Irrigation
A. Zahiri; Kh. Ghorbani; H. Feiz Abady; H. Sharifan
Abstract
IntroductionReservoirs are crucial for water supply to human societies, making their proper and planned management essential. Dams serve multiple purposes, including urban water supply, agricultural irrigation, flood control, and hydroelectric power generation. In order to properly manage and monitor ...
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IntroductionReservoirs are crucial for water supply to human societies, making their proper and planned management essential. Dams serve multiple purposes, including urban water supply, agricultural irrigation, flood control, and hydroelectric power generation. In order to properly manage and monitor the consumption of these important reserves, it is inevitable to know their capacities. Using water stage and the reservoir's initial volume-area-elevation curve, which is prepared with the hydrographic operations, is a common method for estimating the storage capacity of reservoirs at different water levels. Over time, the occurrence of numerous sedimentations, often due to factors such as floods, can alter the initial volume-area-elevation curve of a reservoir, requiring it to be updated. Hydrographic operations, using tools like eco-sounders, are conventional methods for updating this curve; however, these methods are both expensive and time-consuming. In recent years, various studies have focused on remote sensing techniques aimed at estimating the volume of water stored in reservoirs, using water levels to establish the surface area-elevation curve. The basis of these studies is the separation of water-land masks using spectral indices, the calculation of water levels, and the development of reservoir surface area-elevation curves through linear or polynomial relationships. However, the main limitation of these methods is the inaccuracy of linear or polynomial relationships in fitting the surface area-elevation curves at the beginning and end points of the water stage change interval, which correspond to the empty or full states of the reservoir. This inaccuracy arises due to factors such as drought or flood events. In this research, the limitation of linear and polynomial relationships in accurately predicting the points of the reservoir surface area-elevation curves, where observational data are unavailable due to non-occurrence, was addressed by using the Modified Strahler method to draw the hypsometric curve. This method allows for the calculation of the storage capacity of the reservoir between successive water levels and the determination of the final volume of water stored in the reservoir. By comparing the volumes of water stored at the present and initial reservoir capacities, the sedimentation rate and the useful life of the Negarestan Dam reservoir were estimated. Material and MethodsNegarestan Dam (Kabudval) is located on the Qarasu (Zarin Gol) river, 45 km east of Gorgan in the Golestan Province. This dam is used for purposes such as supplying urban water to Aliabad city and supplying water needed for the agricultural irrigation network of Qarasu. In this study, landsat8 satellite images were used to estimate the useful life of the Negarestan reservoir. The required images of the ROI were downloaded through the USGS database and pre-processed in Envi5.3 software. Using visible and infrared spectral bands, water indices NDWIMCFeeters, NDWIGao, MNDWI, AWEISh and TCWet were calculated to separate land-water masks. After evaluating the accuracy of the obtained water level results by comparing it with the initial volume-area-elevation curve of Negarestan reservoir, the MNDWI index was used as the most accurate index to calculate water levels. In this study, the modified Strahler method was used to obtain the hypsometric curve of the surface area-elevation of the reservoir, which has high accuracy in extrapolating the beginning and end points of the curve. By using the hypsometric curve, water levels were extracted for arbitrary water levels, and with the help of the prismoidal method, the volume between consecutive water levels was calculated. The sum of these volumes equaled the current storage capacity of the reservoir. To estimate the sedimentation rate of the Negarestan Dam reservoir, the current storage capacity was compared with the initial storage capacity in 2015. Based on this comparison, the useful life of the reservoir was accurately predicted. Results and DiscussionValidation results for calculating water surface areas using NDWIMCFeeters, NDWIGao, MNDWI, AWEISh and TCWet water indices showed that the MNDWI index with an average water surface areas calculation error equal to 5% is more accurate than other indices. Therefore, the MNDWI index was used in this study. Additionally, the comparison of the volume of water stored in the Negarestan reservoir with its initial storage capacity at the time of operation revealed that, over a period of 9 years, the storage capacity of the reservoir (at a water level of approximately 189.5 meters), which is close to the overflow crest level, had significantly decreased. It has decreased from about 24 to 20 million cubic meters, based on which the average annual sedimentation rate of the reservoir was estimated, to about 1.6%. The results showed that in a period of 9 years, the average level of the bathymetry of Negarestan reservoir has increased by 10 meters due to the accumulation of sediments, and the minimum level of the batymetry has reached from 160 to about 170 meters. According to the statistics of the International Commission on Large Reservoirs (ICOLD), the average annual sedimentation rate of the world's reservoirs is reported to be about 0.95%, and the results show that this amount in the Nagaristan Dam reservoir is almost 2 times the average rate. It is universal. According to the results obtained from this research and assuming constant climatic conditions, the useful life of the Nagarestan dam reservoir was estimated to be about 53 years from the beginning of 2024. ConclusionConsidering the increasing importance of water resources management, including dam reservoirs, this study employed a fast and cost-effective method based on remote sensing to calculate the volume of water stored in dam reservoirs and estimate their useful life. In addition to providing appropriate accuracy, this method was able to overcome the limitations of previous approaches in estimating the volume of accumulated sediment in the deeper parts of the reservoir. As a result, it offers a reliable tool for the effective management of water resources.
sajjad ebrahimzadeh; javad bazrafshan
Abstract
Drought can affects by reduced water resources, agricultural productivity, change in vegetation cover, and accelerate the desertification of areas. In order to drought monitoring, we need to quantify drought effects by using drought indices. These indices based on type of available data are divided ...
