mahboobeh hajibigloo

Land use/cover changes (LU/LC) are considered as one of the most important issues in natural resource management, sustainable development and the environmental changes on a local, national, regional and global scale. Changing uses into each other and changing permissible uses into impermissible uses such as changing agricultural lands into residential regions or changing rangelands into eroded and low-yielding dry farming lands are always considered as importand issues in natural resources. Detection of the patterns of the land use changes and prediction of the changes in the future to carry out suitable planning for optimal utilization of uses in natural resource management reveal the need for modeling spatial and temporal changes of LU/LC. This study aims to assess the efficiency of the integrated model of Markov chain automatic cell (CA-Markov model) in simulation and prediction of spatial and temporal changes of Land use/Land cover (LU/LC) in Gorgan-rud river basin by applying three-dimensional Pentius-Melinus analysis in calibration of land use changes by using three assessment indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit as new indices in the assessment of the accuracy of CA-Markov model.

 In this research, the Earth observing sensor images of Landsat-5 Thematic Mapper (TM) and Landsat-8 Operational Land Imager (OLI) acquired from the U.S. geographical site dependent on the U.S. Geographical Survey (USGS) were used to predict land use changes by using the integrated model of Markov chain automatic cell in Gorgan-rud river basin. Seven land use classes were separated for Gorgan-rud river basin including forest land class with the use code 1, agricultural land class with the use code 2, rangeland class (a mixture of shrubbery,langeland,agriculture) with the use code 3, water bodies class with the use code 4, barren land class (barren, rangeland, agriculture) with the use code 5, residential and industrial region class with the use code 6, streambed class with the use code 7. In this study, object-oriented classification method and  Support Vector Machine (SVM) algorithm were used to classify Landsat 5 and 8 satellite images and extract the land use classes of Gorgan-rud river basin. Segmentation scale  in this algorithm on a 50 unit scale (SL 50) was selected to classify the satellite images of 1987, 2000, 2009 and 2017. The assessment of the accuracy of Support Vector Machine algorithm in the object-based classification of satellite images was done by representing overall accuracy, Kappa cefficient, user accuracy, producer accuracy, commission error and omission error for four study periods. To understand how the changes in the region were created during the period of the study three decades and which classes had the area expansion and which classes had the area decrease, changes in the limits of the classes were revealed and percent of the changes in each class were obtained by using the classification maps and IDRISI software. CA-Markov model predicts the changes of different groups of LU/LC units based on spatial neighbourhood concept, transition probability matrix. Preparing land suitability maps is necessary to predict land use changes so that spatial changes can be controlled for each use by probability rules via filtering suitability maps. Validation of Markov model was performed by using three-dimensional Pentius-Melinus analysis with three assessment indices of Figure of Merit, Quantity Disagreement and Allocation Disagreement.

 Support Vector Machine algorithm in the classification of the land use based on object-oriented showed that the highest rate of commission error and omission error were observed in rangelands and agricultural lands with 19.12 and 18.55 percent respectively in the land use map of the year 2009. The lowest accuracy of the producer with 71.49 percent belongs to the rangeland use class in the land use map of the year 2009 and the lowest use accuracy with 71.45 percent belongs to agricultural land use class in the land use map of the year 2017. In keeping with the obtained results, the highest positive change belongs to the agricultural land use increase and the highest negative changes belong to rangeland and forest land use decrease during the period of three decades from 1987 to 2017. The highest forest land decrease with 4.8 percent, the highest agricultural land increase with 5.3 percent, the highest rangeland decrease with 9 percent, the highest barren land increase with 4.6 percent and the highest residential and industrial land increase with 0.8 happened during the periods of 2000-2017, 1987-2017, 2009-2017, 2009-2017, and 1987-2017 respectively. After validating the predicted land use chnges in CA-Markov model, based on the analysis of the 5 existing states in three-dimensional Pentius-Melinus analysis, the CA-Markov model with the accurate prediction of simulation of 89.92 percent showed the high efficiency of CA-Markov model in simulation process. After the implementation of the CA-Markov model analysis on the obtained land use map from the classification of the satellite images, one transition probability matrix and one transitioned area matrix were created. In predictions made by using CA-Markov model in 2017 to 2033, the most changes relate to barren and forest land expansion decrease to 16966 and 6961 hectare respectively and in contrast to the use decrease, rangeland, residential and agricultural land expansion increase will be observed to 20397, 3913 and 3825 hectare respectively.

