scholarly journals The impact of the spatiotemporal structure of rainfall on flood frequency over a small urban watershed: an approach coupling stochastic storm transposition and hydrologic modeling

2021 ◽  
Vol 25 (9) ◽  
pp. 4701-4717
Author(s):  
Zhengzheng Zhou ◽  
James A. Smith ◽  
Mary Lynn Baeck ◽  
Daniel B. Wright ◽  
Brianne K. Smith ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Spatial And Temporal Variability ◽  
Return Periods ◽  
Impervious Area ◽  
Spatial Coverage ◽  
Flood Response ◽  
The Impact

Abstract. The role of rainfall space–time structure, as well as its complex interactions with land surface properties, in flood response remains an open research issue. This study contributes to this understanding, specifically for small (<15 km2) urban watersheds. Using a flood frequency analysis framework that combines stochastic storm transposition (SST)-based rainfall scenarios with the physically based distributed Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model, we examine the role of rainfall spatial and temporal variability in flood frequency across drainage basin scales in the highly urbanized Dead Run watershed (14.3 km2), Maryland, USA. The results show the complexities of flood response within several subwatersheds for both short (<50 years) and long (>100 years) rainfall return periods. The impact of impervious area on flood response decreases with increasing rainfall return period. For extreme storms, the maximum discharge is closely linked to the spatial structure of rainfall, especially storm core spatial coverage. The spatial heterogeneity of rainfall increases flood peak magnitudes by 50 % on average at the watershed outlet and its subwatersheds for both small and large return periods. The framework of SST–GSSHA-coupled frequency analysis also highlights the fact that spatially distributed rainfall scenarios are needed in quick-response flood frequency, even for relatively small basin scales.

scholarly journals The impact of spatiotemporal structure of rainfall on flood frequency over a small urban watershed: an approach coupling stochastic storm transposition and hydrologic modeling

2021 ◽  
Author(s):  
Zhengzheng Zhou ◽  
James A. Smith ◽  
Mary Lynn Baeck ◽  
Daniel B. Wright ◽  
Brianne K. Smith ◽  
...  
Keyword(s):  
Land Surface ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Spatial And Temporal Variability ◽  
Return Periods ◽  
Impervious Area ◽  
Spatial Coverage ◽  
Flood Response ◽  
The Impact

Abstract. The role of rainfall space-time structure, as well as its complex interactions with land surface properties, in flood response remains an open research issue. This study contributes to this understanding, specifically in small (< 15 km2) urban watersheds. Using a flood frequency analysis framework that combines stochastic storm transposition-based rainfall scenarios with the physically-based distributed GSSHA model, we examine the role of rainfall spatial and temporal variability in flood frequency across drainage scales in the highly-urbanized Dead Run watershed (14.3 km2) outside of Baltimore, Maryland, USA. The results show the complexities of flood response within several subwatersheds for both short (< 50 years) and long (> 100 years) rainfall return periods. The impact of impervious area on flood response decreases with increasing rainfall return period. For extreme storms, the maximum discharge is closely linked to the spatial structure of rainfall, especially storm core spatial coverage. The spatial heterogeneity of rainfall increases flood peak magnitudes by 50 % on average at the watershed outlet and its subwatersheds for both small and large return periods. The results imply that commonly-made assumption of spatially uniform rainfall in urban flood frequency modeling is problematic even for relatively small basin scales.

scholarly journals The Impact of the Construction of a Dam on Flood Management

2021 ◽  
Vol 7 (2) ◽  
pp. 343-356
Author(s):  
Iliasse Khaddor ◽  
Mohammed Achab ◽  
Mohamed Rida Soumali ◽  
Abdelkader Benjbara ◽  
Adil Hafidi Alaoui
Keyword(s):  
Frequency Analysis ◽  
Flood Frequency ◽  
Peak Discharge ◽  
Likelihood Method ◽  
Flood Frequency Analysis ◽  
Numerical Comparison ◽  
Return Periods ◽  
Routing Method ◽  
The Impact ◽  
Extreme Flow

