Towards automatic calibration of 2-D flood propagation models

Abstract. Hydraulic models for flood propagation description are an essential tool in many fields and are used, for example, for flood hazard and risk assessments, evaluation of flood control measures, etc. Nowadays there are many models of different complexity regarding the mathematical foundation and spatial dimensions available, and most of them are comparatively easy to operate due to sophisticated tools for model setup and control. However, the calibration of these models is still underdeveloped in contrast to other models like e.g. hydrological models or models used in ecosystem analysis. This has two primary reasons: first, lack of relevant data against which the models can be calibrated, because flood events are very rarely monitored due to the disturbances inflicted by them and the lack of appropriate measuring equipment in place. Second, 2-D models are computationally very demanding and therefore the use of available sophisticated automatic calibration procedures is restricted in many cases. This study takes a well documented flood event in August 2002 at the Mulde River in Germany as an example and investigates the most appropriate calibration strategy for a simplified 2-D hyperbolic finite element model. The model independent optimiser PEST, that enables automatic calibrations without changing model code, is used and the model is calibrated against over 380 surveyed maximum water levels. The application of the parallel version of the optimiser showed that (a) it is possible to use automatic calibration in combination of 2-D hydraulic model, and (b) equifinality of model parameterisation can also be caused by a too large number of degrees of freedom in the calibration data in contrast to a too simple model setup. In order to improve model calibration and reduce equifinality, a method was developed to identify calibration data, resp. model setup with likely errors that obstruct model calibration.

Download Full-text

Towards automatic calibration of 2-dimensional flood propagation models

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-6-6833-2009 ◽

2009 ◽

Vol 6 (6) ◽

pp. 6833-6864

Author(s):

P. Fabio ◽

G. T. Aronica ◽

H. Apel

Keyword(s):

Model Calibration ◽

Degrees Of Freedom ◽

Flood Control ◽

Flood Hazard ◽

Element Model ◽

Control Measures ◽

Automatic Calibration ◽

Calibration Data ◽

Spatial Dimensions ◽

Model Setup

Abstract. Hydraulic models for flood propagation description are an essential tool in many fields, e.g. civil engineering, flood hazard and risk assessments, evaluation of flood control measures, etc. Nowadays there are many models of different complexity regarding the mathematical foundation and spatial dimensions available, and most of them are comparatively easy to operate due to sophisticated tools for model setup and control. However, the calibration of these models is still underdeveloped in contrast to other models like e.g. hydrological models or models used in ecosystem analysis. This has basically two reasons: first, the lack of relevant data against the models can be calibrated, because flood events are very rarely monitored due to the disturbances inflicted by them and the lack of appropriate measuring equipment in place. Secondly, especially the two-dimensional models are computationally very demanding and therefore the use of available sophisticated automatic calibration procedures is restricted in many cases. This study takes a well documented flood event in August 2002 at the Mulde River in Germany as an example and investigates the most appropriate calibration strategy for a full 2-D hyperbolic finite element model. The model independent optimiser PEST, that gives the possibility of automatic calibrations, is used. The application of the parallel version of the optimiser to the model and calibration data showed that a) it is possible to use automatic calibration in combination of 2-D hydraulic model, and b) equifinality of model parameterisation can also be caused by a too large number of degrees of freedom in the calibration data in contrast to a too simple model setup. In order to improve model calibration and reduce equifinality a method was developed to identify calibration data with likely errors that obstruct model calibration.

