Spatial flood extent modelling. A performance based comparison

The rapid development of Geographical Information Systems (GIS) has together with the inherent spatial nature of hydrological modelling led to an equally rapid development in the integration between GIS and hydrological models. The advantages of integration are particularly apparent in flood extent modelling. In this thesis, the integration of hydrological models and GIS is approached on the basis of performance, with performance taken as the balance of computational efficiency, flexibility of application, and most importantly the reliability of the integrated model. It is shown that predictive reliability is dominated by model uncertainties, particularly in model roughness parameters. These roughness parameters are found to be more conceptual than physical as they represent bulk momentum loss parameters at the reach scale. Limited data on spatial extent of flooding is available to constrain these uncertainties, and where such data is lacking the simplest numerical approach may be as reliable as more complex approaches. The overall performance of the simple approach is then higher as this is more easily integrated within GIS. Observations of flood extent from aerial photographs may help constrain uncertainties, though much more value is found from distributed water level observations in the floodplain. The lack of hydrological data also results in high resolution GIS data of elevation or land use being of limited value. As sufficient hydrological data is unavailable and perhaps impossible to acquire, model predictions made are recommended to be considered probabilistically, irrespective the level of integration with GIS.Civil Engineering and Geoscience

A comparison of flood extent modelling approaches through constraining uncertainties on gauge data

A comparison is made of 1D, 2D and integrated 1D-2D hydraulic models in predicting flood stages in a 17 km reach of the River Saar in Germany. The models perform comparably when calibrated against limited data available from a single gauge in the reach for three low to medium flood events. In validation against a larger event than those used in calibration, extrapolation with the 1D and particularly the integrated 1D-2D model is reliable, if uncertain, while the 2D model is unreliable. The difference stems from the way in which the models deal with flow in the main channel and in the floodplain and with turbulent momentum interchange between the two domains. The importance of using spatial calibration data for testing models giving spatial predictions is shown. Even simple binary (eye-witness) observations on the presence or absence of flooding in establishing a reliable model structure to predict flood extent can be very valuableCivil Engineering and Geoscience

Reduction of Monte-Carlo simulation runs for uncertainty estimation in hydrological modelling

International audienceMonte-Carlo (MC) simulation based techniques are often applied for the estimation of uncertainties in hydrological models due to uncertain parameters. One such technique is the Generalised Likelihood Uncertainty Estimation technique (GLUE). A major disadvantage of MC is the large number of runs required to establish a reliable estimate of model uncertainties. To reduce the number of runs required, a hybrid genetic algorithm and artificial neural network, known as GAANN, is applied. In this method, GA is used to identify the area of importance and ANN is used to obtain an initial estimate of the model performance by mapping the response surface. Parameter sets which give non-behavioural model runs are discarded before running the hydrological model, effectively reducing the number of actual model runs performed. The proposed method is applied to the case of a simple two-parameter model where the exact parameters are known as well as to a widely used catchment model where the parameters are to be estimated. The results of both applications indicated that the proposed method is more efficient and effective, thereby requiring fewer model simulations than GLUE. The proposed method increased the feasibility of applying uncertainty analysis to computationally intensive simulation models. Keywords: parameters, calibration, GLUE, Monte-Carlo simulation, Genetic Algorithms, Artificial Neural Networks, hydrological modelling, Singapor

Estimating the benefits of single value and probability forecasting for flood warning

Flood risk can be reduced by means of flood forecasting, warning and response systems (FFWRS). These systems include a forecasting sub-system which is imperfect, meaning that inherent uncertainties in hydrological forecasts may result in false alarms and missed floods, or surprises. This forecasting uncertainty decreases the potential reduction of flood risk, but is seldom accounted for in estimates of the benefits of FFWRSs. In the present paper, a method to estimate the benefits of (imperfect) FFWRSs in reducing flood risk is presented. These benefits include not only the reduction of flood losses due to a warning response, but also consider the costs of the warning response itself, as well as the costs associated with forecasting uncertainty. The method allows for estimation of the benefits of FFWRSs that use either deterministic or probabilistic forecasts. Through application to a case study, it is shown that FFWRSs using a probabilistic forecast have the potential to realise higher benefits at all lead-times. However, it is also shown that provision of warning at increasing lead time does not necessarily lead to an increasing reduction of flood risk, but rather that an optimal lead-time at which warnings are provided can be established as a function of forecast uncertainty and the cost-loss ratio of the user receiving and responding to the warning.Hydraulic EngineeringCivil Engineering and Geoscience