Validation of Grid APHRODIT Daily Precipitation Estimates and Estimates derived from spatial interpolation of Precipitation in the Khuzestan province

Available accurate and reliable precipitation data are so important in water resources management and planning. In this study,to determine the best method of regional precipitation estimate in Khuzestan province, estimated daily precipitation data from the best interpolation method and APHRODIT Daily Grid Precipitation data during the 2000-2007 years were compared with 44 meteorological stations. Four interpolation methods i.e. Inverse Distance Weighted, Ordinary Kriging, Cokriging, and Regression Kriging were assessed to determine the most appropriate interpolation method for daily precipitation.For the variography analysis in Kriging models, five variogram models including spherical, exponential, linear, linear to sill and Gaussian fitted on the precipitation data. Near neighbor method was used to compare APHRODIT Daily Precipitation data with station recorded data. Cross validation technique was employed to evaluate the interpolation methods and the most appropriate method was determined based on Root Mean Square Error,Mean Bias Error, Mean Absolute Error indices and regression analysis. The result of error evaluation of interpolation methods showed that regression Kriging method has the highest accurate to interpolation of daily precipitation data in Khuzestan province. Therefore, regression-based interpolation methods which using covariates would be improved precipitation evaluate accurate in the area. Comparison of error indices and regression analysis of regression Kriging interpolation method and estimate of APHRODITE show that on most days the accurately estimate of regression Kriging is higher than the APHRODITE. Therefore to understanding of spatial distribution and estimate of daily precipitation data in Khuzestan Province, Regression Kriging interpolation method is more accurate than available APHRODITE dat

Effect of Water Deficit Stress on Peach Growth under Commercial Orchard Management Conditions

In order to study the sensitivity of vegetative growth to water deficit stress of a late-maturing peach (Prunus persica L. cv. Elberta) under orchard conditions, an experiment was conducted as randomized complete-block design with three treatments and four repetitions in Shahdiran commercial orchard in Mashhad during 2011. Three irrigation treatments including 360 (low stress), 180 (moderate stress) and 90 (severe stress) m3ha-1week-1 using a drip irrigation system (minimum stem water potential near harvest: -1.2, -1.5 and -1.7 MPa, respectively) from the mid-pit hardening stage (12th of June) until harvest (23rd of Sep.) applied. Predawn, stem and leaf water potentials, leaf photosynthesis, transpiration, stomatal conductance and leaf temperature, the number of new shoots on fruit bearing shoots and vegetative shoots lengths during growing season as well as leaf area at harvest were measured. The results showed that water deficit stress had negative effects on peach tree water status, thereby resulting in decreased leaf gas exchange and tree vegetative growth. As significant decreased assimilate production of tree was resulted from both decreased leaf assimilation rate (until about 23 % and 50 %, respectively under moderate and severe stress conditions compared to low stress conditions) and decreased leaf area of tree (until about 57% and 79%, respectively under moderate and severe stress conditions compared to low stress conditions at harvest). The significant positive correlation between leaf water potential and vegetative growth of peach revealed that shoot growth would decrease by 30% and 50% of maximum at leaf water potential of –1.56 and –2.30 MPa, respectively

Verification of Temperature and Precipitation Simulated Data by Individual and Ensemble Performance of Five AOGCM Models for North East of Iran

Scince climatic models are the basic tools to study climate change and because of the multiplicity of these models, selecting the most appropriate model for the studying location is very considerable. In this research the temperature and precipitation simulated data by BCM2, CGCM3, CNRMCM3, MRICGCM2.3 and MIROC3 models are downscaled with proportional method according A1B, A2 and B1 emission scenarios for Torbat-heydariye, Sabzevar and Mashhad initially. Then using coefficient of determination (R2), index of agreement (D) and mean-square deviations (MSD), models were verified individually and as ensemble performance. The results showed that, based on individual performance and three emission scenarios, MRICGCM2.3 model in Torbat-heydariye and Mashhad and MIROC3.2 model in Sabzevar had the best performance in simulation of temperature and MIROC3.2, MRICGCM2.3 and CNRMCM3 models have provided the most accurate predictions for precipitation in Torbat-heydariye, Sabzevar and Mashahad respectively. Also simulated temperature by all models in Torbat-heydariye and Sabzevar base on B1 scenario and, in Mashhad based on A2 scenario had the lowest uncertainty. The most accuracy in modeling of precipitation was resulted based on A2 scenario in Torbat-heydariye and, B1 scenario in Sabzevar and Mashhad. Investigation of calculated statistics driven from ensemble performance of 5 selected models caused notable reduction of simulation error and thus increase the accuracy of predictions based on all emission scenarios generally. In this case, the best fitting of simulated and observed temperature data were achieved based on B1 scenario in Torbat-heydariye and Sabzevar and, A2 scenario in Mashhad. And the best fitting simulated and observed precipitation data were obtained based on A2 scenario in Torbat-heydariye and, B1 scenario in Sabzevar and Mashhad. According to the results of this research, before any climate change research it is necessary to select the optimum GCM model for the studying region to simulate climatic parameters