Agricultural Meteorology
S. Pourentezari; K. Esmaili; A.R. Faridhosseini; E. Ghafari
Abstract
Introduction Precipitation is one of the most important input parameters of the hydrological models for rainfall-runoff simulation, which due to the lack of proper dispersion of rain gauge stations and the newly established some of these stations in most basins of the country, the use of these precipitation ...
Read More
Introduction Precipitation is one of the most important input parameters of the hydrological models for rainfall-runoff simulation, which due to the lack of proper dispersion of rain gauge stations and the newly established some of these stations in most basins of the country, the use of these precipitation data faces serious challenges. Therefore, the use of remote-sensing methods is one of the ways that can be used for the streamflow simulation using hydrological models. Runoff is also one of the most important hydrological variables and rainfall-runoff modeling is one of the key items in hydrological sciences to estimate runoff characteristics such as volume, peak flow and arrival time to peak flow. In the present study, we used reanalyzed precipitation data and then evaluated the simulated streamflow using this precipitation data in the Zoshk subbasin. The precipitation data was validated with in situ data, of Kashafrood basin.Materials and Methods The reanalysis precipitation data was selected from the ERA5 precipitation data, and the HEC-HMS was used for the rainfall-runoff simulation. The basin parameters were calculated by the GIS menu. This menu is the newest option in the HEC-HMS software that needs only the DEM basin for calculating the basin parameters. In the present study, we should validate the ERA5 reanalysis precipitation data with in situ data, so we did that in the Kashafrood basin. The number of the rain gauge stations were 34, but some of the stations didn't have complete data and omitted them from the list of the rain gauge stations. For the validation ERA5 reanalysis precipitation data was used from the R, NSE, RMSE, Bias, FAR, POD and TS statistical indicators. These indicators were calculated by programming in EXCEL Visual Basic. The ERA5 precipitation data was evaluated for the Kashfarood basin at daily and monthly time steps. The DEM Zoshk was downloaded with the spatial resolution of 12.5 meters from ALOS-PALSAR satellite and then the basin parameters were calculated by the GIS menu. The SCS curve number was selected as a loss method. In this method, the calculations related to the percentage of impermeability and the average curve number of each sub-basin were obtained through land use and curve number layers, respectively. The SCS unit hydrograph was selected as a transform method. The recession method was selected as a base flow method. NSE and PBias were used for the calibration and validation events in HEC-HMS. In this way, at first the HEC-HMS model was calibrated by tow in situ rainfall-runoff events (91/1/11 and 91/2/6), and then validated by one in situ rainfall-runoff event (99/1/23). For validation streamflow of the ERA5 reanalysis precipitation data, the event on 99/1/23 was used and their streamflow hydrographs were evaluated with each other in Zoshk station.Results and Discussion The results showed that the reanalysis precipitation data of ERA5 had underestimation in daily and monthly time steps. Also in monthly time step, the accuracy of these precipitation dataset in detecting precipitation events (in terms of FAR, TS, and POD indices) was higher than a daily one. In addition, in monthly time steps it had worse accuracy in summer months than the rest of the year in detecting precipitation events (in terms of FAR, TS, and POD indices). For streamflow evaluation, in the calibration phase both NSE was in very good and good ranges, and PBias was in very good, good and acceptable ranges. In addition, the model underestimated the observational one. Finally the ERA5 reanalysis precipitation data was compared by 99/1/23 hydrograph event. The streamflow hydrograph from the ERA5 reanalysis precipitation data was underestimated due to ERA5 underestimation of the precipitation at the Zoshk rain gauge on the days corresponding to the 23/6/99 incident. The ERA5 reanalyzed precipitation data with NSE and Bias percentage coefficients in unacceptable range (NSE≤0.5 and PBias≤±25), compared to flow hydrograph obtained from Zoshk station precipitation data, the efficiency of this precipitation dataset is low. The range of the streamflow hydrograph from the ERA5 precipitation data was unsatisfactory in compared to the observational hydrograph (NSE = -0.47 and PBias = -55.16).Conclusion In general, the accuracy of the flow hydrograph of this product compared to the flow hydrograph of the precipitation data of Zoshk station (NSE = 0.64 and PBias = -15.82), cannot be a relatively reliable source instead of in situ rainfall data in hydrological simulation. The suggestion for future studies is to evaluate other rainfall data based on remote sensing methods in hydrological modeling.
