Evaluating the SSM Model Efficiency in Simulating the Wheat Growth under Water Stress Conditions

IntroductionWheat (Triticum aestivum L.) has become very important as a valuable strategic product with high energy level. The importance of investigating environmental stresses and their role in predicting and evaluating the growth and crops yield is essential. A wide range of plant response to stress is extended to morphological, physiological and biochemical responses. Considering the rapid advancement in computer model development, plant growth models have emerged as a valuable tool to predict changes in production yield. These growth simulation models effectively incorporate the intricate influences of various factors, such as climate, soil characteristics, and management practices on crop yield. By doing so, they offer a cost-effective and time-efficient alternative to traditional field research methods. Material and MethodsThis research was conducted in the research farm of Varamin province, which has a silty loam soil texture. The latitude and longitude of the region are 35º 32ʹ N and 51º 64ʹ E, respectively. Its height above sea level is 21 meters. According to Demarten classification, Varamin has a temperate humid climate. The long-term mean temperature of Varamin is 11.18 ° C and the total long-term rainfall is 780 mm. In this study, in order to simulate irrigated wheat cv. Mehregan growth under drought stress, an experimental based on completely randomized blocks (CRBD) including: non-stress as control (NS), water stress at booting stage (WSB), water stress at flowering stage (WSF), water stress at milking stage (WSM) and water stress at doughing stage (WSD) with three replications during growth season 2019-2020 was carried out in Varamin, Iran. Crop growth simulation was done using SSM-wheat model. This model simulates growth and yield on a daily basis as a function of weather conditions, soil characteristics and crop management (cultivar, planting date, plant density, irrigation regime). Results and DiscussionBased on the results, the simulation of the phenological stages of irrigated wheat cv. Mehregan under water stress condition using SSM-wheat model showed that there was no difference between observed and simulated values. Summary, the values of day to termination of seed growth (TSG) were observed under non- stress, stress in the booting stage, flowering, milking and doughing of the grains, 222, 219, 219, 221, 221 days, respectively andsimulation values with 224, 221, 220, 221, respectively. However, with their simulation values, there were slight differences with 224, 221, 220, 221, respectively. Acceptable values of RMSE (11.7 g.m-2) and CV (3.5) indexes showed the high ability of the SSM model in simulating the grain yield of irrigated wheat cv. Mehregan under water stress conditions. Grain yield values were observed in non-stress conditions of 5783, water stress in booting, flowering, milking and doughing of the grain stages in 5423, 5160, 5006 and 5100 kg. h-1, respectively. While the simulated values were 5630, 5220, 4920, 4680 and 4880 kg. h-1, respectively. Based on the findings, observed and simulated values of leaf area index (LAI) were observed under water stress condition in the booting, flowering, milking and doughing of the grain stages (4.3 and 4.47), (4.33) and 4.46), (4.4 and 4.57) and (4.4 and 4.58) cm-2, respectively. Evaluation of the 1000-grain weight of irrigated wheat cv. Mehregan under the water stress showed that the SSM model was highly accurate. RMSE (4.6 g.m-2) and CV (1.8) values indicate the ability of the SSM model to simulate the 1000-grain weight of irrigated wheat cv. Mehregan. Also, the simulated values of the harvest index were 34.7 % in non-stress conditions, which decreased by 6 % compared to the observed value. Harvest index values were observed under water stress conditions in the in the booting, flowering, milking and doughing of the grain stages in 30.2, 29.3, 29.9 and 29.5 %, respectively. Compared to its observed values, it was reduced by 3, 3.5, 5, and 5.5 %, respectively. ConclusionBased on the findings, the slight difference between the observed and simulated values demonstrates the SSM model's capability to accurately capture water stress impacts on the phenological stages, grain yield, and yield components of irrigated wheat cv. Mehregan during critical growth stages, including booting, flowering, milking, and doughing. The results indicate that the SSM model is effective in simulating wheat growth under water stress conditions, showcasing its potential as a valuable tool for modeling irrigated wheat growth. The model's ability to account for water stress and its effects on various growth parameters makes it a reliable and efficient tool for predicting crop performance in water-limited environments

Quantifying Anthropogenic Stress on Groundwater Resources.

