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Regional debris flow susceptibility analysis in mountainous peri-urban areas through morphometric and land cover indicators
Published in María Carolina Rogelis Prada, Operational Flood Forecasting, Warning and Response for Multi-Scale Flood Risks in Developing Cities, 2020
Regarding the hypsometric indicator, since the hypsometric integral decreases as mass is removed from the watershed it follows that an inverse relationship between hypsometric skewness and the hypsometric integral exists [Harlin, 1984]. This condition was found in the study area with a determination coefficient of 0.71. The same behaviour is exhibited by the density skewness (R2=0.82) and hypsometric Kurtosis (R2=0.45), where small values are characteristic of large integral values and small skewness. The density kurtosis shows no correlation with the hypsometric integral, this is reflected in the low correlation of this parameter with the corresponding principal component in the analysis (see Table 2.2). Headward erosion that starts at the lower reaches would represent a higher possibility of debris flow affecting the urbanized fans of the watersheds; therefore this increase in susceptibility would be represented by high hypsometric integrals, low hypsometric skewness and negative density skewness. Furthermore, according to Cohen et al. [2008] higher hypsometric integral values (greater than 0.5) represent catchments dominated by diffusive erosion processes (concave down hypsometric curve) while lower values (less than 0.5) represent fluvial dominated catchments (concave up hypsometric curve). Therefore the hypsometric integral is linked to erosion processes, landform curvature and landscape morphology.
The age and origin of Sydney Harbour and the Parramatta River: the Cenozoic history of the coastal rivers of central New South Wales
Published in Australian Journal of Earth Sciences, 2023
Second, rather than following a more straightforward route downstream of Drummoyne, the river cuts through the upland barrier of the Hornsby Plateau to reach the sea at Port Jackson. Three alternative mechanisms may be employed to explain discordant drainage patterns of this sort. The first is that the river has developed along a pre-existing line of weakness. The main part of Sydney Harbour is aligned parallel to a suite of local, east-southeast-aligned dyke and joint sets (Herbert, 1983a; Herbert, 1983b, pp. 105–106; Norman, 1968, p. 13, figure 44) and lies along the northern edge of the Lachlan Transverse Zone (Figure 8a), which appears to form a broad trough on the eastern side of the Cumberland Basin (Herbert, 1983c, p. 115). These features may have controlled the alignment of a downcutting stream or the location of a headwardly eroding river. It is unlikely, however, that they could have directed the course of a river across an established topographic barrier. Unless headward erosion captured an existing basin on the inland side of the obstacle, it is difficult to see how the catchment of the river could have become sufficiently enlarged to create a regional-scale drainage path. Significantly, there is no evidence in the landscape of either stream piracy or drainage re-routing to support the idea of an antecedent upstream basin whose discharge has been redirected along the lower Parramatta River.
Risk models for assessing the derived disasters caused by watershed landslides using environmental indicators
Published in Geomatics, Natural Hazards and Risk, 2020
Chao-Yuan Lin, Tzu-Ching Chen, Cheng-Yu Lin
By extending the model proposed by Lin et al. (2017) for delineating large-scale landslides, this study developed a risk assessment model for derived disasters in watersheds. The original model was divided into two parts: the landslide risk of the watersheds (Equation 1) and landslide scale. The landslide risk was derived from multiplying hazard (rainfall measured during event) with vulnerability (areas of weak planes); maximum daily rainfall for landslide events induced by Typhoon Morakot was employed for the calculation of hazard, whereas rainfall during event, road development, and green deterioration indexes composed vulnerability, which was then corrected by slope (Equation 2). The potential of landslide scale was estimated using river concavity, headward erosion, and dip slope indexes (Equation 3), and was corrected by vegetation index to reflect slope debris amount. Due to unit variation, the indices used in this study were all normalized within the range of 0–1 before calculations. To assess the potential of the derived disasters caused by watershed landslides on protected objects (lands reserved for indigenous people), spatial distribution of related environmental indicators for the subdivisions encompassed by indigenous reserved lands were extracted from the original model (Figure 3).
Application of NRCS-CN method for estimation of watershed runoff and disaster risk
Published in Geomatics, Natural Hazards and Risk, 2019
The Chenyoulan watershed is susceptible to flooding when a typhoon or rainstorm event occurs, which has resulted in the scouring of riverbanks, collapse of side slopes, and even headward erosion. These can affect a wide area and take a considerable time to recover, thus hampering economic development, and posing a direct threat to the safety of lives and properties. For this reason, the importance of identifying potential flooded areas quickly, accurately, and at a low cost cannot be overemphasized. This study employed big data of which the hazard and vulnerability data were dynamic and were updated with hydrologic and soil data—for the mapping of flood risk areas. Typhoon Morakot was used as a case study, and its rainfall data were used as the hazard value for runoff depth estimation (Figure 9(b)), which was corrected with monitored data (Figure 9(c)). By contrast, vulnerability data were estimated based on TWIs (Figure 9(d)). Flooding risk was then determined as a probability resulting from the interplay between the hazard and vulnerability values (Figure 9(e)). The assessment of estimated damage and actual damage was conducted by clustering flooding risks into 10 grades. These data were then compared with the sites actually struck by disasters during Typhoon Morakot (Yang et al. 2009). Figure 10 shows the risk grades of the disaster sites, which all fell in the range of 1 − 7. The grading of the flooding risks also provides some insights into the relationship between the flooding risks and number of disaster sites (Figure 11).