Irrigation
M. Fallahi khoshhi; A.R. Karbalaee Doree; Z. Hedjazizadeh; P. Hamezadeh
Abstract
Introduction
The large temporal and spatial changes of precipitation, especially in mountainous areas, have turned it into a controversial variable in climate models. Measuring precipitation (rain and snow) along with its distribution and changes is very important to improve our understanding of global ...
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Introduction
The large temporal and spatial changes of precipitation, especially in mountainous areas, have turned it into a controversial variable in climate models. Measuring precipitation (rain and snow) along with its distribution and changes is very important to improve our understanding of global water cycle and energy, water resources monitoring, hydrological modeling. Lack of reliable data is one of the most important challenges in rainfall analysis. Due to the significant temporal and spatial variability of precipitation in mountainous areas, accurate spatially distributed data is crucial for effective water resource assessment and management. However, many mountainous regions have limited rain gauge stations. Today, satellite products are commonly used to measure precipitation in these areas, but the variability among these products raises concerns about their accuracy in mountainous regions. Additionally, the quality of satellite products differs between various products and across different climatic regions, making it essential to thoroughly evaluate them before use. The purpose of this research was to evaluate the precipitation data of two satellite products (GPM, PERSIAN) and reanalysis data (ECMWF) in the estimation of precipitation in mountainous areas without stations in Lorestan province.
Method
This study utilized rainfall data from 24 synoptic and rain gauge stations across Lorestan province. Emphasis was placed on stations situated in or near mountainous regions. The selected stations were chosen based on their suitable spatial distribution and record length. The rainfall data spanned the period from 2015 to 2021 and included daily, monthly, and annual measurements. To evaluate satellite rainfall algorithms and estimate rainfall in regions with limited data, data from the GPM and PERSIAN satellites were employed, along with ECMWF reanalysis data. The PERSIAN rainfall algorithm is a remote sensing-based method that utilizes artificial neural networks. It calibrates infrared data with passive microwave estimates and converts longwave infrared images into rainfall estimates using a three-step process. The spatial resolution of this product is 0.25° x 0.25°, and it offers hourly, daily, and monthly temporal resolution. The PERSIAN rainfall algorithm data can be accessed from https://chrsdata.eng.uci.edu. The GPM mission aims to provide continuous observations of Earth's precipitation. It employs the GPM Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) to observe both snow and rain. The final product, called IMERG, is generated through multiple runs of the algorithm for each observation time. Initial estimates are quickly provided, and subsequent estimates improve as more information becomes available. The spatial resolution of the GPM product is 1° x 1°, and it offers hourly, daily, and monthly temporal resolution. IMERG data can be obtained from https://gpm.nasa.gov/data. CMWF reanalysis data is derived from the combination of short-term simulations of numerical weather prediction models with ground-based observational data. These simulations are controlled with observational data, and the resulting reanalysis database provides global coverage from 1979 with a spatial resolution ranging from 0.125° x 0.125° to 3°. The temporal resolution of ECMWF reanalysis data is hourly, daily, and monthly. More information about ECMWF data can be found at https://www.ecmwf.int/ (Azizi, 2019). To evaluate the accuracy of the products, R-squared correlation (R2), root mean square error (RMSE), standard deviation (MAD), correlation coefficient (R), error deviation (MBE) and Nash-Sutcliffe coefficient (NS) were used. Also, the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) indices were used to validate the data.
Results
The results showed that none of the three products are suitable for estimating daily precipitation in mountainous areas. However, on a monthly scale, these products provide reasonable estimates. Among the three, the GPM satellite product demonstrated better accuracy on a monthly scale, based on error levels and the spatial distribution of estimated precipitation. On an annual scale, GPM also performed best, as indicated by both statistical errors and the spatial patterns of average annual precipitation. According to the MBE index, on daily and monthly scales, the ECMWF product tended to overestimate precipitation, while the PERSIANN and GPM products underestimated it. On an annual scale, GPM and ECMWF products overestimated precipitation, whereas PERSIANN underestimated it.
Irrigation
K. Raispour; B. Salahe; B. Abad
Abstract
Introduction Precipitation is the most important element of water level that recognizing its temporal-spatial characteristics at different scales is an important step towards better understanding and modeling of the hydrological cycle and related phenomena such as floods. Drought, landslides, snow ...
