جستجوی مقالات مرتبط با کلیدواژه "زنجیره مارکوف" در نشریات گروه "آب و خاک"

Land use changes are one of the main factors in the amount of surface runoff changes in watersheds. Therefore, it is necessary to investigate it to reduce the damages (human and financial) caused by floods and to modify watershed management. The watershed of Nahre Azam is located in the north of Shiraz city and a lot of loss of life and money to the residents of Shiraz due to floods has occurred in previous years. The present research was conducted to investigate the relationship between land use change and runoff in the Nahre Azam watershed in Shiraz using the SWAT model in the period of 2004-2020. The model was calibrated using data from 2004 to 2014 and validated for 2015 to 2020. These images were classified into 6 main land uses using the supervised classification method after performing necessary pre-processing, and a land use map was prepared for 2040 using the Markov chain method. Then, the effect of the land use change in 2003 and 2040 on the amount of simulated runoff was evaluated with the recalibrated model. The calibration results of Nahre Azam watershed for the values of statistical parameters in the calibration step for the coefficient of determination, P-Facor and R-Facor are 0.77, 0.72, and 2.43, respectively, and for the validation step we obtained 0.69, 0.65, and 2.3 respectively. The analysis of the land use map showed that the main land use change in the region related to the conversion of pastures to agricultural land and urban land, which caused a decrease in pastures. Also, the results of the model simulation using the land use maps of 2003 and 2040 indicated that the amount of runoff decreased. The results revealed that if all the uncertainties are minimized, the calibrated SWAT model can produce acceptable hydrological simulation results for the user, which is useful for water resource and environmental managers and politicians as well as city managers of Shiraz.

Prediction of land use changes in explaining the interactions between ecosystems and human activities is important for helping decision makers. A Land use map is considered an information resource in natural resource management. Optimal resource management needs to be investigated, as recognition of changes and resource degradation in the past, and proper and principled planning in order to control and control possible future degradation. The purpose of this study was to evaluate the performance of the Markov chain model (CA Markov) in determining and predicting land use changes for the future in the Ghaleh Jogh area located in the city of Torbat-e-Heydarieh.

Satellite imagery, remote sensing, and the Markov chain model were used for modeling and detecting land use change. The study area of the Ghaleh Jogh Watershed is from Torbat-e-Heydarieh province. A topographic map with a scale of 1: 25,000 and OLI, ETM +, and Landsat MSS images of 1987, 2002, and 2015 were used to prepare land use maps and the changes process. Satellite imagery with first order polynomial equation was corrected by the method of closest neighboring geometric correction and corrected using linear and histogram explanations. For the categorization of images, the controlled classification method and the maximum probability algorithm with acceptable accuracy are used. A Land use map was prepared in 4 ranges of pasture land, Bayer lands, crops, and gardens. In this research, using the CA Markov model, the 2015 forecast map was compared with the monitored map. The percentage of land use, garden, rangeland, lawn, and arable land use varies from 5, 54, 24, and 17 percent in 1987 to 7, 4, 7, 36, 51, and 7.6 percent in 2015, and the user's map Lands for 2025 and 2040 are foreseen. Model calibration was used for predicting the 2015 model.

The results showed that according to the Kappa coefficient, the prepared map has high accuracy. Based on the results of the detection and simulation of the pasture and cultivating lands, they are among the most unstable classes. Garden lands due to more attention and economic significance, there have not been any significant changes in their extent. In general, the largest changes occurred in pasture and agricultural land. The corrective work done in the region has reduced the area of arable land and added to the rangelands.

One of the Principles of water resources management is the optimal use of the reservoirs as the main sources of surface water, and this issue has a special importance in the science of water engineering. In this research, the new K-means clustering method to discretize reservoir inflow has been presented for the Stochastic Dynamic Programming(SDP). In addition, the Moran's method is used to discretize the reservoir storage. By the programming in the Python environment, the historical reservoir inflow in each season is classified to different clusters and obtained the best inflow cluster for each season. The effects of this clustering is also considering in the SDP of Jamishan reservoir. In general, the change in inflow classification will lead to a fundamental change in the transition probability matrix. Thus, the use of K-means method for the reservoir inflow discretization, due to the possibility of optimizing the number of clusters in each time period, can be very useful for the SDP. finally, it is strongly recommended to use K-means method to discretize reservoir inflow for reservoir operation by SDP.

