A Bayesian Copula Approach for Flood Analysis

This study aims to provide joint modelling of rainfall characteristics in Peninsular Malaysia using two-dimensional copula. Two commonly regarded as important variables in the field of hydrology, namely rainfall severity and duration were derived using the Standard Precipitation Index (SPI) and their univariate marginal distributions are further identified by fitting into several distributions. The paper uses a Bayesian framework to estimate the parameter values in the marginal and copula model. The approximation of the posterior distribution by random sampling has been done by Monte Carlo Markov Chain (MCMC). Next, the authors compared these findings with those based on the classical procedure. The results indicated that the Bayesian approach can be substantially more reliable in parameter estimation for small samples.

Characteristics of the floods in Pechea village, Galați county.

This study aims to develop risk analysis methodologies on floods, analyze floods produced on the Suhu River in Pechea village (Galați county), and factors that favor flooding. Flood analysis represents one of the main concerns of researchers in hydrology in the context of climate change. It is increasingly leaving its mark on the frequency of precipitation and, implicitly, on the production of floods. We presented the definitions of floods, and we presented the study area in the first part of the article. The monthly and seasonal frequency of floods were analyzed, and there were calculated specific parameters of a flood produced in the study area. Then, the factors that favor the occurrence of floods were analyzed. The results obtained will contribute to the complete information on floods in small basins in the plain area on the Romanian territory.

Urban Flood Analysis in Ungauged Drainage Basin Using Short-Term and High-Resolution Remotely Sensed Rainfall Records

Analyzing flooding in urban areas is a great challenge due to the lack of long-term rainfall records. This study hereby seeks to propose a modeling framework for urban flood analysis in ungauged drainage basins. A platform called “RainyDay” combined with a nine-year record of hourly, 0.1° remotely sensed rainfall data are used to generate extreme rainfall events. These events are used as inputs to a hydrological model. The comprehensive characteristics of urban flooding are reflected through the projection pursuit method. We simulate runoff for different return periods for a typical urban drainage basin. The combination of RainyDay and short-record remotely sensed rainfall can reproduce recent observed rainfall frequencies, which are relatively close to the design rainfall calculated by the intensity-duration-frequency formula. More specifically, the design rainfall is closer at high (higher than 20-yr) return period or long duration (longer than 6 h). Contrasting with the flood-simulated results under different return periods, RainyDay-based estimates may underestimate the flood characteristics under low return period or short duration scenarios, but they can reflect the characteristics with increasing duration or return period. The proposed modeling framework provides an alternative way to estimate the ensemble spread of rainfall and flood estimates rather than a single estimate value.

Visualization of radar-observed rainfall for hydrological risk assessment

Short-duration high-intensity rainfall constitutes a major hydro-meteorological hazard, with impacts such as pluvial (urban) flooding and debris flow. There is a great demand in society for improved information on small-scale rainfall extremes, both in real time (e.g. for early warning) and historically (e.g. for post-flood analysis). Observing this type of events is notoriously difficult, because of their extreme small-scale space-time variability. However, owing to recent advances in weather radar technology as well as integration with ground-based sensors, observational products potentially applicable in this context are now available. In this paper we present a visualization prototype tailored for hydrological risk assessment by using sub-basins as spatial units, by allowing temporal aggregation over different durations (i.e. accumulation periods) and by expressing high rainfall intensities in terms of return period exceedance. The radar-based data is evaluated by comparison with gauge observations and the quality is deemed sufficient for the intended applications. Different stakeholders have shown great interest in the prototype, which is openly accessible online.

Probabilistic Flood Assessment Methodology for Nuclear Power Plants Considering Extreme Rainfall

Abnormal weather conditions due to climate change are currently increasing on both global and local scales. It is therefore important to ensure the safety of the areas where major national facilities are located by analyzing risk quantitatively and re-evaluating the existing major facilities, such as nuclear power plants, considering the load and capacity of extreme flood conditions. In this study, a risk analysis method is developed that combines flood hazard curves with fragility curves using hydraulic and hydrological models by GIS tools and the @RISK model for the probabilistic flood analysis of nuclear power plant sites. A two-dimensional (2D) analysis is first carried out to estimate flood depths in various watershed scenarios, and a representative hazard curve for both external and internal flooding is made by applying a verified probability distribution type for the flood watersheds. For the analysis of flooding within buildings, an internal grid is constructed using GIS with related design drawings, and based on the flood depth results of the 2D analysis, a hazard curve for the representative internal inundation using a verified probability distribution type is presented. In the present study, walkdowns with nuclear experts are conducted around the nuclear power plant area to evaluate the fragile structures and facilities under possible flooding. After reviewing the 2D inundation analysis results based on the selected major equipment and facilities, the zones requiring risk assessment are re-assigned. A fragility curve applying probability distribution for the site’s major equipment and facilities is also presented. Failure risk analysis of the major facilities is then conducted by combining the proposed hazard and fragility curves. Results in the form of quantitative values are obtained, and the indicators for risks as well as the reliability and optimal measures to support decision-making are also presented. Through this study, it is confirmed that risk assessment based on the proposed probabilistic flood analysis technique is possible for flood events occurring at nuclear power plant sites.

PREDIKSI DEBIT SUNGAI BENGAWAN SOLO MENGGUNAKAN NUMERICAL WEATHER MODEL GLOBAL FORECAST SYSTEM DAN INTEGRATED FLOOD ANALYSIS SYSTEM (Prediction of discharge in Bengawan Solo River using Numerical Weather Model Global Forecast System and Integrated Flood Analysis System)

Flood is one of the most common hydro-meteorological disasters. Bengawan Solo is one of the watersheds in Indonesia that also hit by this disaster. This study discusses the flood disaster in the Bengawan Solo area in early March 2019. The purpose of this study is to conduct a discharge simulation using numerical weather model Global Forecast System (GFS) data through Integrated Flood Analysis System (IFAS) so it is possible to predict discharge in the future. There are three types of numerical weather model GFS data that have been downscale using weather research and forecasting model which differentiated based on spin-up time. The numerical weather model product is then used as rainfall data input for IFAS simulation. Based on the analysis, the flood discharge simulation using an 84-hour spin-up time has a satisfactory performance in describing the change in discharge with respect to time. This happens because numerical weather models will be better at quantifying processes that occur on a meso scale with spatial scale of 10 to 1000 km. The result of this research shows that it is possible to predict river discharge up to 84 hours before the disaster so this is can support the mitigation process for hydrometeorological disasters.

Applicability of High-Resolution Geospatial Data Obtained by UAV Photogrammetry to Develop Drainage System Models for Pluvial Flood Analysis

Integrated flood models have been previously developed to simulate diverse inundation situations and combined with models of storm surges and river floods. However, drainage systems, ground elevation, and surface information of human settlements have only been digitized in large cities. Digitization of surface information is essential for developing a drainage system model for pluvial flood analysis. Occasionally, suburban drainage areas exhibit various complex land-use conditions, including residential and green areas, agricultural land with drainage, and irrigation channels. Herein, UAV photogrammetry was applied to obtain high-resolution geospatial data associated with small-scale flood-prone areas whose elevation, land-use, and waterway networks have not been elucidated sufficiently. The resolution of elevation and land-use data ranged from 2.61–5.22 cm/mesh. The measurement accuracy of the width and depth of the open channels was high, and the errors were mostly within 10%. A drainage system model was developed using data on open channel, elevation, and land-use.