Groundwater modeling often associates with uncertainties caused by incomplete knowledge of the underlying system or uncertainties due to natural variability in system processes and field conditions. Uncertainty in groundwater modeling has been evaluated by researchers in three main sources that can be classified as parameter uncertainty, conceptual uncertainty (model structure uncertainty) and input uncertainty (observation uncertainty). So far, there are few studies that they assess groundwater uncertainty in the country, and quantifying uncertainty has been limited to the statistical methods. Due to the importance of the water resources in the country and the necessity of estimating the uncertainty in order to achieve accurate and reliable results, in this study, parameter uncertainty of an arid region’s groundwater flow model was assessed by using a Monte Carlo-based simulation technique.
First, conceptual groundwater model of Birjand plain, located in the southern province, was developed based on collecting all available data, including topography, observed and withdrawal wells information, recharge information, hydrodynamic properties of aquifer, surface elevation data. Then, the MATLAB-based MODFLOW model was used to simulate the groundwater flow. After initial calibration in steady state, for assessing parameter uncertainty in transient mode two scenarios were defined. In the first scenario uncertainty analysis was performed by assuming that the hydraulic conductivity is one of the major contributors to the model uncertainty. So the aquifer was divided into 17 homogeneous zones according to initial calibration of hydraulic conductity results, and the parameter uncertainty was assessed using Monte Carlo (MC) sampling technique, namely, the generalized likelihood uncertainty estimation (GLUE). In the second scenario, 9 recharge zones were additionally considered as the second parameters, and their influence on the hydraulic conductivity and the total uncertainty were estimated by the GLUE.
Posterior parameter plots of hydraulic conductivity in the 17 homogeneous regions and recharge in the 9 inflow pathways and also, 95% confidence intervals for the simulated water table depth, were obtained as main results. The Indices, as criteria for the comparison, were used to quantify the goodness of uncertainty performance and the sensitive regions in the aquifer were specified by implementing global sensitivity analysis of the model.
Results indicate up to 86% of observed data bounded in the 95% confidence intervals that is emphatic the good performance of the GLUE and also the likelihood function, Weighted Root Mean Squared Error (WRMSE), in the assessment of parameter uncertainty in a groundwater simulation model.

The concept of this study is related to different stakeholders which include policy makers, financial market participants, asset managers and company managers. First, policymakers and government agencies should be aware that uncertainty related to economic policies (monetary policies, fiscal spending, regulations, changes in taxes, national debt, etc.) can have decisive effects on investor sentiment and asset prices. Government agencies should provide uninterrupted communication with elected officials to help clarify the government's policy stance on economic matters. This limits uncertainty about the future direction of certain government policies, which should lead to less volatile and better priced financial assets. Second, financial market participants and asset managers should be aware of changes and direction of change in policy uncertainty and position their portfolios in response to changes in policy uncertainty, which can have short-term and long-term effects on the stock market. Asset managers and investors with long-term mandates and time horizons should be equally concerned about policy uncertainty and its asymmetric effects. Finally, corporate managers may use policy uncertainty to help them time their capital raising activities. Therefore, by taking advantage of policy uncertainty, firm managers may time their firm's capital raising activities to minimize their firm's cost of capital and maximize firm value. Therefore, considering the importance of the capital market in the country's economy on the one hand and the occurrence of numerous shocks in the field of monetary policy, the main purpose of this study is to investigate the effect of monetary policy uncertainty on stock market uncertainty in Iran. In this study, according to the type and purpose of the research, it is applied and in terms of controlling the variables, it is a non-experimental and descriptive-correlation research. Conducting research in the form of inductive analogy and its information is event type. In this study, information on Iran's economy during the years 1368-1400 has been examined. The source of the data used is the database of the World Bank and the Central Bank of the Islamic Republic of Iran, and ARDL and NARDL methods have been used to estimate the desired models. It should be mentioned that Eviews11 software was used to estimate the model. The research model with Elha is specified from the two studies of Battabial and Killins (2021) and Fu and Lu (2021). The results of the ARDL linear model show that in the long term, the uncertainty of monetary policies causes the intensification of uncertainty in the stock market. On the other hand, in the non-linear ARDL model, the results showed that monetary policy uncertainty shocks do not have an asymmetric effect on stock market uncertainty in the first place, and secondly, negative shocks have a positive effect on stock market uncertainty in the long term. Other results showed that government size and economic growth do not play a role in stock market uncertainty. While, the exchange rate, inflation rate and interest rate have an effect on the uncertainty of the stock market. This research was conducted with the aim of investigating the effect of monetary policy uncertainty on stock market uncertainty in Iran using the NARDL approach. In this study, in addition to using the NARDL approach, the ARDL approach has also been used, and in some ways, the linear and non-linear approach has been compared. In the nonlinear model, the positive and negative shocks of monetary policy uncertainty were calculated using the NARDL approach and included in the model. The results of linear and non-linear models showed that the uncertainty of monetary policies affects the uncertainty of the stock market in the long term. In other words, the uncertainty in the application and implementation of monetary policies increases uncertainty in the stock market. The results in the non-linear approach showed that; First, the effect of negative and positive uncertainty shocks is not asymmetric. Second, only the negative shock of monetary policy uncertainty has an effect on stock market uncertainty. In other words, with the occurrence of a negative shock in the uncertainty of monetary policies, the uncertainty in the stock market increases. The results of other variables showed that economic growth and government size, despite their importance in the formation of trust in the capital market, have an insignificant effect on stock market uncertainty. On the other hand, the occurrence of inflationary conditions in the country, both due to the inflation rate and the increase in the exchange rate, causes an increase in uncertainty in the stock market. However, an increase in interest rates will reduce uncertainty in the stock market.