Download Full-text

Adaptive forecast of multi-month lake level elevations

Canadian Journal of Civil Engineering ◽

10.1139/l94-084 ◽

1994 ◽

Vol 21 (5) ◽

pp. 778-788 ◽

Cited By ~ 4

Author(s):

Saad Bennis ◽

Gabriel J. Assaf

Keyword(s):

Water Level ◽

Lake Erie ◽

Flood Control ◽

Ordinary Least Squares ◽

Water Levels ◽

Control Measures ◽

Type Model ◽

Model Parameters ◽

Time Step ◽

Kalman Predictor

The early and precise prediction of the water levels in lakes is a major concern for public authorities. Such predictions describe the evolution of the water levels and are essential for appropriate flood control measures. In this paper, a new ARMAX-type model is developed to predict, months in advance, the monthly fluctuations of the water level of Lake Erie. The predictive variables used in the model are the past monthly water levels of Lakes Erie, Superior, and Michigan–Huron along with the estimated response times between water flow entries and exits. Two scenarios are compared. The first scenario is based on the ordinary least squares (OLS) technique in order to identify the parameters of the ARMAX-type model, to filter measurement and model noises, using the ARMAX Kalman predictor (AKP), and to optimize predictions. In this scenario, the model parameters remain unchanged throughout the simulation. The second scenario is based on the mutually interactive state parameter (MISP) technique in order to readjust the parameters of the model at each time step and to filter measurement and modelling noises through the Kalman predictor. In this scenario, the parameters of the model change with time. The analysis shows that the MISP–AKP framework has a slightly higher Nash coefficient than the OLS–AKP framework for the first month. In subsequent months, however, the quality of the predictions based on the OLS–AKP technique improves significantly. This observation also applies to the persistence and extrapolation coefficients as well as to the sample autocorrelation functions for the residuals of the Lake Erie water level forecast. It was therefore decided to apply the MISP–AKP technique to obtain the first prediction of the Lake Erie level, and the OLS–AKP technique to compute subsequent predictions. Key words: adaptive, forecast, Kalman's filter, lake levels, MISP algorithm, Great Lakes.

Download Full-text

Flood hazard zonation of upper kolodyne using GIS and multi criteria decision analysis in Mizoram, India

Disaster Advances ◽

10.25303/1412da001012 ◽

2021 ◽

Vol 14 (12) ◽

pp. 1-12

Author(s):

Ch Vabeihmo ◽

Malsawm Tluanga ◽

John Blick ◽

Sathing Sangchungnunga ◽

Francis Zodinthara

Keyword(s):

Geographic Information Systems ◽

Decision Analysis ◽

Flood Control ◽

Flood Hazard ◽

Control Measures ◽

Hazard Zonation ◽

Multi Criteria Decision Analysis ◽

The Past ◽

International Border ◽

Very High

Kolodyne is the largest river in Mizoram. The river originates in Myanmar where it flows in a southerly direction and enters Mizoram where it is called Chhimtuipui river and it becomes the international border between India and Myanmar. The Kolodyne river meets several rivers in Mizoram before it enters Chin State in Myanmar again. The upper Kolodyne river has caused destructive floods recently, however, attempts to delineate the flood hazard zones have not been carried out. This river is a source of livelihood for many families in the region and it had wrecked havoc in the past monsoon seasons with the loss of lives and property. The potential flood hazard zonation of the upper Kolodyne watershed using geographic information systems and multi-criteria decision analysis has revealed that about 40% of the total watershed fall in the high and very high potential zones and flood control measures are needed to be updated.

Download Full-text

Has dyke development in the Vietnamese Mekong Delta shifted flood hazard downstream?

10.5194/hess-2017-123 ◽

2017 ◽

Cited By ~ 1

Author(s):

Nguyen Van Khanh Triet ◽

Nguyen Viet Dung ◽

Hideto Fujii ◽

Matti Kummu ◽

Bruno Merz ◽

...

Keyword(s):

