Agricultural Meteorology
M. Amirabadizadeh; Mahdieh Frozanmehr; M. Yaghoobzadeh; Saeideh Hosainabadi
Abstract
IntroductionNowadays, climate change is one of the human challenges in the exploitation and management of water resources. Temperature along with precipitation is one of the most important climatic elements and is one of the main factors in zoning and climatic classification. Due to location of ...
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IntroductionNowadays, climate change is one of the human challenges in the exploitation and management of water resources. Temperature along with precipitation is one of the most important climatic elements and is one of the main factors in zoning and climatic classification. Due to location of Iran within the drought belt and proximity to the high-pressure tropical zone, this country has an arid and semi-arid climate and suffers from drought in majority of years. Therefore, temperature fluctuations and variability are important issues, and make the study of temperature changes a necessity. In the current study, four data mining algorithms in selecting predictors for downscaling of maximum temperature in Birjand synoptic station have been studied, compared and the superior algorithm has been introduced. As the number of large scale features are high, selection of machine learning algorithm will play as an important role in statistical downscaling of climatic variables such as maximum temperature. Materials and MethodsToday, the data set is such that many variables are used to describe the climatic phenomenon in environmental studies. As the number of data is huge, choosing the predictors is one of the most important steps in preprocessing machine learning. In this study, four machine learning methods including stochastic approximation of simultaneous turbulence (SPSA), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge and Gradient Boosting Method (GBM) in selecting important features in downscaling of maximum temperature in Birjand synoptic station during the statistical period of 1961-2019 were studied and compared. It is a mechanism to find a combination of predictors that with a minimum number of predictors can produce an acceptable evaluation index in estimating the variable under study. For the present study, the weather information of Birjand Synoptic Meteorological Station has been prepared by the Meteorological Organization of Iran. In order to calibrate and validate the machine learning algorithms, 70% and 30% of the available monthly data, respectively, were allocated for this purpose. To conduct this research, coding in R-Studio environment and Caret and Fscaret packages were used. In this study, to evaluate the performance of the algorithms, three indices includes relative Nash-Sutcliffe Efficiency (rNSE), Volume Efficiency (VE) and Kling-Gupta Efficiency (KGE) were used.Results and DiscussionBefore using the algorithms in selecting large-scale predictors, the correlation between these variables and the maximum observational temperature at Birjand station was investigated. Large scale variables mslp, P1_v, P8_v, P8_u, P850 Temp, with a maximum correlation temperature of 0.6 showed that the correlation is acceptable given the complexity of the climate change phenomenon. In addition, these results show that all the algorithms used the important factors including F1, F2, F15, F16, F18, F20 and F26 by more than 50% and the first variable (mean pressure at the ocean surface) was the most important parameter in downscaling of maximum temperature. Also, the highest importance was for P1_v and the lowest value related to P5_u, as 73.2% and 15%, respectively. Violin plots of downscaled maximum temperature in validation step of different algorithms along with the observed maximum temperature in Birjand synoptic station in each of the algorithms showed that the values of the first and third quartiles in the output data of SPSA algorithm compared to other algorithms were closer to the observed data. According to the evaluation criteria, SPSA algorithm has a higher performance than other algorithms in reproducing the maximum monthly temperature values in Birjand synoptic station. Also, based on the volumetric efficiency evaluation criteria and relative Nash-Sutcliffe, GBM algorithm was more successful in selecting predictors than Ridge and LASSO algorithms. It is also observed that SPSA algorithm shows different results than other algorithms. In comparison of mean and variance of downscaled and observed maximum temperature, the results of t-test and F-test showed that SPSA algorithm has higher efficiency than other algorithms in regenerating mean and variance of observed maximum temperature in Birjand synoptic station at the 5% significance level.ConclusionThe data used in this study included large scale atmospheric variables and the maximum observed temperature at Birjand station. The algorithms were used to select important predictors and the performance of these methods in the validation part. According to the results of this study, the highest importance among large-scale variables is related to P1_v and the lowest value is related to P5_u, the values of which were 73.2% and 15%, respectively. The SPSA algorithm also performs better than other algorithms in selecting predictors and consequently the maximum temperature.