A possible strategy to mitigate the effects of flooding from an area identified as having high runoff potential will reduce the volumes of water that overflow the drainage area and build a system of a storage location in the coastal city of Tangier. The study is based on two main axes: (i) the extreme flow frequency analysis, using eight probability laws adjusted by the Maximum Likelihood method, and (ii) the estimation of the flood outflows at the dam outlet using the routing method in order to assess the effect of detention dams on water flood. Annual (Maximum) series based flood sampling procedure is adopted for constructing the Flood Frequency analysis. A numerical comparison of AIC criteria and BIC has allowed a proceeding to the selection of the most fitted law distributions. The result shows that the Gumbel law is best adapted to the predetermination of the extreme flow estimation in the Mghogha watershed for different return periods. The reservoir routing method along with rainfall-runoff processes were applied by the mean of the HEC-HMS model. The model was run under two different scenarios. Scenario 1 simulates the Mghogha basin with the absence of the reservoir. Meanwhile, scenario 2 simulates the same basin by taking into account the existence of the Ain Mechlawa reservoir within different return periods of from 2 to 200 years. Peak discharges downstream have been dramatically attenuated and water volumes have been decreased with the prolongation of the return period. For the 100 and 200 return periods, the peak discharge of flood reduction for scenario 1 and scenario 2 were 52.06 and 52.17 %, respectively, and for the flood volume was 22.46 and 22.82% respectively. Finally, the results of investigations showed a good performance of the model in the estimation of outflow peak discharge of the Ain Mechlawa Dam. Doi: 10.28991/cej-2021-03091658 Full Text: PDF

scholarly journals Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates

2013 ◽  
Vol 17 (8) ◽  
pp. 3189-3203 ◽  
Author(s):  
J. López ◽  
F. Francés
Keyword(s):  
Frequency Analysis ◽  
Western Mediterranean ◽  
Flood Frequency ◽  
Additive Models ◽  
Stationary Time Series ◽  
Flood Frequency Analysis ◽  
Climate Indices ◽  
Flood Regime ◽  
Mediterranean Oscillation ◽  
The Impact

Abstract. Recent evidences of the impact of persistent modes of regional climate variability, coupled with the intensification of human activities, have led hydrologists to study flood regime without applying the hypothesis of stationarity. In this study, a framework for flood frequency analysis is developed on the basis of a tool that enables us to address the modelling of non-stationary time series, namely, the "generalized additive models for location, scale and shape" (GAMLSS). Two approaches to non-stationary modelling in GAMLSS were applied to the annual maximum flood records of 20 continental Spanish rivers. The results of the first approach, in which the parameters of the selected distributions were modelled as a function of time only, show the presence of clear non-stationarities in the flood regime. In a second approach, the parameters of the flood distributions are modelled as functions of climate indices (Arctic Oscillation, North Atlantic Oscillation, Mediterranean Oscillation and the Western Mediterranean Oscillation) and a reservoir index that is proposed in this paper. The results when incorporating external covariates in the study highlight the important role of interannual variability in low-frequency climate forcings when modelling the flood regime in continental Spanish rivers. Also, with this approach it is possible to properly introduce the impact on the flood regime of intensified reservoir regulation strategies. The inclusion of external covariates permits the use of these models as predictive tools. Finally, the application of non-stationary analysis shows that the differences between the non-stationary quantiles and their stationary equivalents may be important over long periods of time.

scholarly journals Non-stationary flood frequency analysis in continental Spanish rivers, using climate and reservoir indices as external covariates

2013 ◽  
Vol 10 (3) ◽  
pp. 3103-3142 ◽  
Author(s):  
J. López ◽  
F. Francés
Keyword(s):  
Frequency Analysis ◽  
Western Mediterranean ◽  
Flood Frequency ◽  
Additive Models ◽  
Stationary Time Series ◽  
Flood Frequency Analysis ◽  
Climate Indices ◽  
Flood Regime ◽  
Mediterranean Oscillation ◽  
The Impact

