The goal of the North American Land Data Assimilation System (NLDAS) is to construct quality-controlled, and spatially and temporally consistent, land-surface model (LSM) datasets from the best available observations and model output to support modeling activities. Specifically, this system is intended to reduce the errors in the stores of soil moisture and energy which are often present in numerical weather prediction models, and which degrade the accuracy of forecasts. NLDAS is currently running in near real-time on a 1/8th-degree grid over central North America; retrospective NLDAS datasets and simulations also extend back to January 1979. NLDAS constructs a forcing dataset from gauge-based observed precipitation data (temporally disaggregated using Stage II radar data), bias-correcting shortwave radiation, and surface meteorology reanalyses to drive several different LSMs to produce model outputs of surface fluxes, soil moisture, and snow cover. For more information visit: http://ldas.gsfc.nasa.gov/nldas/NLDAS is a collaboration project among several groups: NOAA/NCEP's Environmental Modeling Center (EMC), NASA's Goddard Space Flight Center (GSFC), Princeton University, the University of Washington, the NOAA/NWS Office of Hydrological Development (OHD), and the NOAA/NCEP Climate Prediction Center (CPC). NLDAS is a core project with support from NOAA's Climate Prediction Program for the Americas (CPPA). Data from the project can be accessed from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) as well as from the NCEP/EMC NLDAS website. Data from the project can be accessed from the NASA Goddard Earth Science Data and information Services Center (GES DISC), http://disc.sci.gsfc.nasa.gov/hydrology/data-holdings, as well as from the NCEP/EMC NLDAS.For more information visit http://ldas.gsfc.nasa.gov/nldas/For more information visit http://ldas.gsfc.nasa.gov/nldas/For more information visit http://ldas.gsfc.nasa.gov/nldas/For more information visit http://ldas.gsfc.nasa.gov/nldas/For more information visit http://ldas.gsfc.nasa.gov/nldas/For more information visit http://ldas.gsfc.nasa.gov/nldas/

The total land water storage anomalies are aggregated from the Global Land Data Assimilation System (GLDAS) NOAH model. GLDAS outputs land water content by using numerous land surface models and data assimilation. For more information on the GLDAS project and model outputs please visit https://ldas.gsfc.nasa.gov/gldas. The aggregated land water anomalies (sum of soil moisture, snow, canopy water) provided here can be used for comparison against and evaluations of the observations of Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO over land. The monthly anomalies are computed over the same days during each month as GRACE and GRACE-FO data, and are provided on monthly 1 degree lat/lon grids in NetCDF format.