Irrigation
M. Fouladi Nasrabad; M. Amirabadizadeh; M. Pourreza-Bilondi; M. Yaghoobzadeh
Abstract
IntroductionThe watershed acts as a hydrological unit regulating the quantity and quality of the water cycle, and human beings have incurred high costs due to ignorance of this complex cycle and lack of planning of projects in terms of the relationship between water management and community development.Knowledge ...
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IntroductionThe watershed acts as a hydrological unit regulating the quantity and quality of the water cycle, and human beings have incurred high costs due to ignorance of this complex cycle and lack of planning of projects in terms of the relationship between water management and community development.Knowledge of features such as maximum flood discharge is essential for the design of hydraulic structures, such as dams, spillways, bridges, and culverts, in order to reduce potential damages and predict when peak discharges will be reached in the downstream areas when discussing flood warning. Rainfall-runoff modeling is one of the key tools in hydrology to achieve flood characteristics, such as peak rate and peak time. In current research, the performance of IHACRES model using "Hydromad" R package has been implemented to simulate flow in the Shoor river basin in Ghaen on a monthly scale. The model simulation was done to investigate the effect of selecting "ARMAX" and "EXPUH" methods in the linear part of the target function. Also, the modeling process and the optimized values of the model parameters were investigated.Materials and MethodsThe Shoor river basin with an area of 2412.92 square kilometers located in Ghaen between 59 degrees and 12 minutes to 59 degrees and 14 minutes east longitude and 33 degrees and 42 minutes to 33 degrees and 45 minutes north latitude. The study catchment with an average altitude of 1420 m above sea level and an average long-term annual rainfall of 173 mm has a dry climate. This river is the largest river in Ghaenat city which flows into Khaf Salt field. In this research, the IHACRES model was implemented using the Hydromad R package. To perform the flow simulation, precipitation, flow rate and temperature data on a monthly scale during the years 1998 to 2017 were used. The IHACRES model has two parts: the first part, which converts precipitation into effective precipitation at each time stage and the second part, which converts effective precipitation into modeled flow. These sections are called nonlinear and linear modules, respectively. To implement each of the sections of nonlinear modules and linear modules according to the data and conditions in the study area, methods with different parameters can be used. In this research, in the non-linear module section, the "CWI" method and in the linear module section, "ARMAX" and "EXPUH" methods have been used for proper routing in the "reverse" calibration section. In the validation section of the "ls" method, the performance criteria of KGE, NS and R2 were used to evaluate the performance of the model in both calibration and validation process. Result and DiscussionComparison of obtained results in this study with previous studies showed that in terms of examining the performance of the model with the EXPUH linear method, the obtained results are consistent with the results of Sadeghi et al. (2015) and Lotfi Rad et al. (2015) and the model with the EXPUH linear method. The NS criteria has shown acceptable performance. According to the results of the model in the calibration section, in terms of evaluation criteria NS, KGE and , and in terms of simulation of peak flow values and the time to peak using EXPUH method in the linear part showed better performance than ARMAX method. The value of these criteria in EXPUH method is equal to 0.86, 0.93, and 0.86 and in ARMAX method are equal to 0.7, 0.85 and 0.73, respectively. In the validation section, the evaluation criteria in EXPUH method were equal to 0.51, 0.63, and 0.54 and in ARMAX method were equal to 0.55, 0.73 and 0.65, respectively, indicating better performance of the model by ARMAX method. Comparison of the EXPUH method, and also the model with ARMAX method showed more accurate performance in terms of peak discharges, quantity and time of occurrence. The values of NS, KGE and evaluation criteria in this section were 0.51, 0.63, and 0.54 using EXPUH method and 0.55, 0.73 and 0.65 with ARMAX method, respectively.ConclusionAccording to the results, the IHACRES model using ARMAX method in the linear section resulted in more accurate performance than EXPUH method in simulation of peak flow values and time to peak.