In groundwater modeling, an array of factors are the cause of deviation between estimated results and observed data which demonstrate uncertainty in groundwater resources system. In this study, with the aim of investigating the uncertainty of groundwater flow model in Ali Abad plain of Qom with the area of 1712 km2, the parameter of hydraulic conductivity as the most important source of uncertainty was used to analyze the parametric uncertainty. For this purpose, in order to simulate ground water level, different uncertainty ranges in 90% of confidence interval of hydraulic conductivity was investigated by Monte Carlo method and the uncertainty assessment criteria were used to quantify the uncertainty. Eventually, the most appropriate uncertainty range was defined. The Rosenblueth’s Point Estimation Method (RPEM) was used to reduce the calculation process and time and then its performance for analyzing the parametric uncertainty was compared to Monte Carlo method using assessment criteria. Results showed that the most appropriate uncertainty range with RPEM and Monte Carlo method for hydraulic conductivity was obtained by increasing of 40% and reduction of 30% of this parameter. RPEM method showed a better performance account for 43% compared to Monte Carlo method.

The main goal of the current study is to investigate the contagion of uncertainty between sectors (finance, housing and macroeconomics) using a dynamic approach. In this regard, using monthly data in the period from 2008:4 to 2020:3, DCC-GARCH models and generalized forecast error variance decomposition (GFEVD), The overall dynamic relationship as well as the directional pairs dynamic relationship of uncertainty indicators between the mentioned sections are investigated. The results of this study show that the housing sector, with the exception of the beginning of 2017, is a net receiver of uncertainty from the other two sectors. Also, the results of the current research show the dual role of the financial sector in the mechanism of inter-sector uncertainty transfer, so that at some periods it is a net receiver of uncertainty and at some other periods (including the years 2017 and 2018) it is a net source and transmitter of uncertainty in the three indicators system. Inflation uncertainty, as an index of macroeconomic uncertainty, is a major source of uncertainty and a transmitter of uncertainty to the financial and housing sectors.

The uncertainty in forecasted precipitation remains a major source of uncertainty in real time flood forecasting. Precipitation uncertainty consists of uncertainty in the magnitude, temporal distribution, and spatial distribution of the precipitation. Due to uncertainty propagation of precipitation in flood forecasting model of HEC-1, temporal disaggregation method is applied by using the framework of fuzzy Extension Principle supported by a normal genetic algorithm. The uncertainty due to the unknown temporal distribution of the precipitation is achieved by randomly disaggregation of the precipitation into subperiods. Uncertainty in discharge and volume of flood hydrograph due to precipitation with temporal disaggregation and precipitation without temporal disaggregation is estimated and is compared with each others. The results show that in all forecasts the uncertainty in discharge and volume of flood hydrograph due to precipitation with temporal disaggregation is significantly more than uncertainty due to precipitation without temporal disaggregation. So that for forecast subperiod equal to six and membership function of precipitation magnitude equal to zero, the uncertainty in peak discharge due to precipitation with temporal disaggregation and without temporal disaggregation are 33. 7% and 16%, respectively. Also the uncertainty in volume of flood hydrograph due to precipitation with temporal disaggregation and without temporal disaggregation are 23. 5% and 14. 8%, respectively. Also the uncertainty in peak discharge due to the uncertainty in the temporal and spatial distribution can be significantly dominant over the uncertainty due to the uncertainty in the magnitude of the precipitation. So that for forecast subperiod equal to six and membership function of precipitation magnitude equal to zero، the uncertainty in peak discharge due to the temporal and spatial distribution and the magnitude of precipitation are 17. 7% and 16%, respectively. Therefore using space- and time-averaged precipitation over the basin may lead to erroneous forecasts.

