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
Nazila Shamloo; Mohammad Taghi Sattari; Khalil Valizadeh Kamran; Halit Apaydin
Abstract
Introduction
Drought is one of the greatest challenges of our time due to the dangers it poses to the world. In arid and semi-arid regions, it is necessary to continuously monitor agricultural systems that face water shortages and frequent droughts. Therefore, it is necessary to have large-scale information ...
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Introduction
Drought is one of the greatest challenges of our time due to the dangers it poses to the world. In arid and semi-arid regions, it is necessary to continuously monitor agricultural systems that face water shortages and frequent droughts. Therefore, it is necessary to have large-scale information about agricultural systems and land use for managing and making decisions for the sustainability of food security. Continuous monitoring of drought requires a large amount of information to be processed with great speed and accuracy. Due to the complexity and impact of various factors on drought, in recent years, the methods of combining several factors to create a comprehensive drought index have received much attention. Machine learning and deep learning methods can provide a more accurate and efficient tool to predict droughts and be used in drought risk management. The review of sources shows that until now no studies have been conducted in the field of drought monitoring using deep learning approach and satellite images in the catchment area of Lake Urmia in Iran. A large part of its economic activities is dedicated to agriculture. The increase in temperature, the increase in evaporation-transpiration and the excessive use of water resources for agriculture have caused an upward trend in the frequency of droughts in this basin during consecutive years, one of the harmful effects of which is a significant decrease in the lake level. Therefore, for drought management in this basin, it is very important to identify drought behavior so It is very important to determine appropriate and reliable indicators to measure and predict the effects of droughts. According to the investigations, it was observed that most of the studies in the field of drought in this basin have been carried out from the meteorological point of view, or by individual plant indicators, so in this study, using the approach of principal component analysis, we tried to provide a composite drought index for drought modeling and forecasting.
Materials and Methods
In this research, satellite images and deep learning and machine learning methods have been used to predict the Combined Drought Index. For this purpose, satellite images were first obtained for the study area and pre-processing was done on the data. Then, all the data were converted to a scale with a spatial resolution of 500 meters, and the VCI index was calculated using NDVI data, the TCI index using the land surface temperature product, and the CWSI index using the Modis evapotranspiration product, and finally, CDI drought index was calculated using principal component analysis method. Then the correlation between CDI data and other meteorological variables including evapotranspiration, potential evapotranspiration, land surface temperature during the day, and land surface temperature at night was calculated. Finally, the CDI index is modeled using deep learning and machine learning methods.
Results and Discussion
This study modeled the Combined Drought Index based on a different combination of input variables and deep learning and machine learning methods. Examining the results showed that the variables of the normalized difference vegetation index, the land surface temperature during the day and at night, evapotranspiration, and potential evapotranspiration were the most influential parameters for modeling the CDI index, and all four methods with acceptable accuracy and error have been able to model the combined drought index. The CART model with a correlation coefficient of 0.96, RMSE equal to 0.029, and Nash Sutcliffe coefficient of 0.92 was chosen as the best model among the methods.
Conclusion
In this research, different combinations of input variables extracted from satellite image products were evaluated in the form of 6 independent scenarios to predict the Combined Drought Index. By examining the evaluation parameters including correlation coefficient, Nash Sutcliffe coefficient, and root mean square error, it was found that all four methods can estimate the combined drought index with acceptable accuracy and error. Among all the methods, the CART method performed better (R=0.96 and RMSE=0.029) than the other methods for predicting the time series of the Combined Drought Index. On the other hand, the SVM method has been able to model the combined drought index with acceptable accuracy (R=0.94 and RMSE=0.034). However, contrary to expectations, two deep learning methods were able to model the combined drought index with less accuracy than machine learning methods. In general, by examining the results, it was found that with the method presented in this research, it is possible to accurately predict the CDI combined drought index time series and predict drought in different periods of plant growth, and use its results for regional drought management and policies, especially in Basins without statistics.
Agricultural Meteorology
Sakineh Khansalari; Mahmood Omidi; Mozhgan Fallahzadeh
Abstract
Introduction
Due to global warming and climate change, droughts and extreme precipitation events are increasing. Therefore, it is of special importance to know the characteristics of precipitation in the region in order to manage water resources effectively especially during torrential rainfall events. ...
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Introduction
Due to global warming and climate change, droughts and extreme precipitation events are increasing. Therefore, it is of special importance to know the characteristics of precipitation in the region in order to manage water resources effectively especially during torrential rainfall events. This can help to reduce the risk of these events and increase water reserves with proper management. These precipitation characteristics which are the objectives of the present study, include the temporal-spatial distribution of precipitation in different parts of the study area, as well as the number of days with and without precipitation and the maximum precipitation occurring in the region. Also, these precipitation characteristics should give us information about extreme precipitation events.
Materials and Methods
This research analyzed the characteristics of precipitation in Markazi province over a 30-year period (from the crop year 1991-1992 to 2020-2021) using statistical methods and the spatial distribution was drawn and analyzed with ArcGIS software. This province includes the 12 meteorological stations of Arak, Mahalat, Saveh, Tafresh, Ashtiyan, Komeijan, Khondab, Shazand, Khomein, Delijan, Farmahin and Gharqabad, which the precipitation data of these stations were investigated. The trend of precipitation changes in monthly, seasonal, and annual time scales were also examined using the Mann-Kendall test. Moreover, extreme precipitation was assessed using four indices: total extreme precipitation (R95p), number of days with precipitation above the station’s extreme precipitation threshold (R95d), absolute intensity of extreme precipitation (AEPI) and the fraction of total rainfall from events exceeding the extreme threshold (R95pT). The latter index represents the ratio of extreme precipitation to annual precipitation in rainy days (daily rainfall above 1 mm).
Results and Discussion
This study reveals that, on average, 53% of the annual precipitation is accounted for by the maximum index of R95pT, which indicates the percentage of extreme precipitation that occurred at each station relative to its the precipitation of the corresponding year. Knowing the timing of these extreme events can help to manage floods and optimize water resources. More than 20% of these precipitations occurred in March. The spatial distribution of rainfall in Markazi province shows that the south-west regions have the highest average annual and seasonal rainfall, except for the summer season, while the eastern regions have the lowest. The winter season has the highest rainfall on average, followed by spring and autumn. March is the rainiest month with a coefficient of variation of 0.8 and an average monthly rainfall of 55.6 mm during the studied period. Due to most extreme precipitation events occurring in this month, it has the highest importance for water storage and management throughout the year. The average precipitation in March ranges from 32.6 mm (Saveh station) to 91.6 mm (Shazand station) across the stations of the province. The maximum rainfall in this month varies from 124.4 to 254.6 mm among the stations of the Markazi province, which is a considerable amount compared to the provincial average crop year. The standard deviation of precipitation in this month is between 28.7 and 61.3 mm, and the coefficient of variation at the stations of the province is between 0.6 and 0.9. Moreover, in terms of average monthly rainfall 22Nov-21Dec, 20Feb-19Mar, and 23Oct-21Nov are the next priority months for water storage management after 20Mar-19Apr, with average monthly rainfall of 39.3, 38.2, and 36.3 mm, respectively. The Mann-Kendall non-parametric test results did not reveal a consistent trend, but it showed that most of the meteorology stations in Markazi province had a significant decreasing trend in the rainfall in 21Jan-19Feb at a 90% confidence level. The analysis of extreme precipitation indices indicated that Shazand station had the highest extreme precipitation threshold value (28 mm), while Saveh and Delijan stations had the lowest (15 mm). The extreme precipitation threshold average of 30 years in other meteorological stations of Markazi province are 21mm in Arak, 17mm in Tafresh, 21mm in Khomeyn, 19mm in Mahallat, 17mm in Komeijan, 16mm in Farmahin, 21mm in Khondab, 17mm Gharqabad and 18mm in Ashtiyan.
Conclusion
The spatial distribution of rainfall in Markazi Province shows that the southwest regions have the highest average annual and seasonal precipitation, except for summer, while the east regions have the lowest. The average monthly rainfall also indicates that March has the highest rainfall among all months of the year, and that about 20% of the annual extreme precipitation occurs in this month.
Agricultural Meteorology
Kh. Javan; A. Movaghari
Abstract
Introduction
The most important effect of global warming is the increase in extreme weather events. According to AR5 reports, between 1951 and 2010, the number of warm days and nights increased and the number of cold days and nights has declined globally. In addition, the duration and frequency of hot ...
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Introduction
The most important effect of global warming is the increase in extreme weather events. According to AR5 reports, between 1951 and 2010, the number of warm days and nights increased and the number of cold days and nights has declined globally. In addition, the duration and frequency of hot periods, including thermal waves, have increased since the middle of the twentieth century. The trend analysis of temperature extreme indices is important in estimating the trend of global warming. Temperature Changes are affected by many complex factors. A significant part of these changes is due to the elements of the general circulation of the atmosphere and the sea surface temperature. Given that extreme weather events are one of the most devastating natural hazards and have harmful effects on different parts of society, therefore, many researchers have studied the changes in the past and future of extreme events and the mechanisms that trigger these changes. This research attempts to study the trend of changes in extreme temperature indices in North-West of Iran, and also their relation with general circulation of atmosphere.
Materials and Methods
At first, diurnal data of minimum and maximum temperature of 20 synoptic stations of the Northwest of Iran, which have long-term and reliable statistics, extracted for the period of 1986-2010 and quality control and data homogeneity of them were investigated. afterwards, 16 Extreme temperature indices introduced by ETCCDMI were applied. In general, these indices are categorized into five categories of absolute indices, based on percentiles, based on thresholds, periodic, and amplitudes that measure the frequency, severity and duration of the temperature. These indices are estimated by RClimDex software and the trend rate of the changes in indices was shown through maps. To measure the changes in the general circulation of atmosphere the annual mean circulation composites extracted for the periods of 1961-1985 and 1986 -2016 based on the reanalysis data of the NCEP / NCAR. Then the difference maps plotted using grads software.
Results
The regional trend of extreme indices and the percentage of stations with a positive and negative trend were identified and the spatial distribution of the gradient of each of the indices was mapped. The results show that all absolute temperature indices have an increasing trend. On average, the maximum temperature (TXx and TXn) has increased by about 0.04 degrees over the decade. The increase rate of TNx is about 0.03 degrees, while the TNn increased by about 0.1 degrees Celsius per decade during the study period. Therefore, in the north-west of Iran, temperature increase has mainly occurred at night. The values of cold days (TX10) and cold nights (TN10) decreased with a gradient of -0.46 and -0.42 days in the decade. The warm days (TX90) and warm nights (TN90) have an increasing trend in 95% of the stations in the area. Frost days (FD) and icing days (IDs) have a decreasing trend, whereas, summer days (SU25) and tropical nights (TR20) have an increasing trend. The number of frost days with a gradient of -0.95 and the number of icing days with a gradient of -0.63 days in decade are decreasing. While, the number of summer days with a gradient of 0.81 and the number of tropical nights with gradient of 0.31 days in decade are increasing. In the northwest of Iran, all stations have been experiencing the increasing trend in Warm Spell Duration Index (WSDI), but the Cold Spell Duration Index (CSDI) in 70% of the stations in the region has decreased. Growing season length, as an effective index especially in agriculture, is increasing by an average of 1.1 days per decade. Based on the results of research carried out globally and at Iran, the trend of Daily Temperature Range (DTR) is negative, while this index has a positive and increasing trend in 65% of North-West stations in Iran. Except TNx and TNn indices that have positive trend in most stations in the region, Comparison of warm and cold extreme indices indicates that warm indices have a positive and incremental trend, while cold indicators show a decreasing trend. The positive gradient of these indices also corresponds to the decreasing trend of cold day and night indices, which indicates an increase in temperature and a decrease in cold days and nights. The study of large-scale changes in atmospheric circulation shows that the study area has got warmer in the spring and summer and colder in autumn and winter.
