Find research datasets worth reusing

opencc-by-4.0May 2022View details →

The basic data , precipitation data, and simulation data of a drainage basin in Dalian

The basic data , precipitation data, and simulation data for flood simulation in a drainage basin in Dalian

opencc-by-4.0Feb 2022View details →

Modelling dynamic precipitation in pre-aged aluminium alloys

Calculation of precipitation kinetics under deformation This repository contains the data plotted in https://doi.org/10.1016/j.actamat.2022.118036 as well as the code used to generate them. The code can be run reading the instructions contained in README.txt

opencc-by-4.0Jun 2022View details →

Data for "Physically Based Deep Learning Framework to Model Intense Precipitation Events at Engineering Scales"

The dataset consists of high resolution (250 m) and low resolution (0.025 degree) climate model outputs in netCDF format. Each file contains data for one variable and one month. Low resolution files follow the naming scheme: montrealC_0025deg_200x200_ERA5_1m_YYYYMM_VAR.nc High resolution files follow the naming scheme: montrealC_250m_324x324_ERA5_TEB_100_noconv_YYYYMM_VAR.nc YYYYMM stands for the year (first 4 digits) and month (last 2 digits). _VAR indicates the variable contained in the file: <ul> <li>_UU700 stands for the east-west component of wind at a pressure level of 700 hPa (hourly frequency)</li> <li>_VV700 stands for the north-south component of wind at a pressure level of 700 hPa (hourly frequency)</li> <li>When _VAR is omitted, the variable is precipitation at 1-minute temporal resolution</li> </ul>

opencc-by-4.0Jun 2022View details →

Disentangling the impact of event- and annual-scale precipitation extremes on critical-zone hydrology in semiarid loess: A case study in apple tree plantation

The dataset is the basic data of the author's paper ' Disentangling the impact of event-and annual-scale precipitation extremes on critical-zone hydrology in semiarid loess - a case study in apple tree plantation '. The main content of this paper is to study the hydrological effect of extreme precipitation on the critical area of semi-arid loess. Taking apple plantation as an example, the data set includes the soil moisture and soil temperature data monitored in the field and the apple tree transpiration data. The measured data are used to calibrate and verify the model used in this paper. The water vapor flux, apple tree evapotranspiration and soil leakage data of the simulated soil profile are also included to analyze the hydrological effect of extreme precipitation on the critical area of loess.

opencc-by-4.0Jan 2022View details →

Binarized landuse and percentile statistics for precipitation trends analyzing in YDM

This repository is complementary material for storing and sharing the scientific data used in the article 'Precipitation Trends Analysis using Gridded Dynamic Sampling Zones: Case Study Yangtze Delta Megalopolis'. It includes: <ul> <li>binarized landuse images in geotiff format,</li> <li>calculated time series of percentile statistics in csv format.</li> </ul>

Adaptation of pathogens to their local plant host, Silphium integrifolium, along a precipitation gradient

All figures and code were generated in RStudio 2022.02.3+492 "Prairie Trillium" Release. All packages needed to run the R code are shown in the RMD’s.   Code to generate figures and statistical analysis:   <ul> <li>DVTindex_help.Rmd <ul> <li>Code to generate figure 3</li> </ul> </li> <li>Figure5_PrairieVSCommonGarden <ul> <li>Code to generate figure 5</li> </ul> </li> <li>PATHOFigs_for_Manu_FEB22.Rmd <ul> <li>Code to generate figures 1, 2, 4</li> </ul> </li> <li>SUPP_Figure3_DimPathoEDIT.Rmd <ul> <li>Code to generate supplementary figure 3 (fig S3)</li> </ul> </li> <li>STATSAnalysis_PathoDim2B.Rmd <ul> <li>Code to generate all tables and statistical analysis in the manuscript</li> </ul> </li> </ul>   Description of data files: <ul> <li>2019and2020Prairie_data.txt <ul> <li>This file contains data collected from the prairie sites in 2019 and 2020</li> </ul> </li> <li>Dim2b_latlon.csv <ul> <li>The latitudinal and longitudinal coordinates for the common garden sites and prairie sites as well as precipitation data</li> </ul> </li> <li>DVTINDEX_from_DVT.csv <ul> <li>A separate file that contains the data that produced fig 3. This data file is sourced from SEPT2019_2020_COMPILED_2b_DATACOLL.txt</li> </ul> </li> <li>SEPT2019_2020_COMPILED_2b_DATACOLL.txt</li> <li>SEPT2019_2020_COMPILED_2b_DATACOLL_plots.txt</li> <li>SEPT2019_2020_COMPILED_2b_DATACOLL_plots_longversion.txt <ul> <li>These 3 data files are different versions of the same raw data that was collected from the common garden sites in 2019 and 2020. The code calls for all 3 at different points to generate plots and run statistical analyses</li> </ul> </li> <li>Summary [ insert unique name here] <ul> <li>Various data frames generated from r mark downs that contain summary statistics of the data. These are used to produce the figures in PATHOFigs_for_Manu_FEB22.Rmd graphs.</li> </ul> </li> </ul>

Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation

The supplementary figures contain 2380 precipitation-type flood events recorded by the DFO compared with  results derived based on GRACE and MODIS. "DFO-GRACE-MODIS" folder means comparison among flood derived from DFO, GRACE and MODIS while "DFO-GRACE" folder means comparison between DFO and GRACE due to lack of MODIS-derived flood inundation. As for detailed information, please refer to the corresponding paper.

Data for: Deep Learning of Model- and Reanalysis- Based Precipitation and Pressure Mismatches over Europe

This study focuses on using UNet Convolutional Neural Networks to predict the spatiotemporal mismatches (errors) between TSMP-G2A model-based and COSMO-REA6 reanalysis-based precipitation and surface pressure over Europe. The following data are provided in this dataset: 1) The remapped and NetCDF-merged TSMP-G2A and COSMO-REA6 precipitation and surface pressure over the study area (EU-11 EUROCORDEX, ~0.11 degrees) for the years 1995-2017. Files: COSMO-REA6_PREPROCESSED.zip and TSMP_PREPROCESSED.zip 2) The actual and predicted spatiotemporal mismatch data for training, validation, and testing periods (1995-2017). Files: MISMATCH_ACTUAL.zip and MISMATCH_PREDICTED.zip   References for original TSMP-G2A and COSMO-REA6 data: TSMP-G2A: http://doi.org/10.17616/R31NJMGR COSMO-REA6: doi:10.1002/qj.2486, 2015

opencc-zeroJul 2022View details →

Precipitation Efficiency Constraint on Climate Change

Precipitation efficiency (PE) relates cloud condensation to precipitation and intrinsically binds atmospheric circulation to the hydrological cycle. Due to PE's inherent microphysical dependencies, definitions and estimates vary immensely. Consequently, PE's sensitivity to greenhouse warming and implications for climate change are poorly understood. Here, we quantify PE's role in climate change by defining a simple index as the ratio of surface precipitation to condensed water path. This macroscopic metric is reconcilable with microphysical PE measures and higher is associated with stronger mean Walker circulation. We further find that state-of-the-art climate models disagree on the sign and magnitude of future changes. This sign disagreement originates from models' convective parameterizations. Critically, models with increasing under greenhouse warming, in line with cloud-resolving simulations, show greater slowdown of the large-scale Hadley and Walker circulations and a two-fold greater increase in extreme rainfall than models with decreasing.

opencc-by-4.0Aug 2022View details →

Drop characteristics from the UNIGE calibration device for non-catching precipitation instruments

These data are from the paper "Calibration uncertainty of non-catching precipitation gauges", by Baire, Q. et al., accepted for publication on the journal Sensors. A rain drop generator, developed at the University of Genova, is presented in that paper using two high-precision syringe pumps, with a capacity of 20 and 1 ml (20 and 4.5 mm piston diameter, respectively), to produce water drops of the required volume. An electric field, generated by a high voltage trigger, allows releasing each single drop on demand. Each drop is generated at the tip of a suitable nozzle by dispensing the necessary volume to achieve the desired drop size and then detached by exploiting a 5 kV potential difference, where the water is negatively charged and attracted by a metal ring (positively charged), positioned just below the tip of the nozzle. By using different nozzles/needles and the proper syringe pump, drops of various size are produced. Validation of drop size measurements was obtained by weighing the total volume of samples of about 20 to 45 drops with a precision balance having a resolution of 0.001 g. Drops were released at 1.20 m above the center of the measurement plane of the camera. Results are summarized in Table 2 in the paper in terms of the average drop diameter obtained from the software and the balance, and their difference. Drop size characteristics as obtained from the photogrammetric system are included in the database for the generated sets of drops. A second set of tests was conducted using the photogrammetric system alone, without weighing the overall water volume. Statistics of the detected drop diameter and fall velocity are listed in Tables 3 and 4 in the paper, together with the number of released drops during each test, while the single drop size and fall velocity characteristics are included in the dataset.