description: Global Land Data Assimilation System Version 2 (hereafter, GLDAS-2) has two components: one forced entirely with the Princeton meteorological forcing data (hereafter, GLDAS-2.0), and the other forced with a combination of model and observation based forcing data sets (hereafter, GLDAS-2.1). This data set, GLDAS-2.0 0.25 degree monthly generated through temporal averaging of the 3-hourly data, contains a series of land surface parameters simulated from the Noah Model 3.3, currently covers from 1948 to 2010 and will be extended to recent years as the data set becomes available. The model simulation was initialized on simulation date January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulation was forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). The simulation used the common GLDAS data sets for land cover (MCD12Q1: Friedl et al., 2010), land water mask (MOD44W: Carroll et al., 2009), soil texture (Reynolds, 1999), and elevation (GTOPO30). The MODIS based land surface parameters are used in the current GLDAS-2.x products while the AVHRR base parameters were used in GLDAS-1 and previous GLDAS-2 products (prior to October 2012). The main objective for Version 2 is to create more climatologically consistent data sets using the Princeton forcing data sets extending from 1948. In Version 1, forcing sources switched several times throughout the record from 1979 to present, which introduced unnatural trends and exhibited highly uncertain forcing fields in 1995-1997. Other enhancements made in Version 2 include model version upgrade, switching to MODIS based land surface parameter data sets, and initialization of soil moisture over desert. In NOAH model, the bottom layer temperature data set was also updated. More details regarding the land surface parameter data changes at http://ldas.gsfc.nasa.gov/gldas/. WGRIB or other GRIB reader is required to read the files. The data set applies a user-defined parameter table to indicate the contents and parameter numbers. The GRIBTAB file (http://disc.sci.gsfc.nasa.gov/hydrology/grib_tabs/gribtab_GLDAS_V2.txt) shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. There are four vertical levels for the Soil Moisture (PDS 086) and Soil Temperature (PDS 085) in the Noah GRIB files. For more information, please see the README Document at http://hydro1.sci.gsfc.nasa.gov/data/s4pa/GLDAS/GLDAS_NOAH025_3H.020/doc/README.GLDAS2.pdf or the GrADS ctl file at ftp://hydro1.sci.gsfc.nasa.gov/data/gds/GLDAS/GLDAS_NOAH025_M.020.ctl.; abstract: Global Land Data Assimilation System Version 2 (hereafter, GLDAS-2) has two components: one forced entirely with the Princeton meteorological forcing data (hereafter, GLDAS-2.0), and the other forced with a combination of model and observation based forcing data sets (hereafter, GLDAS-2.1). This data set, GLDAS-2.0 0.25 degree monthly generated through temporal averaging of the 3-hourly data, contains a series of land surface parameters simulated from the Noah Model 3.3, currently covers from 1948 to 2010 and will be extended to recent years as the data set becomes available. The model simulation was initialized on simulation date January 1, 1948, using soil moisture and other state fields from the LSM climatology for that day of the year. The simulation was forced by the global meteorological forcing data set from Princeton University (Sheffield et al., 2006). The simulation used the common GLDAS data sets for land cover (MCD12Q1: Friedl et al., 2010), land water mask (MOD44W: Carroll et al., 2009), soil texture (Reynolds, 1999), and elevation (GTOPO30). The MODIS based land surface parameters are used in the current GLDAS-2.x products while the AVHRR base parameters were used in GLDAS-1 and previous GLDAS-2 products (prior to October 2012). The main objective for Version 2 is to create more climatologically consistent data sets using the Princeton forcing data sets extending from 1948. In Version 1, forcing sources switched several times throughout the record from 1979 to present, which introduced unnatural trends and exhibited highly uncertain forcing fields in 1995-1997. Other enhancements made in Version 2 include model version upgrade, switching to MODIS based land surface parameter data sets, and initialization of soil moisture over desert. In NOAH model, the bottom layer temperature data set was also updated. More details regarding the land surface parameter data changes at http://ldas.gsfc.nasa.gov/gldas/. WGRIB or other GRIB reader is required to read the files. The data set applies a user-defined parameter table to indicate the contents and parameter numbers. The GRIBTAB file (http://disc.sci.gsfc.nasa.gov/hydrology/grib_tabs/gribtab_GLDAS_V2.txt) shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units. There are four vertical levels for the Soil Moisture (PDS 086) and Soil Temperature (PDS 085) in the Noah GRIB files. For more information, please see the README Document at http://hydro1.sci.gsfc.nasa.gov/data/s4pa/GLDAS/GLDAS_NOAH025_3H.020/doc/README.GLDAS2.pdf or the GrADS ctl file at ftp://hydro1.sci.gsfc.nasa.gov/data/gds/GLDAS/GLDAS_NOAH025_M.020.ctl.

The total land water storage anomalies are aggregated from the Global Land Data Assimilation System (GLDAS) NOAH model. GLDAS outputs land water content by using numerous land surface models and data assimilation. For more information on the GLDAS project and model outputs please visit https://ldas.gsfc.nasa.gov/gldas. The aggregated land water anomalies (sum of soil moisture, snow, canopy water) provided here can be used for comparison against and evaluations of the observations of Gravity Recovery and Climate Experiment (GRACE) and GRACE-FO over land. The monthly anomalies are computed over the same days during each month as GRACE and GRACE-FO data, and are provided on monthly 1 degree lat/lon grids in NetCDF format. Currently, the days included in these monthly anomaly computation are same as GRACE-FO monthly Level-2 RL06.3 JPL solutions.