K-means is an object-based algorithm that selects representative clusters from the data itself rather than averaging them. K-means of a cluster is the most central element of a cluster. The purpose of this method is to reduce sensitivity to large values in the data set. Each cluster is introduced with one of the data close to the centers. According to the number of data categories (k), the value of the least squares function is minimized and the data are categorized in the best way. In addition, the Moran's method is used to discretize the reservoir storage. In this method, the upper and lower limit of the range of changes and the upper limit of each category are used as indicators of discretization of the reservoir volume. The study area includes Jamishan reservoir sub-basin with an area of 527.07 km2 located in the southwest of Sanghar city near the Pirsalman hydrometric station. This watershed is the part of Kermanshah province which is between 32-34° to 34-53° North latitude and 47-22° to 47-52° East longitude. The annual average of rainfall, evaporation and temperature are 441 mm, 1534 mm and 10 degrees Celsius, respectively.

Evaluating the performance of the K-means model in 4 different seasons, showed that among the 19 considered clusters, the best result in seasonalclassification is obtained by the 5 inflow clusters according to the performance rate in fall, winter, spring and summer seasons - 142.57, -176.90,-475.36 and -2.10, respectively. The results of the First-order Markov chain, the possible values are given in Table 1 for 4 seasons in 5 clusters,and in this table, the specified numbers indicate the probability of moving each cluster for each season.In thefollowing, using the backward recursive function, the calculations are continued until reaching the stationary state condition. Finally, the value of l* was obtained for all 4 periods and different combinations of k and i as Fig.1. The results of Steady-state condition showed that l* happened mostly in spring up to 5 clusters of the reservoir storage and the least happened in summer with onecluster. Then, the calculations of the reservoir release probability in each period for each class of inflow and storage have been made. The highest value has occurred for reservoir storage class 4 in the autumn, winter, and spring seasons but in the summer season, due to less inflow and high water demand, it has happened in reservoir storage class 5.

In this research, the Stochastic Dynamic Programming (SDP) of the Jamishan dam reservoir is discussed using the K-means method in classifying the inflow discharge seasonally for the 41 years of historical data. Moran's method is also used to classify the storage volume of the reservoir into 7 classes. To calculate the transition probability matrix during the first-order Markov chain process, it is necessary to have the flow class in each period. For this purpose, the k-means method is used. The reservoir inflow in each season is classified from 2 to 20 classes by programming in the Python environment and especially with the Scikit-learn library. Evaluating the performance of the K-means model in 4 different seasons, showed that among the 19 considered clusters, the best result in seasonal classification is obtained by the 5 inflow clusters. Changing the number of inflow clusters leads to changes in the transition probability matrix and this process would change the results of reservoir operation. It can be said that the use of this flow classification method can have a significant impact on the management and optimization of dam reservoir performance. In general, the use of new classification methods such as the K-means method in the discretization of reservoir inflow for the reservoir stochastic dynamic programming can be very beneficial and effective.

Reducing the water level of Urmia Lake and its effects on the environment around the lake has been one of the important national and international issues and challenges in the last two decades. In accord with the studies, one of the critical factors affecting this declining trend has been the rise in harvest, especially for agriculture. Accordingly, the purpose of this study is to simulate the future status of water resources in the Urmia Lake basin, influenced by the area of agricultural land uses.

  For this purpose, Landsat satellite image data for the period 2000 to 2020 are firstly classified using the SVM algorithm in ENVI5.3 software and the classification accuracy is analyzed using the Kappa Coefficient algorithm.In the following, the statistics and information related to the change of cultivation pattern (from arable to garden) and water sources discharging Lake Urmia are calculated. In the next step, the simulation of land use changes for 2030 and 2040 is done using two LCM and CA-MARKOV methods. And finally, after determining the amount of changes in each land use, the amount of water required for agricultural affairs in the catchment is simulated using NETWAT model.

The results show that the area of two uses, irrigated agriculture and garden will increase from 1450 and 395 square kilometers in 2000 to more than 3600 and 1650 square kilometers in 2040, respectively, This will increase the amount of water Needed or agriculture from 1,500 million cubic meters in 2000 to more than 4,100 million cubic meters in 2040.

From 2000 to 2020, water consumption in irrigated agriculture has increased by 1253.05 Km2; which according to Markov's prediction method, this amount will reach 2049.54 Km2 in 2040 that raises the amount of water consumption by 1 billion and 473 million cubic meters. The gardens land use has increased by 688.02 Km2 from 2000 to 2020, and according to Markov's prediction method, this amount will reach 1276.14 Km2 in 2040, which raises the amount of water consumption by 703 million cubic meters. From 2000 to 2020, 367.06 Km2 has been added to the drayland farming, which according to the prediction of Markov method, this amount will reach 531 Km2 in 2040, which soars the amount of water consumption by 253 MCM.