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Introduction Precipitation is the most important element of water level that recognizing its temporal-spatial characteristics at different scales is an important step towards better understanding and modeling of the hydrological cycle and related phenomena such as floods. Drought, landslides, snow and climate change are on a regional and global scale. Despite the large number of studies conducted in this field, there is still a lot of research need in many parts of the world for reasons such as lack of weather stations to access ground observation data and the non-uniform spatial distribution of these stations. Nowadays, with significant technological advances, including the advent of various satellites, access to a variety of precipitation data has been greatly facilitated. Among the latest precipitation products of various satellites, we can refer to the Global Precipitation Measurement (GPM) satellite data. Related to the subject of the present study, it is stated that most of the studies on rainfall in the Jazmourian catchment area have been based on station data, which due to the poor distribution of meteorological stations; it is not possible to estimate the temporal-spatial distribution of precipitation in the study basin. In this study, the temporal-spatial analysis of precipitation using GPM satellite precipitation products as one of the most important climatic parameters in the basin Due to the undeniable importance of rainfall in this basin, it seems that the analysis of variable rainfall can provide valuable climatic information to researchers and planners. To pave the way for new study platforms.Materials and Methods In this study, satellite data (GPM) with a spatial resolution of 0.1 × 0.1 degrees from January 2001 to December 2019 have been used for spatiotemporal analysis of precipitation in the Jazmourian catchment. The GPM satellite provides more accurate and realistic estimates than other TRMM satellites. In this study, a calibrated precipitation product of level 3 of 6 GPM satellite versions was used. Relevant data are in NCDF format and have UTM image system with WGS84 datum, which after quality control and preprocessing, by specialized software (ENVI, ArcGIS and EXCELL) is converted into network data and data tables and the necessary outputs based on the geographical boundary of the catchment was extracted. The average monthly rainfall was extracted from the average daily rainfall belonging to each month and the seasonal average was extracted from the average of three months related to each season. Spatially, the values of each pixel are the conditions of the average amount of precipitation related to each time series (monthly, seasonal and annual) during the statistical period.Results and Discussion Based on the results, the average rainfall in the Jazmourian catchment was estimated as 144 mm, the spatial distribution of which ranged from 83 to 232 mm. The maximum rainfall occurred in the northern and western parts and the minimum occurred in the central and eastern parts of the basin. Furthermore; based on the annual distribution of rainfall during the statistical period under study, the highest rainfall was in 2019 with 239 mm and the lowest with 53 mm in 2001. In terms of seasonal distribution, winter and spring with values of 118 and 88 mm, respectively, showed the highest and autumn and summer with values of 22 and 45 mm, showed the lowest values of precipitation. Also, during the statistical period under study, winter 2005 with 193 mm had the highest and autumn 2003 with 1 mm had the lowest seasonal rainfall in the basin. In addition, an interesting point is the spatial displacement of high-pressure nuclei in different seasons of the year; so that these nuclei are observed in the cold seasons of the year in the northern and western parts and in the warm seasons of the year in the southwestern and southeastern parts of the basin. The spatial distribution of monthly precipitation indicates the occurrence of the highest monthly precipitation in February and March and the lowest in May and September. Also, the monthly rainfall time series indicates the maximum incidence of precipitation in February 2001 (94 mm) and it’s minimum in January 2001 (no precipitation).Conclusion Precipitation as a source of fresh water on Earth is one of the most important hydrological parameters, the importance of which is undeniable in the survival of human communities and natural ecosystems. Due to the large temporal-spatial variations of precipitation, its study seems necessary. But one of the main challenges for studying this phenomenon is the lack of ground stations as well as their improper distribution. Today, with advancement of technology and remote sensing, a diverse range of satellite data has become available to environmental scientists. In this regard, in the present study, using GPM satellite data and in the statistical period 2001-2019, the temporal-spatial distribution of precipitation in the Jazmourian catchment area in southeastern Iran has been investigated. In general, the high variability of rainfall in Jazmourian catchment in different months and seasons of the year, shows the dominance of arid and low climate in this basin. Therefore, due to the rainfall situation and its high fluctuations under climate change conditions, in the near future, this basin will face serious challenges and crises in water resources management and the sustainability of natural ecosystems. The GPM satellite data used in this study showed appropriate and expected results from the spatial-temporal distribution of precipitation in the Jazmourian catchment and showed a good correlation with meteorological stations. In general, the use of GPM satellite data in the present study is appropriate, which due to its appropriate spatio-temporal separation, gives reliable and satisfactory results. On the other hand, inadequate spatial coverage of meteorological stations and their large statistical vacuum in such a relatively large basin justify the use of this valuable and useful satellite data.