This data set contains the forcing data for Phase 1 of the North American Land Data Assimilation System (NLDAS-1). The data are in 1/8th degree grid spacing and range from Aug. 1996 to Dec. 2007. The temporal resolution is monthly. The file format is WMO GRIB-1. The NLDAS-1 monthly forcing data, containing 17 variables, are generated from the NLDAS-1 hourly forcing data.

Brief description about the NLDAS-1 hourly forcing data can be found from the GCMD DIF for GES_DISC_NLDAS_FOR0125_H_V001 at http://gcmd.gsfc.nasa.gov/getdif.htm?GES_DISC_NLDAS_FOR0125_H_V001.

The data set applies a user-defined parameter table to indicate the contents and parameter number. The GRIBTAB file (http://disc.sci.gsfc.nasa.gov/hydrology/grib_tabs/gribtab_NLDAS_FOR_monthly.001.txt) shows a list of parameters for this data set, along with their Product Definition Section (PDS) IDs and units.

The variables, DLWRFsfc, DSWRFsfc, PRESsfc, SPFH2m, TMP2m, UGRD10m, and VGRD10m, are the monthly average from 00Z01 of month to 23:59Zlastdayofmonth.

The variables, BRTMPsfc and CAPEsfc, are the monthly average from 00Z01 of month to 23:59Zlastdayofmonth, except if any hour has an undefined value of -9999, then do not include the hour in the monthly average.

The variables, PARsfc and RGOESsfc, are the monthly average from 00Z01 of month to 23:59Zlastdayofmonth, except if any hour has an undefined value of -9999, then reassign the variable as zero and include the hour in the monthly average.

The variables, ACPCPsfc, APCPsfc, PEDASsfc, and PRDARsfc, are the monthly accumulation from 00Z01 of month to 23:59Zlastdayofmonth. However, the ACPCPsfc is actually the sum of the (ACPCPsfc/PEDASsfc)*APCPsfc from each hour, where the ratio of (ACPCPsfc/PEDASsfc) is the fraction of convective precipitation from EDAS, and then multiplied by the APCPsfc to get the convective precipitation. For PRDARsfc accumulation, if hourly PRDARsfc is undefined or negative, fill the hour with a zero value.

The last variable, RSWRFsfc, is the monthly average from 00Z01 of month to 23:59Zlastdayofmonth, except represents the monthly average of the hourly "blend" of the DSWRFsfc from EDAS and RGOESsfc from GEOS. The blend algorithm is that, for each hour, the RGOESsfc from GEOS is used for all the grid points where it is available, but for where it is not available, the DSWRFsfc from EDAS is used. Because the spatial extent/availability of GEOS varies from hour to hour, this blend is done for hourly data first, and then the monthly average is applied to the hourly blended data. This last variable thus best represents the shortwave radiation flux downwards at the surface that is used in the NLDAS-1 LSMs. More about this blending/supplementation can be found from http://ldas.gsfc.nasa.gov/nldas/NLDAS1forcing.php.

For more information, please see the README Document at ftp://hydro1.sci.gsfc.nasa.gov/data/s4pa/NLDAS/README.NLDAS1.pdf.

Le système d'assimilation des données terrestres (LDAS, Land Data Assimilation System) combine plusieurs sources d'observations (telles que les données des pluviomètres, les données satellite et les mesures radar des précipitations) pour produire des estimations des propriétés climatologiques à la surface de la Terre ou à proximité. Cet ensemble de données est le fichier de forçage principal (par défaut) (fichier A) pour la phase 2 du North American Land Data Assimilation System (NLDAS-2). Les données sont espacées d'un huitième de degré sur la grille et la résolution temporelle est d'une heure. NLDAS est un projet collaboratif entre plusieurs groupes : le Centre de modélisation environnementale (EMC) de NOAA/NCEP, le Centre de vol spatial Goddard (GSFC) de la NASA, l'université de Princeton, l'université de Washington, l'Office of Hydrological Development (OHD) de NOAA/NWS et le Centre de prévision climatique (CPC) de NOAA/NCEP. NLDAS est un projet essentiel soutenu par le programme de prédiction du climat pour les Amériques (CPPA) de la NOAA. Documentation : Readme Tutoriel Documentation GES DISC Hydrology Documentation sur les tiges de données GES DISC

All files below were prepared by Collin B. Lawrence.

