najmeh khalili; Kamran Davary; Amin Alizadeh; Hossein Ansari; Hojat Rezaee Pazhand; Mohammad Kafi; Bijan Ghahraman
Abstract
Introduction:Many existing results on water and agriculture researches require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. Therefore, the required daily climate data should be generated based on the limited available data. ...
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Introduction:Many existing results on water and agriculture researches require long-term statistical climate data, while practically; the available collected data in synoptic stations are quite short. Therefore, the required daily climate data should be generated based on the limited available data. For this purpose, weather generators can be used to enlarge the data length. Among the common weather generators, two models are more common: LARS-WG and ClimGen. Different studies have shown that these two models have different results in different regions and climates. Therefore, the output results of these two methods should be validated based on the climate and weather conditions of the study region.
Materials and Methods:The Sisab station is 35 KM away from Bojnord city in Northern Khorasan. This station was established in 1366 and afterwards, the meteorological data including precipitation data are regularly collected. Geographical coordination of this station is 37º 25׳ N and 57º 38׳ E, and the elevation is 1359 meter. The climate in this region is dry and cold under Emberge and semi-dry under Demarton Methods. In this research, LARG-WG model, version 5.5, and ClimGen model, version 4.4, were used to generate 500 data sample for precipitation and temperature time series. The performance of these two models, were evaluated using RMSE, MAE, and CD over the 30 years collected data and their corresponding generated data. Also, to compare the statistical similarity of the generated data with the collected data, t-student, F, and X2 tests were used. With these tests, the similarity of 16 statistical characteristics of the generated data and the collected data has been investigated in the level of confidence 95%.
Results and Discussion:This study showed that LARS-WG model can better generate precipitation data in terms of statistical error criteria. RMSE and MAE for the generated data by LAR-WG were less than ClimGen model while the CD value of LARS-WG was close to one. For the minimum and maximum temperature data there was no significant difference between the RMSE and CD values for the generated and collected data by these two methods, but the ClimGen was slightly more successful in generating temperature data. The X2 test results over seasonal distributions for length of dry and wet series showed that LARS-WG was more accurate than ClimGen.The comparison of LARS-WG and ClimGen models showed that LARS-WG model has a better performance in generating daily rainfall data in terms of frequency distribution. For monthly precipitation, generated data with ClimGen model were acceptable in level of confidence 95%, but even for monthly precipitation data, the LARS-WG model was more accurate. In terms of variance of daily and monthly precipitation data, both models had a poor performance.In terms of generating minimum and maximum daily and monthly temperature data, ClimGen model showed a better performance compared to the LARS-WG model. Again, both models showed a poor performance in terms of variance of daily and monthly temperature data, though LAR-WG was slightly better than ClimGen. For lengths of hot and frost spells, ClimGen was a better choice compared to LARS-WG.
Conclusion:In this research, the performances of LARS-WG and ClimGen models were compared in terms of their capability of generating daily and monthly precipitation and temperature data for Sisab Station in Northern Khorasan. The results showed that for this station, LARS-WG model can better simulate precipitation data while ClimGen is a better choice for simulating temperature data. This research also showed that both models were not very successful in the sense of variances of the generated data compared to the other statistical characteristics such as the mean values, though the variance for monthly data was more acceptable than daily data.
N. Khalili; K. Davary; A. Alizadeh; M. Kafi; H. Ansari
Abstract
Modeling of crop growth plays an important role in evaluation of drought impacts on rainfed yield, choosing an optimum sowing date, and managerial decision-makings. Aquacrop model is a new crop model that developed by Food and Agriculture Organization (FAO), that is a model for simulation of crop yield ...
