Agricultural Meteorology
Nasrin Ebrahimi; Azar Zarrin; Abbas Mofidi; Abbasali Dadashi-Roudbari
Abstract
IntroductionClimate change has led to changes in the frequency, intensity, duration, and spatial distribution of climate extremes. During the last decade (2011-2020), the average global temperature was 0.1 ± 1.1 oC higher than in the preindustrial era. Iran and especially the Urmia Lake ...
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IntroductionClimate change has led to changes in the frequency, intensity, duration, and spatial distribution of climate extremes. During the last decade (2011-2020), the average global temperature was 0.1 ± 1.1 oC higher than in the preindustrial era. Iran and especially the Urmia Lake basin is one of the most vulnerable areas to climate change. Urmia lake basin has received the special attention of policymakers and planners since it is the location of Lake Urmia, and it also holds nearly 7% of Iran's water resources. A huge program of dam construction and irrigation networks has been started in this basin in the northwest of Iran since the late 1960s. Despite the increasing attention to Lake Urmia since 1995, the water level of this lake has decreased. During the drought of 1990-2001, Lake Urmia experienced a decrease in its level without any recovery and is decreasing at an alarming rate. Therefore, it is necessary to project the future climate of the Urmia Lake basin and especially extreme precipitation based on the latest climate change models. Materials and MethodsThe CMIP6 models were used to investigate the future projection of extreme precipitation in the Lake Urmia basin. Considering the horizontal resolution, availability of daily data, and climate sensitivity, we selected five models including GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL. The horizontal resolution of all five models is 0.5o. The 25-year historical period (1990-2014) and the 25-year projection period for the near future (2026-2050) were chosen to analyze the extreme precipitation in the Urmia Lake Basin. The future projection was considered under three shared socioeconomic pathways (SSPs) scenarios. These scenarios include SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Mean bias error (MBE) and Normalized Root Mean Square Error (NRMSE) were computed to evaluate the individual models and the multi-model ensemble generated by Bayesian Model Average (BMA) method. To assess extreme precipitation, we used four indices including the Number of heavy precipitation days (R10mm), the number of very heavy precipitation days (R20mm), the Maximum 1-day total precipitation (Rx1day), and the Simple Daily Intensity Index (SDII). Results and DiscussionThe performance of five CMIP6 individual models and the multi-model ensemble in the Lake Urmia basin during the period of 1990 to 2014 was evaluated against eight ground stations. The investigation of the annual precipitation showed that this variable is underestimated in CMIP6 models in the basin averaged. The maximum and minimum bias values model was seen in Saqez station by -9.64 mm for the MRI-ESM2-0 and -0.43 mm for the UKESM1-0-LL, respectively. The highest average MBE in the Urmia Lake basin was respectively obtained for GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL models. Among the examined models, MPI-ESM1-2-HR has shown the highest efficiency among the examined individual models.Variations in the number of heavy precipitation days during the historical period (1990-2014) have distinguished three main areas for the Lake Urmia basin. The main hotspot of heavy precipitations in the Urmia Lake basin is located in the southwest of Kurdistan province with a long-term average of 25.4 days. The next hotspots are the northwest and the northeast of the basin. In the historical period (1990-2014), the precipitation intensity index Rx1day experienced considerable variability. Based on CMIP6-MME, the value of the Rx1day index in the Urmia Lake basin is estimated between a minimum of 16.3 mm and a maximum of 63.3 mm. The maximum variation of this index is seen in the southern areas of the basin, especially on the border with Iraq. ConclusionEvaluation of individual CMIP6 models showed that these models underestimated precipitation in the Lake Urmia basin during the historical period (1990-2014). The CMIP6-MME has significantly improved precipitation estimation. The results of the investigation of days with heavy and very heavy precipitation showed that the two indices R10mm and R20mm are increasing in most areas of the Lake Urmia basin by the middle of the 21st century. Trend analysis showed that the days with heavy and very heavy precipitation will increase under different SSP scenarios in most areas of the Lake Urmia basin, especially in the northern and western regions. Also, days with heavy and very heavy precipitation will have a greater contribution than normal precipitation days in the future. It is expected that the intensity of precipitation will increase in the coming decades in the Lake Urmia basin, and this increase is more for the western and northern regions than for other regions of the basin. This result may potentially increase the flood risk in Lake Urmia.