Abstract. Recent evidences of the impact of persistent modes of regional climate variability, coupled with the intensification of human activities, have led hydrologists to study flood regime without applying the hypothesis of stationarity. In this study, a framework for flood frequency analysis is developed on the basis of a tool that enables us to address the modelling of non-stationary time series, namely, the "generalized additive models for location, scale and shape" (GAMLSS). Two approaches to non-stationary modelling in GAMLSS were applied to the annual maximum flood records of 20 continental Spanish rivers. The results of the first approach, in which the parameters of the selected distributions were modeled as a function of time only, show the presence of clear non-stationarities in the flood regime. In a second approach, the parameters of the distributions are modeled as functions of climate indices (Arctic Oscillation, North Atlantic Oscillation, Mediterranean Oscillation and the Western Mediterranean Oscillation) and a reservoir index that is proposed in this paper. The results when incorporating external covariates in the study highlight the important role of interannual variability in low-frequency climate forcings when modelling the flood regime in continental Spanish rivers. Also, with this approach is possible to properly introduce the impact on the flood regime of intensified reservoir regulation strategies and to be used as predictive tools. Application of non-stationary analysis shows that the differences between the quantiles obtained and their stationary equivalents may be important over long periods of time.

scholarly journals Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images

2021 ◽  
Vol 13 (11) ◽  
pp. 2053
Author(s):  
Aqil Tariq ◽  
Hong Shu ◽  
Alban Kuriqi ◽  
Saima Siddiqui ◽  
Alexandre S. Gagnon ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Satellite Images ◽  
Flood Frequency ◽  
Spatial Extent ◽  
Flood Frequency Analysis ◽  
Army Corps ◽  
Landsat 8 ◽  
Return Periods ◽  
Indus River ◽  
Study Region

Rivers play an essential role to humans and ecosystems, but they also burst their banks during floods, often causing extensive damage to crop, property, and loss of lives. This paper characterizes the 2014 flood of the Indus River in Pakistan using the US Army Corps of Engineers Hydrologic Engineering Centre River Analysis System (HEC-RAS) model, integrated into a geographic information system (GIS) and satellite images from Landsat-8. The model is used to estimate the spatial extent of the flood and assess the damage that it caused by examining changes to the different land-use/land-cover (LULC) types of the river basin. Extreme flows for different return periods were estimated using a flood frequency analysis using a log-Pearson III distribution, which the Kolmogorov–Smirnov (KS) test identified as the best distribution to characterize the flow regime of the Indus River at Taunsa Barrage. The output of the flood frequency analysis was then incorporated into the HEC-RAS model to determine the spatial extent of the 2014 flood, with the accuracy of this modelling approach assessed using images from the Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that a supervised classification of the Landsat images was able to identify the LULC types of the study region with a high degree of accuracy, and that the most affected LULC was crop/agricultural land, of which 50% was affected by the 2014 flood. Finally, the hydraulic simulation of extent of the 2014 flood was found to visually compare very well with the MODIS image, and the surface area of floods of different return periods was calculated. This paper provides further evidence of the benefit of using a hydrological model and satellite images for flood mapping and for flood damage assessment to inform the development of risk mitigation strategies.

An Example of the Impact that Filled-In Peakflow Data Can Have on Flood Frequency Analysis

2010 ◽  
Author(s):  
Steven K. Starrett ◽  
Travis Heier ◽  
Yunsheng Su ◽  
Mark Bandurraga ◽  
Denny Tuan ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
The Impact

scholarly journals Regional Flood Frequency Analysis Using An Artificial Neural Network Model

Geosciences ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 127
Author(s):  
Sasan Kordrostami ◽  
Mohammad A Alim ◽  
Fazlul Karim ◽  
Ataur Rahman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Predictor Variables ◽  
Return Periods ◽  
Ann Model ◽  
Regional Flood Frequency Analysis ◽  
Regional Flood Frequency

