Rahim kazemi; Bagher Ghermezcheshmeh; Reza Bayat
Abstract
IntroductionResearch on low flow is important, not only from a fundamental point of view but also in terms of sustainable water resource management. Optimum water resources management is one of the most crucial challenges of the 21st century, and due to population growth and climate change, water supply ...
Read More
IntroductionResearch on low flow is important, not only from a fundamental point of view but also in terms of sustainable water resource management. Optimum water resources management is one of the most crucial challenges of the 21st century, and due to population growth and climate change, water supply in the future will probably depend on sustainable water sources. The World Meteorological Organization (WMO) introduces low flow as a flow of rivers that continues during the dry period of the climate. Low flow is affected by climate changes, topography, geology, soil, and human activities. The geographical location and climatic conditions of Iran cause a lack of rainfall and water scarcity. Therefore, the recognition and analysis of sustainable water resources is the main component in the surface water resources management of Iran. This research has been done with the aim of investigating the characteristics of scientific publications regarding low flow research in Iran and the world and providing a perspective of the current situation and direction of future research.Materials and methodsThe data relating to low flow research in environmental; agricultural and biological sciences were retrieved from the Science direct database in the period 1999 to 2022 and SCImago Journal Rank indicator (SJR) from https://www.scimagojr.com as well as scientific information database of Jihad Daneshgahi (SID) and data from Iranian Research Institute for Information Science and Technology (IranDoc). A total of 22875 publications were obtained and with following aspects analysed intensively:(1) Distribution of international low flow-related publications (2) low flow-related publications from Iran; (2) distribution of subject categories; (3) core journals; (4) distribution of Iranian articles related to low flow; (5) frequency of low flow-related articles in Middle East and countries around Iran; (6) research trends. Bibliometric techniques, including citation analysis, five-year impact factor, JCR classification, coverage period and h-index were used to evaluate and interpretation of the results.Results and discussionThe results showed that the general trend of global scientific publications in low-flow research was with a positive slope and a growth rate of 1.52%. Also, the trend of international publications from Iran had a positive slope with a 0.94 coefficient of determination and a growth rate of 1.60%. The results of the subject classification of publications at the global level showed that most publications with 85.75% belong to research papers and the least amount to conference papers and editorials. International papers originating from Iran were of the first order of importance to publications with 90.04%, which has a larger share of the total papers than global papers. The results of the analysis of the most important keywords related to low flow showed that more than 72% of the titles of papers and theses were assigned to "base flow" and "flow duration curve (FDC) keywords ".ConclusionsThis research is a systematic bibliographic analysis of texts related to low flow research publication. By summarizing and analysing the growth curve of publications, it can be concluded that the total number of international publications related to low flow research corresponds to the theoretical fitting line and shows the proportionality of the potential of low flow research in the world with the actual amount. The general result of the analysis of international publications from Iran shows a high potential for low flow research in Iran and indicates a change in the attention of the Iranian scientific community to the publication of articles on the topic of low flow at the international level.
Bagher Ghermezcheshmeh; Rahim Kazemi; Mojtaba Nassaji
Abstract
Recognizing and analyzing the behavior of low flow indices is a prerequisite for water resource management and cropping pattern planning in arid and semi-arid regions. The purpose of this study was to analyze low flow in order to provide regional relations and identifying vulnerable areas. In this study, ...
Read More
Recognizing and analyzing the behavior of low flow indices is a prerequisite for water resource management and cropping pattern planning in arid and semi-arid regions. The purpose of this study was to analyze low flow in order to provide regional relations and identifying vulnerable areas. In this study, 26 stations with a common statistical period were selected by examining the daily stream flow data of hydrometric stations in Karkheh Basin. The daily stream flow time series was prepared for each year. Then, three, five, seven, 10, 15, 30 and 60 days low flow and average annual discharge were extracted for each year and each station. A new index was defined as the "low flow index". Then the trend detection of low flow index and its regional analysis was performed. Single and multiple regression between independent parameters of height, area, slope, distance to the outlet and related low flow indices extracted and results were analyzed. The accuracy of simulation was also estimated through the coefficient of determination. The results showed that by increasing the distance from the outlet of Karkheh basin upwards, the trend of the index was higher and in the branches, the obtained index was less than the downstream. This indicates that the branches were more vulnerable and should be focused on better water resources management in the branches.
Mehdi Ahmadi; Bagher Ghermezcheshmeh
Abstract
In the last decades, greenhouse gases in atmosphere have increased as a result of natural and human activities and thus, earth temperature has increased. Rising global temperature, in turn, leads to significant changes in related fields, especially water resources and agriculture. So, investigating and ...
