Hydrogeomorphology
Aghil Madadi; sayyad Asghari Saraskanrood; Hossein Hajatpourghaleroodkhany
Abstract
Monitoring of land use changes and destruction of vegetation as one of the dominant parameters in soil erosion is one of the important issues for assessment and control in natural resource management. The Hyrcanian forests of Gilan province, over the past years, have deteriorated due to neglect and have ...
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Monitoring of land use changes and destruction of vegetation as one of the dominant parameters in soil erosion is one of the important issues for assessment and control in natural resource management. The Hyrcanian forests of Gilan province, over the past years, have deteriorated due to neglect and have taken on a different face. So; The purpose of this research is to reveal the changes in land use and the destruction of forest cover and its effects on soil erosion in the watershed of Ghaleroodkhan Fuman. For this purpose, the changes in land use that took place between 1371 and 1402 were extracted using Landsat images and object-oriented classification techniques and were classified (agriculture, forest, pasture, water, and residential). In the next step, by identifying the effective factors in the erosion of the area and preparing the information layers of each criterion in GIS, the standardization of the layers was done using the fuzzy membership function, the weighting of the criteria using the CRITIC method and the final modeling was done using the MARCOS multi-criteria analysis method. The study of the changes in watershed use shows that the forest cover in 1992, with an area of 222.17 square kilometers, had the largest area among the land uses, and in 2023, its area decreased to 205.03 square kilometers. Also considering the results; Residential use with an increase of 27.17 square kilometers has changed the most during the 30 years of study. According to the erosion zoning map, respectively; The area of the floor with very high and high erosion potential has increased from 18.04 and 31.05 percent in 1992 to 22.52 and 32.34 percent in 2023. According to the obtained results, it is possible to reduce the forest cover and convert it into residential areas, agricultural lands, and pastures, as well; He considered the conversion of agricultural lands to residential areas and the increase of residential and agricultural use in the boundaries and riverbeds as the most important factors involved in increasing the soil erosion potential of the basin.
maryam bayatikhatibi
Abstract
1-IntroductionIn the drainage basins of arid and semi-arid areas where the ecosystem is not able to recover quickly, extreme care should be taken with land use. The hydrological effects of changes in land use are manifested in the form of changes in runoff depth, minimum flow, maximum flow, soil moisture, ...
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1-IntroductionIn the drainage basins of arid and semi-arid areas where the ecosystem is not able to recover quickly, extreme care should be taken with land use. The hydrological effects of changes in land use are manifested in the form of changes in runoff depth, minimum flow, maximum flow, soil moisture, and evapotranspiration. Increasing runoff production in a particular area, in addition to increasing the potential for flooding, has other effects. Due to the type of soil and the topographic and climatic characteristics, the hydrogeomorphological changes caused by human encroachment on slopes and land use changes have been significant in Ojan Chai area (from the sub-basins located on the eastern slopes of Sahand Mountain). Due to erosion in the slopes of Ojan Chay area, it seems that the changes in the amount of runoff are very significant due to land use changes in the area. The study area is one of the rangelands of the country and unfortunately, cultivation is done in an unprincipled manner in the slopes that are not suitable for cultivation. In the coming days, the turbulence of the slopes will be intensified, the amount of runoff will increase, and the number of destructive floods will increase. Often, the soil of the slopes is severely eroded by runoff due to the extreme cultivation in the rangelands.2-MethodologyTo simulate the effects of land use change in a region or watershed, there are many hydrological models, one of which is the L-THIA. This model is a way to evaluate the long-term hydrological effects in a basin by which relative changes that occurred due to a change of use in the runoff can be simulated.The above model is a good tool to help measure the potential effects of land use change on surface runoff. This model is based on the Curve Number (CN) method of the US Soil Conservation Organization (SCS). Expresses CN values range between 0-100, where high values are for urban uses and low values are for areas with high permeability, such as wetlands and pastures with high vegetation density. One of the benefits of L-THIA is that it does not require calibrating the model with real area data. Model calibration is performed automatically using various default CN combinations available in L-THIA GIS. In this paper, to use the L-THIA model, station precipitation was prepared and Landsat satellite images (TM and ETM sensors) and specialized L-THIA software and Arc Map were used. In addition, the probability of a pixel being placed in a particular class is calculated, then the probability of its placement in other classes is estimated and classified according to the maximum similarity (maximum probability) in one of the classes. The above process is expressed based on Equation 1. (Eq.1). Where P (X) is the probability of the presence of the class wᵢ in the image, / x) wᵢ P (probability of each pixel with the spectral characteristic x belonging to the class wᵢ and p (wᵢ / x) the probability of belonging of each pixel with the spectral characteristic x appearing in the image Class wᵢ and p (X) is the probability of the presence of a pixel with a spectral characteristic. The error matrix, kappa coefficient and overall accuracy are used to evaluate the classification accuracy of the images using Equation 2.