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H. Su, M. F. McCabe, E. F. Wood, Z. Su, and J. H. Prueger

Abstract

The Surface Energy Balance System (SEBS) model was developed to estimate land surface fluxes using remotely sensed data and available meteorology. In this study, a dual assessment of SEBS is performed using two independent, high-quality datasets that are collected during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX). The purpose of this comparison is twofold. First, using high-quality local-scale data, model-predicted surface fluxes can be evaluated against in situ observations to determine the accuracy limit at the field scale using SEBS. To accomplish this, SEBS is forced with meteorological data derived from towers distributed throughout the Walnut Creek catchment. Flux measurements from 10 eddy covariance systems positioned on these towers are used to evaluate SEBS over both corn and soybean surfaces. These data allow for an assessment of modeled fluxes during a period of rapid vegetation growth and varied hydrometeorology. Results indicate that SEBS can predict evapotranspiration with accuracies approaching 10%–15% of that of the in situ measurements, effectively capturing the temporal development of surface flux patterns for both corn and soybean, even when the evaporative fraction ranges between 0.50 and 0.90. Second, utilizing high-resolution remote sensing data and operational meteorology, a catchment-scale examination of model performance is undertaken. To extend the field-based assessment of SEBS, information derived from the Landsat Enhanced Thematic Mapper (ETM) and data from the North American Land Data Assimilation System (NLDAS) were combined to determine regional surface energy fluxes for a clear day during the field experiment. Results from this analysis indicate that prediction accuracy was strongly related to crop type, with corn predictions showing improved estimates compared to those of soybean. Although root-mean-square errors were affected by the limited number of samples and one poorly performing soybean site, differences between the mean values of observations and SEBS Landsat-based predictions at the tower sites were approximately 5%. Overall, results from this analysis indicate much potential toward routine prediction of surface heat fluxes using remote sensing data and operational meteorology.

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T. B. Parkin, T. C. Kaspar, Z. Senwo, J. H. Prueger, and J. L. Hatfield

Abstract

Soil respiration is an important component of the carbon dynamics of terrestrial ecosystems. Many factors exert controls on soil respiration, including temperature, soil water content, organic matter, soil texture, and plant root activity. This study was conducted to quantify soil respiration in the Walnut Creek watershed in central Iowa, and to investigate the factors controlling this process. Six agricultural fields were identified for this investigation: three of the fields were cropped with soybean [Glycine max (L.) Merr.] and three were cropped with corn (Zea mays L.). Within each field, soil respiration was measured at nine locations, with each location corresponding to one of three general landscape positions (summit, side slope, and depression). Soil respiration was measured using a portable vented chamber connected to an infrared gas analyzer. Soil samples were collected at each location for the measurement of soil water content, pH, texture, microbial biomass, and respiration potential. Field respiration rates did not show a significant landscape effect. However, there was a significant crop effect, with respiration from cornfields averaging 37.5 g CO2 m−2 day−1 versus an average respiration of 13.1 g CO2 m−2 day−1 in soybean fields. In contrast, laboratory measurements of soil respiration potential, which did not include plant roots, showed a significant landscape effect and an insignificant cropping system effect. Similar relationships were observed for soil organic C and microbial biomass. Additional analyses indicate that corn roots may be more important than soybean roots in their contribution to surface CO2 flux, and that root respiration masked landscape effects on total soil respiration. Also, the failure to account for soil respiration may lead to biased estimates of net primary production measured by eddy covariance.

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Evan J. Coopersmith, Michael H. Cosh, Walt A. Petersen, John Prueger, and James J. Niemeier

Abstract

Soil moisture monitoring with in situ technology is a time-consuming and costly endeavor for which a method of increasing the resolution of spatial estimates across in situ networks is necessary. Using a simple hydrologic model, the estimation capacity of an in situ watershed network can be increased beyond the station distribution by using available precipitation, soil, and topographic information. A study site was selected on the Iowa River, characterized by homogeneous soil and topographic features, reducing the variables to precipitation only. Using 10-km precipitation estimates from the North American Land Data Assimilation System (NLDAS) for 2013, high-resolution estimates of surface soil moisture were generated in coordination with an in situ network, which was deployed as part of the Iowa Flood Studies (IFloodS). A simple, bucket model for soil moisture at each in situ sensor was calibrated using four precipitation products and subsequently validated at both the sensor for which it was calibrated and other proximal sensors, the latter after a bias correction step. Average RMSE values of 0.031 and 0.045 m3 m−3 were obtained for models validated at the sensor for which they were calibrated and at other nearby sensors, respectively.

