جستجوی مقالات مرتبط با کلیدواژه « weighting » در نشریات گروه « آب و خاک »

Climate change affects the hydrological cycle, available water resources and water demand by changing temperature and precipitation. In this regard, predicting temperature and precipitation changes by the models of the Sixth Climate Change Assessment Report due to more accuracy in their outputs can be helpful to plan and manage water resources in the future. In addition, identifying appropriate models among these climate models will pave the way for future research into the effects of climate change. In order to achieve these objectives, the present study was conducted.

At first Step, 138 and 113 meteorological stations were selected to study precipitation and temperature based on the appropriate location distribution in Iran, respectively. Then, monthly precipitation and temperature data of the historical period (1995-2014) were zoned in 30 basins of the country by Thiessen polygon method. In the next step, the results of simulation of precipitation and temperature of 10 CMIP6 models were extracted from website. Then, in order to equalize the time period of the base period of climate models with the observation period, the simulation results of models were used on a monthly basis.For each model, the position of network cells covering 30 basins of the country was determined and according to the percentage of the area of each cell in that basin, the average temperature and precipitation of GCM models were calculated at the level of each basin.Moreover, to increase the accuracy of the results of climatic models, the output of 10 climatic models in the historical period (2020-2039) were compared to observational data and were ranked based on KGE criteria. Then, the models were weighted based on the obtained rankings. In the next step, the forecasting results of climate models under three scenarios SSP126, SSP245 and SSP585 in the next period were obtained, after combining the output of the models based on their weight. Finally, the average changes in temperature and precipitation for the period of 2020-2039 compared to the historical period, were presented for all the basins.

The results of climate models ranking based on their ability in climate forecasting showed that the high score models are different depending on the variety climate in regions and their diversity in precipitation simulation is greater than temperature. So that, among 10 CMIP6 models, 6 high score models for simulation of precipitation and 4 high score models for temperature simulation in different parts of the country were determined. Furthermore, the results of climate forecasts indicate a change in precipitation from about +19 to -12% and an increase in temperature on average in the range of 0.1 to 0.6 degrees Celsius in different basins of the country over the next 20 years. Based on the average temperature changes of 20 next year, it can be seen that the northern regions of the country will experience more temperature increases than the southern regions of the country in the coming period. Increasing the temperature in the next period, while increasing evapotranspiration, will also increase water demand and consumption in different sectors. Therefore, it is necessary to develop policies to adapt to climate change in the agricultural, drinking and industrial sectors. Moreover, the western basins of the country will experience the greatest decrease in rainfall during this period. Due to the strategic location of some of these basins, both in terms of proximity to neighboring countries and in terms of dependence on agricultural activities to create food security and the lack of water resources, reduced rainfall can lead to several problems. Therefore, it is necessary to take measures to reduce water consumption, including changing consumption patterns and planning to compensate for economic losses and establish a balance between water supply and demand.

River flow is one of the most important components of the hydrological cycle, which depends on several climatic factors and its accurate estimation is used in various fields of water resources management. Therefore, in the present study, random forest (RF) and support vector machine (SVM) models were used to predict the monthly streamflow of the Maroon River in the period of 1981- 2017. One of the important steps in the application of artificial intelligence models is the definition of input patterns and determining the effective variables in the modeling process. The Shannon entropy method was used to select the most efficient inputs among precipitation, evaporation, and minimum, maximum, and average temperatures. The results showed that the total weight of precipitation and evaporation was more than 85 percent. In the next step, three different structures were developed for modeling. In the first case, climate-based patterns were defined that used meteorological data as input. In the second case, nonlinear periodicity was added to the climate-based patterns, and in the third case, the climate-based input data were decomposed using five mother wavelet functions, and W-RF and W-SVM hybrid models were created. The performance evaluation of the standalone RF and SVM models showed that by considering the periodic term, the accuracy is somewhat increased compared to the climate-based inputs, but the analysis of the data with wavelet theory significantly reduced the modeling error. In the meantime, the performance of the two models W-RF and W-SVM was very close to each other, but according to the violin plot, the W-SVM model is suggested as the most suitable option for predicting the monthly streamflow of the Maroon River.