iman babaeian; Maryam Karimian; Hamed Ashouri; Rahele Modirian; Leili Khazanedari; Sharare Malbusi; Mansure Kuhi; Azade Mohamadian; Ebrahim Fattahi
Abstract
Introduction: Southeast watersheds of Iran including Great Karoon, Karkheh, Jarrahi and Zohreh have the most significant contribution in the water supply of the agriculture, industry, drinking water and hydroelectric power plants over Iran. 25 percent of the country’s electricity is produced from ...
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Introduction: Southeast watersheds of Iran including Great Karoon, Karkheh, Jarrahi and Zohreh have the most significant contribution in the water supply of the agriculture, industry, drinking water and hydroelectric power plants over Iran. 25 percent of the country’s electricity is produced from hydroelectric power plants located in this region. The existence of a monthly relatively high resolution gridded precipitation dataset is of the most important needs of water resources management for such as deciding on the suitable time of dewatering and discharge of dams, calibration of dynamical monthly forecasting models and drought early warning. Even considering all observation stations governed by Meteorological Administration and Ministry of Power, the density of stations is not so enough to use them for calibration of hydro-climate model outputs. To overcome this deficiency, one way to fill the gap is using bias corrected global gridded precipitation dataset such as APHRODITE, CMORPH, PRESIANN and other newly generated data.
Material and Methods: Watershed of Karkheh, great Karoon, Jarrahi and Zohreh are the area of study which covers southwest provinces of Khuzestan, Kermanshah, Ilam, Chaharmohal-Bakhtiari, Kohkiluyeh and Buyerahmad, Isfahan, Hamadan, Fars and Lorestan, which is shown in figure 2. There are 135 observation station in the area of study which governs by Iran Meteorological Organization and Ministry of Power. Area of study covers by 75 grids of 0.5×0.5 degree latitude and longitude. For each grid there is an APHRODITE precipitation data. In the 34% of grids, there is no observation station. The main goal of this study is to attribute a reliable monthly precipitation data to all grids without any observation station. Period of APHRODITE data set is 1987-2007, which is same to observation period. Firstly regional bias of APHRODITE data set has been computed by comparing observed precipitation with APHRODITE one. Then bias corrected APHRODITE precipitation (Composite APHRODITE Observation dataset) has been placed in non-observation grids. Efficiency of composite precipitation data has been determined by statistical parameters of bias, correlation and Nash-Sutcliff indices.
Results and Discussion: In this research the results have been evaluated at monthly and seasonal time scales. In the case of seasonal time scale, we found that the minimum APHRODITE’s bias of 1.2 mm has been occurring in summer, while the maximum bias has been occurring in winter by 40.9mm. It means that the bias is high in the rainy season. Seasonal correlations were statistically acceptable in 0.05 significant levels, showing same seasonal fluctuations in APHRODITE and rain gage data. To provide seasonal composite APHRODITE-Observed precipitation gridded data set, mean seasonal bias of APHRODITE has been removed, while preserving seasonal fluctuation. The highest spatial correlation of 0.8 was detected in autumn, while it was about 0.7 for spring and winter. The minimum seasonal correlation was in summer by 0.5. There were also a good agreement between area averaged observation and APHRODITE data, when considering statistical indices of bias, Nash-Sutcliff and relative percentage errors. Results show the cumulative distribution function of APRODITE data is behind of the observed cumulative distribution function data, meaning that APHRODITE reaches its maximum earlier than observation data. This implies that APHRODITE cannot capture well the extreme monthly precipitation. Monthly correlations are approximately greater than 0.9, but the only exception is September with a correlation coefficient of 0.52. All correlations are significant in 0.05 levels. The highest spatial correlation was occurred in Novembers. Monthly Nash-Sutcliff was 0.96 in monthly time series. The categorical percentage score was 94.1%. These results strongly confirm that APHRODITE precipitation data is a good option for replacement in grid cells without observations. The number of observation stations per cell is varied from 1 to 7. We found that the maximum monthly correlations occur in grid cells of 0.5×0.5 degree latitude and longitude which having at least 3 observation stations. The three-station bias has been applied to APHRODITE data, then bias-removed data has been replaced with grid cells without observations. Spatial patterns of new composite APHRODITE-observation data set has good agreement with observation in the areas having intense observation stations. They also can capture well the spatial precipitation distribution of rainy areas located in the center of basin and low rainfall areas located in the southwest of the region. The results of this research can be used in calibration of dynamical seasonal forecasting outputs, drought early warning and rain-runoff simulation.
