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moghim--sanaz
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Modification of Crop Pattern Considering Climate Change and Efficient Use of Water Resources
, M.Sc. Thesis Sharif University of Technology ; Moghim, Sanaz (Supervisor)
Abstract
In recent decades, the earth is affected by climate change and the seasons has been warmer than the last. Furthermore, the efficiency of agricultural irrigation is low, and it leads to surface water drought and decrease in groundwater level. The Middle East is known for its warm desert climate. Lack of water resources is considered the most limiting factor for sustainable agriculture and water management of irrigation is a crucial issue for agriculture in Middle East. So it’s necessary to devote the cropping pattern considering above changes. The FAO56 Penman-Monteith equation is the standardized ETo equation. A comparison was made between 8 selected methods and FAO56-PM. Thus the method...
Climate Classification of the MENA (Middle East and North Africa) by Introducing a New Index for Clustering Validation
, M.Sc. Thesis Sharif University of Technology ; Moghim, Sanaz (Supervisor)
Abstract
Clustering presents valuable information in discovery of the climatic zones. To use clustering approaches, similarity measure, clustering algorithm, and clustering validity index should be determined. To find climatic zones over Middle East nad North Africa (MENA), this study performs k-means clustering with Euclidean distance as the similarity measure on four monthly precipitation datasets (CRU, GPCC, UDEL, and PREC/L) and two monthly temperature datasets (CRU, NOAA GHCN-CAMS). This study aims to validate clustering results and find a proper number of clusters. For this purpose, five traditional validity indices are examined on experimental datasets. Results show significant differences...
Comparison and Evaluation of Flood Simulation Under Different Scenarios in Kashkan River and Missouri Basins Using Hec-Ras and Lisflood-Fp, and Development of a Method for Downscaling of Flood Discharge
,
M.Sc. Thesis
Sharif University of Technology
;
Moghim, Sanaz
(Supervisor)
Abstract
This study consists of two parts. In the first part, the performance of the LISFLOOD-FP model, which is raster-based, and the HEC-RAS model, are compared and evaluated. This work studied different scenarios under various digital models in two study areas, the Kashkan study basin with mountainous topography and the basin in Nebraska, which has a plain surface. The flood events are among the most severe floods in both study areas that occurred in March and April 2019. This study showed that in mountain topography, the performance of both models is good even with the 30 m high digital models. Although two models perform well, in the Nebraska basin, the performance of the two models in the...
The Effect of Land Use Change on Water Cycle in Forested and Deforested Areas in Australia
, M.Sc. Thesis Sharif University of Technology ; Moghim, Sanaz (Supervisor)
Abstract
Forested areas can cause positive impacts on ecosystems and water supply for agriculture, electricity generation, industry, and human. On the other hand, large-scale deforestation affects the water and energy budget on a regional and global scale. Considering the importance of this issue, this study evaluates the impact of land use and land cover changes (LULC) on the water cycle components, including precipitation, evapotranspiration, runoff, and hydroclimatic variables such as soil moisture, air temperature, soil temperature, and sensible heat flux in southeastern Australia using an ecosystem model called Integrated Biosphere Simulator (IBIS). For this purpose, a two-year time span with...
Modeling and Forecasting of Carbon Dioxide Concentration over the Australian Daintree Rainforest
, M.Sc. Thesis Sharif University of Technology ; Moghim, Sanaz (Supervisor)
Abstract
In recent decades, the concentration of carbon dioxide in the atmosphere has increased. Carbon dioxide is a greenhouse gas that raises the Earth’s temperature, and it can endanger life on Earth. This study uses the 6.2 version of the Regional Atmospheric Modeling System (RAMS) with its simple biosphere model (SiB-2) to simulate and forecast the concentration of carbon dioxide over the Daintree rainforest in Australia. This analysis helps to better understand the interaction between the biosphere and the atmosphere. Results of the carbon dioxide simulation were compared and validated with the observed values of the satellite product (MultiInstrumentFusedXCO2). Based on effective parameters,...
Alarming System for Extreme Weather Events (Case Study: Bangladesh)
, M.Sc. Thesis Sharif University of Technology ; Moghim, Sanaz (Supervisor)
Abstract
Climate and extreme hydrometeorological studies are required to reduce risk and vulnerabilities. This study uses different cumulus and microphysics schemes in Weather Research and Forecasting (WRF) model to simulate heavy rainfall events and Cyclone Sidr in Bangladesh, where many extreme events occur. Results show that WRF can capture the cyclone track, intensity, and landfall position. In addition, regionalization and an ensemble method through Bayesian regression model (BRM) are used to improve WRF rainfall simulations. Although regionalization can improve results of the experiments with different schemes, BRM leads to the best performance. To consider uncertainty and evaluate hazards, a...
