenhouse gas flux (temperature sensitivity) for our analyses because this metric is commonly used in biological and Earth system sciences and is an important parameter in many modeling frameworks. We predicted that laboratory measurements would be less variable, but also less representative of true field conditions. However, there was greater variability in the Q 10 values calculated from lab-based measurements of CO 2 fluxes, because lab experiments explore extremes rarely seen in situ , and reflect the physical and chemical disturbances occurring during sampling, transport, and incubation. Overall, respiration Q 10 values were significantly greater in laboratory incubations (mean = 4.19) than field measurements (mean = 3.05), with strong influences of incubation temperature and climate region/biome. However, this was in part because field measurements typically represent total respiration (Rs), whereas lab incubations typically represent heterotrophic respiration (Rh), making direct comparisons difficult to interpret. Focusing only on Rh-derived Q 10 , these values showed almost identical distributions across laboratory ( n = 1110) and field ( n = 581) experiments, providing strong support for using the former as an experimental proxy for the latter, although we caution that geographic biases in the extant data make this conclusion tentative. Due to a smaller sample size of CH 4 Q 10 data, we were unable to perform a comparable robust analysis, but we expect similar interactions with soil temperature, moisture, and environmental/climatic variables. Our results here suggest the need for more concerted efforts to document and standardize these data, including sample and site metadata.
Abstract Soil respiration (Rs) plays an important role in terrestrial–atmospheric carbon exchange but remains one of the least studied components of the carbon cycle. How environmental changes influence Rs, and in turn, how Rs influences terrestrial carbon storage in China is unclear. Here, we estimated spatial patterns and temporal trends in Rs from 1961 to 2014 to determine the influence of the recent warming hiatus on the Rs temporal variability in China. We evaluated the relationship between Rs and a set of environmental factors and developed biome‐specific Rs models that were parameterized based on 2,862 Rs records and 18 continuous variables (e.g., air temperature, precipitation and leaf area index). Based on the biome‐specific Rs models and environmental information, we estimated the annual Rs as well as the change trend of Rs in China during the study period. The mean annual Rs from 1961 to 2014 was 3.50 (confidence interval ± 0.87) Pg·C·year −1 . Rs in China increased at a rate of 0.008 Pg·C·year −2 between 1982 and 1998 (warming period), but was static between 1999 and 2014 (warming hiatus). This reduction in Rs increase rate may have an important role in enhancing terrestrial carbon sequestration. These findings confirm that temporal heterogeneity in Rs could be influenced by regional environmental changes, and further help in improving the understanding of soil carbon dynamics in China. Highlights No study in China has integrated multiple biotic and abiotic factors and analysed how soil respiration correlates with different factors across biomes. In this study, biome‐specific models, including biotic and abiotic factors, were developed to estimate soil respiration in China. Annual soil respiration in China was estimated to be 3.50 (confidence interval ± 0.87) Pg·C·year ‐1 . Annual soil respiration in China had greater deceleration compared to the global deceleration between 1999 and 2014.
Here, we compiled data from a set of soil health measurements collected across 41 countries around the world, named SoilHealthDB, which includes 5,241 data entries from 281 published studies. The SoilHealthDB includes 42 soil health indicators and 45 background indicators that describe factors such as climate, elevation, and soil type. A primary goal of SoilHealthDB is to enable the research community to perform comprehensive analyses, e.g., meta-analyses, of soil health changes related to cropland conservation management. The database also provides a common framework for sharing soil health, and the scientific research community is encouraged to contribute their own measurements. R code for quality checking and data processing are available at RScripts. All data (including meta data for clerification) were stored in 'SoilHealthDB_V1.xlsx'.
Abstract The high temporal variability of the soil‐to‐atmosphere CO 2 flux (soil respiration, R S ) has been studied at hourly to multiannual time scales but remains less well understood than R S spatial variability. How R S fluxes vary and are autocorrelated at various time lags has practical implications for sampling and more fundamentally for our understanding of its abiotic and biotic underlying mechanisms. We examined the variability, correlation, and sampling requirements of R S over a wide range of temporal scales in a temperate deciduous forest in eastern Maryland, USA, using both automated (temporally continuous, N = 30,036 over 10 months) and survey (spatially diverse, temporally sparse, N = 1,912 over 17 months) data. Data from a global R S database were also used to examine interannual variability in comparable forests. The coefficient of variability of successive measurements generally varied from the minute (median coefficient of variation 16%) to hourly and daily (11–12%) time scales. Successive R S values measured at a given collar exhibited a strong hour‐to‐hour correlation ( r = 0.931) and a moderate correlation at a 2‐hr lag (0.289); day‐to‐day (i.e., 24 hr lag) hourly observations were uncorrelated. Daily R S means were well correlated at a 1‐day lag ( r = 0.856) but not at any further time lag. In a linear mixed‐effects model predicting R S , soil temperature and moisture exerted consistently strong effects regardless of time scale, and model coefficient of variability was generally high (>80%). These results provide new opportunities to explore the drivers and variability of R S fluxes, quantify sampling requirements, and improve error propagation.