This study explores a general framework for quantifying anthropogenic influences on groundwater budget based on normalized human outflow (hout) and inflow (hin). The framework is useful for sustainability assessment of groundwater systems and allows investigating the effects of different human water abstraction scenarios on the overall aquifer regime (e.g., depleted, natural flow-dominated, and human flow-dominated). We apply this approach to selected regions in the USA, Germany and Iran to evaluate the current aquifer regime. We subsequently present two scenarios of changes in human water withdrawals and return flow to the system (individually and combined). Results show that approximately one-third of the selected aquifers in the USA, and half of the selected aquifers in Iran are dominated by human activities, while the selected aquifers in Germany are natural flow-dominated. The scenario analysis results also show that reduced human withdrawals could help with regime change in some aquifers. For instance, in two of the selected USA aquifers, a decrease in anthropogenic influences by ~20% may change the condition of depleted regime to natural flow-dominated regime. We specifically highlight a trending threat to the sustainability of groundwater in northwest Iran and California, and the need for more careful assessment and monitoring practices as well as strict regulations to mitigate the negative impacts of groundwater overexploitation

Investigation of Meteorological Extreme Events in the North-East of Iran

Introduction: Over the past hundred years, human activity has significantly altered the atmosphere and increase of concentration of greenhouse gases lead to warm the earth's surface. This global warming leads to change of climatic extreme index and increases the intensity and frequency of occurrence of extreme climate events. Investigation of extreme values for planning and policy for the agricultural sector and water resource management is important.In this study, a comprehensive review of extreme indices of temperature and precipitation are discussed. This paper aims to investigate extreme temperature and precipitation indices defined in accordance with CCL, and the study of other climatic parameters in the North East of Iran. Materials and Methods: In this research, statistics and data of some stations in the North East of Iran during the period 1992-2012 were used. To evaluate the extreme climate indices trend, 27 indices of rainfall and temperature, were defined by the ETCCDMI. They were calculated by RClimdex software. In this software, prior to the index calculation, data by quality control software became quantitative and incorrect data were controlled and outlier data were examined. The indices were calculated by daily data. 11 rainfall and 16 temperature indices were calculated by this software.The target of the ETCCDMI process is to delineate a standardized set of indices allowing for comparison across regions. These extreme indices were classified in five categories which included the percentile-based extreme indices, the absolute extreme indices, the threshold extreme indices, the periodic extreme indices, and the other indices. They were estimated at the 0.05 significant levels. The Mann-Kendall test was used to investigate the climatic parameters, maximum relative humidity, sunshine duration and maximum wind speed. Results and Discussion: Thermal analysis results are consistent with warming patterns, and they have showed that hot extremes indices have increased. Hot days index (SU25), shows a significant positive trend in all studied stations. Number of tropical nights has a positive trend in all stations. Hot day frequency (TX90P) and hot night frequency (TN90P) in all stations show a positive trend, indicating an increase in the number of warm days and nights. Cold extreme indices show a decreasing trend. (TX10P) and (TN10P) show significant negative trends in all stations and indicate a decrease in cold days and nights. Number of frost day index shows a decreasing trend. Overall, the results revealed a decrease in the severity and frequency of cold events, while warm events during the study period were significantly increased. These results are consistent with the results of the Intergovernmental Panel on Climate Change and global and regional studies. Rising temperatures could lead to increase in the maximum wind speed in the area. In the study of the maximum wind speed process, this trend was observed in most stations, and incremental changes can be associated with a reduction in the maximum relative humidity (which was observed in the results). The sunshine hour parameter depicted a decreasing trend in the most station trend. In the study of all rainfall indices in all studied stations there were a decreasing and negative trend for rainfall, although few significant trends over time were observed. Comparison of years with the highest rainfall and those with the lowest, showed that the amplitude of fluctuations in precipitation in different years is very high and the distribution of rainfall at distinct stations is different. In general, due to the high dispersion and low rainfall in most stations, providing a clear and uniform regional rainfall pattern is not possible. Due to the effects of temperature and precipitation extreme indices in a wide range of human activities, such as agriculture, water management and building design, it is necessary to consider the effects of these extreme climatic events in the future planning and policies in different sectors. Conclusion The results showed that hot extreme indices, such as summer day index, the number of tropical nights, warm days and nights have increased, while, in the period of study, cold extreme indices have decreasing trend, which shows a decrease in the severity and frequency of cold events.The trend of the maximum wind speed was increased in most stations. Rainfall indices show decreasing and negative trends, although over the studied period few significant trends were observed

