Sources of differences between observations and simulations for a case study using the Noah land surface model-based High-Resolution Land Data Assimilation System (HRLDAS) are examined for sensible ...and latent heat fluxes H and LE, respectively; surface temperature Tsub s; and vertical temperature difference Tsub 0 - Tsub s, where Tsub 0 is at 2 m. The observational data were collected on 29 May 2002, using the University of Wyoming King Air and four surface towers placed along a sparsely vegetated 60-km north-south flight track in the Oklahoma Panhandle. This day had nearly clear skies and a strong north-south soil-moisture gradient, with wet soils and widespread puddles at the south end of the track and drier soils to the north. Relative amplitudes of H and LE horizontal variation were estimated by taking the slope of the least squares best-fit straight line LE/H on plots of time-averaged LE as a function of time-averaged H for values along the track. It is argued that observed H and LE values departing significantly from their slope line are not associated with surface processes and, hence, need not be replicated by HRLDAS. Reasonable agreement between HRLDAS results and observed data was found only after adjusting the coefficient C in the Zilitinkevich equation relating the roughness lengths for momentum and heat in HRLDAS from its default value of 0.1 to a new value of 0.5. Using C = 0.1 and adjusting soil moisture to match the observed near-surface values increased horizontal variability in the right sense, raising LE and lowering H over the moist south end. However, both the magnitude of H and the amplitude of its horizontal variability relative to LE remained too large; adjustment of the green vegetation fraction had only a minor effect. With C = 0.5, model-input green vegetation fraction, and our best-estimate soil moisture, H, LE, LE/H, and Tsub 0 - Tsub s, were all close to observed values. The remaining inconsistency between model and observations-too high a value of H and too low a value of LE over the wet southern end of the track-could be due to HRLDAS ignoring the effect of open water. Neglecting the effect of moist soils on the albedo could also have contributed. PUBLICATION ABSTRACT
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Water-limiting conditions in many California vineyards necessitate assessment of vine water stress to aid irrigation management strategies and decisions. This study was designed to evaluate the ...utility of a Crop Water Stress Index (CWSI) using multiple canopy temperature sensors and to study the diurnal signature in the stress index of an irrigated vineyard. A detailed instrumentation package comprised of eddy covariance instrumentation, ancillary surface energy balance components, soil water content sensors and a unique multi-canopy temperature sensor array were deployed in a production vineyard near Lodi, CA. The instrument package was designed to measure and monitor hourly growing season turbulent fluxes of heat and water vapor, radiation, air temperature, soil water content directly beneath a vine canopy, and vine canopy temperatures. April 30–May 02, June 10–12 and July 27–28, 2016 were selected for analysis as these periods represented key vine growth and production phases. Considerable variation in computed CWSI was observed between each of the hourly average individual canopy temperature sensors throughout the study; however, the diurnal trends remained similar: highest CWSI values in morning and lowest in the late afternoon. While meteorological conditions were favorable for plant stress to develop, soil water content near field capacity due to frequent irrigation allowed high evapotranspiration rates resulting in downward trending CWSI values during peak evaporative demand. While the CWSI is typically used to evaluate plant stress under the conditions of our study, the trend of the CWSI suggested a lowering of plant water stress as long as there was adequate soil water available to meet atmospheric demand.
► Thermal energy balance model performance is evaluated under advective conditions. ► Use of remote meteorological data had a minor effect on DTD model performance. ► Variation in LST and LAI ...significantly affected both TSEB and DTD model output. ► With representative LST and LAI inputs, TSEB and DTD models compute reliable ET.