Irrigation
S.F. Mousavizadeh; H. Ansari; A. R. Faridhoseini
Abstract
Introduction: In the last decade, satellite-based methods, including remote sensing and microwave methods, have been used in many studies to detect soil surface moisture regionally. Thermal remote sensing method is quite effective for checking moisture for bare soil but shows poor correlation for vegetated ...
Read More
Introduction: In the last decade, satellite-based methods, including remote sensing and microwave methods, have been used in many studies to detect soil surface moisture regionally. Thermal remote sensing method is quite effective for checking moisture for bare soil but shows poor correlation for vegetated surfaces. In addition, there is a widespread use of this method in the presence of temperature differences during the day. Satellite imagery enables the ability to measure humidity according to the environmental conditions at the surface. Thus, compared to field measurements, remote sensing techniques are promising because they are capable of spatial measurements at a relatively low cost. Water supply is one of the main causes of evapotranspiration, which can affect it. Soil moisture can be considered as the most direct and important variable describing drought and is the main parameter describing water circulation and energy exchange between the surface and the atmosphere. Scale reduction methods for soil moisture can be divided into three main groups including satellite-based method, GIS data and model-based methods. The same methods have been used extensively in monitoring soil moisture for different spectral patterns at different wavelengths, from visible to microwave remote sensing data. Spectral reflectance decreases with increasing soil moisture in the visible and near-infrared (NIR) range. Therefore, these methods can be used to estimate soil moisture using satellite data for water budgeting and other meteorological and agricultural applications.Materials and Methods: In this study, using the information provided by Zaki (2013), the measured humidity by the sensor was compared with the humidity obtained from the satellite. The soil moisture were measured in 16 points from an area of 13 hectares from Neyshabour plain of Khorasan Razavi province. The novelty of this study is to provide a simple method for using Landsat 7 satellite imagery to estimate the surface moisture of areas of the Earth to eliminate field sampling and optimal use for agriculture. One of the advantages of this method is the reduction of information obtained from the field as input values for crop modeling that can be used to estimate crop yield, so the moisture measured during the winter wheat crop period from November 2012 to March 2013 was used.Results and Discussion: The placement of band numbers 3 and 4 opposite each other to calculate M, the line equation was fitted. Since satellite imagery is not performed daily by satellite, six images were extracted during the growing season. On November 12, which is actually 12 days after planting, the plant is entering the germination stage and the soil is mostly bare. Because the satellite does not receive enough reflected green light, the accuracy of the image in measuring soil moisture decreases, but after the plant grows, the green light is reflected and the amount of digital digit of band 4 is affected, as a result, the amount of moisture in the plant leaves and stem is involved in measuring soil moisture, which is consistent with the results obtained by Petropoulos et al.Conclusion: In general, the results of this study showed that the simple and efficient Red-NIR spatial geometry model has a great ability to estimate soil surface moisture in favorable weather conditions and this method can be used for plant modeling as input data.
R. Garmeh; Alireza Farid-hosseini; majid hashemi nia; A. Hojjati
Abstract
Introduction: Planning and management of water resource and river basins needs use of conceptual hydrologic models which play a significant role in predicting basins response to different climatic and meteorological processes. Evaluating watershed response through mathematical hydrologic models requires ...
Read More
Introduction: Planning and management of water resource and river basins needs use of conceptual hydrologic models which play a significant role in predicting basins response to different climatic and meteorological processes. Evaluating watershed response through mathematical hydrologic models requires finding a set of parameter values of the model which provides thebest fit between observed and estimated hydrographs in a procedure called calibration. Asmanual calibration is tedious, time consuming and requires personal experience, automaticcalibration methods make application of more significant CRR models which are based onusing a systematic search procedure to find good parameter sets in terms of at least oneobjective function.
