1. Data mining issues on improving the accuracy of the rainfall-runoff model for flood forecasting Jia Liu Supervisor: Dr. Dawei Han Email: Jia.Liu@bristol.ac.uk WEMRC, Department of Civil Engineering University of Bristol 24 May 2010

2. Outlines Introduction to the Probability Distributed Model (PDM) Two data mining issues: Selection of data for model calibration Selection of data for model calibration Optimal data time interval in flood forecasting Optimal data time interval in flood forecasting Conclusions and Future work

3. Introduction to rainfall-runoff model Hydrological Cycle Rainfall-Runoff Model Runoff Rainfall (and Evaporation) Rainfall (and Evaporation) A conceptual representation of the hydrological cycle A conceptual representation of the hydrological cycle The fundamental work for any water researches, i.e., The fundamental work for any water researches, i.e., real-time flood forecasting, land-use change evaluations real-time flood forecasting, land-use change evaluations and design of hydraulic structures, etc. and design of hydraulic structures, etc. Rainfall-runoff model

4. Introduction to rainfall-runoff model Hydrological Cycle A conceptual representation of the hydrological cycle A conceptual representation of the hydrological cycle The fundamental work for any water researches, i.e., The fundamental work for any water researches, i.e., real-time flood forecasting, land-use change real-time flood forecasting, land-use change evaluations and design of hydraulic structures, etc. evaluations and design of hydraulic structures, etc. Rainfall-runoff model Probability Distributed Model by Moore (1985) 13 Model Parameters to be calibrated f c, T d, c min, c max, b, b e, k g, b g, S t, k 1, k 2, k b, q c

5. How to cope with the ‘data rich’ environment? Questions proposed: A. How to select the most appropriate data to calibrate the model? 2. Which period the data should be selected from? 1. How long the data should be? Data Length Data Duration B. When used for forecasting, what is the most appropriate sampling rate? Data Time Interval Large quantity Data Fast sampling rate +

6. Calibration data selection: data length and duration Data used for model validation is often determined. We assume that the more similarity the calibration data bears to the validation data, the better performance the rainfall-runoff model should have after calibration. Validation data set A good information quality of the calibration data set = A similar information content to validation data set Calibration data set Comparison of the information quality of the two data sets

7. Calibration data selection: data length and duration An index which can reveal the similarity between the calibration and validation data sets, can be used as a guide for calibration data selection for the rainfall-runoff model. Information Cost Function (ICF) The Information Cost Function (ICF) is a an entropy-like function that gives a good estimate of the degree of disorder of a system Energy of detail Energy of approximation Percentile energy on each decomposition level Fast Fourier Transform Fast Fourier Transform Discrete Wavelet Decomposition Discrete Wavelet Decomposition Flow Duration Curve Flow Duration Curve Liu, J., and D. Han (2010), Indices for calibration data selection of the rainfall-runoff model, Water Resour. Res., 46, W04512, doi:10.1029/2009WR008668.

8. X Z Y X1X1 XNXN YNYN Y1Y1 Z1Z1 ZNZN Forecast lead time Data time interval Model error Error Time interval Error Time interval Long lead time Short lead time Optimal data time interval – for the forecast mode Optimal time interval Sampling theory Lower boundary: Too slow Too fast Leading to numerical problems [Åström, 1968 ; Ljung, 1989] Sampling rate of model input data Hypothetical curve A positive relation Data time interval Forecast lead time

9. Optimal data time interval – for the forecast mode Case study Auto-Regressive Moving Average (ARMA) model for on-line updating Auto-Regressive Moving Average (ARMA) model for on-line updating Four catchments are selected from the Southwest England: Four catchments are selected from the Southwest England:Catchments AREA (km 2 ) LDP (km) DPSBAR (m/km) A Bellever A Bellever21.513.594.9 B Halsewater B Halsewater87.819.485.7 C Brue C Brue135.222.671.1 D Bishop_Hull D Bishop_Hull202.040.298.0 LDP: longest drainage path (km) DPSBAR: mean drainage path slope (m/km) 51°05′N 51°00′N 3°10′W3°05′W3°15′W 4°00′W3°55′W 50°35′N 50°40′N 2°35′W2°30′W2°25′W 51°10′N 51°05′N 3°20′W3°15′W3°10′W 51°05′N 51°00′N BelleverHalsewater BrueBishop_Hull

10. Optimal data time interval – for the forecast mode Case study The positive pattern between the optimal data time interval and the forecast lead time is found to be highly related to the catchment concentration time. The positive pattern between the optimal data time interval and the forecast lead time is found to be highly related to the catchment concentration time.Catchments AREA (km 2 ) LDP (km) DPSBAR (m/km) A Bellever A Bellever21.513.594.9 B Halsewater B Halsewater87.819.485.7 C Brue C Brue135.222.671.1 D Bishop_Hull D Bishop_Hull202.040.298.0 LDP: longest drainage path (km) DPSBAR: mean drainage path slope (m/km) BelleverHalsewater BrueBishop_Hull

11. Conclusions and Future work Selecting data with the most appropriate length, duration and time interval is of great significance in improving the model performance and helps to enhance the efficiency of data utilization in rainfall-runoff modelling and forecasting. More research is needed to explore the applicability of the ICF index for calibration data selection and to verify the hypothetical curve of the optimal data time interval. Weather Research & Forecasting (WRF) Model Rainfall-Runoff Model Runoff Rainfall (and Evaporation) Rainfall (and Evaporation) As real-time inputs Updated by observations

12. The End Thank you for your attention!