Conclusion
In this study, the trend of temperature extreme indices in North-West of Iran and its relation with the large-scale general circulation of the atmosphere have been investigated. The results show that all absolute temperature indices (TXx, TXn, TNx and TNn) are incremental. The indices of cold days (TX10) and nights (TN10) decreased with a gradient of -0.46 and -0.42 days in the decade and the indices of warm days (TX90) and warm nights (TN90) are increasing in 95% of the stations in the area. Frost days and icing days (IDs) show declining trend and summer days (SU25) and tropical nights (TR20) have an increasing trend. In the north-west of Iran, all stations have experienced an increasing trend in warm spell duration index (WSDI), but the cold spell duration index (CSDI) has been decreasing in 70% of the stations in the area. Growing season length (GSL) is increasing by an average of 1.1 days in every decade. Daily temperature range (DTR) has a positive and increasing trend in 65% of stations in north-west Iran. Comparison of warm and cold extreme indices indicates that warming indices have a positive and incremental trend, while cold indices show a decreasing trend. Study of the general circulation of atmosphere of the region by drawing and analyzing difference maps indicates that the study area has been warmer in spring and summer and colder in autumn and winter.
Agricultural Meteorology
H. Fahimi; A. Faraji; B. Alijani
Abstract
Introduction
A subtropical high system that significantly impacts the Iranian climate is the Arabia Anticyclone (Raziei, 2012). This high-pressure system is located southeast of the Red Sea, over the Arabian Peninsula and the Arabian Sea. It is one of the semi-permanent centers in the lower levels of ...
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Introduction
A subtropical high system that significantly impacts the Iranian climate is the Arabia Anticyclone (Raziei, 2012). This high-pressure system is located southeast of the Red Sea, over the Arabian Peninsula and the Arabian Sea. It is one of the semi-permanent centers in the lower levels of the atmosphere, and its influence leads to notable climate changes and characteristics in the region (Karimi, 2016). This system is a component of atmospheric circulation affecting cold-period precipitation in Iran (Karimi et al., 2021); due to its dynamic structure, it has a great ability to provide humid air, especially in the lower layers (Mohammadi & Lashkari, 2018). So far, some studies have been conducted on subtropic high's effect on the Iranian climate, but its impact on the occurrence of precipitation in Iran, especially during the cold period of the year, has received less attention. Therefore, in this study, we aimed to examine the role of the Arabia Anticyclone (AA) subtropical high in the pervasive extreme precipitation during the cold season in Iran. By analyzing its influence at different atmospheric levels, we sought to gain a clearer understanding of how this system affects precipitation patterns in Iran. The study also explores the changes in the AA at various atmospheric levels and its relationship with other atmospheric circulation systems, as well as how these factors contribute to the occurrence of extreme precipitation events in the region.
Materials and Methods
Daily precipitation data of Iranian synoptic stations from 1989 to the end of 2020 were extracted from the Meteorological Organization of Iran. Based on the relative index method, the 95th percentile index of extreme precipitation for all days and all the stations were calculated and extracted by MATLAB software. The criterion was as follows: If at least 20% of the synoptic stations in Iran have extreme precipitation (in case of spatial homogeneity), the days with pervasive/extreme precipitation were obtained, which amounted to 450 days in the entire period. The isohyetal map of 450 days of extreme/pervasive precipitation was drawn in Surfer software to identify the spatial homogeneity of days with extreme/pervasive precipitation because days with this kind of precipitation did not necessarily have spatial homogeneity. The 450 days with extreme/pervasive precipitation were arranged in ascending order, and 7 patterns with the highest extreme precipitation and the highest spatial homogeneity during the cold period of the year (October-March) were selected. Upper atmospheric data of the selected days were then specified to plot and analyze the synoptic maps. The required atmospheric data were geopotential altitude (meter), wind speed (m/s), wind direction, specific humidity (g/km), and average sea-level pressure. The data used were ERA5 data extracted from https://www.ecmwf.int. The data were extracted at three levels: lower, middle, and upper. To synoptically analyze the selected patterns using the selected upper atmosphere data, the following maps were plotted in Grads software:
A) Combined map of geopotential altitude and winds was plotted at the selected levels to determine the geographical location of the subtropical high, its displacement at different levels of the troposphere, its penetration, and its impact on Iran.
B) Combined maps of specific humidity, geopotential altitude, and wind were plotted in order to identify the role of the subtropical high in the transfer of humidity and its transfer to Iran at different levels.
Conclusion
In selected patterns, the AA with its anticyclonic current and passing through tropical warm waters, leads to the transfer of tropical humidity to subtropical and extratropical regions. At different atmospheric levels, the location and establishment of the Anticyclone central nucleus and its degree of expansion towards the north and west are determined by the tropical penetration of the cut off lows and the western trough. The AA has the most dominance over Iran in the lower level. In fact, in the lower level, Iran is dominated by two patterns of cut off low in the western and northern regions and the AA in the southern and eastern regions.
Due to the greater penetration of the southern branch of the westerlies and the orbitalization of the western currents in the middle level, the AA is displaced eastward and southward. The interaction and accompaniment of the AA and the mid-latitude cut off low form an atmospheric river with a tropical origin. The AA plays an important role in transferring the atmospheric river to Iran and its humidity feeding. On the maps, the southerly and easterly displacement of the AA Arabia is an important factor in the lack of formation of an atmospheric river in East Central Africa. By transferring tropical humidity to Iran, the humidity of the extreme pervasive precipitation is supplied. Another major role played by the AA is to strengthen the ITCZ humidity in East Central Africa, where tropical humidity ascends through the cut off low, West trough, and jet streams. The AA takes the Indian Ocean humidity to eastern Central Africa with its anticyclonic movement. At the ground level, the AA diverts humidity from the Arabian Sea and the Persian Gulf to the western and northwestern regions, preventing Turkey low from entering the western and southwestern regions of Iran. Furthermore, by entering the South Red sea, prevents the entry of the Sudan low into the Middle East and prevents the entry of precipitation systems into Iran. However, in supplying humidity to the extreme pervasive precipitation of Iran, it plays a very important role both by creating an atmospheric river in interaction with the mid-latitude cut off low and by transferring humidity through its anticyclonic flow.
Agricultural Meteorology
A. Khedri; A. Saberinasr; N. Kalantari
Abstract
Introduction The comprehension of the hydrogeological conditions of the aquifer and the determination of its hydraulic characteristics, such as hydraulic conductivity, transmissivity coefficient, and specific storage, are crucial for the management and preservation of groundwater resources. Various ...
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Introduction The comprehension of the hydrogeological conditions of the aquifer and the determination of its hydraulic characteristics, such as hydraulic conductivity, transmissivity coefficient, and specific storage, are crucial for the management and preservation of groundwater resources. Various conventional methods, including empirical formulas, laboratory techniques (constant and falling head), tracer tests, field tests (Lugeon, Lefranc, slug, flowmeter, and pumping tests), and groundwater inverse modeling, are employed to establish these characteristics, particularly hydraulic conductivity. Empirical formulas are limited to ideal conditions, and in laboratory methods, the sample must be kept undisturbed. Due to the impracticality of measuring large-scale effective factors, the hydraulic conductivity determined through laboratory methods is also the only representative of the hydraulic conductivity at the sampling point. Tracer studies encounter numerous constraints, such as time, cost, porosity determination, and tracer dispersion in multilayered aquifers. It is also difficult to determine the average hydrodynamic properties of the heterogeneous aquifer based on the data obtained from a specific section of the Lefranc and Slug tests. Consequently, pumping tests are commonly selected for hydraulic parameter estimation. Although costly and time-intensive, these tests provide more precise coefficients. Geophysical methods have been greatly developed during the last two decades and have shown a significant correlation with the hydraulic parameters of the aquifer derived from borehole pumping tests or direct laboratory measurements. This approach minimizes uncertainties in numerical model calibration, improves data coverage, and reduces the time and cost of regional hydrogeological investigations. The conventional approach, known as the electrical resistivity method, is still widely used in global and local research projects for evaluating aquifer hydraulic characteristics (Ige et al., 2018; Arétouyap et al., 2019; Youssef, 2020; Ullah et al., 2020; de Almeida et al., 2021; Lekone et al., 2023). Therefore, this study aims to use the integrated approach of the geophysical method and pumping test as a cost-effective and efficient alternative for estimating the hydraulic parameters of the alluvial aquifer in the northeast of Gachsaran city. Material and Methods The research area is an alluvial aquifer located 5 km to the northeast of Gachsaran, between coordinates 50-52 to 51-09 E longitude and 30-15 to 30-28 N latitude. Using 86 vertical electrical soundings, Archie's equations, and the IPI2win software, the hydraulic characteristics of the aquifer under investigation were estimated. Subsequently, these characteristics were then compared to the coefficients derived from the data of two pumping test wells, which were calculated using the Aquifer test software and obtained via the Cooper-Jacob and Neuman methods. Results and discussion The hydrodynamic coefficients of the aquifer were initially determined using the Cooper-Jacob method in this study. The hydraulic conductivity values for wells one and two are 4.9 m/day and 5.7 m/day, respectively. Correspondingly, the storage coefficient values for wells one and two are 0.015 and 0.021, respectively. Based on the Cooper-Jacob approach, it is deduced that if the storage coefficient values exceed 0.001, the aquifer is classified as unconfined. In this study, the storage coefficient values for both pumping wells suggest that the aquifer is unconfined. Since the vertical flow component and the delayed yield phenomenon should also be taken into account in unconfiend aquifers, the Neuman analytical model has been used in the studied aquifer. The values of specific yield (Sy) for pumping wells one and two, which are related to delayed yield, are 0.05 and 0.04, respectively. These values were calculated by analyzing the first segment of the curve derived from the Neuman logarithmic drawdown-time plot. The storage coefficient values for pumping wells one and two, extracted from the second section of the curve, are 0.015 and 0.021, respectively. Furthermore, the transmissivity value for well number 1 was 323 m2/day, while for well number 2, it was 655.5 m2/day. The vertical electrical sounding (VES) data were subsequently initially analyzed and interpreted using the IPI2win software and the equalization curve method (partial curve matching technique). The coefficients denoted as m and n, indicative of the degree of cementation of the sediments, were determined based on the sedimentary composition prevalent in the area. Archie's equations were employed to calculate the formation factor and porosity parameters. The aquifer exhibits a porosity range of approximately 0.15 in the eastern and southeastern parts (near the outlet of the plain) and around 0.41 in the centeral, northern, and northwestern sections of the area (next to the Asmari Formation). The specific yield (Sy) of the aquifer was calculated using the provided formula: The minimum and maximum specific yield were estimated as 0.006 (in the eastern and southeastern regions) and 0.089 (in the western and northwestern regions of the plain), respectively, with an average value of 0.04. The transmissivity coefficients for the entire aquifer were then calculated based on the fitted relationship between hydraulic conductivity (K) and formation factor (F): The range of transmissivity coefficients varies from a minimum of 63 m2/day (in the western and northwestern sections of the plain) to a maximum of 608.9 m2/day (in the eastern and southeastern areas). The average transmissivity coefficient is calculated as 323.7 m2/day. To ensure the precision of the geoelectric method's coefficients, a comparative analysis was conducted with the hydrodynamic coefficients obtained from the two pumping test wells, as presented in the table below: Well No.K(m/d)T(m2/d)SyPT*VES*PTVESPTVES14.93.63232370.050.0525.75.5655.5632.50.040.03*PT: Pumping Test; VES: Vertical Electrical Sounding Conclusion The evaluation and comparison of the hydrodynamic coefficients derived from the aforementioned methods indicate that the geoelectric method coefficients exhibit acceptable agreement with the pumping test coefficients. In other words, the analysis of the pumping test conducted using the Neuman technique in the unconfined aquifer revealed that well number two displayed a greater transmissivity coefficient, while well number one presented a higher specific yield. These findings are confirmed by the geoelectric approach. Consequently, such hybrid approaches, which include simultaneous analysis of geophysical methods (such as VES) and pumping tests will be a great alternative to multiple costly pumping tests for evaluating the hydrodynamic coefficients of an aquifer. Moreover, employing this hybrid technique enables the generation of dense hydrodynamic coefficients in an aquifer for use as inputs in the groundwater model.