opencc-by-4.0Aug 2022View details →

Monthly water storage anomalies, precipitation, and groundwater recharge in two karstic basins, southwest China (2003-2014)

This dataset includes the regionally-averaged monthly hydrological data in two karstic basins, southwest China which are processed or estimated for the manuscript entitled "A novel approach for assessing groundwater recharge by combining GRACE and baseflow with case studies in karst areas of southwest China" by Huang et al., 2022 (submitted to Water Resources Research, Major Revision). Basin description: The Wujiang River Basin (WRB, ~87,900 km2, ~70% karstification) and Xijiang River Basin (XRB, ~360,000 km2, ~44% karstification) are two typical karstic basins in southwest China. The Wujiang River is the largest tributary in the southern part of the upper Yangtze River. It is originated from the Wumeng Mountain in the Yunnan-Guizhou Plateau and flows from Guizhou to Chongqing. The Xijiang River is the largest tributary of the Pearl River (the largest river in southern China). It is originated from the mountains in eastern Yunnan and flows to Guangxi, Guangdong, and finally into the South China Sea. Data description: 1. TWSA (unit: mm in equivalent water thickness) is the average terrestrial water storage anomaly data obtained from three release 6 GRACE (Gravity Recovery and Climate Experiment)  mascon solutions, i.e., the Center for Space Research (CSR) at the University of Texas (<a href="http://www2.csr.utexas.edu/grace/RL06_mascons.html">http://www2.csr.utexas.edu/grace/RL06_mascons.html</a>), Jet Propulsion Laboratory (JPL, <a href="https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2">https://podaac.jpl.nasa.gov/dataset/TELLUS_GRAC-GRFO_MASCON_CRI_GRID_RL06_V2</a>), and Goddard Space Flight Center (GSFC, <a href="https://earth.gsfc.nasa.gov/geo/data/grace-mascons">https://earth.gsfc.nasa.gov/geo/data/grace-mascons</a>). The anomalies were estimated by removing a mean background value during 2006-2012. 2. SMSA and SWSA (unit: mm in equivalent water thickness) are the soil moisture storage anomaly and surface water storage anomaly data based on the model simulations by WGHM (v2.2d) provided by Dr. Hannes Müller Schmied (email: hannes.mueller.schmied@em.uni-frankfurt.de) at Institute of Physical Geography, Goethe-University Frankfurt. The anomalies were estimated by removing a mean background value during 2006-2012. 3. Precipitation data (unit: mm/month) were based on the monthly gridded (0.5×0.5 degree) data product obtained from China Meteorological Administration (CMA, https://data.cma.cn/) which was interpolated from ground-based data measured by meteorological stations. 4. Groundwater recharge (unit: mm/month) was estimated based on the groundwater budget method, i.e., the summation of groundwater storage change (GWSC) and baseflow. GRACE-based recharge was estimated using the GWSC derived from GRACE TWSA, WGHM-simulated SMSA and SWSA and in situ-based reservoir storage data. Observation-based recharge was estimated using the GWSC based on in situ groundwater-level data and specific yield (or storage coefficient). Both GRACE- and observation-based recharge were estimated using the baseflow separated by a multiple linear regression using the in situ streamflow as predictand, and precipitation and water table depth data as predictors. Time span: 2003-2014. The time lable like "200301" means January, 2003. "200312" means December, 2003.

opencc-by-4.0Aug 2022View details →

Lagrangian Heavy Precipitation Events in convection-permitting Regional Climate Models over the Alps and in the Mediterranean