All ARCIDs correspond to the HydroSHEDS Dataset for Asia (Lehner et al., 2008). (http://www.hydrosheds.org/)

GLDAS data are from the Global Land Data Assimilation System (Rodell et al., 2004). (https://ldas.gsfc.nasa.gov/gldas/)

Q_JJA_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m³ s^-1, and are averaged for the months of June, July, and August from the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).

Q_annual_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m³ s^-1, and are yearly averages for the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).

phi_i_JJA is the accumulated subsurface and surface runoff for the CLM, MOSAIC, NOAH, and VIC model average. The June, July, and August output was averaged over the years 2003 – 2009. Column 1 is ARCID and the accumulated runoff is expressed in m³ s^-1.

phi_i_JJA_err is the standard error of the model mean in phi_i_JJA.

phi_g_JJA contains the accumulated glacier recession flow in m³ s^-1 for the months of June, July, August from 2003 - 2009.

phi_g_JJA_err is the standard error of the model mean in phi_g_JJA.

phi_g_annual contains the annually averaged accumulated glacier recession flow in m³ s^-1 for 2003 – 2009.

lambda.csv contains the fraction of streamflow from glacier recession.

Click anywhere on earth to see how the water balance is changing over time. This app is based on data from GLDAS version 2.1, which uses weather observations like temperature, humidity, and rainfall to run the Noah land surface model. This model estimates how much of the rain becomes runoff, how much evaporates, and how much infiltrates into the soil. These output variables, calculated every three hours, are aggregated into monthly averages, giving us a record of the hydrologic cycle going all the way back to January 2000.

Because the model is run with 0.25 degree spatial resolution (~30 km), these data should only be used for regional analysis. A specific farm or other small area might experience very different conditions than the region around it, especially because human influences like irrigation are not included.

This app can also be seen as a useful template for sharing other climate datasets. If you would like to customize it for your own organization, or use it as a starting point for your own scientific application, the source code is available on github for anyone to use.

Hindcasts were generated for density of Gloeotrichia echinulata, a toxin-producing cyanobacterium, at a nearshore site (South Herrick Cove) in Lake Sunapee, NH, USA, from May-October in 2015 and 2016 using several different Bayesian state-space models as part of a Global Lake Ecological Observatory Network working group project (Lofton et al. 20XX). Hindcasts were produced for one-week to four-week forecast horizons. Models ranged in complexity from a random walk to dynamic linear models with up to two environmental covariates. A subset of the model meteorological driver data for calibration and hindcasting was downloaded from the North American Land Data Assimilation System (NLDAS-2; https://ldas.gsfc.nasa.gov/nldas/) and the Parameter-elevation Regressions on Independent Slopes Model (PRISM; http://www.prism.oregonstate.edu/) for Lake Sunapee, New Hampshire, USA. The model driver data derived from NLDAS-2 data are daily summaries of solar radiation on G. echinulata sampling days from 2009-2016. The model driver data derived from PRISM data are daily sums of precipitation on G. echinulata sampling days from 2009-2016. All other model driver data are also published on the Environmental Data Initiative repository and are specified in the Notes and Comments of this data publication. All code to import data, calibrate models, and generate and analyze hindcasts are available on Github at https://github.com/GLEON/Bayes_forecast_WG/tree/eco_apps_release.

This repository hosts the setup for executing a basin-scale simulation of the Big Spring watershed that drains into Sycamore Creek in northern Arizona, USA, using the TIN-Based Real-Time Integrated Basin Simulator (tRIBS).

This repository consists of :

(1) A data subdirectory, which contains all the necessary data to run the basin-scale simulation of Big Spring, using tRIBS.