Flood Control ◽

Flood Hazard ◽

Mekong Delta ◽

Hydraulic Model ◽

Water Levels ◽

Tidal Dynamics ◽

Flood Peak ◽

Disastrous Flood ◽

The Impact ◽

Flood Characteristics

Abstract. In the Vietnamese part of the Mekong Delta (VMD) the areas with three rice crops per year have been expanded rapidly during the last 15 years. Paddy-rice cultivation during the flood season has been made possible by implementing high-dyke flood defenses and flood control structures. However, there are widespread claims that the high-dyke system has increased water levels in downstream areas. Our study aims at resolving this issue by attributing observed changes in flood characteristics to high-dyke construction and other possible causes. Maximum water levels and duration above the flood alarm level are analysed for gradual trends and step changes at different discharge gauges. Strong and robust increasing trends of peak water levels and duration downstream of the high-dyke areas are found with a step change in 2000/2001, i.e. immediately after the disastrous flood which initiated the high-dyke development. These changes are in contrast to the negative trends detected at stations upstream of the high-dyke areas. This spatially different behavior of changes in flood characteristics seems to support the public claims. To separate the impact of the high-dyke development from the impact of the other drivers, i.e. changes in the flood hydrograph entering the Mekong Delta, and changes in the tidal dynamics, hydraulic model simulations of the two recent large flood events in 2000 and 2011 are performed. The hydraulic model is run for a set of scenarios whereas the different drivers are interchanged. The simulations reveal that for the central VMD an increase of 9–13 cm in flood peak and 15 days in duration can be attributed to high-dyke development. However, for this area the tidal dynamics have an even larger effect in the range of 19–32 cm. However, the relative contributions of the three drivers of change vary in space across the delta. In summary, our study confirms the claims that the high-dyke development has raised the flood hazard downstream. However, it is not the only and not the most important driver of the observed changes. It has to be noted that changes in tidal levels caused by sea level rise in combination with the widely observed land subsidence and the temporal coincidence of high water levels and spring tides have even larger impacts. It is recommended to develop flood risk management strategies using the high-dyke areas as retention zones to mitigate the flood hazard downstream.

Download Full-text

Has dyke development in the Vietnamese Mekong Delta shifted flood hazard downstream?

Hydrology and Earth System Sciences ◽

10.5194/hess-21-3991-2017 ◽

2017 ◽

Vol 21 (8) ◽

pp. 3991-4010 ◽

Cited By ~ 30

Author(s):

Nguyen Van Khanh Triet ◽

Nguyen Viet Dung ◽

Hideto Fujii ◽

Matti Kummu ◽

Bruno Merz ◽

...

Keyword(s):

Flood Control ◽

Flood Hazard ◽

Mekong Delta ◽

Hydraulic Model ◽

Water Levels ◽

Tidal Dynamics ◽

Flood Peak ◽

Disastrous Flood ◽

The Impact ◽

Flood Characteristics

Abstract. In the Vietnamese part of the Mekong Delta (VMD) the areas with three rice crops per year have been expanded rapidly during the last 15 years. Paddy-rice cultivation during the flood season has been made possible by implementing high-dyke flood defenses and flood control structures. However, there are widespread claims that the high-dyke system has increased water levels in downstream areas. Our study aims at resolving this issue by attributing observed changes in flood characteristics to high-dyke construction and other possible causes. Maximum water levels and duration above the flood alarm level are analysed for gradual trends and step changes at different discharge gauges. Strong and robust increasing trends of peak water levels and duration downstream of the high-dyke areas are found with a step change in 2000/2001, i.e. immediately after the disastrous flood which initiated the high-dyke development. These changes are in contrast to the negative trends detected at stations upstream of the high-dyke areas. This spatially different behaviour of changes in flood characteristics seems to support the public claims. To separate the impact of the high-dyke development from the impact of the other drivers – i.e. changes in the flood hydrograph entering the Mekong Delta, and changes in the tidal dynamics – hydraulic model simulations of the two recent large flood events in 2000 and 2011 are performed. The hydraulic model is run for a set of scenarios whereas the different drivers are interchanged. The simulations reveal that for the central VMD an increase of 9–13 cm in flood peak and 15 days in duration can be attributed to high-dyke development. However, for this area the tidal dynamics have an even larger effect in the range of 19–32 cm. However, the relative contributions of the three drivers of change vary in space across the delta. In summary, our study confirms the claims that the high-dyke development has raised the flood hazard downstream. However, it is not the only and not the most important driver of the observed changes. It has to be noted that changes in tidal levels caused by sea level rise in combination with the widely observed land subsidence and the temporal coincidence of high water levels and spring tides have even larger impacts. It is recommended to develop flood risk management strategies using the high-dyke areas as retention zones to mitigate the flood hazard downstream.

