U.S. Climate Reference Network Soil Moisture and Temperature Observations

Jesse E. Bell * Cooperative Institute for Climate and Satellites, North Carolina State University, and NOAA/National Climatic Data Center, Asheville, North Carolina

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Michael A. Palecki +NOAA/National Climatic Data Center, Asheville, North Carolina

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C. Bruce Baker #NOAA/Atmospheric Turbulence and Diffusion Division, Oak Ridge, Tennessee

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William G. Collins +NOAA/National Climatic Data Center, Asheville, North Carolina

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Jay H. Lawrimore +NOAA/National Climatic Data Center, Asheville, North Carolina

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Ronald D. Leeper * Cooperative Institute for Climate and Satellites, North Carolina State University, and NOAA/National Climatic Data Center, Asheville, North Carolina

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Mark E. Hall #NOAA/Atmospheric Turbulence and Diffusion Division, Oak Ridge, Tennessee

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John Kochendorfer #NOAA/Atmospheric Turbulence and Diffusion Division, Oak Ridge, Tennessee

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Tilden P. Meyers #NOAA/Atmospheric Turbulence and Diffusion Division, Oak Ridge, Tennessee

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Tim Wilson #NOAA/Atmospheric Turbulence and Diffusion Division, Oak Ridge, Tennessee

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Howard J. Diamond +NOAA/National Climatic Data Center, Asheville, North Carolina

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Abstract

The U.S. Climate Reference Network (USCRN) is a network of climate-monitoring stations maintained and operated by the National Oceanic and Atmospheric Administration (NOAA) to provide climate-science-quality measurements of air temperature and precipitation. The stations in the network were designed to be extensible to other missions, and the National Integrated Drought Information System program determined that the USCRN could be augmented to provide observations that are more drought relevant. To increase the network’s capability of monitoring soil processes and drought, soil observations were added to USCRN instrumentation. In 2011, the USCRN team completed at each USCRN station in the conterminous United States the installation of triplicate-configuration soil moisture and soil temperature probes at five standards depths (5, 10, 20, 50, and 100 cm) as prescribed by the World Meteorological Organization; in addition, the project included the installation of a relative humidity sensor at each of the stations. Work is also under way to eventually install soil sensors at the expanding USCRN stations in Alaska. USCRN data are stewarded by the NOAA National Climatic Data Center, and instrument engineering and performance studies, installation, and maintenance are performed by the NOAA Atmospheric Turbulence and Diffusion Division. This article provides a technical description of the USCRN soil observations in the context of U.S. soil-climate–measurement efforts and discusses the advantage of the triple-redundancy approach applied by the USCRN.

Corresponding author address: Jesse E. Bell, CICS/NCDC, 151 Patton Ave., Asheville, NC 28801. E-mail: jesse.bell@noaa.gov

Abstract

The U.S. Climate Reference Network (USCRN) is a network of climate-monitoring stations maintained and operated by the National Oceanic and Atmospheric Administration (NOAA) to provide climate-science-quality measurements of air temperature and precipitation. The stations in the network were designed to be extensible to other missions, and the National Integrated Drought Information System program determined that the USCRN could be augmented to provide observations that are more drought relevant. To increase the network’s capability of monitoring soil processes and drought, soil observations were added to USCRN instrumentation. In 2011, the USCRN team completed at each USCRN station in the conterminous United States the installation of triplicate-configuration soil moisture and soil temperature probes at five standards depths (5, 10, 20, 50, and 100 cm) as prescribed by the World Meteorological Organization; in addition, the project included the installation of a relative humidity sensor at each of the stations. Work is also under way to eventually install soil sensors at the expanding USCRN stations in Alaska. USCRN data are stewarded by the NOAA National Climatic Data Center, and instrument engineering and performance studies, installation, and maintenance are performed by the NOAA Atmospheric Turbulence and Diffusion Division. This article provides a technical description of the USCRN soil observations in the context of U.S. soil-climate–measurement efforts and discusses the advantage of the triple-redundancy approach applied by the USCRN.

Corresponding author address: Jesse E. Bell, CICS/NCDC, 151 Patton Ave., Asheville, NC 28801. E-mail: jesse.bell@noaa.gov

1. Introduction

In response to national and international discussions of the state of climate observations in the mid-1990s (Karl et al. 1995a), principles were established to govern the best practices for climate monitoring. The U.S. National Oceanic and Atmospheric Administration (NOAA) then made the decision to deploy climate monitoring stations that were designed with very high standards of quality and reliability (Heim 2001). The first prototype U.S. Climate Reference Network (USCRN) station was deployed at the North Carolina Arboretum in Asheville, North Carolina, during the first World Congress of Botanical Gardens in June 2000, reflecting the close association of the climate measurements with climate user communities. After a few more years of development and testing, the USCRN was first commissioned in January 2004 after extensive development with the first 40 stations, and the full deployment in the lower 48 states was completed in September 2008, and a 29-station expansion into Alaska was started in 2009 (Diamond et al. 2013).

The primary goal of the USCRN is the detection of climate change on a national basis through collection of homogenous in situ temperature and precipitation records that are free from the biases that may be associated with other observing networks. To meet this goal, each station is equipped with triplicate configurations of temperature and precipitation instrumentation, allowing for improved measurement consistency and continuity. Thus, each station has three simultaneous measurements to allow verification of instrument accuracy and to ensure continuous measurements in the event of a sensor failure. Instruments and equipment are also calibrated to standards set by the National Institute of Standards and Technology. The goal is for each station to be functional and stable for the next 50–100 years. The observational time series of USCRN stations is still in its initial phase, but these data have already been used to verify the accuracy of U.S. annual temperatures from the bias-corrected long-term U.S. Historical Climatology Network (USHCN; Menne et al. 2009) over the first 5 years of the USCRN record (Menne et al. 2010). Future USCRN observations will allow researchers to further validate the USHCN record and to observe climate variations without needing to correct for biases that can occur from existing heterogeneous observing networks.

