Michal Podhoranyi

Uncertainty modelling in Rainfall-Runoff simulations based on parallel Monte Carlo method

This article describes statistical evaluation of the computational model for precipitation forecast and proposes a method for uncertainty modelling of rainfall-runoff models in the Floreon+ system based on this evaluation. The Monte-Carlo simulation method is used for estimating possible river discharge and provides several confidence intervals that can support the decisions in operational disaster management. Experiments with other parameters of the model and their influence on final river discharge are also discussed.

Automatic calibration of rainfall-runoff models and its parallelization strategies

For successful decision making in disaster management it is necessary to have very accurate information about disaster phenomena and its potential developmentin time. Rainfall-runoff simulations are an integral part of flood warning and decision making processes. To increase their accuracy, it is crucial to periodically updatetheir parametersin a calibration process.Since calibration is very time consuming process an HPC facility is convenient tool for its speed-up. However, required speed-up can be achieved only avoiding any human-computer interaction in so-called automatic calibration.In order to compare possibilities and efficiency of the automatic calibration, three different fully automatic parallel implementationstrategies were created and tested with our in-house rainfall-runoff model.

Dynamic computing resource allocation in online flood monitoring and prediction

This paper presents tools and methodologies for dynamic allocation of high performance computing resources during operation of the Floreon+ online flood monitoring and prediction system. The resource allocation is done throughout the execution of supported simulations to meet the required service quality levels for system operation. It also ensures flexible reactions to changing weather and flood situations, as it is not economically feasible to operate online flood monitoring systems in the full performance mode during non-flood seasons. Different service quality levels are therefore described for different flooding scenarios, and the runtime manager controls them by allocating only minimal resources currently expected to meet the deadlines. Finally, an experiment covering all presented aspects of computing resource allocation in rainfall-runoff and Monte Carlo uncertainty simulation is performed for the area of the Moravian-Silesian region in the Czech Republic.

Flood evolution assessment and monitoring using hydrological modelling techniques: analysis of the inundation areas at a regional scale

The primary objective of this study is to present techniques that cover usage of a hydrodynamic model as the main tool for monitoring and assessment of flood events while focusing on modelling of inundation areas. We analyzed the 2010 flood event (14th May - 20th May) that occurred in the Moravian-Silesian region (Czech Republic). Under investigation were four main catchments: Opava, Odra, Olse and Ostravice. Four hydrodynamic models were created and implemented into the Floreon+ platform in order to map inundation areas that arose during the flood event. In order to study the dynamics of the water, we applied an unsteady flow simulation for the entire area (HEC-RAS 4.1). The inundation areas were monitored, evaluated and recorded semi-automatically by means of the Floreon+ platform. We focused on information about the extent and presence of the flood areas. The modeled flooded areas were verified by comparing them with real data from different sources (official reports, aerial photos and hydrological networks). The study confirmed that hydrodynamic modeling is a very useful tool for mapping and monitoring of inundation areas. Overall, our models detected 48 inundation areas during the 2010 flood event.

Floreon+: A Web-Based Platform for Flood Prediction, Hydrologic Modelling and Dynamic Data Analysis

The main goal of this article is to describe the overview of Floreon+ system, an online flood monitoring and prediction system, which was primarily developed for the Moravian-Silesian region in the Czech Republic. Moreover, the article specifies the basic processes, which are implemented for running automatic and on-demand simulations that utilize the High Performance Computing (HPC) infrastructure. The main purpose of hydrodynamic models in the disaster management context is to provide an accurate overview of hydrologic situation in a given river catchment. In the event of extreme weather conditions, such as unusually heavy rainfall, these models could provide valuable information about imminent flood risk endangering a particular area. In the disaster management context, time plays a very significant role. Up to date and accurate results obtained in a short time can be very helpful. The availability of such results can be significantly improved by utilization of HPC resources and tools. The article describes the individual parts of the system in terms of data types, dynamic data processing, visualization, and the overall architecture.

Automatization of hydrodynamic modelling in a Floreon+ system

The paper describes fully automatized hydrodynamic modelling as a part of the Floreon+ system. The main purpose of hydrodynamic modelling in the disaster management is to provide an accurate overview of the hydrological situation in a given river catchment. Automatization of the process as a web service could provide us with immediate data based on extreme weather conditions, such as heavy rainfall, without the intervention of an expert. Such a service can be used by non scientific users such as fire-fighter operators or representatives of a military service organizing evacuation during floods or river dam breaks. The paper describes the whole process beginning with a definition of a schematization necessary for hydrodynamic model, gathering of necessary data and its processing for a simulation, the model itself and post processing of a result and visualization on a web service. The process is demonstrated on a real data collected during floods in our Moravian-Silesian region in 2010.