Recorded daily weather data are used as climate input in a number of models that continuously simulate the natural resource systems (Yu, 2003). However, it is sometimes difficult to obtain the required data, and also to process them simply because they are lacking or unavailable. Weather generators have been accordingly developed to produce synthetic weather sequences capturing the essential features of observed weather data needed for running the models. In effect, weather generators are widely used in hydrological, ecological, and crop-yield modeling frameworks. CLIGEN (CLImate GENerator), which was first developed as a component of the Water Erosion Prediction Project (WEPP) (Nicks et al., 1995), is a stochastic weather generator that generates long sequences of daily precipitation (e.g., the pattern of each rainfall event including its duration, time to peak, and intensity), maximum and minimum daily temperature, dew point temperature, solar radiation, and wind speed and direction. CLIGEN has been currently used as a general weather generator beyond its initial intention in many locations around the world. This research was thus conducted to examine the efficiency of CLIGEN in generating the weather data in Sanganeh station in the northeast of Iran.

Knowledge about drought, is the most important prerequisite to reduce risk of drought damage. This study was done in Fars Province, using monthly data of 10 synoptic stations at period of 1990 to 2014. RDI index and Markov chain model used for drought monitoring and forecasting, respectively. Drought severity and expected status were analyzed. Results indicated that, the possibility of transmission to normal class was higher than possibility of transmission to other classes. The probability of drought balances, in monitoring stage, obtained 3.37, 8.88, 8.33, 63.2, 15.68, 0.6 and 0.00% for extremely drought, very drought, moderately drought, normal, moderately wet, very wet and extremely wet periods, respectively. The probability of drought balances, in forecasting stage, obtained 3.95, 10.46, 10.68, 55.51, 18.37, 1.00 and 0.00% for extremely drought, very drought, moderately drought, normal, moderately wet, very wet and extremely wet periods, respectively. Generally in Fars province, in forecasting stage, the frequency of occurrence of drought classes were higher than frequency of occurrence of wet classes that indicate the continuous persistence of drought in this province. Maximum and minimum frequency of drought occurrence classes obtained 31.5% and 20% at Izadkhast and Shiraz synoptic stations, respectively.

Hydrological drought is defined as a significant decrease in the availability of water in all its forms appearing in the land phase of the hydrological cycle. These forms are reflected in various hydrological variables such as stream flow (including snowmelt and spring flow), lake and reservoir level, and groundwater level. A variety of indices and methods for characterizing hydrological drought have been devised. In this study, an index called Streamflow Drought Index (SDI) have been used for characterizing the severity of hydrological droughts. Investigation of the forecasting possibility of hydrological droughts has been carried out by two approaches. First, when the appropriate historical data are available, Markov chain method have been used. The main output of the methodology is the matrix of state transition frequency for a selected pair of reference periods under the hypothesis of a Markov chain for the underlying state process. In other words, the output is a single value of drought state while the probabilities of remaining in the same state or passing to other states in the next reference period are withdrawn from tables which have been obtained off-line. Since, in general, streamflow data are difficult to obtain in real-time, the possibility of using a meteorological drought index was investigated. More specifically, a linear function of SPI was found to predict SDI to an accuracy level which is sufficient for characterizing drought severity. This involves prior calibration of a simple regression equation with modified SPI as the explanatory variable and SDI as the explained variable. The methodology is validated using reliable data from the Nesa and Fashkoh rivers basin in the southwestern margin of Kavir-e Lut (Iran). The results indicate that calibration of rainfall and streamflow relations provides a good opportunity to forecasting of hydrological droughts states in the lack of river flow data. A key consideration is that in this basin, a high degree of successful prediction is observed for the wet period of October to March. In addition, due to lack of storage snow in this basin, which can compensate the deficit rainfall in dry period, drought states predicting for other periods is also possible by using Markov chain methodology.

In order to assess the uncertainty of climate change and its effect on the discharge into the Jamishan dam located in the northwestern province of Kermanshah changes in temperature and precipitation parameters in the periods 2020-2039 and 2040-2059 were calculated using a combination of weighted average seven climate model output under three emission scenarios A1B, B1 and A2 respectively, The daily rainfall and temperature forecasts for future periods under each climate scenario was entered to rainfall-runoff calibrated and validated model ‘IHACRES’ and the daily runoff of future periods under each scenario was predicted. In order to determine periods of high-water and water shortage in the status quo and future periods, using Markov chain transition probability matrix, the Frequency months of wet, normal and drought were calculated. The results showed that in the period 2020-2039, annual rainfall decreases and temperature increases. In the period 2040-2059 the annual temperature changes are most severe between -0.66 and °C. A1B and B1 scenarios show annual rainfall and runoff reduction and increase in the A2 scenario.