fatemeh yaghoubi; Mohammad Bannayan Aval; Ghorban Ali Asadi
Abstract
Introduction: Estimating crop water requirement, crop yield and their temporal and spatial variability using crop simulation models are essential for analysis of food security, assessing impact of current and future climates on crop yield and yield gap analysis, however it requires long-term historical ...
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Introduction: Estimating crop water requirement, crop yield and their temporal and spatial variability using crop simulation models are essential for analysis of food security, assessing impact of current and future climates on crop yield and yield gap analysis, however it requires long-term historical daily weather data to obtain robust predictions. Depending on the degree of weather variability among years, at least 10–20 years of daily weather data are necessary for reliable estimates of crop yield and its inter-annual variability. In many regions where crops are grown, daily weather data of sufficient quality and duration are not available. In this way, gridded weather databases with complete terrestrial coverage are available which require comprehensive validation before any application. These weather databases typically derived from global circulation computer models, interpolated weather station data or remotely sensed surface data from satellites. The aims of this study were to evaluate differences between grided AgMERRA weather data and ground observed data and quantify the impact of such differences on simulated water requirement and yield of rainfed wheat at 9 different locations in Khorasan Razavi province.
Materials and Methods: AgMERRA dataset (NASA’s Modern-Era Retrospective analysis for Research and Applications) was selected as the girded weather data source for use in this study because it is publically accessible. We evaluated AgMERRA weather data against observed weather data (OWD) from 9 meteorological stations (Torbat Jam, Torbat Heydarieh, Sabzevar, Sarakhs, Ghoochan, Kashmar, Gonabad, Mashhad, and Neyshabour) in Khorasan Razavi province. For each weather variable (solar radiation, maximum temperature, minimum temperature, precipitation, and wind speed), the degree of correlation and agreement between OWD and AgMERRA data for the grid cell in which weather stations were located were evaluated. The intercept (b), slope (m), and coefficient of determination (r2) of the linear regression were calculated to determine the strength and bias of the relationship, while the root mean square error (RMSE) and normalized root mean square error (NRMSE) were computed to measure the degree of agreement between data sources. Crop water requirement or actual crop evapotranspiration (ETc) under standard condition was computed using CROPWAT 8.0. The CSM-CERES-Wheat (Cropping System Model-Crop Environment Resource Synthesis-Wheat) model, included in the Decision Support System for Agrotechnology Transfer (DSSAT v4.6) software package was used to calculate rainfed wheat yield. For each location in this study, rainfed wheat grain yield and water requirement were simulated using ground-observed and AgMERRA weather data and outputs were compared with each other.
Results and Discussion: The results of this study showed that AgMERRA daily maximum and minimum temperature and solar radiation showed strong correlation and good agreement with data from ground weather stations. AgMERRA daily precipitation had low correlation and good agreement (mean r2= 0.34, RMSE= 2.25 mm and NRMSE= 4.94% across the 9 locations) with OWD daily values, but correlation with 15-day precipitation totals were much better (mean r2 >0.7 across the 9 locations). There was reasonable agreement between a number of observed dry and wet days with AgMERRA compared to OWD. Results indicated that coefficient of variation of simulated water requirement and yield using AgMERRA weather data was remarkably similar to the degree of variation observed in simulated water requirement and yield using OWD at all locations (distribution of CVs in simulated water requirement and yield using AgMERRA weather data were within ±5% of the CV calculated for simulated water requirement and yield using observed weather data) except Torbat Jam, Torbat Heydarieh and Gonabad for water requirement and Mashhad, Kashmar and Ghoochan for yield. There was good agreement between long-term average yield simulated with AgMERRA weather data and long-term average yield simulated using observed weather data. For example, the distribution of simulated yields using AgMERRA data was within 10% of the simulated yields using observed data at all locations. Using AgMERRA weather data resulted in simulated crop water requirement that were not in close agreement with crop water requirement simulated with ground station data at two location including Gonabad and Torbat Heydarieh.