Download Full-text

An example of the application of statistical techniques to predict lake level elevations

Canadian Journal of Civil Engineering ◽

10.1139/l81-017 ◽

1981 ◽

Vol 8 (2) ◽

pp. 114-121 ◽

Cited By ~ 1

Author(s):

Sivajogi D. Koppula

Keyword(s):

Lake Water ◽

Stormwater Management ◽

Flood Control ◽

Water Levels ◽

Control Measures ◽

Lake Levels ◽

Lake District ◽

Management Objectives ◽

Future Events ◽

Better Than

A knowledge of future lake levels is helpful in the stormwater management of a lake district with respect to flood control measures, land use measures around the lake, etc. The planning of strategies to achieve management objectives often receives wide attention; however, little or no mention is usually made of prediction of future events, e.g., lake water levels, upon which the strategies are based. These future events are of major importance as they determine the type of objectives and hence the strategies that management will have to develop and follow in the future.In the present study, the available data on monthly lake water levels are examined utilizing two statistical methods, namely, the Box-Jenkins time series analysis and harmonic analysis. The statistical models are then used to predict future lake levels; the predictions are compared with actual observations. It has been found that the predictions made by the Box-Jenkins model are closer to the recorded observations. The results from the two independent methods are combined to yield a composite forecast, which gives results that are slightly better than those given by either of the independent methods.

Download Full-text

Flood Hazard Assessment and Decisions Support Using Geographic Information System: A Case Study of Uyo Capital City, Akwa Ibom State, Nigeria

International Journal of Geography and Geology ◽

10.18488/journal.10/2014.3.4/10.4.56.67 ◽

2014 ◽

Vol 3 (4) ◽

pp. 56-67

Author(s):

Uwem J Ituen ◽

Imoh Johnson ◽

Ndifreke Nyah

Keyword(s):

Hazard Assessment ◽

Flood Control ◽

Flash Flood ◽

Flood Hazard ◽

Geographic Information ◽

Control Measures ◽

Capital City ◽

Map Algebra ◽

Adequate Knowledge ◽

Flood Hazard Assessment

The study aimed at assessing flood prone areas in Uyo Capital City with a view to suggesting control measures. It used 2008 NigerSat imagery, soil texture, rainfall, and road network data of Uyo. With Multi-criteria evaluation technique, the use of Geographic Information Systems (GIS), Global Positioning System (GPS), Digital Elevation Model (DEM) and single output map algebra were employed to generate flood hazard map of Uyo. The DEM was used to generate contours, terrain elevation, slope, and aspect surfaces, where aspect provided the direction of slope that contributed to flood inundation. Flood mapping was done to determine flood locations based on a 3D terrain assessment while flood hazard assessment formed the basis for flood control in the area. From the result of the study, flood hazard areas in Uyo Capital City were identified and classified into high, moderate, and low hazard zones. Based on this classification however, flood control measures have also been rated as critical, less critical, and non-critical respectively. Out of the 25 flood locations captured during the 2012 flash flood event, twelve locations were found on the critical control zones while thirteen were found on the less critical control zones. Based on the findings from this study, it was however suggested that town planners, construction companies and individuals should work in consultation with Geographers, Hydrologists and other stakeholders in the field who have adequate knowledge of the terrain and the technical ability in flood hazard modeling. Additionally, non-structural flood control measures have also been strongly advocated for implementation in the capital city of Uyo.