Agricultural Meteorology
M. Abdollahi Fuzi; B. Bakhtiari; K. Qaderi
Abstract
IntroductionSpring frost is considered an important threat to agricultural products in high and middle latitudes. The damage caused by Late Spring Frosts (LSFs) significantly impacts vulnerable plant organs. This event has caused more economic losses to agriculture than any other climatic hazard in Asia, ...
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IntroductionSpring frost is considered an important threat to agricultural products in high and middle latitudes. The damage caused by Late Spring Frosts (LSFs) significantly impacts vulnerable plant organs. This event has caused more economic losses to agriculture than any other climatic hazard in Asia, North America, and Europe. Also, these phenomena have contributed to low crop yields in Iran. The latest statistics released by the Food and Agriculture Organization of the United Nations (FAO) show that Iran is one of the largest producers of agricultural products and the world’s second-biggest producer of pistachios. Kerman province is one of the significant areas of pistachio production. This province has a large share of the pistachio word area plantation. Spring frost damage to pistachio crops has led to low yields in recent years. A key aspect of studying frost is the ability to accurately estimate its occurrence. In this study, artificial neural network methods have been used to estimate late spring frost in the pistachio crop of Kerman city. Materials and MethodsIn this study, the efficiency of this method was investigated in the estimation of minimum temperature. For this purpose, the daily data of the synoptic station of Kerman city were obtained from Iran Meteorological Organization from 2000 to 2020. Meteorological data including mean, maximum, and minimum temperatures, relative humidity, wind speed, saturated vapor pressure, and sunshine hours were used. Five different combinations of these variables was considered as input variables in artificial neural network method for minimum temperatures modeling. After entering data into network and modeling with each combination, RMSE and R2 values were calculated. Finally, the combination of 8 variables including average and maximum temperature, the minimum temperature the previous day and two days prior, relative humidity, wind speed, saturated vapor pressure, and sunny hours were selected as the most suitable combination of variables. Subsequently, a simulation of minimum temperature values was conducted using 10% of the data. The performance of the methods was evaluated using statistical indices of coefficient of determination (R2), mean square of error (RMSE), Mean Bias Error (MBE), and Coefficient of Nash–Sutcliffe (NSE). Results and DiscussionThe accuracy of an analytical method is the degree of agreement between the test results generated by the method and the true value. Upon examining the models, the M1 model was identified as the best due to its lowest RMSE and higher R². ANN model results were evaluated using various performance measure indicators. The simulated outcome of the model indicated a strong association with actual data, where the correlation coefficient was above 0.95, and the MBE index was zero. Also, the RMSE value was positive and close to zero, and the NSE value was above 0.75. Therefore artificial neural network method had high accuracy. In this study, mean annual minimum temperature was estimated using artificial neural network models (from March 10 to May 20). Comparison between the observed and calculated data showed that these data were in good agreement. Also, the results showed that temperature fluctuations were high between March 10 and March 31. From 2011 to 2017, an almost uniform temperature trend has been observed between March 10 and March 31. However, the years 2000, 2006, and 2020 showed a noticeable decrease in temperature. From 2018 to 2020, this trend of temperature reduction continued. In April, the temperature values were between 7 and 10 degrees Celsius. The years 2001, 2005, 2006, 2009, 2016, and 2019 had a noticeable decrease in temperature. In May, the mean minimum temperature was between 10 and 14 degrees Celsius. Therefore, the probability of frost occurrence in early-flowering cultivars was higher in late March than in April and May. The years 2000, 2004, 2005, 2012, 2015, 2019 and 2020 had the highest number of frost days in the last two decades. ConclusionThe results showed that the artificial neural network method had a high performance in estimating the minimum temperature. The values of the statistical indicators were R2=0.963, RMSE=0.027oC, MBE= 0 and NSE=0.966 respectively. In addition, the ANN method performed well in estimating the number of critical frost days for pistachio crops. The results showed that, although reducing the amount of input data in models decreases their output precision, data-driven methods can still be useful tools for minimum temperature estimation.
Agricultural Meteorology
Sh. Katorani; M. Ahmadi; A. Dadashi-Roudbari
Abstract
IntroductionDust emission is considered as one of the environmental hazards in arid and semi-arid regions. Understanding the effective variables in increasing dust mass density is very important for early warning and reducing its imposing damages. One of the main and effective variables in the occurrence ...
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IntroductionDust emission is considered as one of the environmental hazards in arid and semi-arid regions. Understanding the effective variables in increasing dust mass density is very important for early warning and reducing its imposing damages. One of the main and effective variables in the occurrence of dust is the geographical and climate characteristics of the origin areas and areas affected by this phenomenon. Feeding the great rivers of Mesopotamia, it has reduced soil moisture. Also, the wind component is one of the reasons for the increase in dust in these areas. This study examines the relative importance of climatic variables to investigate seasonal and monthly changes in dust emission in West Asia and parts of South and Central Asia. Materials and MethodsThis study has examined West Asian dust from three perspectives spatial distribution, trends, and their relationship with climate variables. For this purpose, the Dust Column Mass Density (DUCMASS) variable output of the MERRA-2 dataset was used to investigate the spatial distribution of the dust mass density trend, and the AgERA5 dataset was used to investigate the seasonal and monthly changes of precipitation, wind speed, and temperature variables from 1981 to 2020. In this study, the modified Mann-Kendall (MMK) trend test method was used to investigate the trend of dust occurrence in the study area, and the Sen's slope estimator (SSE) test was used to investigate the slope of the trend and to better display the changes in dust mass density in the western region. the results of the SSE test have been examined on a decade scale. Results and DiscussionInvestigating the possible climate drivers in the changes of dust mass density for different regions by calculating the correlation between the time series of dust mass density and the variables of temperature, precipitation, and wind speed has been investigated. The results showed that there is an inverse correlation between dust mass density and precipitation and a direct relationship between dust mass density and temperature and wind speed. The highest correlations between dust mass density and temperature have been calculated, and this value has reached 0.9 in the warm months of the year. On the other hand, the highest negative correlations have been calculated in the cold period of the year (winter and autumn seasons) between dust concentration and precipitation with a value of -0.7. The correlation coefficient between dust mass density and wind speed in the months of January to May and November to December was mostly above 0.6. This value shows a lower correlation in the summer season.In most months of the year, dust mass density shows an increasing trend in most regions, from March to July, an increasing trend in active dust springs in Mesopotamia, the deserts of Iraq and Syria, the desert of Rub' Al Khali, Ad-Dahna and Al Nufud Al Kabir were observed in Arabia and Thar desert in Pakistan. This increasing trend started cyclically from the beginning of spring and reaches its peak in June and July, and the intensity of the trend decreases from September and reaches its minimum value in December. The important point is that the cycle of changes in the monthly trend of dust mass density coincides with the cycle of changes in dust mass density. The northern parts of Iran and Turkey have the highest frequency among different months of the year with a decreasing trend of dust mass density. The increasing trend of dust mass density in the spring and summer seasons in Mesopotamia, the deserts of Iraq, Syria, and Yemen, the Sistan Plain, and the Thar desert in Pakistan and the southeast of Iran was significant at the level of 0.05. ConclusionThe results revealed that the seasonal changes in dust mass density show well the active sources of dust in the studied area. In the spring and summer seasons, the activity of the dust centers located in the west of the study area, including the Rub' al Khali, Ad-Dahna and Al Nufud Al Kabir deserts, Mesopotamia, the deserts of Iraq and Syria, increases and on the arrival of dust to the west and southwest Iran affects. The investigation showed that climate variables play a key role in the variability of dust mass density in the study area so the areas corresponding to the summer north wind and the 120-day wind of Sistan have shown the highest dust mass density in annual variability. The correlation coefficient between dust mass density with temperature and direct wind speed and its correlation with negative precipitation have been obtained. The results showed that dust mass density has an increasing trend in most of the regions, so from March to August (spring and summer), the increasing trend of dust mass density is significant at the level of 0.05. The highest intensity of the increasing trend was observed in the spring and summer seasons in Mesopotamia, the deserts of Iraq, Syria, and Yemen, the Sistan Plain, and the Thar desert in Pakistan and southeast Iran.
Agricultural Meteorology
A. Faraji; M. Kamangar; S. Ashrafi
Abstract
IntroductionSnow is a prevalent form of precipitation, particularly in mountainous and high latitude regions, characterized by ice crystals in various microscopic structures. It naturally accumulates in cold and elevated areas through the freezing of air and the unsuccessful melting of water into crystalline ...
