A high resolution coupled hydrologic–hydraulic model (HiResFlood-UCI) for flash flood modeling

HiResFlood-UCI was developed by coupling the NWS's hydrologic model (HL-RDHM) with the hydraulic model (BreZo) for flash flood modeling at decameter resolutions. The coupled model uses HL-RDHM as a rainfall-runoff generator and replaces the routing scheme of HL-RDHM with the 2D hydraulic model (BreZo) in order to predict localized flood depths and velocities. A semi-automated technique of unstructured mesh generation was developed to cluster an adequate density of computational cells along river channels such that numerical errors are negligible compared with other sources of error, while ensuring that computational costs of the hydraulic model are kept to a bare minimum. HiResFlood-UCI was implemented for a watershed (ELDO2) in the DMIP2 experiment domain in Oklahoma. Using synthetic precipitation input, the model was tested for various components including HL-RDHM parameters (a priori versus calibrated), channel and floodplain Manning n values, DEM resolution (10 m versus 30 m) and computation mesh resolution (10 m+ versus 30 m+). Simulations with calibrated versus a priori parameters of HL-RDHM show that HiResFlood-UCI produces reasonable results with the a priori parameters from NWS. Sensitivities to hydraulic model resistance parameters, mesh resolution and DEM resolution are also identified, pointing to the importance of model calibration and validation for accurate prediction of localized flood intensities. HiResFlood-UCI performance was examined using 6 measured precipitation events as model input for model calibration and validation of the streamflow at the outlet. The Nash–Sutcliffe Efficiency (NSE) obtained ranges from 0.588 to 0.905. The model was also validated for the flooded map using USGS observed water level at an interior point. The predicted flood stage error is 0.82 m or less, based on a comparison to measured stage. Validation of stage and discharge predictions builds confidence in model predictions of flood extent and localized velocities, which are fundamental to reliable flash flood warning

Hydraulic impact of Wan River Project with MIKE 11

Hydraulic assessment of Wan River Project was carried out using MIKE 11 model from the Danish Hydraulic Institute (DHI).  The approach for this model leads to unsteady flow simulations along stream channel reach.  The study aimed the development of MIKE 11 model based on stream cross-section (L sections) and water release data.  The global value of the model parameters i.e. manning’s roughness coefficient (n) and ground water leakage coefficient was found as 0.028 and 7.11e-005, respectively.  The hydraulic performance of wan river project was judged in terms of water delivery performance ratio and system performance ratio.  The average water delivery performance ratio WDPR ratio for canal network of the project declines from 1.05 to 0.68, 0.68 to 0.39 and 0.39 to 0.28 for head, middle and tail reach, respectively.  The system performance ratio revealed that the Main canal, Telhara and Warud distributory are drawing excess water, whereas Bathkhed distributory, Branch and Belkhed Branch canal are getting less water.  The study concluded that there was uneven distribution of water among the distributories and hence there is need to reschedule the irrigation.   Keywords: hydraulic assessment, unsteady flow simulation, river modeling, MIKE 11 HD, Wan River projec

Characterization of river kosi in the monsoon of 2010-12

Climatic change, unplanned land use pattern, deforestation, socio-economic change and human intervention are affecting the hydrology of Kosi river basin .In the absence of proper flood management, potential threat of flood damages is increasing in every extreme flood event. The existing structural counter measures in the basin will not be sufficient against probable extreme floods. So, it is necessary to develop non-structural counter measures. The methodology in this study is to develop non-structural counter measures for downstream reach of Kosi river basin so that losses and damages due to flood disaster could be minimized with the simultaneous application of structural as well as non-structural measures. This paper presents the results of statistical analysis as well as model analysis based on Kosi river basin data procured from Central Water Commission office, Patna and TRMM website in order to develop the relationship between various hydrological parameters like rainfall, soil moisture, runoff and hence to develop flood forecasting model using simple statistical tools ( least square methods of best fit technique )and doing inundation analysis using maps available in NRSC website for last three years during monsoon. The results found are satisfactory and can be used for flood forecasting and hence bring down the effect of the menace both in terms of life and propert

