Assessing Climate Change Impact on Urban Drainage Systems

Transcription

1 Assessing Climate Change Impact on Urban Drainage Systems by Asst/Prof Qin Xiaosheng School of Civil & Environmental Engineering, Nanyang Technological University, Blk N1-01c-82, 50 Nanyang Ave., Singapore Tel: ; Fax:

2 Outline Background Motivation of assessing regional extreme rainfall Methodology Statistical method for spatial downscaling & temporal disaggregation Framework of this study Case Study Projection of future conditions Drainage flow assessment

3 BACKGROUND Flooding at Commonwealth Lane --- from Straitimes Number of flood events 2010: : : : Data from Wikipedia Photo taken at a bridge along AYE on Sep. 05, from Straitimes Others 43% Jun 14% The frequency and magnitude of extreme rainfalls could be affected by climate change! Nov 23% Oct 20%

4 BACKGROUND Common methods in climate change assessment Global Circulation Models (GCMs) : However, there is a scale mismatch between the GCM outputs and the needs for urban hydrological impact studies (Arnbjerg-Nielsen, 2008) The results derived from those models present the climate change information spreading over a very large area (from km 2 ) with the time intervals from daily to hourly. Compared to few square kilometres of urban area and drainage system that is sensitively changed corresponding to the precipitation with time interval of minutes

5 BACKGROUND Downscaling techniques can be used to fill this gap: Global Circulation Model (GCM) Dynamical downscaling (Regional Climate Model, RCM) Regional extreme rainfall (resolution: generally 3-6 km, 15min or above) Global Circulation Model (GCM) Statistical downscaling & disaggregation Regional extreme rainfall (resolution: station/point level, 5min or less). Advantages Disadvantages Statistical downscaling Comparatively cheap and computationally efficient Easily transferable to other regions Require long and accepted historical data Highly dependent upon choice of predictors Absence of physical meaning Dynamical downscaling Produces response based on pysically consistent process Computationally intensive Summarized by Fowler et al., (2007)

6 BACKGROUND Statistical downscaling Regression model (e.g. ASD, general linear model, artificial neural network, support vector machine, etc.) Stochastic weather generator (e.g. LARS-WG, RainSim, etc.) Statistical disaggregation Stochastic point process model (BLRP, NSRP) Nonparametric method (KNN) Downscaling Disaggregation Large-scale predictor (daily timescale) Station level (daily timescale) Station level (sub-daily or subhourly timescale)

7 OBJECTIVE Propose a combined statistical downscaling and disaggregation framework to bridge the gap between coarse-resolution climate models and fine-resolution urban hydrological study Apply it to climate change impact assessment on urban drainage flow

8 Proposed Methodology Framework DATA GCM HadCM3 TOOLS Historical daily Rainfall data Historical hourly + min rainfall data Spatial Statistical downscaling Daily rainfall data Disaggregation ASD Tool KNN Method Historical drainage flow data From daily to hourly Flow data series Hydrological model Climate change impact assessment From hourly to min-level SWMM model GCM : Global Circulation Model; ASD : Automated Statistical Downscaling KNN: K-nearest neighbor; SWMM: U.S EPA Storm Water Management

9 METHOD Automated statistical downscaling (ASD) The automated regression-based statistical downscaling (Hessami et al., 2008). The method uses backward stepwise regression and partial correlation coefficients for predictor selection. The modelling of daily precipitation consists of two steps: modelling the precipitation occurrence and then modelling the amount of precipitation when the precipitation occurs: O i 0 j 1 j ij n p n i 0 j 1 j ij i R p e where O i is the daily precipitation occurrence, R i are daily precipitation amounts, p ij are predictors, n is number of predictors, α and β are model parameters and e i is modelling error which is assumed to follow a Gaussian distribution.

10 METHOD K nearest neighbor (KNN) method The K nearest neighbor (KNN) method is a nonparametric resampling method based on historical observed data (Prairie et al., 2007). The neighbors are selected by an optimization procedure: (i) the potential candidate is classified into twenty groups based on the Euclidean distance; (ii) each group would generate one disaggregated results; (iii) for disaggregation from daily to hourly, the root-mean-square-error (RMSE) values of five monthly statistical indicators (i.e. mean, standard deviation, probability of wet hour, 90 th percentile and maximum value) are calculated; for disaggregation from hourly to minute-level, the RMSE of extreme rainfall (extracted from IDF curves) are calculated; (iv) based on the minimum value of RMSE, the best k value would be estimated.

