AWDN Soil Moisture

Ainsworth - Discrete Depths

Station Information

Station ID: a250059
Name: AINSWORTH
Climate Division: 02
County: BROWN
State: NE
Latitude: 42.55
Longitude: -99.82
Elevation: 2509 Feet
Period of Record: 1984/6/4 - Current

Soil Moisture Information

Variable	Units	Start	End	Probe Type	Soil Type
Soil Moisture at 10cm	%	2005	2012	Theta	Silt
Soil Moisture at 25cm	%	2005	2012	Theta	Silt
Soil Moisture at 50cm	%	2005	2012	Theta	Silt
Soil Moisture at 100cm	%	2005	2012	Theta	Clay
Soil Moisture at 10cm	%	1998	2005	Vitel	Silt
Soil Moisture at 25cm	%	1998	2005	Vitel	Silt
Soil Moisture at 50cm	%	1998	2005	Vitel	Silt
Soil Moisture at 100cm	%	1998	2005	Vitel	Clay

Background

Soil moisture plays a critical role in agricultural activities and land atmosphere interaction. In 1998 the High Plains Regional Climate Center upgraded stations in the Automated Weather Data Network (AWDN) to monitor soil water at 14 sites in Nebraska. The Natural Resources Conservation Service (NRCS) has provided soil moisture sensors for some of the sites. Currently, sensors are placed at 10, 25, 50, and 100 cm below the surface at 51 AWDN sites. AWDN technicians maintain the sensors and laboratory facilities were updated to include adequate test and calibration equipment. The network archives the data for a daily time step. Hourly data can be acquired through special requests.

Data on the Web

Currently the Nebraska Soil Moisture web page allows the user community to access graphical displays of recorded soil moisture for each depth and year and calculated root zone volumetric water content for each year. Raw data is available upon request.

Current Applications

Soil moisture data is used by Nebraska officials, who serve on the Climate Assessment and Response Committee (CARC), to monitor drought and climate. This allows the state of Nebraska to conduct an early assessment of hydro-climatic conditions and estimate potential impacts on various sectors of the economy including agriculture. In other words, management of any potential emergency situation related to hydro-climatic conditions is undertaken in a timely fashion before it becomes a crisis.

In previous years, it was difficult and time consuming to obtain soil moisture validation data sets to test water balance models (Robinson and Hubbard, 1990). Today automated sensors lend themselves to real time applications and the resulting models help farmers to assess soil water conditions, crop growth, and irrigation schedules. Farmers can access the service through the HPRCC Online Services. They only need to provide some basic information to run the model and get assistance in irrigation scheduling and an assessment of soil water condition. Power companies are also utilizing soil moisture data to determine future irrigation demand and subsequent power supply.

We are fine tuning this model to estimate soil moisture of the past years when soil moisture instruments were not in place. In addition to soil water, the model estimates various components of energy balance. Thus, application of the model for the past years and under varying crop cover and field management provides data of intra-seasonal and inter-annual variation of soil water condition and corresponding impacts on energy partitioning. This effort will also allow us to develop a model estimated historical soil moisture data set for various types of agricultural and hydro-climatic studies.

Future objectives:

This project will continue to measure and archive soil moisture in coming years. The investigators also hope to increase the number of soil water monitoring sites in the AWDN network. The soil water data will continue to play an important role in drought and climate monitoring in this region and we anticipate that its role will expand in the coming years.

The model estimated historical soil moisture data is crucial for various risk management and vulnerability studies. We also plan to study soil moisture variability at different time-scales and under different crop and natural vegetation cover. These efforts will help us to understand the relationship between soil water and various components of energy balance under varying land use. We also plan to compare our model estimated energy partitioning with meso-scale model estimates and GCM grid-scale estimates. This analysis will improve our knowledge of how to parameterize the interaction between land-surface and boundary layer atmosphere.