Development of a Fast and Accurate Hybrid Model for Floodplain Inundation Simulations

High computational cost is often the most limiting factor when running high-resolution hydrodynamic models to simulate spatial-temporal flood inundation behavior. To address this issue, a recent study introduced the hybrid Low-fidelity, Spatial analysis, and Gaussian Process learning (LSG) model. The LSG model simulates the dynamic behavior of flood inundation extent by upskilling simulations from a low-resolution hydrodynamic model through Empirical Orthogonal Function (EOF) analysis and Sparse Gaussian Process learning. However, information on flood extent alone is often not sufficient to provide accurate flood risk assessments. In addition, the LSG model has only been tested on hydrodynamic models with structured grids, while modern hydrodynamic models tend to use unstructured grids. This study therefore further develops the LSG model to simulate water depth as well as flood extent and demonstrates its efficacy as a surrogate for a high-resolution hydrodynamic model with an unstructured grid. The further developed LSG model is evaluated on the flat and complex Chowilla floodplain of the Murray River in Australia and accurately predicts both depth and extent of the flood inundation, while being 12 times more computationally efficient than a high-resolution hydrodynamic model. In addition, it has been found that weighting before the EOF analysis can compensate for the varying grid cell sizes in an unstructured grid and the inundation extent should be predicted from an extent-based LSG model rather than deriving it from water depth predictions.Niels Fraehr, Quan J. Wang, Wenyan Wu, and Rory Natha

An adaptive multi-fidelity optimization framework based on co-Kriging surrogate models and stochastic sampling with application to coastal aquifer management

Surrogate modelling has been used successfully to alleviate the computational burden that results from high-fidelity numerical models of seawater intrusion in simulation-optimization routines. Nevertheless, little attention has been given to multi-fidelity modelling methods to address cases where only limited runs with computationally expensive seawater intrusion models are considered affordable imposing a limiting factor for single-fidelity surrogate-based optimization as well. In this work, a new adaptive multi-fidelity optimization framework is proposed based on co-Kriging surrogate models considering two model fidelities of seawater intrusion. The methodology is tailored to the needs of solving pumping optimization problems with computationally expensive constraint functions and utilizes only small high-fidelity training datasets. Results from both hypothetical and real-world optimization problems demonstrate the efficiency and practicality of the proposed framework to provide a steep improvement of the objective function while it outperforms a comprehensive single-fidelity surrogate-based optimization method. The method can also be used to locate optimal solutions in the region of the global optimum when larger high-fidelity training datasets are available

Lower Scotts Creek Floodplain and Habitat Enhancement Project

Scotts Creek, located in northern Santa Cruz County, maintains the southernmost persistent population of Central California Coast (CCC) Coho Salmon (endangered) in addition to CCC steelhead (threatened). Fisheries biologists believe overwinter mortality due to lack of refuge habitat is the primary factor limiting salmonid production. Instream rearing habitat may also be limiting, especially during drought years. The legacy effects of historic land use practices, including dredging, wood removal, and the construction of levees, continued to limit refuge and rearing opportunities. A restoration project was implemented to improve refuge and rearing opportunities for salmonids along lower Scotts Creek by removing portions of the deteriorating levee, grading new connections with existing off-channel features, enhancing tributary confluences, constructing alcove habitat features at the margins of the stream channel, and constructing large wood complexes (LWCs) instream. Novel restoration techniques were employed on an experimental basis. Whole in-situ alder trees were pushed into the stream channel with their root systems left partially intact to establish living key pieces. Individual log, boulder, and rootwad LWC components were attached together with couplers that permitted some freedom of independent movement among the individual components. LWCs were braced against live, standing trees and stabilized with boulder ballasts placed on the streambed, which eliminated excavation of the streambed/banks and the need to dewater or divert the stream during construction. Project performance, changes to physical habitat characteristics, and changes to stream morphology associated with implementation were monitored using habitat assessment methods derived from the California Department of Fish and Wildlife’s (CDFW) salmonid habitat survey protocol (Flosi et al. 2010), and topographic survey techniques and data analysis adapted from Columbia Habitat Monitoring Protocol (Bouwes et al. 2011). Preliminary results indicated that LWCs remained stable and functional. In addition, implementation of the restoration project increased pool frequency, low-flow pool volume, instream cover, frequency of instream, alcove, and off-channel refuge habitat features, and frequency of points of connectivity with the floodplain. Long-term monitoring will be required to determine the survivorship, decay rates, and overall persistence of alder recruits