The Investigation The Amount of Interception in Sprinkler Irrigationin Wheat and Soybean

Interception is one of the important and effective parameters on ET and hydrological relation, which is ignored in many situations. In order to investigate the effectiveness of LAI and extinction coefficient on amount of interception, in this study wheat and soybean were cultivated in thelysimeters of agricultural school of Fredowsi Uni. of Mashhad, in Spring and Summer 2012 in the same treatments. The results showed that there is relationship between interception and LAI and extinction coefficient. By increasing LAI, interception increased significantly (slope 0.15). The maximum amount of interception was 1.19 cm in soybean by 6.19 LAI and in wheat cultivars was 1.1cm in 4.58LAI. Also by decreasing the extinction coefficient, interception increased by the rate of 1.023. Results showed that in the same LAI (3.2), wheat interception was more than soybean, 0.74 and 0.5 respectively. While in the same extinction coefficient interceptions was the same in two crops. Standardization the amount of interception by LAI, showed that the effect of the crop on interception is still remained, while by standardize the interception by extinction coefficient, the influence of crop on standard interception removed. The obtained result showed that the type of crop has a significant effect on interception, which can be shown by extinction coefficient

Validation of Grid APHRODIT Daily Precipitation Estimates and Estimates derived from spatial interpolation of Precipitation in the Khuzestan province

Available accurate and reliable precipitation data are so important in water resources management and planning. In this study,to determine the best method of regional precipitation estimate in Khuzestan province, estimated daily precipitation data from the best interpolation method and APHRODIT Daily Grid Precipitation data during the 2000-2007 years were compared with 44 meteorological stations. Four interpolation methods i.e. Inverse Distance Weighted, Ordinary Kriging, Cokriging, and Regression Kriging were assessed to determine the most appropriate interpolation method for daily precipitation.For the variography analysis in Kriging models, five variogram models including spherical, exponential, linear, linear to sill and Gaussian fitted on the precipitation data. Near neighbor method was used to compare APHRODIT Daily Precipitation data with station recorded data. Cross validation technique was employed to evaluate the interpolation methods and the most appropriate method was determined based on Root Mean Square Error,Mean Bias Error, Mean Absolute Error indices and regression analysis. The result of error evaluation of interpolation methods showed that regression Kriging method has the highest accurate to interpolation of daily precipitation data in Khuzestan province. Therefore, regression-based interpolation methods which using covariates would be improved precipitation evaluate accurate in the area. Comparison of error indices and regression analysis of regression Kriging interpolation method and estimate of APHRODITE show that on most days the accurately estimate of regression Kriging is higher than the APHRODITE. Therefore to understanding of spatial distribution and estimate of daily precipitation data in Khuzestan Province, Regression Kriging interpolation method is more accurate than available APHRODITE dat

Assessment of Fluctuation Patterns Similarity in Temperature and Vapor Pressure Using Discrete Wavelet Transform

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

Using Discrete Wavelet Transform for Trend Analysis and Oscillatory Patterns Identification of Temperature (Case Study: Mashhad Synoptic Station)

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, Tren

Verification of Temperature and Precipitation Simulated Data by Individual and Ensemble Performance of Five AOGCM Models for North East of Iran

