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The Jishishan Ms 6.2 earthquake occurred at 23:59 on December 18, 2023 in Gansu Province, China. We conducted a field survey to assess the hazards and damages caused by the earthquake and its associated geo-activities. Subsequently, we organized a seminar to discuss the possible causes of the destruction of a prehistoric site-Lajia Settlement-dated back to four thousand years B.P. and located only several kilometers away from the epicenter of the Jishishan earthquake. The Jishishan earthquake was unique for its hazard and disaster process, which featured ground shaking and a series of complex geological and geomorphological activities: sediment and soil spray piles, liquefaction, collapse, landslide, and mudflow along water channels. We define this phenomenon as the Jishishan earthquake ripple hazard (JERH). The most recent evidence from the JERH suggests that a prehistoric earthquake similar to the JERH, instead of riverine floods or earthquake-induced landslide dam outburst flood, as previously hypothesized, destroyed the Lajia Settlement.
RESUMEN
Artificial dams are one of the most common hydraulic structures for mitigating debris flow disasters in alpine valley regions. However, performance alteration and failure after successive debris flows can lead to dam failure, releasing large amounts of materials within a very short time; moreover, the contribution of artificial dam failures to debris flows is poorly understood. This study quantitatively analyzed the artificial dam failure effects based on the numerical simulations of the Zhouqu '8.8' debris flow, with three scenarios: all nine dams failed (S1); no dams were ever built (S2); all nine dams remained intact (S3). The results showed that artificial dam failures had a significant amplifying effect on the magnitude of a debris flow. The maximum velocity and flow depth decreased by 20% and 11.2% if all the dams did not collapse; comparison of S1 and S2 showed that discharge and velocity at the front of the debris flow increased by 54.6% and 89%, the bulk density and yield stress increased by 3.3% and 5.7%, due to artificial dam failures. This could increase the destructive capacity of a debris flow and the possibility of a river blockage. A single artificial dam failure could locally amplify the magnitude of debris flow. Overall, on the catchment scale, the magnitude of a debris flow was dominated by topography and channel geometry, which can reduce the amplification effect of dam failures at locations where the channel was curved. However, where the channel was straight and flat, the flow velocity and discharge increased cumulatively by 3 m/s and 637 m3/s due to cascading failure. In addition, a comprehensive scheme combining ecological and engineering measures to mitigate debris flow disasters is discussed. This quantitative study is important and urgent needed to understand the amplification effect of dam failures and to implement debris flow mitigation in alpine valley regions.
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Desastres , Ríos , China , IngenieríaRESUMEN
Geological conditions along the Karakorum Highway (KKH) promote the occurrence of frequent natural disasters, which pose a serious threat to its normal operation. Landslide susceptibility mapping (LSM) provides a basis for analyzing and evaluating the degree of landslide susceptibility of an area. However, there has been limited analysis of actual landslide activity processes in real-time. The SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) method can fully consider the current landslide susceptibility situation and, thus, it can be used to optimize the results of LSM. In this study, we compared the results of LSM using logistic regression and Random Forest models along the KKH. Both approaches produced a classification in terms of very low, low, moderate, high, and very high landslide susceptibility. The evaluation results of the two models revealed a high susceptibility of land sliding in the Gaizi Valley and the Tashkurgan Valley. The Receiver Operating Characteristic (ROC) curve and historical landslide verification points were used to compare the evaluation accuracy of the two models. The Area under Curve (AUC) value of the Random Forest model was 0.981, and 98.79% of the historical landslide points in the verification points fell within the range of high and very high landslide susceptibility degrees. The Random Forest evaluation results were found to be superior to those of the logistic regression and they were combined with the SBAS-InSAR results to conduct a new LSM. The results showed an increase in the landslide susceptibility degree for 2808 cells. We conclude that this optimized landslide susceptibility mapping can provide valuable decision support for disaster prevention and it also provides theoretical guidance for the maintenance and normal operation of KKH.