A method for estimating flash flood peak discharge in a poorly gauged basin: Case study for the 13–14 January 1994 flood, Giofiros basin, Crete, Greece

2010 ◽  
Vol 385 (1-4) ◽  
pp. 150-164 ◽  
Author(s):  
Aristeidis G. Koutroulis ◽  
Ioannis K. Tsanis
Keyword(s):  
Flash Flood ◽  
Peak Discharge ◽  
Flood Peak

scholarly journals Estimating Flash Flood Peak Discharge in Gidra and Parná Basin: Case Study for the 7-8 June 2011 Flood

2012 ◽  
Vol 60 (3) ◽  
pp. 206-216 ◽  
Author(s):  
Pavla Pekárová ◽  
Aleš Svoboda ◽  
Pavol Miklánek ◽  
Peter Škoda ◽  
Dana Halmová ◽  
...  
Keyword(s):  
Flash Flood ◽  
Temporal Distribution ◽  
Peak Discharge ◽  
Flood Events ◽  
Flood Peak ◽  
Direct Measurements ◽  
Extreme Flood ◽  
Rainfall Runoff Model ◽  
Runoff Model

Estimating Flash Flood Peak Discharge in Gidra and Parná Basin: Case Study for the 7-8 June 2011 FloodWe analyzed the runoff and its temporal distribution during the catastrophic flood events on river Gidra (32.9 km2) and Parná (37.86 km2) of the 7th June 2011. The catchments are located in the Small Carpathian Mountains, western Slovakia. Direct measurements and evaluation of the peak discharge values after such extreme events are emphasized in the paper including exceedance probabilities of peak flows and of their causal flash rainfall events. In the second part of the paper, plausible modeling mode is presented, using the NLC (Non Linear Cascade) rainfall-runoff model. Several hypothetical extreme flood events were simulated by the NLC model for both rivers. Also the flood runoff volumes are evaluated as basic information on the natural or artificial catchment storage.

scholarly journals Unmanned Aerial Systems-Aided Post-Flood Peak Discharge Estimation in Ephemeral Streams

2020 ◽  
Vol 12 (24) ◽  
pp. 4183
Author(s):  
Emmanouil Andreadakis ◽  
Michalis Diakakis ◽  
Emmanuel Vassilakis ◽  
Georgios Deligiannakis ◽  
Antonis Antoniadis ◽  
...  
Keyword(s):  
Flash Flood ◽  
Traditional Approach ◽  
Peak Discharge ◽  
Unmanned Aerial Systems ◽  
Monitoring Networks ◽  
Response Dynamics ◽  
Hydrologic Modelling ◽  
Flood Peak ◽  
Field Evidence ◽  
Aerial Systems

The spatial and temporal scale of flash flood occurrence provides limited opportunities for observations and measurements using conventional monitoring networks, turning the focus to event-based, post-disaster studies. Post-flood surveys exploit field evidence to make indirect discharge estimations, aiming to improve our understanding of hydrological response dynamics under extreme meteorological forcing. However, discharge estimations are associated with demanding fieldwork aiming to record in small timeframes delicate data and data prone-to-be-lost and achieve the desired accuracy in measurements to minimize various uncertainties of the process. In this work, we explore the potential of unmanned aerial systems (UAS) technology, in combination with the Structure for Motion (SfM) and optical granulometry techniques in peak discharge estimations. We compare the results of the UAS-aided discharge estimations to estimates derived from differential Global Navigation Satellite System (d-GNSS) surveys and hydrologic modelling. The application in the catchment of the Soures torrent in Greece, after a catastrophic flood, shows that the UAS-aided method determined peak discharge with accuracy, providing very similar values compared to the ones estimated by the established traditional approach. The technique proved to be particularly effective, providing flexibility in terms of resources and timing, although there are certain limitations to its applicability, related mostly to the optical granulometry as well as the condition of the channel. The application highlighted important advantages and certain weaknesses of these emerging tools in indirect discharge estimations, which we discuss in detail.