Agricultural Meteorology
S. Javidan; M.T. Sattari; Sh. Mohsenzadeh
Abstract
IntroductionPrecipitation is one of the most important components of water cycle. Accurate precipitation measurement is essential for flood forecasting and control, drought analysis, runoff modeling, sediment control and management, watershed management, agricultural irrigation planning, and water quality ...
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IntroductionPrecipitation is one of the most important components of water cycle. Accurate precipitation measurement is essential for flood forecasting and control, drought analysis, runoff modeling, sediment control and management, watershed management, agricultural irrigation planning, and water quality studies. Determining the correct amount of precipitation in cities and rural areas is also important for managing floods. The precipitation process is completely non-linear and involves randomness in terms of time and space. Therefore, it is not easy to explain that with simple linear models due to various climatic factors and may contain major errors. Therefore, various methods and models have been proposed to evaluate, and predict precipitation. This study aimed to estimate the daily precipitation of Tabriz based on hybridized tree-based and Bagging methods by using neighboring stations.Materials and MethodsIn the present study, the rainfall data of adjacent stations in Urmia lake basin (Sahand, Sarab, Urmia, Maragheh and Mahabad) were employed in 1986-2021 to estimate the daily rainfall in Tabriz. About 70% of data were considered for calibration and 30% of data were applied for validation. Using the correlation matrix and Relief algorithm, various input components were identified. Modeling was performed using tree-based data mining methods including M5P, RT and REPT and Bagging method. The daily precipitations of Tabriz was decomposed into their components by seasonal-trend analysis method. Its components, including trend, seasonal and residual, were used in different input scenarios to investigate the effect of these components on improving the modeling results. To evaluate the modeling performance, the indices of correlation coefficient, Root Mean Square Error, Nash-Sutcliffe Efficiency and modified Wilmot coefficient were applied.Results and DiscussionRT and REPT methods increased the accuracy of the model and decreased its error when they were used as the basic algorithm of the Bagging method. This was not the case with the M5P method, as the results were slightly weaker. It was also observed that Tabriz rainfall is largely influenced by Sahand rainfall, as the most models gave reliable estimates by using the rainfall data for Sahand station. This can be explained by the high correlation between Tabriz rainfall and Sahand. The results showed that the first scenario (Sahand) for M5P, RT, REPT and B-M5P method, the fifth scenario (Sahand, Sarab, Urmia, Maragheh and Mahabad) for the B-RT method, and the fourth scenario (Sahand, Sarab, Urmia and Mahabad) for the B-REPT method were the best scenarios. The best performance was found for the scenario 1 of the M5P decision tree model, followed by the Bagging method with the M5P base algorithm. In general, it was concluded that application of the Bagging method produced reliable results. Modeling without considering the decomposition components was compared with modeling with decomposition components. Adding seasonal, trend and residual components to the modeling input combinations significantly improved the accuracy of the results. Application of Bagging method in most cases also increased the modeling accuracy. The first scenario (Sahand and residual) for M5P and B-M5P methods, the tenth scenario (residual, trend, seasonal, Sahand and Sarab) for RT, REPT and B-REPT methods, and the eighth scenario (residual, trend and Sahand) for B-RT method were selected as the best scenarios. As a result, among the stations, Sahand, due to proximity and high correlation, and Sarab, due to greater correlation, had a great impact on precipitation in Tabriz. In general, the Bagging method with the basic M5P algorithm (B-M5P) was best suited in the first scenario. Thus, adding precipitation analysis components and using the Bagging method improve the modeling results with tree-based data mining methods.ConclusionOur results showed that Bagging method provided acceptable results in most cases. In the first case, the first scenario of M5P method including Sahand precipitation data was selected as the superior method and scenario. As a result, Sahand was the most effective station in estimating Tabriz rainfall with the highest correlation and the shortest distance from Tabriz. In the second case, with the decomposition components, the accuracy of the results increased significantly. The Bagging method with the basic M5P algorithm, the parameters of Sahand precipitation and the residual of Tabriz precipitation was considered as the best modeling algorithm. It can be concluded that using Bagging method and decomposition components with the closest station to the studied station results in the highest accuracy. Therefore, Bagging models with tree-based algorithm can be considered as simple and widely used methods.
