Multifilter rotating shadowband radiometers are deployed in the United States, Canada, and New Zealand by the USDA
(United States Department of Agriculture) UV-B (ultraviolet-B) Monitoring and Research Program to measure UV-B
irradiances at seven discrete wavelengths. A synthetic model is used to construct the continuous spectral distribution,
from which irradiance integrals can be performed for various purposes. The derived spectral data are posted for public
use through a web accessible database. Although the synthetic model has been validated with a certain data set, few
works have been seen to compare the results of the synthetic model with simulations of other widely accepted models
such as TUV. Through this comparison the validation of the synthetic model can be further confirmed and alternative
techniques for constructing spectral irradiances from discrete narrowband measurements can also be explored.
In this study the data from the USDA UV-B Monitoring and Research Program are used to evaluate the synthetic model
and to explore the capability of the TUV model for constructing continuous spectra from discrete measurements.
Simulations of the TUV model are compared with discrete measurements, erythema-weighted broadband measurements,
and the results of the synthetic model. Good agreements between derived results by using TUV model and the synthetic
model with measurements in general further confirm the validation of the synthetic model. Generally, the spectral
irradiances constructed by using synthetic model are lower than those by using the TUV model at very shorter
wavelengths (<301 nm) and at the wavelengths of 315-342 nm, but are higher at other wavelengths. The ratio of
erythemal doses derived by using the TUV simulation to broadband measurements varies between 0.87-1.02.
Constructed erythemal doses by using the TUV simulation are closer to broadband measurements than those obtained by
using the synthetic model. These results suggest that the TUV model may be a good alternative to accurately estimate
continuous spectral distributions from discrete measurements.
Field experiments and laboratory tests have shown multiple effects of enhanced ultraviolet-B (UV-B) radiation on cotton
growth, development, and yield. Adverse effects include development of chlorotic and necrotic patches on leaves,
reductions in total leaf area, plant height, photosynthesis, and yield. However, little work has been carried out to
incorporate these experimental results into a simulation model and to estimate the effects of UV-B radiation under field
conditions with varied environments and management practices. This study incorporates experimental results of UV-B
effects on cotton crop into a cotton simulation model, GOSSYM, which is being used widely in various applications. In
this work, first modules were modified to incorporate the effects of UV-B radiation on canopy photosynthesis, leaf area
expansion, and stem and branch elongation. Then, the modified model was used to test the validity of model assumptions
and algorithms on independent experimental data sets. Finally, preliminary studies were performed to simulate the
effects of UV-B radiation in the field conditions at Stoneville, Mississippi using 30-year (1964-1993) climate data.
Simulation results agreed well with experimental measurements, proving the validation of the model. Our results suggest
that cotton lint yield declined with increased UV-B radiation. The reductions were 20% when UV-B irradiance was 12
kJ m-2 under irrigated conditions. Similar reductions in yield were predicted at lower UV-B radiation (11 kJ m-2) under
rain-fed conditions. The modified model will be useful to simulate the impacts of UV-B radiation on cotton growth and
yield under current and future climatic conditions and to suggest management options to mitigate the adverse effects.
Ultraviolet (UV) radiation is the source energy for tropospheric photolysis processes, while harmful for living
organism of the earth. It is thus necessary to incorporate UV radiation for an integrated earth modeling system to predict
interactions between climate, chemistry and ecosystem processed. The widely-used NCAR TUV (Tropospheric
Ultraviolet and Visible) radiation model has been coupled with the state-of-the-art mesoscale CWRF (Climate extension
of the Weather Research and Forecasting model) to predict the UV dependence of local climate conditions and its
impacts on air quality and crop growth. The original TUV v4.2 has been significantly improved by (1) replacing the core
radiation transfer solver, DISORT v1.1 with the latest v2.0beta; (2) adding a new aerosol scheme based on the Shettle
(1989); (3) recoding the entire model to follow the CWRF F90 standard with dynamic memory allocation and modular
design; and (4) developing a flexible interface for coupling with CWRF.