scholarly journals Deriving Design Flood Hydrograph Based on Conditional Distribution: A Case Study of Danjiangkou Reservoir in Hanjiang Basin

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Changjiang Xu ◽  
Jiabo Yin ◽  
Shenglian Guo ◽  
Zhangjun Liu ◽  
Xingjun Hong
Keyword(s):  
Joint Probability ◽  
Flood Frequency ◽  
Peak Discharge ◽  
Flood Frequency Analysis ◽  
Design Flood ◽  
Copula Functions ◽  
Danjiangkou Reservoir ◽  
Flood Peak ◽  
Flood Hydrograph

Design flood hydrograph (DFH) for a dam is the flood of suitable probability and magnitude adopted to ensure safety of the dam in accordance with appropriate design standards. Estimated quantiles of peak discharge and flood volumes are necessary for deriving the DFH, which are mutually correlated and need to be described by multivariate analysis methods. The joint probability distributions of peak discharge and flood volumes were established using copula functions. Then the general formulae of conditional most likely composition (CMLC) and conditional expectation composition (CEC) methods that consider the inherent relationship between flood peak and volumes were derived for estimating DFH. The Danjiangkou reservoir in Hanjiang basin was selected as a case study. The design values of flood volumes and 90% confidence intervals with different peak discharges were estimated by the proposed methods. The performance of CMLC and CEC methods was also compared with conventional flood frequency analysis, and the results show that CMLC method performs best for both bivariate and trivariate distributions which has the smallest relative error and root mean square error. The proposed CMLC method has strong statistical basis with unique design flood composition scheme and provides an alternative way for deriving DFH.

Towards Predicting Flood Event Peak Discharge in Ungauged Basins by Learning Universal Hydrological Behaviors with Machine Learning

2021 ◽  
Author(s):  
Akhil Sanjay Potdar ◽  
Pierre-Emmanuel Kirstetter ◽  
Devon Woods ◽  
Manabendra Saharia
Keyword(s):  
Machine Learning ◽  
Spatial Variability ◽  
Spatial Organization ◽  
Flash Flood ◽  
Rainfall Variability ◽  
Peak Discharge ◽  
Machine Learning Techniques ◽  
Major Influence ◽  
Flood Peak ◽  
Basin Scale

AbstractIn the hydrological sciences, the outstanding challenge of regional modeling requires to capture common and event-specific hydrologic behaviors driven by rainfall spatial variability and catchment physiography during floods. The overall objective of this study is to develop robust understanding and predictive capability of how rainfall spatial variability influences flood peak discharge relative to basin physiography. A machine learning approach is used on a high-resolution dataset of rainfall and flooding events spanning 10 years, with rainfall events and basins of widely varying characteristics selected across the continental United States. It overcomes major limitations in prior studies that were based on limited observations or hydrological model simulations. This study explores first-order dependencies in the relationships between peak discharge, rainfall variability, and basin physiography, and it sheds light on these complex interactions using a multi-dimensional statistical modeling approach. Amongst different machine learning techniques, XGBoost is used to determine the significant physiographical and rainfall characteristics that influence peak discharge through variable importance analysis. A parsimonious model with low bias and variance is created which can be deployed in the future for flash flood forecasting. The results confirm that although the spatial organization of rainfall within a basin has a major influence on basin response, basin physiography is the primary driver of peak discharge. These findings have unprecedented spatial and temporal representativeness in terms of flood characterization across basins. An improved understanding of sub-basin scale rainfall spatial variability will aid in robust flash flood characterization as well as with identifying basins which could most benefit from distributed hydrologic modeling.

scholarly journals The Impact of Reforestation Induced Land Cover Change (1990–2017) on Flood Peak Discharge Using HEC-HMS Hydrological Model and Satellite Observations: A Study in Two Mountain Basins, China

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1347 ◽  
Author(s):  
Crispin Kabeja ◽  
Rui Li ◽  
Jianping Guo ◽  
Digne Edmond Rwabuhungu Rwatangabo ◽  
Marc Manyifika ◽  
...  
Keyword(s):  
Land Cover ◽  
Agricultural Land ◽  
Flash Flood ◽  
Peak Discharge ◽  
Substantial Impact ◽  
Flood Peak ◽  
Mountainous Regions ◽  
Hydrological Responses ◽  
Basin Scale ◽  
The Impact