Application and validation of many thermal remote sensing-based energy balance models involve the use of local meteorological inputs of incoming solar radiation, wind speed and air temperature as well as accurate land surface temperature (LST), vegetation cover and surface flux measurements. For operational applications at large scales, such local information is not routinely available. In addition, the uncertainty in LST estimates can be several degrees due to sensor calibration issues, atmospheric effects and spatial variations in surface emissivity. Time differencing techniques using multi-temporal thermal remote sensing observations have been developed to reduce errors associated with deriving the surface-air temperature gradient, particularly in complex landscapes. The Dual-Temperature-Difference (DTD) method addresses these issues by utilizing the Two-Source Energy Balance (TSEB) model of Norman et al. (1995) 1, and is a relatively simple scheme requiring meteorological input from standard synoptic weather station networks or mesoscale modeling. A comparison of the TSEB and DTD schemes is performed using LST and flux observations from eddy covariance (EC) flux towers and large weighing lysimeters (LYs) in irrigated cotton fields collected during BEAREX08, a large-scale field experiment conducted in the semi-arid climate of the Texas High Plains as described by Evett et al. (2012) 2. Model output of the energy fluxes (i.e., net radiation, soil heat flux, sensible and latent heat flux) generated with DTD and TSEB using local and remote meteorological observations are compared with EC and LY observations. The DTD method is found to be significantly more robust in flux estimation compared to the TSEB using the remote meteorological observations. However, discrepancies between model and measured fluxes are also found to be significantly affected by the local inputs of LST and vegetation cover and the representativeness of the remote sensing observations with the local flux measurement footprint.
Accurately representing complex land-surface processes balancing complexity and realism remains one challenge that the weather modelling community is facing nowadays. In this study, a ...photosynthesis-based Gas-exchange Evapotranspiration Model (GEM) is integrated into the Noah land-surface model replacing the traditional Jarvis scheme for estimating the canopy resistance and transpiration. Using 18-month simulations from the High Resolution Land Data Assimilation System (HRLDAS), the impact of the photosynthesis-based approach on the simulated canopy resistance, surface heat fluxes, soil moisture, and soil temperature over different vegetation types is evaluated using data from the Atmospheric Radiation Measurement (ARM) site, Oklahoma Mesonet, 2002 International H₂O Project (IHOP_2002), and three Ameriflux sites. Incorporation of GEM into Noah improves the surface energy fluxes as well as the associated diurnal cycle of soil moisture and soil temperature during both wet and dry periods. An analysis of midday, average canopy resistance shows similar day-to-day trends in the model fields as seen in observed patterns. Bias and standard deviation analyses for soil temperature and surface fluxes show that GEM responds somewhat better than the Jarvis scheme, mainly because the Jarvis approach relies on a parametrised minimum canopy resistance and meteorological variables such as air temperature and incident radiation. The analyses suggest that adding a photosynthesis-based transpiration scheme such as GEM improves the ability of the land-data assimilation system to simulate evaporation and transpiration under a range of soil and vegetation conditions.
•Effect of the calculation method for evaluation height using the flux-gradient approach was tested.•The analysis focused on metolachlor data collected over an eight year period.•Under unstable ...conditions, historical estimation methods caused a 15% error in the flux, on average.•The results indicate that the exact evaluation height should be used whenever possible.
Volatilization represents a significant loss pathway for many pesticides, herbicides and other agrochemicals. One common method for measuring the volatilization of agrochemicals is the flux-gradient method. Using this method, the chemical flux is estimated as the product of the vertical concentration gradient and a turbulent-transfer coefficient (eddy diffusivity). For computational simplicity, the evaluation height needed to calculate the eddy diffusivity is typically approximated as either the geometric or logarithmic mean. Both of these estimation methods are based on simplifying assumptions and can be a significant source of error, particularly when the separation distance between the measurement heights is large. Using data collected over an eight-year period at the USDA-ARS OPE3 experimental watershed, this study compared fluxes of metolachlor, a commonly-used herbicide, computed using the approximated evaluation heights with those calculated using the exact evaluation height. While it was found that the primary factor influencing the accuracy of the flux estimates using the approximate evaluation heights was atmospheric stability, errors in the estimate of the evaluation height can result in significant (>10%) errors in the flux estimates. Based on these results, it is recommended that the exact evaluation height be used with the flux-gradient technique.