Materials and Methods: Conceptual hydrologic models play a significant role inpredicting a basin’s response to different climatic and meteorological processes within natural systems. However, these models require a number of estimated parameters. Model calibration is the procedure of adjusting the parametervalues until the model predictions match the observed data. Manual calibration of high-fidelity hydrologic (simulation) models is tedious, time consuming and sometimesimpractical, especially when the number of parameters islarge. Moreover, the high degrees of nonlinearity involved in different hydrologic processes and non-uniqueness ofinverse-type calibration problems make it difficult to find asingle set of parameter values. In this research, the conceptual HEC-HMS model is integrated with the Particle Swarm Optimization (PSO) algorithm.The HEC-HMS model was developed as areplacement for HEC-1, which has long been considered as astandard model for hydrologic simulation. Most of thehydrologic models employed in HEC-HMS are event-basedmodels simulating a single storm requiring the specificationof all conditions at the beginning of the simulation. The soil moistureaccounting model in the HEC-HMS is the onlycontinuous model that simulates both wet and dry weatherbehavior.Programming of HEC –HMS has been done by MATLAB and techniques such as elite mutation and creating confusion have been used in order to strengthen the algorithm and improve the results. The event-based HEC-HMS model simulatesthe precipitation-runoff process for each set of parameter values generated by PSO. Turbulentand elitism with mutation are also employed to deal with PSO premature convergence. The integrated PSO-HMS model is tested on the Kardeh dam basin located in the Khorasan Razavi province.
Results and Discussion: Input parameters of hydrologic models are seldomknown with certainty. Therefore, they are not capable ofdescribing the exact hydrologic processes. Input data andstructural uncertainties related to scale and approximationsin system processes are different sources of uncertainty thatmake it difficult to model exact hydrologic phenomena.In automatic calibration, the parameter values dependon the objective function of the search or optimization algorithm.In characterizing a runoff hydrograph, threecharacteristics of time-to-peak, peak of discharge and totalrunoff volume are of the most importance. It is thereforeimportant that we simulate and observe hydrographs matchas much as possible in terms of those characteristics.
Calibration was carried out in single objective cases. Model calibration in single-objective approach with regard to the objective function in the event of NASH and RMSE were conducted separately.The results indicated that the capability of the model was calibrated to an acceptable level of events. Continuing calibration results were evaluated by four different criteria.Finally, to validate the model parameters with those obtained from the calibration, tests perfomed indicated poor results. Although, based on the calibration and verification of individual events one event remains, suggesting set is a possible parameter.
Conclusion: All events were evaluated by validations and the results show that the performance model is not desirable. The results emphasized the impossibility of obtaining unique parameters for a basin. This method of solution, because of non-single solutions of calibration, could be helpful as an inverse problem that could limit the number of candidates. The above analysis revealed the existence of differentparameter sets that can altogether simulate verificationevents quite well, which shows the non-uniqueness featureof the calibration problem under study. However, the methodologyhas benefited from that feature by finding newparameter intervals that should be fine-tuned further inorder to decrease input and model prediction uncertainties.The proposed methodology performed well in the automatedcalibration of an event-based hydrologic model;however, the authors are aware of a drawback of the presentedanalysis – this undertakingwas not a completely fair validationprocedure. It is because validation events represent possiblefuture scenarios and thus are not available at the time ofmodel calibration. Hence, an event being selected as a validationevent should not be used to receive any morefeedback for adjusting parameter values and ranges.However,this remark was not fully taken into consideration, mostlybecause of being seriously short of enough observed eventsin this calibration study. Therefore, the proposed methodology,although sound and useful, should be validated inother case studies with more observed flood events.
maysam majidi; a. Alizade; m. vazifedoust; a. faridhosseini
Abstract
Introduction: Water when harvested is commonly stored in dams, but approximately up to half of it may be lost due to evaporation leading to a huge waste of our resources. Estimating evaporation from lakes and reservoirs is not a simple task as there are a number of factors that can affect the evaporation ...
Read More
Introduction: Water when harvested is commonly stored in dams, but approximately up to half of it may be lost due to evaporation leading to a huge waste of our resources. Estimating evaporation from lakes and reservoirs is not a simple task as there are a number of factors that can affect the evaporation rate, notably the climate and physiography of the water body and its surroundings. Several methods are currently used to predict evaporation from meteorological data in open water reservoirs. Based on the accuracy and simplicity of the application, each of these methods has advantages and disadvantages. Although evaporation pan method is well known to have significant uncertainties both in magnitude and timing, it is extensively used in Iran because of its simplicity. Evaporation pan provides a measurement of the combined effect of temperature, humidity, wind speed and solar radiation on the evaporation. However, they may not be adequate for the reservoir operations/development and water accounting strategies for managing drinking water in arid and semi-arid conditions which require accurate evaporation estimates. However, there has not been a consensus on which methods were better to employ due to the lack of important long-term measured data such as temperature profile, radiation and heat fluxes in most lakes and reservoirs in Iran. Consequently, we initiated this research to find the best cost−effective evaporation method with possibly fewer data requirements in our study area, i.e. the Doosti dam reservoir which is located in a semi-arid region of Iran.