Conclusions: These results supported the use of uncorrected AgMERRA daily maximum and minimum temperature and solar radiation in areas that their weather stations only have a few years of daily weather records available or areas without weather station. Considering the advantage of continuous coverage and availability, use of AgMERRA dataset appears to be a promising option for simulation of long-term average yield and water requirement, as well as for assessing impact of climate change on crop production and also estimating the magnitude of existing gaps between yield potential and current average farm yield in Khorasan Razavi province. But they are not very reliable for accurate simulation of water requirement and yield in a specific year and estimate their inter-annual variation.
M. Shokouhi; Seied Hosein Sanaei-Nejad
Abstract
Introduction: Many researchers studied and emphasized on determining the importance of climatic factors that affect crop yield. As the most source of moisture in rainfed cultivation, precipitation is the most important climate factor. Spatial and temporal change of this factor effects crop yield. Standardized ...
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Introduction: Many researchers studied and emphasized on determining the importance of climatic factors that affect crop yield. As the most source of moisture in rainfed cultivation, precipitation is the most important climate factor. Spatial and temporal change of this factor effects crop yield. Standardized Precipitation Index (SPI) is useful to characterize the condition of the moisture supply before and during the growing season of crops. Studies have shown that in some areas there is little correlation between spring wheat yield and SPI, while in other areas there is significant relationship between wheat yield and SPI. This difference indicates SPI as an indicator of moisture supply, depend on the study area .The purpose of this study was to determine the most effective period of precipitation during growing season for rainfed barley using variables obtained from moisture supply and precipitation periods in Tabriz. The most effective period of precipitation can be used for the management of rainfed cultivation.
Materials and Methods: Daily temperature and precipitation data of Tabriz station were collected from Iran Meteorological Organization for the years 1955 to 2013. In addition, barley yields data were collected for the years 1977 to 2013. In this study, the occurrence of phenological stages (germination, tillering, anthesis, ripening and harvesting) were estimated using growing degree days (GDD). The SPI value for 28-week time scale of the first week after planting (SPI28) was considered as an indicator of the moisture supply during growing season. SPI28 values less than zero and greater than zero representing different classes of drought and humidity respectively. For correlation analysis, 128 weekly variables were defined at different time scales of daily precipitation data (Table 2). The relationship between the crop yield and precipitation variables were analyzed by linear correlation.
Results and Discussion: The correlation coefficient (r) between precipitation and annual rainfed barley yield were presented in Table 2. The highest correlation between yield and precipitation occurred during the 10-week period between 25 February and 6 May, which was mostly observed at the end of April to mid-May that was coincide with the beginning of anthesis. So it can be concluded that the anthesis stage was the most critical stage to water stress in barley. Based on the SPI28 value greater than zero (wet conditions) or less than zero (dry conditions), the amount of precipitation (between 25 February and 6 May) was divided into two groups. The amount of precipitation between 25 February and 6 May explained 78% of the yield variations when SPI28 was greater than zero (wet conditions). One mm increase in precipitation in this period increased the yield with the rate of 2/76 kg / ha. If early planting conditions is dry (SPI 28
vajiheh mohammadi sabet; Mohammad Mousavi Baygi; Hojat Rezaee Pazhand
Abstract
Introduction: The Southern Oscillation is a large scale phenomenon that changes the Normal oscillating air pressure on both sides of the Pacific Ocean. It disrupted the normal conditions and the patterns of temperature and precipitation change in the nearby region and other regions of the world. This ...