As the USCRN was being deployed, a movement led by the Western Governors’ Association to monitor and respond to the impacts of drought led to legislation forming the National Integrated Drought Information System (NIDIS) Program [National Integrated Drought Information System Act of 2006, Public Law 109-430, 120 STAT 2918 (2006)]. As drought and other climate extremes have been predicted to increase in frequency and magnitude in the future (Seneviratne et al. 2012; Easterling et al. 2008), it is increasingly important to improve drought-monitoring capacities across the United States. USCRN’s original aboveground instrumentation suite was not sufficient for completely assessing all climate factors associated with drought; subsurface observations are very important for understanding this issue. Fortunately, the USCRN stations were engineered to be extensible to new instrumentation and new missions. A major initiative through NIDIS established the resources for adding soil-climate instrumentation, data handling, and drought-oriented applied science to the USCRN program beginning in fiscal year 2009. The addition of soil observations will not only directly improve U.S. drought-monitoring capabilities, but USCRN will now be able to assist in validation of newly or soon-to-be deployed satellites for soil moisture monitoring [e.g., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP)].

In March 2009, national and international soil-measurement experts from multiple government agencies and academic institutions were invited to Oak Ridge, Tennessee, for a workshop on the system design for a USCRN soil-climate–monitoring network (at the time of writing, a report from the workshop could be found online at the NIDIS U.S. Drought Portal at http://www.drought.gov/workshops/crn/USCRN_SMST_workshop_summary.pdf). These discussions provided important input for the final decisions made by the USCRN program. The workshop members suggested the use of an instrument capable of measuring both soil moisture and temperature simultaneously, based on existing coaxial impedance dielectric sensor technology. Most important, like USCRN air temperature and precipitation measurements, soil moisture and temperature are recorded in triplicate at each USCRN station. Following these criteria, in locations that have suitable soil depths, soil probes were installed in three independent locations in undisturbed soil several meters from the USCRN instrument tower at five standard depths: 5, 10, 20, 50, and 100 cm. The primary factors used in selecting USCRN stations were isolation and land-use stability; soil characteristics were not considered in the original criteria for station selection, and some locations are not on deep soils that are uninterrupted by mineral layers or rocks. Stations located on shallow or rocky soils were instrumented only at the 5- and 10-cm depths, and, in one case, there were no probes installed at a station located on solid rock. Atmospheric relative humidity sensors were also added to the existing accompaniment of ancillary observations, which include solar radiation, surface IR temperature, 1.5-m wind speed, and a wetness detector.

This article provides a brief review of the nature of soil-climate observations in the United States and the role that USCRN will play in soil-climate monitoring. The quality control of USCRN soil moisture and temperature data will also be discussed, including issues of network maintenance. Applications of the USCRN soil moisture and temperature to science and operations will be presented, with special focus on the benefits of the triplicate soil-sensor system. Conclusions will be drawn, and future plans will be discussed for satellite validation, model validation, and drought monitoring.

2. U.S. soil observations and the USCRN

Soil moisture and temperature are recognized as important variables that influence plant productivity in both agricultural and natural ecosystems. The U.S. Department of Agriculture (USDA) has a rich history of soil-science work relating to the movement of water within soil and the use of soil water by plants (Landa and Nimmo 2003). Scientists in USDA laboratories had developed crude electrical methods to measure soil temperature and moisture by 1897 (Whitney and Briggs 1897; Whitney et al. 1897). Lysimeters were used to establish the relative water requirements of plants by carefully measuring water inputs into a soil–plant system container on accurate scales (Briggs and Shantz 1914). Despite these early developments and many more experimental advances through the twentieth century, it was first and foremost the mission of the USDA to find practical means to assist agricultural practitioners. Although the U.S. Weather Bureau collected meteorological data as part of the USDA, no national, long-term quantitative time series of soil moisture exists. Soil erosion increasingly became the focus of USDA soil scientists, and the Soil Conservation Service was formed in the mid-1930s after the devastation of agricultural land by soil erosion in the South and the Great Plains (Helms 1992). Methods used initially to track the state of soil moisture at various locations in the United States were based on a qualitative scale that analyzed the appearance of the soil and its response to handling, a system (USDA 1998) that is still available for use today. Although the Weekly Weather and Crop Bulletin has been published for almost 100 years, it was only in the early 1980s that statewide percentages began to be given regularly for four soil moisture classes: very short, short, adequate, and surplus. Practical devices were developed around the same time to measure soil moisture without taking manual gravimetric samples, and some states established networks of quantitative soil moisture measurements even before the federal government commenced this activity in the 1990s. Therefore, many of the longest soil moisture time series in the United States are specific to states or experimental sites and do not extend more than approximately 30 years into the past.

Although federal soil temperature records extend over a longer time period in the United States, they are not well distributed geographically and were mainly installed at cooperative observer program (COOP) stations in agricultural areas of the central and southern states. Isolated stations began measuring soil temperatures at 10 cm as early as the late 1950s, and network development continued even after the U.S. Weather Bureau separated from the USDA and became NOAA’s National Weather Service in 1970. The network reached 200 stations in the early 1980s (Hu and Feng 2004) and currently has 301 stations. An average trend for 38 of the most complete stations from 1967 to 2002 indicated the rate of temperature increase to be 0.31°C decade−1 at the 10-cm level (Hu and Feng 2003), considerably higher than the comparable trend (0.10°C decade−1) for air temperature (Karl et al. 1995b) and indicating the importance of soil temperature measurements over time. However, since the 1980s, the COOP-based soil temperature network has been in decline, with considerably fewer stations still active.

During the 1980s, the state of Illinois became one of the first in the United States to regularly use observing-sensor technology to quantify soil moisture across a large-scale network of sites (Hollinger and Isard 1994; Scott et al. 2010). In the 1990s, other states started to react to the lack of quantitative measurements of soil-climate variables, with the most extensive networks eventually developed by Oklahoma (Brock et al. 1995; McPherson et al. 2007; Illston et al. 2008) and Nebraska (You et al. 2010). Also in the 1990s, the first federal effort to develop a national soil moisture/soil temperature network was launched by the USDA (Schaefer et al. 2007). The 21 stations in this experimental network were incorporated into the Soil Climate Analysis Network (SCAN) when it was established by the USDA’s National Resources Conservation Service (NRCS) in 1999 (NRCS is the successor to the Soil Conservation Service). SCAN was the only national soil-climate observational network in the United States for the next decade, eventually reaching 183 stations in total by 2012.

While the goal of a 1000-station SCAN network (Schaefer et al. 2007) has not yet been realized, the USDA also funded the addition of SCAN-like soil moisture and temperature instruments to more than 380 snowpack-telemetry (SNOTEL; Serreze et al. 1999) sites since 1999. Some Remote Automatic Weather Stations (RAWS) deployed for the management of wildfire danger and the fulfillment of other research needs (Zachariassen et al. 2003) have also been augmented with soil moisture and temperature sensors. However, both of these networks and the SCAN are distributed somewhat unevenly across the United States, with the SNOTEL and RAWS stations being predominantly located in the western United States and SCAN stations being largely confined to agricultural regions and experimental watersheds.

