Integrated Modeling Approach for the Development of Climate-Informed, Actionable Information

Abstract

Flooding is a prevalent natural disaster with both short and long-term social, economic, and infrastructure impacts. Changes in intensity and frequency of precipitation (including rain, snow, and rain-on-snow) events create challenges for the planning and management of resilient infrastructure and communities. While there is general acknowledgment that new infrastructure design should account for future climate change, no clear methods or actionable information are available to community planners and designers to ensure resilient designs considering an uncertain climate future. This research demonstrates an approach for an integrated, multi-model, and multi-scale simulation to evaluate future flood impacts. This research used regional climate projections to drive high-resolution hydrology and flood models to evaluate social, economic, and infrastructure resilience for the Snohomish Watershed, WA, USA. Using the proposed integrated modeling approach, the peaks of precipitation and streamflows were found to shift from spring and summer to the earlier winter season. Moreover, clear non-stationarities in future flood risk were discovered under various climate scenarios. This research provides a clear approach for the incorporation of climate science in flood resilience analysis and to also provides actionable information relative to the frequency and intensity of future precipitation events.

“Garbage in, Garbage Out” Does Not Hold True for Indigenous Community Flood Extent Modeling in the Prairie Pothole Region

“…Various modeling approaches have been proposed and reviewed for flood mapping, such as hydrological models [50], hydrodynamic approaches [51,52], and integrated modeling [53]. In the context of Prairies, however, the combined WDPM and DEM process provided a diagnostic approach to assess flood extent with minimum data requirements.…”

Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks

“…Floods are one of the most devastating and deadliest natural disasters affecting human life worldwide (Dassanayake, Burzel, & Oumeraci, 2015; Elnazer, Salman, & Asmoay, 2017; Kousky, 2018). Originated by climatic‐hydrologic causes, the flood phenomenon refers to a situation where the river flow and water level rise unexpectedly (Judi, Rakowski, Waichler, Feng, & Wigmosta, 2018; Schumm & Lichty, 1965). Flood intensity is dependent on the geographical location and the climatic and geological conditions (Li, Wu, Dai, & Xu, 2012; Testa, Zuccala, Alcrudo, Mulet, & Soares‐Frazão, 2007).…”

An Assessment of the Influence of Uncertainty in Temporally Evolving Streamflow Forecasts on Riverine Inundation Modeling

“…Mapping inundation in a timely and accurate manner for the consequential floods caused by major weather events is never easy and depends on a cascading chain of model structures with uncertainties accumulated at every node. A typical model chain usually takes an ensemble of numerical weather predictions (NWPs) as the boundary conditions to force a hydrologic model to simulate rainfall-runoff processes over the catchment and then adopts the simulated overland runoff as the successive boundary conditions to drive a hydraulic channel routing model to predict streamflow [1][2][3]. The chain of models coupled at various scales makes up the backbone of the current continental-scale and global-scale streamflow forecasting platforms, such as the European Flood Forecasting System (EFFS), the U.S. National Oceanic and Atmospheric Administration's National Water Model (NWM), or the Global Flood Awareness System (GloFAS).…”