Javad ramezani moghadam; Mostafa Yaghoubzadeh; Ahmad Jafarzadeh
Abstract
Introduction & Background: Assessment of climate change impacts on hydrology is relied on the information of climate changes in adequate scale. Due to outputs of GCMs (General Circulation Models) that are the most confident tools for simulating climate change impacts but are available in coarse resolution. ...
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Introduction & Background: Assessment of climate change impacts on hydrology is relied on the information of climate changes in adequate scale. Due to outputs of GCMs (General Circulation Models) that are the most confident tools for simulating climate change impacts but are available in coarse resolution. Downscaling process which is classified to several methods such as transfer function, weather generator and weather typing is performed for improving of GCMs projection and using them in local scale. Meanwhile feature selection is the main essential step in downscaling with transfer function. Because the main goal of downscaling is the improvement of GCMs projections, several researches examined vary approaches for feature selection. This study aims to assess performance of downscaling daily precipitation under four different selection methods such as PCA, CA, SRA and ParCA using comprehensive comparison tests.
Materials and Methods: Measured daily rainfall for Ardebil (with cold semi-arid climate) and Birjand (arid climates) were collected for the period from 1977 to 2004. The CanESM2 (Canadian Earth System Model) outputs were used as GCM for simulating of climate change impacts on precipitation pattern. So of CanESM2 outputs (large scale predictors) and measured daily precipitation (local scale predictants) were considered as input and target for downscaling respectively. The Artificial Neural Network (ANN) which widely has been used in climate change researches was selected as downscaling method. Despite of the most of literature have used only efficiency criteria for distinguishing from different approaches in downscaling, this study reveals performance of feature selection methods based on either them or statistical tests. The comparison tests between measured and downscaled rainfall such as assessment criteria, statistics characteristics comparison, contingency table event for wet and dry series diagnostics and Violin plot were used as tools for skill assessment of feature selection approaches.
Results and Discussion: Results showed that although different methods of predictor selection had includes various subsets, predictors such as relative humidity at surface and zonal velocity component at 500-hPa pressure levels in Birjand and mean temperature at 2m, mean sea level pressure and rotation of the air in Ardebil are the most descriptive features which have more relationship with measured daily precipitation. The efficiency criteria of comparing measured and downscaled precipitation indicated that CA method is superior to other in Birjand station and SRA’s results were better than those of other in Ardebil station. Value of RMSE, R and NSE was achieved 1.2 mm/day, 0.55 and 0.25 in Birjand and 1.75 mm/day, 0.14 and 0.013 in Ardebil respectively. The examination of measured and downscaled statistical characteristics reveals that CA has the better influence on downscaling than those of others in Birjand station. In this comparative test most of downscaled statistical components such as mean, median and skewness under CA have more similarity to measured values. But in Ardebil, with cold and arid climate, performance of SRA to downscale was the same as performance of CA to it. Also both SRA and CA were better than ParCA. The skill assessment of different methods to fit measured and downscaled variability by violin plot showed that generally ParCA outperformed other method in Birjand station. The comparison of violin plots, in Ardebil, revealed that no one of predictor selection methods has acceptable accuracy for fitting measured variability. Outcomes of contingency table event showed although all feature selection methods have not remarkable capability for distinguishing from the measured wet and dry series in Ardebil station, performance of ParCA and SRA were acceptable in Birjand station. The values of CSI for ParCA and SRA were calculated 0.25 and 0.22 in Birjand and it shows that more of 20 percent of ParCA and SRA’s diagnostics was correct.
Conclusions: By assessing of results, it can be inferred that generally downscaling of daily rainfall in Birjand station is outperforming Ardebil. In other expression daily downscaling of precipitation in arid climate has better results than cold and arid climate. Also different tests have various results about feature selection methods. In Ardebil, SRA in efficiency criteria test and both SRA and CA in statistics characteristics have better performance than others. But in this region no methods have remarkable performance in violin and dry and wet tests.
mostafa yaghoobzadeh; Saeid Boroomand Nasab; Zahra Izadpanah; Hesam Seyyed Kaboli
Abstract
Introduction: Accurate estimation of evapotranspiration plays an important role in quantification of water balance at awatershed, plain and regional scale. Moreover, it is important in terms ofmanaging water resources such as water allocation, irrigation management, and evaluating the effects of changing ...
