Development of Integrated Water Balance Analysis System

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive

Enhanced DEM-based flow path delineation methods for urban flood modelling

In order to simulate surface runoff and flooding, one-dimensional (1D) overland flow networks can be automatically delineated using digital elevation models (DEM). The resulting network comprises flow paths and terrain depressions/ponds and is essential to reliably model pluvial (surface) flooding events in urban areas by so-called 1D/1D models. Conventional automatic DEM-based flow path delineation methods have problems in producing realistic overland flow paths when detailed high-resolution DEMs of urban areas are used. The aim of this paper is to present the results of research and development of three enhanced DEM-based overland flow path delineation methods; these methods are triggered when the conventional flow path delineation process stops due to a flow obstacle. Two of the methods, the 'bouncing ball and buildings' and 'bouncing ball and A*' methods, are based on the conventional 'bouncing ball' concept; the third proposed method, the 'sliding ball' method, is based on the physical water accumulation concept. These enhanced methods were tested and their results were compared with results obtained using two conventional flow path delineation methods using a semi-synthetic test DEM. The results showed significant improvements in terms of the reliability of the delineated overland flow paths when using these enhanced methods

The influence of digital elevation model resolution on overland flow networks for modelling urban pluvial flooding

This paper presents the developments towards the next generation of overland flow modelling of urban pluvial flooding. Using a detailed analysis of the Digital Elevation Model (DEM) the developed GIS tools can automatically generate surface drainage networks which consist of temporary ponds (floodable areas) and flow paths and link them with the underground network through inlets. For different commercially-available Rainfall-Runoff simulation models, the tool will generate the overland flow network needed to model the surface runoff and pluvial flooding accurately. In this paper the emphasis is placed on a sensitivity analysis of ponds and preferential overland flow paths creation. Different DEMs for three areas were considered in order to compare the results obtained. The DEMs considered were generated using different acquisition techniques and hence represent terrain with varying levels of resolution and accuracy. The results show that DEMs can be used to generate surface flow networks reliably. As expected, the quality of the surface network generated is highly dependent on the quality and resolution of the DEMs and successful representation of buildings and streets

Overland flow and pathway analysis for modelling of urban pluvial flooding

Research on improving an overland flow model is presented for urban pluvial flooding under the dual-drainage concept where sewer flow dynamically interacts with overland flow. This occurs during heavy storms when the sewer system is surcharged. The system becomes pressurised and overland flow increases by the additional volume flowing out from the sewer. To represent the overland flow realistically, a new methodology was developed to automatically create the overland flow network which can interact with the drainage system. Use is made of high-resolution, accurate Digital Elevation Model data collected by the LiDAR technique. This approach updates the current urban drainage models to urban flood models with detailed representation of overland flow processes such as pond forming, flow through preferential surface pathways and surface drainage capacity. This work advances new areas of urban flood management including improvement in real-time control and of links with rainfall now-casting, and short term urban flood forecasting. The dual-drainage approach is appropriate for real-time applications

Real time control of nature-based solutions: Towards Smart Solutions and digital twins in Rangsit Area, Thailand

The intensity and frequency of hydro-meteorological hazards have increased due to fast-growing urbanisation activities and climate change. Hybrid approaches that combine grey infrastructure and Nature-Based Solutions (NBSs) have been applied as an adaptive and resilient strategy to cope with climate change uncertainties and incorporate other co-benefits. This research aims to investigate the feasibility of Real Time Control (RTC) for NBS operation in order to reduce flooding and improve their effectiveness. The study area is the irrigation and drainage system of the Rangsit Area in Thailand. The results show that during the normal flood events, the RTC system effectively reduces water level at the Western Raphiphat Canal Station compared to the system without RTC or with additional storage. Moreover, the RTC system facilitates achieving the required minimum volume and increasing the volume in the retentions. These findings highlight the potential of using RTC to improve the irrigation and drainage system operation as well as NBS implementation to reduce flooding. The RTC system can also assists in equitable water distribution between Klongs and retention areas, while also increasing the water storage in the retention areas. This additional water storage can be utilized for agricultural purposes, providing further benefits. These results represent an essential starting point for the development of Smart Solutions and Digital Twins in utilizing Real-Time Control for flood reduction and water allocation in the Rangsit Area in Thailand.BT/Environmental Biotechnolog

Using Multiple Space Assests with In-Situ Measurements to Track Flooding in Thailand

Increasing numbers of space assets can enable coordinated measurements of flooding phenomena to enhance tracking of extreme events. We describe the use of space and ground measurements to target further measurements as part of a flood monitoring system in Thailand. We utilize rapidly delivered MODIS data to detect major areas of flooding and the target the Earth Observing One Advanced Land Imager sensor to acquire higher spatial resolution data. Automatic surface water extent mapping products delivered to interested parties. We are also working to extend our network to include in-situ sensing networks and additional space assets

St-corabico : A spatiotemporal object-based bias correction method for storm prediction detected by satellite

Advances in near real-time rainstorm prediction using remote sensing have offered important opportunities for effective disaster management. However, this information is subject to several sources of systematic errors that need to be corrected. Temporal and spatial characteristics of both satellite and in-situ data can be combined to enhance the quality of storm estimates. In this study, we present a spatiotemporal object-based method to bias correct two sources of systematic error in satellites: displacement and volume. The method, Spatiotemporal Contiguous Object-based Rainfall Analysis for Bias Correction (ST-CORAbico), uses the spatiotemporal rainfall analysis ST-CORA incorporated with a multivariate kernel density storm segmentation for describing the main storm event characteristics (duration, spatial extension, volume, maximum intensity, centroid). Displacement and volume are corrected by adjusting the spatiotemporal structure and the intensity distribution, respectively. ST-CORAbico was applied to correct the early version of the Integrated Multi-satellite Retrievals for the Global Precipitation Mission (GPM-IMERG) over the Lower Mekong basin in Thailand during the monsoon season from 2014 to 2017. The performance of ST-CORABico is compared against the Distribution Transformation (DT) and Gamma Quantile Mapping (GQM) probabilistic methods. A total of 120 storm events identified over the study area were classified into short and long-lived storms by using a k-means cluster analysis method. Examples for both storm event types describe the error reduction due to location and magnitude by ST-CORAbico. The results showed that the displacement and magnitude correction made by ST-CORAbico considerably reduced RMSE and bias of GPM-IMERG. In both storm event types, this method showed a lower impact on the spatial correlation of the storm event. In comparison with DT and GQM, ST-CORAbico showed a superior performance, outperforming both approaches. This spatiotemporal bias correction method offers a new approach to enhance the accuracy of satellite-derived information for near real-time estimation of storm events.</p