Abstract. Downstream changes in particle size that occur in the Waipaoa River, a 104-km-long gravel bed river in which rapid aggradation in the historic (post-1800) period was triggered by the conversion of native forest to pasture, are summarized in this paper. The textural data presented are unique for a field situation, not only because of the spatial resolution and extent of the sampling program but also because they provide information about the pattern of fining at different points in time. They are supported by equally comprehensive topographic survey data from which local rates of aggradation can be derived. Despite variability induced by lateral sediment inputs, there is an essentially continuous pattern of fining along the entire length of the river. Fining occurs in both the fine and coarse size fractions of the bed material. The highest rates of fining occur in the larger percentiles of the subsurface bed material and in the surface bed material. Downstream fining in the Waipaoa River appears to be a response to changes in flow hydraulics that are regulated by the concave configuration of the long profile. The fining gradient developed rapidly (in <45 years). It does not appear to be influenced by the rate of aggradation (nor the overall rate of sediment supply to the channel system), because, in the short term, aggradation has a negligible impact on the inherited form of the long profile.
The MW7.8 14 November 2016 Kaikoura earthquake generated more than 10000 landslides over a total area of about 10000 km2, with the majority concentrated in a smaller area of about 3600 km2. The largest landslide triggered by the earthquake had an approximate volume of 20 (±2) M m3, with a runout distance of about 2.7 km, forming a dam on the Hapuku River. In this paper, we present version 1.0 of the landslide inventory we have created for this event. We use the inventory presented in this paper to identify and discuss some of the controls on the spatial distribution of landslides triggered by the Kaikoura earthquake. Our main findings are: 1) the number of medium to large landslides (source area 10000 m2) triggered by the Kaikoura earthquake is smaller than for similar sized landslides triggered by similar magnitude earthquakes in New Zealand; 2) seven of the largest eight landslides (from 5 to 20 x 106 m3) occurred on faults that ruptured to the surface during the earthquake; 3) the average landslide density within 200 m of a mapped surface fault rupture is three times that at a distance of 2500 m or more from a mapped surface fault rupture ; 4) the "distance to fault" predictor variable, when used as a proxy for ground-motion intensity, and when combined with slope angle, geology and elevation variables, has more power in predicting landslide probability than the PGA or PGV variables typically adopted for modelling; and 5) for the same slope angles, the coastal slopes have landslide point densities that are an order of magnitude greater than those in similar materials on the inland slopes, but their source areas are significantly smaller.
A New Zealand Landslide Database has been developed to hold all of New Zealand's landslide data and provide factual data for use in landslide hazard and risk assessment, including a probabilistic landslide hazard model for New Zealand, which is currently being developed by GNS Science. Design of a national Landslide Database for New Zealand required consideration of existing landslide data stored in a variety of digital formats and future data yet to be collected. Pre-existing landslide datasets were developed and populated with data reflecting the needs of the landslide or hazard project, and the database structures of the time. Bringing these data into a single database required a new structure capable of containing landslide information at a variety of scales and accuracy, with many different attributes. A unified data model was developed to enable the landslide database to be a repository for New Zealand landslides, irrespective of scale and method of capture. Along with landslide locations, the database may contain information on the timing of landslide events, the type of landslide, the triggering event, volume and area data, and impacts (consequences) for each landslide when this information is available. Information from contributing datasets include a variety of sources including aerial photograph interpretation, field reconnaissance and media accounts. There are currently 22,575 landslide records in the database that include point locations, polygons of landslide source and deposit areas, and linear landslide features. Access to all landslide data is provided with a web application accessible via the Internet. This web application has been developed in-house and is based on open-source software such as the underlying relational database (PostGIS) and the map generating Web Map Server (GeoServer). Future work is to develop automated data-upload routines and mobile applications to allow people to report landslides, adopting a consistent framework.