Read More
In the last decades, greenhouse gases in atmosphere have increased as a result of natural and human activities and thus, earth temperature has increased. Rising global temperature, in turn, leads to significant changes in related fields, especially water resources and agriculture. So, investigating and modeling climate changes can be considered as a very important factor in water resources management planning. Different studies have been done in the field of climate change issues in the world, but, at the moment, AOGCM model is the most reliable tool to generate climate scenarios. It is necessary to downscale AOGCM data using different techniques in station scale and compare linear and nonlinear downscaling models. In liner method SDSM and in nonlinear method ANN programming were used in MATLAB. For investigating the amount of error, mean biomass monthly and annual and for extreme data, variance and for analyzing uncertainty Man-Witney test were used in 95 percent level. Results showed the amount of mean monthly errors are 0.75, 12, 11 and 7 mm in Ghaemshahr, Babolsar, Ghoran Talar and Bandpey in SDSM model and 3, 2, 26 and 4 mm in ANN model and the amount of mean annual errors are 9, 146, 141 and 87 mm in SDSM model and 45, 32, 321 and 48 mm in ANN model (increased or decreased), respectively. Examining the performance of variance showed that ANN model was somewhat better than SDSM model. Also, results of uncertainty for 12 months in Ghaemshar, Babolsar, Quran Talar and Bandpey stations showed 8, 3, 6 and 4 in SDSM model and 4, 2, 2 and 3 in ANN model, respectively. In general, this study showed that in studies on climate change effects on runoff, uncertainty, and when limited data are available, SDSM model should be used and when the aim is investigating the flood and its minimum and maximum estimation, it is better to use ANN model.
Marzieh Hajimohammadi; Abolfazl Azizian; Bagher Ghermezcheshmeh
Abstract
Knowledge of climate variabilities and their behavior in future periods and their effects in various fields has great importance especially in strategic and macro planning in water resources. This study aims to evaluate the effect of climate change on hydrological condition of the Kan Watershed. For ...
Read More
Knowledge of climate variabilities and their behavior in future periods and their effects in various fields has great importance especially in strategic and macro planning in water resources. This study aims to evaluate the effect of climate change on hydrological condition of the Kan Watershed. For this purpose the HadCM3model under the A2 emission scenario and also statistical downscaling model (SDSM) were applied for temperature and rain variables simulation. Then, SWAT model was used for monthly runoff simulation and SUFI-2 algorithm was used in SWAT-CUP software pack for calibrating and uncertainly analyzing. The performance of SDSM model was evaluated base on MBE and NRMSE parameters, the result indicated that temperature variable was simulated more accurate than of precipitation. The result of the predicting temperature in period (2011-2040) compare with the base period (1961-2001) showed the maximum and minimum temperature will increase by 1.3 and 0.8 °C, respectively. Also, the rainfall will decrease by 3-4 percent for all of selected stations. The most rainfall reduction will be for spring. While in some months of winter an increase of precipitation was predicted. The result of calibration and validation of SWAT model agreed well with the observed data, so that Nash-Sutcliff efficiency coefficient, as objective function, was 0.82 and 0.71, in calibration period (1983-1991) and validation period (1992-1996) respectively. Finally, results of runoff prediction showed an increase in winter and a decrease in other seasons based on climate scenarios. Overall, according to obtained results runoff will decrease by seven percent for future period.
Mojtaba Rezaei; Mehdi Vafakhah; Bagher Ghermezcheshmeh
Abstract
Flood is a sudden happening and quick and destructive event that causes death and financial sensible and unsensiable damages in different parts on the world and Iran annually. Control or decreasing these destructive impositions needs precise and accurate studies. So, recognition of the places with runoff ...
Read More
Flood is a sudden happening and quick and destructive event that causes death and financial sensible and unsensiable damages in different parts on the world and Iran annually. Control or decreasing these destructive impositions needs precise and accurate studies. So, recognition of the places with runoff generation potential is very important. In current study, two major aims of investigation of the application of distributed ModClark model in flood hydrograph simulation and determination of flood source area in distributed and sub-watershed condition were investigated in the Khanmirza watershed, Chaharmahal-e-Bakhtiari Province. For this reason, at first, inputs of model were extracted by ArcGIS 9.3 and then model was calibrated and validated. In next step, in order to determine flood source area for cell units and sub-watersheds, by applying "Unit Flood Response" method, at first, design rainfall with return periods of 25, 50, and 100 years at the Aloni station were extracted and then influence of each cell and sub-watershed on output hydrograph of the outlet watershed were obtained. The results of model based on comparison between equal-width discharges show that at validation step, the model was simulated the flood hydrograph with high precision with root mean square error, efficiency coefficient, and R2 of 1.53, 0.89, and 0.74, respectively. Also, according to the results of current study, based on the flood volume relative error, peak discharge, base time, and time to peak, ModClark model had lower error in predicting the flood volume and peak discharge. The final results showed that flood source area increases in sub-watersheds from downstream to upstream, while it doesn’t follow any distribution in cell units.
