Risk assessment of agricultural water requirement based on a multi-model ensemble framework,southwest of Iran     

Zamani  Reza  Akhond-Ali  Ali-Mohammad  Roozbahani  Abbas  Fattahi  Rouhollah 《Theoretical and Applied Climatology》2017,129(3-4):1109-1121

Water shortage and climate change are the most important issues of sustainable agricultural and water resources development. Given the importance of water availability in crop production, the present study focused on risk assessment of climate change impact on agricultural water requirement in southwest of Iran, under two emission scenarios (A2 and B1) for the future period (2025–2054). A multi-model ensemble framework based on mean observed temperature-precipitation (MOTP) method and a combined probabilistic approach Long Ashton Research Station-Weather Generator (LARS-WG) and change factor (CF) have been used for downscaling to manage the uncertainty of outputs of 14 general circulation models (GCMs). The results showed an increasing temperature in all months and irregular changes of precipitation (either increasing or decreasing) in the future period. In addition, the results of the calculated annual net water requirement for all crops affected by climate change indicated an increase between 4 and 10 %. Furthermore, an increasing process is also expected regarding to the required water demand volume. The most and the least expected increase in the water demand volume is about 13 and 5 % for A2 and B1 scenarios, respectively. Considering the results and the limited water resources in the study area, it is crucial to provide water resources planning in order to reduce the negative effects of climate change. Therefore, the adaptation scenarios with the climate change related to crop pattern and water consumption should be taken into account.

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Geochemical influences on the quality of groundwater in eastern part of Semnan Province,Iran     

Mohamad Sakizadeh  Rouhollah Mirzaei  Hadi Ghorbani 《Environmental Earth Sciences》2016,75(10):917

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Reservoir Modeling by Data Integration via Intermediate Spaces and Artificial Intelligence Tools in MPS Simulation Frameworks     

Rouhollah Ahmadi  Ehsan Khamehchi 《Natural Resources Research》2013,22(4):321-336

Conditioning stochastic simulations are very important in many geostatistical applications that call for the introduction of nonlinear and multiple-point data in reservoir modeling. Here, a new methodology is proposed for the incorporation of different data types into multiple-point statistics (MPS) simulation frameworks. Unlike the previous techniques that call for an approximate forward model (filter) for integration of secondary data into geologically constructed models, the proposed approach develops an intermediate space where all the primary and secondary data are easily mapped onto. Definition of the intermediate space, as may be achieved via application of artificial intelligence tools like neural networks and fuzzy inference systems, eliminates the need for using filters as in previous techniques. The applicability of the proposed approach in conditioning MPS simulations to static and geologic data is verified by modeling a real example of discrete fracture networks using conventional well-log data. The training patterns are well reproduced in the realizations, while the model is also consistent with the map of secondary data.  相似文献   

Determination of Trace Levels of Nickel and Manganese in Soil,Vegetable, and Water     

Rouhollah Khani  Farzaneh Shemirani 《洁净——土壤、空气、水》2010,38(12):1177-1183

A simple and reliable method for rapid and selective extraction and determination of trace levels of Ni²⁺ and Mn²⁺ was developed by ionic liquid (IL) based dispersive liquid–liquid microextraction coupled to flame atomic absorption spectrometry (FAAS) detection. The proposed method was successfully applied to the preconcentration and determination of nickel and manganese in soil, vegetable, and water samples. After preconcentration, the settled IL‐phase was dissolved in 100 µL of ethanol and aspirated into the FAAS using a home‐made microsample introduction system. Injection of 50 µL of each analyte into an air–acetylene flame provided very sensitive spike‐like and reproducible signals. Effective parameters such as pH, amount of IL, volume of the disperser solvent, concentration of the chelating agent, and effect of salt concentration were inspected by a (2^5‐1) fractional factorial design to identify the most important parameters and their interactions. Under optimum conditions, preconcentration of 10 mL sample solution permitted the detection of 0.93 µg L^?1 Ni²⁺ and 0.52 µg L^?1 Mn²⁺ with enrichment factors 77.2 and 82.6 for Ni²⁺ and Mn²⁺, respectively. The accuracy of the procedure was evaluated by analysis of a certified reference material (CRM TMDW‐500, drinking water).  相似文献