 Detecting land use changes by using LCM tool for the period of three decades 1987-2017 in Gorgan-rud river basin showed that the forest, agricultural and residential use has had significant changes in this region. The obtained results of the prediction of the land use changes during the coming eighteen years by using the integrated model of Markov chain automatic cell following the detected changes by LCM tool show that we will face extreme deforestation phenomenon in this area. Investigation of the obtained results from the implementation of the future use network model by using Markov transition estimator showed that the future use changes can be predicted based on the existing environmental conditions showing that the agriculture will extremely increase in Gorgan-rud river basin during the coming eighteen years. Thus we can protect water and soil resources with comprehensive and long-term management and prevent the degradation of these valuable resources. Three indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit in three-dimensional Pentius-Melinus analysis had an important role in representation of the accuracy rate and calibration of the land use classification and the land use prediction corresponding with the obtained results from the carried out studies concerning the accuracy assessment with indices of Quantity Disagreement, Allocation Disagreement and Figure of Merit. The results of the studied land use changes by using LCM tool and the integrated model of Markov chain automatic cell during the period of 1987 to 2035 show the degradation of more than 24309 hectare of the forest lands and agriculture increase in an area about 62421 hectare indicating human interfernces and deforestation we face in this area.

Land cover and land use are an important variable in natural land processes. Land use change in environmental protection programs and natural resource management plays an important role in the intensification of natural crises such as floods. The Gorganrood River basin in the Golestan province has historically experienced land use conversion. In this research was selected for land use classification using Landsat 8 OLI satellite images of the 25 June 2017. The goal of this study is to assess the accuracy of two approaches, pixel-based supervised classification and the object-oriented one base on quantity and allocation disagreement indexes. The accuracy assessment results indicated verified that for land use mapping the SVM algorithm using a 50 pixel segmentation in the object-based classification having a quantity disagreement of 2.03, an allocation disagreement of 4.58, and an overall accuracy of 92.65% and a kappa coefficient of 0.91 was more accurate than other algorithms in the object-based classification and other algorithms in the pixel-based classification. Based on this algorithm, the lowest of omission and commission error showed in forest lands and residential and industrial areas of 0.58% and 1.59% respectively. The highest of producer and user accuracy showed in forest lands and the water body of 99.44% and 99.41% respectively. The largest area of land use in the Gorganrood River basin is related to the Barren/Rangeland/Cropland class of 314110 ha. Finally, the SVM-SL50 algorithm in the object-based classification is suggested as an optimal classifier with a high accuracy for classification of land use classification maps in order to manage natural resources in Golestan province.

Estimation of suspended sediment basins of data and information is readily available but for basins without statistics, other methods require. The experimental method known as sediment rating curves or relationship "discharge sediment load" is provided. This study seeks to provide better separation of data and the preparation and suspended sediment curve refine hydrometric stations Maraveh Tape, Houtan, Qazanqayeh on the Atrak River in the Atrak watershed is located in Golestan province. To increase the accuracy of the classification scale monthly, quarterly, all samples, wet and dry periods and classifieds sediment discharge with frequency curve fitting techniques to fit a linear, linear fit by adjusting FAO, fitting combination (multi-line ), graphical approach (maximum concentration) and is fitted intermediate categories. For this purpose, statistical indicators were used to select the top. The results showed that sediment rating curve model by fitting a line to the correct application of FAO data Maraveh Tape station, method fitting a line dividing adjusted by applying FAO data on dry and wet period in the station Qazanqayeh and method Fitting a line segmentation classification data on discharges in Houtan Station Among the models tested with the least amount of mean square error and the predictability of the study is to estimate precipitation stations. Selected on the basis of the average amount of sediment suspended for watershed Atrak to Maraveh Tape station, Qazanqayeh and Houtan respectively 705, 683 and 781 Ton/Km2.day was estimated.

Flood risk management is a comprehensive approach which includes various evaluation indicators in river basins, it is an effective and sustainable but complex method. Flood risk assessment provides valuable information to assess vulnerability and exposure to hazard. For assessing the flood risk in river basins, several models such as RF, DSS, ANN, RFM, SVM, and GA are used to study the effective parameters of flood risk which have been used in different watersheds for the determination of the importance of these indicators. Determining a suitable model for use on the basin scale requires the consideration of the model suitability for the basin local conditions, data requirements, model complexity, accuracy and validity of the model, model hypothesis, spatial and temporal variation of the model, model components, and user target. In general, there is no model suitable for all conditions. Problems related to the inability to identify, the lack of uniqueness of the model, and the physical incomprehensibility of calibration parameters are some of the issues that are caused by the application of distributive and physical models. These issues also exist in complex conceptual models. To meet the growing needs of field administrators, it is necessary to have tools that can be fully used to investigate the spatial distribution of floods and the transport of river materials. As a general and practical rule, the development of distributed models with a relatively low physical complexity is recommended.