The completion of the USCRN soil-probe deployment in the continental United States in August 2011 increases U.S. coverage of soil moisture and temperature observations. In addition, the USCRN sites are sampling a variety of natural environments in addition to agricultural settings that predominate in some networks. The USCRN use of three sets of probes for soil-climate measurements at each depth allows for quantification of observation variation over time that is due to measurement errors and soil-matrix effects. Because of the unique triplicate configuration, the USCRN soil moisture and soil temperature observations will play a specific role in understanding U.S. soil-climate variations over time and will also provide uncertainty estimates for these measurements that have not been previously available. The addition of soil probes to USCRN will help to expand global efforts to unify soil-monitoring networks for better understanding changes in soil moisture and to assist in satellite and land surface model calibration (Robock et al. 2000). As an active international partner assisting in global in situ databases, USCRN is providing soil observational data to the International Soil Moisture Network (Dorigo et al. 2011).

3. USCRN network configuration

USCRN stations are located at 114 sites across the continental United States (Fig. 1). The station grid spacing was determined by a requirement to explain 98% of the U.S. annual temperature variance and 95% of the U.S. annual precipitation variance, using a method described by Vose and Menne (2004). Before a site is selected for installation, multiple locations are surveyed in each grid cell for station installation. A panel of NOAA scientists and engineers analyze and determine the surveyed location that is most likely to be removed from future human development while still being representative of the surrounding area. Ideally, the site selected will be devoid of further human development for the next 50+ years. The station, thus, will provide a long-term climate record for that given location and serve as a controlled reference to the surrounding stations that are not a part of the USCRN. As most of the stations were installed before the decision to incorporate soil observations, not all locations are ideal for the installation of soil temperature/moisture probes. Of the 114 stations in the continental United States, 90 stations had probes installed at the five standard WMO levels (5, 10, 20, 50, and 100 cm), 23 were instrumented only at the 5- and 10-cm levels, and the Torrey station located in Capitol Reef National Park in Utah was bolted to solid rock and could not accommodate any soil instruments.1

Fig. 1.
Fig. 1.

Installation of the USCRN soil-climate instrumentation was completed for the contiguous United States in 2011. Of the 114 stations, 90 stations had all five depths installed (black dots), 23 had only the top two depths installed (green dots), and one could not support any soil probes (red dot).

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0146.1

a. Soil probes and station instrumentation

A coaxial impedance dielectric sensor with a built-in thermistor is used to collect both soil moisture and soil temperature data, and three of these sensors are located at every depth at all USCRN sites. The model currently used by USCRN, the Hydra Probe II Soil Sensor Model SDI-12 of Stevens Water Monitoring Systems, Inc.,2 produces electromagnetic waves at 50 MHz that are reflected from the soil after which the return signal is processed. The probes are designed to convert the received signal into a number of variables, but USCRN downloads and archives only the real dielectric permittivity and soil temperature because of transmission bandwidth limitations. Dielectric measurements can then be converted to volumetric soil moisture content (m3 m−3) using an empirical relationship (Seyfried et al. 2005).

In addition to soil moisture and temperature, each USCRN station measures surface climate variables, including air temperature, precipitation, wetness-sensor data, relative humidity, solar radiation, surface infrared temperature, and 1.5-m wind speed. All listed variables, plus the 5-cm-level soil moisture and soil temperature observations, are being formatted for 5-min resolution. The remaining soil variables will be reported only as an hourly average, since the lower layers change so slowly. Before the hourly Geostationary Operational Environmental Satellite Data Collection System transmission of soil measurements and other climate variables to NCDC, the data are stored on a data logger at the station. This configuration makes the data available in cases of transmission failure.

b. Instrument placement and triplicate configuration

The soil-probe configuration is designed to obtain three independent samples of soil moisture and soil temperature for each instrumented soil depth (5, 10, 20, 50, and 100 cm). Observations are made at three points in a 5-m radius around the main instrument tower. Each probe is installed a minimum of 1.5 m away from the USCRN instrument tower, at 0°, 120°, and 240° compass directions around the base (Fig. 2). The probes are installed in a vertical profile for 5–50 cm, and the lowest depth is installed by drilling an oblique hole to 100-cm depth. Stations with soils that were not conducive to deep installations had only the top 5- and 10-cm probes installed.

Fig. 2.
Fig. 2.

Configuration of the USCRN soil probes around the base of the main instrument tower near Harrison, Nebraska. The wire leads from each set of soil probes and travels through the gray conduits to a circuit board through which the probes connect to the data logger. The three plots are located at 0° (north), 120° (southeast), and 240° (southwest) around the base of the tower. Each soil probe set must be a minimum of 1.5 m from the base of the tower in undisturbed soil.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0146.1

A chart of a typical set of soil moisture values averaged from three measurements for each depth is shown for one of the Asheville, North Carolina, USCRN stations in Fig. 3. As can be seen, soil moisture levels gradually decline during the growing season from March to September, with the downward trend broken only by precipitation events. The response of soil moisture to precipitation is rapid in the 5-, 10-, and 20-cm layers, but changes are much slower at the 50- and 100-cm layers.

Fig. 3.
Fig. 3.

USCRN layer averages of soil moisture from the Asheville 13 station. The 5- (black line), 10- (blue line), 20- (dark green line), 50- (green line), and 100-cm (red line) lines demonstrate the seasonal response. Similar figures can be created online at the U.S. Drought Portal (http://www.drought.gov).

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0146.1

4. Characteristics of the soil data

Only the real dielectric permittivity and soil temperature are recorded and transmitted from each site, as was discussed earlier. These raw data are subject to quality control examinations, and, in the case of dielectric, translation into volumetric water content (vwc) of the soil. They are also combined into layer averages for each site. After the soil data are quality controlled and soil physical property variables are determined, soil moisture data can be used to generate additional derived variables for drought assessment.

a. Data monitoring and quality control

After probe installation at a station, there is a 240-day trial period to allow the soil conditions to stabilize and to identify any faulty soil observations. The first 60 days are excluded from the record and are not included in the general public release. This procedure reduces the possibility of reporting specious measurements caused by soil settling around the probes. The remaining 180 days of the trial period and all ensuing daily observations are available for public use, as long as the data are not flagged for quality issues.