Scince climatic models are the basic tools to study climate change and because of the multiplicity of these models, selecting the most appropriate model for the studying location is very considerable. In this research the temperature and precipitation simulated data by BCM2, CGCM3, CNRMCM3, MRICGCM2.3 and MIROC3 models are downscaled with proportional method according A1B, A2 and B1 emission scenarios for Torbat-heydariye, Sabzevar and Mashhad initially. Then using coefficient of determination (R2), index of agreement (D) and mean-square deviations (MSD), models were verified individually and as ensemble performance. The results showed that, based on individual performance and three emission scenarios, MRICGCM2.3 model in Torbat-heydariye and Mashhad and MIROC3.2 model in Sabzevar had the best performance in simulation of temperature and MIROC3.2, MRICGCM2.3 and CNRMCM3 models have provided the most accurate predictions for precipitation in Torbat-heydariye, Sabzevar and Mashahad respectively. Also simulated temperature by all models in Torbat-heydariye and Sabzevar base on B1 scenario and, in Mashhad based on A2 scenario had the lowest uncertainty. The most accuracy in modeling of precipitation was resulted based on A2 scenario in Torbat-heydariye and, B1 scenario in Sabzevar and Mashhad. Investigation of calculated statistics driven from ensemble performance of 5 selected models caused notable reduction of simulation error and thus increase the accuracy of predictions based on all emission scenarios generally. In this case, the best fitting of simulated and observed temperature data were achieved based on B1 scenario in Torbat-heydariye and Sabzevar and, A2 scenario in Mashhad. And the best fitting simulated and observed precipitation data were obtained based on A2 scenario in Torbat-heydariye and, B1 scenario in Sabzevar and Mashhad. According to the results of this research, before any climate change research it is necessary to select the optimum GCM model for the studying region to simulate climatic parameters

Determining of most Effective Factors on drought Useing of Panel Data Analyse (Khorasan Razavi Province)

Drought is a natural creeping event that starts due to lower moisture compared to normal condition. This phenomenon impacts all aspects of human activities. However there is neither any detailed definition nor a general and proper index for drought monitoring In the present study using the Drought indices SPI and RDI to monitor drought in 10 synoptic stations in the province were studied over a period of 24 years(1991-2010). After using panel data analysis of annual and seasonal drought tried to detecte effective the parameters above were measured using two indicators. Based on the results of monitoring Drought was found a severe drought that the 2008 in the province. Also, analyse of Panel data was show all six parameters mean of maximume tempretuer, mean of minimum tempreture, sun shine, precipitation, relative humidity and mean wind speed in 2 meters that to calculate the drought index RDI, not required to calculate Drought in time scale of annual and seasonal in 10 stations; due time scale, only of some these parameters are required. Based on SPI, precipitation is necessary for time scale annual and seasonal droghut

Daily soil temperature modeling using ‘panel-data’ concept

<p>The purpose of this research was to predict soil temperature profile using ‘panel-data’ models. Panel-data analysis endows regression analysis with both spatial and temporal dimensions. The spatial dimension pertains to a set of cross-sectional units of observation. The temporal dimension pertains to periodic observations of a set of variables characterizing these cross-sectional units over a particular time-span. This study was conducted in <i>Khorasan-Razavi</i> Province, Iran. Daily mean soil temperatures for 9 years (2001–2009), in 6 different depths (5, 10, 20, 30, 50 and 100 cm) under bare soil surface at 10 meteorological stations were used. The data were divided into two sub-sets for training (parameter training) over the period of 2001–2008, and validation over the period of the year 2009. The panel-data models were developed using the average air temperature and rainfall of the day before (<math><mrow><msub><mi>T</mi><mrow><mi>d</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></math> and <math><mrow><msub><mi>R</mi><mrow><mi>t</mi><mo>−</mo><mn>1</mn></mrow></msub></mrow></math>, respectively) and the average air temperature of the past 7 days (<i>T</i>_w) as inputs in order to predict the average soil temperature of the next day. The results showed that the two-way fixed effects models were superior. The performance indicators (<i>R</i>² <i>=</i> 0.94 to 0.99, RMSE = 0.46 to 1.29 and MBE = −0.83 and 0.74) revealed the effectiveness of this model. In addition, these results were compared with the results of classic linear regression models using <i>t</i>-test, which showed the superiority of the panel-data models.</p