Vegetated surfaces, such as grasslands and croplands, constitute a significant portion of the earth's surface and play an important role in land-atmosphere exchange processes. This study focuses on ...one important parameter used in describing the exchange of moisture from vegetated surfaces: the minimum canopy resistance (rsub csub min). This parameter is used in the Jarvis canopy resistance scheme that is incorporated into the Noah and many other land surface models. By using an inverted form of the Jarvis scheme, rsub csub min is determined from observational data collected during the 2002 International H2O Project (IHOP_2002). The results indicate that rsub csub min is highly variable both site to site and over diurnal and longer time scales. The mean value at the grassland sites in this study is 96 s msup -1 while the mean value for the cropland (winter wheat) sites is one-fourth that value at 24 s msup -1. The mean rsub csub min for all the sites is 72 s msup -1 with a standard deviation of 39 s msup -1 . This variability is due to both the empirical nature of the Jarvis scheme and a combination of changing environmental conditions, such as plant physiology and plant species composition, that are not explicitly considered by the scheme. This variability in rsub csub min has important implications for land surface modeling where rsub csub min is often parameterized as a constant. For example, the Noah land surface model parameterizes rsub csub min for the grasslands and croplands types in this study as 40 s msup -1. Tests with the coupled Weather Research and Forecasting (WRF)-Noah model indicate that the using the modified values of rsub csub min from this study improves the estimates of latent heat flux; the difference between the observed and modeled moisture flux decreased by 50% or more. While land surface models that estimate transpiration using Jarvis-type relationships may be improved by revising the rsub csub min values for grasslands and croplands, updating the rsub csub min will not fully account for the variability in rsub csub min observed in this study. As such, it may be necessary to replace the Jarvis scheme currently used in many land surface and numerical weather prediction models with a physiologically based estimate of the canopy resistance. PUBLICATION ABSTRACT
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
► We model spatially distributed energy balance fluxes and evapotranspiration. ► We use high resolution airborne multispectral and thermal infrared imagery. ► We compare modeled fluxes and soil water ...content with measurements. ► Estimated soil water content is assimilated and used to update water balance. ► Improvements in soil water content estimates in the soil profile are obtained.
Remote sensing of evapotranspiration (ET) has evolved over the last 20 years with the development of more robust energy balance approaches and the availability of timely remotely sensed imagery from satellite sensors. This has allowed the use of remote sensing for near-real time water management in irrigated systems in the western United States. In this paper a hybrid ET approach is applied to irrigated and non-irrigated cotton fields at the BEAREX08 experimental site using airborne remote sensing inputs under highly advective conditions, taking advantage of the available root zone soil water content measurements for verification of model output. The modeling approach is based on coupling the Two-Source-Energy Balance (TSEB) and the reflectance-based crop coefficient models. The TSEB model provides estimates of real crop ET while the reflectance-based crop coefficient approach allows for updating the basal crop coefficient and the interpolation and extrapolation of ET between the dates of remote sensing inputs facilitating the maintenance of a soil water balance in the root zone of the crop. Actual ET estimates using the TSEB model were compared with measured ET using eddy covariance systems deployed in four cotton fields during the BEAREX08 experiment. Estimates of soil water content in the soil profile of both irrigated and rain fed cotton fields were compared with measurements at different depths using neutron probe observations. Data assimilation techniques were applied to update soil water content values using estimates based on actual ET from the TSEB model. Results indicate that the hybrid ET modeling approach using data assimilation produced reliable daily ET interpolated between remote sensing observations and significantly improved soil water content estimates throughout the root zone profile compared to applying the crop-coefficient technique in a water balance model without the actual ET inputs.