(Eq. 2). Where OA is overall accuracy, N is the number of experimental pixels, Pii∑ is the sum of the elements of the original diameter of the error matrix.The kappa index is calculated from Equation 3.(Eq. 3). Where po correctly observed, pc shows the expected agreement. The error matrix shows the interference or conversion of uses to each other. Land use maps have been prepared for two periods (1988, 2018) as well as land use change maps. 3-Results and DiscussionIn this research, using THIA L- model, the type of soil was determined according to the available soil maps, prepared samples, soil reports of studies of other organizations and field experiences, soil hydrological group in the study area as the basis of the model used. In the prepared map, it is clear that the range of hydrological group A is observed in the southern and southwestern parts. The area related to hydrological group B is mostly scattered in the northern, northeastern, and central parts. Hydrological group C is spread around the flood plains in the central part of the basin, and finally hydrological group D, which is the largest part of the basin surrounding Ojan largely.According to the land use map of 1988, the largest area is related to rangeland use with an area of 544.6575181 square kilometers and the smallest area is related to water use equal to 0.189899975 square kilometers. According to the land use map of the year 2018, the largest area is related to agricultural use with an area of 510.5889519 square kilometers and the smallest area is related to road use equal to 0.5715 square kilometers. Examination of runoff depth maps for 1988 and 2018 shows that significant changes have been made in terms of quantity and location. Examining the height of runoffs and comparing two different periods in a specific use in relation to changing the rainfall parameter shows that a change in the rainfall parameter can significantly increase runoff in agricultural areas. This situation in relation to the range of the gardens is different, especially in recent years, showing a complex situation. In the case of pastures between 2018 and 1988, there is no significant difference in the height of runoff. Runoff depth in different land uses and rainfall shows that in areas with low rainfall, the highest runoff height is seen in lands under agricultural use. With increasing rainfall, pastures produce the most runoff and again with increasing rainfall, the highest runoff production is related to agricultural lands. In agricultural lands, the amount of runoff has increased in three decades and decreased in pastures.4-ConclusionThe results show that over the past three decades, many rangelands have been cultivated. The area of agricultural lands has increased from 368.4917957 square kilometers in 1988 to 510.5889519 square kilometers in 2018. The results of calculations in such lands show that the height and volume of runoff has doubled from 1988 to 2018. In fact, increasing the area of cultivated land and land use changes from pasture to agricultural land has increased the amount of runoff. The results of studies on soils located on slopes show that the hydrological group of soils in this area is impermeable and with maximum daily rainfall that has increased in recent years, they can produce high-volume deep surface runoff in a short time. These slopes were considered pastures in 1988 (about 90 square kilometers of pastures have been converted into agricultural land). This has caused row crops to produce more runoff in these areas. The results of the studies with the model used and the result of this research in the area of Ojan Chay basin show that the main reason for the increase in height and volume of runoff was land use changes.Keywords: Land use changes, Runoff, Erosion, Flood, L-THIA model, Ojan Chay basin5-ReferencesKhaligi, B., Mahdavi, M., Sagafiyan, B. (2005). Investigating the effect of land use change on flooding using NRCS model, Natural Resources of Iran,vol,58,No,4,p 41-58.Razvizadeh, S., Salajegehe, A., Khaligi, S., Gafari, M. (2014). Investigating the effect of land use change on flooding using, HEC-HMS model (case study: Taleghan watershed) Journal of Rangeland and Watershed Management, Vol. 66, No.3, pp 373-386.Sadati, H, Golami, S., Sharifi, F., Ayobzadeh, A. (2008). Investigating the effect of land use change on runoff, Journal of Rangeland and Watershed Management, Vol. 4. No. 3, pp 301-315.-Hentati, A., Akira Kawamura, Hideo Amaguchi, Yoshihiko Iseri. (2010).Evaluation of sedimentation vulnerability at small hillside reservoirs in the semi-arid region of Tunisia using the Self-Organizing Map, Geomorphology, No. 122, 56–64-Kakembo,V., W.W. Xanga, K. Rowntree.(2009).Topographic thresholds in gully development on the hillslopes of communal areas in Ngqushwa Local Municipality, Eastern Cape, South Africa, Geomorphology, No. 110.188–194-Khairulmaini Osman Salleh and Fatemeh Mousazadeh.(2011).Gully erosion in semiarid regions,Procedia Social and Behavioral Sciences No.19, 651–661.Vahidi, Mohammadjavad; Rasoul Mirabbasi Najafabadi; Mohsen Ahmadi. (2020). Analysis and ranking of soil erosion prevention methods using multi-criteria decision-making methods in rural areas of Darmian County, South Khorasan, Hydrogeomorphology, Vol. 6, No, 23.209-233.Yamani, Mojtaba, Hamid Ganjaeian; Lila Garoso; Mahnaz Javedan. (2020). Identification of susceptible areas for the development of agricultural lands based on parameters Hydro geomorphology (Case study: Sanandaj city), Hydrogeomorphology, Vol. 6, No, 23.1-20.