Identifying land potentials based on environmental resources in each region such as climate, soil and topography is the first step in developing a sustainable agricultural system. To this end, in this study, the land suitability of Jiroft city for rainfed and irrigated Rapeseed cultivation was studied using Fuzzy-AHP model in GIS. Therefore, the weight and importance of information layers such as climate (temperature, precipitation and Growth Degree Days), topography (slope & altitude), soil (EC, pH and soil type) along with access to water resources were separately obtained for irrigated and rainfed Rapeseed cultivation based on analytical hierarchy process (AHP) method. Then the information layers were classified and fuzzified according to the environmental requirements of the Rapeseed crop in the GIS environment. Finally, the overlap of the layers was done by multiplying the weight obtained using the AHP method by the fuzzy layers. Finally, the land suitability map was separately prepared and classified for irrigated and rainfed Rapeseed cultivation. The results of land suitability assessment for irrigated Rapeseed cultivation showed that 14.7, 24.75, 21.61, 26.02 and 12.91% of the total area of the study area have located in very suitable, suitable, moderate suitability, low suitability and unsuitable classes, respectively. Also, the results of land suitability assessment for rainfed Rapeseed cultivation showed that 12.65, 18.46, 24.4, 22.82 and 21.76% of the total area of the study area have located in very suitable, suitable, moderate suitability, low suitability and unsuitable classes, respectively. In this classification, the factors of access to water resources for irrigated cultivation and rainfall for rainfed cultivation were the main factors limiting canola growth. These results can be useful for policy makers and farmers to design a suitable cultivation pattern.

Optimal water consumption and the supply & demand management would require a better focus on the economy of water resources. This study evaluates the effects of virtual water on the economic value of water resources using a mathematical model. The model has been applied to the Arak Plain, central Iran, for four strategic crops of wheat, barley, alfalfa and corn with three weighting methods of “cultivated area”, “water consumption volume”, and “income”. Meteorological and agricultural data, water consumption data as well as incomes and expenses of the water section in 2015-2016 were used in this research. Results show that the economic value of agricultural water resources in the Arak Plain increased for all the three weighting methods by considering the virtual water that led to substituting the products. Replacing alfalfa with sorghum would increase the economic value of water resources by 36.0%, 28.0%, and 40.5%, using the three weighting methods of “cultivated area”, “water consumption volume”, and “income”, respectively. Increasing the economic value of water resources is one of the main ways towards sustainable development and food security.

Water resources management depends on the precise assessment of water storage and access in each region, as well as environmental interactions of these resources. The man objective of this study was to delineate the potential zones of groundwater storage using FAHP. Mapping and assessment of it required maps of geomorphology, drainage, density, lineament density, slope and vegetation, which were initially prepared as the input layers in FAHP; the appropriate weights were attributed to them based on FAHP. Potential zones of ground water were classified into five classes of poor, average, good, very good and excellent. The number and density of available wells and springs in the study area dealt with the potential of the region for groundwater storage. So, ROC was used to assess the validation of results, considering spring points as signs of water resources. According to the results, classes of very good, good, average, weak, and very weak were ranked as the first to the last in terms of privilege order with an area of 37.7, 55, 40, 107, and 98.4 square kilometers, respectively.

In this paper, the effect of inter-basin water transfer on the economic value of water resources has been investigated through a case-study of the Kamal Saleh dam from the Sarband Basin to the central Arak Basin. For this purpose, by using the mathematical model developed in this research, the economic value of agricultural water was investigated without regard to transferred water resources and considering its impact for wheat, barley, alfalfa and corn. The input data for this study was included meteorological and agricultural information, volume of consumed water, wastewater volume, volume of water transfer from Kamal Saleh Dam, as well as information about costs and incomes of the crop year 2015-2016. The results showed that water transfer to Arak city would increase the economic value of agricultural water resources in this city. So, the economic value of water estimated from three methods including weighing the area under cultivation, the volume of water consumption and product income for increasing crop area of corn were increased 10, 7.5 and 9 percentage, respectively . In addition, by increasing crop area of alfalfa the economic value of water estimated from all three methods was increased 2%. Increasing the economic value of water resources can be one of the solutions to sustainable development and food security. However, inter-basin water transfer projects should be implemented with all technical and economic and environmental factors, with emphasis on social and political considerations.

The main purpose of this paper is to investigate the Shannon’s Entropy in surface water quality evaluation. To achieve this aim, the Shannon’s entropy and the World Health Organization methods have been used to assign weights of the water quality parameters. Monthly hydrometric data including; Ca, Mg, HCO3, Na, SO4, EC, pH, TDS were analyzed, form 1968-2015, which were measured and recorded at Ahwaz, Darkhovein, Gotvand and Molasani stations of Karun River and as well as; Quran-Talar and Koshtargah hydrometric statins of Babolrood River. The above mentioned rivers are flowing in south and north of IRAN, respectively. The result demonstrated the accuracy of the Shannon’s Entropy performance. As the result showed, the Shannon’s Entropy result gave an encouraging level of agreement with the WHO’s result. However, there were small discrepancies between these two results. Shannon’s entropy weights and the effective parameters of Karun River water quality at different hydrometric stations are Ahwaz pH=0.15, Darkhovein TDS=0.11, Gotvand EC=0.13 and Molasani pH=0.15, respectively, and in a same way for Babolrood River at stations; Quran-Talar pH=0.15 and Koshtargah TDS=0.14, respectively. According to the water quality index values of Karun River, its waters are classified in both excellent and good conditions. However, based on the mentioned values the Babolrood River is classified in excellent condition.