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IntroductionSnow is a prevalent form of precipitation, particularly in mountainous and high latitude regions, characterized by ice crystals in various microscopic structures. It naturally accumulates in cold and elevated areas through the freezing of air and the unsuccessful melting of water into crystalline form (WMO, 2022). Snow cover plays a crucial role in determining water reserves, especially during warmer seasons. Monitoring snow cover is a specialized field within weather and climatology. Snow cover plays a key role in the balance of energy due to its high albedo. Climatologists and meteorologists, who analyze global climate changes, emphasize the significance of snow monitoring due to its impact on both daily weather patterns and long-term climate shifts (Bashir et al., 2010). Spatial studies of snow cover by using satellite data have become one of the high priority topics in geomatics research due to their applicability and high accuracy. Considering that the snow cover area in many regions of the world, including mountainous regions, affects water resources and meteorology, accurate spatial analysis and investigation of changes in the area of snow cover is very important. In this regard, use of satellite data and new tools in the spatial analysis of the snow cover area, as an efficient method in geomatics research, has received much attention (Cheng et al., 2019).Data and MethodThis research examines the changes in snow cover in the western part of Iran from 2001 to 2021. The study area includes the provinces of Kurdistan, Kermanshah, Ilam, Hamadan, and Lorestan, covering a total area of 466,121 square kilometers. The region is located between latitudes 31°51'36" to 36°49'45" N and longitudes 45°27'18" to 50°04'26" E. It encompasses the northern part of the Zagros Mountain range, which serves as the entry point for western weather systems into the country. Snow cover was assessed using Modis satellite images, with the NDSI index used to identify snow. The analysis revealed a trend in snow cover, which was further examined using the Mann-Kendall method. The spatial distribution and changes in spatial components (length, width, and height) were assessed using the G* Index. Results and Discussion To analyze snow cover in the western region, the snow cover index was calculated by averaging the images for each period. The area of snow cover was then determined for each period. Analysis of the winter snow cover area revealed a decreasing trend. The application of the Mann-Kendall method confirmed this decreasing trend, which is statistically significant at the 95% confidence level. Additionally, considering the annual sinusoidal behavior of snow, it can be concluded that the seasonal component is the dominant factor in the region, with temperature changes primarily driven by seasonal variation due to its relative distance from the equator. Spatial analysis indicated that the distribution of snow cover follows a northwest-southeast direction, as evidenced by the standard deviation ellipse. More than 99% of snowfall is concentrated in high-altitude areas with a specific spatial arrangement. The hotspot map shows that surface snow cover is clustered in the west and southeast directions, predominantly at altitudes above 2200 meters. The clustering pattern of snow cover is more pronounced at higher latitudes and towards the western borders. These findings have important implications for water resource management, drought prediction, and the development of strategies to mitigate droughts. Conclusion This research demonstrates a decreasing trend in the area of snow cover during the winter season in the western part of Iran. Spatial analysis reveals that the major axis of snow distribution follows a northwest-southeast orientation, aligned with the mountainous terrain in that direction. The hotspot map highlights that surface snow cover is concentrated in the west and southeast directions, particularly at altitudes above 2200 meters. Hotspot analysis indicates that snow cover is clustered towards higher latitudes and more pronounced towards the western borders.
Agricultural Meteorology
S. Mirshekari; F. Yaghoubi; S.A. Hashemi
Abstract
Introduction
The 21st century is witnessing the increase of climate change as an important challenge due to its destructive environmental and socio-economic effects. Extreme climatic conditions have become frequent and more intense in recent decades as a result of human activities. Iran, as one of the ...
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Introduction
The 21st century is witnessing the increase of climate change as an important challenge due to its destructive environmental and socio-economic effects. Extreme climatic conditions have become frequent and more intense in recent decades as a result of human activities. Iran, as one of the countries in the Middle East with a different climate in each region of the country, has suffered significant adverse effects of climate change. Considering the importance of the climate change, it is important to investigate the changes in climate variables to know the future conditions and make management decisions. In the field of climate research, global climate models are useful tools that are often used to investigate the global climate system, including historical and projected periods. Since the use of the CMIP6 dataset provides improved clarity and accuracy for predicting future climate forecasts, the main objective of the present study is to predict the temperature and precipitation changes in the near, mid, and far future in Sistan-va-Baluchestan province.
Materials and Methods
The minimum temperature, maximum temperature, and precipitation data of 10 general circulation models (GCMs) of the 6th IPCC report for the baseline (1990-2014) were downloaded from the Global Climate Research Program database (https://esgf-node.llnl.gov). Then GCMs were including ACCESS-CM2, CMCC-ESM2, CNRM-CM6-1-HR, CNRM-ESM2-1, EC-Earth3-CC, EC-Earth3-Veg-LR, INM-CM4-8, INM-CM5-0, MIROC6, and NorESM2-MM. Four statistical indicators including correlation coefficient (R2), RMSE, Nash-Sutcliffe efficiency (NSE), and mean absolute error (MAE) were used to evaluate the performance of 10 GCMs. Based on the results obtained from the these indicators, the models that had higher performance in predicting the temperature and precipitation data were selected as the best models for forecasting in the future. The ensemble of these models under two SSP2-4.5 and SSP5-8.5 scenarios for the near, middle, and far future (2026-2050, 2051-2075, and 2076-2100) were extracted from the World Climate Research Program database.
CMhyd (Climate Model data for hydrologic modeling) tool was used to bias correction climate data of the selected models. In order to choose the best bias correction method, the R2, RMSE, NSE, and MAE were estimated.
After bias correction, the climate data of selected models were ensembled and then the changes in precipitation and maximum and minimum temperature in three future periods compared to the baseline was estimated.
Results and Discussion
The results showed that out of 10 GCMs, seven models had good performance (R2 > 0.40, 4.23 < RMSE < 12.02°C, 0.12 < NSE < 0.74, and 3.36 < MAE < 9.59°C) in simulating daily minimum and maximum temperature. However, the performance of all models in simulated daily precipitation was poor (R2 > 0.19, 1.24 < RMSE < 3.70 mm, -7.41 < NSE < -0.57, and 0.23 < MAE < 0.85 mm).
Among the different bias correction methods of temperature and precipitation available in CMhyd, the distribution mapping method had the best performance.
In all three regions, compared to the baseline, the average annual minimum and maximum temperature under two scenarios will increase in the future periods and precipitation will decrease in most periods and scenarios. These changes will be mainly in the SSP5-8.5 scenario compared to SSP2-4.5 and also in the far future period compared to the middle and near future. Averaged across all locations, annual maximum temperature showed increases in near, middle, and far projected periods of 1.3, 2.1, and 2.8°C under SSP2-4.5 and 1.6, 3.1, and 5.1°C under SSP5-8.5, respectively (Fig. 2), while for minimum temperature, the increases will be of 1.6, 2.6, and 3.4°C for SSP2-4.5 and 1.9, 3.9, and 6.3°C for SSP5-8.5. The range of annual precipitation among all sites was from –58.22 to 49.33% under SSP5-8.5 in the near and far future periods in Zabol and Iranshahr, respectively.
The annual increase in the average maximum and minimum temperature will be mainly due to the increase in air temperature in the months of January, February, August, September, October, November and December. The annual decrease in precipitation will mainly result from the decrease in precipitation in January, February, March, November, and December, and the annual increase in precipitation will result from the significant increase in precipitation in May and October compared to the baseline.
Conclusion
The results showed that under different scenarios of climate change, the maximum and minimum temperatures in the near, middle, and far future periods will face an increase compared to the baseline. However, the precipitation changes in the future time periods are not the same as compared to the baseline, and in some periods the precipitation will decrease and in others it will increase. But in general, the decrease in precipitation will be more than its increase. Therefore, it is very important to formulate and implement appropriate management programs for the needs of each region, in order to properly manage water resources and adapt to extreme temperatures and their consequences.
Agricultural Meteorology
A. Gholami; H. Mir Mousavi,; M. Jalali; K. Raispour
Abstract
Introduction
Clouds can be considered as one of the most complex and influential variables of the atmosphere system in forming of the climate structure of the earth. When the condensation process takes place at a higher altitude than the earth's surface, it creates clouds. Cloudiness represents ...
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Introduction
Clouds can be considered as one of the most complex and influential variables of the atmosphere system in forming of the climate structure of the earth. When the condensation process takes place at a higher altitude than the earth's surface, it creates clouds. Cloudiness represents the percentage of the atmosphere that is covered by clouds. Clouds, as one of the most complex variables of the climate system, besides changing the energy balance, are also effective in the spatial and temporal distribution of many climate variables. Clouds have a lot of temporal and spatial variability and can affect the climate through many complex relationships and affect the water cycle. The investigation of clouds holds great significance as they serve as the bridge between synoptic systems and the Earth's surface climatic conditions. Any alteration in cloud-related parameters can trigger a domino effect, influencing various other climatic variables. It's worth noting that Iran exhibits a lower average cloud cover of 26%, notably less than the global average of 50%. This places Iran in the category of countries with relatively minimal cloud cover.Hence, possessing insights into the atmospheric cloud cover conditions in Iran becomes imperative for early detection and management of hydroclimatic crises, particularly in the context of water scarcity and drought-related challenges.
Data and Methods
In the current research, the cloud data of 93 synoptic meteorological stations of Iran have been used in the daily time period during the statistical period of 1991-2021. The amount of cloudiness is an estimate of the nearest octa (eighth) and values 0 and 8 are completely clear and completely cloudy, respectively. In the present study, Kolmogorov-Smirnov, Anderson-Darling and Lilliefors test were used to determine the normality of the data at the 95% confidence level for annual, monthly and seasonal scales.
In the subsequent phase, we employed both parametric and nonparametric methods to discern trends within the cloudiness time series. The parametric approach involved a linear regression test based on the least squared error method, while the nonparametric method employed the Mann-Kendall test. These tests allowed us to identify data trends, accounting for both normal and non-normal distributions of cloudiness. Furthermore, we explored the interplay between cloud cover and spatial factors, namely latitude and longitude, employing Pearson's correlation coefficient. This analysis shed light on the relationships between these variables. Conclusively, we created a spatial distribution map depicting the extent of cloudiness across various stations. This mapping allowed us to dissect the temporal-spatial distribution of cloudiness, comprehend alterations in cloud cover, and investigate the contributing factors behind these changes.
Results and Discussion
The results of Normality Tests according to the Kolmogorov-Smirnov test showed that all the stations did not have a normal distribution however, during the other two tests, except Arak, Kashan, Sarakhs, Takab, Kahnuj, Ramhormoz and Ramsar, other stations had normal distribution. The tests to determine the trend based on the parametric linear regression test based on the least squares error method showed a decreasing trend in 44 stations and an increasing trend in 3 stations of Ardabil, Qom and Sarab. According to the non-parametric Mann-Kendall test, among the stations without normal distribution, Kahnuj, Ramhormoz and Sarakhs stations have a decreasing trend, and no special trend was observed in other stations. The relationship between the two factors of latitude and longitude with the cloudiness variable using the Pearson correlation coefficient indicates a negative relationship (-0.42) between the cloudiness variable and the longitude factor as the amount of cloudiness in Iran's atmosphere decreases with the increase of latitude. Hwoever, the relationship between cloudiness variable and latitude, a positive relationship (0.75) was obtained as the amount of cloudiness increases with the increase of latitude. The survey of the annual cloudiness map of the stations shows the highest amount of cloudiness is in the South, Southwest and East of Caspian Sea. The lowest amount of annual rainfall was in South and Southeast of Iran. The statistical analysis of annual cloudiness data in Iran showed that the amount of cloudiness in Iran is 27.5%. Examining the normal distribution of monthly and seasonal values indicates the non-normality of the data with the Kolmogorov-Smirnov test, but based on the Lilliefors and Anderson-Darling tests, the winter and spring seasons and the months of December, January, February, April and May had a normal distribution and the autumn and summer seasons and the months of June, July, August, September and October did not have normal distribution. Seasonal and monthly trend with linear regression method shows a decreasing trend in winter and spring seasons and cold months of the year. According to the Mann-Kendall method, there was a decreasing trend in the fall season and no significant trend was observed in the summer season.