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Modeling of crop growth plays an important role in evaluation of drought impacts on rainfed yield, choosing an optimum sowing date, and managerial decision-makings. Aquacrop model is a new crop model that developed by Food and Agriculture Organization (FAO), that is a model for simulation of crop yield based on “yield response to water“ with meteorological, crop, soli and management practices data as inputs. This model has to be calibrated and validated for each crop species and each location. In this paper, the Aquacrop has been calibrated and evaluated for rainfed wheat in Sisab station (Northern Khorasan). For this purpose, daily meteorological data and historical yield data from two cropping season (2007-2008 and 2008-2009) in the Sisab station have been used to calibrate this model. Next, meteorological data and historical yield data of five cropping season (2002-2003 to 2006-2007) are used to validate the model. The result shows that the Aqucrop can accurately predict crop yield as R2, RMSE, NRMSE, ME, and D-Index are achieved 0.86, 0.062, 5.235, 0.917 and 0.877, respectively.
N. Khalili; K. Davari; H. Ansari; A. Alizadeh
Abstract
Abstract
Drought is one the most complicated and unknown natural disasters and rainfed agriculture is often the first sector to be affected by drought. In this research, we consider the drought monitoring from both meteorological and agricultural points of view. We have selected Standardized Precipitation ...
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Abstract
Drought is one the most complicated and unknown natural disasters and rainfed agriculture is often the first sector to be affected by drought. In this research, we consider the drought monitoring from both meteorological and agricultural points of view. We have selected Standardized Precipitation Index (SPI) among the meteorological indices, with a one month time scale for the synoptic station of Bojnurd. Although there are few exceptions in during (1996-2005) in 1996, 1998, 1999, and 2000, in which the severely and extremely dry category have been matched to the growth season of the rainfed, the results of SPI index from precipitation data of this station and the trend of drought variations from 1996 to 2005 show that in Bojnord synoptic station, the meteorological drought has not happened in the growth season of the rainfed wheat (23 Oct. To 17 June) or at least it has been near normal category. The periods from June 1998 to May 1999 and from June 2004 to June 2005 have been the driest and wettest periods, respectively. The meteorological indices such as SPI, either are only the function of precipitation, or consider a long term time scale. In the first case they do not give a comprehensive analysis on the drought phenomena and cannot give be used for the monitoring of the crop moisture situation and in the later case, they are not applicable for short term time scales such as daily or weekly monitoring. Therefore, to monitor the agricultural drought and influence the other factors such as the temperature along with precipitation, the crop moisture index (CMI) has been introduced for weekly monitoring. To achieve this goal, we have used the climatic data of Bojnord synoptic station over ten years from 1996 to 2005. The results from CMI index show that in the last week of grain filling, around the last week of May, extremely drought (-2.7>CMI>-3) has happened. Also, during the crop maturity, a exceptional drought has been monitored with CMI
A. Alizadeh; N. Khalili
Abstract
Abstract
Solar radiation, nowadays has a lot of application in different fields of agriculture, irrigation, and hydrology engineering and due to these various applications, different models has been proposed for it’s estimation. Angstrom-Prescott equation is one of the most important well known models ...
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Abstract
Solar radiation, nowadays has a lot of application in different fields of agriculture, irrigation, and hydrology engineering and due to these various applications, different models has been proposed for it’s estimation. Angstrom-Prescott equation is one of the most important well known models for solar radiation estimation. This equation has empirically coefficient that various for each location. In this paper, the data gathered in Mashhad Synoptic station during 1378 and 1380, Angstrom-Prescott coefficient has been identified according to the ratio of actual sunshine hours (n) to the maximum sunshine hours (N). Also a Regression local equation has been proposed considering several meteorology parameters including daily gathered data of saturation vapor pressure deficit, precipitation, air temperature mean, relative humidity percentage and n/N. Finally the proposed model has been evaluated according to the independent measured data during 1381 to 1382. The statistical analysis of the results not show a significant difference between multi coefficients-local equation with Angstrom-Prescott equation, and therefore without more accuracy and more additional meteorology data and only with the data including sunshine hours and calculating extraterrestrial solar radiation, global solar radiation can be used with a high precision. For instance our model for Mashhad can be used with a=0.23 and b=0.44 which are the coefficient of the Angstrom-Prescott equation. This coefficient should be calibrated and validated for each zone individually.
Keywords: Angstrom-Prescott, Solar radiation, Sunshine hours, Mashhad
