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767 results for “Soil Moisture”
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-14 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-14 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Meteorology and soil moisture data collected at multiple frequencies from the Cross-scale Interactions Study (CSIS) Block-15 site automated monitoring stations: Jornada Basin LTER, 2013 - ongoing
This dataset contains summary data collected at the Jornada Basin LTER program's Cross Scale Interactions Study (CSIS) Block-15 site automated weather station and associated soil substation at several temporal scales. Precipitation data are collected at 1-second frequency during rain events, air temperature and wind are summarized every 5-minutes, and all aboveground sensors are summarized at 30-minute, hourly and daily frequencies. Soil moisture is measured at a 30-minute frequency and summarized daily. Observed values include average/maximum/minimum air temperature, relative humidity, and wind speed; average wind direction; soil moisture, temperature and conductivity. Aboveground sensors are measured and calculated based on 1-second scan rate. Soil moisture is measured every 30-minutes near the weather station and approximately 30-meters distance at a nearby substation. Wind speed is measured at 37cm, 75 cm, 150 cm, and 300 cm, wind direction at approximately 3m, and air temperature and relative humidity at approximately 2.5m. Soil sensors are installed at 10, 20 and 30cm depths.
Soil Moisture on the Main Cropping System Experiment at the Kellogg Biological Station, Hickory Corners, MI (1989 to 2019)
Dataset Abstract Measurements of soil moisture began in 1989 for all treatments on the LTER main site and in 1993 on the successional and forest sites. Soil moisture is analyzed on the baseline soil samplings which are collected twice monthly or monthly during the growing season. The percent gravimetric moisture content is calculated on a dry weight basis. Other datasets from the baseline soil samplings include Inorganic nitrogen and Total N and Total C. original data source http://lter.kbs.msu.edu/datasets/18
Soil moisture determinations by Electrical Resistivity (ERT) Experiment at the Kellogg Biological Station, Hickory Corners, MI (2009)
Dataset AbstractLarge-scale conversion of croplands to perennial biofuel crops could substantially impact regional water, nutrient, and C cycles due to the longer growing seasons and differences in rooting systems compared with most annual crops. However, these differences in crop water use are not well known due to the limited tools available to nondestructively study the spatiotemporal patterns of root water uptake in situ at field scales. Geophysical imaging tools such as electrical resistivity (ER) reveal changes in water content in the soil profile. Data used in: https://doi.org/10.1002/vzj2.20124original data source http://lter.kbs.msu.edu/datasets/222
Soil Moisture and Temperature following experimental drought in the LEF
We used throughfall exclusion shelters to determine effects of short-term (3 month) drought on trace gas fluxes and nutrient availability in humid tropical forests in Puerto Rico. Exclusion and control plots were replicated within and across three topographic zones (ridge, slope, valley) to account for spatial heterogeneity typical of these ecosystems. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) surface variable outputs (SWE, snowmelt, streamflow, soil moisture), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of snow water equivalent, snow melt, and runoff, as well as the model configuration file. Outputs of precipitation, total evapotranspiration, actual evapotranspiration, as well as model inputs are archived separately on the Environmental Data Initiative.
Supplemental soil moisture and temperature data from the saddle catchment sensor network, 2019 - 2021.
Hand-held soil moisture measurements were taken at 8 of 16 soil moisture sensors within the sensor network at Niwot Ridge to supplement the continuous measurement system at these locations. The hand-held measurements occur much less frequently than the 10 min sensor data, but they are important in determining the spatial variability of soil moisture in the alpine.
Monsoon Rainfall Manipulation Experiment (MRME) Soil Temperature, Moisture and Carbon Dioxide Data from the Sevilleta National Wildlife Refuge, New Mexico
The Monsoon Rainfall Manipulation Experiment (MRME) is designed to understand changes in ecosystem structure and function of a semiarid grassland caused by increased precipitation variability, by altering rainfall pulses, and thus soil moisture, that drive primary productivity, community composition, and ecosystem functioning. The overarching hypothesis being tested is that changes in event size and frequency will alter grassland productivity, ecosystem processes, and plant community dynamics. Treatments include (1) a monthly addition of 20 mm of rain in addition to ambient, and a weekly addition of 5 mm of rain in addition to ambient during the months of July, August and September. It is predicted that changes in event size and variability will alter grassland productivity, ecosystem processes, and plant community dynamics. In particular, we predict that many small events will increase soil CO2 effluxes by stimulating microbial processes but not plant growth, whereas a small number of large events will increase aboveground NPP and soil respiration by providing sufficient deep soil moisture to sustain plant growth for longer periods of time during the summer monsoon.