2) A doc subdirectory, which contains a jupyter notebook for analyzing and visualizing this specific benchmark case, alongside additional documentation.

(3) A src subdirectory that is currently empty but intended to contain source code for the tRIBS executable, which can be obtained here.

(4) A bin subdirectory, that is currently empty but intended to store the tRIBS executable, with instructions to build the model here.

(5) A results directory, which contains reference model output for results comparison.

Instructions for running this benchmark are available in the README.html file within the repository, which can be opened with your web browser, and can also be found in the latest tRIBS documentation.

Data from this model were obtained from a number of public sources:

Soil properties were obtained from ISRIC (https://www.isric.org), with tRIBS input parameters derived from the ROSETTA pedotransfer functions (https://github.com/usda-ars-ussl/rosetta-soil). Precipitation and vegetation parameters related to tree height and vegetation fraction were provided by Salt River Project. Meteorological forcing was obtained from NLDAS-2 (https://ldas.gsfc.nasa.gov/nldas/v2/forcing),with rainfall data provided from Salt River Project rain gauges.

NOTE: If you encounter a situation where, after downloading and extracting the big_spring.gz file, the extracted file has no extension, please follow these steps:

Rename the File: Manually rename the extracted file to big_spring.zip.

Extract Again: After renaming, unzip the big_spring.zip file to access the full folder with all the necessary files.

This issue may occur if the file extraction process doesn't automatically add the .zip extension. By renaming the file and unzipping it again, you should be able to access the complete set of files without any issues.

