Comprehensive flood mitigation and management in the Chi River Basin, Thailand

Severe flooding of the flat downstream area of the Chi River Basin occurs frequently. This flooding is causing catastrophic loss of human lives, damage and economic loss. Effective flood management requires a broad and practical approach. Although flood disasters cannot completely be prevented, major part of potential loss of lives and damages can be reduced by comprehensive mitigation measures. In this paper, the effects of river normalisation, reservoir operation, green river (bypass), and retention have been analysed by using integrated hydrologic and hydraulic modelling. Every tributary has been simulated by a process-based hydrological model (SWAT) coupled with the 1D/2D SOBEK river routing model. Model simulation results under the design rainfall event, i.e. flood depth, flood extent, and damages for the situation with and without flood mitigation measures have been compared and evaluated to determine an optimal set of mitigation measures. The results reveal that a combination of river normalisation, reservoir operation, and green river (bypass) is most effective as it can decrease the extent of the 100-year flood event by approximately 24% and 31% for the economic damage. The results of this study will be useful for improving the present flood defence practice in the Chi River Basi

Rule-based committees of data-driven models for hydrological forecasting

Nowadays, data-driven techniques are being used for solving complex problems inhydrology. In this field, many researches seem to point to the combination of differentmodels for the improvement of the modelling process. The combination of models isapplied in many fields, especially in complex situations, where the individual model haslimitations. This is especially visible in seasonal problems where the overfitting and themisrepresentation of noisy data affect the overall performance of the model. In thisthesis the combination of data-driven models (committee model) is used to performflow forecasting.In a committee of models several models are trained. In operation, either only onemodel is used or the results of running several models are combined. In this work, bothapproaches are used. An important problem is the incorporation of hydrologicalknowledge into the rules for splitting data into subsets to be used in training localmodels. The novelty of this work is in using baseflow separation methods and inbuilding a separate data-driven model only for baseflow.The applicability of these techniques was studied by doing three case studies withdifferent hydrological conditions. The seasonal effect and the importance of the correctidentification of regimes for committee of models were highlighted. The hydrologicaldomain knowledge was incorporated in the splitting criteria.The model performance was assessed by the comparison between the committee ofmodels and a global model trained on the whole data set. The application showed theoverall improvement in the use of different committee models approaches

Hybrid models for hydrological forecasting: Integration of data-driven and conceptual modelling techniques

This book presents the investigation of different architectures of integrating hydrological knowledge and models with data-driven models for the purpose of hydrological flow forecasting. The models resulting from such integration are referred to as hybrid models. The book addresses the following topics: A classification of different hybrid modelling approaches in the context of flow forecasting. The methodological development and application of modular models based on clustering and baseflow empirical formulations. The integration of hydrological conceptual models with neural network error corrector models and the use of committee models for daily streamflow forecasting. The application of modular modelling and fuzzy committee models to the problem of downscaling weather information for hydrological forecasting.WatermanagementCivil Engineering and Geoscience

Decomposing satellite-based rainfall errors in flood estimation : Hydrological responses using a spatiotemporal object-based verification method

A spatiotemporal object-based rainfall analysis method is used to evaluate the hydrological response of two systematic satellite error sources for storm estimation in the Capivari catchment, Brazil. This method is called Spatiotemporal Contiguous Object-based Rainfall Analysis (ST-CORA) specifically evaluates the error structure of satellite-based rainfall products using a 3D pattern clustering algorithm. Errors due to location and magnitude in the Near Real-time (NRT) CMORPH product are subtracted by adjusting the shift and the intensity distribution with respect to a storm object obtained from gauge-adjusted weather radar. Synthetic scenarios of each error source are used as forcing for hourly calibrated distributed hydrological ‘wflow-sbm’ model to evaluate the main sources of systematic errors in the hydrological response. Two types of storm events in the study area are evaluated: short-lived and a long-lived storm. The results indicate that the spatiotemporal characteristics obtained by ST-CORA clearly reflect the main source of errors of the CMORPH storm detection. It is found that location is the main source of error for the short-lived storm event, while volume is the main source in the long-lived storm event. The subtraction of both errors leads to an important reduction of the simulated streamflow in the catchment. The method applied can be useful in bias correction schemes for satellite estimations especially for extreme precipitation events.</p

Combining semi-distributed process-based and data-driven models in flow simulation: A case study of the Meuse river basin

One of the challenges in river flow simulation modelling is increasing the accuracy of forecasts. This paper explores the complementary use of data-driven models, e.g. artificial neural networks (ANN) to improve the flow simulation accuracy of a semi-distributed process-based model. The IHMS-HBV model of the Meuse river basin is used in this research. Two schemes are tested. The first one explores the replacement of sub-basin models by data-driven models. The second scheme is based on the replacement of the Muskingum-Cunge routing model, which integrates the multiple sub-basin models, by an ANN. The results show that: (1) after a step-wise spatial replacement of sub-basin conceptual models by ANNs it is possible to increase the accuracy of the overall basin model; (2) there are time periods when low and high flow conditions are better represented by ANNs; and (3) the improvement in terms of RMSE obtained by using ANN for routing is greater than that when using sub-basin replacements. It can be concluded that the presented two schemes can improve the performance of process-based models in the context of flow forecasting.Water Resources SectionCivil Engineering and Geoscience