Conclusion
The purpose of this research was to investigate the temporal and spatial changes of cloudiness in Iran. The results showed a decreasing trend in 47 stations and an increasing trend in only 3 stations and no significant trend was observed in other stations. Also, in monthly and seasonal scales results indicated a decreasing trend in all stations in the cold months of the year and winter, spring and autumn seasons. Examining the relationship between the spatial factors of longitude and latitude with the cloudiness variable using Pearson's correlation coefficient also indicates a negative correlation with longitude and a positive correlation with latitude, and this indicates a large spatial difference in the amount of cloudiness in the country. In general, it can be said that spatial factors (longitude and latitude) were internal factors in the spatial changes of clouds and climatic systems such as Siberian high pressure, sub-tropical high pressure, westerlies system and moisture from the seas of Oman, India and the Persian Gulf and sometimes the Red Sea as external factors were in the temporal changes of clouds. So, cloudiness was a variable that was directly related to other climate variables. Thus, cloud cover was a variable that was directly related to other climatic variables, and its decrease or increase causes the values of elements such as temperature, precipitation, and humidity to change. Therefore, studying this important climate variable and investigating its changes is very important and especially in the discussions of droughts and water crises, it has a special place.
Agricultural Meteorology
A. Yahyavi Dizaj; T. Akbari Azirani; Sh. Khaledi; Kh. Javan
Abstract
IntroductionEvapotranspiration is the combination of two separate processes, soil moisture evaporation, and plant transpiration, which amount depends on various meteorological elements. Therefore, identifying the effective factors and the amount of their impact on reference evapotranspiration (ET0) is ...
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IntroductionEvapotranspiration is the combination of two separate processes, soil moisture evaporation, and plant transpiration, which amount depends on various meteorological elements. Therefore, identifying the effective factors and the amount of their impact on reference evapotranspiration (ET0) is important. This component plays an important role in various agricultural studies, including the design of irrigation and drainage systems, reservoir design, and irrigation planning (Ahmadyan et al., 2023). Accurate estimates of evaporation and transpiration play an important role in studies such as global climate change, and environmental evolution, and in various scientific fields such as hydrology, agriculture, forest and pasture management, and water resources management (Kazemi, 2020). Materials and MethodsThe research was conducted in Iran, and the data analyzed encompass various meteorological parameters, including maximum, average, and minimum temperatures, average relative humidity, wind speed, and sunshine hours. These data were collected on a daily basis from 40 synoptic stations across the country. The dataset spans from 1976 to 2020 and was sourced from the Meteorological Organization of the country (IMO, 2022).The research employed the FAO Penman-Monteith method, specifically the 56th version, to estimate seasonal ET0 (evapotranspiration) values.In this research, for statistical evaluations of ET0 and revealing the trend of time series on a seasonal scale, the non-parametric Mann-Kendall (M-K) test; (Kendall, 1948; Mann, 1945) was used. To identify the changing trend of the ET0 time series, the ITA method was used on a seasonal scale. Four meteorological stations and the 45-year time scale (1976-2020) used in the current research, it had a better performance than other interpolation methods, which was used as the superior method. To understand the possible changes of one or more meteorological variables in ET0, the sensitivity of Reference Evapotranspiration to six meteorological variables (relative humidity, hours of sunshine, average temperature, maximum temperature, minimum temperature, and wind speed) was estimated. For this purpose, Sobol's method (Sobol, 1993). Sensitivity analysis was used. Results and DiscussionAccording to the ET0 survey results, the highest amount of ET0 was observed in the spring season in the south and south-eastern parts, and the highest average value was 1050 mm/year in Zabul station. The increase of ET0 in these areas can be due to the sun's radiation and more warming of the earth's surface in the southern latitudes of the country. In summer due to the length of the day and higher temperature, we saw an increase in ET0, especially in the southern and southeastern regions of the country. In autumn, due to the decrease in the length of the day and the decrease in temperature, the amount of ET0 has also decreased significantly in the northern parts of the country. In winter, with a decrease in temperature and an increase in relative humidity, which is more noticeable in northern than southern regions.In the summer season, all stations generally showed an increasing trend in ET0. In most of the stations, the significance level was 5% and it did not follow a specific pattern. In the autumn season, an increasing trend of ET0 was observed at a significant level of 5% in Khoy and Saqez stations, and a significant decreasing trend was observed in Qazvin and Shiraz stations. In the winter season, in the western and northwestern regions, all study stations showed an increasing trend of ET0. Finally, the overall results indicate that there is a significant increasing trend of ET0 during the summer in Iran. The graphical results of the ET0 trend by the four seasons on a scale of 44 showed that, in general, there was an increasing trend in ET0 in both high and low areas in all seasons. The values of meteorological variables have been changed by the Sobol method in the range of 40% to investigate the effect of meteorological elements on ET0 in different seasons of the year. The ranking of the sensitivity coefficient of the most effective meteorological parameter on the increase of the seasonal ET0 using Sobol's method showed that, in general, in the spring season, the minimum temperature had the greatest effect on the reference evaporation and transpiration rate. Also, the ratings obtained in the summer season indicate that wind speed has the greatest effect on the ET0 amount. In the autumn season, wind speed is still the first rank in affecting the rate of evaporation and transpiration. Finally, in the winter, the maximum temperature is the most important influencing factor among the other meteorological parameters. ConclusionAccording to the results, the amount of ET0 was increasing and it has been noteworthy in the eastern half of Iran in recent years. The trend of changes in ET0 showed that most stations had a positive value. The ET0 seasonal time series analysis with the ITA method indicated that in Kerman station; ET0 increased in all seasons and these results were at Bandar Anzali station. It was also observed that the seasonal trend of ET0 was increasing. The results of the sensitivity analysis graphs showed that relative humidity generally had a negative effect, and the other parameters indicates a positive effect in increasing the ET0. Also, the results explained that in spring, summer, autumn, and winter, meteorological variables of minimum temperature, wind speed, and maximum temperature played a greater role in increasing ET0. The findings of the present research and the results of the ranking of the sensitivity of factors affecting the ET0 rate showed that in each period, different conditions prevail in terms of the influence of meteorological elements on the ET0 rate.
Agricultural Meteorology
S. Shiukhy Soqanloo; M. Mousavi Baygi; B. Torabi; M. Raeini Sarjaz
Abstract
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 ...
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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.
Agricultural Meteorology
N. Torabinezhad; A. Zarrin; A.A. Dadashi-Roudbari
Abstract
Introduction
Drought is a costly natural hazard with wide-ranging consequences for agriculture, ecosystems, and water resources. The purpose of this research is to determine the characteristics of drought and its types in Iran during the last four decades. Drought turns into different types in the water ...
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Introduction
Drought is a costly natural hazard with wide-ranging consequences for agriculture, ecosystems, and water resources. The purpose of this research is to determine the characteristics of drought and its types in Iran during the last four decades. Drought turns into different types in the water cycle and imposes many negative consequences on natural ecosystems and different socio-economic sectors. According to International Disaster Database (EM-DAT), drought accounts for 59% of the economic losses caused by climate change. Many parts of the world have experienced extensive and severe droughts in recent decades. In Iran, droughts have occurred frequently during the last four decades and have become more severe in the last decade.
Materials and Methods
In this research, we used precipitation, temperature, wind speed, and sunshine hours of 49 synoptic meteorological stations during 1981-2020. Drought has been investigated with The Standardized Precipitation-Evapotranspiration Index (SPEI) in four scales of 3, 6, 12, and 24 months, which represent meteorological, agricultural, hydrological, and socio-economic droughts. To calculate the SPEI, the precipitation variable (P) is analyzed with the cumulative difference between P and potential evapotranspiration (PET). In other words, surplus/deficit climate water balance (CWB) is considered. The FAO Penman-Monteith method was used to calculate PET. Then, using the RUN theory, the characteristics of drought, including its magnitude, duration, intensity, and frequency, were determined for all four investigated scales.
Results and Discussion
The results showed that the frequency of drought events fluctuates from a minimum of 12.13% to a maximum of 18.13% in different regions of the country during 1981-2020. The climatological study of drought characteristics shows that the most frequent drought events occurred in the west, southwest, and southern coasts of the Persian Gulf and northwest of Iran compare to other regions of the country. This is while the duration of the drought period is longer in the eastern and interior regions of Iran. Examining the types of droughts shows that more than 60% of the droughts occurring in Iran are moderate droughts. Moderate and severe droughts are mostly seen in the west, southwest, and northwest of Iran. The duration of Iran's drought varies from at least 3 months in meteorological drought to more than 8 months in socio-economic drought. Therefore, droughts are more frequent in the western regions and longer in the eastern regions. The intensity of drought is also higher in the eastern and interior regions than in the western and northwestern regions of Iran. The decadal changes of drought show that the duration and magnitude of drought in Iran have increased and the severity of the drought has decreased during recent decades.
Conclusion
The intensity, magnitude, and duration of the drought period in Iran increased with the increase of the investigated scales from 3 months to 24 months. Examining the average frequency of drought showed that as we move from meteorological drought to socio-economic drought, the frequency of drought increases, which confirms the previous findings. The eastern and southeastern parts of Iran have experienced a longer duration and larger magnitude of drought than the western and northwestern Iran, which can be caused by the climate conditions of this region, i.e., high temperature and evapotranspiration and less precipitation, and seasonality.
The maximum magnitude of drought in Iran is related to socio-economic drought (SPEI-24) followed by hydrological drought (SPEI-12). This characteristic has increased especially in the last two decades (2001-2020) compared to the previous decades (1981-2000). This is while the magnitude of meteorological (SPEI-3) and agricultural (SPEI-12) droughts do not increase much in the last two decades compared to the previous decades.
Anthropogenic activities play a more prominent role in increasing the magnitude of socio-economic (SPEI-24) and hydrological (SPEI-12) droughts than natural forcing. With the construction of many dams and the digging of countless deep wells, as well as changing the direction of rivers, the water cycle has been completely affected by human activities during the last four decades in Iran. Obviously, anthropogenic activities play an important role in increasing the magnitude of hydrological and socio-economic droughts. In contrast, meteorological and agricultural droughts have not shown many changes in Iran.
The results of the decadal average of drought intensity showed that this characteristic of drought in the last decade (2011-2020) has decreased compared to previous decades (1981-2010). On the other hand, as mentioned earlier, the magnitude and duration of drought, especially for hydrological and socio-economic droughts, have increased in the last two decades (2001-2020). Therefore, the reason for the decrease in the severity of the drought has a statistical explanation before it has a climatic reason because the severity of the drought is calculated by dividing the magnitude of the drought by its duration.
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
Firooz Abdolalizadeh; Ali Mohammadkhorshiddoust
Abstract
IntroductionHeavy rains often occur in small areas, but they may be the result of large-scale systems and their energy and moisture are provided from distant areas (Mohamadei et al., 2010). Therefore, identification of synoptic systems is of great importance in order to predict precipitation. Although ...
