Global long term daily 1 km surface soil moisture dataset with physics informed machine learning.

Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution soil moisture datasets are still limited. Here we use physics-informed machine learning to generate a global, long-term, spatially continuous high resolution dataset of surface soil moisture, using International Soil Moisture Network (ISMN), remote sensing and meteorological data, guided with the knowledge of physical processes impacting soil moisture dynamics. Global Surface Soil Moisture (GSSM1 km) provides surface soil moisture (0-5 cm) at 1 km spatial and daily temporal resolution over the period 2000-2020. The performance of the GSSM1 km dataset is evaluated with testing and validation datasets, and via inter-comparisons with existing soil moisture products. The root mean square error of GSSM1 km in testing set is 0.05 cm3/cm3, and correlation coefficient is 0.9. In terms of the feature importance, Antecedent Precipitation Evaporation Index (APEI) is the most important significant predictor among 18 predictors, followed by evaporation and longitude. GSSM1 km product can support the investigation of large-scale climate extremes and long-term trend analysis.

Influence of Mg on the mechanical properties and degradation performance of as-extruded ZnMgCa alloys: In vitro and in vivo behavior.

The influence of Mg content on the mechanical properties, degradation behavior, in vitro cell adhesion, and in vivo behavior of as-extruded Zn-xMg-0.1Ca (x = 0.5 wt%, 1.0 wt%, 1.5 wt%) alloys was investigated. A high Mg content could increase the volume fraction of the hard Mg2Zn11 phase distributed at grain boundaries. This condition could significantly improve yield strength and ultimate tensile strength. Mg addition could adjust the degradation rate of Zn alloys and influence cytocompatibility. ZnMg1Ca0.1 alloy showed the highest adhesion density of bone marrow-derived mesenchymal stem cells (BMSCs) because the degradation rate of ZnMg1Ca0.1 alloy could supply appropriate pH and [Zn2+] for BMSCs. Mg addition could improve the cytocompatibility of ZnMgCa alloys. However, a Mg content threshold was observed, and the Mg content should be exactly controlled. Combined with the mechanical properties, the degradation rate of zinc alloy implants could be adjusted to match the healing of tissues by adding Mg. In vivo results showed that the degradation rate of the optimized ZnMgCa alloy could match the healing of local tissues or organs. Animal implant results revealed alloy safety.

Prediction of CH4 emissions from potential natural wetlands on the Tibetan Plateau during the 21st century.

The alpine wetlands on the Tibetan Plateau (TP) are ecosystems vulnerable to global climate change. It has been recognized that future climate change may have a significant impact on methane (CH4) emissions from the plateau, while less attention has been paid to predicting temporal and spatial variations in CH4 emissions from TP natural wetlands. In this study, we used an integrated model framework based on the CH4MODwetland, TOPMODEL and TEM models to predict CH4 emissions from potential natural wetlands on the TP under IPCC AR5 scenarios from 2006 to 2100. The model estimates suggest that the mean area-weighted CH4 fluxes will increase from 4.45 ±â€¯0.42 g m-2 yr-1 in 2006 to 4.79 ±â€¯0.72, 5.99 ±â€¯0.85 and 11.53 ±â€¯1.33 g m-2 yr-1 under 3 Representative Concentration Pathway scenarios (RCP 2.6, RCP 4.5 and RCP 8.5 scenarios), respectively, by 2100. The dominant drivers stimulating CH4 emissions are air temperature, precipitation and net primary productivity (NPP). Spatially, CH4 fluxes and emissions showed a decreasing trend from south to north and from east to west. In response to climate change, a total of 0.42 ±â€¯0.06, 0.54 ±â€¯0.09 and 1.01 ±â€¯0.12 Tg yr-1 of CH4 emissions will be emitted from the TP's potential natural wetlands by the end of this century under the RCP 2.6, RCP 4.5 and RCP 8.5 scenarios, respectively.

Global wetlands: Potential distribution, wetland loss, and status.

Even though researchers have paid a great deal of attention to wetland loss and status, the actual extent of wetland loss on a global scale, especially the loss caused directly by human activities, and the actual extent of currently surviving wetlands remains uncertain. This paper simulated the potential distribution of global wetlands by employing a new Precipitation Topographic Wetness Index (PTWI) and global remote sensing training samples. The results show earth would have approximately 29.83millionkm2 of wetlands, if humans did not interfere with wetland ecosystems. By combining datasets related to global wetlands, we found that at least 33% of global wetlands had been lost as of 2009, including 4.58millionkm2 of non-water wetlands and 2.64millionkm2 of open water. The areal extent of wetland loss has been greatest in Asia, but Europe has experienced the most serious losses. Wetland-related datasets suffer from major inconsistencies, and estimates of the areal extent of the remaining global wetlands ranged from 1.53 to 14.86millionkm2. Therefore, although it is challenging, thematic mapping of global wetlands is necessary and urgently needed.

Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades.

Both anthropogenic activities and climate change can affect the biogeochemical processes of natural wetland methanogenesis. Quantifying possible impacts of changing climate and wetland area on wetland methane (CH4) emissions in China is important for improving our knowledge on CH4 budgets locally and globally. However, their respective and combined effects are uncertain. We incorporated changes in wetland area derived from remote sensing into a dynamic CH4 model to quantify the human and climate change induced contributions to natural wetland CH4 emissions in China over the past three decades. Here we found that human-induced wetland loss contributed 34.3% to the CH4 emissions reduction (0.92 TgCH4), and climate change contributed 20.4% to the CH4 emissions increase (0.31 TgCH4), suggesting that decreasing CH4 emissions due to human-induced wetland reductions has offset the increasing climate-driven CH4 emissions. With climate change only, temperature was a dominant controlling factor for wetland CH4 emissions in the northeast (high latitude) and Qinghai-Tibet Plateau (high altitude) regions, whereas precipitation had a considerable influence in relative arid north China. The inevitable uncertainties caused by the asynchronous for different regions or periods due to inter-annual or seasonal variations among remote sensing images should be considered in the wetland CH4 emissions estimation.