Hafez Mirzapour; Ali Haghizadeh; Naser Tahmasebipour; Hossein Zeinivand
Volume 6, Issue 20 , December 2019, , Pages 79-99
Abstract
1- IntroductionAccurate detection of changes land use in Accurate and timely, Basis for a better understanding of the relationships and interactions of human and natural phenomena to manage and provides better use of resources. Principal land use management requires accurate and timely information in ...
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1- IntroductionAccurate detection of changes land use in Accurate and timely, Basis for a better understanding of the relationships and interactions of human and natural phenomena to manage and provides better use of resources. Principal land use management requires accurate and timely information in the form of a map. Regarding the widespread and unsustainable changes in land use, including the destruction of natural resources in recent years, Investigating how landslide changes during time periods are essential for satellite imagery. Since conservation of natural resources requires monitoring and continuous monitoring of an area, Land-use change models are now used to identify and predict land-change trends and land degradation one of the most widely used models in predicting land use change is the Auto-Markov cell model. the aim of present study is to monitor land use changes in the past years and predict changes in the coming years in Badavar-Nurabad watershed in the Lorestan province with an area of 71600 hectares. 2- MethodologyThe Markov chain method analyzes a pair of land cover images and outputs a transition probability matrix, a transition area matrix, and a set of conditional probability images. The transition probability matrix shows the probability that one land-use class will change to the others . The transition area matrix tells the number of pixels that are expected to change from one class to the others over the specified period (Ahadnejad 2010). Automatic cells are models in which adjacent and continuous cells, such as cells that may include a quadrilateral network, change their state or attributes through simple application of simple rules. CA models can be based on cells that are defined in several dimensions. The rules for changing the state of a cell from one mode to another can be either a combination of growth or decrease, such as a change to a developed cell or without development. This change is the source of the change that occurs in the adjacent cell. Neighborhood usually occurs in adjacent cells or in cells that are close together(Ghorbani et al, 2013). In order to detect land use changes in the studied area, TM , ETM+ and OLI satellite images of Landsat were used during three time periods of 1991, 2004 and 2016. After applying geometric and atmospheric corrections to images, the land use map for each year was prepared using the maximum probability method. The Kappa coefficient for the classified images of 1991, 2004 and 2016was 0.81, 0.85 And 0.90 obtained. Then, to model land use changes using the Auto-Markov cell model for 2028 horizons, First, in the Idrisi Selva software using Markov chain, the map was selected as input from the years 1991 and 2004, the 12-year prediction of the changes was considered by 2016 to determine the likelihood of a change in application. Then, using the CA-Markov method, the data from the Markov chain and the map of 2016 were used as input data for the automated-Markov cell method. 3- ResultsAssessment of the match between simulated and actual map of 2016 with 0.97 kappa index showed that this model is an appropriate model for simulating of land use change. The results from monitoring satellite imagery that in 1991 to 2016, the extent of residential areas, land is Dry farming, garden and irrigated farming land added in front of vast pastures, shrubbery and other is reduced. After verifying the model's accuracy, a 2028 map was prepared to predict the changes over the coming years. Well as the results show that the vast pastures of the forecast is reduced in the amount of 659.89hectares and 395.47 hectares will be added to the extent of irrigated farming. 4- Discussion and conclusionThe results of the Auto-Markov cell model showed that if the current trend continues, the size of the ranges will decrease sharply. Comparison of simulated map of 2016 by model and actual map with Kappa index showed that Auto-Markov cell model is a suitable model for predicting land use change and can accurately assess the future status of land use and vegetation to predict. Therefore, it is suggested protective measures and make appropriate management decisions to control non-normative changes continue to apply more than ever.
