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Showing 4 results for Jahanbakhsh ASL
Saeid Jahanbakhsh Asl, Behruz Sari Sarraf, Hosein Asakereh, Soheila Shirmohamadi,
Volume 7, Issue 1 (5-2020)
Volume 7, Issue 1 (5-2020)
Abstract
The study of temporal - spatial changes of high extreme rainfalls in west of Iran (1965-2016)
Extended Abstract
Introduction
Rainfall is one of the appropriate weather parameters not only in describing weather condition in one specific area but also is in estimating potential impacts of climate change in the environment and in many economic and social systems. Some studies show that during half a century weather patterns by more and severe raining events and by changes in scheduling and rain status has been changed. From 1960s with its much slope, the abundance and severity of extreme rainfalls throughout the world has increased and it is expected to continue the increase until the end of the current century. So understanding the behavior of extreme events is one of the main aspects of climate change and the increase of information about heavy rains has utmost importance for society, especially for the population who lives in areas with increased flood risk.
According to above mentioned cases and abnormal behavior and irregular rainfalls in Iran and its high variability from one hand and Iran's west region ability to heaviness and extension of rainfalls on the other hand, the necessity of understanding and study of temporal and spatial dangerous rainfalls is recognized. Among extreme rainfall characteristics, the portion of such rainfalls in total rain production is studied less. Due to the experiments carried out, the increase of annual rainfall in Iran happens through heavy rainfalls. Therefore heavy rainfall portions out of total annual rainfalls can be defined as an index of crisis. The increase of this index implies the heavy floods in rainy years and severe drought and drought years.
Data and Method
Iran's west region including East and West Azerbaijan provinces, Zanjan, Kurdistan, Kermanshah, Hamadan, Lorestan, and Ilam consists of about 14 percent of Iran's total area. The height of this region includes a domain of 100 to about 4000 meters. Zagros mountain ranges are the most important characteristic of west of Iran, which are drawn from north-west to south-east.
In this research, we used network data from interpolation daily rainfall observation of 823 meteorology stations from January 1st up to December 31st, 2016 by using Kriging interpolation method and by separating 6×6 km spatial. The results formed matrix interpolation process by dimension of 18993×6410. This matrix has the rain status of 6410 points of west of Iran for every day rainfall (18993). Extreme rain falls are identified in terms of threshold of 95 percentile in each point and each day of year. The rainfall of each day and each pixel is compared to that related pixel and corresponding to that day and those days which their rainfalls rates were equal to or larger than threshold were identified for studying extreme rain fall portion in total yearly rainfall, the total of equal rainfalls and more than 95 percentile is calculated for each year and each of pixel and, it is divided to total of the same pixel rainfalls in that year.
We used the least squarely error for understanding temporal- spatial behavior of regression.
Results and Discussion
The average extreme rain falls in west of Iran is under the influence of their roughness and placement and also synoptic rainfall. The proof of this claim identifies through placement of average extreme rainfall over altitudes of region. By increasing geographical latitude in Iran's western provinces, it is decreased both of total extreme rainfalls and portion of such rainfall out of total yearly rainfall. Total extreme rainfall trend shows a frequency in a domain with 16 mm in each year. The negative trend of total rainfall with the area of 74.72 percent consists of three quarters of Iran's west.
The narrow strip of the west of Kurdistan and south-west of west Azerbaijan have the highest amount of positive trend which is meaningful in certainty level of 95 percent.
The study of process showed the ratio of extreme rainfalls portion to total yearly rainfall, which is increasing about 60.7 percent of west area of this country extreme rainfalls in total yearly rainfall and the greatest part of this area is located in southern half of the studied area.
The negative trend also is located in northern half and they have consisted of 39.29 percent of studied area of these, only in 29.81 percent of region, the trend ratio of extreme rainfalls to total yearly rainfalls are meaningful in certainty level of 95 percent.
Keywords: Extreme Rainfalls, Trend, 95 Percentile, Rainfall Portion, west of Iran.
Mr Mohammad Hossein Aalinejad, Pro Saeed Jahanbakhsh Asl,
Volume 8, Issue 1 (5-2021)
Volume 8, Issue 1 (5-2021)
Abstract
Simulation of runoff from Gamasiab basin snowmelt with SRM model
Abstract
Snow cover in a basin affect its water balance and energy balance. So, snow cover variation is a major factor in climate change of a region. Study of temporal variation of snowmelt and snow water equivalent depth is very important in flood forecasting, reservoir management and agricultural activities of an area. In the most of the mountainous basins of the country, information on snow cover were not available. Also, the number of meteorological stations in high altitude areas do not match with information needed for snowmelt simulation. Therefore, indirect methods such as the analysis of satellite images to obtain the needed parameters for simulation is necessary, which is the one of the most effective methods in estimation of runoff originated from snow. Using the NOAA satellite data for zoning the snow cover of area started firstly in the USA since the 1961 and continuous until today (spatial and temporal resolution of satellite images increased by starting the MODIS work).
