Runoff models and flood frequency statistics for design flood estimation in Austria – Do they tell a consistent story?

Rogger, Magdalena; Köhl, Bernhard; Pirkl, Herbert; Viglione, Alberto; Komma, Jürgen; Kirnbauer, R.; Merz, Ralf; Blöschl, Günter

doi:10.1016/j.jhydrol.2012.05.068

Cited by 100 publications

(94 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the density of those stations can be very sparse in developing countries, which limits their capability to assess the accurate significance and impact of flood events [6]. Statistical and model prediction approaches were applied to estimate or predict the potentially inundated area and impacts of future flood disasters [14][15][16][17]. These approaches are, unfortunately, not able to handle irregular changes induced by real objective causes.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Dao

Liou

2015

Remote Sensing

View full text Add to dashboard Cite

Cambodia is one of the most flood-prone countries in Southeast Asia. It is geographically situated in the downstream region of the Mekong River with a lowland floodplain in the middle, surrounded by plateaus and high mountains. It usually experiences devastating floods induced by an overwhelming concentration of rainfall water over the Tonle Sap Lake's and Mekong River's banks during monsoon seasons. Flood damage assessment in the rice ecosystem plays an important role in this region as local residents rely heavily on agricultural production. This study introduced an object-based approach to flood mapping and affected rice field estimation in central Cambodia. In this approach, image segmentation processing was conducted with optimal scale parameter estimation based on the variation of objects' local variances. The inundated area was identified by using Landsat 8 images with an overall accuracy of higher than 95% compared to those derived from finer spatial resolution images. Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index products were utilized to identify the paddy rice field based on seasonal inter-variation between vegetation and water index during the transplanting stage. The rice classification result was well correlated with the statistical data at a commune level (R 2 = 0.675). The flood mapping and affected rice estimation results are useful to provide local governments with valuable information for flooding mitigation and post-flooding compensation and restoration.

show abstract

Section: Introductionmentioning

confidence: 99%

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Dao

Liou

2015

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The simplest types of methods, which utilize rainfall depth data, are referred to as deterministic or design storm methods. They are based on the selection of a design storm, which is defined by a rainfall depth, duration, return period (all three from an intensity-duration-frequency curve) and shape, and which is transformed into a design flood hydrograph using an event-based rainfall-runoff model (Rogger et al, 2012). The simplicity of these types of methods and their computational efficiency means that they are widely used in many countries and still dominate the methods used in practice (ASCE, 1996;DVWK, 1999;FEH, 1999;Ball et al, 2016).…”

Section: Deterministic or Design Storm Approachesmentioning

confidence: 99%

“…Saghafian et al (2014) note that even though some of the uncertainties associated with discharge measurements and their length are removed by these methods in favour of less uncertain rainfall depth measurements, rainfall loss and rainfall-runoff models constitute new sources of uncertainties. Moreover, design storm methods rely on the following critical assumptions and choices to be made: the design storm event generates a design flood of the same return period (Adams et al, 1986;Rogger et al, 2012;Viglione et al, 2009); the subjective choice of the design storm hyetograph (Asquith et al, 2003); the subjective estimation of initial catchment conditions prior to the design storm event (Camici et al, 2011); and the need to estimate the initial and continuous losses from the design storm (Gamage et al, 2015;Ball et al, 2016). …”

Section: Deterministic or Design Storm Approachesmentioning

confidence: 99%

An Application of a Stochastic Semi-Continuous Simulation Method for Flood Frequency Analysis: A Case Study in Slovakia

Valent

Paquet

2017

Slovak Journal of Civil Engineering

View full text Add to dashboard Cite

show abstract

“…In general, the stochastic PQRUT model gives higher values than the GEV distribution, which might be due to the uncertainty in estimating the parameters for the GEV distribution. For example, the study by Rogger et al (2012) shows that the flood frequency analysis based on fitting a Gumbel distribution to AMAX series underestimates high flows in catchments with a high storage capacity, where a step change in the flood frequency curve occurs. The results of the study by Rogger et al (2012) can be explained by the fact that the Gumbel distribution, which 20 has a shape parameter of 0 and so is not as flexible as the GEV distribution.…”

mentioning

confidence: 99%

A stochastic event-based approach for flood estimation in catchments with mixed rainfall/snowmelt flood regimes

Filipova¹,

Lawrence²,

Skaugen³

2018

Preprint

View full text Add to dashboard Cite

Abstract. The estimation of extreme floods is associated with high uncertainty, in part due to the limited length of streamflow records. Traditionally, flood frequency analysis or event-based model using a single design storm have been applied. We propose here an alternative, stochastic event-based modelling approach. The stochastic PQRUT method involves Monte Carlo procedure to simulate different combinations of initial conditions, rainfall and snowmelt, from which a distribution of flood peaks can be constructed. The stochastic PQRUT was applied for 20 small and medium-sized catchments in Norway and the 5 results show good fit to the observations. A sensitivity analysis of the method indicates that the soil saturation level is less important than the rainfall input and the parameters of the PQRUT model for flood peaks with return periods higher than 100 years, and that excluding the snow routine can change the seasonality of the flood peaks. Estimates for the 100-and 1000-year return level based on the stochastic PQRUT model are compared with results for a) statistical frequency analysis, and b) a standard implementation of the event-based PQRUT method. The differences between the estimates can be up to 200% for 10 some catchments, which highlights the uncertainty in these methods.

show abstract

Runoff models and flood frequency statistics for design flood estimation in Austria – Do they tell a consistent story?

Cited by 100 publications

References 22 publications

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

Object-Based Flood Mapping and Affected Rice Field Estimation with Landsat 8 OLI and MODIS Data

An Application of a Stochastic Semi-Continuous Simulation Method for Flood Frequency Analysis: A Case Study in Slovakia

A stochastic event-based approach for flood estimation in catchments with mixed rainfall/snowmelt flood regimes

Contact Info

Product

Resources

About