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IntroductionHeavy rains often occur in small areas, but they may be the result of large-scale systems and their energy and moisture are provided from distant areas (Mohamadei et al., 2010). Therefore, identification of synoptic systems is of great importance in order to predict precipitation. Although rain has many positive effects on human life, heavy rain can cause one of the most dangerous and damaging natural disasters, namely floods. Every year, floods cause many human and financial losses in different regions of the world. Floods are more effective in vulnerable areas and cause the loss of human lives, damage to property and products, disruption of transportation and services, and other economic losses (Kheradmand et al., 2018). In March 2019, heavy rains occurred in Golestan province, which caused flooding in parts of this province, especially in the cities of Gonbad-Kavus and Aqqala. Most of this heavy rain and flood occurred in the Gorgan River basin. According to meteorological reports, the rain started from the night of 03.17.2019 and continued until 03.21.2019, although the heaviest rainfall occurred from the 03.18.2019. The volume of the flood was so great that the dams on the Gorgan River could not accommodate it. According to the reports of the regional water company of Golestan province, the flood entered the Bostan dam at 1 am on 03/19/2019, and after passing through it, entered the Vashmgir dam at 6 am, and then on 03.21.2019 entered the city of Aqqala. The damage of this flood was estimated at about 4800 billion Tomans, which includes damage to 17800 residential units, damage to farms, transportation infrastructure, 40% reduction in tourism, damage to industrial units, unemployment of about 3000 people, and damage to the nomads of the province. (Islamic Republic News Agency, 04.09.2019). Considering the heavy damage caused by the mentioned heavy rain and flood in Golestan province, it is necessary to identify and analyze the causes of its occurrence in order to plan and take the necessary measures to prepare and deal with such incidents.Materials and MethodsThe study area is Gorganrood watershed, most of this area is located in Golestan province. Golestan province is one of the northern provinces of the country and is located in the southeast of Caspian sea. In this research, in order to identify and analyze the heavy rain that occurred in Golestan province in March 2019, which led to severe flooding, several types of data were used (data from meteorological stations, NCEP/NCAR reanalyzed data, MODIS satellite images, GPM precipitation products). First, using the rainfall data of the synoptic stations located in the Gorgan River watershed, the time of heavy rainfall was identified, and then using the data of the aforementioned stations and several stations outside the basin, a rainfall zoning map was prepared. MODIS satellite images were also used to check the position of precipitation system and cloudiness of region. Using GPM satellite rainfall products called IMERG, which were extracted on a half-hourly basis, as well as the main synop reports of meteorological stations, which are reported on a six-hourly basis, the intensity of rainfall was investigated. In addition, the physical conditions of the basin were investigated using the topography and slope map of the basin prepared from the DEM layer of the region. In the following, using the reanalyzed data of the NCEP/NCAR database (National Center for Environmental Prediction - National Center for Atmospheric Research of the United States), synoptic maps including maps of land surface pressure, geopotential height of the upper atmosphere, Omega (indicates the speed of vertical movements of the atmosphere), wind direction and speed, moisture flux convergence function, frontal function, specific humidity, atmospheric precipitable water and Hoff-Müller diagram were drawn to identify the synoptic and dynamic factors of the mentioned precipitations.Results and DiscussionThe results of the present research in the analysis of flood factors can be summarized as follows:Survey of the topography and slope of the Gorganrood basin revealed that the physical conditions of the basin are such that the potential for flooding is high.The amount of rainfall in 24, 6 and a half hour intervals in the study area were investigated and it was shown that the rainfall occurred on March 17, 18 and 19, especially on March 18, in terms of the intensity of rainfall were very intense.Investigation of the state of the middle troposphere showed that the formation of the Rossby wave and the meridional expansion of one of its troughs, along with the creation of a positive vorticity that dominated the studied area on the seventeenth of March, are the main factors in the creation of a baroclinic atmosphere and the dynamic ascent of air.Investigation of the synoptic-dynamic conditions of the lower levels of the troposphere showed that in the lower levels of the low-altitude synoptic system with closed meters, at the same time as the deep trough reigns over the region, it has been formed and strengthened during peak rainfall times and has led to a strong rise of air.Investigating the state of atmospheric humidity in the study area and identifying sources of moisture supply using special humidity maps, moisture flux convergence function and atmospheric flow paths were carried out.Investigating the omega variable in the vertical profile of the atmosphere using the Hoff-Mueller diagram showed that during the times of precipitation events, upward movements prevailed in all levels of the troposphere, especially during the peak of precipitation, the upward movements became more intense in the lower levels.Identifying the type of clouds using MODIS products showed that during heavy rains, especially on March 18, deep convective clouds with a high density of water were formed in the region, which extended up to a height of 300 hectopascals and were very thick.
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.
Agricultural Meteorology
S. Pourentezari; K. Esmaili; A.R. Faridhosseini; E. Ghafari
Abstract
Introduction Precipitation is one of the most important input parameters of the hydrological models for rainfall-runoff simulation, which due to the lack of proper dispersion of rain gauge stations and the newly established some of these stations in most basins of the country, the use of these precipitation ...
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Introduction Precipitation is one of the most important input parameters of the hydrological models for rainfall-runoff simulation, which due to the lack of proper dispersion of rain gauge stations and the newly established some of these stations in most basins of the country, the use of these precipitation data faces serious challenges. Therefore, the use of remote-sensing methods is one of the ways that can be used for the streamflow simulation using hydrological models. Runoff is also one of the most important hydrological variables and rainfall-runoff modeling is one of the key items in hydrological sciences to estimate runoff characteristics such as volume, peak flow and arrival time to peak flow. In the present study, we used reanalyzed precipitation data and then evaluated the simulated streamflow using this precipitation data in the Zoshk subbasin. The precipitation data was validated with in situ data, of Kashafrood basin.Materials and Methods The reanalysis precipitation data was selected from the ERA5 precipitation data, and the HEC-HMS was used for the rainfall-runoff simulation. The basin parameters were calculated by the GIS menu. This menu is the newest option in the HEC-HMS software that needs only the DEM basin for calculating the basin parameters. In the present study, we should validate the ERA5 reanalysis precipitation data with in situ data, so we did that in the Kashafrood basin. The number of the rain gauge stations were 34, but some of the stations didn't have complete data and omitted them from the list of the rain gauge stations. For the validation ERA5 reanalysis precipitation data was used from the R, NSE, RMSE, Bias, FAR, POD and TS statistical indicators. These indicators were calculated by programming in EXCEL Visual Basic. The ERA5 precipitation data was evaluated for the Kashfarood basin at daily and monthly time steps. The DEM Zoshk was downloaded with the spatial resolution of 12.5 meters from ALOS-PALSAR satellite and then the basin parameters were calculated by the GIS menu. The SCS curve number was selected as a loss method. In this method, the calculations related to the percentage of impermeability and the average curve number of each sub-basin were obtained through land use and curve number layers, respectively. The SCS unit hydrograph was selected as a transform method. The recession method was selected as a base flow method. NSE and PBias were used for the calibration and validation events in HEC-HMS. In this way, at first the HEC-HMS model was calibrated by tow in situ rainfall-runoff events (91/1/11 and 91/2/6), and then validated by one in situ rainfall-runoff event (99/1/23). For validation streamflow of the ERA5 reanalysis precipitation data, the event on 99/1/23 was used and their streamflow hydrographs were evaluated with each other in Zoshk station.Results and Discussion The results showed that the reanalysis precipitation data of ERA5 had underestimation in daily and monthly time steps. Also in monthly time step, the accuracy of these precipitation dataset in detecting precipitation events (in terms of FAR, TS, and POD indices) was higher than a daily one. In addition, in monthly time steps it had worse accuracy in summer months than the rest of the year in detecting precipitation events (in terms of FAR, TS, and POD indices). For streamflow evaluation, in the calibration phase both NSE was in very good and good ranges, and PBias was in very good, good and acceptable ranges. In addition, the model underestimated the observational one. Finally the ERA5 reanalysis precipitation data was compared by 99/1/23 hydrograph event. The streamflow hydrograph from the ERA5 reanalysis precipitation data was underestimated due to ERA5 underestimation of the precipitation at the Zoshk rain gauge on the days corresponding to the 23/6/99 incident. The ERA5 reanalyzed precipitation data with NSE and Bias percentage coefficients in unacceptable range (NSE≤0.5 and PBias≤±25), compared to flow hydrograph obtained from Zoshk station precipitation data, the efficiency of this precipitation dataset is low. The range of the streamflow hydrograph from the ERA5 precipitation data was unsatisfactory in compared to the observational hydrograph (NSE = -0.47 and PBias = -55.16).Conclusion In general, the accuracy of the flow hydrograph of this product compared to the flow hydrograph of the precipitation data of Zoshk station (NSE = 0.64 and PBias = -15.82), cannot be a relatively reliable source instead of in situ rainfall data in hydrological simulation. The suggestion for future studies is to evaluate other rainfall data based on remote sensing methods in hydrological modeling.
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.
Agricultural Meteorology
M. Fashaee; S.H. Sanaei Nejad; M. Quchanian
Abstract
Introduction Drought analysis in agriculture can not only be achieved by measuring precipitation changes but also by using other parameters such as soil moisture. Due to the fact that soil moisture affects plant growth and yield, it is often considered for monitoring agricultural drought. Remote ...
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Introduction Drought analysis in agriculture can not only be achieved by measuring precipitation changes but also by using other parameters such as soil moisture. Due to the fact that soil moisture affects plant growth and yield, it is often considered for monitoring agricultural drought. Remote sensing data are often provided from three sources: microwave, visible and thermal. Most satellite soil moisture-based algorithms rely on passive microwave images, active microwaves, or a combination of data from several different sensors. Among the various remote sensing methods, the microwave electromagnetic spectrum has fewer physical limitations than other spectrum in measuring soil moisture. However, microwave soil moisture data often have very large pixel dimensions (more than 10 km), making it difficult to use them on a small scale.Materials and Methods In this study, in order to calculate the agricultural drought index at the field-scale, AMSR2 Retrieval data were calibrated first using field moisture measurement data in the Neishabour plain during 2017 to 2019. During the research period, 560 soil samples (20 samples in 28 shifts) were collected and soil moisture was measured in the laboratory of the Department of Water Science and Engineering, Ferdowsi University of Mashhad. LPRM_AMSR2_ SOILM3_001 is one of the third level products of the AMSR2 sensor, which is produced on a daily basis with a spatial resolution of 25 × 25 km2. Land surface parameters including surface temperature, surface soil moisture and plant water availability were obtained by passive microwave data using the Land parameter Retrieval Method (LPRM). Then, by using Modis sensor images (NDVI and LST), linear downscaling equations were extracted. The dimensions of the AMSR2 images were reduced from 25 kilometers to 1000 meters using these equations. In next step, SMADI Agricultural Drought Index, which is a combination of vegetation characteristics, soil moisture and land surface temperature, was used to monitor agricultural drought at the field-scale. Statistical indicators such as coefficient of determination (R^2), mean absolute error (MAE) and root mean square error (RMSE) were also used to evaluate the statistical performance.Results and DiscussionBy visual analysis of the role of vegetation and land unevenness, it was found that these two factors affect the regression relationships extracted for calibration of remote sensing data. The RMSE and MAE values for the regression equations used in the calibration process were calculated in the range of 1.6 to 4%, which can be considered acceptable in comparison with the mean values of the soil moisture data (15 to 20). The results showed that changes in SMADI index in three land use zones including rainfed cultivation (R1), medium rangeland (R2) and poor rangeland (R3) have experienced a similar trend to precipitation changes, illustrating that precipitation is one of the most effective factors in major changes in SMADI agricultural drought index fluctuations. It was also observed that SMADI index changes with a delay of 1 to 8 days compared to the precipitation changes in all three zones. In all three zones, the SMADI index followed a similar trend to in-situ soil moisture changes. At mot 80% of the changes in SMADI-R1 index can be explained by in-situ SM-R1, and the rest of the changes were related to other environmental factors or measurement error. This decreases to 68% in the R3 zone. It should be noted that soil moisture monitoring can more accurately reflect the impact of environmental factors on the changes in agricultural drought index such as SMADI than other variables; because the rainfall recorded at the meteorological station does not necessarily occur uniformly throughout the study area. On the other hand, any amount of precipitation will not necessarily lead to an effective change in soil moisture storage. This also renders assessment of the performance of agricultural drought indicators difficult.Conclusion Examination of statistical indices of coefficient of determination (R2), mean absolute error value (MAE) and root mean square error (RMSE) showed that the algorithm used in downscaling as well as estimating SMADI agricultural drought index is well able to reflect the interactions between precipitation, soil moisture, vegetation and changes in canopy temperature profile. This feature justifies and strengthens its application in agrometeorological analysis.