Gamasiab River is one of the important branches of Karkheh basin. Its basin area is about 11040 km2 between latitude 47 degrees 7 minutes to 49 degrees 10 minutes east and latitude 33 degrees 48 minutes 4 degrees 85 minutes north. The altitude of this basin is 1275 to 3680 meters above sea level. In this study, for simulation of runoff originated from melting snow, firstly snow cover in the basin of Gamasiab in 2014 to 2017 calculated by using the satellite images of MODIS in the google earth engine system. Also, air temperature and precipitation data of synoptic stations in the area of study and daily stream flow discharges of Polechehr hydrometric station, from November of 2014 to July of 2017 was used. Then, weather and snow cover area included as the input of SRM for simulation of snowmelt runoff. To obtain the information needed to the model, physiographic characteristics of the basin including the area and different classes of height obtained from the Arc-Hydro and Hec_GeoHMS in DEM maps of GIS software. Then the snow cover areas obtained from the images of MODIS in daily interval that obtained by google earth engine system.
Using the digital elevation map (DEM) and the accession of the Arc-Hydro and Hec_GeoHMS software of GIS, firstly flow direction map plotted. Secondly flow accumulation and stream flow network maps plotted, and by introducing the basin output to the program (Polechehr hydrometric station) borders of the basin identified and classification of the basin accomplished according to the three distinct height classes. Monitoring the snow surface cover during the daily time interval showed that the area covered with snow in winter season. This area decreases as the air temperature increases. The SRM model simulated the snowmelt of Gamasiab basin with good accurately, in which, the percent of volume error or Vd was lose than 2% and the R2 was above 0.9.
The results of this research showed that the using the images of MODIS yields a reasonable estimation of the snow cover area of Gamasiab with local of data. Also simulation results showed the high capability of the SRM in snowmelt runoff of the area under study. Result showed that the coefficient of determination and volume percent of error of model was 0.93 and %0.3 for 2014-2015 and it was 0.9 and 3.33 for 2015-2016 years, respectively. The results of this study, was in consistent with the previous studies fading in which in addition of model's parameters, physiographic characteristics, basin play a major role in the accuracy of the simulation. According to the calculated and observed runoff diagram, in both years of study, peak temperatures begin in March, as the weather warms and the snow melts, and will continue until April. Considering the snow cover, it can be concluded that the main runoff of March Peak is related to snowmelt, but with the change in the shape of precipitation from snow to rain and the warming of the weather, April peak is related to rain. Regardless of acceptable simulation results of the model, the lack of snow survey station in the study area, (yield the model to face with difficulty) in process. To overcome this shortcoming, we used the presumptions of the model and recommended values of the model.
Keywords: MODIS; Remote sensing; Runoff Snow; SRM; Gamasiab.
Mr Mohammad Hossein Aalinejad, Pro Saeed Jahanbakhsh Asl, Pro Ali Mohammad Khorshiddoust,
Volume 8, Issue 3 (12-2021)
Volume 8, Issue 3 (12-2021)
Abstract
Investigation of Temperature and Precipitation Changes in the Seymarreh Basin by Using CMIP5 Series Climate Models
Abstract
Panel reports on climate change suggest that climate change around the world is most likely due to human factors. Temperature and precipitation are two important parameters in the climate of a region whose variations and fluctuations affect different areas such as agriculture, energy, tourism and so on. Seymareh basin is one of the most significant sub-basins of Karkheh. The purpose of this study is to predict the impact of climate change on precipitation and temperature of the Seymareh Basin in 2021-2040 period. These effects were analyzed at selected stations with uncertainties related to atmospheric general circulation models (GCMs) of CMIP5 models under two scenarios of RCP45 and RCP85 through LARS-WG statistical model. Then the uncertainties of the models and scenarios were investigated by comparing the monthly outputs of the models by the coefficients of determination coefficient (R2) in the forthcoming period (2021-2040) with the base period (1980–2010). The root mean square error (RMSE) calculations presented the best model and scenarios for generating future temperature and precipitation data.
The Seymareh catchment is the largest and the main Karkheh sub-basin that covers parts of Kermanshah, Lorestan and Ilam provinces. The length of the largest river at the basin level to the site of the Seymareh Reservoir Dam is approximately 475 km, and the area of the basin is 26,700 km2. Geographic coordinates of the basin are from 33° 16 ́ 03 ̋to 34°59 ́ 29 ̋north latitudes and 46°6 ́9 ̋to ̋ 5 ́ 0 ° 49 Eastern longitudes, minimum basin height 698 m at the dam outlet and its maximum height 3,638 m. It is on the western highlands of Borujerd.