Impacts of DEM Type and Resolution on Deep Learning-Based Flood Inundation Mapping

This paper presents a comprehensive study focusing on the influence of DEM type and spatial resolution on the accuracy of flood inundation prediction. The research employs a state-of-the-art deep learning method using a 1D convolutional neural network (CNN). The CNN-based method employs training input data in the form of synthetic hydrographs, along with target data represented by water depth obtained utilizing a 2D hydrodynamic model, LISFLOOD-FP. The performance of the trained CNN models is then evaluated and compared with the observed flood event. This study examines the use of digital surface models (DSMs) and digital terrain models (DTMs) derived from a LIDAR-based 1m DTM, with resolutions ranging from 15 to 30 meters. The proposed methodology is implemented and evaluated in a well-established benchmark location in Carlisle, UK. The paper also discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. The study found that DTM performs better than DSM at lower resolutions. Using a 30m DTM improved flood depth prediction accuracy by about 21% during the peak stage. Increasing the resolution to 15m increased RMSE and overlap index by at least 50% and 20% across all flood phases. The study demonstrates that while coarser resolution may impact the accuracy of the CNN model, it remains a viable option for rapid flood prediction compared to hydrodynamic modeling approaches

A Systematic Review of Real-time Urban Flood Forecasting Model in Malaysia and Indonesia -Current Modelling and Challenge

Several metropolitan areas in tropical Southeast Asia, mainly in Malaysia and Indonesia have lately been witnessing unprecedentedly severe flash floods owing to unexpected climate change. The fast water flooding has caused extraordinarily serious harm to urban populations and social facilities. In addition, urban Southeast Asia generally has insufficient capacity in drainage systems, complex land use patterns, and a largely susceptible population in confined urban regions. To lower the urban flood risk and strengthen the resilience of vulnerable urban populations, it has been of fundamental relevance to create real-time urban flood forecasting systems for flood disaster prevention agencies and the urban public. This review examined the state-of-the-art models of real-time forecasting systems for urban flash floods in Malaysia and Indonesia. The real-time system primarily comprises the following subsystems, i.e., rainfall forecasting, drainage system modeling, and inundation area mapping. This review described the current urban flood forecasting modeling for rainfall forecasting, physical-process-based hydraulic models for flood inundation prediction, and data-driven artificial intelligence (AI) models for the real-time forecasting system. The analysis found that urban flood forecasting modeling based on data-driven AI models is the most applied in many metropolitan locations in Malaysia and Indonesia. The analysis also evaluated the existing potential of data-driven AI models for real-time forecasting systems as well as the challenges towards i

One- and Two-Dimensional Hydrological Modelling and Their Uncertainties

Earth processes, which occur in land, air and ocean in different environment and at different scales, are very complex. Flooding is also a part of the complex processes, which need to be assessed accurately to know the accurate spatial and temporal changes of flooding and their causes. Hydrological modelling has been used by several researchers in river and floodplain modelling for flood analysis. In this chapter, factors affecting flash flood, possible options of basic input parameters in one- and two-dimensional hydrological models in data sparse environment, some case studies and uncertainty in hydrological modelling were discussed. This discussion will help the readers to understand the flooding factors, selection of input parameters in data sparse environment, a brief insight of one- and two-dimensional hydrological models and uncertainties in their input and model parameters and model structures

Development of An Integrated GIS-Based System for Surface Water Quality Assessment and Management (GIS-SWQAM)

