S. S. Eslamian, A. Zarei, A. Abrishamchi,
Volume 8, Issue 1 (4-2004)
Abstract
An approach for regional low flow frequency analysis is to use multiple regression techniques for obtaining relationships between low flows with specific return periods and catchments characteristics. In this paper, this method has been used. After single-site frequency analysis for 20 stream gauging stations, homogeneity test was performed. Regional relationships between low flows with return periods 2 , 5 , 10, and 20 years and catchments characteristics were derived.
For this purpose, catchment area, mean elevation, minimum elevation, shape factor, main channel length, length of main chanel from catchment centroid to outlet, forest area, mean annual rainfall, and mean catchment slope as model inputs were examined and cachment area, mean elevation, and mean catchment slope entered to the models. Finally, the mean relative error of models for different return period, 2, 5, 10, and 20 years, was computed 41.1, 41.3, 45.0, 47.2 percent, respectively that in comparison with other studies, it displays smaller errors.
A.r. Emadi, S. Fazeli, M. Hooshmand, S. Zamanzad-Ghavidel, R. Sobhani,
Volume 27, Issue 1 (5-2023)
Abstract
The agricultural sector as one of the most important sectors of water consumption has great importance for the sustainability of the country's water resources systems. The objective of this study was to estimate the river water abstraction (RWA) for agricultural consumption in the study area of Nobaran in the Namak Lake basin. The RWA was estimated using variables related to morphological, hydrological, and land use factors, as well as a combination of their variables collected through field sampling. Data mining methods such as adaptive-network-based fuzzy inference systems (ANFIS), group method of data handling (GMDH), radial basis function (RBF), and regression trees (Rtree) were also used to estimate the RWA variables. In the current study, the GMDH24 model with a combined scenario including the variables of river width, river depth, minimum flow, maximum flow, average flow, crop, and the garden cultivated area was adopted as the best model to estimate the RWA variable. The RMSE value for the combined scenario of the GMDH24 model was found to be 0.046 for estimating RWA in the Nobaran study area. The results showed that the performance of the GMDH24 model for estimating RWA for maximum values is very acceptable and promising. Therefore, modeling and identifying various variables that affect the optimal RWA rate for agricultural purposes fulfills the objectives of integrated water resources management (IWRM).
I. Kazemi Roshkhari, A. Asadi Vaighan, M. Azari,
Volume 28, Issue 1 (5-2024)
Abstract
Due to climate change and human activities, the quality and quantity of water have become the most important concern of most of the countries in the world. In addition, changes in land use and climate are known as two important and influential factors in discharge. In this research, four climate change models including
HADGEM2-ES, GISS-E-R, CSIRO-M-K-3-6-0, and CNRM-CM5.0 under two extreme scenarios RCP2.6 and RCP8.5 were used as climate change scenarios in the future period of 2020-2050. The future land use scenario (2050) was prepared using the CA-Markov algorithm in IDRISI software using land use maps in 1983 and 2020. The SWAT model was calibrated to better simulate hydrological processes from 1984 to 2012 and validated from 2013 to 2019 and was used to evaluate the separate and combined effects of climate change and land use on discharge. The prediction of the climate change impact on discharge showed a decrease in most of the models under the two scenarios RCP2.6 and RCP8.5. The average maximum decrease and increase under the RCP2.6 scenario is 60 and 30 percent, respectively. This significant reduction is greater than that predicted under the RCP8.5 scenario. Examining the combined effects of climate and land use change revealed that the average decrease in discharge in the months of October, November, December, and January under two scenarios is 46.2 and 58%, respectively. The average increase in discharge is predicted to be 47% under the RCP8.5 in the months of April and May in the HadGEM2ES.
