IMPROVING ACCURACY, LEAD TIME AND CONTINGENCY IN FLUVIAL FLOOD FORECASTS TO ENHANCE EMERGENCY MANAGEMENT Federico Groppa 1, Jon Wicks 2, Andy Barnes 3 1Halcrow Group, Brisbane, QLD, [email protected] 2Halcrow Group, Swindon, UK, [email protected] 3Halcrow Group, Exeter, UK Abstract Flood forecasting techniques have become more advanced over recent years in response to increased demands for more accurate, timely and reliable flood warnings. The National Flood Forecasting System (NFFS) is the UK Environment Agency’s flood forecasting platform and hosts the flood forecasting models and techniques for all seven regions of the Environment Agency. The NFFS has a high level of resilience, but the risk of partial or total failure cannot be completely removed. Thus, contingency forecasting techniques that are completely independent of the NFFS are required to provide flood forecasting duty officers with alternative forecasting methods. This paper first describes and evaluates the improvements in flood forecasting that have been achieved through development of real-time hydrological and hydrodynamic models for the Meon, Beaulieu and Lymington catchments in southern England. Performance of the models has been assessed at a range of lead-times to determine the typical accuracy of the forecasts. Importantly, this provides flood forecasting duty officers with an indication of the level of confidence that can be placed in the forecasts. The paper’s focus then shifts to the contingency forecasting techniques that have been developed for these and other catchments in southern England. The contingency forecasting techniques include rate-of-rise extrapolation, peak-to-peak correlations and rainfall correlations, as well as more innovative techniques such as multivariate analyses. Each of the contingency techniques is described, followed by an assessment of their strengths and weaknesses. The paper finally describes how the improved forecasts are currently used within the flood warning decision making process. Introduction Flood warning systems provide a well-established way to help to reduce risk to life, and to allow communities and the emergency services time to prepare for flooding and to protect possessions and property (Sene, 2008). Flood warning systems comprise numerous components that must be coordinated in a clear and efficient way in order to make the system effective. The Australian Government’s Flood Warning manual (2009) provides a thorough analysis of the different elements required to develop an effective flood warning system. Fluvial flood forecasts are a key element in a flood warning system. Abundant literature is available on the topic, however the experience gained during the development and operation of the forecasting systems, and detailed catchment knowledge are always fundamental to understand the full potential of the forecasting tools available. 1 This paper reviews a range of fluvial flood forecasting tools that were implemented in southern England in the past five years, emphasising the different elements that were considered at each stage and the lessons learnt during development of the tools. Flood forecasting models Rainfall runoff models are a form of hydrological model and predict river flow based on observed and/or forecast rainfall. If observed rainfall is used, the maximum lead time achievable is generally similar to the typical response time of the catchment to rainfall. However, factors such as the antecedent catchment conditions, influence of snowmelt, direction and speed of the rainfall event and available storage capacity of reservoirs within the catchment will all affect the response time. In many cases, the lead time provided by the use of observed rainfall (from raingauges or weather radar) can be sufficient, but may be extended by the use of rainfall forecasts (Sene, 2008). Three flood forecasting models were developed in catchments located in southern England, as shown in Figure 1. The forecasting models developed have two components: a hydrological component that uses PDM (Probability Distributed Model) and a hydraulic component built using an ISIS 1D hydraulic model (www.halcrow.com/isis ). Two-dimensional models are rarely used in real-time flood forecasting in the UK, due to their complexity and time taken to run. Flood forecasting models must be sufficiently robust to ensure they are capable of simulating a wide range of scenarios whilst providing the required level of accuracy and meeting maximum run time requirements. The latter is essential for real time flood forecasting, since it helps maximise the available lead-time and allows mitigation plans to be put into action as early as possible. Figure 1 – Location of the catchments where flood forecasting models were developed 2 Model updating One distinguishing feature of forecasting models compared to off-line simulation models is the ability to use observed data from telemetered river monitoring stations to modify forecasts as they are generated. This real