Climate change and human activities are the main driving forces of water erosion changes, especially in the Qinghai-Tibet plateau (QTP). In this paper, water erosion in the QTP during 1982-2015 was quantified by the Revised Universal Soil Loss Equation (RUSLE). Grid-by-grid Spearman's rank correlation test, spatial overlay analysis, and Mann-Kendall test were used to investigate the response of water erosion changes to climate change and human activities. The results indicated that water erosion modulus varied between 12.5 and 29.1 t·ha -1 ·yr -1 during 1982-2015 with a multi-year mean value of 19.4 t·ha -1 ·yr -1 . Severe erosion areas are mainly distributed along the Yarlung Tsangpo River and in the western part of the Hengduan Mountain. Temporally, water erosion mutations occurred in 1989 and 2006. Compared to 1982-1989, water erosion was mitigated in both 1990-2006 and 2007-2015, with a 19.5% and 6.2% decrease, respectively. Precipitation in 74.1% area of the QTP was not linearly correlated with water erosion due to low precipitation intensity, rising vegetation cover, and the influence of land use types such as urban residential land, water bodies, sandy, Gobi, and bare rocky gravel land. NDVI increases and human activities implement offset water erosion caused by climate change. During 1982-2015, water erosion reduction was mainly distributed in the Xinjiang, Qinghai and Tibet border areas, southern Tibet, the Qaidam Basin surrounding areas, and eastern Qinghai, accounting for 74.2% area of the whole QTP. Even so, there was still an increase in erosion of 2007-2015 compared to 1990-2006 that should still draw attention to prevent possible increased erosion risk in the future. This study is the first attempt to study the effects of climate change and human activities on soil erosion in the QTP, and the results provide new insights into soil erosion changes in the QTP.
Abstract. Soil erosion is a major threat to soil resources, continuing to cause environmental degradation and social poverty in many parts of the world. Many field and laboratory experiments have been performed over the past century to study spatio-temporal patterns of soil erosion caused by surface runoff under different environmental conditions. However, these historical data have never been integrated together in a way that can inform current and future efforts to understand and model soil erosion at different scales. Here, we designed a database (SoilErosionDB) to compile field and laboratory measurements of soil erosion caused by surface runoff, with data coming from sites across the globe. The SoilErosionDB includes 18 columns for soil erosion related indicators and 73 columns for background information that describe factors such as latitude, longitude, climate, elevation, and soil type. Currently, measurements from 99 geographic sites and 22 countries around the world have been compiled into SoilErosionDB. We provide examples of linking SoilErosionDB with an external climate dataset and using annual precipitation to explain annual soil erosion variability under different environmental conditions. All data and code to reproduce the results in this study can be found at: Jian, J., Du, X., Stewart, R., Tan, Z. and Bond-Lamberty, B.: jinshijian/SoilErosionDB: First release of SoilErosionDB, Zenodo, https://doi.org/10.5281/zenodo.4030875, 2020b. All data are also available through GitHub: https://github.com/jinshijian/SoilErosionDB.
Conservation management practices – including agroforestry, cover cropping, no-till, reduced tillage, and residue return – have been applied for decades to control surface runoff and soil erosion, yet results have not been integrated and evaluated across cropping systems. In this study we collected data comparing agricultural production with and without conservation management strategies. We used a bootstrap resampling analysis to explore interactions between practice type, soil texture, surface runoff, and soil erosion. We then used a correlation analysis to relate changes in surface runoff and soil erosion to 13 other soil health and agronomic indicators, including soil organic carbon, soil aggregation, infiltration, porosity, subsurface leaching, and cash crop yield. Across all conservation management practices, surface runoff and erosion had respective mean decreases of 67% and 80% compared with controls. Use of cover cropping provided the largest decreases in erosion and surface runoff, thus emphasizing the importance of maintaining continuous vegetative cover on soils. Coarse- and medium-textured soils had greater decreases in both erosion and runoff than fine-textured soils. Changes in surface runoff and soil erosion under conservation management were highly correlated with soil organic carbon, aggregation, porosity, infiltration, leaching, and yield, showing that conservation practices help drive important interactions between these different facets of soil health. This study offers the first large-scale comparison of how different conservation agriculture practices reduce surface runoff and soil erosion, and at the same time provides new insight into how these interactions influence the improvement or loss of soil health.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
