Sharif Digital Repository

Numerous studies yet have been carried out on downscaling of the large-scale climate data using both dynamical and statistical methods to investigate the hydrological and meteorological impacts of climate change on different parts of the world. This study was also conducted to investigate the capability of feedforward neural network with error back-propagation algorithm to downscale the provincial segmentation of Iran (30 provinces) on a daily scale. This model was proposed for the downscaling daily temperature, precipitation and wind speed data, and it was calibrated and verified by using the daily outputs derived from the National Center for Environmental Prediction (NCEP) database... 

General Circulation Models (GCMs) are advanced tools for impact assessment and climate change studies. Previous studies show that the performance of the GCMs in simulating climate variables varies significantly over different regions. This study intends to evaluate the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) GCMs in simulating temperature and precipitation over Iran. Simulations from 37 GCMs and observations from the Climatic Research Unit (CRU) were obtained for the period of 1901–2005. Six measures of performance including mean bias, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), linear correlation coefficient (r), Kolmogorov-Smirnov... 

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... 

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... 

This paper addresses the impacts of climate change on hydrology and water resources in the Karkheh River Basin (KRB), which is the third most productive basin in Iran and has great potential for hydropower generation. The total potential capacity of reservoirs in this basin is more than 15 ×109 Mm3, of which 40% has been built. The sensitivity of the KRB to potential climate change is investigated by simulating basin streamflow response under different climate change scenarios. A conceptual rainfall-runoff model (IHACRES) was first calibrated by using hydrological and streamflow observations. The model was then applied by downscaling two general circulation model outputs (CGCM3 and HadCM3)... 

A downscaling model capable of explaining the temporal and spatial variability of regional hydroclimatic variables is essential for reliable climate change studies and impact assessments. This study proposes a novel statistical approach based on generalized hierarchical linear model (GHLM) to downscale precipitation from the outputs of general circulation models (GCMs) at multiple sites. GHLM partitions the total variance of precipitation into within- and between-site variability allowing for transferring information between sites to develop a regional downscaling model. The methodology is demonstrated by downscaling precipitation using the outputs of eight GCMs in Lake Urmia basin in... 

Precipitation (P), plant water use, and evaporation from the soil surface control the travel time of streamflow (Q) and evapotranspiration (ET) in a complex way. However, the impact of soil moisture and energy availability on the travel time distribution (TTD) of evaporated and transpired waters are yet less understood. In this study, we investigate how the seasonal variability of P and ET in terms of phase shift and rate influences the temporal dynamics of TTDs. To this end, we choose four contrasting climate types described as in-phase P and ET, out-of-phase P and ET, year-round constant P with seasonal ET, and year-round constant ET with seasonal P. We use a physically-based hydrological... 

Utilizing ground-based measurements to obtain water budget components, especially in large scale basins, is challenging due to the limitation in the spatiotemporal availability of in-situ data. In this paper, we propose a probabilistic framework for estimating water budgets in low runoff regions using remote sensing products. By studying water budgets in the Central Basin of Iran (CBI) over 8 years period (2009–2016), we investigate the locations and time scales at which the water budget calculated from satellite products provides most closure. To this end, we use precipitation from the Tropical Rainfall Measuring Mission (TRMM), evapotranspiration from the Water Productivity Open Access... 

Deposition of asphaltenes upon precipitation is a main flow assurance concern, which propelled the development of various experimental and modeling techniques to accurately predict its occurrence. This work develops an integrated approach combining thermodynamic and deposition modules with a multiphase flow simulator to simultaneously model asphaltenes precipitation and deposition in wellbores. The Peng-Robinson equation of state and the modified Miller-Flory-Huggins theory are used to calculate the thermodynamic properties of the oil and asphaltenes precipitation, respectively. The deposition module is based on conservation laws for asphaltenes transport and is linked to the flow simulator... 

Meteorological droughts due to the concurrent occurrence of low-precipitation and high-temperature events can lead to severe negative impacts on agriculture, economy, ecosystem, and society. This study proposes a novel framework to characterize such drought conditions based on the joint variability of precipitation–temperature, particularly under climate change. Generalized hierarchical linear model is used to downscale precipitation and temperature at multiple stations from the outputs of nine General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5. A bivariate drought index called Precipitation–Temperature Deciles Index (PTDI) is developed using... 

Meteorological droughts due to the concurrent occurrence of low-precipitation and high-temperature events can lead to severe negative impacts on agriculture, economy, ecosystem, and society. This study proposes a novel framework to characterize such drought conditions based on the joint variability of precipitation–temperature, particularly under climate change. Generalized hierarchical linear model is used to downscale precipitation and temperature at multiple stations from the outputs of nine General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5. A bivariate drought index called Precipitation–Temperature Deciles Index (PTDI) is developed using... 

Globally, the impacts of climate change can vary across different regions. This study uses a probability framework to evaluate recent historical (1976–2016) and near-future projected (until 2049) climate change across Europe using Climate Research Unit and ensemble climate model datasets (under RCPs 2.6 and 8.5). A historical assessment shows that although the east and west of the domain experienced the largest and smallest increase in temperature, changes in precipitation are not as smooth as temperature. Results indicate that the maximum changes between distributions of the variables (temperature and precipitation) mainly occur at extreme percentiles (e.g., 10% and 90%). A group analysis... 

Estimating precipitation at high spatial-temporal resolution is vital in manifold hydrological, meteorological and water management applications, especially over areas with un-gauged networks and regions where water resources are on the wane. This study aims to evaluate five downscaling methods to determine the accuracy and efficiency of which on generating high-resolution precipitation data at annual and monthly scales. To establish precipitation-Land surface characteristics relationship, environmental factors, including Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST) and Digital Elevation Model (DEM), were considered as proxies in the spatial downscaling...