Materials and Methods: Our study site was the Doosti dam reservoir located between Iran and Turkmenistan borders, which was constructed by the Ministry of Water and Land Reclamation of the Republic of Turkmenistan and the Khorasan Razavi Regional Water Board of the Islamic Republic of Iran. Meteorological data including maximum and minimum air temperature and evaporation from class A pan were acquired from the Doosti Dam weather station. Relative humidity, wind speed, atmospheric pressure and precipitation were acquired from the Pol−Khatoon weather station. Dew point temperature and sunshine data were collected from the Sarakhs weather station. Lake area was estimated from hypsometric curve in relation to lake level data. Temperature measurements were often performed in 16−day periods or biweekly from September 2011 to September 2012. Temperature profile of the lake (required for lake evaporation estimation) was measured at different points of the reservoir using a portable multi−meter. The eighteen existing methods were compared and ranked based on Bowen ratio energy balance method (BREB).
Results and Discussion: The estimated annual evaporation values by all of the applied methods in this study, ranged from 21 to 113mcm (million cubic meters). BREB annual evaporation obtained value was equal to 69.86mcm and evaporation rate averaged 5.47mm d-1 during the study period. According to the results, there is a relatively large difference between the obtained evaporation values from the adopted methods. The sensitivity analysis of evaporation methods for some input parameters indicated that the Hamon method (Eq. 16) was the most sensitive to the input parameters followed by the Brutsaert−Stricker and BREB, and radiation−temperature methods (Makkink, Jensen−Haise and Stephen−Stewart) had the least sensitivity to input data. Besides, the air temperature, solar radiation (sunshine data), water surface temperature and wind speed data had the most effect on lake evaporation estimations, respectively. Finally, all evaporation estimation methods in this study have been ranked based on RMSD values. On a daily basis, the Jensen−Haise and the Makkink (solar radiation, temperature group), Penman (Combination group) and Hamon (temperature, day length group) methods had a relatively reasonable performance. As the results on a monthly scale, the Jensen−Haise and Makkink produced the most accurate evaporation estimations even by the limited measurements of the input data.
Conclusion: This study was carried out with the objective of estimating evaporation from the Doosti dam reservoir, and comparison and evaluation of conventional method to find the most accurate method(s) for limited data conditions. These examinations recognized the Jensen−Haise, Makkink, Hamon (Eq. 17), Penman and deBruin methods as the most consistent methods with the monthly rate of BREB evaporation estimates. The results showed that radiation−temperature methods (Jensen−Haise and Makkink) have appropriate accuracy especially on a monthly basis. Also deBruin, Penman (combination group), Hamon and Papadakis (temperature group) methods produced relatively accurate results. The results revealed that it is necessary to calibrate and adjust some evaporation estimation methods for the Doosti dam reservoir. According to the required input data, sensitivity and accuracy of these methods, it can be concluded that Jensen−Haise and Makkink were the most appropriate methods for estimating the lake evaporation in this region especially when measured data were not available.
A. Mianabadi; A. Alizadeh; Seied Hosein Sanaei-Nejad; M. Bannayan Awal; A. Faridhosseini
Abstract
Precipitation is a key input to different crop and hydrological models. Usually, the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network, this data does not have a desirable accurate. So estimation ...
