K. S. Humes

Bio: K. S. Humes is an academic researcher from University of Idaho. The author has contributed to research in topics: Sensible heat & Thematic Mapper. The author has an hindex of 26, co-authored 41 publications receiving 3837 citations. Previous affiliations of K. S. Humes include University of Oklahoma & United States Department of Agriculture.

Seasonal and Synoptic Variations in Near-Surface Air Temperature Lapse Rates in a Mountainous Basin

Abstract: 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 (Tmax, Tmin, and Tavg) 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 Tmax 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 Tmin and Tavg 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 0.65°C (100 m) 1 ] is solely applicable to maximum temperature and often grossly overestimates Tmin and Tavg lapse rates. Regional lapse rates perform better than the environmental lapse rate for Tmin and Tavg, 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.

Dielectric Loss and Calibration of the Hydra Probe Soil Water Sensor

Abstract: Widespread interest in soil water content (θ, m3 m−3) information for both management and research has led to the development of a variety of soil water content sensors. In most cases, critical issues related to sensor calibration and accuracy have received little independent study. We investigated the performance of the Hydra Probe soil water sensor with the following objectives: (i) quantify the inter-sensor variability, (ii) evaluate the applicability of data from two commonly used calibration methods, and (iii) develop and test two multi-soil calibration equations, one general, “default” calibration equation and a second calibration that incorporates the effects of soil properties. The largest deviation in the real component of the relative dielectric permittivity \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\left({\epsilon}_{r}^{{^\prime}}\right)\) \end{document} determined with the Hydra Probe using 30 sensors in ethanol corresponded to a water content deviation of about 0.012 m3m−3, indicating that a single calibration could be generally applied. In layered (wet and dry) media, er′ determined with the Hydra Probe was different from that in uniform media with the same water content. In uniform media, θ was a linear function of √er′. We used this functional relationship to describe individual soil calibrations and the multi-soil calibrations. Individual soil calibrations varied independently of clay content but were correlated with dielectric loss. When applied to the 19-soil test data set, the general calibration outperformed manufacturer-supplied calibrations. The average θ difference, evaluated between \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{{\epsilon}}_{\mathrm{r}}^{{^\prime}}\ =\ 4\) \end{document} and \batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(\mathrm{{\epsilon}}_{\mathrm{r}}^{{^\prime}}\ =\ 36\) \end{document}, was 0.019 m3m−3 for the general equation and 0.013 m3m−3 for the loss-corrected equation.

Sensible heat flux - Radiometric surface temperature relationship for eight semiarid areas

Abstract: Measurements of sensible heat flux, radiometric surface temperature, air temperature, and wind speed made at eight semiarid rangeland sites were used to investigate the sensible heat flux-aerodynamic resistance relationship. The individual sites covered a wide range of vegetation (0.1-4 m tall) and cover (3%-95% bare soil) conditions. Mean values of k/B, a quantity related to the resistance of heat versus momentum transfer at the surface, for the individual sites varied between 3.5 and 12.5. A preliminary test of the utility of an excess resistance based on the mean value of k/B showed that the difference between the mean estimated and measured sensible heat fluxes varied +/- 60 W/sq m for the eight semiarid sites. For the eight sites the values of k/B were plotted against the roughness Reynolds number. The plot showed considerable scatter with values ranging between and beyond the theoretical curves for bluff rough and permeable rough surfaces.

Lidar Remote Sensing for Ecosystem Studies

Abstract: Articles R emote sensing has facilitated extraordinary advances in the modeling, mapping, and understanding of ecosystems. Typical applications of remote sensing involve either images from passive optical systems, such as aerial photography and Landsat Thematic Mapper (Goward and Williams 1997), or to a lesser degree, active radar sensors such as RADARSAT (Waring et al. 1995). These types of sensors have proven to be satisfactory for many ecological applications , such as mapping land cover into broad classes and, in some biomes, estimating aboveground biomass and leaf area index (LAI). Moreover, they enable researchers to analyze the spatial pattern of these images. However, conventional sensors have significant limitations for ecological applications. The sensitivity and accuracy of these devices have repeatedly been shown to fall with increasing aboveground biomass and leaf area index (Waring et al. 1995, Carlson and Ripley 1997, Turner et al. 1999). They are also limited in their ability to represent spatial patterns: They produce only two-dimensional (x and y) images, which cannot fully represent the three-dimensional structure of, for instance, an old-growth forest canopy.Yet ecologists have long understood that the presence of specific organisms, and the overall richness of wildlife communities, can be highly dependent on the three-dimensional spatial pattern of vegetation (MacArthur and MacArthur 1961), especially in systems where biomass accumulation is significant (Hansen and Rotella 2000). Individual bird species, in particular, are often associated with specific three-dimensional features in forests (Carey et al. 1991). In addition, other functional aspects of forests, such as productivity, may be related to forest canopy structure. Laser altimetry, or lidar (light detection and ranging), is an alternative remote sensing technology that promises to both increase the accuracy of biophysical measurements and extend spatial analysis into the third (z) dimension. Lidar sensors directly measure the three-dimensional distribution of plant canopies as well as subcanopy topography, thus providing high-resolution topographic maps and highly accurate estimates of vegetation height, cover, and canopy structure. In addition , lidar has been shown to accurately estimate LAI and aboveground biomass even in those high-biomass ecosystems where passive optical and active radar sensors typically fail to do so. The basic measurement made by a lidar device is the distance between the sensor and a target surface, obtained by determining the elapsed time between the emission of a short-duration laser pulse and the arrival of the reflection of that pulse (the return signal) at the sensor's receiver. Multiplying this …