Quality assessment of soil observations is a continuous process for accurately diagnosing defective probes and periods with faulty data. The USCRN quality control method starts by flagging values that are out of the expected physical range or when soil moisture measurements are recorded in below-freezing soil temperatures. The actual values are retained so that future users can reassess the quality control results. Raw values are identified as missing when data are not received from the satellite transmission, and derived values are not calculated and are considered missing when the raw values have been flagged by the quality control procedure.

As each value is subject to quality control, there is an initial check to see if the data logger door is open, indicating maintenance is under way and data should not be used from this period. Then, for the range checks, dielectric values outside the range from 0.1 to 70 are flagged. Since the dielectrics of air (1.0) and pure water (80) are beyond the physically possible range of dielectric values in soil, the quality control range limits are exceeded only in cases with clear instrument problems. The temperature quality control limits are from −30° to 65°C, where the lower value matches the lower limit of the instrument’s calibration range and the higher value is 10°C above the instrument’s upper calibration limit. This quality control temperature range allows for warmer temperatures to be recorded without flags, although with decreased accuracy, while still helping to eliminate completely erroneous values. In fact, the 5-cm layers at two desert stations, Stovepipe Wells, California, and Yuma, Arizona, have reached 57.0° and 56.3°C, respectively, during the short history of the network. The lowest temperatures in the 5-cm layers have been observed at Riley, Oregon (−15.5°C), and Wolf Point, Montana (−14.9°C). The next test for dielectric values is dependent on probe temperature; if the probe temperature is less than 0.5°C, the dielectric value is flagged as potentially being impacted by freezing water in the soil. The slightly-above-zero temperature limit is intended to avoid cases in which the temperature is above zero at the probe thermistor but ice crystals are forming in the soil around the instrument. Before completing the quality process, a list of known faulty sensors is consulted to determine whether the value is from a known unreliable sensor; if so, it is flagged.

On a regular basis, postprocessing of soil temperature and moisture observations occurs to assist in identifying potentially malfunctioning probes. Time periods that are found to have continuous or intermittent sensor malfunctions, even if the measurements are usually within gross error checks, are put on a “faulty sensor list” that automatically flags and removes values from production until the instrument is replaced and/or returned to service. Postprocessing of soil observations includes an automated analysis of the number of missing values, gross errors, sensor noise, spikes, and irregular jumps in the data record. An expert analyst then inspects each occurrence and if appropriate puts the data period in the faulty sensor list (Fig. 4). Visual inspection of the data is currently necessary to identify faulty sensors, as natural inconsistencies among the three sensors are common with heterogeneous soils, and an automated system could improperly flag functional sensors. No flagged data on the list of faulty sensors are used in generating the layer averages. As soon as repairs are made or a new sensor is installed, the probe is once again available with no further flagging. Once the flagging of the raw data is completed, calculated values are then derived.

Fig. 4.
Fig. 4.

USCRN triplicate redundancy allows for quick identification of faulty sensors. Presented here is an example using triplicate redundancy at the USCRN Lincoln 8 site to determine a malfunctioning sensor. The data series for probe 1 (light blue) and probe 2 (green) are greatly different from probe 3 (dark blue) in the middle of the time series. After probe 3 was replaced near the end of the time series, the three probes go back to agreement with each other. Note that, although each probe is in fairly close proximity to the others, the individual response to drying and wetting is variable even for functional probes.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0146.1

b. Calculated soil moisture

At most measurement depths, soil moisture measurements are recorded at the station as dielectric values in 15-min intervals and then averaged into 1-h values for transmission and storage. Although this has been true of the 5-cm-depth measurements, starting in 2012 the 5-cm-depth values have been collected in 5-min intervals and transmitted as twelve 5-min values hourly for improved satellite calibration/validation. The 5-min values allow for better synchronization in satellite comparisons. For example, Fig. 5 shows that using a 5-min value for 5-cm soil moisture gives better definition to the start of soil moistening during a rain event at the Port Aransas, Texas, USCRN site.

Fig. 5.
Fig. 5.

USCRN 5-cm soil moisture averages for the Port Aransas station, comparing 5-min (green solid) and 1-h (red dash) soil moisture values during a rain event (blue bars).

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0146.1

After the data are ingested at NCDC, the quality-controlled dielectric values are converted to vwc values (water fraction by volume; m3 m−3) using Eq. (1) on the basis of Seyfried et al. (2005) with updated coefficients:
e1
Since the general equation does not work well for very small dielectric values, the soil moisture is assumed to be zero for practical purposes. Individual probe values are then combined into hourly averages for each depth and into 5-min averages for the 5-cm depth. The averaged values can be accessed online at the USCRN (see below).

During soil-probe installation, soil samples are extracted at all depths for every installation. The samples are then sent to the USDA National Soil Survey Center in Lincoln, Nebraska, for further evaluation. Samples are analyzed to determine bulk density, field capacity (at −33 kPa), permanent wilting point (at −1500 kPa), and other general soil characteristics. Soil water content at the permanent wilting point and field capacity are determined for the soils at the depth of each probe, allowing for an estimate of plant available water. Plant available water, being an indication of the amount of water available in the soil for plant growth and development, will likely be added as an additional calculated soil moisture variable to assist in drought monitoring.

c. Calculated soil temperature

Soil temperature measurements are recorded in degrees Celsius every 15 min and then converted to hourly averages prior to transmission. Similar to the soil moisture measurements, 5-cm-depth temperatures will also be transmitted in 5-min intervals by the end of 2012. Once the data arrive at NCDC, the soil temperature values go through the quality control procedures mentioned earlier. The independent and quality-controlled probe values at each depth are then averaged to produce an integrated hourly soil temperature value. The final depth-averaged product is stored at USCRN for public access online. Individual probe values of soil temperature and moisture are stored at NCDC.

d. Data access

NCDC provides online access to hourly and daily soil-climate data from the USCRN (http://www.ncdc.noaa.gov/crn/qcdatasets.html). This directory contains monthly, daily, and hourly USCRN datasets, although soil moisture and soil temperature are only contained in the Daily01 and Hourly02 subdirectories. Both Daily01 and Hourly02 contain subdirectories for each individual year for which station years of data are recorded and a subdirectory with period-of-record files for each station, whichever is preferable. There is a “README.txt” text file to explain the data format for each type of file. There is also online access to individual soil-probe measurements through the data lister (http://www.ncdc.noaa.gov/crn/observations.htm).