Agricultural Meteorology
S.M. Ebnehejazi; H. Yazdanpanah; S. Movahedi; M.A. Nasr-Esfahani; M. Moradizadeh
Abstract
Introduction Agricultural products frost in spring imposes heavy financial losses to agriculture particularly in northwest of Iran’s orchards. Not only temperature is one of the most important climate parameters but also it is a very crucial element in the agricultural sector. Untimely temperature ...
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Introduction Agricultural products frost in spring imposes heavy financial losses to agriculture particularly in northwest of Iran’s orchards. Not only temperature is one of the most important climate parameters but also it is a very crucial element in the agricultural sector. Untimely temperature fluctuations and rise and fall which are usually unexpected will cause shock and heavy damages. Therefore taking into consideration the agricultural products frost and offering an approach would be of great importance for reducing relevant damages. In studies carried out by Omidvar and Dehghan Banadoki (2012) and Hesari et al. (2015) characteristics and different types of frosts have been considered in relation to the agricultural products. Different models were introduced to predict flowering date in different investigated regions. In more studies, in addition to determining the best model for predicting the date of occurance of flowering stage, probable date of last frost has been estimated as well. Investigating long term temperature changes is a method which applied by Martínez-Lüscher (2017) and Vitasse et al. (2018) to find out about established changes in flowering date and also changes in the last frost date. Nasr Esfahani and Yazdanpanah (2019) realized that 48-hour early warning for frost occurrence can be performed with adequate precision. Despite all studies in the field of products frost particularly during flowering date, it seems a rapid frost warning system must be established and provided to make early warning for each orchard. In this essay, since our goal is to make such early warning three days before frosting, so we have to investigate accuracy and validity of 72-hour minimum temperature simulation using WRF model. On the other hand, we must know phonological stage of each product in each orchard to inform the farmer about frost hazards based on critical temperature, therefore the second goal of this research is to detect phonological stages through Landsat 7 and Landsat 8 images.Materials and MethodsIn order to achieve the aim of current study, 72-hour minimum temperature simulation through the Weather Research and Forecasting (WRF) model was investigated and values of vegetation index were derived for a 30 meters pixel at an experimental orchard in Kahriz, West Azerbaijan Province, in 2016-2107. Computational grid for 2 meters temperature simulation using WRF model contains of three nested grid with horizontal resolution of 27, 9 and 3 kilometers. Horizontal resolution of terrain height and land use data is equal to 30 second (about 1 km). The initial and 3-h boundary conditions with 0.5º horizontal resolution from the Global Forecast System (GFS) were obtained from National Centers for Environmental Information (NCEI). Based on the previous research KFMYJ physical scheme configuration for WRF model were used in this research. Model's hindcasts at 03:00 UTC hour for each of 51 synoptic weather stations of northwest of Iran in internal computational grid were interpolated by MATLAB software with interp 3 function using linear method, then the obtained values were compared to minimum temperature observed in the stations by using MAE, MSE, RMSE and MSSS indicators. Phenological statistics, the time of beginning and end of growth stages were obtained from Iran Meteorological Organization. Besides, 77 Landsat 7 satellite images of ETM+ sensor, and 41 Landsat 8 images of OLI sensor were downloaded from United States Geological Survey website from March to September 2007-2016 with a spatial resolution of 30 meters. In this research, atmospheric and radiometric correction were performed with the FLAASH method on the metadata file in the ENVI software environment and then vegetation index was calculated using NDVI index.Results and DiscussionExamining the evaluation indicators of the WRF model, results revealed a significant correlation and regression model between 2 meters temperature variable from WRF model output and minimum temperature variable observed in the entire stations for 72-hour simulation. As a result WRF model can be applied in 72-hour temperature simulation in the area of study. Another finding of this research indicated that in comparison to the field-recorded data, NDVI values gained from Landsat images properly indicates changes of phenology stages in the relevant apple orchard. In this study, the indicators used to evaluate the model error showed model hindcasts are more accurate for 24-hour and then 48-hour simulations than for 72-hour simulation, but the 72-hour simulation accuracy is not much different from 24-hour and 48-hour simulations. In northwestern Iran, which is a mountainous region, it is very difficult to simulate airflow in areas with complex topography, therefore the total correlation coefficient of all stations in all three simulations is in the range of 0.5, and the error rates of MAE and RMSE, respectively reaches about 2.8 and 3.8 Celsius. According to the second finding of this research, the NDVI indicator obtained from Landsat 7 and Landsat 8 satellite images can show the progress and changes in the phenological stages of apple trees.Conclusion This study showed the efficiency of the WRF model for 72-hour simulation of the minimum temperature as well as the potential of Landsat 7 and Landsat 8 images in detecting apple phenological stages in the study area. Therefore, by using the WRF model for 72-hour minimum temperature simulation and recognizing the phenological stages from Landsat images, if the temperature in any orchard reaches a critical level in the next 72 hours due to the phenological stage, frost warning can be announced and then frost mitigation should be done by the farmer.
Agricultural Meteorology
S.M. Afzali; J. Khoshhal Dastjerdi; A. Torahi
Abstract
Introduction: One of the most critical human issues globally is producing more food for the world's growing population. The climate of each region is an effective factor in the agricultural sector and the amount of its production. Iran is one of the world's date-producing countries, which ranks second ...
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Introduction: One of the most critical human issues globally is producing more food for the world's growing population. The climate of each region is an effective factor in the agricultural sector and the amount of its production. Iran is one of the world's date-producing countries, which ranks second in terms of date production and exports. This plant has 200 genera and 4000 species, each of which can adapt to arid regions and can have the highest production and economic efficiency in its proper place. It is a monocotyledonous plant from the Palmaceae family that needs at least 10 degrees Celsius for continued growth. Growth will stop at temperatures below 10 degrees Celsius, and temperatures below 4 degrees Celsius will encounter cold stress. This plant is sensitive to environmental conditions and cannot live qualitatively and quantitatively in all hot and dry regions. On the other hand, the palm tree is a plant that lives up to several hundred years, and some of its varieties bear fruit up to 200 years old, but their valuable and economic life is on average about 50 years. It is noteworthy that this tree did not produce an economic crop until ten years ago. Dates have an important role in currency exchange, job creation, food security, and strengthening global competitiveness by providing income from non-oil exports. Therefore, the construction of a palm tree is a risky long-term investment in the country. Dates have different varieties, each capable of adapting to a region of arid regions and can produce the most production and economic efficiency in its proper location. Global warming, its impact on different regions of the earth in the future, and the response of the living creatures of these regions in the last century have led planners and scientists of many disciplines, especially climatology researchers, and in particular agricultural climatologists, to understand climate conditions and design long-lived sustainable plants that can survive in future environmental conditions and have good economic returns, design programs, and awareness algorithms.Materials and Methods: One of the best is the maximum entropy model (MaxEnt). By applying this algorithm, it can be predicted how the species will exist in different regions based on the presence of the species. The present study was conducted by field method, descriptive, and library statistics. The data used included WordClim site data (bioclimatic variables), presence data of two cultivars of date palm, Gantar and Halawi, daily meteorological data, elevation, and land slope based on the suitable land slope for palm tree cultivation, high and low temperatures, and phonological data. CCSM4 model with quadratic scenarios of 2.6, 4.5, 6.0, and 8.5 was used to predict and estimate different country regions in terms of talent for cultivation of two selected date varieties. Due to the higher value of AUC in Scenario 4.5, this scenario was considered as the selected scenario. This study is different from previous studies using the CCSM4 climatic model, new diffusion scenarios (RCP), and prediction of date distribution concerning its cultivars, while previous studies on prediction of date distribution have not paid any attention to it.Results and Discussion: The results showed that the distribution and cultivation area of Gantar and Halawi are different, and in the future, the suitable area of cultivation of Gantar cultivar will decrease, and the suitable area of cultivation of Halawi cultivar will increase. Jacknife test showed that the model successfully predicted the potential of cultivation area based on the AUC criterion and temperature-related biological variables (Bio 1, Bio 6, Bio 8, and Bio 10) had the most significant impact on the distribution modeling of cultivars. Therefore, with the rising temperature, parts of the country, especially the foothills of the plains, become more susceptible to cultivation. So that at present, when the maximum height for the optimal growth of cultivars is about 700 meters, it will reach about 1200 meters in the coming decades. At present, Iranshahr city in Sistan and Baluchestan province has the most desirable area of Gantar and Halawi cultivar cultivation. However, in the next decade, the most desirable cultivation area will be the Gontar cultivar in Ahvaz city and Halawi cultivar in Jask city. It was also found that using WorldClim site data for perennial and especially long-lived plants was not sufficient. Because in these data, high and low temperatures that can destroy the plant during its life or shorten its life and reduce the economic fruit of cultivation are not included, and of course gardening and fruit trees are a long-term investment. The risk of investing should not be increased.
Agricultural Meteorology
R. Maleki Meresht; B. Sobhani; M. Moradi
Abstract
Introduction: Heat waves (HWs) are one of the most important climatic disasters that have devastating environmental consequences in nature every year). The purpose of this study is investigation of the effect of heat waves on the intensification of thermal islands in Sanandaj city from 1989 to 2018. ...
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Introduction: Heat waves (HWs) are one of the most important climatic disasters that have devastating environmental consequences in nature every year). The purpose of this study is investigation of the effect of heat waves on the intensification of thermal islands in Sanandaj city from 1989 to 2018. The constant rise in temperature of the city as an urban heat island and the sudden occurrence of HW's as one of the major climatic hazards, is an important concern of urban management policy makers; because intensify heat of this city and cause a lot of environmental damage.
Materials and Methods: In order to identify HWs in Sanandaj city, from 1989 to 2018, by using Fumiaki Index and MATLAB software, days whit temperature above +2 standard deviation or above the mean Normalized Thermal Deviation (NTD) that lasted at least two days, were identified as the day with HWs and calculated by equation 1.
(1)
Where, T (i, j, n) is temperature of day ith from month jth in year nth indicates the average temperature of day i from month j. To eliminate the noise in the mean, a 9-day moving average filter was performed on these data three times and calculated by the following equation.
(2)
Where, ∆T= (i, j, n) indicates absolute deviation of temperature from the average on day jth of the month i th, in year n th compared to the average temperature of the same day. In order to the values of temperature deviation of different times and places to be comparable at a certain time and place, it is necessary to standardize these absolute values of temperature deviation by means of temperature diffraction. Like day-to-day changes, diffuse T∆ at 31 days for each day is calculated by the following equation.
(3)
The value is the average temperature deviation in 31 days that is calculated by the following equation.
(4)
Finally, (NTD) is calculated by the following equation.
(5)
Where .Then in MATLAB software, days with temperatures +2 above average (NTD) and lasting at least two days, were selected as the day with the HW.