Performance Evaluation of Machine Learning and Statistical Approaches for Wildfire Modeling and Prediction
, M.Sc. Thesis Sharif University of Technology ; Moghim, Sanaz (Supervisor)
Abstract
Wildfires are complex phenomena with many indeterminate and highly unpredictable driving factors that have remained unresolved. During the last decade, machine learning methods have successfully excelled in wildfire prediction as an alternative to traditional field research methods by elucidating the relationship between historical wildfire events and various important variables. The main purpose of this research is to evaluate the random forest machine learning approach and the logistic regression statistical approach to prepare a wildfire susceptibility map using data related to historical wildfires and effective variables in the Okanogan region in Washington province of the United States...
The Investigation and Tracking of Dust Particles and Their Properties over Hamoun Lake and the Aral Sea
, M.Sc. Thesis Sharif University of Technology ; Moghim, Sanaz (Supervisor)
Abstract
Dry bed of lakes and surrounding areas play an important role in dust production. This work tries to develop an Aerosol Type Detection Index for the two regions of Lake Hamon in Sistan, Iran, and the Aral Sea in Central Asia. Using the parameters of Extinction Coefficient and Depolarization Ration of CALIPSO satellite, the proposed index differentiates and categorizes fine dust particles and clouds according to the physical behavior of each in absorbing and backscattering satellite rays. This index shows values between zero and 0.15 for desert dust and -0.3 to zero for polluted desert dust particles. In order to verify the accuracy of determining the type of particles by the proposed index,...
Analysis of Heat and Cold Waves Indices
, M.Sc. Thesis Sharif University of Technology ; Moghim, Sanaz (Supervisor)
Abstract
Heat waves and cold waves are among the most important extreme weather events, which could directly affect human health. The heat wave and cold wave are defined as a period of consecutive days (usually at least 3 days) when hot or cold air persists over a region. However, there is no universal definition of these two phenomena in different scientific references, and the available definitions differ depending on the type of temperature index, the minimum required duration, and the defined threshold temperature. This research aims to examine the main indices of heat waves and cold waves and analyze them in 6 major cities: Tehran, London, Madrid, New York, Shanghai, and Vancouver which have...
Assessment of Vulnerability and Resilience to Land Degradation
, M.Sc. Thesis Sharif University of Technology ; Moghim, Sanaz (Supervisor)
Abstract
In recent decades, especially with the increase of greenhouse gases and global warming, land degradation has become one of the important global environmental issues. Land degradation has significant impacts on soil performance, vegetation cover, crops, and even the dispersion of dust particles. Land degradation is a complex phenomenon that is affected by multiple factors. Thus, it is necessary to identify these factors and then assess the degree of vulnerability and resilience to it globally. Proper indicators can clarify the integration and effective linkage between related parameters. This study aims to develop a Land Degradation Vulnerability Index (LDVI) using parameters such as...
Downscaling Tehran’s Temperature Field Using Machine Learning Algorithms and Geospatial Interpolation
, M.Sc. Thesis Sharif University of Technology ; Moghim, Sanaz (Supervisor)
Abstract
Due to climate change and the increase in the emission of greenhouse gasses, the temperature of the large cities is increasing. Tehran, which is the capital of Iran and the most populated city of it, is no exception. One of the significant tools for characterizing heat in the cities is having access to the temperature field of the region. Different tools can be used for achieving the temperature field. Two methods for doing so are remote sensing and numerical models. Each one of the mentioned methods has their own strength and weaknesses. In this research, the WRF-ARW model (version 3.7) is used for deriving meteorological fields for the city of Tehran. One of the many merits of using...
Reliability Assessment of Wind Power Density in Khouzestan Province
, M.Sc. Thesis Sharif University of Technology ; Moghim, Sanaz (Supervisor)
Abstract
This study seeks to evaluate reliability of wind power density over Khouzestan province using WRF simulations. Based on 10 meter wind speed data extracted from eight synoptic stations over Khouzestan province, two different domains were selected in south and south-west of khouzestan as promising regions with strong wind speeds. WRF model were used to simulate atmospheric data during 2016 and over two selected regions with 2 kilometers grid spacing. Simulated data were verified with comparing to in-situ data via three parameters of RMSE, BIAS and correlation. Results indicate that model configuration had acceptable accuracy in simulating wind speed data. In this study, Monte Carlo...
Recognition of Dust Sources of Lake Urmia Basin (Remote Sensing) and its Relationship with Climate and Meteorological Parameters
, M.Sc. Thesis Sharif University of Technology ; Moghim, Sanaz (Supervisor)
Abstract
Lake Urmia, the largest domestic lake in Iran due to climate change and lack of attention to sustainable development in the past two decades, has lost a significant part of its area. The contaminated bed of dried lakes is one of the main centers of dust generation.These materials are transported along with the wind to distant areas and have an irreversible impact on human health and the environment, so it is very important to determine the sources of dust production on the lake of Urmia to reduce the adverse effects of it.In this study, after determining the arid areas in the Lake Urmia basin using the Angstrom Exponent (AE) and the Aerosol Optical Depth (AOD), an index has been introduced...
