Meysam Salarijazi | Gorgan University of Agricultural Science and Natural Resources (original) (raw)

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Published Papers by Meysam Salarijazi

reach is a suitable tool for the tidal flood risk and environmental management and design for riv... more reach is a suitable tool for the tidal flood risk and environmental management and design for rivers that are located in coastal area. The reaches of the river that affected by river flow and tidal wave are called as tidal limit and water level in this limit is a function of flood discharge and water level in upstream and downstream, respectively. In tidal limit, tidal waves propagate along the river to the upstream and combination of these waves and upstream flood discharge leads to an increase in water level. This mechanism increases flood plain limit and potential of damage and the risk. In this study, the reach between Ahvaz and Khorramshahr in Karun River in Iran is selected as case study and different linear models for the prediction of water level in tidal flood condition are investigated. Results show that the simple linear regression is not acceptable because of its variable variance of residuals. Transformed nonlinear models that are a form of linear models are used for modeling too. Two equations (Logarithmic and Power) with improved coefficient of determination, relatively constant variance and normal distribution of residuals of models are concluded from detailed analysis. These selected models are used for acceptability of these models considering their simplicity.

Papers by Meysam Salarijazi

Iranian journal of science and technology. Transactions of civil engineering/Civil engineering, Mar 4, 2024

Applied Water Science, Oct 16, 2023

Physics and Chemistry of the Earth, Parts A/B/C, Dec 31, 2023

Journal of Water and Climate Change

This research utilized Bayesian and quantile regression techniques to analyze trends in discharge... more This research utilized Bayesian and quantile regression techniques to analyze trends in discharge levels across various seasons for three stations in the Gorganroud basin of northern Iran. The study spanned a period of 50 years (1966–2016). Results indicate a decrease in high discharge rates during springtime for the Arazkouseh and Galikesh stations, with a steep slope of −0.31 m3/s per year for Arazkouseh and −0.19 and −0.17 for Galikesh. Furthermore, Tamar station experienced an increase in very high discharge during summer, with a slope of 0.12 m3/s per year. However, low discharge rates remained relatively unchanged. Arazkouseh station showed a higher rate of decreasing discharge levels and this trend was most prominent during spring. Additionally, the Bayesian quantile regression model proved to be more accurate and reliable than the frequency-oriented quantile regression model. These findings suggest that quantile regression models are a valuable tool for predicting and managi...

Journal of Water and Climate Change, Jun 23, 2023

The results reveal statistically significant trends with distinct slopes in distinct quantiles fo... more The results reveal statistically significant trends with distinct slopes in distinct quantiles for each station and season so that the changes in the trend of high values (quantile 0.9) of evaporation were more than low values (quantile 0.1). In the spring, medium and high evaporation rates increased in the northern regions of the province (the highest trend slope, 0.15 mm/decade in the northeast), while they fell (with a slope of À0.15 mm/decade) in the southern regions. But the high values of evaporation in summer have increased in most of the stations (the highest trend slope, 0.15 mm/decade). In contrast, high levels of evaporation in autumn and winter grew at a rapid rate in the eastern part of the province (the highest slope according to season, 0.15 and 0.2 mm/decade), but they declined in the western half (the highest slope, À0.1 and À0.15 mm/decade, respectively). In general, there was a significant decreasing trend for evaporation, mostly in the western half, but there was an increasing trend, mostly in the eastern half of the province. Significant increases in daily evaporation, particularly during the dry season, will diminish water supplies, destabilize the agricultural sector, and eventually desertify the area.

urban climate, May 1, 2023

Physics And Chemistry Of The Earth, Parts A/b/c, Oct 1, 2023

Research Square (Research Square), Mar 23, 2023

In most arid and semiarid environments, groundwater is one of the precious resources threatened b... more In most arid and semiarid environments, groundwater is one of the precious resources threatened by water table decline and desiccation, thus it must be constantly monitored. Identifying the causes in uencing the variations of the subsurface water level, such as meteorological drought, is one approach for monitoring these uctuations. In the present study, the effect of two meteorological drought indices SPI and SPEI on the uctuations of the underground water level was evaluated, as was their relationship with the drought index of the subsurface water level (SWI) using multivariate linear regression and M5 decision tree regression. After calculating climatic and hydrological drought indicators in a 6-month time window for a long-term statistical period (1989-2018), the semi-deep aquifers of Golestan province, which is located in northern Iran, were considered as a research location for this purpose. The results demonstrated that the effect of meteorological drought does not immeddergiately manifest in the changes of the subsurface water table and the hydrological drought index. By adding the meteorological drought index with a 6-month lag step, the average air temperature, and the total rainfall from the previous 6 months as new variables, the correlation with the SWI index increases, so that in the best-case scenario, the M5 decision tree model provides the best result in predicting the SWI index. The second half of the year yielded a coe cient of determination of 0.92 and an error value of RMSE = 0.27 for the SPEI index. Among the meteorological drought indicators, the SPEI index, which is based on precipitation and evapotranspiration, created a stronger link with the SWI index, which highlights the signi cance of potential evapotranspiration. It is a warning that, as a result of global warming, subsurface water tables in this region may fall in the future.

Idojaras, 2022

Investigation of river flow volume in different conditions as a function of temperature and rainf... more Investigation of river flow volume in different conditions as a function of temperature and rainfall variables can be quite effective in understanding the hydrological and hydro-climatic conditions of the watershed. Multiple linear regression models were applied in estimating river flow in several studies due to their straightforwardness and appropriate interpretation of results. In this study, to overcome the limitations of the multiple linear regression model, the Bayesian quantile regression model was used to estimate the river flow volume as a function of rainfall and temperature, and the results were compared. The data and information used for the Qareh-Sou basin in northern Iran are of substantial environmental and socioeconomic importance. Five data series, including spring, summer, autumn, winter, and annual series, were created and used for this study. It was found that the Bayesian quantile regression model has considerable flexibility to model the volume of flow for different quantiles, predominantly upper and lower quantiles, and can be used to model high and low flows. With increasing the values of quantiles, a limited decreasing pattern in the effect of rainfall on the volume of flow was identified, which can be due to increasing the effect of other factors in the formation of extreme flows of the river. For summer data in high quantiles, the effect of rainfall on river flow volume shows an increasing pattern. This pattern is different from the other studied series, which may be due to the low base flow in summer. The results confirm that the application of Bayesian quantile regression compared to multiple linear regression leads to much more valuable information on the impact of rainfall and temperature on river flow volume.