The csv datafile contains a set of heavy precipitation events identified in cpRCMs. Each of the entries represents an event and is described with detailed properties: 'Start Date [YYYYMMDD.HOUR/24]', 'Latitude [°]', 'Longitude [°]',        'Duration [h]', 'Volume [km² h]', 'P99(pr) [mm h-1]',        'P90(pr) [mm h-1]', 'P75(pr) [mm h-1]',        'P50(pr) [mm h-1]', 'P25(pr) [mm h-1]',        'P10(pr) [mm h-1]', 'Total Precipitation [m3]',        'Maximum Precipitation [mm h-1$]',        'Mean Precipitation [mm h-1$]', 'Direction [°]',        'Distance Traveled [km]', 'Eccentricity [-]', 'Track Eccentricity [-]',        'Mean Ellipsicity [-]', 'Track Ellipsicity [-]', 'Mean Major Angle [°]',        'Track Major Angle [°]', 'Mean Major Axis [-]', 'Track Major Axis [-]',        'My Orientation [°]', 'My Track Orientation [°]', 'max(Elevation) [m]',        'min(Elevation) [m]', 'Start Year [YYYY]', 'Start Month [MM]',        'LandFallSea [-]', 'Scenarios', 'Models', 'situations', 'Ensemble',        'Speed [km h$^{-1}$]', 'Mean(Area) [km²]', 'orographic [-]',        'Severity [-]', 'I/O OBS [-]', 'Region [-]', 'orographic1500 [-]',        'orographic2000 [-]', 'orographic2500 [-]', 'orographic3000 [-]']

South Pacific precipitation dataset: PACRAIN, TRMM, ERA5 and calibrated precipitation series.

This dataset contains a set of stations located in the South Pacific region, obtained from the PACRAIN (Pacific Rainfall Database). Similarly, the TRMM (Tropical Rainfall Measuring Mission) satellite precipitation series and the ERA5 reanalysis precipitation series have been extracted for the aforementioned stations. For the stations corresponding to IDs  NZ75400, NZ82400, NZ84317, NZ99701, SP00646, US14000 and US14690, the  following daily variables are included:   <ul> <li>TRMM raw</li> <li>ERA5 raw</li> <li>PACRAIN </li> <li>Weather type associated</li> <li>TRMM calibrated (scaling) </li> <li>TRMM calibrated (empirical quantile mapping) </li> <li>TRMM calibrated (scaling conditioned) </li> <li>TRMM calibrated (empirical quantile mapping conditioned) </li> </ul> where the last four are a consequence of the application of 4 different calibration methods. A summary of the stations is shown in the following table: <table> <caption> </caption> <tbody> <tr> <td>Station Id</td> <td>Station Name</td> <td>Longitude</td> <td>Latitude</td> <td>Start</td> <td>End</td> <td>% Missing Data</td> <td>Altitude</td> </tr> <tr> <td>NZ75400</td> <td>Kolopelu (Wallis and Futuna)</td> <td>-178.12</td> <td>-14.32</td> <td>1998-01-01</td> <td>2012-01-01</td> <td>9.74</td> <td>36</td> </tr> <tr> <td>NZ82400</td> <td>Alofi (Niue)</td> <td>-163.93</td> <td>-19.07</td> <td>1998-01-01*</td> <td>2010-09-02</td> <td>2.68</td> <td>59</td> </tr> <tr> <td>NZ84317</td> <td>Rarotonga (Cook Islands)</td> <td>-159.8</td> <td>-21.2</td> <td>1999-09-28</td> <td>2012-01-12</td> <td>11.36</td> <td>4</td> </tr> <tr> <td>NZ99701</td> <td>Raoul Island (New Zealand)</td> <td>-177.93</td> <td>-29.23</td> <td>1998-01-01*</td> <td>2012-01-01</td> <td>0.72</td> <td>49</td> </tr> <tr> <td>SP00646</td> <td>Port Vila (Vanuatu)</td> <td>168.3</td> <td>-17.72</td> <td>2000-01-26</td> <td>2013-06-01</td> <td>18.13</td> <td>24</td> </tr> <tr> <td>US14000</td> <td>Aoloau (American Samoa)</td> <td>-170.77</td> <td>-14.3</td> <td>1998-01-01*</td> <td>2019-12-31*</td> <td>21.72</td> <td>408</td> </tr> <tr> <td>US14690</td> <td>Nu'uuli (American Samoa)</td> <td>-170.70</td> <td>-14.32</td> <td>1998-01-01*</td> <td>2019-12-31*</td> <td>0.037</td> <td>3</td> </tr> </tbody> </table>