Open AccessData sources This study was based on the soil microbial metabolic quotient dataset in Xu et al. (2017), which synthesized data spanning from 1970 to 2016. In this study, we further updated that dataset to 2020. The same criteria for data compilation in Xu et al (2017) have been applied to update the dataset in this study. Specifically, we searched publications in Google Scholar (https://scholar.google.com/) using the keyword combinations of “basal respiration”, “soil microbial biomass”, “soil microbial turnover rate”, “soil microbial metabolic quotient”, and “soil microbial residence time”. We screened the papers via following criteria: 1) both soil basal respiration and microbial biomass C were reported; 2) any of soil microbial turnover rate, soil microbial metabolic quotient, and MRT estimated based on basal respiration rate in lab conditions was clearly reported; 3) no contamination and disturbance occurred during sampling; and 4) lab incubation for basal respiration is less than 40 days as long incubation experiments may cause a shift in microbial community, which does not represent MRT in the sampled soils. Collectively, the final dataset included 2627 observations retrieved from 232 papers, covering 9 biomes (i.e., boreal forest, temperate broadleaf forest, temperate coniferous forest, tropical/subtropical forest, grassland, shrubland, bare soils/desert, natural wetlands, and cropland) (Fig. 1). Cropland, temperate broadleaf forest, grassland, and temperate coniferous forest accounted for approximately 46%, 13%, 11%, and 9%, respectively, whereas all other biomes combined accounted for 21% of the whole dataset. The majority of the field sites are located in Europe, Asia, and North America, whereas a relatively small number of observations are from South America, Africa, Australia, and Antarctica. For data points without coordinate information reported, we searched the geographical coordinates based on the names of the study site, city, state, and country. The geographical information was further used for locating the sampling points on the global map to extract climate, edaphic properties, vegetation productivity, and soil microclimate long-term data from global datasets (Xu et al. 2017). Climate, edaphic, vegetation, and microbial data Climatic, edaphic, vegetation, and microbial variables were not fully reported in published studies, we extracted such variables from global datasets following our previous studies (Xu et al. 2013, Xu et al. 2017, Guo et al. 2020, He et al. 2020). For climatic variables, we extracted mean annual temperature (MAT) and mean annual precipitation (MAP) during 1970-2000 from the WorldClim database version 2 with the spatial resolution of 30 seconds (https://www.worldclim.org/data/worldclim21.html). In addition, we obtained monthly and annual mean soil moisture (SM) and soil temperature (ST) of top 10 cm during 1979-2018 from the NCEP/DOE AMIP-II Reanalysis (https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.gaussian.html). We also obtained the data of soil pH and soil texture (i.e., sand, silt, and clay) from the Harmonized World Soil Database (HWSD, https://daac.ornl.gov/cgibin/dsviewer.pl?ds_id=1247) at a spatial resolution of 0.05° × 0.05°. Soil bulk density (BD), soil C, and total (TN) were extracted from the IGBP-DIS dataset (IGBP, https://daac.ornl.gov/SOILS/guides/igbp-surfaces.html), at a spatial resolution of 0.5′ × 0.5′. Root C density (Croot) data were extracted from the global dataset of a 0.5-degree resolution based on observational data (Gibbs and Ruesch 2008, Song et al. 2017). We extracted topsoil porosity data from a global dataset produced by Global Land Data Assimilation System (GLDAS, https://ldas.gsfc.nasa.gov/gldas/) at a spatial resolution of 0.25° × 0.25°. Annual net primary productivity (NPP) for the period of 2000-2015 was obtained from the MODIS gridded dataset with a spatial resolution of 30 seconds (http://files.ntsg.umt.edu/data/NTSG_Products/). Soil microbial biomass C (MBC) and nitrogen (MBN) were retrieved from a compiled global soil microbial biomass C and nitrogen (N) dataset archived at Oak Ridge National Laboratory (Xu et al. 2015b). The auxiliary datasets used included the global land area database and global vegetation distribution dataset. The global vegetation distribution dataset was obtained from a spatial map of 11 major biomes: boreal forest, temperate forest, tropical/subtropical forest, mixed forest, grassland, shrubland, tundra, desert, natural wetlands, cropland, and pasture, which have been used in our previous publications (Xu et al. 2013, Xu et al. 2017, Guo et al. 2020, He et al. 2020). The global land area database was from the surface data map of 0.5° × 0.5° generated for E3SM (https://web.lcrc.anl.gov/public/e3sm/inputdata/lnd/clm2/surfdata_map/). To generate the global map of MRT, the global datasets of varied spatial resolutions were resampled to 0.5 degree using the “bilinear” algorithm. For datasets formatted as NetCDF, we performed the interpolation using the function of “linint2_Wrap” in NCAR Command Language (Version 6.3.0). For datasets in other formats, the interpolation was conducted using the platform of ArcGIS 10.6 (Esri, Redlands, CA, USA). Temperature correction for lab incubations Soil basal respiration is defined as the steady rate of respiration in soil, which originates from the mineralization of organic matter (Bloem et al. 2005). The temperature response of basal respiration follows the exponential function (Moyano et al. 2007). The sensitivity of microbial respiration to temperature is commonly described by Q10, a factor by which carbon dioxide (CO2) production increases with a 10°C increase in temperature. Under steady‐state conditions, soil microbial biomass does not change over a long term. The specific growth rate of soil microbial community is equivalent to microbial biomass turnover rate, corresponding to its inverse as soil microbial biomass residence time as below, equation 1) where MRT is the microbial residence time, MBC is microbial biomass C, and BR is the basal respiration rate. Due to the differences between lab incubation temperature and in situ soil temperature, temperature correction is necessary for comparing estimated MRT across studies in a quantitative manner. We adjusted the reported basal respiration to their long-term (1979-2018) average ST following the equation 2. This function has been previously used to mathematically simulate the temperature dependence of microbial respiration (Rey and Jarvis 2006, Wei et al. 2014). The corrections were performed under the assumption that basal respiration is temperature dependent, while soil microbial biomass remains unchanged during the typically short soil incubations. equation 2) where T1 and T2 are temperatures in Celsius, is basal respiration at a given temperature of T2, is the estimated basal respiration at T1, and Q10 is the temperature sensitivity parameter. Temperature sensitivity of Q10 is an important parameter in modeling temperature effects on basal respiration. In the past several decades, Q10 has been extensively investigated. Experimental studies ubiquitously indicated large spatial heterogeneity of Q10. It has been found that Q10 is not a constant, the reported Q10 values were different among soils and ecosystems (Davidson et al. 1998, Wang et al. 2019). Despite the uncertainties in Q10 values, a fixed Q10 of 2.0 has gained wide acceptance in modelling ecosystem respiration responses to climate change (Sistla et al. 2014, Xu et al. 2014). Although the Q10 values are commonly reported as 2.0, the reported values varied among studies, ranging from 1.4 to 2.6 (Mahecha et al. 2010, Wang et al. 2010, Hamdi et al. 2013, Wang et al. 2019, Li et al. 2020). To fully consider the variations in reported Q10 values among studies, we therefore selected seven Q10 values (i.e., 1.4, 1.6, 1.8, 2.0, 2.2, 2.4, and 2.6) with an interval of 0.2 within 1.4-2.6 centered around 2.0 to calibrate basal respiration from lab incubation temperature to in situ soil temperature. In the dataset, there were seventeen studies without explicit incubation temperature indicated. The ISO 16072 (2002)recommends an incubation temperatures range of 20-30°C. The incubation temperature is closely associated with Q10values, and the Q10 value of 25°C was proved to be a threshold incubation temperature for smaller variations in Q10values. A significant decrease occurs in Q10 values when temperature was less than 25°C. When incubation temperature was above 25°C, the mean Q10 remained relatively constant (Wang et al. 2019). Therefore, for studies without incubation temperature reported, we performed the temperature correction for lab incubations assuming an incubation temperature of 25°C. Model selection The MRT exhibited clear biogeographic patterns, indicating the important role of environmental factors on MRT distribution (Fig. S1-6, Fig. 2). Therefore, we created a generalized linear model to quantify the independent and interactive impacts of soil microbes (MBC and MBN), climate (MAP and MAT), soil microclimate (ST and SM), vegetation (NPP and Croot), and edaphic properties (silt, sand, soil pH, BD, topsoil porosity, soil C, and TN) on the MRT. Based on the generalized linear model, we further built an empirical model for the mean MRT by selecting the most important factors in explaining the variation in the mean MRT. To identify the most important factors in explaining the variation in the mean MRT, we repeatedly removed the least important variables (<0.1%) from the generalized linear model. Finally, we selected 23 most important variables in explaining the variations in mean MRT. In addition, we randomly splitting the dataset to two portions. A portion (75%) of data were used to train the model; and other 25% was used for model