The information used in this study was obtained from the Meteorological Organization of the country. For this study, three synoptic stations of Kermanshah, Hamadan and Khorramabad, which had the highest statistical records and had appropriate distribution at basin level, were used. These data included daily and monthly temperature and precipitation information, and sunshine hours.
The LARS-WG fine-scale exponential model was proposed by Rasko et al., Semnoff and Barrow (1981). We used daily data at stations under current and future weather conditions. In order to select the best GCM model from the models mentioned above, minimum temperature, maximum temperature, precipitation and sunshine data were entered daily in the base period (1980–2010) and data were generated for five models under two scenarios of RCP45 and RCP85 for the period 2040–2021. The data were generated in 100 random series and the mean of required variables (minimum temperature, maximum temperature and rainfall) were extracted monthly in the period 2021-2040. Then, root mean square error (RMSE) and determination coefficient (R2) were used to evaluate the performance of the models and compare the results.
To ensure the models' ability to generate data in the coming period, computational data from the model and observational data at the stations under study should have been compared. The capability of the LARS-WG model in modeling the minimum temperature, maximum temperature, and radiation at the stations under study was completely consistent with the observed data. The model's ability to exemplify rainfall was also acceptable, however the highest modeling error was related to March rainfall.
By comparing the observed and produced data including monthly average precipitation, minimum and maximum temperatures through five mentioned models with their indices, the best model and scenario for future fabrication were determined. The results of this comparison showed that among the available models, HADGEM2-ES model under RCP 4.5 scenario had the best result for precipitation and HADGEM2-ES under RCP 8.5 scenario predicted the best result for maximum temperature. Determining the best model, precipitation data, minimum temperature and maximum temperature produced in the selected models and scenarios were analyzed to investigate the climate change temperature and precipitation for the future period.
The results of this study indicated that due to the wide range of output variations of different models and scenarios, by not taking into account the uncertainties of the models and scenarios can have a great impact on the results of the studies. It was also found in this study that the LARS-WG exponential model was capable of modeling precipitation data and baseline temperature in the study area, so that the radiation data, minimum and maximum temperatures were completely consistent with the data.
The observations are consistent and the models' ability to predict rainfall is very good and acceptable manner. In investigating the uncertainties caused by atmospheric general circulation models and existing scenarios, the best model to predict precipitation in the study area is HADGEM2-ES model under RCP 8.5 scenario, the best model for temperature estimation model HADGEM2-ES under RCP scenario No. 4.5.
The overall results of this study revealed that the average precipitation in the basin will decrease by 4.5% on average, while the minimum temperature will be 1.5° C and the maximum temperature will be 2.17° C. The highest increase will be due to the warmer months of the year. Notable are the disruptions of rainfall distribution and the high temperatures will have significantly negative consequences than rainfall reduction.
Abstract
Panel reports on climate change suggest that climate change around the world is most likely due to human factors. Temperature and precipitation are two important parameters in the climate of a region whose variations and fluctuations affect different areas such as agriculture, energy, tourism and so on. Seymareh basin is one of the most significant sub-basins of Karkheh. The purpose of this study is to predict the impact of climate change on precipitation and temperature of the Seymareh Basin in 2021-2040 period. These effects were analyzed at selected stations with uncertainties related to atmospheric general circulation models (GCMs) of CMIP5 models under two scenarios of RCP45 and RCP85 through LARS-WG statistical model. Then the uncertainties of the models and scenarios were investigated by comparing the monthly outputs of the models by the coefficients of determination coefficient (R2) in the forthcoming period (2021-2040) with the base period (1980–2010). The root mean square error (RMSE) calculations presented the best model and scenarios for generating future temperature and precipitation data.
The Seymareh catchment is the largest and the main Karkheh sub-basin that covers parts of Kermanshah, Lorestan and Ilam provinces. The length of the largest river at the basin level to the site of the Seymareh Reservoir Dam is approximately 475 km, and the area of the basin is 26,700 km2. Geographic coordinates of the basin are from 33° 16 ́ 03 ̋to 34°59 ́ 29 ̋north latitudes and 46°6 ́9 ̋to ̋ 5 ́ 0 ° 49 Eastern longitudes, minimum basin height 698 m at the dam outlet and its maximum height 3,638 m. It is on the western highlands of Borujerd.