Bagher Ghermezcheshmeh; Aliakbar Rasuli; Majid Rezaei-Banafsheh; Alireza Massah; Alimohammad Khorshiddoost
Abstract
In the statistical downscaling methods which is based on the relationship between AOGCMs data and ground based climatic variables (such as rain and temperature), the future period of those variables are simulated. Since in the simulation, all effective parameters cannot be modeled, estimated values suffers ...
Read More
In the statistical downscaling methods which is based on the relationship between AOGCMs data and ground based climatic variables (such as rain and temperature), the future period of those variables are simulated. Since in the simulation, all effective parameters cannot be modeled, estimated values suffers from be uncertainty. The outputs of downscaling models are used as inputs to agriculture and water resources models; therefore, identifying the models inputs’ error or uncertainty is essential to realize the reliability of obtained results. In this research, an attempt is made to investigate the uncertainty of Artificial Neural Network (ANN) as a downscaling model in a case study in the northwest of Iran. For this purpose, precipitation, minimum and maximum temperature variables were used in the designed ANN model, and the NCEP data was employed for its calibration and validation. The HadCM3 was the selected AOGCM in this study. Observed daily time series were gathered at all stations in the study period and on the basis of bootstrap method the 99% confidence interval was calculated for all the variables. In the next step, the simulated (downscaled) mean and variance of the variables by the ANN model, compared to the calculated confidence interval. To compare the results, the criterion of the number of station-month was used. The results showed that the average maximum temperature at 14 station-months were within the confidence interval. The results of monthly analysis showed that the accuracy of ANN model in summer was low and its uncertainty is more than the other seasons. In the simulation of minimum temperature based on this criterion, 18 station-months were within the confidence interval. The accuracy of ANN to estimate the minimum temperature in summer was low with high uncertainty in almost all the stations. Moreover, in June and August in any of the stations estimated values were not within the confidence interval. Due to the high variability of rainfall in relation to temperature, confidence range was very high, and in some stations was more than 50% of average monthly precipitation. Because of the high confidence rang of precipitation, in 53 Stations-month cases were within the confidence interval.
Bagher Ghermezcheshmeh; Aliakbar Rasuli; Majid Rezaei Banafsheh; Alireza Massah Bovani; Alimohammad Khorshiddust
Abstract
Increasing Green House Gases (GHG) may change the climate in different areas. Investigation of parameters are difficult due to induced changes in climate parameters, such as precipitation and temperature. For predicting global climate change, different climate scenarios are defined, using AOGCM models. ...
Read More
Increasing Green House Gases (GHG) may change the climate in different areas. Investigation of parameters are difficult due to induced changes in climate parameters, such as precipitation and temperature. For predicting global climate change, different climate scenarios are defined, using AOGCM models. AOGCMs are able to simulate global atmospheric circulation patterns. However, the spatial resolutions of such models are coarse; for example HadCM3 has spatial resolutions of 3.75 and 2.5 in longitude and latitude, respectively. Therefore, to study climate change in a given area, the outputs of the used AOGCMs must be downscaled properly. For this reason, statistical and dynamical methods have been developed. Statistical methods establish a relationship between AOGCM outputs and climate parameters such as precipitation and temperature. For example, many statistical methods use multiple regressions to predict future climate parameters. However, the accuracy of downscaling procedure varies depending on the geographical position of the studied station in relative to the nearby AOGCM grids. In this research, the accuracy of SDSM was tested in different synoptic stations of northwest Iran. This area has a complex topography and climate due to intrusion of different rain bearing weather systems to the region. First of all, daily climate data (precipitation, maximum and minimum temperature) were collected and their time series created. HadCM3 data for the girds over the studied area was obtained and SDSM model was applied for each climate parameters of all synoptic stations in the region. Then, the difference between the SDSM outputs and observed parameters were evaluated for all the stations and the performance of the downscaled outputs were evaluated by comparing the mean and variance of the model outputs and those of the NCEP/NCAR for the present climate. The morpho-climatic parameters were derived for each station and their relations with the magnitude of the model error were evaluated. Results showed that the error in precipitation has significant relation with the distance to the grid center, whereas the error in maximum temperature is related to the difference between the elevation of a given station and the mean elevation of the HadCM3 grids. For example, in Urmia station, the error is the highest of 104 mm while in Saqez the error is the lowest of 9.4 mm. Also, the maximum temperature accuracy in stations with elevation near to mean elevation of the grid is higher. Pars Abad station with 32 m elevation and with high elevation difference with the grid mean elevation, showed 1.14 ºC of error and Tabriz station with less elevation difference to grid mean elevation, showed 0.0.08 ºC of error.
