Quantifying uncertainty in urban flooding analysis considering hydro-climatic projection and urban development effects

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Abstract

How will the combined impacts of land use change, climate change, and hydrologic modeling influence changes in urban flood frequency and what is the main uncertainty source of the results? Will such changes differ by catchment with different degrees of current and future urban development? We attempt to answer these questions in two catchments with different degrees of urbanization, the Fanno catchment with 84% urban land use and the Johnson catchment with 36% urban land use, both located in the Pacific Northwest of the US. Five uncertainty sources ĝ€" general circulation model (GCM) structures, future greenhouse gas (GHG) emission scenarios, land use change scenarios, natural variability, and hydrologic model parameters ĝ€" are considered to compare the relative source of uncertainty in flood frequency projections. Two land use change scenarios, conservation and development, representing possible future land use changes are used for analysis. Results show the highest increase in flood frequency under the combination of medium high GHG emission (A1B) and development scenarios, and the lowest increase under the combination of low GHG emission (B1) and conservation scenarios. Although the combined impact is more significant to flood frequency change than individual scenarios, it does not linearly increase flood frequency. Changes in flood frequency are more sensitive to climate change than land use change in the two catchments for 2050s (2040ĝ€"2069). Shorter term flood frequency change, 2 and 5 year floods, is highly affected by GCM structure, while longer term flood frequency change above 25 year floods is dominated by natural variability. Projected flood frequency changes more significantly in Johnson creek than Fanno creek. This result indicates that, under expected climate change conditions, adaptive urban planning based on the conservation scenario could be more effective in less developed Johnson catchment than in the already developed Fanno catchment. © 2011 Author(s).

Figures

  • Fig. 1. Fanno and Johnson Creek catchment boundary, river network, and the Portland urban growth boundary (UGB).
  • Fig. 2. Monthly runoff rate (%) that indicates the ratio of monthly runoff to monthly 3 precipitation for 2000-2006 and monthly coefficient of determination between the 4 Fanno daily streamflow (USGS 14206950) and the Johnson daily streamflow (USGS 5 14211500) 6 7
  • Table 1. PRMS model parameters for calibration. D: Digital elevation map, LU: Land use map, S: Soil map, OPT: Optimized (modified from Chang et al., 2010).
  • Table 2. Description of the three land cover scenarios used in this study to simulated land cover projections within the Fanno and Johnson Creek catchments by 2050 (Source: Hulse et al., 2004; Franczyk and Chang, 2009).
  • Fig. 3. Relation between urban land use (%) and mean impervious surface (%). Data are obtained from USGS Report 2006-5101-D (Waite et al., 2008, Table 1).
  • Fig. 4. Changes in precipitation according to three GCMs and two emission scenarios in Fanno Creek and Johnson Creek catchments.
  • Fig. 5. Land use categories (%) for reference land use in 2001 and two future land use change scenarios for the 2050s.
  • Fig. 6. Variation of flood frequency by climate change scenarios, with recurrence intervals of 2, 5, 10, 25, 50, and 100 years for the 2050s with respect to the reference period of 1960–1989. The blue dot indicates flood frequency using observed climate data and symbol (x) indicates outliers.

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CITATION STYLE

APA

Jung, I. W., Chang, H., & Moradkhani, H. (2011). Quantifying uncertainty in urban flooding analysis considering hydro-climatic projection and urban development effects. Hydrology and Earth System Sciences, 15(2), 617–633. https://doi.org/10.5194/hess-15-617-2011

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