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Introduction: Accurate estimation of evapotranspiration plays an important role in quantification of water balance at awatershed, plain and regional scale. Moreover, it is important in terms ofmanaging water resources such as water allocation, irrigation management, and evaluating the effects of changing land use on water yields. Different methods are available for ET estimation including Bowen ratio energy balance systems, eddy correlation systems, weighing lysimeters.Water balance techniques offer powerful alternatives for measuring ET and other surface energy fluxes. In spite of the elegance, high accuracy and theoretical attractions of these techniques for measuring ET, their practical use over large areas might be limited. They can be very expensive for practical applications at regional scales under heterogeneous terrains composed of different agro-ecosystems. To overcome aforementioned limitations by use of satellite measurements are appropriate approach. The feasibility of using remotely sensed crop parameters in combination of agro-hydrological models has been investigated in recent studies. The aim of the present study was to determine evapotranspiration by two methods, remote sensing and soil, water, atmosphere, and plant (SWAP) model for wheat fields located in Neishabour plain. The output of SWAP has been validated by means of soil water content measurements. Furthermore, the actual evapotranspiration estimated by SWAP has been considered as the “reference” in the comparison between SEBAL energy balance models.
Materials and Methods: Surface Energy Balance Algorithm for Land (SEBAL) was used to estimate actual ET fluxes from Modis satellite images. SEBAL is a one-layer energy balance model that estimates latent heat flux and other energy balance components without information on soil, crop, and management practices. The near surface energy balance equation can be approximated as: Rn = G + H + λET
Where Rn: net radiation (Wm2); G: soil heat flux (Wm2); H: sensible heat flux (Wm2); and λET: latent heat flux (Wm2). Simulations were carried out by SWAP model for two different sites in Faroub and Soleimani fields. The SWAP is a physically based one-dimensional model which simulates vertical transport of water flow, solute transport, heat flow and crop growth at the field scale level. The period of simulation covered the whole wheat growing season (from 1st of December2008 to 30th of July2009. 16 MODIS images was used to determine evapotranspiration during wheat growing season. Inverse modeling of evapotranspiration (ET) fluxes was followed to calibrate the soil hydraulic. While SWAP model has the advantage of producing the right amount of irrigation and evapotranspiration at high temporal resolution, SEBAL can estimate crop variables like leaf area index, NDVI index, net radiation, Soil heat flux, Sensible heat flux and evapotranspiration athigh spatial resolution.
Results and Discussion: Actual and potential evapotranspiration were estimated for SWAP Model during the whole wheat growing season around669.5 and 1259.6 mm for Farub field and 583.7 and 1331.2 mm for Soleimani field, respectively. In contrast with NDVI and net radiation,spatial distribution of SEBAL parameters indicated that soil heat flux, sensible heat flux, and surface temperature of land have the same behavior. At the planting date, evapotranspiration was low and about 1 mm/day, but at the peak of plant growth, it was about 9 mm/day. Moreover, evapotranspiration declined at late growing season to about 3 mm/ day. SWAP model has been calibrated and validated with meteorological data and the data of field measurements of soil moisture. The amount of RMSE of 0.635 and 0.674 (mm/day) and MAE of 0.15 and 0.53 (mm/day) and also coefficient of determination (R2) of 0.915 and 0.964 obtained from comparison of SEBAL algorithm with SWAP model for Farub and Soleimani fields showed that no significant differences was seen between results of two models.
Conclusion: The present study supports the use of SEBAL as the most promising algorithm that requires minimum input data of ground based variables. Results of comparison of SEBAL and SWAP model showed that SEBAL can be a viable tool for generating evapotranspiration maps to assess and quantify spatiotemporal distribution of ET at large scales. Also, it feels that SEBAL and SWAP models can be applied in a wide variety of irrigation conditions without the need for extensive field surveys. This helps significantly in identifying performance indicators and water accounting procedures in irrigated agriculture, and to obtain their likely ranges.
