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 ...
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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.
A.R. Araghi; M. Mousavi Baygi; majid hashemi nia
Abstract
Introduction: Studying long-term trend changes of meteorological parameters is one of the routine methods in atmospheric studies, especially in the climate change subject. Among the meteorological parameters, temperature is always considered as one of the most atmospheric elements and studying it in ...
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Introduction: Studying long-term trend changes of meteorological parameters is one of the routine methods in atmospheric studies, especially in the climate change subject. Among the meteorological parameters, temperature is always considered as one of the most atmospheric elements and studying it in order to gain a better understanding of the climate change phenomenon, has been effective. In addition to identifying trends, extraction of oscillatory patterns in the atmospheric phenomena and parameters occurrence can be an applicable and reliable method to explore the complex relations between atmospheric-oceanic cycles and short term or long term consequences of meteorological parameters.
Materials and Methods: In this paper, monthly average temperature time series in Mashhad synoptic station in 55 years period (from 1956 to 2010) in monthly, seasonal, annual and seasons separately (winter, spring, summer and autumn) have been analyzed. Discrete wavelet transform and Mann-Kendall trend test were the main methods for performing this research. Wavelet transform is a powerful method in signal processing and it is an advanced version of short time Fourier transforms. Moreover, it has many improvements and more capabilities compared with Fourier transform. In the first step, temperature time series in various time scales (which was mentioned above) have been decomposed via discrete wavelet transforms into approximation (A) and detail (D) components. For the second step, Mann-Kendall trend test was applied to the various combinations of these decomposed components. For detecting the most dominant periodic component for each of the time scales datasets, results of Mann-Kendall test for the original time series and the decomposed components were compared to each other. The nearest value indicated the most dominant periodicity based on the D component’s level. To detect the similarity between results of the Mann-Kendall test, relative error method was employed. Additionally, it must be noted that before applying Mann-Kendall test, time series has to be assessed for its autocorrelation status. If there are seasonality patterns in the studied time series or lag-1 autocorrelation coefficient of data is significant, then some modified versions of the Mann-Kendall test have to be employed.
Results and Discussion: Results of this study showed that the temperature trend at every time scaled dataset (monthly, seasonal, annual and seasons separately) is positive and significant. Autocorrelation coefficients indicated that only seasonal time series and winter datasets did not have significant ACFs. On the other hand, monthly and seasonal datasets had seasonality pattern. Based on these results, Hirsch and Slack’s modified version of Mann-Kendall test was employed for monthly and seasonal time series and for the winter temperature data, the original version of the Mann-Kendall test was applied. For the remaining time series, the Hamed and Rao’s modified version of the Mann-Kendall trend test was employed. Dominant periodicities in monthly, seasonal and annual, confirmed the oscillatory behavior of each other. However, in the seasons, it seems that periodic patterns with the same temperature ranges are more similar. On the other hand, due to the greater similarity between the results of the Mann-Kendall test in the warmer seasons and the data with monthly, seasonal and annual time scale, it seems that yearly warm period has more noticeable impacts on the positive and significant trend of temperature in the study area. It must be noted that in any of the studied time series, results of the Mann-Kendall test for detail (D) component was not significant and after adding approximation (A) component, Mann-Kendall statistics turned to a significant value. This happens because the long term variations or trends appear in approximation components in most of the time series.
Conclusion: In this study, a powerful signal processing method called wavelet transform was employed to detect the most dominant periodic components in temperature time series in various time scales, in Mashhad synoptic station. Results showed that using frequency-time analysis methods has more benefits compared with the use of only classic statistical methods, since one can explore any time series with more accuracy. Because most of the meteorological variables have periodic structures, it seems that using advanced signal processing methods like wavelet for analysis of these variables can have many advantages compared with linear-based methods. It can be suggested for future studies to use and employ signal processing methods for exploring the large scaled phenomena (e.g. ENSO, NAO, etc.) and discovering the relationship between these phenomena and climate change in recent decades.
Keywords: Discrete wavelet transforms, Mann-Kendall test, Oscillatory pattern, Trend
A. Araghi; M. Mousavi Baygi; S.M. Hasheminia
Abstract
Period and trend are two main effective and important factors in hydro-climatological time series and because of this importance, different methods have been introduced and applied to study of them, until now. Most of these methods are statistical basis and they are classified in the non-parametric tests. ...
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Period and trend are two main effective and important factors in hydro-climatological time series and because of this importance, different methods have been introduced and applied to study of them, until now. Most of these methods are statistical basis and they are classified in the non-parametric tests. Wavelet transform is a mathematical based powerful method which has been widely used in signal processing and time series analysis in recent years. In this research, trend and main periodic patterns similarity in temperature and vapor pressure has been studied in Babolsar, Tehran and Shahroud synoptic stations during 55 years period (from 1956 to 2010), using wavelet method and the sequential Mann-Kendall trend test. The results show that long term fluctuation patterns in temperature and vapor pressure have more correlations in the arid and semi-arid climates, as well as short term oscillation patterns in temperature and vapor pressure in the humid climates, and these dominant periods increase with the aridity of region.
