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.
Agricultural Meteorology
Sepideh Dowlatabadi; Mahdi Amirabadizadeh; Mahdi Zarei
Abstract
Introduction
The sustainable availability of water resources and the qualitative and quantitative status of these resources are threatened by many natural and antropogenic factors, among which climate change plays an important role. Climate change can have profound effects on the hydrological cycle ...
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Introduction
The sustainable availability of water resources and the qualitative and quantitative status of these resources are threatened by many natural and antropogenic factors, among which climate change plays an important role. Climate change can have profound effects on the hydrological cycle through changes in the amount and intensity of precipitation, evapotranspiration, soil moisture, and increasing temperature. On the other hand, the distribution of rainfall in different parts of the world will be uneven. So that some parts of the world may face a significant decrease in the amount and intensity of precipitation, as well as major changes in the timing of wet and dry seasons. Therefore, sufficient knowledge about the effects of climate change on hydrological processes and water resources will be of particular importance. In this research, as the first comprehensive study, the effect of future climate change on the water resources components of Neyshabur-Rookh watershed was investigated by a set of one hydrological model and six General Circulation Models under the RCP4.5 scenario.
Materials and Methods
The Neyshabur-Rookh watershed with an area of 9449 square kilometers is a sub-basin of Kavir-e Markazi-e Iran and a part of the Kalshoor Neyshabur watershed, which is located between of 58 degrees and 13 minutes and 59 degrees and 30 minutes and east longitude and 35 degrees and 40 minutes and 36 degrees and 39 minutes north latitude. The study area with an average altitude of 1549.6 m above sea level and average annual precipitation of 246.83 mm, a mean annual temperature of 13.3 Celsius has an arid to semi-arid climate. For hydrological simulation of the watershed using WetSpass-M model, maps of Digital Elevation Model (DEM), land-use, soil texture, slope, and distribution map of groundwater depth, Leaf Area Index (LAI), and climate data (rainfall, mean temperature, potential evapotranspiration, wind speed and the number of rainy days) per month in 1991-2017 period were used. Then the prepared model was calibrated and validated. The climatic data of six General Circulation Models (GCMs) under the RCP4.5 scenario (Representative Concentration Pathways) were downscaled using the Quantile Mapping Bias-Corrected method. The downscaled GCM models were ranked and weighted in each station according to results of the Leave one out cross validation method and utilized as an ensemble for projecting the near-future climatic conditions of the water resources components of the watershed. By importing the monthly maps of precipitation, average temperature and evapotranspiration in the period of 2026-2052 into the calibrated hydrological model, the hydrological response of watershed to near future climate change was determined and evaluated.
Results and Discussion
WetSpass-M was calibrated by changing the calibration parameters in five hydrometric stations and the compared measured and simulated streamflow. The values of four evaluation criteria NS, R2, MB, and RMSE indicated the good performance of the model during the calibration and validation process. By predicting climatic parameters in near future and preparing and importing maps of monthly precipitation, mean temperature, and evapotranspiration to WetSpass-M, the hydrologic simulation of the watershed was done in the 2026-2052 period. The results indicated that the mean annual temperature and precipitation would be respectively increased by 4.66% and 1.21°C under RCP4.5 in the near-future period compared to the baseline period. The average temperature will increase in all months so that the most changes will occur in September and the least changes will occur in March. The rainfall of the watershed will increase in March, April, May, October, and December and will decrease in the rest of the months. The highest and lowest rainfall changes will happen in April and August, respectively. The analysis of the components of water resources in the near future shows that annual total runoff, groundwater recharge, and actual evapotranspiration will increase by 5.9%, 14.85%, and 1.42% compared to the base period, and annual direct runoff and interception will decrease by 15.15% and 3.54%, respectively.
Conclusion
Considering the importance and major role of the Neyshabur watershed in the economy of agricultural products of Razavi Khorasan province, the results of this research will be of great help to the managers and policymakers of the country's water resources management in order to make appropriate decisions with the aim of reducing the effects of climate change on the water resources of the Neyshabur-Rookh Basin.
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.