This paper presents the results from a study on the application of an artificial neural network (ANN) model for regional flood frequency analysis (RFFA). The study was conducted using stream flow data from 88 gauging stations across New South Wales (NSW) in Australia. Five different models consisting of three to eight predictor variables (i.e., annual rainfall, drainage area, fraction forested area, potential evapotranspiration, rainfall intensity, river slope, shape factor and stream density) were tested. The results show that an ANN model with a higher number of predictor variables does not always improve the performance of RFFA models. For example, the model with three predictor variables performs considerably better than the models using a higher number of predictor variables, except for the one which contains all the eight predictor variables. The model with three predictor variables exhibits smaller median relative error values for 2- and 20-year return periods compared to the model containing eight predictor variables. However, for 5-, 10-, 50- and 100-year return periods, the model with eight predictor variables shows smaller median relative error values. The proposed ANN modelling framework can be adapted to other regions in Australia and abroad.

scholarly journals Improved Mixed Distribution Model Considering Historical Extraordinary Floods under Changing Environment

Water ◽  
2018 ◽  
Vol 10 (8) ◽  
pp. 1016 ◽  
Author(s):  
Jianzhu Li ◽  
Yanchen Zheng ◽  
Yimin Wang ◽  
Ting Zhang ◽  
Ping Feng ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Distribution Model ◽  
Return Periods ◽  
Design Flood ◽  
Mixed Distribution ◽  
Pearson Type ◽  
The Difference ◽  
Pearson Type Iii Distribution

Historical extraordinary floods are an important factor in non-stationary flood frequency analysis and they may occur at any time, regardless of whether the environment is changing or not. Based on mixed distribution (MD) modeling, this paper proposed an improved mixed distribution (IMD) model to consider the discontinuity and non-stationarity of flood samples simultaneously, which adds historical extraordinary floods in both sub-series divided by a change point. As a case study, the annual maximum peak discharge and volume series of Ankang hydrological station, located in the upper Hanjiang River Basin of China, were selected to identify non-stationarity by using the variation diagnosis system. MD and IMD were used to fit the flood characteristic series and a genetic algorithm was employed to estimate the optimal parameters. Compared with the design flood values fitted by the stationary Pearson type-III distribution, the results computed by IMD decreased at low return periods and increased at high return periods, with the difference varying from −6.67% to 7.19%. The results highlighted that although the design flood values of IMD are slightly larger than those of MD with different return periods, IMD provided a better result than MD. IMD provides a new perspective for non-stationary flood frequency analysis.

scholarly journals Frequency Analysis of Snowmelt Flood Based on GAMLSS Model in Manas River Basin, China

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2007
Author(s):  
Chaofei He ◽  
Fulong Chen ◽  
Aihua Long ◽  
Chengyan Luo ◽  
Changlu Qiao
Keyword(s):  
Time Series ◽  
Frequency Analysis ◽  
Flood Frequency ◽  
Additive Models ◽  
Flood Frequency Analysis ◽  
Climate Indices ◽  
Design Flood ◽  
Stationary Model ◽  
Snowmelt Flood ◽  
The Impact

With the acceleration of human economic activities and dramatic changes in climate, the validity of the stationarity assumption of flood time series frequency analysis has been questioned. In this study, a framework for flood frequency analysis is developed on the basis of a tool, namely, the Generalized Additive Models for Location, Scale, and Shape (GAMLSS). We introduced this model to construct a non-stationary model with time and climate factor as covariates for the 50-year snowmelt flood time series in the Kenswat Reservoir control basin of the Manas River. The study shows that there are clear non-stationarities in the flood regime, and the characteristic series of snowmelt flood shows an increasing trend with the passing of time. The parameters of the flood distributions are modelled as functions of climate indices (temperature and rainfall). The physical mechanism was incorporated into the study, and the simulation results are similar to the actual flood conditions, which can better describe the dynamic process of snowmelt flood characteristic series. Compared with the design flood results of Kenswat Reservoir approved by the China Renewable Energy Engineering Institute in December 2008, the design value of the GAMLSS non-stationary model considers that the impact of climate factors create a design risk in dry years by underestimating the risk.

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