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D. W. Meek, J. H. Prueger, W. P. Kustas, and J. L. Hatfield

Abstract

Two eddy covariance instrument comparison studies were conducted before and after the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) field campaign to 1) determine if observations from multiple sensors were equivalent for the measured variables over a uniform surface and to 2) determine a least significant difference (LSD) value for each variable to discriminate between daily and hourly differences in latent and sensible heat and carbon dioxide fluxes, friction velocity, and standard deviation of the vertical wind velocity from eddy covariance instruments placed in different locations within the study area. The studies were conducted in early June over an alfalfa field and in mid-September over a short grass field. Several statistical exploratory, graphical, and multiple-comparison procedures were used to evaluate each daily variable. Daily total or average data were used to estimate a pooled standard error and corresponding LSD values at the P = 0.05 and P = 0.01 levels using univariate procedures. There were no significant sensor differences in any of the daily measurements for either intercomparison period. Hourly averaged data were used to estimate a pooled standard error and corresponding LSD values at the P = 0.05 and P = 0.01 levels using mixed model procedures. Sensor differences for pre- and post-intercomparisons were minimal for hourly and daily values of CO2, water vapor, sensible heat, friction velocity, and standard deviation for vertical wind velocity. Computed LSD values were used to determine significant daily differences and threshold values for the variables monitored during the SMACEX campaign.

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William P. Kustas, John H. Prueger, Karen S. Humes, and Patrick J. Starks

Abstract

Radiometric surface temperature observations T R(ϕ), near-surface meteorological/surface energy flux (METFLUX), and atmospheric boundary layer (ABL) data were collected during the Washita ’94 Experiment conducted in the Little Washita Experimental Watershed near Chickasha, Oklahoma. The T R(ϕ) measurements were made from ground and aircraft platforms near the METFLUX stations located over vegetated surfaces of varying amounts of cover and over bare soil. Continuous, half-hourly averaged ground-based T R(ϕ) measurements essentially at the point scale were calibrated with periodic ground transect and aircraft-based T R(ϕ) observations at coarser resolutions so that the continuous T R(ϕ) measurements would be representative of surface temperatures at the field scale (i.e., on the order of 104 m2). The METFLUX data were collected nominally at 2 m above the surface, while ABL measurements were made in the lower 8–10 km of the atmosphere. The “local” wind speed, u, and air temperature, T A, from the METFLUX stations, as well as the mixed-layer wind speed, U M, and potential temperature, ΘM, were used in a two-source energy balance model for computing fluxes with continuous T R(ϕ) measurements from the various surfaces. Standard Monin–Obukhov surface layer similarity was used with the “local” u and T A data from the METFLUX stations. Bulk similarity approaches were used with the U M and ΘM data referenced either to ABL height or the top of the surface layer. This latter approach of using mixed-layer data to drive model computations for the different sites is similar to the so-called flux-aggregation schemes or methods proposed to account for subgrid variability in atmospheric models, such as the“tile” or “mosaic” approach. There was less agreement between modeled and measured fluxes when using mixed-layer versus local meteorological variables data for driving the model, and the type of bulk formulation used (i.e., whether local or regional surface roughness was used) also had a significant impact on the results. Differences between the flux observations and model predictions using surface layer similarity with local u and T A data were about 25% on average, while using the bulk formulations with U M and ΘM differences averaged about 30%. This larger difference was caused by an increase in biases and scatter between modeled and measured fluxes for some sites. Therefore, computing spatially distributed local-scale fluxes with ABL observations of mixed-layer properties will probably yield less reliable flux predictions than using local meteorological data, if available. Given the uncertainty in flux observations is about 20%, these estimates are still considered reasonable and moreover permit the mapping of spatially distributed surface fluxes at regional scales using a single observation of U M and ΘM with high resolution T R(ϕ) data. Such T R(ϕ) observations with a 90-m pixel resolution will be available from the Advanced Spaceborne Thermal Emission and Reflection Radiometer to be launched on NASA’s Earth Observing System.

