A Study on the Temporal and Local Distribution of Showers Generating Flood in Zolachai River Basin by Using Intensity-Duration-Frequency-Area Curves Relationships

2009 ◽  
Vol 9 (10) ◽  
pp. 1922-1928
Author(s):  
Zahra Hejazizade ◽  
Elahe Jahanshiri ◽  
Mohammad Hossein Naserzadeh
Keyword(s):  
River Basin ◽  
Local Distribution ◽  
Intensity Duration Frequency ◽  
Frequency Area

scholarly journals How size and trigger matter: analyzing rainfall- and earthquake-triggered landslide inventories and their causal relation in the Koshi River basin, Central Himalaya

2018 ◽  
Author(s):  
Jianqiang Zhang ◽  
Cees J. van Westen ◽  
Hakan Tanyas ◽  
Olga Mavrouli ◽  
Yonggang Ge ◽  
...  
Keyword(s):  
River Basin ◽  
Causal Relation ◽  
Anthropogenic Activities ◽  
Slope Angle ◽  
Central Himalaya ◽  
Triggering Mechanism ◽  
Susceptibility Maps ◽  
Triggering Mechanisms ◽  
Koshi River Basin ◽  
Frequency Area

Abstract. Inventories of landslides caused by different triggering mechanisms, such as earthquakes, extreme rainfall events or anthropogenic activities, may show different characteristics in terms of distribution, causal factors and frequency-area relationships. This research aims to study such differences in landslide inventories, and the effect they have on landslide susceptibility assessment. Koshi River basin in central Himalaya was taken as study area. Detailed landslide inventories were generated based on visual interpretation of remote sensing images and field investigation for different time periods and triggering mechanisms. Maps and images from the period 1992 to 2015 were used to map 5,858 rainfall-triggered landslides and after the 2015 April 25 Gorkha earthquake, an additional 1138 co-seismic landslides were mapped. A set of topographic, geological and land cover factors were employed to analyze their correlation with different types and sizes of landslides. The results show that the frequency – area distributions of rainfall and earthquake–triggered landslides varied considerably, with the former one having a larger frequency of small landslides. Also topographic factors varied considerably for the two triggering events, with both elevation and slope angle showing significantly different patterns for earthquake-triggered and rainfall-triggered landslides. Landslides were classified into two size groups, in combination with the main triggering mechanism (rainfall- or earthquake-triggered). Susceptibility maps for different combinations of landslide size and triggering mechanism were generated using logistic regression analysis. The different triggers and sizes of landslide data were used to validate the models. The results showed that susceptible areas for small and large size rainfall- and earthquake-triggered landslides differed substantially, while susceptibility maps for different size of earthquake-triggered landslides were similar.

scholarly journals Development of Intensity Duration Frequency (IDF) Curves for Rainfall Prediction within the Middle Niger River Basin

2020 ◽  
Vol 4 (2) ◽  
pp. 382-397
Author(s):  
J. O. Ehiorobo ◽  
O.C. Izinyon ◽  
R. I. Ilaboya
Keyword(s):  
River Basin ◽  
Rainfall Intensity ◽  
Rainfall Data ◽  
Annual Maximum ◽  
Return Periods ◽  
Maximum Rainfall ◽  
Niger River ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
Best Fit

Rainfall Intensity-Duration-Frequency (IDF) relationship remains one of the mostly used tools in hydrology and water resources engineering, especially for planning, design and operations of water resource projects. IDF relationship can provide adequate information about the intensity of rainfall at different duration for various return periods. The focus of this research was to develop IDF curves for the prediction of rainfall intensity within the middle Niger River Basin (Lokoja and Ilorin) using annual maximum daily rainfall data. Forty (40) year’s annual maximum rainfall data ranging from 1974 to 2013 was employed for the study. To ascertain the data quality, selected preliminary analysis technique including; descriptive statistics, test of homogeneity and outlier detection test were employed. To compute the three hours rainfall intensity, the ratio of rainfall amount and duration was used while the popular Gumbel probability distribution model was employed to calculate the rainfall frequency factor. To assess the best fit model that can be employed to predict rainfall intensity for various return periods at ungauged locations, four empirical IDF equations, namely; Talbot, Bernard, Kimijima and Sherman equations were employed. The model with the least calculated sum of minimized root mean square error (RMSE) was adopted as the best fit empirical model. Results obtained revealed that the Talbot model was the best fit model for Ilorin and Lokoja with calculated sum of minimized error of 1.32170E-07 and 8.953636E-08. This model was thereafter employed to predict the rainfall intensity for different durations at 2, 5, 10, 25, 50 and 100yrs return periods respectively.

