Nooshin Ahmadibaseri; A.A. Sabziparvar; M. Khodamoradpour; L. Alados Arboledas
Abstract
Introduction: Surface Solar Radiation (SSR) as the largest source of land-surface energy is an important parameter in meteorological and climatological studies. Limitations in ground-based measurements have encouraged the users to approach low cost and reliable methods to estimate radiation components, ...
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Introduction: Surface Solar Radiation (SSR) as the largest source of land-surface energy is an important parameter in meteorological and climatological studies. Limitations in ground-based measurements have encouraged the users to approach low cost and reliable methods to estimate radiation components, for the regions where the ground-based radiation data are sparse. Different methods have been developed for estimating SSR including empirical models, radiative transfer models, semi-empirical models, and models based on satellite and reanalysis products. In most studies in Iran, empirical methods have been investigated. Despite the simplicity of these models, they do not accurately represent SSR variations because of not considering all the parameters affecting radiation variations, at large spatial scales with different climates. The Global Land Data Assimilation System (GLDAS) is a combination of measured and satellite data that uses advanced land surface modeling and data assimilation methods. One of the strengths of this model that makes GLDAS unique is that it has global coverage, high spatial-temporal resolution and is available for free. GLDAS is a terrestrial modeling system uncoupled to the atmosphere. This work was aimed to evaluate SSR derived from GLDAS using ground measurements over Iran from 2012 to 2015 on a daily basis.
Materials and Methods: In this study, measured SSR in 24 radiometer stations of Iran from 2012 to 2015 was extracted. Since the measured data are associated with some errors, the quality of the data must be checked and screened before use. In this study, Moradi's proposed method was used to control data quality. The studied areas were classified into three zones of coastal, arid and semi-arid climates based on Digital Elevation Model (DEM) and UNESCO climate classification approach. The GLDAS SSR outputs were extracted with a spatial and temporal resolution of 0.25° grid cell and 3-hourly from 2012 to 2015. The GLDAS is one of the LDAS projects and has been extended jointly by the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) and the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP). The purpose of GLDAS is to produce high quality temporal and spatial land surface data. GLDAS drives three land surface models: Mosaic, Noah, and CLM. GLDAS assessments SSR at the land surface using a method and cloud and snow products from the Air Force Weather Agency's (AFWA) Agricultural Meteorology modeling system (AGRMET). Since the GLDAS data are created using the gridded Binary format, the nearest neighborhood interpolation method was used to match these data with ground-based data and GLDAS datasets were generated for station points using CDO software. In this study, GLDAS datasets were compared against measured SSR datasets by four validation metrics. The metrics used are determination coefficient (R2), the mean bias error (MBD), the mean absolute error (MABD), relative mean absolute error (RMABD) and root mean squared error (RMSE).
Results and Discussion: Statistical analysis showed that the performance of GLDAS in SSR evaluation is reasonable in Iran with a high-efficiency coefficient of 0.88. Also, it was shown that the GLDAS has a higher ability to estimate SSR under clear sky (warm seasons) conditions than cloudy conditions (cold seasons). Similar to the obtained results, Träger-Chatterjee et al. (2010); Jia et al. (2013); Boilley and Wild (2015) and Heidary Beni and Yazdanpanah (2017) also showed that the ERA- Interim, NCEP-DOE, RegCM4 and angstrom model are also more capable of estimating SSR in warm seasons. Seasonal bias variations at three studied areas showed that the most changes occurred in summer and least changes in winter. The highest overestimation was also observed in the coastal areas in summer and the lowest overestimation in the semi-arid regions in spring. The evaluation of the GLDAS performance against the site measured SSR data suggests that the GLDAS tends to underestimate in 71% of the studied stations. Moreover, the stations located in the arid region provided a better estimation of SSR as compared with semi-arid and coastal locations. These results were compared with those of Boilley and Wald (2015) that showed ERA-Interim and MERRA reanalysis models have high uncertainty in areas with tropical humid climates, and in regions with arid climates, models perform better in SSR estimation. Our findings were also in good agreement with their results.
Conclusion: GLDAS SSR outputs can be used for agricultural studies. This is due to the facts that arid and semi-arid climates are dominant in Iran and the growing season is mostly in the warm season.
Ali Chavoshian; P.S. Katiraie-Boroujerdy
Abstract
Introduction: Precipitation has an important role not only in the variety of scientific applications including climate change, climate simulations, weather modeling, and forecasting but also in decision making such as water management, hydrology, agriculture, drought, and crisis management. Different ...
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Introduction: Precipitation has an important role not only in the variety of scientific applications including climate change, climate simulations, weather modeling, and forecasting but also in decision making such as water management, hydrology, agriculture, drought, and crisis management. Different temporal resolutions and coverages of data are required for this and other applications. For example, long term meteorological data are needed for monitoring the climate variability and trends and for climate simulation assessments in local and global scales. Also, present data are used to assimilate into forecast models to improve the predictions. Historical and present precipitation data are the main requirements to monitor and predict droughts which help to early warning system and water management decisions in a country. The recent rainfall data are also the primary input of hydrological models to flood forecast in a basin. The accurate estimation of precipitation amount is vital for these applications.