11 STUDY CASE Location: Telok Blangah Rise Total area: 36.4 ha. The system consists : 55 Subcatchments, 50 conduits, 50 manholes, and 1 Outlet, all the subcatchments are finally routed to the outfall (O1) of the study area. The two water level sensors installed corresponding to: TB01 at junction node J7 TB02 at Out1

12 MODEL VALIDATION The model is calibrated by using a number of rainfall events at two gauging stations namely TB01 and TB02. For model verification, additional rainfall events are selected. The figure shows the result of one example rainfall event (on July 3, 2012). Generally, the simulation of flows in the drainage system produces very close results to the observed data, especially the peak flows. Qm-TB01 Qp-TB01 Qm-TB02 Qp-TB02 Rainfall hyetograph and Runoff hydrograph for observed and simulated data of TB01 and TB02 on July 3, 2012 (Qm: observed data, Qp: simulated data).

13 Pwet Max (mm/day) Mean (mm/day) STD (mm/day) RAIN ANALYSIS RESULT The calibration and verification of the ASD model are conducted using the observed daily rainfall data of the closest weather station, NCEP reanalysis data, and the projected data from the HadCM3 model under B2 scenario. The result indicates that the ASD model performs well in reproducing the daily precipitation, but slightly underestimates the P wet value from April to October compared to the observed data (a) OBS SIM AVE SIM Boundary J F M A M J J A S O N D (c) J F M A M J J A S O N D (b) J F M A M J J A S O N D (d) J F M A M J J A S O N D Comparison of observed and simulated statistical properties of daily rainfall for current period ( ) based on HadCM3 B2 scenario data.

14 Mean (mm/hour) STD (mm/hour) Max (mm/hour) RAIN ANALYSIS RESULT The disaggregation process involves two steps: disaggregating the daily rainfall data to hourly data and then disaggregating hourly data to generate min-level rainfall data The KNN method performs well in fitting the observed hourly data. The mean hourly rainfalls between observed data and simulated data are fairly close, except for small underestimation from July to September (a) OBS SIM AVE SIM Boundary (b) J F M A M J J A S O N D (c) 180 J F M A M J J A S O N D (d) Pwet (hour) J F M A M J J A S O N D J F M A M J J A S O N D Comparison of observed and simulated statistical properties of hourly rainfall for current period ( ) based on HadCM3 B2 scenario data.

15 5min Rainfall (mm) RAIN ANALYSIS RESULT Figure 6 shows the generated 5-min IDF curve versus the observed official record (PUB, 2014). It is indicated that observed record falls in the ensemble range of the simulated results. The IDF curves can also be developed for the time intervals of 15 minutes, 30 minutes and 1 hour to support assessment of the climate change impact on drainage flow Official 5min IDF curve for current period Return Period (Year) Comparison of observed and simulated IDF curves for 5-min rainfall for current period ( ) based on HadCM3 B2 scenario data.

16 Annual Maximum Hourly Annual Max Daily Annual Rainfall (mm) PROJECTION RESULTS 3600 (a) Baseline ( ) SIM AVE 3200 SIM Boundary (b) 300 The baseline scenario is from 1980 to 2000 and the future scenarios are from 2011 to In order to further examine the impact of climate change on urban hydrological systems, 1 out of 20 ensemble results (which corresponds to the most serious condition) for the period 2011 to 2040 is used as input for drainage flow modeling (c) Projection of annual rainfall (a), maximum daily data (b) and maximum hourly data (c) for the future period

17 DRAINAGE MODELING RESULTS Comparison between drainage flow between the period of to Conduits Percentage difference in peak discharge (%) 15-min duration 30-min duration 60-min duration Return period (year) In terms of the surcharge condition under the changing climate for the period only the 60-min design rainfall duration would lead to a reduction of the total flooded volume as well as the number of conduits being surcharged. The biggest decrease is associated with the 100-years return period with about 5000 m 3 reduction of flooded water, and 4 conduits not surcharged. However, the 15-min rainfall duration with 100-years return period would result in the worse flood problem with 26 conduits suffering from surcharge condition and an overflow of about 11,000 m 3 of water. C C C C C C C C C C Average

18 CONCLUSION The impact of climate change on drainage flow is assessed by using a framework consisting of spatial downscaling, temporal disaggregation and hydrological modeling. The results from downscaling and disaggregation, which were based on automated statistical downscaling (ASD) and KNN methods, were close to the observation data with ensemble uncertainties. The climate change scenario was projected for the period from 2011 to The drainage flow of such a period was compared to the flow for the current condition by using SWMM model. The result indicated that for 1-hr rainfall duration, there would be a slight decrease in peak discharge for the studied drainage system; in contrast, the shorter durations of 15-min and 30-min rainfalls would lead to more serious flooding problems.

19 Thank you very much! Qin Xiaosheng Assistant Professor, School of Civil & Environmental Engineering, Nanyang Technological University, Blk N1-01c-82, 50 Nanyang Ave., Singapore Tel: ; Fax: