Yutaka Hagimoto

NCAR/CU Surface, Soil, and Vegetation Observations during the International H2O Project 2002 Field Campaign

Abstract The May–June 2002 International H2O Project was held in the U.S. Southern Great Plains to determine ways that moisture data could be collected and utilized in numerical forecast models most effectively. We describe the surface and boundary layer components, and indicate how the data can be acquired. These data document the eddy transport of heat and water vapor from the surface to the atmosphere (in terms of sensible heat flux H and latent heat flux LE), as well as radiative, atmospheric, soil, and vegetative factors that affect it, so that the moisture and heat supply to the atmosphere can be related to surface properties both for observational studies and tests of land surface models. The surface dataset was collected at 10 surface flux towers at locations representing the major types of land cover and extending from southeast Kansas to the Oklahoma Panhandle. At each location, the components of the surface energy budget (H, LE, net radiation, and soil heat flux) are documented each half-hour, ...

Application of Landsat to Evaluate Effects of Irrigation Forbearance

Thirty-meter resolution Landsat data were used to evaluate the effects of irrigation management in the Wood River Valley, Upper Klamath Basin, Oregon. In an effort to reduce water use and leave more of the water resource in-stream, 4,674 ha of previously flood irrigated pasture was managed as dryland pasture. Ground-based measurements over one irrigated and one unirrigated pasture site were used to monitor the difference in evapotranspiration (ET) using the Bowen ratio-energy balance method. These data sets represent point measurements of the response to irrigation, but do not allow for the spatial integration of effects of irrigated versus unirrigated land treatment. Four Landsat scenes of the Wood River Valley during the 2004 growing season were evaluated using reconstructed METRIC algorithms. Comparisons of ET algorithm output with ground-based data for all components of the energy balance, including net radiation, soil heat flux, sensible heat flux and evapotranspiration, were made for the four scenes. The excellent net radiation estimates, along with reasonable estimates of the other components, are demonstrated along with the capability to integrate results to the basin scale.

Interpretation of In Situ Observations in Support of P -Band Radar Retrievals

Current estimates of Net Ecosystem Exchange (NEE) at regional and continental scales contain significant uncertainty. It has been shown that root-zone soil moisture (RZSM) content has a first-order effect on NEE. The objective of the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project is to provide measurements to estimate RZSM using a P -band airborne radar. Flight operations are conducted over representative sites of the nine major North American biomes which have been instrumented with soil water content sensors in various locations and over various depth layers which will be correlated to the P -band radar return signal. The hypothesis of the AirMOSS project is that integrating spatially and temporally resolved observations of RZSM into ecosystem dynamics models can significantly reduce the uncertainty of NEE estimates and carbon balance estimates. The calibration of the sensor systems is described and results are demonstrated for multiday up to annual time series at various sites. The success of fitting four models (e.g., power, first-, second-, and third-order polynomials) to the RZSM content with depth is tested by using in situ data collected in Tonzi Ranch, CA, USA, in 2014. Under the testing condition, all models satisfy the mission goal most of the duration. The second- and third-order models perform better during the dry season than the simpler power and first-order models. However, during periods of rapidly changing values of RZSM content, none of the models are able to meet the root-mean-squared error objective.

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

A method is described which uses the absence of radiocarbon in industrial chemicals and fuels made from petroleum feedstocks which frequently contaminate the environment. This radiocarbon signal — or rather the absence of signal — is evenly distributed throughout a contaminant source pool (unlike an added tracer) and is not impacted by biological, chemical or physical processes (e.g., the 14C radioactive decay rate is immutable). If the fossil-derived contaminant is fully degraded to CO2, a harmless end-product, that CO2 will contain no radiocarbon. CO2 derived from natural organic matter (NOM) degradation will reflect the NOM radiocarbon content (usually <30,000 years old). Given a known radiocarbon content for NOM (a site background), a two end-member mixing model can be used to determine the CO2 derived from a fossil source in a given soil gas or groundwater sample. Coupling the percent CO2 derived from the contaminant with the CO2 respiration rate provides an estimate for the total amount of contaminant degraded per unit time. Finally, determining a zone of influence (ZOI) representing the volume from which site CO2 is collected allows determining the contaminant degradation per unit time and volume. Along with estimates for total contaminant mass, this can ultimately be used to calculate time-to-remediate or otherwise used by site managers for decision-making.

Interpretation of in situ observations in support of P-band radar retrievals

Current estimates of Net Ecosystem Exchange (NEE) at regional and continental scales contain significant uncertainty. It has been shown that root-zone soil moisture (RZSM) content has a first-order effect on NEE. The objective of the NASA Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project is to provide measurements to estimate RZSM using a P-band airborne radar. Flight operations are conducted over representative sites of the nine major North American biomes which have been instrumented with soil water content sensors in various locations and over various depth layers which will be correlated to the P-band radar return signal. The hypothesis of the AirMOSS project is that integrating spatially and temporally resolved observations of RZSM into ecosystem dynamics models can significantly reduce the uncertainty of NEE estimates and carbon balance estimates. The calibration of the sensor systems is described and results are demonstrated for multiday up to annual time series at various sites. The success of fitting four models (e.g., power, first-, second-, and third-order polynomials) to the RZSM content with depth is tested by using in situ data collected in Tonzi Ranch, CA, USA, in 2014. Under the testing condition, all models satisfy the mission goal most of the duration. The second- and third-order models perform better during the dry season than the simpler power and first-order models. However, during periods of rapidly changing values of RZSM content, none of the models are able to meet the root-mean-squared error objective.