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 ...
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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.
H. Moradi; H. Ansari; majid hashemi nia; A. Alizadeh; A. Vahidian Kamyad; S.M.J. Mosavi
Abstract
Evapotranspiration is one of the major components of hydrologic cycle and estimation of irrigation needs. In recent years the use of intelligent systems for estimating hydrological phenomena has increased significantly.In this study the possibility of using fuzzy inference system efficiency, creating ...
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Evapotranspiration is one of the major components of hydrologic cycle and estimation of irrigation needs. In recent years the use of intelligent systems for estimating hydrological phenomena has increased significantly.In this study the possibility of using fuzzy inference system efficiency, creating a bridge between meteorological parameters and evapotranspiration, and comparing the accuracy of reference evapotranspiration using these systems were investigated. After analyzing the different models and different combinations of daily meteorological data, five models for estimating daily reference evapotranspiration were presented. For these models, the calculated evapotranspirationfrom Penman-Monteith-FAO equation was considered as a baseand the efficiency of other models was evluated using statistical methods such as root mean squared error, error of the mean deviation, coefficient of determination,Jacovides(t) and Sabbaghet al. (R2/t) criteria. The used data were collected from Mashhad’s meteorological synoptic station for a period of 50-years (from 1339 to 1389).From the available data, 75 percentwas used for training the model and the rest of 25 percent was utilized for the testing purposes. The results derived from the fuzzy models with different input parameters as compared with Penman-Monteith-FAO and Hargreaves-Samani methods showed that fuzzy systems were very well able to estimate the daily reference evapotranspiration.Fuzzy model so that the highest correlation with the four input variables (r=0.99) had in mind and evaluate other parameters, the model with two parameters, temperature and relative humidity (RMSE=0.96, MBE =0.18, R2=0.95, t=22, = and R2 / t=0.04) match very well with the model Penman - Monteith - FAO had stage training. In the test phase, training phase was very similar results and the model with the second phase of temperature and relative humidity will get the best match. According to the results of this study it can be concluded that fuzzy model approach is an appropriate method to estimatethe daily reference evapotranspiration. In addition, the fuzzy models do not require complex calculations which are required forcombination methods.
S.H. Sanaei-Nejad; S. Noori; M. Hashemi nia
Abstract
Abstract
Evapotranspiration (ET) determination is a key factor for irrigation scheduling, water balance, irrigation system design and management and crop yields simulation. Therefore many scientists have tried to estimate evapotranspiration in different spatial and temporal scales. Remote sensing is ...
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Abstract
Evapotranspiration (ET) determination is a key factor for irrigation scheduling, water balance, irrigation system design and management and crop yields simulation. Therefore many scientists have tried to estimate evapotranspiration in different spatial and temporal scales. Remote sensing is a one of new technique in estimation of ET in regional scales. So, in this study it’s tried to estimate spatial distribution daily actual ET for Mashhad’s sub basin using MODIS image data related to 4th June, 1st July and 26th July 2009 and surface energy balance algorithm for land (SEBAL) taking into account topographic effects. The results showed that MODIS image data and SEBAL method were able to estimate actual daily ET in Mashhad sub-basin properly. Based on the results, areas which had dense vegetation and low temperatures had high ET rates, while in areas with sparse vegetation and high temperatures the ET rate was low.
Keywords: Evapotranspiration, MODIS, Remote sensing, SEBAL
A. Emami; B. Ghahraman; K. Davary; M. Hashemi nia; S. Tamassoki
Abstract
Abstract
Deficit irrigation is a method to promote water use efficiency (WUE) in a farm under water shortage conditions, however, the consequences is that yield per area decreases. To determine production functions for three cotton cultivars, an experiment was conducted during 1381 growing season on ...
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Abstract
Deficit irrigation is a method to promote water use efficiency (WUE) in a farm under water shortage conditions, however, the consequences is that yield per area decreases. To determine production functions for three cotton cultivars, an experiment was conducted during 1381 growing season on a silty clay loam soil in HashemAbad Agricultural Research Station in Gorgan. This study was performed using a split-plot design with 3 replications on three cotton cultivars. A line-source sprinkler irrigation system was used with 54 plots in each side of the line (3cultivars* 6treatments* 3replications). To estimate root zone water deficit, climatic data and cotton crop coefficients during the growing season were used. For each cultivar second order equations were derived to relate yield and applied water. However, linear relationships were established to relate yield and evapotranspiration. In addition, based on Doorenbos and Kassam formula yield response factors were found to be 1.02, 0.96 and 1.01 for Sahel, Say Ocra, and 818-312 cultivars, respectively. Such yield response factors can be used to optimize irrigation planning under water shortage conditions.
Key words: water production function, yield response factor, line source irrigation, Gorgan