SEV-LTER Mean Variance Experiment Desert Shrubland Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Desert Shrubland ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV-LTER Mean Variance Experiment Juniper Savanna Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Juniper Savanna ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SEV-LTER Mean x Variance Experiment Pinon Juniper Soil Moisture and Temperature
We designed novel field experimental infrastructure to resolve the relative importance of changes in the climate mean and variance in regulating the structure and function of dryland populations, communities, and ecosystem processes. The Mean x Variance Experiment (MVE) adds three novel elements to prior designs (Gherardi & Sala 2013) that have manipulated interannual variance in climate in the field by (i) determining interactive effects of mean and variance with a factorial design that crosses a drier mean with increased (more) variance, (ii) studying multiple dryland ecosystem types to compare their susceptibility to transition under interactive climate drivers, and (iii) adding stochasticity to our treatments to permit the antecedent effects that occur under natural climate variability. This new infrastructure enables direct experimental tests of the hypothesis that interactions between the mean and variance of precipitation will have larger ecological impacts than either the mean or variance in precipitation alone. A subset of plots have soil moisture and temperature sensors to evaluate treatment effectiveness by addressing, How do MVE manipulations alter the mean and variance in soil moisture and temperature? And, how does micro-environmental variation among plots influence how much MVE treatments alter soil moisture profiles over three soil depths? This data package includes soil moisture and temperature sensor data from the Mean x Variance Climate experiment in the Pinon Juniper ecosystem at the Sevilleta National Wildlife Refuge, Socorro, NM.
SGD-SM: Generating Seamless Global Daily AMSR2 Soil Moisture Long-term Products (2013-2019)
<p><strong>If you used our dataset, please cite our reference:</strong></p> <p><strong>Zhang, Q., Yuan, Q., Li, J., Wang, Y., Sun, F., and Zhang, L.: Generating seamless global daily AMSR2 soil moisture (SGD-SM) long-term products for the years 2013–2019, Earth Syst. Sci. Data, 13, 1385–1401, https://doi.org/10.5194/essd-13-1385-2021, 2021.</strong></p> <p><strong>Description:</strong></p> <ul> <li>A <strong>seamless global daily</strong> (<strong>SGD</strong>) AMSR2 soil moisture long-term (2013-2019) dataset is generated through the proposed model. This daily products include <strong>2553</strong> global soil moisture NetCDF4 files, starting from Jan 01, 2013 to Dec 31, 2019 (about <strong>20GB</strong> memory after uncompressing this zip file).</li> <li>To further validate the effectiveness of these products, three verification ways are employed as follow: 1) In-situ validation; 2) Time-series validation; And 3) simulated missing regions validation. More validation results can be viewed at <strong><a href="https://qzhang95.github.io/Projects/Global-Daily-Seamless-AMSR2">SGD-SM</a></strong>.</li> <li>An example Python code of extracting this dataset is also available at <strong><a href="https://github.com/qzhang95/SGD-SM">https://github.com/qzhang95/SGD-SM</a></strong>.</li> <li>Official LPRM AMSR2 Descending L3 soil moisture products indeed only have 28 daily files in May 2013 (missing data files in date May 11, May 12, and May 13).</li> <li>This soil moisture dataset is comprised of netCDF4 (*.nc) files. Therefore, users need to install <strong>netCDF4</strong> toolkit before reading the data: <pre><code class="language-python">pip install netCDF4 pip install numpy</code></pre> <p> </p> </li> <li>It should be noted that the original and reconstructed soil moisture data are both recorded in one NC file. User can read the original data, reconstructed data, and mask data as follows:</li> <li> <pre><code class="language-python">Data = nc.Dataset(NC_file_position) Ori_data = Data.variables['original_sm_c1'] Rec_data = Data.variables['reconstructed_sm_c1'] Ori = Ori_data[0:720, 0:1440] Rec = Rec_data[0:720, 0:1440] Mask_ori = np.ma.getmask(Ori)</code></pre> <p> </p> </li> </ul>
Dataset of Sentinel-1 surface soil moisture time series at 1 km resolution over Southern Italy
<p>The dataset consists of a time series of the Sentinel-1 (S-1) surface soil moisture (SSM) product at 1 km spatial resolution validated in Balenzano et al. (2021 a) over the Southern Italy. The specifications of the S-1 SSM product are provided in Balenzano et al. (2021 b). The SSM time series was obtained in correspondence of the ascending (RON A146) S-1 Interferometric Wide swath (IW) acquisition dates from January 2015 to December 2018 with a temporal gap between consecutive of 6 days (when both S-1A and S-1B data are available) or 12 days. On each date (183 in total), two co-registered layers are provided: mean SSM [m3/m3] and its standard deviation [m3/m3], which provides the SSM uncertainty. The retrieval algorithm is a time series short term change detection (STCD) that is implemented in the “Soil MOisture retrieval from multi-temporal SAR data” (SMOSAR) code (Balenzano et al. 2013).</p>