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Introduction: The Southern Oscillation is a large scale phenomenon that changes the Normal oscillating air pressure on both sides of the Pacific Ocean. It disrupted the normal conditions and the patterns of temperature and precipitation change in the nearby region and other regions of the world. This phenomenon is caused by changing the water slope in the Pacific Ocean between Peru (northwestern South America) and Northern Australia (about Indonesia and Malaysia). ENSO phenomenon is formed of Elnino (warm state) and La Niña (cold state). There is high pressure system in the East and low pressure system in the West Pacific Ocean in normal conditions (Walker cycle). The trade winds blow from East to West with high intensity. ENSO start when the trade winds and temperature and pressure balance on both sides of the PacificOcean change. High pressure will form in the west and low pressure will form in the East. As a result, west will have high and east will have low rainfall. Temperature will change at these two locations. Enso longs about 6 to 18 months. This research investigated the impact of ENSO on monthly precipitation and temperature of Mashhad.The results showed that temperature and rainfall have a good relation with ENSO.This relation occurs in 0-5 month lag.
Materials and Methods: The severity of ENSO phenomenon is known by an index which is called ENSO index. The index is the anomaly of sea surface temperature in the Pacific. The long-term temperature and precipitation data of Mashhad selected and analyzed. The Rainfall has no trend but temperature has trend. The trend of temperature modeled by MARS regression and trend was removed.The rainfall data changed to standard and temperature changed to anomaly for comparison with ENSO index. The 2016 annual and monthly temperature of Mashhad is not available. The 2016 Annual temperature was forecasted by ARMA (1,1) model. Then this forecast disaggregated to monthly temperature. For each period of occurring high ENSO, these three indexes (ENSO index, standardized rainfall and anomalies temperature) were compared. The co-variation of these indexes was compared. Also, the correlation and cross correlation for each period of occurring ENSO, with rain and temperature of Mashhad was calculated.
Results and Discussion: Mashhad monthly temperature and precipitation were compared with the extreme values of ENSO index in periods of the occurrence this phenomenon (1950-2016). In addition, the correlation and cross-correlation between ENSO-Rainfall index and ENSO-temperature index for this period were calculated.Forecasted temperature for 2016 by ARMA (1,1) was 13.2 Degrees Celsius, which has 0.2 degree increase in comparison to last year. Results showed thatthere is no an obvious relation between ENSO-Temperature and ENSO-Rainfall in interval (-1, +1). But there are good relation between ENSO-Temperature and ENSO-Rainfall beyond of (-1,+1). The results of Elnino showed that the monthly precipitation and temperature increase with a lag of 2 to 5 months and 0 to 4 months, respectively. The results of Lanina showed that the monthly precipitation and temperature decrease with a lag of 3 to 5 months and 1 to 4 months, respectively. Also when ENSO index is located in the interval (-1, +1), there is no certain harmony with temperature and precipitation of Mashhad.
Conclusions: The aim of this study was evaluating the effect of the ENSO phenomenon on monthly temperature and precipitation of Mashhad.Mashhad monthly temperature and precipitation, respectively, for 132 and 124 years were available.Precipitation was static and has no trend, but temperature was not static and has two changed (jumped) point in 1976 and 2000. MARS regression was used for patterning the process. Removing the trend was done by MARS model and the data was obtained without trend. Monthly ENSO index since 1950 from reliable websites worldwide (NOAA) was obtained. Mashhad monthly temperature data was animalized and precipitation data was standardized. This was performed for comparing Temperature and Rain with ENSO index. The effect of the ENSO phenomenon on Mashhad precipitation and temperature in both graphical and cross-correlation was performed.As a final result, there is a good relation with latency zero up to 5 months for temperature and precipitation of Mashhad beyond the interval (-1, + 1). It cannot be claimed that after the phase of La Nina, El Nino must be entered and vice versa. This note is important for forecasting the temperature and precipitation of 2016coming months. If ENSO index in the coming months, especially in autumn and winter, decrease and inter in La Nina phase, the winter will be cold with low rainfall.
Nooshin Ahmadibaseri; A. Shirvani; mohammad jafar nazemosadat
Abstract
In this study, the artificial neural networks (ANNs) and regression models were used to downscale the simulated outputs of the general circulation models (GCMs). The simulated precipitation for 25.18 º N to 34.51 º N and 45 º E to 60 º E, geopotential height at 850 mb and zonal wind at 200 mb for ...