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William P. Kustas, John H. Prueger, J. Ian MacPherson, Mengistu Wolde, and Fuqin Li

Abstract

Eddy covariance measurements of wind speed u and shear velocity u * from tower- and aircraft-based systems collected over rapidly developing corn- (Zea mays L.) and soybean [Glycine max (L.) Merr.] fields were used in determining the local and regional (effective) surface roughness length zo and 〈zo〉, respectively. For corn, canopy height increased from ∼1 to 2 m and the leaf area index changed from ∼1 to 4 during the study period, while for soybean, canopy height increased from ∼0.1 to 0.5 m and the leaf area index increased from ∼0.5 to 2. A procedure for the aggregation of local roughness values from the different land cover types based on blending-height concepts yielded effective surface roughness values that were from ∼1/2 to 1/4 of the magnitude estimated with the aircraft data. This indicated additional kinematic stress caused by form drag from isolated obstacles (i.e., trees, houses, and farm buildings), and the interaction of adjacent corn- and soybean fields were probably important factors influencing the effective surface roughness length for this landscape. The comparison of u * measurements from the towers versus the aircraft indicated that u * from aircraft was 20%–30% higher, on average, and that u * over corn was 10%–30% higher than over soybean, depending on stability. These results provide further evidence for the likely sources of additional kinematic stress. Although there was an increase in zo and 〈zo〉 over time as the crops rapidly developed, particularly for corn, there was a more significant trend of increasing roughness length with decreasing wind speed at wind speed thresholds of around 5 m s−1 for the aircraft and 3 m s−1 for the tower measurements. Other studies have recently reported such a trend. The impact on computed sensible heat flux H using 〈zo〉 derived from the aggregation of zo from the different land cover types, using the blending-height scheme, and that estimated from the aircraft observations, was evaluated using a calibrated single-source/bulk resistance approach with surface–air temperature differences from the aircraft observations. An underestimate of 〈zo〉 by 50% and 75% resulted in a bias in the H estimates of approximately 10% and 15%, respectively. This is a relatively minor error when considering that the root-mean-square error (rmse) value between single-source estimates and the aircraft observations of H was 15 W m−2 using the aircraft-derived 〈zo〉, and only increased to approximately 20 and 25 W m−2 using the 1/2 and 1/4 〈zo〉 values, as estimated from the blending-height scheme. The magnitude of the excess resistance relative to the aerodynamic resistance to heat transfer was a major contributing factor in minimizing the error in heat flux calculations resulting from these underestimations of 〈zo〉.

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Fuqin Li, William P. Kustas, John H. Prueger, Christopher M. U. Neale, and Thomas J. Jackson

Abstract

Two resistance network formulations that are used in a two-source model for parameterizing soil and canopy energy exchanges are evaluated for a wide range of soybean and corn crop cover and soil moisture conditions during the Soil Moisture–Atmosphere Coupling Experiment (SMACEX). The parallel resistance formulation does not consider interaction between the soil and canopy fluxes, whereas the series resistance algorithms provide interaction via the computation of a within-air canopy temperature. Land surface temperatures were derived from high-resolution Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM) scenes and aircraft imagery. These data, along with tower-based meteorological data, provided inputs for the two-source energy balance model. Comparison of the local model output with tower-based flux observations indicated that both the parallel and series resistance formulations produced basically similar estimates with root-mean-square difference (RMSD) values ranging from approximately 20 to 50 W m−2 for net radiation and latent heat fluxes, respectively. The largest relative difference in percentage [mean absolute percent difference (MAPD)] was for sensible heat flux, which was ≈35%, followed by a MAPD ≈ 25% for soil heat flux, ≈10% for latent heat flux, and a MAPD < 5% for net radiation. Although both series and parallel versions gave similar results, the parallel resistance formulation was found to be more sensitive to model parameter specification, particularly in accounting for the effects of vegetation clumping resulting from row crop planting on flux partitioning. A sensitivity and model stability analysis for a key model input variable, that is, fractional vegetation cover, also show that the parallel resistance network is more sensitive to the errors vegetation cover estimates. Furthermore, it is shown that for a much narrower range in vegetation cover fraction, compared to the series resistance network, the parallel resistance scheme is able to achieve a balance in both the radiative temperature and convective heat fluxes between the soil and canopy components. This result appears to be related to the moderating effects of the air temperature in the canopy air space computed in the series resistance scheme, which represents the effective source height for turbulent energy exchange across the soil–canopy–atmosphere system.

