COMPARISON OF FLOOD DISTRIBUTION MODELS FOR JOHOR RIVER BASIN

2015 ◽  
Vol 74 (11) ◽  
Author(s):  
Ahmad Zuhdi Ismail ◽  
Zulkifli Yusop ◽  
Zainab Yusof
Keyword(s):  
River Basin ◽  
Peak Flow ◽  
Maximum Flow ◽  
Flood Event ◽  
Distribution Model ◽  
Annual Maximum ◽  
Flow Data ◽  
Water Control ◽  
Distribution Models ◽  
Annual Peak

One of the most useful and commonly used parameters to describe a flood event is peak flow or annual maximum flood. In many localities, storm water control facilities are required and their sizes are determined based on certain peak flow magnitude. This study aimed at estimating the average recurrent interval (ARI) of flood event for Johor River basin based on the distributions of annual peak flow. The analysis used annual maximum flow data from July 1965 to June 2010 recorded at the Rantau Panjang gauging station. Five distribution models, namely Generalized Extreme Value (GEV), Lognormal, Pearson 5, Weibull and Gamma were tested. The goodness fit test (GOF) of Kolmogorov-Smirnov (K-S) was used to evaluate and estimate the best-fitted distribution. The results reaffirm the current practice that GEV is still the best-fitted distribution model for fitting the annual peak flow data. On the other hand, gamma distribution showed the poorest result.

scholarly journals Flood Frequency Analysis at the Downstream of Sg. Perak River Basin using Annual Maximum Flow Discharge Data

2018 ◽  
Vol 7 (4.35) ◽  
pp. 709 ◽  
Author(s):  
Munir Snu ◽  
Sidek L.M ◽  
Haron Sh ◽  
Noh Ns.M ◽  
Basri H ◽  
...  
Keyword(s):  
Probability Distribution ◽  
River Basin ◽  
Frequency Analysis ◽  
Return Period ◽  
Maximum Flow ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Annual Maximum ◽  
Discharge Data ◽  
Flow Discharge

The recent flood event occurred in 2014 had caused disaster in Perak and Sungai Perak is the main river of Perak which is a major natural drainage system within the state. The aim of this paper is to determine the expected discharge to return period downstream for Sg. Perak River Basin in Perak by using annual maximum flow data. Flood frequency analysis is a technique to assume the flow values corresponding to specific return periods or probabilities along the river at a different site. The method involves the observed annual maximum flow discharge data to calculate statistical information such as standard deviations, mean, sum, skewness and recurrence intervals. The flood frequency analysis for Sg. Perak River Basin was used Log Pearson Type-III probability distribution method. The annual maximum peak flow series data varying over period 1961 to 2016. The probability distribution function was applied to return periods (T) where T values are 2years, 5years, 10years, 25years, 50years, and 100years generally used in flood forecasting. Flood frequency curves are plotted after the choosing the best fits probability distribution for annual peak maximum data. The results for flood frequency analysis shows that Sg. Perak at Jambatan Iskandar much higher inflow discharge  which is 3714.45m3/s at the 100years return period compare to Sg. Plus at Kg Lintang and Sg. Kinta at Weir G. With this, the 100years peak flow at Sg Perak river mouth is estimated to be in the range of 4,000 m3/s. Overall, the analysis relates the expected flow discharge to return period for all tributaries of Sg. Perak River Basin.

scholarly journals Examining controls on peak annual streamflow and floods in the Fraser River Basin of British Columbia

2017 ◽  
Author(s):  
Charles L. Curry ◽  
Francis W. Zwiers
Keyword(s):  
British Columbia ◽  
Soil Moisture ◽  
River Basin ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Snow Accumulation ◽  
Fraser River ◽  
Annual Maximum ◽  
Daily Streamflow ◽  
Annual Peak

