Showing 3 results for Sabziparvar
Z Maryanji, A Sabziparvar, F Tafazoli, H Zare Abianeh, H Banzhad, M Ghafouri, M Mousavi,
Volume 12, Issue 46 (1-2009)
Abstract
Under different climatic conditions of Iran, the evaluation of evapotranspiration (ETo) models sensitivity to meteorological parameters, prior to introducing the superior performance model, seems quite necessary. Using a 35-year (1971-2005) climatological observations in Hamedan, this study compares the sensitivity of different commonly used evapotranspiration models to different meteorological parameters within the IPCC recommended variability range of 10 to 20% during the growing season (April-October). The radiation and temperature-based ETo models include: Penman-Monteith -FAO56 [PMF56], Jensen-Haise [JH1,2], Humid Turc [TH], Arid (semi) arid Turc [TA], Makkink [MK], Hansen [HN], and Hargreaves-Samani [HS]. Results indicate that all the above-mentioned ETo models show the highest sensitivity to radiation and temperature parameters. This implies that special care is required when we apply model-generated radiation and albedo parameters in such ETo models. It is predicted that by 2050, as a result of global warming, the cold semi-arid climates of Iran will cause an average evapotranspiration rise of about 8.5% in crop reference during the growing season.
A.a Sabziparvar, H Tabari, A Aeini,
Volume 14, Issue 52 (sumer 2010)
Abstract
Soil temperature is one of the important variables in hydrology, agriculture, meteorology and climatology studies. Owing to the fact that soil temperature is only measured at synoptic stations, reconstruction of this variable in other places is of great importance for many relevant agricultural surveys. Using 10-year (1996-2005) daily meteorological observations, including: air temperature, global solar radiation, precipitation, relative humidity, vapor pressure, wind speed and air pressure data, different empirical relationships are suggested. At statistically significant level (P<0.05), the suggested regressions are reliable for estimating soil temperature in various depths (5, 10, 20, 30, 50 and 100 cm) and different climate types. Using soil temperature as the dependent variable and the other meteorological parameters as the independent variables, the multivariable relationships are classified accordingly. The results indicate that the impact of meteorological parameters on soil temperature is not the same. At statistically significant level (P<0.05), the mean daily air temperature presented the highest correlation coefficients with soil temperature for all climate types (on average, from R2>0.91 for warm semi-arid, to R2>0.85 for humid climates). Other results highlighted that the correlation coefficients decreased as the soil depth increased. The behavior of statistical validation criteria of the suggested relations are also discussed for all the mentioned climates.
. A. A. Sabziparvar1, S. Ebrahimzadeh2, M. Khodamoradpour3,
Volume 21, Issue 4 (Winter 2018)
Abstract
The most important factor in determining crop water requirement is estimation of evapotranspiration (ET). Majority of the methodsestimate ET apply series of relatively complex formula,which is then used to determine crop evapotranspiration (ETc). The parameters used in aforesaid methods are: Solar radiation, wind speed, humidity, etc. Unfortunately, in Iran and many countries, long-term records of these parameters are not readily available. The purpose of this study is to calculate the Selianinov Hydrothermic Index that merely requires daily temperature and precipitation data in order to determine correlation coefficients (r) versus ET and Crop Water Requirement (CWR) of some agricultural crops of Iran. First, the Selianinov index is calculated from daily precipitation and temperature during the growth season. Further, the results are correlated against both ETc and CWR. The model results indicate inverse (negative) strong exponential and polynomial relations between the dependent and independent variables. Coefficient of determination (R2) for polynomial equations (on average 0.84) in all crops was better than exponential equations (on average 0.72). Correlation between Selianinov index and CWR indicates that coefficient of determination in both equations was close together (0.83 for polynomial equations and 0.82 for exponential equations).