It is an fact that surface water receives a large volume of pollutants from industrial, agricultural, and municipal sources. The adverse health and environmental effects of surface water pollution have been a major concern in environmental management. Water quality models are useful tools to simulate the complex transport and fate of pollutants in a water body and predict the short-term and long-term effects on water quality variation. The emergence of spatial information technologies, such as Geographic Information System (GIS) make it possible to assess and predict surface water quality with more details with respect to spatial information. The focuses of this thesis is to develop a comprehensive system named as GIS-SWQAM, which includes: (1) the development of a GIS-based water quality assessment system to assess the water quality and provide spatial distribution of water quality variables; (2) the development of an artificial neural network model to predict the change of water quality variables; (3) the development of a user interface that integrates the above models and functions; furthermore, a comparative analysis of the modeling approach developed in the GIS-SWQAM and the commercial model MIKE 21 was performed through field case studies. The GIS-based water quality and ecological risk assessment models (MWQ module for marine water quality assessment and LWQ module for lake water quality assessment) are developed by integrating a fuzzy risk assessment model, a eutrophication risk assessment model, a heavy metal risk assessment model, a dynamic database, the ArcGIS Engine, and a graphical user interface (GUI). The assessment results are both spatially and visually presented in the form of contour maps and color-coded maps that indicate risk levels. A large amount of data with both spatial and temporal distributions is managed by the developed system and analyzed by the assessment modules. The developed MWQ and LWQ modules are respectively applied in the Liaodong Bay of China and Lake Champlain. The MWQ and LWQ produce risk maps that depict the spatial distribution of integrated water quality index values, eutrophication risk levels and heavy metal risk levels in the study area. The maps generated can provide a better understanding of the distribution of the water quality and ecological risk levels. The primary factors that affect the water quality are subsequently examined using the visualized results. An artificial neural network model with the back-propagation algorithm (BPANN) is first developed using Matlab to predict the chlorophyll-a concentration in Lake Champlain. Then, the algorithm of the BPANN model is built using the C# programing language and integrated with GIS and the database to build the ANN module, which is applied to predict the total phosphorus concentration in Lake Champlain. The best performing model is determined among the results of models built with different combination of input variables, which are preliminarily selected by linear correlation analysis and domain knowledge. Subsequently, the performances of the BPANN models are validated by a new set of field data. Similar to the MWQ and LWQ modules, the ANN module also produces the spatial distribution maps of the predicted concentrations; errors made during the prediction are presented in the user interface. The results indicate that the developed BPANN models can provide acceptable prediction results and can be used to provide a quick modeling assessment of water quality variation for managers. In this thesis, the MIKE 21 FM software is also used to establish a hydrodynamic model coupled with a transport model to simulate the total phosphorus concentration in Lake Champlain. A comparative analysis is performed between the results of the MIKE 21 model and the BPANN model. The results of the MIKE 21 model are acceptable, but not as good as that of the BPANN model. This further verifies that the developed BPANN model is a reliable tool to assess the lake eutrophication and to help managing lake water quality. The developed system can be also applied to surface water management in other area

The Application of Hydraulic and Sediment Transport Models in Fluvial Geomorphology

After publishing the famous “Fluvial Processes in Geomorphology” in the early 1960s, the work of Luna Leopold, Gordon Wolman, and John Miller became a key for opening the door to understanding rivers and streams. They first illustrated the problem to geomorphologists and geographers. Later, Chang, in his “Fluvial Processes in River Engineering”, provided a basis for engineers, showing this group of professionals how to deal with rivers and how to understand them. Since then, more informative studies have been published. Many of the authors started to combine fluvial geomorphology knowledge and river engineering needs, such as “Tools in Fluvial Geomorphology” by G. Mathias Kondolf and Hervé Piégay, or focused more on river engineering tasks, such as “Stream Restoration in Dynamic Fluvial Systems: Scientific Approaches” by Andrew Simon, Sean Bennett, and Janine Castro. Finally, Luna Leopold summarized river and stream morphologies in the beautiful “A view of the river”. It appears that we continue to explore this subject in the right direction. We better understand rivers and streams, and as engineers and fluvial geomorphologists, we can establish tools to help bring rivers alive. However, there is still a hunger for more scientific tools that we could use to further understand rivers and to support the development of healthy streams and rivers with high biodiversity in the present world, which has started to face water scarcity

Modelling of Floods in Urban Areas

This Special Issue publishes the latest advances and developments concerning the modelling of flooding in urban areas and contributes to our scientific understanding of the flooding processes and the appropriate evaluation of flood impacts. This issue contains contributions of novel methodologies including flood forecasting methods, data acquisition techniques, experimental research in urban drainage systems and/or sustainable drainage systems, and new numerical and simulation approaches in nine papers with contributions from over forty authors