Probable maximum precipitation 24 hours estimation: A case study of Zanjan province of Iran

One of the primary concerns in designing civil structures such as water storage dams and irrigation and drainage networks is to find economic scale based on possibility of natural incidents such as floods, earthquake, etc. Probable maximum precipitation (PMP) is one of well known methods, which helps design a civil structure, properly. In this paper, we study the maximum one-day precipitation using 17 to 50 years of information in 13 stations located in province of Zanjan, Iran. The proposed study of this paper uses two Hershfield methods, where the first one yields 18.17 to 18.48 for precipitation where the PMP24 was between 170.14 mm and 255.28 mm. The second method reports precipitation between 2.29 and 4.95 while PMP24 was between 62.33 mm and 92.08 mm. In addition, when the out of range data were deleted from the study of the second method, precipitation rates were calculated between 2.29 and 4.31 while PMP24 was between 76.08 mm and 117.28 mm. The preliminary results indicate that the second Hershfield method provide more stable results than the first one

Evaluation the Impact of Climate Change on Future Temperature Trend of Abhar Plain

Temperature is one of the major parameters of plant growth and influences the amount of water requirement.Therefore, in this paper, the future temperature trend in the Abhar area was studied under the influence of climate change during the future time periods and compared with the observation period. In this way, the observation period of 1986-2010, the horizon near 2045-2011, the middle horizon of 2046-2070, and the far horizon of 2080-2100 was considered. The LARS-WG software was used in order to downscale, scenario file generation and general circulation atmospheric model simulation. In order to simulate the climate was used, the HadCM3 model by A2 scenario. In generating a scenario file, the numbers of both series (GCM PREDICTIONS and LARS-WG PARAMETERS) were considered, and the scenario file was generated for each of the three horizons future near, middle and far. The results of this study show the increasing trend of minimum, average and maximum temperature parameters over the next horizons. The amounts of the increase of the parameters studied during the mentioned horizons, respectively, in the parameter of the minimum annual temperature of 0.63,1.64 and 3.34°C, the mean annual temperature was 0.26, 0.72 and 1.46 °C and maximum temperature parameter will be 0.32, 0.55 and 0.81°C

Hydropolitics and hydrology issues in Hirmand/Helmand international river basin

This paper presents an empirical study to find the steady state of Hirmand/Helmand international basin. Using Markov chain method and by considering seven states including very dry, dry, semi-dry, average, semi-wet, wet and very wet, the proposed study uses historical data over the period 1952-1997 and determines the steady state of the region. The results of the survey indicate that the likelihood of having very dry, dry, semi-dry, average, semi-wet, wet and very wet states are 10.4, 27.6, 9.5, 17.5, 18.5, 11.1 and 5.4 percent, respectively. In other words, there is a chance of 47.5% for having dry or very dry state, 35% for having semi and very wet and a likelihood of 17.5% for having normal condition

Predicting Yield and Water Use Efficiency in Saffron Using Models of Artificial Neural Network Based on Climate Factors and Water

The predicted models for crops yield are developing rapidly by the creation of new statistical techniques and neural networks. For this purpose, a research was carried out in the Torbat-e-Heydarieh region for predicting yield and water use efficiency of saffron by using an artificial neural network model. The model was calibrated and validated by using crop yield and climate parameters data during 2009-2010. The models were evaluated by using indices of correlation coefficient (R2), root mean squares error normalized (RMSEn), and mean squares error (MSE). The results showed that the suggested neural network (model No. 9) with having 2 hidden layers, 8 neurons, and R2= 0.97 (for saffron yield); and 1 hidden layer, 7 neurons, and R2= 0.90 (for water use efficiency) had a high accommodation with these two factors. Also, according to the indices RMSEn and MSE, model No. 9 simulated the yield and WUE of saffron with a high accuracy, such that RMSEn and MSE for yield in this model obtained were 2.78% and 0.0041, respectively; and for WUE they were calculated to be 5.41% and 0.0073, respectively. Also, the results of sensitivity analysis indicated that irrigation is the most important parameter for predicting yield and WUE, and after that is precipitation and solar radiation. Generally, use of the suggested neural network in this research can improve saffron cultivation in the Torbat-e-Heydarieh region

The Effect of Climate Change on Sorghum's Yield in the Abhar Plain

Temperature is one of the factors affecting plant growth. Hence, in this paper, future temperature trends in Abhar region affected by climate change during future periods was evaluated and compared with the period of observation. Plant yield in the future and different cultivation periods was later simulated and estimated through AquaCrop simulation model of plant yield. The study observation period was considered as 1986-2010 AD, near horizon 2011-2045, the average horizon 2046-2079 and 2080-2100 horizon in the current study. LARS-WG software was used in HadCM3 model and A2 scenario in order to downscale the results of general atmosphere circulation's simulation model. Furthermore, the scenario file was generated in this study. According to the results obtained, the highest yield wil be cultivated on May 26 with 46.29 tons per hectare and the lowest yield will be produced on June 5 with 40.6 tons per hectare. If we change the traditional cultivation time from May 15 to May 26, a growth of 0.28 tons per hectare will be expected.  The highest yield will be on May 15 in the future. Moreover, the sorghum's yield will decrease. Lesser photosynthesis during the shorter growing season and C4 photosynthetic system of this product could be involved in this yield loss