5. Challenges associated with the USCRN soil-measurement technique

To maintain consistency and redundancy in the design of USCRN soil-climate measurements, a uniform probe model was used at all stations across the network during the initial deployment. Issues can arise at locations that are less suitable for coaxial impedance dielectric sensors operating at 50 MHz. Unique environmental and physical conditions can result in sensor errors and increase uncertainties in measurements. Deeper probes (100 and, on occasion, 50 cm) in some locations are experiencing repeated dramatic oscillations between hourly measurements (Fig. 6). A preliminary investigation indicated that this problem is likely caused by the proximity of these sensors to the water table and their exposure to saturated soil conditions. Some soil moisture sensors are experiencing dramatic oscillations at all depths. For example, all soil layers at the Coos Bay, Oregon, site are very saline because of the marine estuarine environment, which causes soil probes to report dielectric values that are greater than those of pure water (not shown). In several other locations, soil clay content can produce a high cationic exchange capacity that likely interferes with the soil dielectric measurements. Although moisture data from ineffective probes are excluded from the final quality-controlled datasets, all raw data from these sensors are being recorded and archived. Research is currently being conducted on whether to remove these probes and to install different sensors for these locations.

Fig. 6.
Fig. 6.

Volumetric water concentration from the six 100-cm probes at the dual Lincoln 11 (green) and Lincoln 8 (shades of red) stations for the first six months in 2011. The top three soil moisture concentrations are from the Lincoln 8 station (probes 1, 2, and 3) and are more variable than the three probes from Lincoln 11 at the same depth. Higher variability, especially in the lower soil depths, is likely from probes being inundated with water.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0146.1

6. Applications of multiple soil moisture and temperature measurements

The triplicate-sensor installation at individual soil depths provides many possibilities for studying the variability and uncertainty of soil measurements. The original intent of the triplicate soil-probes configuration was to follow the USCRN protocol of redundant precipitation and temperature measurements, which is used to assist with quality control and system maintenance. Although the triplicate design does allow for an investigation of congruency among sensors, the amount of natural variability in the soil matrix and the effects of even small installation differences at an individual station affect the USCRN soil measurements in ways that are distinct from the redundant aboveground measurements.

Each station’s unique variability among the three soil sensors provides multiple scientific opportunities to better understand the role of soil heterogeneity and measurement uncertainty in analysis of soil moisture and temperature at an individual location. Here we use a preliminary investigation of soil-climate variability among triplicate probe sets over the entire network to explore some expected and some unexpected patterns across depth and season. The goal of this section is to highlight the scientific possibilities that can arise from measurements with triple redundancy. The first analysis focused on hourly data for all available stations for June 2011. Individual station statistics [mean, standard deviation, and coefficient of variation (CV)] were calculated for each triplicate set at each depth. These individual station statistics (means, standard deviation, and CV) of soil moisture and temperature were averaged across the network and plotted to examine the relationship with depth (Fig. 7).

Fig. 7.
Fig. 7.

Soil-climate means (blue bars) and standard deviations (orange bars) produced from five soil depths across all stations recording measurements during June 2011 for (a) soil moisture (%) and (b) soil temperature (°C). The line graph (right axis) is the average CV.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0146.1

The results show that—as would be expected for most midlatitude regions in the United States during the beginning of summer—mean soil moisture increases with depth and mean soil temperature decreases with depth. Interestingly, the standard deviations of soil moisture measurements among the three probes at each level show a modest increasing trend with greater depth from the soil surface. As was noted earlier, some of the probe sets show strong variations in soil moisture at 100 cm because of the effects of clay cations and saturation that is likely the cause of the increasing trend in standard deviation for soil moisture. Showing contrasting behavior to that of the calculated standard deviation of soil moisture, standard deviations of soil temperature measurements among triplicate probes are at a maximum at the surface, as would be expected, and decline monotonically with depth. However, where mean temperature and moisture change monotonically with depth, the coefficient of variation did not demonstrate the same consistency because of the more complex nature of the changes in the relationship between standard deviation and mean with depth. The standard deviation and the CV of soil temperature follow a decreasing pattern with depth very similar to that of the mean, except for the increase of CV with 100-cm measurements. The decreasing pattern in the top four depths is due to the physical properties of heat transfer through soil. The increase in CV at the 100-cm depth is probably the result of some sensors being saturated or of difficulties that can occur with deep-soil-depth installations. Hence, variability at the 100-cm depth could be an artifact of multiple natural or artificial causes. Because soil moisture mean and standard deviations generally increase with depth but not at the same rate, the CV varies substantially with depth.

As each station is making a transition through unique geospatial and environmental conditions, water content and infiltration can vary greatly because of soil type, bioperturbation, or natural debris. In addition, the timing of this sample, in June, may influence the use of water from different layers under vegetation in various phenological stages. Thus, the rates of change with depth can vary greatly among stations, and it is hoped that this fact will spur researchers to investigate the potential relationships that can occur.

An investigation of seasonal patterns was performed on the triplicate redundant soil moisture measurements to determine whether any temporal changes occur among the three sensors. Using the same format as the June analysis, an examination of the change in soil moisture over the entire network was performed on the spring (March–May), summer (June–August), and autumn (September–November) seasons. Seasonal patterns of mean soil moisture revealed a similar increase with depth to that seen in the June 2011 example (Fig. 8). The pattern for most depths is a decrease in the mean soil moisture amount during the summer and then an increase in the autumn, except for the 100-cm depth, which continues to diminish into the autumn. Soil water recharge starts in the upper layers following plant senescence, but the downward percolating water does not reach the 100-cm layer until after autumn in many places and is likely the cause of the decreasing trend across seasons at the 100-cm depth. The seasonal patterns for soil moisture standard deviations are different from the seasonal patterns of the means at all depths, maintaining the same inconsistencies that were seen with changes in depth for June. However, the patterns of CV were more visible, with the greatest CV at all depths occurring when soil moisture amounts were at their lowest. Thus, the variability among sensors at a given depth tended to increase with drying of the soil. This behavior is most likely due to differing rates of soil drying caused by variations in soil characteristics among the three sensors. To ensure that the variability is truly a product of the soil characteristics and not from inconsistencies that will inevitably occur with sensor installation, gravimetric soil moisture measurements near many sensor plots will be conducted to validate the probe measurements. Even with gravimetric calibration, there will likely be measurement variability among triplicate soil sensors because of differences in soil characteristics, weather, and topography.

Fig. 8.
Fig. 8.

Averaged soil moisture values for each depth and season for the entire network during 2011: (top left) mean, (top right) standard deviation, and (bottom) CV. The bars indicate the depth of the layer: 5 (light green), 10 (orange), 20 (red), 50 (blue), and 100 (dark green) cm.

Citation: Journal of Hydrometeorology 14, 3; 10.1175/JHM-D-12-0146.1

7. Conclusions

The USCRN has completed the installation of soil-monitoring probes in the conterminous United States. Under ideal conditions, stations have triplicate measurements of soil moisture and soil temperature at five depths (5, 10, 20, 50, and 100 cm). Stations that cannot support the five depths have installations in the top two depths, and one station that is installed on bare rock does not have soil observations. In addition to making quality-controlled soil temperature and moisture data publicly available, the USCRN program is developing derived products that will be useful for the analysis of growing seasons and drought.

USCRN provides the layer averages of soil moisture and soil temperature for the quality-controlled datasets online (see section 4d), and there are graphical representations available online from both USCRN (http://www.ncdc.noaa.gov/crn/) and the U.S. Drought Portal (http://www.drought.gov/portal/server.pt/community/drought.gov/crn_soil_data). Besides the direct association with NIDIS and drought monitoring, USCRN soil observations will have many uses, including numerical weather prediction and climate modeling; validating satellite estimates of surface soil moisture; and for many applications in agriculture, hydrology, and environmental science. USCRN soil observations are continually being developed to improve quality and usefulness. Some of the future projects for the USCRN soil observations include drought-monitoring products, satellite validation, gravimetric calibrations, and researching the spatial interrelation between stations.

Furthermore, the triplicate design of the network will not only assist in assuring quality soil-climate data but will also provide a unique opportunity to research the variability of soil temperature and moisture. Preliminary work has already shown some changes and patterns for depth and season. Although the individual values for the three sensors can be variable at any point in time, the seasonal patterns and responses to change are consistent among all sensors at a single depth.

Acknowledgments

We are grateful to the NIDIS Program and its director, Dr. Roger S. Pulwarty, for the foresight in providing the resources to the USCRN program to allow the operation and maintenance of the network’s soil sensors, as well as in facilitating the science work related to analyzing the USCRN soil data that underlie the research in this paper. This work was supported by NOAA through the Cooperative Institute for Climate and Satellites—North Carolina under Cooperative Agreement NA09NES4400006. We greatly appreciate the work of the USCRN staff at NOAA’s NCDC and ATDD for ensuring the continual success of the program. We especially thank Scott Embler, Diana Kantor, and Rocky Bilotta for technical assistance. We also appreciate Maggie Robinson for editorial remarks. In addition, we thank Anthony Arguez and Pierre Guillevic for assisting in the NOAA internal review process.

REFERENCES

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  • Brock, F. V., Crawford K. C. , Elliot R. L. , Cuperus G. W. , Stadler S. J. , Johnson H. L. , and Eilts M. D. , 1995: The Oklahoma Mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 519.

    • Search Google Scholar
    • Export Citation
  • Diamond, H. J., and Coauthors, 2013: U.S. Climate Reference Network after one decade of operations: Status and assessment. Bull. Amer. Meteor. Soc.,94, 485–498.

  • Dorigo, W. A., and Coauthors, 2011: The International Soil Moisture Network: A data hosting facility for global in situ moisture measurements. Hydrol. Earth Syst. Sci., 15, 16751698.

    • Search Google Scholar
    • Export Citation
  • Easterling, D. R., and Coauthors, 2008: Measures to improve our understanding of weather and climate extremes. Weather and Climate Extremes in a Changing Climate, T. R. Karl et al., Eds., NOAA, 117–126.

  • Heim, R. R., 2001: New network to monitor climate change. Eos, Trans. Amer. Geophys. Union, 82, 143.

  • Helms, D., 1992: Readings in the history of the Soil Conservation Service. USDA Historical Notes 1, USDA Soil Conservation Service, Washington, D.C., 174 pp. [Available online at http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb1043484.pdf.]

  • Hollinger, S. E., and Isard S. A. , 1994: A soil moisture climatology of Illinois. J. Climate, 7, 822833.

  • Hu, Q., and Feng S. , 2003: A daily soil temperature dataset and soil temperature climatology of the contiguous United States. J. Appl. Meteor., 42, 11391156.

    • Search Google Scholar
    • Export Citation
  • Hu, Q., and Feng S. , 2004: U.S. soil temperature and its variation: A new dataset. Bull. Amer. Meteor. Soc., 85, 2931.

  • Illston, B. G., Basara J. B. , Fisher D. K. , Elliott R. , Fiebrich C. A. , Crawford K. C. , Humes K. , and Hunt E. , 2008: Mesoscale monitoring of soil moisture across a statewide network. J. Atmos. Oceanic Technol., 25, 167182.

    • Search Google Scholar
    • Export Citation
  • Karl, T. R., and Coauthors, 1995a: Critical issues for long-term climate monitoring. Climatic Change, 31, 185221, doi:10.007/BF01095146.

    • Search Google Scholar
    • Export Citation
  • Karl, T. R., Knight R. W. , and Plummer N. , 1995b: Trends in high frequency climate variability in the twentieth century. Nature, 377, 217220.

    • Search Google Scholar
    • Export Citation
  • Landa, E. R., and Nimmo J. R. , 2003: Soil history: The life and scientific contributions of Lyman J. Briggs. Soil Sci. Soc. Amer. J., 67, 681693.

    • Search Google Scholar
    • Export Citation
  • McPherson, R. A., and Coauthors, 2007: Statewide monitoring of the mesoscale environment: A technical update on the Oklahoma mesonet. J. Atmos. Oceanic Technol., 24, 301321.

    • Search Google Scholar
    • Export Citation
  • Menne, M. J., Williams C. N. Jr., and Vose R. S. , 2009: The U.S. Historical Climatology Network monthly temperature data, version 2. Bull. Amer. Meteor. Soc., 90, 9931007.

    • Search Google Scholar
    • Export Citation
  • Menne, M. J., Williams C. N. Jr., and Palecki M. A. , 2010: On the reliability of the U.S. surface temperature record. J. Geophys. Res., 115, D11108, doi:10.1029/2009JD013094.

    • Search Google Scholar
    • Export Citation
  • Robock, A., Vinnikov K. Y. , Srinivasan G. , Entin J. K. , Hollinger S. E. , Speranskaya N. A. , Liu S. , and Namkhai A. , 2000: The Global Soil Moisture Data Bank. Bull. Amer. Meteor. Soc., 81, 12811299.