Download Full-text

Kalibrasi Model Soil Moisture Accounting dengan Software HEC-HMS di Stasiun Debit Pasir Pangaraian

Journal of Infrastructure and Civil Engineering ◽

10.35583/jice.v1i1.2 ◽

2021 ◽

Vol 1 (1) ◽

pp. 13-21

Author(s):

Yohanna Lilis Handayani ◽

Gopal Adya Ariska ◽

David Imannuel Ketaren

Keyword(s):

Soil Moisture ◽

Model Calibration ◽

Daily Rainfall ◽

Automatic Calibration ◽

Calibration Data ◽

Discharge Data ◽

Climatological Data ◽

Soil Moisture Accounting ◽

Model Soil ◽

Daily Discharge

This research aims to compare the results of the calibration of the Soil Moisture Accounting (SMA) model using Percent Error in Volume (PEV) and Peak Weighted Root Mean Square Error (RMSE). The SMA model calibration uses the HEC-HMS (Hydrologic Engineering Center – Hydrologic Modeling System). There are 12 calibrated parameters by automatic calibration. The input data are the area of the watershed, daily rainfall, daily discharge data and climatological data. The data used is data from 2008 to 2017. The results show that PEV performance shows good results. While the RMSE showed poor results. PEV results are best at 7 years of calibration and 3 years of verification. The length of the calibration data has not affected the verification results.

Download Full-text

Threat prediction in complex distributed systems using artificial neural network technology

CPT2020 The 8th International Scientific Conference on Computing in Physics and Technology Proceedings ◽

10.30987/conferencearticle_5fce27725ade42.71922144 ◽

2020 ◽

Author(s):

Evgeny Palchevsky ◽

Olga Khristodulo ◽

Sergey Pavlov

Keyword(s):

Neural Network ◽

Distributed Systems ◽

Flood Control ◽

Least Squares Method ◽

Mathematical Formulation ◽

Point Of View ◽

Water Levels ◽

Control Measures ◽

Forecasting Methods ◽

The Neural Network

In the context of this article, a method for detecting threats based on their forecasting and development in complex distributed systems is proposed. Initially, the relevance of the research topic is substantiated from the point of view of the prospective use of various methods in the framework of threat management and their forecasting in complex distributed systems. Based on the analysis of these methods, a proprietary forecasting method based on the second generation recurrent neural network (RNN) was proposed. The mathematical formulation of the problem is presented, as well as the structure of this neural network and its mathematical model of self-learning, which allows achieving more accurate (with less error) results in the framework of threat prediction (in this case, the level of water rise at gauging stations) in complex distributed systems. An analysis was also made of the effectiveness of the existing and proposed forecasting methods, which showed the stability of the neural network in relation to other forecasting methods: the error of the neural network is 3-20% of actual (real) water levels; the least squares method reaches up to 34.5%, the numerical method in a generalized form - up to 36%; linear regression model – up to 47.5%. Thus, the neural network allows a fairly stable forecast of the flood situation over several days, which allows special services to carry out flood control measures.

Download Full-text

Proababilistic Approach to Deterministic Inundation Map Informed by Geographical Factors

10.5194/egusphere-egu2020-21498 ◽

2020 ◽

Author(s):

Jae-Ung Yu ◽

Minkyu Jung ◽

Jin-Young Kim ◽

Hyun-Han Kwon

Keyword(s):

Logistic Regression ◽

Flood Risk ◽

Flood Control ◽

Hydrological Cycle ◽

Flood Hazard ◽

Control Measures ◽

Geographical Information ◽

Target Area ◽

Hazard Map ◽

Flood Hazard Map

Urbanization causes extension of impervious surface interrupting natural hydrological cycle, which may increase in the number of disaster factors causing difficulties in terms of flood management. Flood control measures should prioritize identification of areas where flooding is expected to occur, considering various spatial characteristics distributed over the areas at risk. In this study, a probabilistic flood risk assessment was performed. The flood hazard map for a 100-year return level was used to illustrate the concept of a probabilistic model. Here, we trained the model to obtain the relationship between the estimated inundation area and potential predictors such as elevation, slope, curve number, and distance to the river. In this study, a Bayesian logistic regression analysis was performed to impose probabilities on the inundation for each grid. Finally, the flood risk was provided with the population for the entire target area through the model.&#160;Keywords: Bayesian Inference, Flood Hazard Map, Geographical Information, Logistic Regression&#160;AcknowledgementThis work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 19AWMP-B121100-04)

Download Full-text