Understanding the effect of land use and land cover (LULC) type change on watershed hydrological response is essential for adopting applicable measures to control floods. In China, the Grain to Green Program (GTGP) and the Natural Forest Conservation Program (NFCP) have had a substantial impact on LULC. We investigate the effect of these conservation efforts on flood peak discharge in two mountainous catchments. We used a series of Landsat images ranging from 1990 to 2016/2017 to evaluate the LULC changes. Further to this, the hydrological responses at the basin and sub-basin scale were generated by the Hydrologic Modeling System (HEC-HMS) under four LULC scenarios. Between 1990 and 2016/2017, both catchments experienced an increase in forest and urban land by 18% and 2% in Yanhe and by 16% and 8% in Guangyuan, respectively. In contrast, the agricultural land decreased by approximately 30% in Yanhe and 24% in Guangyuan, respectively. The changes in land cover resulted in decrease in flood peak discharge ranging from 14% in Yanhe to 6% in Guangyuan. These findings provide a better understanding on the impact of reforestation induced LULC change on spatial patterns of typical hydrological responses of mountainous catchment and could help to mitigate flash flood hazards in other mountainous regions.

Controls of flash flood peak discharge in Mediterranean basins and the special role of runoff-contributing areas

2018 ◽  
Vol 565 ◽  
pp. 846-860 ◽  
Author(s):  
Yair Rinat ◽  
Francesco Marra ◽  
Davide Zoccatelli ◽  
Efrat Morin
Keyword(s):  
Flash Flood ◽  
Peak Discharge ◽  
Special Role ◽  
Flood Peak

scholarly journals Effect of Flood Peak Discharge Control by a Small Reservoir in an Urbanized Area—Case Study in the Kurabe River Basin, Japan

2017 ◽  
Vol 07 (04) ◽  
pp. 314-330
Author(s):  
Kouzo Ito ◽  
Manabu Segawa ◽  
Hiroshi Takimoto ◽  
Toshisuke Maruyama
Keyword(s):  
River Basin ◽  
Peak Discharge ◽  
Small Reservoir ◽  
Flood Peak ◽  
Urbanized Area

scholarly journals Methodology for post-event analysis of flash floods - Svacenický Creek case study

2011 ◽  
Vol 41 (3) ◽  
pp. 235-250 ◽  
Author(s):  
Lotta Blaškovičová ◽  
Oliver Horvát ◽  
Kamila Hlavčová ◽  
Silvia Kohnová ◽  
Ján Szolgay
Keyword(s):  
Hydrological Model ◽  
Flash Flood ◽  
Curve Number ◽  
Flash Floods ◽  
Event Analysis ◽  
Distributed Hydrological Model ◽  
Radar Rainfall ◽  
Flood Peak ◽  
Spatially Distributed

Methodology for post-event analysis of flash floods - Svacenický Creek case study In this paper a methodology for a post-event analysis of a flash flood and estimation of the flood peak and volume are developed and tested. The selected flash flood occurred on the 6th of June, 2009 in the Svacenický Creek Basin. To understand rainfall-runoff processes during this extreme flash flood, the runoff response was simulated using the spatially-distributed hydrological model KLEM (Kinematic Local Excess Model). The distributed hydrological model is based on the availability of raster information about the landscape's topography, soil and vegetation properties and radar rainfall data. In the model, the SCS-Curve Number procedure is applied to a grid for the spatially-distributed representation of the runoff-generating processes. A description of the drainage system's response is used to represent the runoff's routing. The simulated values achieved by the KLEM model were comparable with the maximum peak estimated on the basis of the post-event surveying. The consistency of the estimated and simulated values from the KLEM model was evident both in time and space, and the methodology has shown its practical applicability.

Sign in / Sign up

Export Citation Format

Share Document