Materials and Methods: However, rainfall is discontinuous and varies greatly both in time and space which makes it parallel with difficulties in the actual measurements. The two main sources of observational precipitation datasets are ground-based rain gauge measurements and space-based remote sensing satellite estimations each one with its own limitations and strengths. Historically, rain-gauge measurements have been considered as the “ground truth”, but they have mostly limited to land surface, the measurements are sparse or nonexistent in some regions like deserts or high topographic areas. Although rain gauges measure rainfall directly, their data are only representative for a limited spatial extent and may be subjected to some errors caused by local effects such as topography or wind-induced undercatch. An alternative approach which can provide relatively homogenous estimates with complete coverage over most of the globe is based on using satellite observations. Therefore, satellite data are capable to estimate precipitation over the oceans and over remote areas where few or no ground measurements are available. The satellite-based precipitation estimates are derived mainly from visible, infrared (IR) and passive microwave (PMW) radiances which are measured by satellites. Although the visible channels cannot be used at night, the IR data are available in fine spatial resolution (about 3-4 km) with high temporal sampling (15 min) which are provided by geosynchronous satellites. Another source of data is PMW that can be used to estimate rainfall more directly. Low-altitude polar-orbiting satellites serve to measure the PMW data. Although, the microwave sensors can penetrate into the clouds and provide more information about the cloud characteristics such as water vapor, cloud particles, and structure of hydrometeors, but at the expense of temporal sampling. In recent years, different algorithms have been developed using the combination of the IR, Visible (VIS) and PWM observations to provide more accurate rainfall estimations in high spatial and temporal resolutions. To demonstrate the similarities and differences between the spatial distribution of different satellite-based and gauge-based precipitation datasets over Iran we compared seven different datasets. For comparisons all datasets are regridded to 0.25-degree latitude longitude spatial resolutions. Then the spatial distribution of the mean and relative standard deviations of annual precipitation of these datasets have been calculated. We also used more than 2000 rain gauges to evaluate the selected datasets. To reduce error only 228 pixels, include at least 3 rain gauges are used for comparisons of spatial average of monthly, seasonal and annual precipitation of gauge and seven datasets.
Results and Discussion: The results showed a large amount of differences in annual precipitation between seven selected datasets. The most differences pronounce in wet areas in the north of Alborz Mountain, in the semi-arid and arid regions of the central desert and in the high mountainous areas of the southern Zagros. The reason for these differences is that not only satellite-based but gauge-based datasets have large uncertainties estimating areal precipitation in such high topographic areas. The satellite products are prone to some errors arising from not fully understood physical process, sampling error and parameter estimation. Therefore, verification of precipitation datasets is one of the most important parts of the data development and refinements. In this paper, the spatial distribution of seven different global-observational precipitation datasets over Iran are compared for the period 2003-2007. At first all datasets were regridded to 0.25° spatial resolutions using linear interpolation method. Then, the mean and relative standard deviation of annual precipitation of the datasets were calculated to analyze the spatial discrepancies between datasets. The areal average of annual precipitation and the contribution of seasonal precipitation were calculated for comparison purposes. The results showed that areal average of annual and seasonal precipitation for 228 selected pixels for PERSIANN-CDR, TRMM, and GPCP which are satellite-based and gauge adjusted datasets are more similar to the rain gauge data than other datasets. The results for the above datasets are even better than CRU and APHRODITE which are gauge-based datasets.
Conclusion: The results showed that the satellite estimates are not capable to show the precipitation (detection and amount) over the coast of Caspian Sea and the high areas of the Zagros Mountain as well as other parts of the country. There are some useful recommendations for data users at the end of this paper. In fact, in this paper our spatial focus is on Iran and we introduced a web address which data users can access freely from one of the most popular and widely used satellite-based products in easy-to-use format only for Iran. The results show considerable differences between the datasets. The difference is about 0.8 times of mean annual precipitation (about 300 mm in a year) for the coast of Caspian Sea. The satellite-based estimations were less accurate over the coast of Caspian Sea and high mountainous area of the southwest of Zagros comparing to other parts of the country. While spring precipitation shows maximum contributions in annul precipitation for in-situ datasets, winter precipitation shows maximum contribution in annual precipitation for other datasets. The results showed that areal average of monthly, seasonal and annual precipitation over 228 selected pixels for PERIANN-CDR, TRMM and GPCP were consistent with rain gauge data. CMORPH and PERSIANN underestimate areal average of monthly and seasonal precipitation over the pixels.
Mahdi Delghandi; Saeid Boroomand Nasab
Abstract
Field experiments for quantifying optimal breeding strategies are time-consuming and expensive. Crop simulation models can provide an alternative, less time-consuming and inexpensive means of determining the optimum breeding strategies. These models consider the complex interactions between weather, ...
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Field experiments for quantifying optimal breeding strategies are time-consuming and expensive. Crop simulation models can provide an alternative, less time-consuming and inexpensive means of determining the optimum breeding strategies. These models consider the complex interactions between weather, soil properties and management factors. CERES-Wheat is one of best models which can simulate the growth and development of wheat. Therefore, in present paper DSSAT 4.5-CERES-Wheat was evaluated for predicting growth, phenology stages and yield of wheat (cultivar of Chamran) for Ahwaz region. For this purpose, one Experimental research was designed at the experimental farm of the Khuzestan Agriculture And Natural Resources Research Center (KANRC), located at Ahwaz in 2010-2011 growth season. Using results of this research and two another research, CERES-Wheat model was evaluated. Results of evaluation showed that most and less NRMSE were abtained for simulation of maximum Leaf Area Index (6%) and phenology stages (2%), respectively. Therefore, it can conclude that CERES-Wheat is a powerful model in order to simulation of growth, phenology stages and yield of wheat.