Abstract. The Fraser River basin (FRB) of British Columbia is one of the largest and most important watersheds in Western North America, and is home to a rich diversity of biological species and economic assets that depend implicitly upon its extensive riverine habitats. The hydrology of the FRB is dominated by snow accumulation and melt processes, leading to a prominent annual peak streamflow invariably occurring in June–July. However, while annual peak daily streamflow (APF) during the spring freshet in the FRB is historically well correlated with basin-averaged, April 1 snow water equivalent (SWE), there are numerous occurrences of anomalously large APF in below- or near-normal SWE years, some of which have resulted in damaging floods in the region. An imperfect understanding of which other climatic factors contribute to these anomalously large APFs hinders robust projections of their magnitude and frequency. We employ the Variable Infiltration Capacity (VIC) process-based hydrological model driven by gridded observations to investigate the key controlling factors of anomalous APF events in the FRB and four of its subbasins that contribute more than 70 % of the annual flow at Fraser-Hope. The relative influence of a set of predictors characterizing the interannual variability of rainfall, snowfall, snowpack (characterized by the annual maximum value, SWEmax), soil moisture and temperature on simulated APF at Hope (the main outlet of the FRB) and at the subbasin outlets is examined within a regression framework. The influence of large-scale climate modes of variability (the Pacific Decadal Oscillation (PDO) and the El Niño-Southern Oscillation (ENSO)) on APF magnitude is also assessed, and placed in context with these more localized controls. The results indicate that next to SWEmax (which strongly controls the annual maximum of soil moisture), the snowmelt rate, the ENSO and PDO indices, and rate of warming subsequent to the date of SWEmax are the most influential predictors of APF magnitude in the FRB and its subbasins. The identification of these controls on annual peak flows in the region may be of use in the context of seasonal prediction or future projected streamflow behaviour.

scholarly journals Spatial rainfall variability and runoff response during an extreme event in a semi-arid catchment in the South Pare Mountains, Tanzania

2009 ◽  
Vol 13 (9) ◽  
pp. 1659-1670 ◽  
Author(s):  
M. L. Mul ◽  
H. H. G. Savenije ◽  
S. Uhlenbrook
Keyword(s):  
Peak Flow ◽  
Extreme Event ◽  
Rainfall Variability ◽  
Maximum Flow ◽  
Base Flow ◽  
Flood Event ◽  
Hydrograph Separation ◽  
The South ◽  
Data Set ◽  
Extreme Flood

Abstract. This paper describes an extreme flood event that occurred in the South Pare Mountains in northern Tanzania. A high spatial and temporal resolution data set has been gathered in a previously ungauged catchment. This data was analysed using a multi-method approach, to gather information about the processes that generated the flood event. On 1 March 2006, extreme rainfall occurred in the Makanya catchment, (300 km2), where up to 100 mm were recorded in Bangalala village in only 3 h. The flood was devastating, inundating large parts of the flood plain. The spatial variability of the rainfall during the event was very large, even in areas with the same altitude. The Vudee sub-catchment (25.8 km2) was in the centre of the rainfall event, receiving about 75 mm in 3 h divided over the two upstream tributaries: the Upper-Vudee and Ndolwa. The peak flow at the weir site has been determined using the slope-area method and gradually varied flow calculations, indicating a peak discharge of 32 m3 s−1. Rise and fall of the flood was very sharp, with the peak flow occurring just one hour after the peak of the rainfall. The flow receded to 1% of the maximum flow within 24 h. Hydrograph separation using hydrochemical parameters indicates that at the floodpeak 50% of the flow was generated by direct surface runoff (also indicated by the large amount of sediments in the samples), whereas the recession originated from displaced groundwater (>90%). The subsequent base flow in the river remained at 75 l s−1 for the rest of the season, which is substantially higher than the normal base flow observed during the previous rainy seasons (15 l s−1) indicating significant groundwater recharge during this extreme event.

scholarly journals Spatial rainfall variability and runoff response during an extreme event in a semi-arid catchment in the South Pare Mountains, Tanzania

2008 ◽  
Vol 5 (4) ◽  
pp. 2657-2685 ◽  
Author(s):  
M. L. Mul ◽  
H. H. G. Savenije ◽  
S. Uhlenbrook
Keyword(s):  
Peak Flow ◽  
Extreme Event ◽  
Rainfall Variability ◽  
Maximum Flow ◽  
Base Flow ◽  
Flood Event ◽  
Hydrograph Separation ◽  
The South ◽  
Data Set ◽  
Extreme Flood

