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To accurately estimate near-surface (2 m) air temperatures in a mountainous region for hydrologic prediction models and other investigations of environmental processes, the authors evaluated daily and seasonal variations (with the consideration of different weather types) of surface air temperature lapse rates at a spatial scale of 10 000 km 2 in south-central Idaho. Near-surface air temperature data (T max , T min , and T avg ) from 14 meteorological stations were used to compute daily lapse rates from January 1989 to December 2004 for a medium-elevation study area in south-central Idaho. Daily lapse rates were grouped by month, synoptic weather type, and a combination of both (seasonal-synoptic). Daily air temperature lapse rates show high variability at both daily and seasonal time scales. Daily T max lapse rates show a distinct seasonal trend, with steeper lapse rates (greater decrease in temperature with height) occurring in summer and shallower rates (lesser decrease in temperature with height) occurring in winter. Daily T min and T avg lapse rates are more variable and tend to be steepest in spring and shallowest in midsummer. Different synoptic weather types also influence lapse rates, although differences are tenuous. In general, warmer air masses tend to be associated with steeper lapse rates for maximum temperature, and drier air masses have shallower lapse rates for minimum temperature. The largest diurnal range is produced by dry tropical conditions (clear skies, high solar input). Cross-validation results indicate that the commonly used environmental lapse rate [typically assumed to be Ϫ0.65°C (100 m) Ϫ1] is solely applicable to maximum temperature and often grossly overestimates T min and T avg lapse rates. Regional lapse rates perform better than the environmental lapse rate for T min and T avg , although for some months rates can be predicted more accurately by using monthly lapse rates. Lapse rates computed for different months, synoptic types, and seasonal-synoptic categories all perform similarly. Therefore, the use of monthly lapse rates is recommended as a practical combination of effective performance and ease of implementation.

also use soil dielectric properties to determine . These alternative sensors have received relatively little inde-Widespread interest in soil water content (, m 3 m Ϫ3 ) information pendent study; and critical practical issues related to for both management and research has led to the development of a calibration methodology and application have not been variety of soil water content sensors. In most cases, critical issues addressed. related to sensor calibration and accuracy have received little independent study. We investigated the performance of the Hydra Probe soil The Hydra Probe is an example of the alternative water sensor with the following objectives: (i) quantify the intersensors now available. 1 It is currently in widespread use sensor variability, (ii) evaluate the applicability of data from two (e.g., the Soil Climate Analysis Network of the Natural commonly used calibration methods, and (iii) develop and test two Resource Conservation Service) and has proven to be multi-soil calibration equations, one general, "default" calibration robust under a variety of field conditions. Previous reequation and a second calibration that incorporates the effects of soil search demonstrated that Hydra Probe measurements properties. The largest deviation in the real component of the relative are precise and accurate in fluids with known dielectric dielectric permittivity (ε r ) determined with the Hydra Probe using 30 properties and highly correlated with in soils, indicatsensors in ethanol corresponded to a water content deviation of about ing the potential of the instrument for quantitative mea-0.012 m 3 m Ϫ3 , indicating that a single calibration could be generally surement (Seyfried and Murdock, 2004). It was also applied. In layered (wet and dry) media, ε r determined with the Hydra Probe was different from that in uniform media with the same water found that the calibration relationship varied considercontent. In uniform media, was a linear function of ͌ε r . We used ably among soils and that the manufacturer-supplied this functional relationship to describe individual soil calibrations and calibrations were not accurate for some soils. Important the multi-soil calibrations. Individual soil calibrations varied indepenpractical considerations regarding the use of the Hydra dently of clay content but were correlated with dielectric loss. When Probe remain. These include: (i) the degree of variation applied to the 19-soil test data set, the general calibration outperin response among different sensors (i.e., the inter-senformed manufacturer-supplied calibrations. The average difference, sor variability), which determines if sensor specific calievaluated between ε r ϭ 4 and ε r ϭ 36, was 0.019 m 3 m Ϫ3 for the general brations are required, (ii) the optimal experimental equation and 0.013 m 3 m Ϫ3 for the loss-corrected equation.83712;

Soil moisture is an important component in many hydrologic and land–atmosphere interactions. Understanding the spatial and temporal nature of soil moisture on the mesoscale is vital to determine the influence that land surface processes have on the atmosphere. Recognizing the need for improved in situ soil moisture measurements, the Oklahoma Mesonet, an automated network of 116 remote meteorological stations across Oklahoma, installed Campbell Scientific 229-L devices to measure soil moisture conditions. Herein, background information on the soil moisture measurements, the technical design of the soil moisture network embedded within the Oklahoma Mesonet, and the quality assurance (QA) techniques applied to the observations are provided. This project also demonstrated the importance of operational QA regarding the data collected, whereby the percentage of observations that passed the QA procedures increased significantly once daily QA was applied.