Effects of hydrological events on morphological evolution of a fluvial system

Abstract

This study quantifies morphological evolution of the Dez River, Iran, from 1955 to 2016. The approach uses a sequence of Landsat images, aerial photos, and topographic maps. In addition, the hydrological data including average daily discharge and yearly maximum discharge at the Dezful hydrological station for the period (1955-2016) were used. The study reach was divided into 48 meander loops from upstream to downstream. Active channel width (w) was determined at 10 m intervals and changes assessed along the study reach of the Dez River. Morphological indices including sinuosity index; straight meander length; centerline flow length; erosion area; erodible length channel migration; centerline elongation; and radius of curvature were calculated in the reach. Results showed that the study reach of the Dez River changed dramatically in response to major floods, although the general trend is towards a narrowing of active channel width by 38% in the period 1955-2016. Results show that most of the meander loops in the study area extended and expanded. Between 1989 and 1995, all types of meander change were observed. There was a direct correlation between the frequency of hydrological events (flood days) bigger than 2-years return period and elongation of bends. 1. Introduction Rivers represent an important and dynamic landscape feature in the world's great plains and valleys. Fluvial systems in these regions are, however, under threat, coming under increasing pressure from growing populations and the demand placed on water, particularly in drier zones. River systems are used as a source of energy, food, transport and subjected to geopolitics (Surian and Rinaldi, 2003; Yousefi et al., 2017a). Fluvial ecosystems are among the most threatened ecosystems in the world, where pollution, urbanization, bio-invasion or changes in water regime results in degraded landscapes (Das et al., 2014; Luppi et al., 2009a; Thakur et al., 2012). Change in river morphology results in adjustment of other morphological and ecological characteristics until the river adapts to the new conditions (

Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran

“…However, there are no universal guidelines on choosing influential factors for each natural disaster. Therefore, based on the relevant literature [5,47,[53][54][55][56][57][58][59], different topo-hydrological, geo-environmental, and morphometric factors were selected for testing ( Figure 3). The first step of the study is to record the location of disaster occurrences (snow-avalanches, rockfalls, and floods) over previous years (2010-2018).…”

“…Avalanches, rockfalls, and floods frequently occur in mountainous regions of Iran and cause severe damage to buildings, people, and natural environments [11,12,46,47]. This study investigates multi-hazard exposure using different machine learning approaches due to the following reasons: (1) Machine learning (ML) is a subfield of artificial intelligence where models can learn and improve themselves based on historical events; (2) ML models can easily identify trends and patterns in a large volume of data and involve continuous improvement during operation, which lets them make better decisions [48]; (3) they are capable of handling data that are multi-dimensional and multi-variety [30]; and (4) they can directly extract knowledge of natural disaster processes based on previous disaster occurrences and geo-environmental factors without human intervention, thus, they do not need experts' experiences and judgements to determine the importance of predictive variables [49].…”

“…47,[53][54][55][56][57][58][59], different topo-hydrological, geo-environmental, and morphometric factors were selected for testing (Figure 3).Remote Sens. 2019, 11, x FOR PEER REVIEW 5 of Predictive factors: (a) Elevation, (b) TPI, (c) TRI, (d) TWI, (e) LS, (f) RSP, (g) VRM, (h) WEI, (i) aspect, (j) slope, (k) distance from stream, (l) profile curvature, (m) lithology, and (n) land use.…”

Prediction of factors affecting activation of soil erosion by mathematical modeling at pedon scale under laboratory conditions

“…Researchers have accurately identified different forms of soil erosion that can occur under different environmental conditions, due to rain, wind, human, or animal influences [15][16][17][18][19] or caused by morphological and hydrogeological factors 20,21 . Soil erosion induced by human activities, such as agriculture, could be minimized if correct monitoring and assessment were carried out to develop efficient preventive measures.…”

Estimation and prediction of the oscillation pattern of meandering geometry in a sub-catchment basin of Bhagirathi-Hooghly river, West Bengal, India

“…The character of fluctuating discharge is a significant factor for migration. The quantity of discharge is responsible for altering meandering geometry, bend curvature, which hastens the speed of cut-off [5][6][7]. For accelerating the bank erosion procedure, flow velocity affects the river bank's convex and concave side.…”