Vineyards in many semi-arid regions globally face limited water resources. Monitoring evapotranspiration (ET) of vineyards is critical for water resource management, but remains difficult due to the ...complex biophysics of the surfaces. Both measurement and modeling approaches for estimating turbulent water vapor transport rely on implicit assumptions that exchanges occur in a reasonably regular fashion over the time scales generally used for averaging. However, heterogeneous vegetation in semi-arid climates, such as many vineyards, presents inherent factors, including canopy row/row space structure and frequent periods of light wind, unstable conditions, that can create episodic transport characteristics. Eddy covariance data were collected above and within the canopy of two vineyards in the Central Valley of California during the Grape Remote sensing Atmospheric Profile & Evapotranspiration eXperiment (GRAPEX). The goal was to document and quantify the existence of intermittent turbulence transport of water vapor, and associated episodic canopy venting. These effects were found to correlate with periods light winds and highly unstable/convective conditions. Power and cross-spectra for intermittent periods documented enhancement of low-frequency water vapor exchange events compared to more steady periods, and diminished time scale correlation between humidity within the canopy and above the canopy. Analyses show that intermittent cases can necessitate longer flux-averaging periods (up to 2 h) than more steady conditions. Episodic exchange events were isolated and summed to determine their relative contribution to the overall water vapor flux. Since light wind, unstable conditions are relatively common in many arid vineyard regions, these findings have implications for mechanistic ET models that rely on time-averaged vertical gradients, which implies reasonably steady transport.
Surface renewal (SR) is a biometeorological technique that uses high frequency air temperature measurements above a crop surface to estimate sensible heat flux (
H
). The
H
derived from SR is then ...combined with net radiation (
R
n
) and ground heat flux (
G
) measurements to estimate latent heat flux (LE) as the residual of an energy balance equation. Recent advances in SR theory enabled its use beyond research settings, and led to the development of an inexpensive, stand-alone SR system for use in commercial agricultural settings. However, these commercial applications require replacing expensive net radiometers with clear sky models designed to estimate
R
n
for the energy balance approach, while also assuming
G
is zero on a daily basis. The accuracy of substituting
R
n
measurements with modelled values is unknown, and the assumption of an inconsequential
G
requires additional testing. Here, we compare the accuracy of the SR derived estimates of
H
and LE when
R
n
is either measured directly or modelled, and we compare results to two eddy covariance (EC) LE observations, namely LE measured via EC with an infrared gas analyzer (EC
IRGA
) and LE solved as a residual in the surface energy balance (EC
resid
). These measurements were collected at the Grape Remote sensing Atmospheric Profile & Evapotranspiration eXperiment (GRAPEX) conducted over a vineyard within the Lodi, CA wine growing region. LE from SR using tower
R
n
data measured directly onsite was significantly correlated with LE from EC
resid
and from EC
IRGA
with a least squares regression slope ~ 1. LE derived with the modelled incoming solar radiation (SWi) and DisALEXI
R
n
approaches were also significantly correlated with LE from EC
resid
, but both modelling approaches overestimated LE at higher fluxes. Patterns were similar, but with more scatter for correlations between LE from EC
IRGA
and LE from SR using either modelled or remotely sensed
R
n
. Incorporating direct measurements of
G
had minimal impact on the agreement of several SR approaches and LE from both EC approaches, however, when differences did occur direct measures of
G
reduced scatter and bias especially for the empirical SR approach. Our results suggest that LE derived from the new SR method requires fairly accurate
R
n
modelling approaches to obtain reliable and unbiased estimates of daily LE in comparison to measured LE using EC techniques.
Display omitted
•Assessed common metrics with data from 34 cropland, grazing & integrated systems.•Compared production and phenology metrics from eddy covariance, PhenoCam and Landsat.•Correlations ...among metrics varied across diverse U.S. agroecosystems.•Devised a metric assessment framework to streamline decision making for monitoring.
Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series.
Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam GCC (70.4 days, padj < 0.0001) and EC GPP (79.6 days, padj < 0.0001). Landscape heterogeneity did not explain differences in SOS and EOS estimates. Annual integrated estimates of productivity from EC GPP and PhenoCam GCC diverged from those estimated by Landsat GPP and NDVI at sites where annual production exceeds 1000 gC/m−2 yr−1. Based on our results, we developed a “metric assessment framework” that articulates where and how metrics from satellite, eddy covariance and PhenoCams complement, diverge from, or are redundant with one another. The framework was designed to optimize instrumentation selection for monitoring, modeling, and forecasting ecosystem functioning with the ultimate goal of informing decision-making by land managers, policy-makers, and industry leaders working at multiple scales.
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