(6)
Then the thermal island was calculated in Sanandaj city using Equation 7.
SUHI= MLSTurban –FLSTrural
(7)
Where, SUHI is the island surface heat index, MLSTurban and FLSTrural are the average surface temperature of urban and rural areas, respectively.
Results and Discussion: The results showed that, during the study period (1989-2008), the highest frequency of HW hazards in this city was in September, February, March, and October 1991. The maximum duration of HWs was 6 days, which occurred in December 2017 and 2005, therefore long-term HWs have been experienced in this city. Results also showed, in both HW and NHW conditions, in the hot and cold months of the year, often a cold island is formed in the city center during the day and a heat island is formed at night. Results also showed that short-time heat waves have been effective in intensifying heat islands. Examination of the intensity of thermal islands in this city showed that during the day in both HW and NHW conditions, which in the HW conditions dominance of the cold island compared to normal day, it has been reduced and in the last months of winter (February), even during the day, a heat island has been created in the center of the city. At night time, in both HW and NHW conditions, a heat island was created in Sanandaj center, but the intensity of night- time heat islands in HW conditions is often significantly higher than normal conditions especially in the winter. Investigation of the condition of thermal islands in the warm months of the year showed that in both HWs, a cold island has been created in the city center that the intensity of cold islands during the HW conditions, especially in the summer months, was often higher than NHW conditions. At night time, there was often a heat island in the city center that was more intense than normal day. Also, in HW conditions, wind speed and especially relative humidity has decreased significantly more than the cold months of the year.
Conclusion: According to the results the highest incidence of HW hazards occurred in the winter and early spring. Also, long-term (6-days) HW occurred in this period. The increasing trend, frequency and continuation of HW, especially in the cold months of the year, can be the effects of climate change and global warming. Severe and continuous HWs occurred in Sanandaj city, especially in late winter, can cause early germination and flowering of crops and gardens and it will negatively affect agriculture and horticulture and will lead to great economic losses. The effects of HWs on heat islands occurred in the suburbs due to having a clear sky without pollution, with minimal vegetation and lack of surface water resources and ground with low heat capacity is affected by HWs faster than the city center and as the land surface around the city becomes warmer than its center, a cold island is formed in the city center. At night, the suburbs due to low heat capacity, lose absorbed heat faster and as a result, the heat island is formed in the city center. In general, the occurrence of heat waves in the intensification of thermal islands in the Sanandaj city, especially in the warm months of the year, has a significant effect, and it is likely to intensify in the coming decades, especially at night during the hot months of the year.
Agricultural Meteorology
S. Bayati; Kh. Abdollahi
Abstract
Introduction: Rainfall data are required for planning, designing, developing and managing water resources projects as well as hydrological studies. Some previous studies have suggested increasing the density of the rain gauge network to reduce the estimation error. However, more operational stations ...
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Introduction: Rainfall data are required for planning, designing, developing and managing water resources projects as well as hydrological studies. Some previous studies have suggested increasing the density of the rain gauge network to reduce the estimation error. However, more operational stations require more installation costs and monitoring. Some common techniques including statistical methods, spatial interpolation, information-based theory and combination are used to evaluate and design the network. Chaharmahal va Bakhtiari province is a mountainous region; hence, a denser rainfall network is expected in this mountainous environment. The aim of this study was to evaluate the condition of rain gauge stations in Chaharmahal va Bakhtiari province using two approaches, i.e. geostatistical methods and entropy theory.
Materials and Methods: The main required data set for this study is a time series of rainfall data. These data were collected on a daily scale from the Regional Water Company of Chaharmahal va Bakhtiari. After performing statistical tests, the annual data series was prepared for 46 rain gauge stations. A statistical period of 2000 to 2016 was used. The homogeneity of data was investigated by double mass test and histogram drawing methods using Excel and SPSS software, and the existence of trend in the time series of data was investigated by applying a Spearman test. Then, the adequacy of rain gauges in the gauging network was investigated. Annual rainfall interpolation maps and their standard error maps were prepared using the kriging method. Contribution of each station in reducing or increasing the error in the rain gauge network was investigated by removing each station in a cross validation procedure. The efficiency of the rain gauge network was evaluated using the concept of discrete entropy and the values of entropy indices. The value of keeping the rain gauge stations was determined using the net exchange information index.
Results and Discussion: There was no homogeneity problem and significant trend in the data series. Considering the permissible error percentage of 5%, there is a need to add 15 new rain gauge stations to the network. To apply the geostatistical method, we applied it once without deleting any station; then, the kriging interpolation error was calculated for the precipitation data. Then, only one station was removed at each stage, and both the error and the contribution of each station in increasing or decreasing the error compared to the case without Station deletion were obtained. The results indicated that Ab-Turki, Shahrekord, Borujen and Barez stations were more important than other stations. Two stations namely Chaman-Goli and Ben stations can also be considered as the influential stations in error due to the density of stations in the region and error maps. Similarly, the results of the entropy theory method were found effective in evaluating the design of the rain gauge network. The highest value of H(x) was observed in the data of Armand station (3.26) and the lowest value was observed in Abbasabad station (2.28). Since H(x) shows the uncertainty of measuring data, the maximum and minimum uncertainty were found for Armand and Abbasabad sites, respectively. Based on the Net Exchange Information Index, Bardeh, Bareh Mardeh and Dezkabad stations were ranked 1 to 3, respectively, indicating that they transmit and receive more information than other stations. On the other hand, a number of stations including Dorak anari, Abtorki and Chelo stations had the lowest values.
Conclusion: Due to the vast extent of the area and also considering the permissible error percentage of 5%, the number of the stations in this area was found to be insufficient. Thus, although calculating the kriging error maps showed that some stations do not have a significant share in increasing the error, removing the stations is not recommendable. Regarding the new stations, new 15 rain gauge stations are needed to check out the error maps. According to the field observations, the higher priority should be given to the northwestern area (which had the largest interpolation error) in the first place. For the regions with lower error, such as northeast, east, southeast, west and southwest that do not have rain gauge stations, additional rain gauge stations should be constructed.
Agricultural Meteorology
S.F. Ziaei Asl; A.A. Sabziparvar
Abstract
Introduction: It is possible to guide the agricultural experts to achieve a suitable genotype and adapt to climatic conditions in proportion to the length of the modified growing season by identifying the impact of climate change in recent years on the cumulative rate of degree-days of plant growth. ...
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Introduction: It is possible to guide the agricultural experts to achieve a suitable genotype and adapt to climatic conditions in proportion to the length of the modified growing season by identifying the impact of climate change in recent years on the cumulative rate of degree-days of plant growth. This will prevent the waste of capital and agricultural inputs and ultimately prevent the reduction of the final crop due to the mismatch of genotype-crop with the current climate. In the present study, an attempt has been made to study and compare the trend in the start and end of the growing season, the growing season length (GSL), and growing degree-days(GDD) during 1959-2018 in the elevated and coastal areas of Iran.Materials and Methods: For this study, the daily temperature of 27 synoptic stations were used including 19 stations in elevated areas and 8 stations in coastal areas during 1959-2018. The first day with a minimum daily temperature equal to or greater than 0, 5, and 10 °C was considered as the start of the growing season (SGS). Moreover, the first day after the start of the growing season which has a minimum daily temperature of less than 0, 5, and 10 °C was considered as the end of the growing season (EGS). Trend analysis was performed in time series of GSL and GDD based on thresholds of 0, 5, and 10 °C using the Mann-Kendall test. To compare the results, the statistical period of 60 years was divided into two periods of 30 years (1959-1988 and 1989-2018). In both periods, the statistical characteristics of the GSL and GDD based on the three thresholds mentioned in coastal and elevated areas were surveyed and compared. In this study, deviation from the mean was used to complete the study of changes in the GSL. This index shows the scatter of data around the mean.Results and Discussion: The GSL extension came from both the advance in SGS and the delay in EGS. Comparison results of the two 30-year periods (1959-1988 and 1989-2018) showed that during 1989-2018, in most stations the GSL has increased. During this period, based on 0 °C, the earliest and latest SGS were on February 24 and April 30 in Yazd and Shahrekord, respectively. Accordingly, the earliest and latest EGS were on October 15 and December 11 in Shahrekord and Gorgan, respectively. Based on 5 °C, the earliest and latest SGS were on February 10 and June 2 in Abadan and Gorgan, respectively. Accordingly, the earliest and latest EGS on September 17 and December 6 were at Shahrekord, Bam, and Abadan, respectively. Based on 10 °C, the earliest and latest SGS was on February 11 and June 20 at stations, respectively. Accordingly, the earliest and latest EGS were on August 27 and December 8 in Shahrekord and Bushehr, respectively. The shortest and longest GSLs based on all three thresholds of 0, 5, and 10 °C were Shahrekord and Bandar Abbas, respectively. The highest and lowest coefficient of variation of GSL were 20.8% in Zanjan and 4.9% in Bandar Abbas, respectively. Based on 0, 5, and 10 °C, the lowest GDDs in GSL are 3233, 1767, and 880 °C.d, respectively, and all of them were obtained at Shahrekord. On the other hand, the highest GDD0, GDD5, and GDD10 in GSL were 6783, 7372, and 5761 °C.d, respectively, in Yazd, Abadan, and Bandar Abbas. The most significant trend in GSL was in Zanjan, Zahedan, and Khorramabad.Conclusion: Examination of changes in the GSL indicates the existence of a significant trend in a limited number of stations. Also, with increasing the threshold from 0 to 5 and from 5 to 10 °C, there is a significant decreasing trend in more stations. At the threshold of 10 °C a significant and decreasing trend of GSL was observed in Urmia, Sanandaj, Khorramabad, Birjand, and Bandar Abbas stations, In following, a significant increasing trend was observed in Tabriz, Tehran, Kermanshah, Isfahan, Yazd, and Bushehr stations. The results of the studies showed fewer changes in the time series of the GSL based on thresholds of 0 and 5 °C in the statistical period of 1989-2018. On the other hand, the results showed that the GSL trend is significant in more stations in the recent period based on the threshold of 10 °C. Deviation from the average GSL in coastal areas was greater than the elevated areas so that the GSL based on 10 °C in both areas increased with greater slope and continuity. This increasing trend of deviation from the average in the coastal areas from the early '70s and the elevated areas from the early '90s and continues until now. In this regard, Bandar Abbas station and then Bushehr station had the longest GSL, and Shahrekord station had the shortest GSL among other stations which has been studied. Comparison of GDDs of the GSL during 1989-2018 showed the decrease of GDDs from south to north and from west to east of the country. Accordingly, in the southern stations of the country, the conditions for tropical plants (threshold of 10 °C) have become more suitable than the cold stations of the west and northwest, Time series analysis of the average annual GDDs based on the three thresholds during 1989-2018 showed a significant increasing (positive) trend in 93% of the stations. During the second 30-years period, Shahrekord and Shiraz stations did not show a significant trend in all three mentioned thresholds. However, the analysis of the annual average of GDDs during 1959-1988 showed the trend in 41% of the stations. According to the results of this study, it can be concluded that in cold regions, due to the increase in GDDs, the supply of cooling units for plants with certain cooling needs is more difficult. In the south of the country, as the total required GDD is achieved earlier, the GSL gets shorter, and therefore less dry biomass will accumulate in the product. This likely leads to a reduction in crop yields in this part of the country.