Land use changes investigation requires the use of rapid methods and new techniques, respectively. The use of remote sensing and GIS and integrating them with accurate information and field data prepared multipurpose decision. In this study, used images of Landsat 2, sensor MSS June 1975, Landsat 7, ETM 2002, and Landsat 8 sensor OLI June 2013, from USGS site for developing maps and survey land use changes over the period of 38 years in ENVI and ERDAS software. The Landsat 5, TM sensor image, June 1991, used to prepare land use maps and compared with 2002 and 2013, to predict land use change in 2024. Results related to land use changes in the past 38 years show a reduction of 12% (9/7060 hectare) orchard area and an increase of 7% and 5% of residential areas and agricultural lands, respectively. Also the predicted land use changes in 2024 represents a decrease of 2% compared to the current state of the orchard, while the almost constant level of agricultural land, and residential area will increase 2 percent.

Drought is a climatic anomaly that associates with a significant decrease (lack) of precipitation and water resources availability, which spreads on vast temporal and spatial scales, and significantly affects various aspects of life and environment. One of the most common methods of drought assessing and monitoring is calculating drought indices (DIs). Drought areal and temporal extent and its severity are determined by these indices. In this study, an aggregate drought index (Hydro-Meteorological) has been developed for the assessment of hydrological and meteorological droughts in Sarbaz river basin located in southeastern of Iran. The Aggregate Drought Index (ADI) comprehensively considers all physical forms of drought (meteorological, hydrological, and agricultural) through selection of variables that are related to each drought type. In this case, monthly values of Stream flow Drought Index (SDI) and Standardized Precipitation Index (SPI) indicators were used for four similar reference periods with principle component analysis and aggregate hydro-meteorological index was defined based on its first component. The study time span was set between 1981-82 to 2010-11, which begins of October in Iran. Results based on the aggregate drought index (ADI) revealed that a long period of hydro-meteorological drought occurred from 1999-2000 to 2005/06 in southeast of Iran, in which, 2003/04 water year has been extremely a drought year. The ADI methodology provides a clear, objective approach for describing the intensity of drought. This index is appropriately able to represent the behavior of Hydro-Meteorological droughts and recommended as an integrated index for assessing and monitoring of regional droughts. Finally, different states of hydro-meteorological drought have been extracted based on conventional regional thresholds, and have been modeled by Markov chain. This made the estimation of drought state transition frequency possible, and made the prediction of next drought state time more real. State transition frequency matrices, are the main instruments for predicting drought states in real time. Results of validation tests and conforming the predicted results with real data indicate that predicting hydrological drought state transitions in the study area using Markov chain method is valid.

In the present study, monitoring, prediction and zoning of drought in Khuzestan province were investigated using time series provided by Standard Precipitation Index (SPI) and Markov Chain. The obtained results showed that in all stations, based on the SPI index and during a six-month period, the almost normal condition has the highest frequency and is existent in between 33 to 42 percent of all cases. Probability of passing from a specific condition to the same condition in sequential periods is extremely high, and in turn, the probability of passing from the dry condition to wet condition and the other way around is quite low. In average, the possibility of reaching a balance between dry, normal and wet periods in stations of the region are 30.8, 36.5 and 32.7 percent respectively. The average continuity of drought periods predicted on a 6-month scale varied from a minimum of 3 months in Paye- pole station to a maximum of 4.6 months in Shohadaye-Behbahan Dam station. It is predicted that in the next ten years, Khuzestan province will be dealing with 35.5 months of drought.

Agricultural scientists have developed considerable interest in modeling and generation of rainfall as new ways of analyzing rainfall data and assessing its impact on agriculture. A combination of Markov chain and gamma distribution function is recognized as a simple approach and is demonstrated to be effective in generating daily rainfall data for many environments. Thus the availability of the weather data limits the applicability of the simulation method. When these model parameters are evaluated over time and at different places, however, certain general characteristics are revealed. First, the transitional probability of a wet day followed by a wet day tends to be greater but parallel to the transitional probability of a dry day followed by a wet day. This phenomenon leads to a linear relationship of the transitional probabilities to the fraction of wet days per month. Second, the beta parameter, which is used to describe the amount of rainfall, is related to the amount of rain per wet day owing to the positive skew ness of the rainfall distribution. Based on these relationships, a simple method is introduced, by which model parameters can be estimated from monthly summaries instead of from daily values. The suggested method, therefore, provides a convenient vehicle for applying weather simulation models to areas in which its use had been impossible because of the unavailability of long series of daily weather data.

In the present study severity, duration and frequency of droughts were analyzed using monthly rain data in Lorestan province. At first, the rainfall data was normalized and then rain time series Z score was created. Dry months and periods were separated by applying run theory and by determining critical. Threshold Then, the severity, duration and frequency (SDF) curves of the droughts were plotted. By applying Markov chain, transition probability matrix and region’s static probability matrix were extracted and the number of dry years and duration of the droughts in the region was forcasted for the next 15 years.