Detailed studies on the sediment budget may reveal valuable insights into the successive build-up of the Canterbury Plains and their modification by Holocene fluvialaction connected to major braided rivers. Additionally, they bear implications beyond these fluvial aspects. Palaeoseismological studies claim to have detected signals of major Alpine Fault earthquakes in coastal environments along the eastern seaboard of the South Island (McFadgen and Goff, 2005). This requires high connectivity between the lower reaches of major braided rivers and their mountain catchments to generate immediate significant sediment pulses. It would be contradictory to the above mentioned hypothesis though. Obtaining better control on sediment budgets of braided rivers like the Waimakariri River will finally add significant value to multiple scientific and applied topics like regional resource management. An essential first step of sediment budget studies Is to systematically map the geomorphology, conventionally in the field and/or using remote-sensing applications, to localise, genetically identify, and classify landforms or entire toposequences of the area being investigated. In formerly glaciated mountain environments it is also indispensable to obtain all available chronological information supporting subsequent investigations.
Geospatial liquefaction models aim to predict liquefaction using data that is free and readily-available. This data includes (i) common ground-motion intensity measures; and (ii) geospatial parameters (e.g., among many, distance to rivers, distance to coast, and Vs30 estimated from topography) which are used to infer characteristics of the subsurface without in-situ testing. Since their recent inception, such models have been used to predict geohazard impacts throughout New Zealand (e.g., in conjunction with regional ground-motion simulations). While past studies have demonstrated that geospatial liquefaction-models show great promise, the resolution and accuracy of the geospatial data underlying these models is notably poor. As an example, mapped rivers and coastlines often plot hundreds of meters from their actual locations. This stems from the fact that geospatial models aim to rapidly predict liquefaction anywhere in the world and thus utilize the lowest common denominator of available geospatial data, even though higher quality data is often available (e.g., in New Zealand). Accordingly, this study investigates whether the performance of geospatial models can be improved using higher-quality input data. This analysis is performed using (i) 15,101 liquefaction case studies compiled from the 2010-2016 Canterbury Earthquakes; and (ii) geospatial data readily available in New Zealand. In particular, we utilize alternative, higher-quality data to estimate: locations of rivers and streams; location of coastline; depth to ground water; Vs30; and PGV. Most notably, a region-specific Vs30 model improves performance (Figs. 3-4), while other data variants generally have little-to-no effect, even when the “standard” and “high-quality” values differ significantly (Fig. 2). This finding is consistent with the greater sensitivity of geospatial models to Vs30, relative to any other input (Fig. 5), and has implications for modeling in locales worldwide where high quality geospatial data is available.