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Drought can affects by reduced water resources, agricultural productivity, change in vegetation cover, and accelerate the desertification of areas. In order to drought monitoring, we need to quantify drought effects by using drought indices. These indices based on type of available data are divided into two general categories of ground- and satellite- based indices. The aim of this study was to compare the capability of detection and classification of vegetation changes occurred due to the drought, between one ground-based drought index (Standardized Precipitation Index (SPI)) and four satellite drought indices derived from AVHRR-NOAA (normalized difference vegetation index (NDVI), temperature condition index (TCI), ratio vegetation index (RVI), standardized vegetation index (SVI) in the Kermanshah province. To do this, the change vector (CV) analysis was used as one of the important change detection algorithms. In this method, the change occurred in vegetation has been shown by two components, change magnitude and change direction. The results of implementation of the CVA on the maps of drought indices during the growing season (March to August) in selected years (two normal years, one wet year, and one drought year) showed the best response to the drought in the study years (except the wet year 1992), obtained by SVI. The lowest similarity was obtained between the SPI and TCI, for wet and normal years. Finally, the study suggests mostly the satellite indices based on the vegetation conditions, rather than the temperature indices, for assessing the effect of drought on vegetation cover.
A. Hezarjaribi; F. Nosrati Karizak; K. Abdollahnezhad
Abstract
Cation Exchange Capacity (CEC) is an important characteristic of soil in view point of nutrient and water holding capacity and contamination management. Measurement of CEC is difficult and time-consuming. Therefore, CEC estimation through other easily-measurable properties is desirable. The purpose ...
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Cation Exchange Capacity (CEC) is an important characteristic of soil in view point of nutrient and water holding capacity and contamination management. Measurement of CEC is difficult and time-consuming. Therefore, CEC estimation through other easily-measurable properties is desirable. The purpose of this research was to investigate CEC estimating using easily accessible parameters with Artificial Neural Network. In this study, the easily accessible parameters were sand, silt and clay contents, bulk density, particle density, organic matter (%OM), calcium carbonate equivalent (%CCE), pH, geometric mean diameter (dg) and geometric standard deviation of particle size (σg) in 69 points from a 1×2 km sampling grid. The results showed that Artificial Neural Network is a precise method to predict CEC that it can predict 82% of CEC variation. The most important influential factor on CEC was soil texture. The sensitivity analysis of the model developed by using of Artificial Neural Network represented that clay%, silt%, sand%, geometric mean diameter and geometric standard deviation of particle size, OM% and total porosity were the most sensitive parameters, respectively. The model with clay%, silt%, sand%, geometric mean diameter and geometric standard deviation of particle size as inputs data was selected as the base model to predict CEC at studied area.
Kh. Ghorbani
Abstract
So far several methods have been developed for mapping and interpolation of isohyets.one of the recently accepted methods is geographically weighting regression which is suitable for evaluation of spatial heterogeneity of dependent variable by using local regressions. In order to evaluate annually precipitation ...
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So far several methods have been developed for mapping and interpolation of isohyets.one of the recently accepted methods is geographically weighting regression which is suitable for evaluation of spatial heterogeneity of dependent variable by using local regressions. In order to evaluate annually precipitation spatial variation, this study was conducted in Gilan province which precipitation is distributed non-uniform due to different environmental conditions. The results of geographically weighting regression method were compared with another interpolation methods including global polynomial, local polynomial, inverse distance weighting (IDW), spiline, kriging and co-kriging and . In this study, average of 20 years annually precipitation data of 185 meteorological observations over Gilan Province and its neighboring stations was used for modeling of spatial distribution variations of mean annual precipitation by using other variables like elevation and position of points to the sea level. Cross validation technique was used to assessment accuracy of each interpolation methods. The result showed that geographically weighting regression method had minimum error with RMSE=147 and had significant difference with the kriging method which was in the second rank with RMSE=187. Finally the best method for mapping isohyets in Gilan province is geographically weighting regression method.
Kh. Ghorbani; A. Khalili; S.K. Alavinezhad; Gh. Nakhaezadeh
Abstract
Abstract
Precipitation is an important variable which is used in definition of drought. Based on precipitation amounts, some indices are devised for drought monitoring including, the Standardized Precipitation Index (SPI) and the Standard Index of Annual Precipitation (SIAP). Each of these Drought indices ...
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Abstract
Precipitation is an important variable which is used in definition of drought. Based on precipitation amounts, some indices are devised for drought monitoring including, the Standardized Precipitation Index (SPI) and the Standard Index of Annual Precipitation (SIAP). Each of these Drought indices are classified into some classes so that each class descripts a given severity of drought. Investigation of simultaneously occurrence situation for two drought indices can be an appropriate measure to evaluate the agreement of indices. Association Rules in DATA MINING is used to find rules and patterns in database. In this paper, two drought indices, SPI and SIAP, were computed at 11 meteorological stations belong to Ministry of Energy in Kermanshah province. Then, based on Association Rules, simultaneously occurrence situation of drought severity classes for both indices were determined in seasonally, half yearly and yearly time scales. Results showed that there is not any good agreement between most of drought category from these indices (less than 50 percent) and Shows different behavior of drought.
Keywords: Data Mining, Drought, Association Rules, Kermanshah