opencc-zeroSep 2022View details →

Soil microbes respond to four-year warming and precipitation alteration

The alpine grasslands of the Tibetan Plateau store 23.2 Pg soil organic carbon (SOC), which becomes susceptible to microbial degradation with climate warming. However, accurate prediction of how the soil carbon (C) stock changes under future climate warming is hampered by our limited understanding of below-ground complex microbial communities. Here, we show that 4 years of warming strongly stimulated methane (CH4) uptake by 93.8% and aerobic respiration (CO2) by % in the soils of alpine grassland ecosystem. Due to no significant effects of warming on net ecosystem CO2 exchange (NEE), the warming-stimulated CH4 uptake enlarged the carbon sink capacity of whole ecosystem. Furthermore, precipitation alternation didn't alter such warming effects, despite the significant effects of precipitation on NEE and soil CH4 fluxes were observed. Metagenomic sequencing revealed that warming led to significant shifts in the overall microbial community structure and the abundances of functional genes, which contrasted to no detectable changes after 2 years of warming. Carbohydrate utilization genes were significantly increased by warming, corresponding with significant increases in soil aerobic respiration. Increased methanotrophic genes and decreased methanogenic genes were observed under warming, which significantly (R2 = 0.59, P < 0.001) correlated with warming-enhanced CH4 uptakes. Furthermore, 212 metagenome-assembled genomes (MAGs) were recovered, including many populations involved in the degradation of various organic matter and a highly-abundant methylotrophic population of the Methyloceanibacter genus. Collectively, our results provide compelling evidence that specific microbial functional traits for CH4 and CO2 cycling processes respond to climate warming with differential effects on soil greenhouse gas emissions. Alpine grasslands may play huge roles in mitigating climate warming through such microbially-enhanced CH4 uptake.

opencc-by-4.0Sep 2022View details →

Atlas of Depth-Duration-Frequency Relationships for Extreme Precipitation in South America and Africa

A continental-scale parameterization of a stochastic weather generator (CLIGEN) was used to determine depth-duration-frequency relationships with coverage of Africa and South America.

opencc-by-4.0Oct 2022View details →

How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation? - Observed precipitation data

The dataset contains the rain gauge hourly rainfall series used in the paper "How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation?". Each rain gauge series is saved in one Matlab variable, organized as a structure S with five fields: S.name: the identification name of the rain gauge station S.vals_mm: series of hourly rainfall in millimeter S.time_utc: time steps series, in UTC time S.elev_m: elevation of the station, in m a.s.l. S.xy_utm: station coordinates X and Y in meter in the Reference system WGS84/UTM zone 32N

Widespread variation in functional trait-vital rate relationships in tropical tree seedlings across a precipitation and soil phosphorus gradient

A fundamental assumption of functional ecology is that functional traits are related to interspecific variation in performance. However, the relationship between functional traits and performance is often weak or uncertain, especially for plants. A potential explanation for this inconsistency is that the relationship between functional traits and vital rates (e.g., growth and mortality) is dependent on local environmental conditions, which would lead to variation in trait-rate relationships across environmental gradients. In this study, we examined trait-rate relationships for six functional traits (seed mass, wood density, maximum height, leaf mass per area, leaf area, and leaf dry matter content) using long-term data on seedling growth and survival of woody plant species from eight forest sites spanning a pronounced precipitation and soil phosphorus gradient in central Panama. For all traits considered except for leaf mass per area-mortality, leaf mass per area-growth, and leaf area-mortality relationships, we found widespread variation in the strength of trait-rate relationships across sites. For some traits, trait-rate relationships showed no overall trend but displayed wide site-to-site variation. In a small subset of cases, variation in trait-rate relationships was explained by soil phosphorus availability. Our results demonstrate that environmental gradients have the potential to influence how functional traits are related to growth and mortality rates, though much variation remains to be explained. Accounting for site-to-site variation may help resolve a fundamental issue in trait-based ecology – that traits are often weakly related to performance – and improve the utility of functional traits for explaining key ecological and evolutionary processes.