Read More
Precipitation is a key input to different crop and hydrological models. Usually, the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network, this data does not have a desirable accurate. So estimation of daily areal rainfall can be obtained by spatial interpolation of rain gauges data. However, direct application of these techniques may produce inaccurate results. In the last years, applying the remote sensing for estimation of rainfall have got so popular all around the word and many techniques have been developed based on the satellite data with high temporal and spatial resolution. In this paper, CMORPH model was validated for precipitation estimation over the northeast of Iran. Results showed that this model could not estimate precipitation accurately in daily scale, but in monthly and seasonal scale the estimation was more accurate. Farooj and Namanloo station had the highest correlation equal to 0.31 in daily scale. The highest correlation in monthly scale was equal to 0.62 for Barzoo, Namanloo and Se yekAb station. In Seasonal scale Gholaman station had the highest correlation which was equal to 0.63. Also, the probability of detection has been estimated accurately by CMORPH. But this technique did not have an accurate estimation for wet and dry days, mean annual precipitation and the number of non-rainy days.
D. Houshmand; K. Esmaili; A. Keshavarzi; A. Faridhosseini
Abstract
The existence of bridge pier in streamflow causes a complex 3D flow formation, which also causes the scouring around bridge pier. Since rivers are usually curved, it is necessary to investigate the impact of change in flow patterns caused by passage of flow through the curve on the scouring around bridge ...
Read More
The existence of bridge pier in streamflow causes a complex 3D flow formation, which also causes the scouring around bridge pier. Since rivers are usually curved, it is necessary to investigate the impact of change in flow patterns caused by passage of flow through the curve on the scouring around bridge pier. By developing Computational Fluid Dynamics (CFD), there is a possibility to simulate the flow pattern around the bridge piers. Therefore, the purpose of this research is modeled a 3D flow stream near the bridge piers in a curved channel. For this purpose a fluent model software was employed, and solved by stream equations using finite volume method of centralism. For discretization of Navier Stocks equation, three turbulence models of K-ε, K-ω, and RSM were used. In order to consider free surface, Fluid Volume Method was applied. The numerical model was validated with measured experimental data around the bridge piers in the meandering flume with 5 sequential curve paths. The results showed that the RSM turbulence model performed well compared to the other two models. When comparing the flow of upstream to downstream of bridge piers it can be observed that the placement of bridge piers in the middle of curved shape channel may lead secondary flow towards the inner curve of a channel. Also, the resulted vortex continues with a 150 degree curve.
A. Moghaddam; A. Alizadeh; Alinaghi Ziaei; A. Farid Hosseini; D. Fallah Heravi
Abstract
Genetic Algorithm as a one of the main evolutionary algorithms has had a most successful role in the water distribution network optimization.This algorithmhas been undergoing many reforms and improved versions are published. A type of genetic algorithms is Fast Messy Genetic Algorithm (FMGA), that has ...
Read More
Genetic Algorithm as a one of the main evolutionary algorithms has had a most successful role in the water distribution network optimization.This algorithmhas been undergoing many reforms and improved versions are published. A type of genetic algorithms is Fast Messy Genetic Algorithm (FMGA), that has the ability to increase the convergence rate in solving optimization problems with reducing the length of chromosomes and removing the inefficient genes, meanwhile studying the chromosomes which are not equal in terms of gene strings.In this paper, for evaluation of the FMGA performance in solving water distribution network optimization problems, after the sensitivity analysis and determining the best values of these parameters, two benchmark networks and a real network are analyzed, which are named Two-loop network, the Hanoi network and Jangal City network, respectively, and the results were compared with previous researches. Least-cost in two loop network was estimated after 2880 number of function evaluations that had significant improvements compared to the results of previous researches. In Hanoi network, the minimum cost obtained equal to 6.045×106 $ that is less than other researchers results are issued so far. After proving the efficiency of algorithm, its performance was shown in design of real Jangal city network according to increasing network size and design constraints.
S. Kermanshahi; K. Davari; majid hashemi nia; A. Farid Hosseini; H. Ansari
Abstract
The requiring of reducing agricultural water demand as the world’s largest consumer of water, for having sustainable water resources is not concealed to anyone. With measurements such as increasing irrigation efficiency, changing in cropping pattern, reducing the cultivation area, etc, this goal can ...