Take a look at the trend of macroeconomic variables such as exchange rate and inflation in Iran, shows that these variables have had a relatively Instable trend in recent years. However, the effects of the uncertainty of these variables and their interactions have received very little attention in the country. Therefore, this study investigating the impact of exchange rate uncertainty on inflation and interest rate uncertainty and the role monetary and fiscal policy credibility on it using quarterly data during the period 1991-2019. For this purpose, initially using the three-variable VECH and BEKK methods, uncertainty in the variables is estimated. The results show that BEKK method is more suitable for estimating uncertainty than VECH method. Then using the VAR structure, Investigating the Impact of Exchange Rate Uncertainty on Inflation and Interest Rate Uncertainty and the Role Monetary and fiscal policy credibility on it. The results show that exchange rate uncertainty and inflation rate uncertainty affect each other. Also, exchange rate uncertainty and interest rate uncertainty intensify each other. Rising interest rates increase interest rate uncertainty and rising inflation increases inflation uncertainty. This result means the confirmation of the Friedman-Ball hypothesis and the rejection of the Pourgerami and Maskus hypothesis. The results also show that the credit of monetary and fiscal policies reduces the effect of exchange rate uncertainty on interest rate uncertainty and inflation.

The present study aims to investigate the impact of financial market risks on the value of financial assets. In this study, first, by studying the research literature, financial market risk indicators including bad economic uncertainty and good economic uncertainty were identified. Then, the most important indicators from the perspective of experts were determined by the pairwise comparison questionnaire and AHP method. In the next step, the uncertainty indices were calculated by GARCH regression and derivation of conditional standard deviation. Then, the effect of financial market risks on the value of financial assets was examined by VAR regression. The statistical population of the data is related to the financial market and macroeconomy of Iran in the period 1986-2020. The results showed that the response of consumption of financial assets to the uncertainty of the stock index in different periods is high and increasing and the reaction of consumption of financial assets to the uncertainty of economic growth is negative. The production of financial assets is not affected by stock market uncertainty and economic growth uncertainty. Stock market profits are not initially affected by stock market uncertainty, but in later periods this effect increases and stock market profits react negatively to economic growth uncertainty. The reaction of stock market income to stock market uncertainty and economic growth uncertainty is very small. The reaction of stock market investment to stock market uncertainty and economic growth uncertainty is very strong and negative. Also, the stock market risk rate does not have much effect on stock market uncertainty and economic growth uncertainty.