    • Search Google Scholar
    • Export Citation
  • Schaefer, G. L., Cosh M. H. , and Jackson T. J. , 2007: The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN). J. Atmos. Oceanic Technol., 24, 20732077.

    • Search Google Scholar
    • Export Citation
  • Scott, R. W., Krug E. C. , Burch S. L. , Mitsdarfer C. R. , and Nelson P. , 2010: Investigation of soil moisture variability under sod in east-central Illinois. Rep. of Investigation 119, Illinois State Water Survey, Champaign, IL, 71 pp. [Available online at http://www.isws.illinois.edu/pubdoc/RI/ISWSRI-119.pdf.]

  • Seneviratne, S. I., and Coauthors, 2012: Changes in climate extremes and their impacts on the natural physical environment. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, C. B. Field et al., Eds., Cambridge University Press, Cambridge, United Kingdom, 109–230.

  • Serreze, M. C., Clark M. P. , Armstrong R. L. , McGinnis D. A. , and Pulwarty R. S. , 1999: Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data. Water Resour. Res., 35, 21452160.

    • Search Google Scholar
    • Export Citation
  • Seyfried, M. S., Grant L. E. , Du E. , and Humes K. , 2005: Dielectric loss and calibration of the Hydra Probe soil water sensor. Vadose Zone J., 4, 10701079.

    • Search Google Scholar
    • Export Citation
  • USDA, 1998: Estimating soil moisture by feel and appearance. Program Aid Number 1619, USDA Natural Resources Conservation Service, Washington, D.C., 12 pp.

  • Vose, R. S., and Menne M. J. , 2004: A method to determine station density requirements for climate observing networks. J. Climate, 17, 29612970.

    • Search Google Scholar
    • Export Citation
  • Whitney, M., and Briggs L. J. , 1897: An electrical method of determining the temperature of soils. USDA Division of Soils Bulletin 7, U.S. Government Printing Office, Washington, D.C., 15 pp. [Available online at http://ia601600.us.archive.org/11/items/electricalmethod07whit/electricalmethod07whit.pdf.]

  • Whitney, M., Gardner F. D. , and Briggs L. J. , 1897: An electrical method of determining the moisture content of arable soils. USDA Division of Soils Bulletin 6, U.S. Government Printing Office, Washington, D.C., 26 pp. [Available online at http://ia601602.us.archive.org/24/items/electricalmethod06whit/electricalmethod06whit.pdf.]

  • You, J., Hubbard K. G. , Mahmood R. , Sridhar V. , and Todey D. , 2010: Quality control of soil water data in applied climate information system-case study in Nebraska. J. Hydrol. Eng., 15, 200209.

    • Search Google Scholar
    • Export Citation
  • Zachariassen, J., Zeller K. F. , Nikolov N. , and McClelland T. , 2003: A review of the Forest Service Remote Automated Weather Station (RAWS) network. General Tech. Rep. RMRS-GTR-119, Rocky Mountain Research Station, U.S. Forest Service, Fort Collins, CO, 153 pp. [Available online at http://www.fs.fed.us/rm/pubs/rmrs_gtr119.pdf.]

1

Soil sensors will eventually be installed at USCRN stations in Alaska, but the unique nature of the soils, including permafrost, may involve a different configuration profile than in the conterminous United States.

2

The USCRN Program does not endorse any specific commercial instrument models.

Save
  • Briggs, L. J., and Shantz H. L. , 1914: Relative water requirements of plants. J. Agric. Res., 3, 164.

  • Brock, F. V., Crawford K. C. , Elliot R. L. , Cuperus G. W. , Stadler S. J. , Johnson H. L. , and Eilts M. D. , 1995: The Oklahoma Mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 519.

    • Search Google Scholar
    • Export Citation
  • Diamond, H. J., and Coauthors, 2013: U.S. Climate Reference Network after one decade of operations: Status and assessment. Bull. Amer. Meteor. Soc.,94, 485–498.

  • Dorigo, W. A., and Coauthors, 2011: The International Soil Moisture Network: A data hosting facility for global in situ moisture measurements. Hydrol. Earth Syst. Sci., 15, 16751698.

    • Search Google Scholar
    • Export Citation
  • Easterling, D. R., and Coauthors, 2008: Measures to improve our understanding of weather and climate extremes. Weather and Climate Extremes in a Changing Climate, T. R. Karl et al., Eds., NOAA, 117–126.

  • Heim, R. R., 2001: New network to monitor climate change. Eos, Trans. Amer. Geophys. Union, 82, 143.

  • Helms, D., 1992: Readings in the history of the Soil Conservation Service. USDA Historical Notes 1, USDA Soil Conservation Service, Washington, D.C., 174 pp. [Available online at http://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb1043484.pdf.]

  • Hollinger, S. E., and Isard S. A. , 1994: A soil moisture climatology of Illinois. J. Climate, 7, 822833.

  • Hu, Q., and Feng S. , 2003: A daily soil temperature dataset and soil temperature climatology of the contiguous United States. J. Appl. Meteor., 42, 11391156.

    • Search Google Scholar
    • Export Citation
  • Hu, Q., and Feng S. , 2004: U.S. soil temperature and its variation: A new dataset. Bull. Amer. Meteor. Soc., 85, 2931.

  • Illston, B. G., Basara J. B. , Fisher D. K. , Elliott R. , Fiebrich C. A. , Crawford K. C. , Humes K. , and Hunt E. , 2008: Mesoscale monitoring of soil moisture across a statewide network. J. Atmos. Oceanic Technol., 25, 167182.

    • Search Google Scholar
    • Export Citation
  • Karl, T. R., and Coauthors, 1995a: Critical issues for long-term climate monitoring. Climatic Change, 31, 185221, doi:10.007/BF01095146.

    • Search Google Scholar
    • Export Citation
  • Karl, T. R., Knight R. W. , and Plummer N. , 1995b: Trends in high frequency climate variability in the twentieth century. Nature, 377, 217220.

    • Search Google Scholar
    • Export Citation
  • Landa, E. R., and Nimmo J. R. , 2003: Soil history: The life and scientific contributions of Lyman J. Briggs. Soil Sci. Soc. Amer. J., 67, 681693.

    • Search Google Scholar
    • Export Citation
  • McPherson, R. A., and Coauthors, 2007: Statewide monitoring of the mesoscale environment: A technical update on the Oklahoma mesonet. J. Atmos. Oceanic Technol., 24, 301321.

