Widespread interest in soil water content (θ, m 3 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 ε r ′ determined with the Hydra Probe using 30 sensors in ethanol corresponded to a water content deviation of about 0.012 m 3 m −3 , indicating that a single calibration could be generally 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 content. In uniform media, θ was a linear function of √ε r ′ 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 and , was 0.019 m 3 m −3 for the general equation and 0.013 m 3 m −3 for the loss‐corrected equation.
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 km2 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.
Abstract 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.
Abstract A relatively simple modeling approach for estimating spatially distributed surface energy fluxes was applied to two small watersheds, one in a semi-arid climate region and one in a sub-humid region. This approach utilized a combination of ground-based meteorological data and remotely sensed data to estimate 'instantaneous' surface energy fluxes at the time of the satellite or aircraft overpasses. The spatial resolution in the watershed grid cells, which was on the order of 100-400 km, was selected to be compatible with ground measurements used for validation. The model estimates of surface energy fluxes compared well with ground-based measurements of surface flux (typically within approximately 40 Wm−2). The model accuracy may be slightly less for bare soil surfaces due to an overestimation of the soil heat flux. In addition to demonstrating the feasibility of computing spatially distributed values of surface energy fluxes, these maps were used to qualitatively infer the dominant factors controlling the energy fluxes for the time period shortly following precipitation events in the basins. For the semi-arid watershed, values of sensible heat flux varied considerably over the watershed and displayed a pattern very similar to that of the spatially variable cumulative precipitation for at least one to eight days prior to the image acquisition. Due to the large fraction of exposed bare soil in a semi-arid ecosystem, even very small precipitation events had a strong influence on the pattern of sensible heat fluxes observed shortly after the event (less than 24 hours). For the sub-humid watershed, the fluxes tended to be more uniform across the watershed, and were influenced by a combination of precipitation total and land cover type. Keywords:: surface energy balanceremote sensing
Arid and semiarid rangelands comprise a significant portion of the earth's land surface. Yet little is known about the effects of temporal and spatial changes in surface soil moisture on the hydrologic cycle, energy balance, and the feedbacks to the atmosphere via thermal forcing over such environments. Understanding this interrelationship is crucial for evaluating the role of the hydrologic cycle in surface–atmosphere interactions. This study focuses on the utility of remote sensing to provide measurements of surface soil moisture, surface albedo, vegetation biomass, and temperature at different spatial and temporal scales. Remote-sensing measurements may provide the only practical means of estimating some of the more important factors controlling land surface processes over large areas. Consequently, the use of remotely sensed information in biophysical and geophysical models greatly enhances their ability to compute fluxes at catchment and regional scales on a routine basis. However, model calculations for different climates and ecosystems need verification. This requires that the remotely sensed data and model computations be evaluated with ground-truth data collected at the same areal scales. The present study (MONSOON 90) attempts to address this issue for semiarid rangelands. The experimental plan included remotely sensed data in the visible, near-infrared, thermal, and microwave wavelengths from ground and aircraft platforms and, when available, from satellites. Collected concurrently were ground measurements of soil moisture and temperature, energy and water fluxes, and profile data in the atmospheric boundary layer in a hydrologically instrumented semiarid rangeland watershed. Field experiments were conducted in 1990 during the dry and wet or "monsoon season" for the southwestern United States. A detailed description of the field campaigns, including measurements and some preliminary results are given.
A primary motivation for using remotely sensed data to estimate components of the surface energy balance is to quantify surface energy fluxes in a spatially distributed manner over various spatial scales. However, all models which utilize remotely sensed data to estimate surface fluxes also require input variables and parameters which cannot be estimated on a spatially distributed basis with remotely sensed data. In this analysis, data from the Monsoon '90 experiment were used to evaluate the limitations in spatially extending a relatively simple energy balance model with remotely sensed data over a semiarid rangeland watershed. Using one ground‐based meteorological and flux station as a reference site, aircraft‐based remotely sensed data (surface temperatures and reflectances) were used to compute energy balance components for seven other locations within the watershed. The results indicated that for clear sky conditions, all components of the surface energy balance could be estimated to within approximately the same level of uncertainty with which the fluxes were measured with ground‐based flux instrumentation. However, under partly cloudy conditions the variability in incoming solar radiation across the watershed significantly degraded the estimation of distributed values of net radiation ( R net ). If ground‐based estimates of incoming solar radiation are used to calculate R net from remotely sensed data, then the spatial extent over which that measurement is valid limits the area over which accurate spatially distributed values of R net can be estimated. Additionally, the results of sensitivity analyses indicate that the level of uncertainty to which the roughness length for momentum, or z 0 m , is typically known for spatially distributed values in an area of naturally variable vegetation can give rise to significant uncertainties in the estimation of sensible heat flux. For areas where the spatial variation in roughness parameters is of the order of several centimeters, the error associated with assuming constant values for the roughness length for momentum is similar in size to the errors associated with temperature variations of the order of several degrees. In order to utilize radiometric temperatures to reliably estimate spatially distributed values of sensible heat flux, techniques such as those explored by Menenti and Ritchie (this issue) are needed to provide spatially distributed information on surface roughness parameters.