NLDAS-2 Sacramento (SAC) Post-processed Daily-mean Soil Moisture Data
<p>This dataset contains North American Land Data Assimilation System Version 2 (NLDAS-2) Sacramento (SAC) post-processed daily-mean soil moisture data from 1993 to 2017 at four layers: 0-10 cm, 10-40 cm, 40-100 cm, and 0-100 cm. Original post-processing of the hourly data was conducted at National Oceanic and Atmospheric Administration (NOAA) by Dr. Youlong Xia and then later provided to the NOAA Physical Sciences Laboratory (PSL). Daily-mean data were generated at PSL from the hourly data by averaging data from 8 times each day (0,3,6,9,12,15,18,21 Z). The data are written in netCDF format and provided in annual netCDF files.</p>
BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH
<p>This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies LLC. </p> <p> </p> <p>Each zip file contains a set of four Level 2 NetCDF files which provides the highest spatial resolution available for each of four products for a given flight location. With the Level 2 data, each flight location and variable can have different spatial resolutions depending on the sensor type, retrieval algorithm, and flight altitude. The file name convention for the zip files is as follows.</p> <p> </p> <p>uas_L2_yyyymmdd_hhmmss_vX.X.zip </p> <p>where</p> <p>L2 = Level 2 data </p> <p>yyyymmdd = year,month,day</p> <p>hhmmss = hour,minute,second</p> <p>vX.X = version number</p> <p>Time is the flight start time in UTC.</p> <p> </p> <p>The NetCDF file format contained in the zip files has a similar format to the zip files with convention</p> <p> </p> <p>uas_<var>_L2_yyyymmdd_hhmmss.nc </p> <p>where</p> <p><var> = vsm, dem, ndvi, or stmp</p> <p>vsm = volumetric soil moisture</p> <p>dem = digital elevation</p> <p>ndvi = normalized difference vegetation index</p> <p>stmp = surface temperature</p> <p> </p> <p>Note that each flight location using the E2 aerial platform required two flights so starting flight times for the soil moisture NetCDF files are different from the other three products.</p> <p><strong>November 2023 update</strong>: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data.</p> <p><strong>December 2023 update</strong>: Version 2.1 updated soil moisture data with a wet bias in v2.0 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.</p>
iRON_Soil Moisture_Calibrated
<p>**<strong>Error has been found in Glenwood Springs station data. The 8in depth soil moisture sensor was mislabeled as the 40in depth sensor and vice versa for data sets uploaded prior to February 2024. Datasets uploaded after Feb 22, 2024 have the sensor labels corrected <em>for all dates going back to installation.</em>**</strong><br><br>***<strong>An error has been caught in time codes. The time codes currently read GMT-5:00. The CORRECT time code is GMT-7:00 for all datasets</strong>. <strong>Files with FullRecord in the name are in correct, UTC time codes</strong>***</p> <p>These data are the complete data set from installation through March 15, 2018 for AGCI's interactive Roaring Fork Observation Network (iRON). The network consists of 9 stations in the Southern Rockies (Colorado, USA). Descriptions of soil type and site locations can be found at: www.agci.org/iron/stations</p> <p><strong>Please note:</strong> Measurements taken for rain are recorded via tipping bucket rain gauge and may not be accurate for winter precipitation events (e.g. snow). Error readings are represented by -888.88, -138.25, or -162.469 represents a sensor error. Absent data is represented by N/A.</p> <p>Sensors used for soil moisture readings are Decagon EC-5 Dielectric probe (5cm/2in depth); Decagon 10-HS (20cm/8in; 50cm/20in depths). All other equipment is Onset, and the logger device is the RX3000 model. Data collection is generally set to every 20 minutes (most stations) or every hour (Independence Pass station), but maintenance resets, etc. may occasionally have contributed to other collection intervals, e.g. every 5 minutes. Conversely, on occasions of logger failure, there may be full days of data absent.</p> <p>Snow data are available for Northstar Transition Zone and Independence Pass by request.</p> <p> </p> <p><strong>Updated calibrations (V2) are anticipated in the summer of 2018 for the following stations: Glassier Ranch (3), Glenwood Springs (5), Northstar Transition Zone (7), </strong><strong>Spring Valley (8)</strong></p> <p>Some instruments have been replaced over the life of a station. In 2015, all original U30 logger boxes were replaced with the RX3000 model. For complete metadata or records of instrumentation, please contact project advisor E. Osenga at eliseo at agci.org.</p> <p><strong>**Glassier Ranch site is a wetland and the soils are often completely saturated (leading to sensor difficulties in taking accurate readings) much of the year.</strong></p>
Streamflow, precipitation, soil moisture, and ephemeral stream nitrogen data for St. Croix, USVI
<p>These datasets were collected from two ephemeral stream sites within the Salt River watershed on St. Croix, USVI using high frequency (15-minute) sensors. The stream nitrogen data were collected via grab samples and were analyzed with a benchtop spectrophotometer. The data were collected to better understand the influence of precipitation and soil moisture conditions on stream nitrogen concentrations. </p>