Tens of thousands of landslides were generated over 10,000 km2 of North Canterbury and Marlborough as a consequence of the 14 November 2016, Mw7.8 Kaikōura Earthquake. The most intense landslide damage was concentrated in 3500 km2 around the areas of fault rupture. Given the sparsely populated area affected by landslides, only a few homes were impacted and there were no recorded deaths due to landslides. Landslides caused major disruption with all road and rail links with Kaikōura being severed. The landslides affecting State Highway 1 (the main road link in the South Island of New Zealand) and the South Island main trunk railway extended from Ward in Marlborough all the way to the south of Oaro in North Canterbury. The majority of landslides occurred in two geological and geotechnically distinct materials reflective of the dominant rock types in the affected area. In the Neogene sedimentary rocks (sandstones, limestones and siltstones) of the Hurunui District, North Canterbury and around Cape Campbell in Marlborough, first-time and reactivated rock-slides and rock-block slides were the dominant landslide type. These rocks also tend to have rock material strength values in the range of 5-20 MPa. In the Torlesse ‘basement’ rocks (greywacke sandstones and argillite) of the Kaikōura Ranges, first-time rock and debris avalanches were the dominant landslide type. These rocks tend to have material strength values in the range of 20-50 MPa. A feature of this earthquake is the large number (more than 200) of valley blocking landslides it generated. This was partly due to the steep and confined slopes in the area and the widely distributed strong ground shaking. The largest landslide dam has an approximate volume of 12(±2) M m3 and the debris from this travelled about 2.7 km2 downslope where it formed a dam blocking the Hapuku River. The long-term stability of cracked slopes and landslide dams from future strong earthquakes and large rainstorms are an ongoing concern to central and local government agencies responsible for rebuilding homes and infrastructure. A particular concern is the potential for debris floods to affect downstream assets and infrastructure should some of the landslide dams breach catastrophically. At least twenty-one faults ruptured to the ground surface or sea floor, with these surface ruptures extending from the Emu Plain in North Canterbury to offshore of Cape Campbell in Marlborough. The mapped landslide distribution reflects the complexity of the earthquake rupture. Landslides are distributed across a broad area of intense ground shaking reflective of the elongate area affected by fault rupture, and are not clustered around the earthquake epicentre. The largest landslides triggered by the earthquake are located either on or adjacent to faults that ruptured to the ground surface. Surface faults may provide a plane of weakness or hydrological discontinuity and adversely oriented surface faults may be indicative of the location of future large landslides. Their location appears to have a strong structural geological control. Initial results from our landslide investigations suggest predictive models relying only on ground-shaking estimates underestimate the number and size of the largest landslides that occurred.
We use a mapped landslide inventory coupled with a 2-m resolution vertical difference model covering an area of 6,875 km 2 to accurately constrain landslide volume-area relationships. We use the difference model to calculate the source volumes for landslides triggered by the M W 7.8 Kaikōura, New Zealand, earthquake of 14 November 2016. Of the 29,519 mapped landslides in the inventory, 28,394 are within the analysis area, and of these, we have calculated the volume of 17,256 source areas that are ≥90% free of debris. Of the 28,394 landslides, about 80% are classified as soil or rock avalanches and the remainder as mainly translational slides. Our results show that both the soil avalanches and the rock avalanches, ignoring their source geology, have area to volume power-law scaling exponents (γ) of 0.921 to 1.060 and 1.040 to 1.138, respectively. These are lower than the γ values of 1.1-1.3 (for soil) and 1.3-1.6 (for rock) reported in the literature for undifferentiated landslide types. They are, however, similar to those γ values estimated from other coseismic landslide inventories. In contrast, for 50 selected rotational, translational (planar slide surfaces), or compound slides, where much of the debris remains in the source area, we found γ values range between 1.46 and 1.47, indicating that their slide surfaces were considerably deeper than those landslides classified as avalanches. This study, like previous studies on coseismic landslides, shows that soil and rock avalanches (disrupted landslides) are the dominant landslide type triggered by earthquakes and that they tend to be shallow.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.