Read More
The requiring of reducing agricultural water demand as the world’s largest consumer of water, for having sustainable water resources is not concealed to anyone. With measurements such as increasing irrigation efficiency, changing in cropping pattern, reducing the cultivation area, etc, this goal can be achieved. In this study, the status of water resources and irrigation demands within the Neyshabour Plane was evaluated by using Water Evaluation and Planning model (WEAP). To assess the effect of these strategies in WEAP model, scenarios with different topics for cropping pattern, reducing cultivation area, and combined scenarios were developed and then the simulations were performed for 20 years in future. The results suggested that above measurements reduced the mean annual water demand of agriculture by 9, 10 and 18 percents respectively and subsequently reduced the average of annual groundwater deficit by 13, 8 and 18 percents. On the other hand these measurements had a significant role in reducing the agricultural water demand, and therefore, in reducing the extraction from different water resources.
N. Sayari; A. Alizadeh; M. Bannayan Awal; A.R. Farid Hossaini; M.R. Hessami Kermani
Abstract
Abstract
The climate change was known to force local hydrology, through changes in the pattern of precipitation, temperature and the other hydrological variables. In this research, the impact of global warming on maximum and minimum temperature, precipitation and evapotranspiration (wheat, corn, tomato ...
Read More
Abstract
The climate change was known to force local hydrology, through changes in the pattern of precipitation, temperature and the other hydrological variables. In this research, the impact of global warming on maximum and minimum temperature, precipitation and evapotranspiration (wheat, corn, tomato and sugar beet) of Kashafrood basin under two climate change scenarios (A2 and B2), and the output of two GCM models (HadCM3 and CGCM2) for three period of times (2010-2039, 2040-2069 and 2070-2099), were investigated. For evaluation two scenarios were downscaled into local level with Automated Statistical Downscaling (ASD) model. Precipitation was expected to decrease and/or increase, depends on applied GCM. The results indicated that the annual precipitation decreased for three periods under CGCM2 model and also for two scenarios (A2 and B2) as much as 13%-16% decreasing, the annual precipitation for three periods under HadCM3 model and two scenarios (A2 and B2) as much as 2%-8% increasing. The maximum and minimum temperatures in the Kashafrood basin was predicted, which increased by CGCM2 and HadCM3 models with two scenarios. Based on the HadCM3 model, maximum and minimum temperatures were expected to increase 2.4 0C to 5.8 0C and 0.6 0C to 3.8 0C, respectively; for 2070-2099 periods. For CGCM2 model, maximum and minimum temperatures were expected to increase 0.06 0C to 2.59 0C and 0.1 0C to 1.9 0C respectively; for 2070-2099. Evapotranspiration under A2 and B2 scenarios and HadCM3 model was increased but increasing in evapotranspiration with CGCM2 model under both scenarios was not significant in many cases. The comparison of two models and also two scenarios indicated that more critical status for A2 scenario by using two GCM models for this basin.
Keywords: Climate change, General circulation model, Downscaling, HadCM3, CGCM2, Kashaf rood basin, Evapotranspiration
M.S. Ghazanfari Moghadam; A. Alizadeh; M. Mousavi baygi; A.R. Farid-Hosseini; M. Bannayan Aval
Abstract
Abstract
Precipitation as the most important factor plays the main role in many application researches which are based on climatic parameters. Many researches in the field of hydrology, hydrometeorology and agriculture employs rain-gauges (such as synoptic and climatologic stations) data. Precipitation ...
Read More
Abstract
Precipitation as the most important factor plays the main role in many application researches which are based on climatic parameters. Many researches in the field of hydrology, hydrometeorology and agriculture employs rain-gauges (such as synoptic and climatologic stations) data. Precipitation characteristics, such as rainfall intensity and duration, usually exhibit significant spatial variation, even within small watersheds; while rain gauge network density could not provide desirable cover. Nearly all related researches use interpolation methods for places without rain gauge data. Many studies showed that the estimated error was usually high by usual interpolation methods. Employing satellite data with high spatial and temporal resolution could provide accurate precipitation estimation. PERSIANN (Precipitation estimation from remotely sensed information using artificial neural network) model works based on the ANN (artificial Neural Network) system which uses multivariate nonlinear input-output relationship functions to fit local cloud top temperature (Tb) to pixel rain rates (R). In this study, PERSIANN model and two interpolation methods (Kriging & IDW) were employed to estimate precipitation for North-Khorasan between the years 2006 until 2008. Results show better correlation between PERSIANN outputs and station data than other two interpolation methods. while correlation coefficient for Kendal`s test is 0.805 between model and Bojnord Station data, this coefficient is 0.488 for IDW and 0.565 for Kriging methods.