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In this study, the artificial neural networks (ANNs) and regression models were used to downscale the simulated outputs of the general circulation models (GCMs). The simulated precipitation for 25.18 º N to 34.51 º N and 45 º E to 60 º E, geopotential height at 850 mb and zonal wind at 200 mb for 12.56° N to 43.25° N and 19.68° E to 61.87° E data sets as the predictors were extracted from ECHAM5 GCM for the period 1960-2005. The observed monthly precipitation data of Abadan, Abadeh, Ahwaz, Bandar Abbas, Bushehr, Shiraz and Fasa stations as the predictand were extracted for the period 1960-2005. The principal components (PCs) of the simulated data sets were extracted and then six PCs were considered as the input file of the ANN and multiple regression models. Also the combinations of the simulated data sets were used as the input file of these models. The periods 1960-2000 and 2001-2005 were considered as the train and test data in the ANN, respectively. The Pearson correlation coefficient and normalized root mean square error results indicated that ANN predicts precipitation more accurate than multiple regression. For the monthly time scale, the geopotential height is the best predictor and for the seasonal time scale (winter) the simulated precipitation is the best predictor in ANN based standardized precipitation principal components.
mohammad jafar nazemosadat; K. Shahgholian
Abstract
The aim of this study is to assess some synoptic characteristics of heavy precipitations in southwestern parts of Iran and evaluate the relationship between them with the Madden-Julian Oscillation (MJO). Research is conducted with regard to distribution of precipitation per month and identifying their ...
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The aim of this study is to assess some synoptic characteristics of heavy precipitations in southwestern parts of Iran and evaluate the relationship between them with the Madden-Julian Oscillation (MJO). Research is conducted with regard to distribution of precipitation per month and identifying their steam sources. Daily records of the November-April precipitation data in Abadan, Ahwaz, Bandar-Abbas, Bushehr, Shahr-e-kord and Shiraz stations for the 1975- 2011 period are collected as well as same panel data for Yasuj station from 1990 to 2011. Rainfall data are sorted in descending order and precipitation values that were fallen within the 5% and 10% of highest records are categorized as the heavy precipitation. The most frequent precipitations occurred in January, February and December. The most frequent heavy precipitations in Ahwaz, Bandar-Abbas, Bushehr, Shahr-e-kord and Shiraz stations occurred in phase 8, while in Abadan station occurred in phases 7 and 8. Apparently, due to the short duration precipitations data at Yasuj station, the most frequent heavy precipitation observed in phase 2.Synoptic maps show that harmonized with eastward movement of convective precipitation in Indian or pacific oceans.Heavy precipitation forms in the west region of Iran and moves toward southwest and south Central of Iran and then appears to Afghanistan.Formation of a cyclonic circulation that encompasses the Mediterranean Sea, Red Sea and Persian Gulf plays an important role for moisture supplement of these storm activities. The synoptic maps have indicated that main sources of these heavy rainfalls are moisture produced at the Arab sea and western parts of the Indian Ocean.
A. Mianabadi; A. Alizadeh; Seied Hosein Sanaei-Nejad; M. Bannayan Awal; A. Faridhosseini
Abstract
Precipitation is a key input to different crop and hydrological models. Usually, the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network, this data does not have a desirable accurate. So estimation ...
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Precipitation is a key input to different crop and hydrological models. Usually, the rain gauge precipitation data is applied for the most management and researching projects. But because of non-appropriate spatial distribution of rain gauge network, this data does not have a desirable accurate. So estimation of daily areal rainfall can be obtained by spatial interpolation of rain gauges data. However, direct application of these techniques may produce inaccurate results. In the last years, applying the remote sensing for estimation of rainfall have got so popular all around the word and many techniques have been developed based on the satellite data with high temporal and spatial resolution. In this paper, CMORPH model was validated for precipitation estimation over the northeast of Iran. Results showed that this model could not estimate precipitation accurately in daily scale, but in monthly and seasonal scale the estimation was more accurate. Farooj and Namanloo station had the highest correlation equal to 0.31 in daily scale. The highest correlation in monthly scale was equal to 0.62 for Barzoo, Namanloo and Se yekAb station. In Seasonal scale Gholaman station had the highest correlation which was equal to 0.63. Also, the probability of detection has been estimated accurately by CMORPH. But this technique did not have an accurate estimation for wet and dry days, mean annual precipitation and the number of non-rainy days.
abolfazl Mosaedi
Abstract
Prediction of precise forage production and proper management strategies requires identifying key climatic factors in different regions. The objective of this research is to compare forage production in different region based on climatic factors and drought indices. The study sites include Arak, Roudshore, ...
