Abstract
Abstract. Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in watersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the uncertainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncertainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In addition, we propose a concise procedure to derive prior parameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the autoregressive error model greatly helps to meet the statistical assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced to 5 times with Bayesian updating, using only few discharge measurements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of uncertainty is crucial to support decision making.
Highlights
Urbanization and the resulting land-use change strongly affect the water cycle in watersheds (Rosso and Rulli, 2002; Ott and Uhlenbrook, 2004; Shepherd, 2005; Brath et al, 2006; Clarke, 2007; Quilbeet al., 2008; Barron et al, 2011; Jung et al, 2011; Schaefli et al, 2011)
We find that the values for N obtained with the empirical formulas roughly vary by a factor of 2, whereas the results for k vary by a factor of 4
The model was calibrated with seven parameters: N and k of the IUH model, A and S for the watershed characteristics, σ and τ of the error model, and σ ζ of the rainfall multipliers
Summary
Urbanization and the resulting land-use change strongly affect the water cycle in watersheds (Rosso and Rulli, 2002; Ott and Uhlenbrook, 2004; Shepherd, 2005; Brath et al, 2006; Clarke, 2007; Quilbeet al., 2008; Barron et al, 2011; Jung et al, 2011; Schaefli et al, 2011). Small urban watersheds in areas of urban sprawl are mostly ungauged (Sivapalan, 2003) and where data are available, records often contain only few years of the most basic hydrological variables, such as rainfall and streamflow. This makes it intrinsically difficult to assess the consequences of urbanization and predictions of such ungauged or poorly gauged basins are considered highly uncertain (Franks, 2002; Sivapalan et al, 2003; Wagener and Gupta, 2005)
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