Keywords: PERSIANN model, IDW, Kriging, Interpolation methods, Precipitation estimation
A. Alizadeh; N. Sayari; M.R. Hessami Kermani; M. Bannayan Aval; A.R. Farid-Hosseini
Abstract
چکیده
تغییر اقلیم دارای اثرات مستقیمی بر فرآیندهای هیدرولوژیکی نظیر تبخیر از سطح آب، تعرق از گیاه، تغذیه آبهای زیرزمینی، رواناب یا ذوب برف دارد. در این مقاله اثرات احتمالی ...
Read More
چکیده
تغییر اقلیم دارای اثرات مستقیمی بر فرآیندهای هیدرولوژیکی نظیر تبخیر از سطح آب، تعرق از گیاه، تغذیه آبهای زیرزمینی، رواناب یا ذوب برف دارد. در این مقاله اثرات احتمالی تغییراقلیم بر تبخیر و تعرق در آینده بررسی شده است. به همین دلیل تأثیر تغییراقلیم بر دما (حداقل، حداکثر و میانگین) و بارش تحت سناریوی A2 و برای سه دوره 2039-2010، 2069-2040 و 2099-2070 و با استفاده از ریزمقیاس نمایی آماری و خروجی های مدل گردش عمومی جو HadCM3 در حوضه کشف رود مورد بررسی قرار گرفت. در مرحله بعدی با استفاده از پارامترهای پیش بینی شده، تبخیر و تعرق گیاهان الگوی کشت این حوضه شامل گندم، چغندرقند، گوجه فرنگی، سیب و ذرت با استفاده از روش هارگریوز و سامانی محاسبه و برای دوره های مختلف مورد مقایسه قرار گرفتند. نتایج حاصل نشان داد که دما (حداقل، حداکثر و میانگین) در هر سه دوره پیش بینی نسبت به دوره پایه 1990-1961 افزایش خواهد یافت. میانگین سالانه بارش پیش بینی شده در دوره های مذکور تفاوت معنی داری نداشت ولی توزیع آن در فصلهای مختلف تغییر خواهد کرد. بدینصورت که مقدار بارش برای ماههای زمستان و تابستان کاهش و برای ماههای پائیز و بهار افزایش خواهد یافت. میزان تبخیر و تعرق محاسبه شده برای تمامی ماهها و برای تمامی دوره ها تحت تأثیر دما افزوده خواهد شد. نتایج نشان می دهد که در صورت افزایش دمای هوا به میزان 1، 2 و 4 درجه سانتی گراد نیاز آبی الگوی کشت گیاهان در دشت کشف رود به ترتیب 6، 10 و 16 درصد افزایش پیدا خواهد کرد.
واژه های کلیدی: مدلهای گردش عمومی جو، ریزمقیاس نمائی آماری، تبخیر وتعرق گیاه، حوضه کشف رود، تغییراقلیم
M. S. Ghazanfari; A. Alizadeh; M. Naseri; A.R. Farid-hosseini
Abstract
Abstract
Urban expansion, pollution augmentation, and extention of major industrial activities in metropolitan areas impacted local climates of major cities. Transforming big cities into heat islands is one of the most prominent results of such a micro-climate change. In this study, variation of precipitation, ...
Read More
Abstract
Urban expansion, pollution augmentation, and extention of major industrial activities in metropolitan areas impacted local climates of major cities. Transforming big cities into heat islands is one of the most prominent results of such a micro-climate change. In this study, variation of precipitation, temperature and some other important climatic parameters including relative humidity, and percentage of cloudiness were reviewed in order to study micro-climate change. The city of Mashhad selected for this study, as metropolitan area. The study was performed by comparing the climate parameters of this city with the neighboring regions, which were identified as the same climate category. Due to the effective role of rainfall in the urban weather modification and decreasing of pollution, rainfall variation is more important and crucial. The result of this research showed that the rainfall variation followed the temperature trend. A significant correlation between temperature and precipitation change showed the effect of heat island on urban climate parameters. The urban heat island phenomenon increase the hot season rainfall while its effects on cold season rainfall decrease.
Keywords: Urban Heat Island, Air pollution, Microclimate, Climate change