scholarly journals Estimating Flood Characteristics Using Geomorphologic Flood Index with Regards to Rainfall Intensity-Duration-Frequency-Area Curves and CADDIES-2D Model in Three Iranian Basins

2020 ◽  
Vol 12 (18) ◽  
pp. 7371
Author(s):  
Farid Faridani ◽  
Sirus Bakhtiari ◽  
Alireza Faridhosseini ◽  
Micheal J. Gibson ◽  
Raziyeh Farmani ◽  
...  
Keyword(s):  
Water Depth ◽  
Large Scale ◽  
Binary Classification ◽  
Depth Map ◽  
Small Region ◽  
Flood Depth ◽  
Intensity Duration Frequency ◽  
Frequency Area ◽  
Flood Index ◽  
2D Model

There is not enough data and computational power for conventional flood mapping methods in many parts of the world, thus fast and low-data-demanding methods are very useful in facing the disaster. This paper presents an innovative procedure for estimating flood extent and depth using only DEM SRTM 30 m and the Geomorphic Flood Index (GFI). The Geomorphologic Flood Assessment (GFA) tool which is the corresponding application of the GFI in QGIS is implemented to achieved the results in three basins in Iran. Moreover, the novel concept of Intensity-Duration-Frequency-Area (IDFA) curves is introduced to modify the GFI model by imposing a constraint on the maximum hydrologically contributing area of a basin. The GFA model implements the linear binary classification algorithm to classify a watershed into flooded and non-flooded areas using an optimized GFI threshold that minimizes the errors with a standard flood map of a small region in the study area. The standard hydraulic model envisaged for this study is the Cellular Automata Dual-DraInagE Simulation (CADDIES) 2D model which employs simple transition rules and a weight-based system rather than complex shallow water equations allowing fast flood modelling for large-scale problems. The results revealed that the floodplains generated by the GFI has a good agreement with the standard maps, especially in the fluvial rivers. However, the performance of the GFI decreases in the less steep and alluvial rivers. With some overestimation, the GFI model is also able to capture the general trend of water depth variations in comparison with the CADDIES-2D flood depth map. The modifications made in the GFI model, to confine the maximum precipitable area through implementing the IDFAs, improved the classification of flooded area and estimation of water depth in all study areas. Finally, the calibrated GFI thresholds were used to achieve the complete 100-year floodplain maps of the study areas.

Distribution choice for the assessment of design rainfall for the city of London (Ontario, Canada) under climate change

2013 ◽  
Vol 40 (2) ◽  
pp. 121-129 ◽  
Author(s):  
S. Das ◽  
N. Millington ◽  
S.P. Simonovic
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Global Climate ◽  
Climate Conditions ◽  
Pearson Type ◽  
Idf Curves ◽  
Intensity Duration Frequency ◽  
City Of London ◽  
Thames River ◽  
The City

With the effect of global climate change the rainfall intensity is changing, and in many places it is drastically increasing. The use of intensity–duration–frequency (IDF) curves based on historic rainfall data might, therefore, underestimate the risk associated with the design and assessment of drainage systems. The theoretical probability distribution function used in the establishment of IDF curves based on historical observations might need to be different for the future conditions. The Gumbel (EV1) distribution is the currently recommended distribution for use in Canada and the EV1 and the Log-Pearson type 3 (LP3) are routinely used in the US. This study investigates potential utility of the generalized extreme value (GEV) distribution for use in climate change impact studies by the City of London that is located in the Upper Thames River Basin. All results point that GEV seems to be the best choice for the use with the Upper Thames River Basin data. We would like to use results of this study and open the discussion on the choice of most appropriate distribution for the development of IDF curves under changing climate conditions in Canada.

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