Reza Bayat; Bagher Ghermez Cheshmeh; Hoseingholi Refahi
Abstract
The rate of soil erosion and sediment yield depends on different factors. Vegetation cover is more variable factor effecting occurrence of soil erosion in comparison to the other factors in a given watershed. The main purpose of this study was to investigate the role of canopy cover resulting from land ...
Read More
The rate of soil erosion and sediment yield depends on different factors. Vegetation cover is more variable factor effecting occurrence of soil erosion in comparison to the other factors in a given watershed. The main purpose of this study was to investigate the role of canopy cover resulting from land management on sediment yield. Taleghan watershed was selected as a study area due to the availability of sufficient data. Necessary information was provided for MPSIAC model and was made in GIS environment and all needed calculations were done for preparing sediment yield map. The result of the comparison of the calculated and the estimated sediment yield indicated similarity of them (i.e. 98.3% similarity). For impact assessment of vegetation cover effect on sediment control, canopy cover was changed in a range of ±5 to 25% percent with 5% interval in both good and bad conditions. The effect of these changes was applied to bare soil percentage as well. The related thematic maps were prepared and sediment yield was determined. The result showed that sediment yield varied because of changing canopy cover. The increase and decrease of canopy cover by 25%, affected the sediment yield to change by -29.6% and 26.8% respectively.
Bahram Saghafian; Bagher Ghermezcheshmeh; Masoud Samiei; Reza Asheghi
Volume 1, Issue 3 , October 2009, , Pages 140-152
Abstract
Study of effective factors on sediment load of river basins has attracted more attention in watershed management. In spite of the short record length, sediment load measured in stations can be used in such studies. In this study, 20 sub-basins with measured sediment data was identified and some 48 physiographic, ...
Read More
Study of effective factors on sediment load of river basins has attracted more attention in watershed management. In spite of the short record length, sediment load measured in stations can be used in such studies. In this study, 20 sub-basins with measured sediment data was identified and some 48 physiographic, climatic, geologic, and vegetation index factors were extracted for the sub-basins using GIS. Surface curvature and satellite image-based vegetation indices were considered for the first time. Based on factor analysis, four factors namely total area, percent of convex area, percent area with northwest aspect and percent area with NDVI>0.4 were the main factors. Cluster analysis was applied to delineate homogeneous regions, which led to two regions. The results indicated that the factors mentioned above are the most influential factors on sediment load.
Bahram Saghafian; Saman Mohammadi; Baghe Ghermezchshme
Volume 1, Issue 1 , May 2009, , Pages 32-45
Abstract
Calculating low flow characteristics is very important for planning of water diversions; providing water for hydropower, water quality threshold in streams, water supply for cities and industries and estimate of sewerage discharge threshold. The objective of this research is analyzing low flow with different ...
Read More
Calculating low flow characteristics is very important for planning of water diversions; providing water for hydropower, water quality threshold in streams, water supply for cities and industries and estimate of sewerage discharge threshold. The objective of this research is analyzing low flow with different duration and return periods and extracting low flow regional models for locations without hydrometric station. This research was carried out in Gilan province where 35 hydrometric stations with long-term and reliable daily discharge data were selected. Low flows of durations 10, 30, 60, 90 and 180 days were estimated. Using frequency analysis, several statistical distributions were examined and log Parson Type 3 was found the best distribution for flow duration over 60 days and log normal best fitted flow durations shorter than 60 days. Then low flows of different return periods including 2, 5, 10, 25, 50, 100 and 200 year were calculated. Independent factors were identified using factor analysis that included basin area, mean annual rainfall, slope, average elevation, and drainage density. Cluster analysis divided the basins in two homogeneous regions. In each homogeneous region, multivariate regression through step-by-step method determined basin area, mean annual rainfall and slope as independent influential parameters in low flow regional models. Regional models were extracted for low flow with various durations and return periods.