Development of a Model for Monitoring and Prediction of Drought under Climate Change at Watershed Scale
, Ph.D. Dissertation Sharif University of Technology ; Abrishamchi, Ahmad (Supervisor) ; Moghim, Sanaz (Supervisor)
Abstract
The objective of this dissertation is to develop a model to monitor and project the long-term changes in meteorological drought under climate change at watershed scale. In this model, drought is defined based on joint precipitation-temperature values since negative correlation between precipitation and temperature implies that drier periods are often warmer, and therefore, the consequences of drought are more severe compared to low-precipitation periods with mild temperature. This is of particular importance considering global warming. To quantify drought, an indicator called precipitation-temperature deciles index, which is an extension of precipitation deciles index, is introduced. Copula...
Impact of climate variation on hydrometeorology in Iran
, Article Global and Planetary Change ; Volume 170 , 2018 , Pages 93-105 ; 09218181 (ISSN) ; Sharif University of Technology
Elsevier B.V
2018
Abstract
Results confirm that Iran like many countries is affected by high climate variability, which has influenced hydroclimatological variables such as temperature, evaporation, precipitation, runoff, and radiation. This study uses Global Land Data Assimilation System (GLDAS) data to assess hydrological cycle changes in Iran during a long period (Jan1948-Jan2017). Results show that hydrometeorological variables have significant changes (p-value<.01) during the period of 2010–2017 relative to the baseline period (2004–2009). Most extreme values of these variables including temperature, evaporation, precipitation, wind, and downward longwave radiation occurred recently (in 2015 to 2017). The average...
Assessment of water storage changes using GRACE and GLDAS
, Article Water Resources Management ; Volume 34, Issue 2 , 2 January , 2020 , Pages 685-697 ; Sharif University of Technology
Springer
2020
Abstract
Water crisis is one of the main global risks that has different impacts on the society. This work uses available data and tools to track water storage changes in Iran, where lack of observations limits hydroclimatological studies and thus causes disasters. Data from Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiment (GRACE) are combined to analyze water storage changes (LWE) in Iran. GRACE signals indicate a large reduction of the water storage in North of Iran along the coast of the Caspian Sea (the largest global inland water body), where the water level has been oscillating significantly. In addition, results show the largest reduction of the water...
Reliability assessment of the wind power density using uncertainty analysis
, Article Sustainable Energy Technologies and Assessments ; Volume 44 , 2021 ; 22131388 (ISSN) ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
Evaluation of the wind energy potential in different climates includes high level of uncertainties. To address the uncertainties, this study performs reliability analysis by defining a limit state function for the wind power density (WPD) to determine failure or success of the system in the probabilistic framework. The probability distributions of the variables including wind speed and air density simulated by the Weather Research and Forecasting (WRF) model with 2 km resolution are used in the limit state function to form a nondeterministic model in southwest of Iran. Given significant correlation between air density and wind speed in most of the pixels, Nataf transformation is applied to...
Characterization of aerosol types over Lake Urmia Basin
, Article 2019 Central Asian DUst Conference, CADUC 2019, 8 April 2019 through 12 April 2019 ; Volume 99 , 2019 ; 25550403 (ISSN) ; Ramezanpoor, R ; Sharif University of Technology
EDP Sciences
2019
Abstract
Atmospheric aerosols affect the Earth's climate, air quality, and thus human health. This study used the Aerosol Optical Depth (AOD) and the Ångström exponent to cluster different particle types over the Lake Urmia Basin. This classification found desert dust and marine (mixed with continental or local-pollution aerosols) as two main aerosol types over the region, while their sources are not well defined. Although different air masses and wind circulation over the study domain in varied months can help to distinguish aerosol sources, measurements are crucial for a complete evaluation
Bias correction of climate modeled temperature and precipitation using artificial neural networks
, Article Journal of Hydrometeorology ; Volume 18, Issue 7 , 2017 , Pages 1867-1884 ; 1525755X (ISSN) ; Bras, R. L ; Sharif University of Technology
2017
Abstract
Climate studies and effective environmental management require unbiased climate datasets. This study develops a new bias correction approach using a three-layer feedforward neural network to reduce the biases of climate variables (temperature and precipitation) over northern South America. Air and skin temperature, specific humidity, and net longwave and shortwave radiation are used as inputs to the network for bias correction of 6-hourly temperature. Inputs to the network for bias correction of monthly precipitation are precipitation at lag 0, 1, 2, and 3 months, and also the standard deviation of precipitation from 3 × 3 neighbors around the pixel of interest. The climate model data are...
Regression-based regionalization for bias correction of temperature and precipitation
, Article International Journal of Climatology ; Volume 39, Issue 7 , 2019 , Pages 3298-3312 ; 08998418 (ISSN) ; Bras, R. L ; Sharif University of Technology
John Wiley and Sons Ltd
2019
Abstract
Statistical bias correction methods are inferred relationships between inputs and outputs. The constructed functions are based on available observations, which are limited in time and space. This study investigates the ability of regression models (linear and nonlinear) to regionalize a domain by defining a minimum number of training pixels necessary to achieve a good level of bias correction performance. Linear regression is used to divide northern South America into five regions. To correct the biases of temperature and precipitation, an artificial neural network (ANN) model was trained with selected pixels within each region and then used to reproduce bias-corrected temperature and...