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Showing 2 results for Guilan Province

Nima Sohrabnia, Dr Bohlol Alijani, Dr Mehry Akbari,
Volume 7, Issue 2 (8-2020)

Modeling the discharge of rivers in selected watersheds of Guilan province during climate change
   In this essay, we investigated the effects of climate change on the rivers of selected basins of Guilan province, one of the northern provinces of Iran for the period 2020 to 2050 under three climate scenarios: RCP2.6, RCP4.5, RCP8.5. For this purpose, rainfall and temperature data from 45 climate data stations and 20 hydrometric stations from 1983 to 2013 were used. The average precipitation and temperature at basin level were calculated by drawing both Isohyet and Isothermal lines by usage Kriging method. Mann-Kendall and Sen’s slope estimator tests were used to determine the significance of the data trends and their slope, respectively. The results showed that temperature has increased in all catchments during the study period and this trend was significant in most of them but no significant trend was observed for precipitation. Discharge has also decreased in most basins and this trend was significant in Shafarood, Navrood and Chafrood basins. However, for future periods, precipitation is not significant in any of the climate scenarios, but the temperature is increasing in all scenarios except for the RCP2.6 scenario. Rivers discharge in the RCP2.6 scenario is not significant in any of the basins, but in the RCP4.5 scenario the Shafarood and Ghasht-Roodkan catchments have a significant reduction in the 95% confidence level. In the RCP8.5 scenario, the Chafrood and Shafarood basins have a 99% confidence reduction trend.
Population and technology growth, increased water consumption and climate change have led many researchers to study and model water resources in the present and future periods. Especially in areas like Iran that are facing a lot of water stresses. The purpose of the present study, which was carried out in the Guilan province, is to provide information on the present and future status of surface water resources, and to prepare them for facing the problems of potential water resources exploitation.
In this study 45 synoptic, evaporative and rain gauge stations and 20 hydrometric stations data with sufficient statistics were used. The period of study is also between 1983 and 2013. In this regard, after calculating the average precipitation and temperature values of each basin using Kriging model, first, the annual average of precipitation and temperature values ​​of each basin were calculated. Then, multivariate regression was used to obtain the regression equations between precipitation, temperature and discharge data, then by using SDSM model and climate scenarios (RCP2.6, RCP4.5, RCP8.5) future temperature and precipitation data were generated. By placing these generated data in the Created regression equations, the discharge of the rivers was calculated for the period 2020 to 2050. The trend of time series and their slope were analyzed respectively by Mann-Kendall and Sense tests.
   The study of the annual average precipitation trend of the selected catchments during the study period showed that all the basins had no significant trend at any of the confidence levels (95% and 99%). However, for the temperature there is an increasing trend. In Chafrood, Zilaki, Chalvand, Lavandevil, Tutkabon, Chubar, Lamir, Hawigh, Dissam, Shirabad, Ponel, Samoosh, and Polrood basins there is significant trend at 95% confidence level. For the Hawigh River basin there is significant trend at 99% confidence level. Also in most of the basins there is a downward trend of rivers discharge. In addition, in the three basins of Chafrood, Navrood and Shafarood, there is a significant decreasing trend at 95% confidence level, which is also significant at 99% confidence level for Navrood and Shafarood rivers.
Analysis of future data showed that precipitation is not significant in any of the climate scenarios, but the temperature is increasing in all scenarios except for the RCP2.6 scenario in RCP2.6 scenario. For rivers discharge there was no significant trend in any of the basins, but in RCP4.5 scenario there is a significant decrease in 95% confidence level in Shafarood and Ghasht-Roodkan. Also in the RCP8.5 scenario, a significant decreasing trend of flow discharge at 99% confidence level is observed for Chafrood and Shafarood basins. Finally, the catchments were grouped according to the level of risk involved with decreasing discharge. The results of grouping showed that most of the basins in the three scenarios were in the medium risk group but Shafarood, Chafrood and Ghasht-roodkhan watersheds have higher risk than the other watersheds, respectively.
Investigation of river discharge trends for the period 2020 to 2050 in different scenarios showed that the basins of Ghasht-roodkhan, Chafrood and Shafarood are more sensitive to climate change than other basins. Overall, escalating temperature trends in future and precipitation irregularities can create very difficult conditions in future to use these resources. Especially, this study's concordance with other studies in Iran and the study area confirms that such crises are more likely to occur..
Keywords: Climate Change Scenarios, Rivers Discharge, Man-Kendall, Sen’s Slope estimator, Guilan Province

Roshanak Afrakhteh, Abdolrasoul Salman Mahini, Mahdi Motagh, Hamidreza Kamyab,
Volume 10, Issue 3 (9-2023)