Soil grid data for 3 agricultural fields in Italy (Soil Moisture, soil organic carbon)
<p><span>Soil data collected in an agricultural area with annual crops in Italy (west-central Lombardia Po Valley, province of Pavia). The data refers to soil properties of 320 soil samples for Soil Moisture and 120 for SOC, collected in the topsoil (around 5-10 cm), considering a regular sampling grid, within three agricultural field with different crops (spring-summer cycle) and soils type, Rice-Loamy, Sorghum-Sandy and, Maize-Clay, in a period (before the seeding of the crops), when the soil was bare, in the framework of the EJP Steropes project.</span></p> <p><span>The aim of the collected dataset was to be able to analyse the influence of soil moisture in SOC (WP2 of the STEROPES project) prediction models from remote sensing.</span></p> <p><span>Data in the form of shape file (one shapefile for each agricultural field, for oth Soil Moisture and SOC), and pictures of the soil surface in .jpg format. </span></p>
A Deep Neural Network Based SMAP Soil Moisture Product
<p>The soil moisture datasets here are based on a deep neural network (DNN) that utilizes the merits of a suite of existing satellite and reanalysis products to produce a new SM product with minimum (maximum) bias (correlation) -- using NASA’s Soil Moisture Active Passive (SMAP) data and ERA5 reanalysis. The benchmark of the network is a bias-adjusted SM with maximum correlation with in situ data over each land-cover type. The bias is adjusted to the product that exhibits a minimum bias over each land-cover type. Consistent with the laws of L-band microwave propagation in soil and canopy, the input variables include polarized SMAP brightness temperatures, incidence angles, vegetation scattering albedo, surface roughness parameter, surface water fraction, effective soil temperatures, bulk density, clay fraction, and vegetation optical depth from the normalized difference vegetation index (NDVI) climatology. The DNN is trained and validated using two years (04/2015--03/2017) of global data and deployed for assessment of its performance from 04/2017 to 03/2021. The testing results against in situ measurements demonstrate that the DNN outputs typically exhibit improved error quality metrics over most land cover types and climate regimes and can properly capture SM temporal dynamics, beyond each SMAP product across regional to continental scales.</p>
High resolution soil moisture and soil temperature data during Hurricane Florence, 2018, over the Carolina region (U.S.)
<p>We set the study domain over the U.S. east coast to cover the Carolinas and the regions that were affected by Hurricane Florence. Therefore, the selected domain covered the area between -86º to -75º longitude and 30º to 40º latitude. We should note that Hurricane Florence made landfall in the Carolinas on September 14, 2018, as a Category 1 storm. Hurricane reports indicate that “Hurricane Florence made landfall near Wrightsville Beach, North Carolina at 7:15 AM EDT (1115 UTC) on September 14 with estimated maximum winds of 90 mph (150 km/h), and a minimum central pressure estimate of 958 millibars. Winds gusts topping 105 mph (169 km/h) were reported in the Outer Banks of North Carolina.” Rainfall from Florence, as per the initial reports, suggest possible new records for North Carolina (breaking the record set by Hurricane Floyd in 1999).</p> <p>we used the latest development of the high-resolution land surface assimilation system (HRLDAS) that was retrieved from the Github repository (<a href="https://github.com/NCAR/hrldas-release">https://github.com/NCAR/hrldas-release</a>). The model was coupled to the Noah land surface modeling system and used the multi-layer soil model, complex canopy resistance with the Penman method for calculating evapotranspiration, and frozen ground physics for the simulations.</p> <p> The atmospheric forcing including 2-meter air temperature, shortwave, and long-wave radiation, 2-meter wind speed, 2-meter specific humidity, and surface pressure data was obtained from the NCEP-DOE Reanalysis 2 (available from <a href="https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html">psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html</a>). For the precipitation, we used the high-resolution GCIP/EOP surface precipitation NCEP/EMC gridded data (Stage IV) with 4 km of grid spacing. All the forcing data have been retrieved at an hourly frequency from 2016 to 2019. The model was configured with the initial soil moisture and soil temperature conditions at 4 depths (0-10, 10-40, 40-100, 100-200 cm), retrieved from the NCEP data. Other initialization fields including skin temperature and water equivalent snow depth were retrieved from NCEP. The NCEP reanalysis data, however, does not provide “plant canopy surface water” data which is required as an initialization field. This data was retrieved from the NLDAS dataset. The first two years of the model run (2016 and 2017) were considered as the spin-up, and the outcome during 2018 was used for further analysis and public release.</p>
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