Das Land Data Assimilation System (LDAS) kombiniert mehrere Beobachtungsquellen (z. B. Daten von Niederschlagsmessgeräten, Satellitendaten und Radarniederschlagsmessungen), um Schätzungen von klimatologischen Eigenschaften an oder nahe der Erdoberfläche zu erstellen. Dieses Dataset ist die primäre (Standard-)Forcing-Datei (Datei A) für Phase 2 des North American Land Data Assimilation System (NLDAS-2). Die Daten haben einen Rasterabstand von 1/8 Grad und eine stündliche zeitliche Auflösung. NLDAS ist ein gemeinsames Projekt mehrerer Gruppen: des Environmental Modeling Center (EMC) von NOAA/NCEP, des Goddard Space Flight Center (GSFC) von NASA, der Princeton University, der University of Washington, des Office of Hydrological Development (OHD) von NOAA/NWS und des Climate Prediction Center (CPC) von NOAA/NCEP. NLDAS ist ein Kernprojekt, das vom Climate Prediction Program for the Americas (CPPA) der NOAA unterstützt wird. Dokumentation: Readme Anleitung GES DISC Hydrology Documentation GES DISC Data Rods Documentation

This repository hosts the setup for executing a point-scale simulation (i.e. single computational element) of the Happy Jack SNOTEL Site in northern Arizona, USA, using the TIN-Based Real-Time Integrated Basin Simulator (tRIBS).

This repository consists of :

(1) A data subdirectory, which contains all the necessary data to run tRIBS at Happy Jack and includes bias corrected dataset of SWE for model comparison.