time updating of forecasts can significantly improve the accuracy of model outputs. Several techniques have been developed for updating forecasts, including error prediction methods and techniques which adjust the internal state of the model components and model parameters respectively. The PDM model has the option to use state correction and error prediction modes, the latter of which also allows the estimation of future errors by means of an auto-regressive moving average (ARMA) model. Analysis of practical cases Some physical characteristics of the three catchments studied are given in Table 1, while Figure 1 shows their location in southern England. Figure 2 shows a picture of Beaulieu river (near Beaulieu gauging station), taken during the March 2008 flood event. The typical time-to-peak of these three catchments is less than 8 hours. In all cases Tipping Bucket Raingauge (TBR) data series were available for at least three raingauges within or near (less than 10km) the catchment boundaries, as well as catchment average radar rainfall data series for the catchments. In all cases 15-min datasets were used. The geology of the Lymington and Beaulieu catchments is broadly uniform, with baseflow representing a very small proportion of the total flow during high-flow events. In contrast, the Meon catchment has a highly permeable stratum mid-catchment that notably modifies the flow regime and characteristics of high-flow events. In this catchment flooding events typically occur during winter when the catchment is partially or totally saturated. Additionally, hydrogeological studies in the Meon catchment have demonstrated that the gauging stations located in the middle reach consistently measure significantly lower flows than those detected upstream, as a result of natural by-passing of the river channel. The forecasting model for the Meon river was modified in order to consider this by-pass effect (and calibrated using observed borehole data), which significantly improved the results. Nevertheless, it should be highlighted that the quality of results for the Meon river was generally lower than that of results obtained for the Beaulieu and Lymington rivers. Mean Number Mean flow Max. Data Urban Area longitudinal of near the obs. series Catchment area [km 2] slope gauging catchment flow length [%] (terrain) [%] stations outlet [m 3/s] [m 3/s] [years] Beaulieu 64.7 2.5 0.7 2 0.86 20.4 7.5 Meon 107.6 7 1.5 5 1.11 8.61 9 Lymington 120.9 3.8 0.6 3 1.37 68.8 10 Table 1 - Physical characteristics of the catchments studied 3 Figure 2 – Flooding in Beaulieu river, March 2008 Main results for the study cases Table 2 shows the performance statistics for the events analysed. The results shown in Table 2 and Figures 5, 6 and 7 reflect model performance without the use of state updating. River River Lymington at Beaulieu River Meon at at Meerut Hartford East West Brockenhurst Mislingford Wickham Road Bridge Meon Meon Peak error [m] 0.080 -0.010 0.020 0.155 0.072 -0.068 0.005 Standard deviation 0.110 0.310 0.144 0.096 0.190 0.110 0.146 (peak) [m] Average R 2 (event) (Nash- 0.70 0.58 0.69 0.38 0.28 0.23 0.33 Sutcliffe) Mean square error (event) 0.067 0.228 0.170 0.043 0.075 0.080 0.060 [m] Table 2 - Performance statistics for the three models Table 2 shows that, even when the peak water level mean error is in line with expectations (i.e. within ±0.20m) in all the cases, there is some important variation in the results. This can be particularly seen for the low R 2 values obtained for the river Meon models. Various causes were suggested to explain these low R 2 values, in particular the low quality of the flow data series and ungauged net water transfers with neighbouring catchments (there is hydrogeological evidence in the Meon catchment confirming this occurs). Figure 3 and 6 show examples of the results obtained for the calibration events, while Figure 5 shows a verification event. These figures show the ability of the flood forecasting models to replicate the different hydrograph characteristics. They also show how the observed rainfall based on TBR and radar (Hyrad) vary quite significantly. 4 82.80 80 82.70 70 82.60 60 H3 82.50 Observed stage 50 H2 Modelled stage Hyrad 82.40 Modelled stage TBR Cumulative Hyrad rainfall 40 H1 Cumulative average TBR rainfall 82.30 Stage [mAOD] Stage 30 82.20 Cumulative rainfall [mm] ] [mm] rainfall Cumulative 20 82.10 82.00 10 81.90 0 22/12/1999 23/12/1999 24/12/1999 25/12/1999 26/12/1999 27/12/1999 28/12/1999 29/12/1999 30/12/1999 Date Figure 3 - Calibration event: Meon river at West Meon 25 20 15 10 Flow at Hartford Bridge (m3/s) Bridge Hartford at Flow 5 0 0 50 100 150 200 250 300 350 400 450 500 Run time (hours) Hartford flow TBR flow Hyrad Flow Figure 4 - Calibration event: Beaulieu river at Hartford Bridge 5 Figure 5 - Verification event: Lymington river at Brockenhurst Results evaluation process and forecasting accuracy assessment The performance of the models was initially evaluated without the use of state updating.