    • Search Google Scholar
    • Export Citation
  • Menne, M. J., Williams C. N. Jr., and Vose R. S. , 2009: The U.S. Historical Climatology Network monthly temperature data, version 2. Bull. Amer. Meteor. Soc., 90, 9931007.

    • Search Google Scholar
    • Export Citation
  • Menne, M. J., Williams C. N. Jr., and Palecki M. A. , 2010: On the reliability of the U.S. surface temperature record. J. Geophys. Res., 115, D11108, doi:10.1029/2009JD013094.

    • Search Google Scholar
    • Export Citation
  • Robock, A., Vinnikov K. Y. , Srinivasan G. , Entin J. K. , Hollinger S. E. , Speranskaya N. A. , Liu S. , and Namkhai A. , 2000: The Global Soil Moisture Data Bank. Bull. Amer. Meteor. Soc., 81, 12811299.

    • Search Google Scholar
    • Export Citation
  • Schaefer, G. L., Cosh M. H. , and Jackson T. J. , 2007: The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN). J. Atmos. Oceanic Technol., 24, 20732077.

    • Search Google Scholar
    • Export Citation
  • Scott, R. W., Krug E. C. , Burch S. L. , Mitsdarfer C. R. , and Nelson P. , 2010: Investigation of soil moisture variability under sod in east-central Illinois. Rep. of Investigation 119, Illinois State Water Survey, Champaign, IL, 71 pp. [Available online at http://www.isws.illinois.edu/pubdoc/RI/ISWSRI-119.pdf.]

  • Seneviratne, S. I., and Coauthors, 2012: Changes in climate extremes and their impacts on the natural physical environment. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, C. B. Field et al., Eds., Cambridge University Press, Cambridge, United Kingdom, 109–230.

  • Serreze, M. C., Clark M. P. , Armstrong R. L. , McGinnis D. A. , and Pulwarty R. S. , 1999: Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data. Water Resour. Res., 35, 21452160.

    • Search Google Scholar
    • Export Citation
  • Seyfried, M. S., Grant L. E. , Du E. , and Humes K. , 2005: Dielectric loss and calibration of the Hydra Probe soil water sensor. Vadose Zone J., 4, 10701079.

    • Search Google Scholar
    • Export Citation
  • USDA, 1998: Estimating soil moisture by feel and appearance. Program Aid Number 1619, USDA Natural Resources Conservation Service, Washington, D.C., 12 pp.

  • Vose, R. S., and Menne M. J. , 2004: A method to determine station density requirements for climate observing networks. J. Climate, 17, 29612970.

    • Search Google Scholar
    • Export Citation
  • Whitney, M., and Briggs L. J. , 1897: An electrical method of determining the temperature of soils. USDA Division of Soils Bulletin 7, U.S. Government Printing Office, Washington, D.C., 15 pp. [Available online at http://ia601600.us.archive.org/11/items/electricalmethod07whit/electricalmethod07whit.pdf.]

  • Whitney, M., Gardner F. D. , and Briggs L. J. , 1897: An electrical method of determining the moisture content of arable soils. USDA Division of Soils Bulletin 6, U.S. Government Printing Office, Washington, D.C., 26 pp. [Available online at http://ia601602.us.archive.org/24/items/electricalmethod06whit/electricalmethod06whit.pdf.]

  • You, J., Hubbard K. G. , Mahmood R. , Sridhar V. , and Todey D. , 2010: Quality control of soil water data in applied climate information system-case study in Nebraska. J. Hydrol. Eng., 15, 200209.

    • Search Google Scholar
    • Export Citation
  • Zachariassen, J., Zeller K. F. , Nikolov N. , and McClelland T. , 2003: A review of the Forest Service Remote Automated Weather Station (RAWS) network. General Tech. Rep. RMRS-GTR-119, Rocky Mountain Research Station, U.S. Forest Service, Fort Collins, CO, 153 pp. [Available online at http://www.fs.fed.us/rm/pubs/rmrs_gtr119.pdf.]

  • Fig. 1.

    Installation of the USCRN soil-climate instrumentation was completed for the contiguous United States in 2011. Of the 114 stations, 90 stations had all five depths installed (black dots), 23 had only the top two depths installed (green dots), and one could not support any soil probes (red dot).

  • Fig. 2.

    Configuration of the USCRN soil probes around the base of the main instrument tower near Harrison, Nebraska. The wire leads from each set of soil probes and travels through the gray conduits to a circuit board through which the probes connect to the data logger. The three plots are located at 0° (north), 120° (southeast), and 240° (southwest) around the base of the tower. Each soil probe set must be a minimum of 1.5 m from the base of the tower in undisturbed soil.

  • Fig. 3.

    USCRN layer averages of soil moisture from the Asheville 13 station. The 5- (black line), 10- (blue line), 20- (dark green line), 50- (green line), and 100-cm (red line) lines demonstrate the seasonal response. Similar figures can be created online at the U.S. Drought Portal (http://www.drought.gov).

  • Fig. 4.

    USCRN triplicate redundancy allows for quick identification of faulty sensors. Presented here is an example using triplicate redundancy at the USCRN Lincoln 8 site to determine a malfunctioning sensor. The data series for probe 1 (light blue) and probe 2 (green) are greatly different from probe 3 (dark blue) in the middle of the time series. After probe 3 was replaced near the end of the time series, the three probes go back to agreement with each other. Note that, although each probe is in fairly close proximity to the others, the individual response to drying and wetting is variable even for functional probes.

  • Fig. 5.

    USCRN 5-cm soil moisture averages for the Port Aransas station, comparing 5-min (green solid) and 1-h (red dash) soil moisture values during a rain event (blue bars).

  • Fig. 6.

    Volumetric water concentration from the six 100-cm probes at the dual Lincoln 11 (green) and Lincoln 8 (shades of red) stations for the first six months in 2011. The top three soil moisture concentrations are from the Lincoln 8 station (probes 1, 2, and 3) and are more variable than the three probes from Lincoln 11 at the same depth. Higher variability, especially in the lower soil depths, is likely from probes being inundated with water.

  • Fig. 7.

    Soil-climate means (blue bars) and standard deviations (orange bars) produced from five soil depths across all stations recording measurements during June 2011 for (a) soil moisture (%) and (b) soil temperature (°C). The line graph (right axis) is the average CV.

  • Fig. 8.

    Averaged soil moisture values for each depth and season for the entire network during 2011: (top left) mean, (top right) standard deviation, and (bottom) CV. The bars indicate the depth of the layer: 5 (light green), 10 (orange), 20 (red), 50 (blue), and 100 (dark green) cm.

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