This paper is a discussion of urban heat islands (UHIs), which unique residential areas are characterized by dense central cores surrounded by less dense peripheral lands. UHIs experience higher temperatures due to impermeable surfaces and specific land use patterns. These temperature variations have negative environmental and social impacts, leading to increased energy consumption, air pollution, and public health concerns. It emphasizes the need for simpler approaches to comprehend UHI temperature dynamics and explains how urban development patterns contribute to land surface temperature variation. The case study of Guilan Plain illustrates the relationship between development patterns and temperature, utilizing techniques like principal component analysis and generalized additive models.
This paper focuses on mapping land use and land surface temperature in the southwestern region of the Caspian Sea, specifically in the low-lying area of Guilan province. The research utilized satellite data from Landsat sensors for three different time periods: 2002, 2012, and 2021. A spatial unit known as a "city block" was employed through object-based analysis using eCognition software. Thermal bands from Landsat, such as TM band 6, ETM+ band 6, and TIR-1 band 10, were used to retrieve land surface temperature. The radiative transfer equation was used to calculate temperature, accounting for atmospheric and emissivity effects.
The study employed the normalized difference vegetation index (NDVI) method to estimate land surface radiance. The main focus of the study was to identify predictive variables for urban land surface temperature within the context of residential city blocks. These variables were categorized as intrinsic (related to the block's structure) and neighboring (related to adjacent blocks) variables. Intrinsic variables included block area, shape index, perimeter-to-area ratio, and central core index, calculated using Fragstats software. Neighboring variables encompassed metrics like shared boundary length, mother polygon area, number of neighboring blocks, average distance to neighboring block centers, average area of neighboring blocks, average shape index of neighboring blocks, and average central core index of neighboring blocks. Principal Component Analysis (PCA) was employed to select significant variables that captured the majority of data variance. Variables with eigenvalues greater than 1 in each principal component were considered significant contributors. Varimax rotation was applied to the PCA results to ensure accurate variable selection.
The study utilized a Generalized Additive Model (GAM) approach, implemented using the mgcv package in R, to model the relationship between urban land surface temperature and predictor variables. Smoothing parameters were estimated using a restricted maximum likelihood method. Model accuracy and interpretability were assessed using the coefficient of determination (R-squared) and the F-test analysis. the study's results include the generation of land use maps for three different time periods using object-based image analysis. Urban block characteristics were aligned with spectral units through density, shape, and scale coefficients. Over the years, the average block size showed variation, increasing from 61.19 hectares to 62.21 hectares. Urban expansion was observed across the years, with the urban area expanding from 9.5% to 11.1% of the region. Surface temperatures ranged from 22.84 to 26.26°C, with urban temperatures spanning 26.14 to 53.04°C. Independent variables were calculated for intrinsic and neighboring categories, with varying characteristics like block size, shape index, and perimeter-to-area ratio. Principal Component Analysis identified influential parameters, leading to the selection of block size, and shared boundary. the polygon area, and perimeter-to-area ratio as main variables for a generalized additive regression model. This model demonstrated non-linear relationships between these predictors and urban temperature. Block size, shared boundary, and mother polygon area exhibited a positive relationship with temperature, while the perimeter-to-area ratio displayed a negative trend. The model's performance was satisfactory, indicated by an R-squared value of 0.619.
The discussion focuses on the challenges and complexities of predicting urban surface temperature through studies on land use patterns. the current study concentrates on analyzing surface temperature within urban block units and categorizing variables into intrinsic and neighboring factors to enhance the understanding of the relationship between urban surface temperature and spatial distribution. Despite calculating urban surface temperature as a seasonal average across years, notable variations in temperatures were observed across different years. These variations are attributed to environmental conditions, climatic factors, and atmospheric influences that fluctuate over time. Consequently, the study aims to mitigate the impact of dynamic parameters by basing its models on cumulative temperature changes over various years. However, despite its reliability, this approach might lead to biased results when dealing with short-term time-series imagery.
The discussion also delves into the study's approach of focusing on spatial indices of urban units as predictive neighboring parameters. This choice stems from the fact that other units, particularly agricultural ones, experience significant changes over shorter periods, which can disrupt model calibration. Principal Component Analysis highlights the importance of block size as a key predictor of urban surface temperature, emphasizing the shift from polygon area to block size as a spatial scale. The study concludes that both block size and aggregation significantly influence urban temperature patterns. The Generalized Additive Model reveals that block size and mother polygon area exhibit a positive relationship with urban surface temperature, while the perimeter-to-area ratio displays an inverse correlation. This parameter indicates that units with smaller central cores and higher perimeter-to-area ratios experience cooler temperatures due to engagement with neighboring units, especially agricultural ones. In conclusion, the findings suggest that urban blocks function as distinct entities where temperature-related factors are influenced by intrinsic attributes like shape, as well as by the positioning of a unit relative to others.
The conclusion highlights the continuous growth of studies investigating the connection between land use patterns and urban surface temperature. Block size emerges as a central factor in determining urban surface temperature, alongside block dispersion and aggregation, which play crucial roles as predictors in residential areas. Additionally, the study emphasizes the importance of spatial configuration and unit structure in shaping urban temperature patterns. The proposed methodology has the potential to enhance understanding of parameter significance in shaping urban temperature patterns across various regions of Iran.

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