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Martha C. Anderson, J. M. Norman, William P. Kustas, Fuqin Li, John H. Prueger, and John R. Mecikalski

Abstract

The effects of nonrandom leaf area distributions on surface flux predictions from a two-source thermal remote sensing model are investigated. The modeling framework is applied at local and regional scales over the Soil Moisture–Atmosphere Coupling Experiment (SMACEX) study area in central Iowa, an agricultural landscape that exhibits foliage organization at a variety of levels. Row-scale clumping in area corn- and soybean fields is quantified as a function of view zenith and azimuth angles using ground-based measurements of canopy architecture. The derived clumping indices are used to represent subpixel clumping in Landsat cover estimates at 30-m resolution, which are then aggregated to the 5-km scale of the regional model, reflecting field-to-field variations in vegetation amount. Consideration of vegetation clumping within the thermal model, which affects the relationship between surface temperature and leaf area inputs, significantly improves model estimates of sensible heating at both local and watershed scales in comparison with eddy covariance data collected by aircraft and with a ground-based tower network. These results suggest that this economical approach to representing subpixel leaf area hetereogeneity at multiple scales within the two-source modeling framework works well over the agricultural landscape studied here.

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Christopher J. Watts, Russell L. Scott, Jaime Garatuza-Payan, Julio C. Rodriguez, John H. Prueger, William P. Kustas, and Michael Douglas

Abstract

The vegetation in the core region of the North American monsoon (NAM) system changes dramatically after the onset of the summer rains so that large changes may be expected in the surface fluxes of radiation, heat, and moisture. Most of this region lies in the rugged terrain of western Mexico and very few measurements of these fluxes have been made in the past. Surface energy balance measurements were made at seven sites in Sonora, Mexico, and Arizona during the intensive observation period (IOP) of the North American Monsoon Experiment (NAME) in summer 2004 to better understand how land surface vegetation change alters energy flux partitioning. Satellite data were used to obtain time series for vegetation indices and land surface temperature for these sites. The results were analyzed to contrast conditions before the onset of the monsoon with those afterward. As expected, precipitation during the 2004 monsoon was highly variable from site to site, but it fell in greater quantities at the more southern sites. Likewise, large changes in the vegetation index were observed, especially for the subtropical sites in Sonora. However, the changes in the broadband albedo were very small, which was rather surprising. The surface net radiation was consistent with the previous observations, being largest for surfaces that are transpiring and cool, and smallest for surfaces that are dry and hot. The largest evaporation rates were observed for the subtropical forest and riparian vegetation sites. The evaporative fraction for the forest site was highly correlated with its vegetation index, except during the dry spell in August. This period was clearly detected in the land surface temperature data, which rose steadily in this period to a maximum at its end.

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W. E. Eichinger, H. E. Holder, R. Knight, J. Nichols, D. I. Cooper, L. E. Hipps, W. P. Kustas, and J. H. Prueger

Abstract

The Soil Moisture–Atmosphere Coupling Experiment (SMACEX) was conducted in the Walnut Creek watershed near Ames, Iowa, over the period from 15 June to 11 July 2002. A main focus of SMACEX is the investigation of the interactions between the atmospheric boundary layer, surface moisture, and canopy. A vertically staring elastic lidar was used to provide a high-time-resolution continuous record of the boundary layer height at the edge between a soybean and cornfield. The height and thickness of the entrainment zone are used to estimate the surface sensible heat flux using the Batchvarova–Gryning boundary layer model. Flux estimates made over 6 days are compared to conventional eddy correlation measurements. The calculated values of the sensible heat flux were found to be well correlated (R 2 = 0.79, with a slope of 0.95) when compared to eddy correlation measurements in the area. The standard error of the flux estimates was 21.4 W m−2 (31% rms difference between this method and surface measurements), which is somewhat higher than a predicted uncertainty of 16%. The major sources of error were from the estimates of the vertical potential temperature gradient and an assumption that the entrainment parameter A was equal to the ratio of the entrainment flux and the surface heat flux.

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