Abstract. This paper describes an extreme flood event that occurred in the South Pare Mountains in northern Tanzania. A high spatial and temporal resolution data set was gathered in a previously ungauged catchment. This data was analysed using a multi-method approach, to gather information about the processes that resulted in the flood event. On 1 March 2006, extreme rainfall occurred in the Makanya catchment, (300 km2), where up to 100 mm were recorded in Bangalala village in only 3 h. Runoff was devastating, inundating large parts of the flood plain. The spatial variability of the rainfall during the event was very large, even in areas with the same altitude. The Vudee sub-catchment (25.8 km2) was in the centre of the rainfall event, receiving about 75 mm in 3 h divided over the two upstream tributaries: the Upper-Vudee and Ndolwa. The peak flow at the weir site has been determined using the slope-area method and gradually varied flow calculations, indicating a peak discharge of 32 m3 s−1. Rise and fall of the flood was very sharp, with the peak flow occurring just one hour after the peak of the rainfall. The flow receded to 1% of the maximum flow within 24 h. Hydrograph separation using hydrochemical parameters indicates that at the peak of the flow 50% was generated by direct surface runoff (also indicated by the large amount of sediments in the samples), whereas the recession originated from displaced groundwater (>90 %). The subsequent base flow in the river remained at 75 l s−1 for the rest of the season, which is substantially higher than the normal base flow observed during the previous rainy seasons (15 l s−1) indicating significant groundwater recharge during this extreme event.

scholarly journals A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks

2021 ◽  
Vol 13 (8) ◽  
pp. 1495
Author(s):  
Jehyeok Rew ◽  
Yongjang Cho ◽  
Eenjun Hwang
Keyword(s):  
Neural Networks ◽  
Species Distribution ◽  
Best Practice ◽  
Deep Neural Networks ◽  
Species Distribution Models ◽  
Species Distribution Model ◽  
Environmental Data ◽  
Distribution Model ◽  
Distribution Models ◽  
Robust Prediction

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.

scholarly journals Process-Based Modeling of the High Flow of a Semi-Mountain River under Current and Future Climatic Conditions: A Case Study of the Iya River (Eastern Siberia)

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1042
Author(s):  
Andrey Kalugin
Keyword(s):  
Water Level ◽  
River Basin ◽  
21St Century ◽  
Residential Buildings ◽  
High Flow ◽  
Peak Discharge ◽  
Future Climate ◽  
Future Climate Change ◽  
Generation Model ◽  
Annual Peak

The purpose of the study was to analyze the formation conditions of catastrophic floods in the Iya River basin over the observation period, as well as a long-term forecast of the impacts of future climate change on the characteristics of the high flow in the 21st century. The semi-distributed process-based Ecological Model for Applied Geophysics (ECOMAG) was applied to the Iya River basin. Successful model testing results were obtained for daily discharge, annual peak discharge, and discharges exceeding the critical water level threshold over the multiyear period of 1970–2019. Modeling of the high flow of the Iya River was carried out according to a Kling–Gupta efficiency (KGE) of 0.91, a percent bias (PBIAS) of −1%, and a ratio of the root mean square error to the standard deviation of measured data (RSR) of 0.41. The preflood coefficient of water-saturated soil and the runoff coefficient of flood-forming precipitation in the Iya River basin were calculated in 1980, 1984, 2006, and 2019. Possible changes in the characteristics of high flow over summers in the 21st century were calculated using the atmosphere–ocean general circulation model (AOGCM) and the Hadley Centre Global Environment Model version 2-Earth System (HadGEM2-ES) as the boundary conditions in the runoff generation model. Anomalies in values were estimated for the middle and end of the current century relative to the observed runoff over the period 1990–2019. According to various Representative Concentration Pathways (RCP-scenarios) of the future climate in the Iya River basin, there will be less change in the annual peak discharge or precipitation and more change in the hazardous flow and its duration, exceeding the critical water level threshold, at which residential buildings are flooded.

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