The information used in this study was obtained from the Meteorological Organization of the country. For this study, three synoptic stations of Kermanshah, Hamadan and Khorramabad, which had the highest statistical records and had appropriate distribution at basin level, were used. These data included daily and monthly temperature and precipitation information, and sunshine hours.
The LARS-WG fine-scale exponential model was proposed by Rasko et al., Semnoff and Barrow (1981). We used daily data at stations under current and future weather conditions. In order to select the best GCM model from the models mentioned above, minimum temperature, maximum temperature, precipitation and sunshine data were entered daily in the base period (1980–2010) and data were generated for five models under two scenarios of RCP45 and RCP85 for the period 2040–2021. The data were generated in 100 random series and the mean of required variables (minimum temperature, maximum temperature and rainfall) were extracted monthly in the period 2021-2040. Then, root mean square error (RMSE) and determination coefficient (R2) were used to evaluate the performance of the models and compare the results.
To ensure the models' ability to generate data in the coming period, computational data from the model and observational data at the stations under study should have been compared. The capability of the LARS-WG model in modeling the minimum temperature, maximum temperature, and radiation at the stations under study was completely consistent with the observed data. The model's ability to exemplify rainfall was also acceptable, however the highest modeling error was related to March rainfall.
By comparing the observed and produced data including monthly average precipitation, minimum and maximum temperatures through five mentioned models with their indices, the best model and scenario for future fabrication were determined. The results of this comparison showed that among the available models, HADGEM2-ES model under RCP 4.5 scenario had the best result for precipitation and HADGEM2-ES under RCP 8.5 scenario predicted the best result for maximum temperature. Determining the best model, precipitation data, minimum temperature and maximum temperature produced in the selected models and scenarios were analyzed to investigate the climate change temperature and precipitation for the future period.
The results of this study indicated that due to the wide range of output variations of different models and scenarios, by not taking into account the uncertainties of the models and scenarios can have a great impact on the results of the studies. It was also found in this study that the LARS-WG exponential model was capable of modeling precipitation data and baseline temperature in the study area, so that the radiation data, minimum and maximum temperatures were completely consistent with the data.
The observations are consistent and the models' ability to predict rainfall is very good and acceptable manner. In investigating the uncertainties caused by atmospheric general circulation models and existing scenarios, the best model to predict precipitation in the study area is HADGEM2-ES model under RCP 8.5 scenario, the best model for temperature estimation model HADGEM2-ES under RCP scenario No. 4.5.
The overall results of this study revealed that the average precipitation in the basin will decrease by 4.5% on average, while the minimum temperature will be 1.5° C and the maximum temperature will be 2.17° C. The highest increase will be due to the warmer months of the year. Notable are the disruptions of rainfall distribution and the high temperatures will have significantly negative consequences than rainfall reduction.
- : Climate Change, Climate Scenarios, Uncertainty, LARS-WG, Seymareh.
Saeid Jahanbakhsh Asl, Behruz Sari Sarraf, Hosein Asakereh, Soheila Shirmohamadi,
Volume 8, Issue 4 (1-2021)
Volume 8, Issue 4 (1-2021)
Abstract
understanding the behavior of extreme events is one of the main aspects of climate change.
In this research, we used network data from interpolation daily rainfall observation of 823 meteorology stations from January 1st up to December 31st, 2016 by using Kriging interpolation method and by separating 6×6 km spatial. The results formed matrix interpolation process by dimension of 18993×6410. This matrix has the rain status of 6410 points of west of Iran for every day rainfall(18993). Extreme rain falls are identified in terms of threshold of 95 percentile in each point and each day of year. for studying extreme rain fall portion in total yearly rainfall, the total of equal rainfalls and more than 95 percentile is calculated for each year and each of pixel and, it is divided to total of the same pixel rainfalls in that year. We used the least squarely error for understanding temporal- spatial behavior of regression. By increasing geographical latitude in Iran's western provinces, it is decreased both of total extreme rainfalls and portion of such rainfall out of total yearly rainfall. Total extreme rainfall trend shows a frequency in a domain with 16 mm in each year. The negative trend of total rainfall with the area of 74.72 percent consists of three quarters of Iran's west. The narrow strip of the west of Kurdistan and south-west of west Azerbaijan have the highest amount of positive trend which is meaningful in certainty level of 95 percent. The study of process showed the ratio of extreme rainfalls portion to total yearly rainfall, which is increasing about 60.7 percent of west area of this country extreme rainfalls in total yearly rainfall and the greatest part of this area is located in southern half of the studied area. The negative trend also is located in northern half and they have consisted of 39.29 percent of studied area of these, only in 29.81 percent of region, the trend ratio of extreme rainfalls to total yearly rainfalls are meaningful in certainty level of 95 percent.
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