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Prediction of precise forage production and proper management strategies requires identifying key climatic factors in different regions. The objective of this research is to compare forage production in different region based on climatic factors and drought indices. The study sites include Arak, Roudshore, Baghic, and Gharahso in Central and Qom provinces. Climatic factors and drought indices include precipitation, temperature, evapotranspiration, standardized precipitation index (SPI), and Reconnaissance Drought Index (RDI). For each climatic variable/or indices, 33 time periods of 1, 2, 3, 4, 6, and 9-month were specified. We have used Principle Component Analysis to decline the number of variables and then, the appropriate time periods were selected. By using stepwise and best subset, the relationship between forage production and each of the climate factors and indices was modeled. To select model, assessment statistics of R, MBE, RMSE, MARE, and IPE were used. Finally, to predict forage production in Roudshore, Baghic, and Gharahso sites, models based on evapotranspiration (RMSE=7.7, r=0.99), RDI (RMSE=3.1, r=0.99) and precipitation (RMSE=4.0, r=0.99) were selected respectively. The best model was based on the combinations of climatic factors and drought indices (RMSE=0.2, r=0.99) for Arak. In general, the relation between forage production and drought condition based on RDI is stronger than its relationship with precipitation and temperature. As we have used precipitation and evapotranspiration simultaneously in RDI, so this index is more precise than SPI.
M. Rezaei; M. Nohtani; A. Moghaddamnia; A. Abkar; M. Rezaei
Abstract
One of the most important problems in the management and planning of water resources is to forecast long-term precipitation in arid region and hyper arid regions. In this study, statistical downscaling model (SDSM) is used for study of climate change effects on precipitation. The data used as input to ...
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One of the most important problems in the management and planning of water resources is to forecast long-term precipitation in arid region and hyper arid regions. In this study, statistical downscaling model (SDSM) is used for study of climate change effects on precipitation. The data used as input to the Model are daily precipitation of Kerman and Bam synoptic stations, NCEP (National Centers for Environmental Prediction) data and the A2 and B2 emission scenarios HadCM3 for the reference period (1971-2001). Using HadCM3 A2, B2 data the precipitation for three period (2010-2039), (2040-2069) and (2070-2099) are predicted and compared with the reference period. We used the first 15 years data (1971-1985) for the calibration and the second 15 years data (1986-2001) for model validation. Research results showed that the precipitation will change and Change directions are positive in some months and negative in other months. After the examination function Indexes results from SDSM model shown that this model has better accuracy and a high ability to predict precipitation in arid region than hyper arid region.
H. Asakereh; R. Khoshraftar; F. Sotudeh
Abstract
Rainfall and debit of rivers are two tempo-spatial non-linear and changeable factors. One way to study and analysis these parameters is investigate appearance and latent oscillations. Spectral Analysis is a useful technique to reveal these oscillations in time series. In this paper it has been attempted ...
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Rainfall and debit of rivers are two tempo-spatial non-linear and changeable factors. One way to study and analysis these parameters is investigate appearance and latent oscillations. Spectral Analysis is a useful technique to reveal these oscillations in time series. In this paper it has been attempted to detect cycles in rainfall and debit time series at Mashinkhaneh station in Talesh (Garakanrood) catchment’s during Mehr 1354 to Shahrivar 1386 in the three time scales (annual, seasonal and monthly). Accordingly, the discharge and precipitation data at Mashinkhaneh station in Talesh (Garakanrood) catchment from Mehr 1354 to Shahrivar 1386 have been used. The results of applying the spectral analysis procedures to discharge and rainfall time series in each three category of time scales, suggested the absence of significant non-sinusoidal (trend) in the 95% confidence level. However, significantly sinusoidal cycles various in the two time series were extracted. The 2-4 year cycle, and 4-5.3 years have the most occurrences in the both time series. In the annual scale, 6.4 years cycle, 2-5.3 years, 7.7 years seasonal and 2-4, 4- 5.3, 6.4, 8, 10.7 and 16 year in the monthly scale cycles has been extracted. Studies carried out by many researchers indicate that the mentioned cycles are in relation with oscillation periods of ENSO, NAO and QBO in other parts of the world.
