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 acknowledgement that new infrastructure design should account for future climate change, no clear methods or actionable information is available to community planners and designers to ensure resilient design considering an uncertain climate future. This research used climate projections to drive high-resolution hydrology and flood models to evaluate social, economic, and infrastructure resilience for the Snohomish Watershed, WA, U.S.A. The proposed model chain has been calibrated and validated. Based on the established model chain, the peaks of precipitation and streamflows were found to shift from spring and summer to earlier winter season. The nonstationarity of peak discharges was discovered with more frequent and severe flood risks projected. The peak discharges were also projected to decrease for a certain period in the near future, which might be due to the reduced rain-on-snow events. This research was expected to provide a clear method for the incorporation of climate science in flood resilience analysis and to also provide actionable information relative to the frequency and intensity of future precipitation events.

A partition of the combined impacts of socioeconomic development and climate variation on economic risks of riverine floods

“…The Bulletin 17C flood‐frequency method (England et al, ) was used to quantify the frequency distribution of annual peak discharge for both historic and projected flow conditions. The Bulletin 17C analysis showed that the RCP4.5 pathway had slightly larger peak flows than the RCP8.5 in the less frequent but more destructive extreme events (Judi et al, ). The RCP8.5 pathway was also found to have relatively lower peaks than RCP4.5 by Madsen, Lawrence, Lang, Martinkova, and Kjeldsen () as a result of reduced winter snowpacks due to increased winter temperatures, so the RCP 4.5 was chosen for use in this study to represent the flood hazard with more serious consequences.…”

“…The RCP8.5 pathway was also found to have relatively lower peaks than RCP4.5 by Madsen, Lawrence, Lang, Martinkova, and Kjeldsen () as a result of reduced winter snowpacks due to increased winter temperatures, so the RCP 4.5 was chosen for use in this study to represent the flood hazard with more serious consequences. The DHSVM model had been previously calibrated using observed flows from the same USGS stream gauge on the Snohomish River for the 1983–1993 period with a validation period from 1996 to 2006 and using weather forcing data from the NLDAS‐2 (Judi et al, ). By comparison, the annual peak discharge of the projected climate state was 19, 22, and 24% larger than the annual extreme of the historic climate state for the 10, 100, and 1,000‐year return intervals, respectively (Figure ).…”

“…The historic flow condition was developed based on the streamflow observations (1964–2014) from the United States Geological Survey (USGS) stream gauge (#12150800, 47°49′52″N, 122°02′50″W) located on the Snohomish River, downstream of the Snoqualmie River and Skykomish River confluence (Figure ). The future flow condition is represented using a projected annual peak discharge distribution derived from downscaled climate projections (Judi et al, ). Specifically, the climate outputs of the general circulation model from the Platform for Regional Integrated Modelling and Analysis were extracted for the Snohomish basin (Gao et al, ; Kraucunas et al, ) and dynamically downscaled using the Weather Research and Forecasting model (Skamarock et al, ).…”

“…The objective of this study is, therefore, to propose and demonstrate a methodology to quantify the contributions of climate change and socioeconomic development to flood risk, using best available data and a multiscale, multimodel approach. The adopted multiscale, multimodel approach consisted of (a) using existing downscaled projections of temperature and precipitation to drive distributed hydrologic modelling, (b) developing historic and projected flood frequency distributions and associated flood extents, (c) creating spatial intersections between flood extents and three historic urban development conditions, and (d) quantifying flood loss using infrastructure fragility relationships (Judi, Rakowski, Waichler, Feng, & Wigmosta, ). In this case, the three historic development conditions span the socioeconomic states just before and after the 2007–2008 global financial crisis.…”

Flood Risk Reduction from Agricultural Best Management Practices

“…For the flood frequency analysis, we used relationships identified by Fuller (1914) to transform SWAT daily mean discharge into instantaneous peak flows, and then Bulletin 17B methods to estimate annual exceedance flow probabilities. While the log-Pearson Type III probability distribution and the methods in Bulletin 17B are widely applied, limitations exist (England et al 2019), in particular the assumption of stationarity (see Judi et al 2018 on this issue), and high uncertainties in estimated quantiles. But due to short periods of record and nonstationarity in observed data, high uncertainty in estimated flow quantiles is common to all hydraulic design and floodplain mapping analyses.…”