| Home | << 1 2 >> |
|
Tariq, A., Beni, L. H., Ali, S., Adnan, S., & Hatamleh, W. A. (2023). An effective geospatial-based flash flood susceptibility assessment with hydrogeomorphic responses on groundwater recharge. Groundwater for Sustainable Development, 23, 100998.
Abstract: Floods are one of the most common natural disasters, resulting in the extensive destruction of infrastructure, property, and human life. The destructive potential of a flood depends on numerous factors, including the size of the flood, the rate of flooding, the time it takes for the water to move through a given area, the river’s planform and cross-section geometry, and other similar factors. The present study is a unique analysis of flood mapping that was accomplished with the help of the Analytical Hierarchy Process (AHP), Frequency Ratio (FR), and hydrogeomorphic response to floods by integrating geospatial analysis and unit stream power modeling. The Indus catchment region of Pakistan is where the subject topic is put into practice. According to the hydrologic analysis of the yearly peak discharge, the hydro-station in Gilgit-Baltistan can move boulders measuring up to 0.5 m in height during significant flooding. On the other hand, there will be no change to the geometry of the cross-section throughout 1980–2020 in Gilgit-Baltistan. The flood susceptibility map is constructed using data from twelve influencing parameters, including elevation, proximity to the drainage network, slope, drainage density, geomorphology, rainfall, the curvature of the topography, flow accumulation, geology, land use, Topographic Wetness Index (TWI), and Stream Power Index (SPI). The area under the curve (AUC) approach, which demonstrates a substantial degree of accuracy (85% and 83%), is utilized to evaluate the effectiveness of the AHP and FR. The current study fills the gaps between the geospatial approach and the hydrogeomorphic assessment of flood to determine flood susceptibility.
|
|
Sardo, M. S., & Jalalkamali, N. (2022). A system dynamic approach for reservoir impact assessment on groundwater aquifer considering climate change scenario. Groundwater for Sustainable Development, 17, 100754.
Abstract: With its arid and semi-arid climate, Iran claims about one-third of the world’s average annual precipitation. Accordingly, the present study investigated whether an effective water resources management (WRM) strategy (both groundwater and reservoir resources) could reduce groundwater drawdown while simultaneously providing secure enough water for preservation of agricultural activities and rural settlements. For this purpose, a comprehensive system dynamics (SD) model incorporating reservoir, surface-water, and groundwater resources was developed. Then, the model was implemented for the Nesa plain in Bam County, Iran, as an example. In this plain, the construction of a dam to supply drinking water to the cities of Bam and the Bam Industrial Zone had devastated the environment and human communities in the downstream areas, leading to the depopulation of as many as 104 villages in the Bam region. The results of the SD model revealed that the artificial recharge of the plain groundwater aquifer along with the management of the operation of the wells and increasing productivity would be very effective. In order to estimate future precipitation data, the SDSM statistical exponential microscale model was used to microscale the large CanESM2 scale model under two scenarios of RCP4.5 and RCP8.5. The continuation of the current trend of the groundwater resources in the plain during the next 20 years will also cause a drop in water level of 8.3 m compared with the existing situation and a reduction of 41 m compared with the long-term average of 1980. Based on this modeling effort, upon releasing 60% of river flow, surplus to downstream demand, for recharging aquifer through artificial recharge projects, the rate of water table fall will decline significantly over a 20-year period and the amount of negative aquifer water balance would most likely improve from 65.5 to 35.17 million cubic meters annually.
|