2) A doc subdirectory, which contains a jupyter notebook for analyzing and visualizing this specific benchmark case, alongside additional documentation.

(3) A src subdirectory that is currently empty but intended to contain source code for the tRIBS executable, which can be obtained here.

(4) A bin subdirectory, that is currently empty but intended to store the tRIBS executable, with instructions to build the model here.

(5) A results subdirectory, which contains reference model output for results comparison.

Instructions for running this benchmark are available in the README.html file within the repository, which can be opened with your web browser, and can also be found in the latest tRIBS documentation.

Data from this model was originally collated by Gretchen Hawkins and Josh Cederstrom for their Master’s Thesis work at Arizona State University and associated publications:

Hawkins, G.A., Vivoni, E.R., Robles-Morua, A., Mascaro, G., Rivera, E., and Dominguez, F. 2015. A Climate Change Projection for Summer Hydrologic Conditions in a Semiarid Watershed of Central Arizona. Journal of Arid Environments. 118: 9–20. https://doi.org/10.1016/j.jaridenv.2015.02.022

Cederstrom, C.J., Vivoni, E.R., Mascaro, G., and Svoma, B. 2024. Forest Thinning Effects on Watershed Responses under Warming. Water Resources Research. (In Review).

Bias corrected SWE data is from:

Sun N, Yan H, Wigmosta MS, Leung LR, Skaggs R, Hou Z. 2019. Regional Snow Parameters Estimation for Large-domain Hydrological Applications in the Western United States. Journal of Geophysical Research: Atmospheres. 124 : 5296–5313.

Data for this model simulation were obtained from a number of public sources:

Meteorological forcing was obtained from the Happy Jack SNOTEL Site (https://www.nrcs.usda.gov/wps/portal/wcc/) and NLDAS-2 (https://ldas.gsfc.nasa.gov/nldas/v2/forcing). Soil properties were obtained from the Soil Survey Geographic Database (https://nrcs.app.box.com/v/soils) with derived parameters informed from the literature and calibration process. Vegetation parameters were obtained from the LANDFIRE REMAP data set (https://landfire.gov).

NOTE: If you encounter a situation where, after downloading and extracting the happy_jack.gz file, the extracted file has no extension, please follow these steps:

Rename the File: Manually rename the extracted file to happy_jack.zip.

Extract Again: After renaming, unzip the happy_jack.zip file to access the full folder with all the necessary files.

This issue may occur if the file extraction process doesn't automatically add the .zip extension. By renaming the file and unzipping it again, you should be able to access the complete set of files without any issues.

Hệ thống đồng hoá dữ liệu trên đất (LDAS) kết hợp nhiều nguồn dữ liệu quan sát (chẳng hạn như dữ liệu đo lượng mưa, dữ liệu vệ tinh và số đo lượng mưa bằng radar) để đưa ra các ước tính về đặc tính khí hậu tại hoặc gần bề mặt Trái Đất. Tập dữ liệu này là tệp bắt buộc chính (mặc định) (Tệp A) cho Giai đoạn 2 của Hệ thống đồng hoá dữ liệu đất đai Bắc Mỹ (NLDAS-2). Dữ liệu có khoảng cách lưới là 1/8 độ; độ phân giải tạm thời là theo giờ. NLDAS là một dự án hợp tác giữa nhiều nhóm: Trung tâm Mô hình hoá Môi trường (EMC) của NOAA/NCEP, Trung tâm Chuyến bay Vũ trụ Goddard (GSFC) của NASA, Đại học Princeton, Đại học Washington, Văn phòng Phát triển Thuỷ văn (OHD) của NOAA/NWS và Trung tâm Dự đoán Khí hậu (CPC) của NOAA/NCEP. NLDAS là một dự án cốt lõi được Chương trình dự đoán khí hậu cho Châu Mỹ (CPPA) của NOAA hỗ trợ. Tài liệu: Readme Hướng dẫn Tài liệu về thuỷ văn của GES DISC Tài liệu về các thanh dữ liệu GES DISC