Riverine wetlands are waterbodies that are located at lowlands alongside rivers and are inundated during floods or recharged by aquifers. In order to identify and locate suitable sites for wetlands, the hydrological properties including watershed physiography, river discharge and its temporal (seasonal) variation as well as groundwater table fluctuation should be studied. Continued population growth and urgent need to conservation, rehabilitation and expansion of natural and valuable environments such as wetlands which support environmental services required by human should be strongly considered for sustainable development. The aim of this study is to assess utilization of biological capacity of rivers to restore the riverine wetlands in order to improve the biological and ecological conditions of the country’s wetlands.
In this research, the potential of Tajan River within the Sarakhs county, was identified considering presence of the streamside tree cover, specific ecotourism capacity, specific bird watching capacity, appropriate morphology of the river and inundation characteristics of floodplains. Creation of riverine wetlands alongside the Tajan River can play an important role in flood mitigation through storing of precipitation induced surface runoff. To this end, all factors affecting on delineation of proposed wetlands boundaries including geomorphological and geological, hydrologic, hydrodynamic, economic, social and developmental characteristics of the Tajan watershed were investigated. During field survey from the study area, on the basis of predominant condition from biodiversity, species, ecological, hydrologic and hydraulic point of view, four sites were determined as suitable for riverine wetlands within the watershed. Then, the cross-section of each of these sites have been surveyed and measured in detail in order to assess their morphological and hydraulic characteristics.
On the basis of flow Hydraulics and river morphology studies, two out of the four selected sites which are located within the Sarakhs’ Jahanbani Forest, due to low velocity of flow and high width of river in these river reaches were assessed as suitable for implementation of vegetation and wetlands restoration measures. Analysis of monthly discharge data of Polekhatoon- Kashafrud hygrometry station in recent years, indicates that the Tajan river flow is nil or negligible during warm months. Therefore, to supply water for the suggested wetlands, it should be relied upon floods and annual discharges from their upstream sub watersheds.
The annual discharge of the Tajan river at one of the suggested sites for the riverine wetlands within the Sarakhs’ Jahanbani Forest was estimated about125.9 MCM. This site due to its high potential for tourism and high diversity of birds can be exploited from various perspectives if tourism activities are developed in the region.

Flood hazard and disaster in Iran is one of the most frequent and damaging types of natural disaster. The Gorganroud watershed in the Golestan province recently has incurred severe damages resulted from flood events. Thus, this work was aimed to assess and map the flood susceptibility areas in the Gorganroud watershed to propose a comprehensive layout for flood monitoring and alarming stations. The Analytic Hierarchy Process (AHP) integrated with Weighted Linear Combination (WLC) approach within the GIS environment was employed to extract flood hazard zones. Results revealed that the cities Maraveh Tape, Bandar Gaz, Gorgan, Galikesh, Kordkooy, Minoodasht, Azadshahr, Kalale, Ramian, Gonbadkavoos, Aghala, Bandar Torkaman, and Gomishan with the ranks of 1 to 10, respectively populated the highest proportion of the "High" hazard class. Additionally, two percentages of the cities Aliabad, Azadshahr, Galikesh, and Gorgan were exposed to the hazard class of "Very High". According to results, about 32% of the villages and 26% of their population in the study area are exposed to the high and very high flood hazard zones. Furthermore, around 50% of the populations within high and very high hazard zones are 65 yr who are vulnerable to natural hazards. Based on the results of the flood hazard zoning, a layout of the monitoring plan for the flood warning system in the Gorganroud basin was proposed. This plan comprised 74 repeater stations and 215 alarming stations and fourteen control and monitoring centers were considered to collect and display data from all stations, as well.

The flood is second destructive incident in Iran. Greater understanding of different points of view Hydro meteo logy and watershed systems and analysis modeling of rainfall, runoff, determination of flood zone reference the hydrological and hydraulic conditions in the watershed, not only in terms of financial and legal, but also to predict and help flood warning systems are helpful ways. The purpose of this research provide the perfect solution to establish flood warning system using hydrological models and hydraulic models in the basin upstream is of the Germichai dam. In hydrological studies after restructuring the data, flood frequency analysis has been done up to a day at the station. To choose distribution for all stations using the Best-fit test based on L- moments of stations, three parameters were normally distributed. Applying the ratio of the maximum one-day flood into flood peak moment values for different return period flood peak moment of the river at the dam site Germichai was calculated. River flooding, especially in the current conditions of relatively high recurrence caused flooding along the river due to the steep terrain of the river is low. By examining the persistence of storms and their levels, early warning alarms were calculated for each of the stations. After the set time alarm events based on the frequency of Miane station, Flood warning system for flood areas within the flood level is designed to suit the threshold.

One of the natural disasters that annually causes a lot of damage to people and society, is flood.Timely flood warning system to ensure that final beneficiaries forecasting system, an appropriate warning is received at the appropriate time and the appropriate response to protect and minimize the damage done to their finances.In this regard, Gharanghoochay river catchment, causing massive floods in Mianeh city has been selected to implement a flood warning system. In this paper, hydrological modeling for the basin using HEC-HMS software was obtained and for the entire sub- basin, Muskingum dynamics equations have been used. The flood located in east Azerbaijan province, floods 2004.06.03 calibrate the model rainfall - runoff flood warning was used. Continuity check with showers and amount of time before the alarm warning signal for each station was calculated. After pre-determined according to abundance of caution showers Hashtrood station, flood warning systems for flooding areas within the mat-o-flood plan has been designed to suit the threshold. Under-taking damage riverbank villages and history of natural disasters in the region and assess the damages incurred to the Mianeh city, emergency action guidelines was prepared.