It is necessary to understand the climate change in next decades to have a suitable environmental planning for adapting and reducing it's effects. In climate change studies, ignoring uncertainties at various stages of impact assessment will reduce confidence in system results. This uncertainty is due to the performance of general circulation models, different emission scenarios and doanscaling process. In this research uncertainty of monthly average temperature of drainage basin of Minab dam is projected in two periods(2016-2045 and 2046-2075) using outcomes of the five general circulation models of the HADGEM2-ES, BNU-ESM, CCSM4, CSIRO-MK3-6, MPI-ESM-MR under three scenarios of RCP2.6, RCP4.5 and RCP8.5. For this purpose, using the method of variants of average tempreture for change factor, future is downscaled. Latin hypercube method wich is an accidental sampelling is used here for checking the uncertainty of models. The results of period uncertainty, in all models and scenarios showed that, the uncertainty of the second period (2046-2075) is greater than in the first period (2016-2045). It means, increasing the length of the forecast period increases the error in predicting climate change models. The results showed the uncertainty of the different models showed that the least uncertainty was related to the CSIRO-Mk3-6 model in the RCP2.6 scenario and the 2016-2045 period, while the highest uncertainty was related to the HadGEM2-ES model in the scenario and period.It is necessary to understand the climate change in next decades to have a suitable environmental planning for adapting and reducing it's effects. In climate change studies, ignoring uncertainties at various stages of impact assessment will reduce confidence in system results. This uncertainty is due to the performance of general circulation models, different emission scenarios and doanscaling process. In this research uncertainty of monthly average temperature of drainage basin of Minab dam is projected in two periods(2016-2045 and 2046-2075) using outcomes of the five general circulation models of the HADGEM2-ES, BNU-ESM, CCSM4, CSIRO-MK3-6, MPI-ESM-MR under three scenarios of RCP2.6, RCP4.5 and RCP8.5. For this purpose, using the method of variants of average tempreture for change factor, future is downscaled. Latin hypercube method wich is an accidental sampelling is used here for checking the uncertainty of models. The results of period uncertainty, in all models and scenarios showed that, the uncertainty of the second period (2046-2075) is greater than in the first period (2016-2045). It means, increasing the length of the forecast period increases the error in predicting climate change models. The results showed the uncertainty of the different models showed that the least uncertainty was related to the CSIRO-Mk3-6 model in the RCP2.6 scenario and the 2016-2045 period, while the highest uncertainty was related to the HadGEM2-ES model in the scenario and period.It is necessary to understand the climate change in next decades to have a suitable environmental planning for adapting and reducing it's effects. In climate change studies, ignoring uncertainties at various stages of impact assessment will reduce confidence in system results. This uncertainty is due to the performance of general circulation models, different emission scenarios and doanscaling process. In this research uncertainty of monthly average temperature of drainage basin of Minab dam is projected in two periods(2016-2045 and 2046-2075) using outcomes of the five general circulation models of the HADGEM2-ES, BNU-ESM, CCSM4, CSIRO-MK3-6, MPI-ESM-MR under three scenarios of RCP2.6, RCP4.5 and RCP8.5. For this purpose, using the method of variants of average tempreture for change factor, future is downscaled. Latin hypercube method wich is an accidental sampelling is used here for checking the uncertainty of models. The results of period uncertainty, in all models and scenarios showed that, the uncertainty of the second period (2046-2075) is greater than in the first period (2016-2045). It means, increasing the length of the forecast period increases the error in predicting climate change models. The results showed the uncertainty of the different models showed that the least uncertainty was related to the CSIRO-Mk3-6 model in the RCP2.6 scenario and the 2016-2045 period, while the highest uncertainty was related to the HadGEM2-ES model in the scenario and period.It is necessary to understand the climate change in next decades to have a suitable environmental planning for adapting and reducing it's effects. In climate change studies, ignoring uncertainties at various stages of impact assessment will reduce confidence in system results. This uncertainty is due to the performance of general circulation models, different emission scenarios and doanscaling process. In this research uncertainty of monthly average temperature of drainage basin of Minab dam is projected in two periods(2016-2045 and 2046-2075) using outcomes of the five general circulation models of the HADGEM2-ES, BNU-ESM, CCSM4, CSIRO-MK3-6, MPI-ESM-MR under three scenarios of RCP2.6, RCP4.5 and RCP8.5. For this purpose, using the method of variants of average tempreture for change factor, future is downscaled. Latin hypercube method wich is an accidental sampelling is used here for checking the uncertainty of models. The results of period uncertainty, in all models and scenarios showed that, the uncertainty of the second period (2046-2075) is greater than in the first period (2016-2045). It means, increasing the length of the forecast period increases the error in predicting climate change models. The results showed the uncertainty of the different models showed that the least uncertainty was related to the CSIRO-Mk3-6 model in the RCP2.6 scenario and the 2016-2045 period, while the highest uncertainty was related to the HadGEM2-ES model in the scenario and period.

The impact of both nominal (inflation) and real (output growth) macroeconomic uncertainty on economic performance is one of the major and complex issues. Costs of high inflation and inflation uncertainty have irreversible and adverse effects on the economy and welfare of the society. Over time, apart from inflation, inflation uncertainty may also affect productivity growth. As inflation uncertainty, output growth uncertainty can also be of positive or negative effect on growth. This study investigates the relationship between inflation, inflation uncertainty, output growth and output growth uncertainty in Iran using quarterly data from 1990:2 to 2012:1. In doing so, a bivariate VARMA, MVGARCH-M is used, and the estimation method is quasi maximum likelihood. The results show that output growth uncertainty has a negative and significant effect on the growth and positive and significant effect on inflation. On the other hand, inflation uncertainty has had a positive and significant effect on inflation over the period, but its effect on output growth has not been significant. Also, the conditional variance–covariance process underlying the output growth inflation exhibits significant non-diagonality and asymmetry.

After the financial crisis, studies of uncertainty have been of particular interest to researchers in the past decade. Given the negative impact of uncertainty on the economic growth and development of the country, policymakers seek to control and reduce the uncertainty to improve economic activities. For this purpose, the value of uncertainty must first be assessed. In this study, after the existing methods of calculating uncertainty are expressed, the index of uncertainty is introduced based on an Internet search and its advantages and disadvantages are examined. Given the foreign exchange turbulence in Iran's economy in recent years, the uncertainty indices in the foreign exchange market during the years 2003-2016 are extracted monthly through this procedure. Furthermore, in the next step, GARCH models are used to estimate uncertainty indices in the foreign exchange market for the same period. It has been found through comparison that the Internet search of indices provides a good image of the foreign exchange price variations in the market. In general, policymakers in various areas need to monitor their policy environment, which can be used to measure uncertainty in some of these areas.