H. Ahmadzadeh; saeed morid; M. Delavar
Abstract
Streamflows, actual evapotranspiration and crops’ yield are the main variables to estimate agricultural water productivity. Thus, simulation of these variables is of great importance in evaluation of different measures to increase water productivity. For this, application of conceptual models is a ...
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Streamflows, actual evapotranspiration and crops’ yield are the main variables to estimate agricultural water productivity. Thus, simulation of these variables is of great importance in evaluation of different measures to increase water productivity. For this, application of conceptual models is a relevant approach and SWAT (soil and water assessment tool) is one of the well known models in this regard. The present paper aims to assess SWAT in simultaneous simulation of streamflows, actual evapotranspiration and the main crops’ yield of the Zarineh Rud basin. The reason for selection of this basin as the study area relates to its role to meet the Urmia Lake’s water requirement. The lake faces with serious water shortage in recent years and escalating water inflow depend to increase water productivity in the upper catchments. To setup SWAT, the basin was divided to 11 subbasins and 908 hydrological response units, which enables us to introduce more accurately the basin’s cropping pattern and water resources, which meet the requirements of the agricultural area. For simulation of the river flows, data from 6 gauging stations were used for calibration and validation of the model for periods of 1987 to 1999 and 2000 to 2006 respectively that resulted R2 and RMSE between 0.49 to 0.71 and 3.9 to 44.9 (m3/sec) for calibration period, and values of 0.54 to 0.77 and 2.07 to 55.7(m3/sec) for validation period respectively. There is no observed data for actual evapotranspiration in the basin. So, it was verified in the wet years by maximum evapotranspiration reported in National Water Document that results presented the values of 0.97 and 52.5(mm/year) for R2and RMSE respectively. Finally, the estimated yields of the 7 staple crops by the model were compared with the recorded data that showed very close values(R2=0.9 and RMSE=1.65(ton/ha)).
F. Fathian; S. Morid; S. Arshad
Abstract
The drawdown trend of the water level in Urmia Lake poses a serious problem for northwestern Iran that will have a negative impact on the agriculture and industry. This research investigated the possible causes of this adversity by estimating trends in the time series of hydro-climatic variables of the ...
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The drawdown trend of the water level in Urmia Lake poses a serious problem for northwestern Iran that will have a negative impact on the agriculture and industry. This research investigated the possible causes of this adversity by estimating trends in the time series of hydro-climatic variables of the basin as well as tracking changes in the land use of the study area, using satellite images. Four non-parametric statistical tests, the Mann-Kendall, Theil-Sen, Spearman Rho and Sen's T test, were applied to estimate the trends in the annual time series of streamflow, precipitation and temperature at 18 stations throughout the case study. Furthermore, by using the LANDSAT satellite images of 1976, 1989, 2002 and 2011, land use classification was determined using maximum likelihood, minimum distance and mahalanobis distance methods. The results showed significant increasing temperature trend throughout the region and an area-specific precipitation trend. The trend tests also confirmed a general decreasing trend in region streamflows that was more pronounced in the downstream stations. The results showed that the classification by the maximum likelihood method wass associated with minimum error. The results of processing the images showed that the irrigated crops, horticultural and dry lands have increased during last 35 years by 412, 485 and 672 percent, respectively. But, the pasture area is decreased by 34 percent. Finally, correlation between streamflow changes with simultaneous changes in climatic variables and land use showed it is significant in case of temperature and land use; and insignificant in case of precipitation. However, the determination coefficient of land use is higher than temperature.