Liquid-liquid phase reaction between crystal violet and sodium hydroxide: kinetic study and precipitate analysis

To investigate reaction order and kinetic parameters of the reaction between crystal violet (CV) and sodium hydroxide (NaOH), various concentrations of the reactants were applied. The present work also verifies the unknown solid product produced under highly concentrated conditions. The reaction orders of CV and NaOH were determined to be 1 and 1.17 by pseudo-rate method, respectively, with a rate constant, k, of 0.084 [(M-1.17) s-1]. In addition to pseudo-rate method, the half-life approach is use to calculated the overall reaction order to verify the accuracy of pseudo-rate method. The overall reaction order is determined to be 1.9 by half-life method. Compare reaction order gained from both methods, the overall reaction order is determined as ~2. The precipitate formation was observed when high concentrations of CV (0.01~0.1 M) and NaOH (1.0 M) were applied. Fourier transform infrared (FTIR) spectroscopy was used to compare the spectra of the precipitate generated and a commercial solvent violet 9 (SV9). Based on the FTIR spectra, it was confirmed that the molecular structure of the precipitate matched that of solvent violet 9.

T cell deficiency precipitates antibody evasion and emergence of neurovirulent polyomavirus

JC polyomavirus (JCPyV) causes progressive multifocal leukoencephalopathy (PML), a life-threatening brain disease in immunocompromised patients. Inherited and acquired T cell deficiencies are associated with PML. The incidence of PML is increasing with the introduction of new immunomodulatory agents, several of which target T cells or B cells. PML patients often carry mutations in the JCPyV VP1 capsid protein, which confer resistance to neutralizing VP1 antibodies (Ab). Polyomaviruses (PyV) are tightly species-specific; the absence of tractable animal models has handicapped understanding of PyV pathogenesis. Using mouse polyomavirus (MuPyV), we found that T cell deficiency during persistent infection, in the setting of monospecific VP1 Ab, was required for outgrowth of VP1 Ab-escape viral variants. CD4 T cells were primarily responsible for limiting polyomavirus infection in the kidney, a major reservoir of persistent infection by both JCPyV and MuPyV, and checking emergence of these mutant viruses. T cells also provided a second line of defense by controlling the outgrowth of VP1 mutant viruses that evaded Ab neutralization. A virus with two capsid mutations, one conferring Ab-escape yet impaired infectivity and a second compensatory mutation, yielded a highly neurovirulent variant. These findings link T cell deficiency and evolution of Ab-escape polyomavirus VP1 variants with neuropathogenicity.

Cyanobacterial- and moss-forming biocrusts consistently mitigate the temperature sensitivity of microbial respiration along a continental precipitation gradient

1. Biocrusts are prevalent and participate in many soil organic carbon (C) processes in drylands. The predicted increase in aridity will expand the biocrust cover and significantly impact soil organic C dynamics. However, how biocrusts change soil organic C decomposition and what factors drive the effect in response to climate warming remains largely unknown at a continental scale. 2. We measured microbial respiration and its temperature sensitivity (Q10) in bare soil lacking biocrusts and two universal biocrusted soils (cyanobacterial- and moss-crusted soil) from 43 sites across a precipitation gradient from 39 mm to 443 mm to evaluate the relative effects of biocrusts on Q10 and the driving forces in northern China's dryland. 3. Microbial respiration increased and Q10 decreased with increasing precipitation in bare soil, cyanobacterial- and moss-crusted soil. Biocrusts positively affected microbial respiration, with a more substantial magnitude by moss crusts than cyanobacterial crusts. Biocrusts negatively impacted Q10, and the magnitudes were similar between moss and cyanobacterial crusts. Most importantly, the relative effects of biocrusts on microbial respiration and Q10 increased with decreasing precipitation. 4. The positive effects of biocrusts on soil organic C content and microbial biomass carbon were positively correlated with the level of increased microbial respiration. Contrastingly, the magnitude of reduced Q10 was attributed to the biocrusts' positive effects on soil organic C quality and adverse effects on the ratio of fungal to bacterial PLFAs (F: B). 5. Our study provides strong evidence that biocrusts decrease the temperature sensitivity of microbial respiration in northern China's dryland. This result suggests that the predicted expanding biocrust cover is crucial for maintaining the soil organic C stability by buffering the positive impacts of climate warming on soil organic C decomposition in drylands.