Given the lack of detailed cloud information in observations, this study focuses on validation of the TUV module
in a standalone mode against the USDA UV-B data under clear-sky conditions. To facilitate this, a cloud detection
scheme based on Long and Ackerman (2000) is incorporated to distinguish clear versus cloudy sky conditions from the
UV-B observations. The model input includes in situ measurements of the column ozone and total aerosol optical depth;
TOMS retrievals of the column ozone (in case missing in situ) and climatologically surface reflectivity; and the NARR
(North American Regional Analysis) meteorological conditions. The TUV results agree well with the UV-B
measurements at 7 narrow spectral bands (300, 305, 311, 317, 325, 332, 368 nm).
The continuing rise in atmospheric CO2 is considered as a main cause of the future changes in global climate. Predicted climate changes include an increase in mean annual air temperature and alterations in precipitation pattern and cloud cover. Elevated atmospheric CO2 and climate changes are expected to influence the ecosystems. The regional climate models (RCMs) will likely remain primary tools for climate prediction in the foreseeable future. The importance of RCMs is increasing in addressing scientific problems associated with climate variability, changes, and impacts at regional scales. The RCMs have been also used in climate impact studies on ecosystems, especially in agricultural crops by generating climate scenarios for input to crop models. With a large volume of satellite remote sensing data of the earth terrestrial surface becoming available, precisely monitoring the dynamics of the land surface state variables for agricultural and land use management becomes possible6. With the effort to study the climate crop interactions we plan to use a CWRF model (a climate extension of the Weather Research and Forecasting model-WRF) developed by the Illinois State Water Survey to form the climate scenarios. The WRF model is based upon the most advanced supercomputing technologies and promises greater efficiency in computation and flexibility in new module incorporation. This extension inclusively incorporates all WRF functionalities for numerical weather predictions while enhancing the capability for climate applications. To represent
the surface-atmosphere interactions the CWRF requires specification of surface boundary conditions (SBCs) over both land and oceans. A comprehensive set of SBCs based on best observational data is desired for CWRF general applications for all effective, dynamically coupled or uncoupled, combinations of the surface modules, as well as for any specific region of the world. This report followed the approach of Liang et al. presents a preliminary work to construct vegetative SBCs for the CWRF modeling effort in China domain by using remote sensing data from TM, AVHRR, MODIS which are freely available. The full list of the CWRF SBCs was defined by Liang.
GOSSYM is a comprehensive crop growth model that has been continuously developed since the late 1970s and widely applied to assist cotton growers, crop consultants, and researchers. The state-of-art CWRF (Climate-Weather Research and Forecasting model) demonstrated skillful simulations of regional water and energy cycle processes that are keenly important to cotton growth. This study focuses on coupling GOSSYM and CWRF to study crop-climate interactions. The coupling procedures include (1) recoding the GOSSYM to follow the CWRF F90 modular implementation; (2) replacing the soil dynamic module of the GOSSYM with the CWRF-predicted soil temperature and moisture while integrating the crop field management or cultural practice component (e.g., irrigation, tillage); (3) providing the GOSSYM with surface air temperature, precipitation, and surface solar radiation from the CWRF; (4) constructing crop height and coverage, leaf and stem area indices, greenness and root profile from the GOSSYM as inputs for the CWRF to represent the crop feedback on solar albedo and infrared emissivity, precipitation interception, and evapotranspiration. This study presents the preliminary results of the GOSSYM driven by the CWRF simulated climate conditions and discusses the model performance on cotton yield, leaf area index and height and their responses to water stress under the irrigation and non-irrigation conditions.
A new parameterization of snow-free land surface albedo is developed using the MODerate resolution Imaging Spectroradiometer (MODIS) products of broadband black-sky and white-sky reflectance and vegetation information as well as the North American and Global Land Data Assimilation System (LDAS) outputs of soil moisture during 2000-20003. It represents the predictable albedo dependences on solar zenith angle, surface soil moisture, fractional vegetation cover, and leaf plus stem area index, while including a statistic correction for static effects specific of local surface characteristics. All parameters are estimated by solving optimization problems of a physically based conceptual model for the minimization of the bulk variances between simulations and observations. A preliminary result showed that, for composites of all temporal and spatial samples of a same land cover category over North America, correlation coefficients between the new parameterization with the MODIS data range from 0.6 to 0.9, while relative errors vary within 5-20%. This is a substantial improvement over the existing state-of-the art Common Land Model (CLM) abide scheme, which has correlation coefficients from -0.5 to 0.5 and relative errors of 20-100%.