Journal articles on the topic 'Flood Frequency Curve'
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1
Ibeje, Andy Obinna, and Ben N. Ekwueme. "Regional Flood Frequency Analysis using Dimensionless Index Flood Method." Civil Engineering Journal 6, no. 12 (December 1, 2020): 2425–36. http://dx.doi.org/10.28991/cej-2020-03091627.
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Hydrologic designs require accurate estimation of quartiles of extreme floods. But in many developing regions, records of flood data are seldom available. A model framework using the dimensionless index flood for the transfer of Flood Frequency Curve (FFC) among stream gauging sites in a hydrologically homogeneous region is proposed. Key elements of the model framework include: (1) confirmation of the homogeneity of the region; (2) estimation of index flood-basin area relation; (3) derivation of the regional flood frequency curve (RFFC) and deduction of FFC of an ungauged catchment as a product of index flood and dimensionless RFFC. As an application, 1983 to 2004 annual extreme flood from six selected gauging sites located in Anambra-Imo River basin of southeast Nigeria, were used to demonstrate that the developed index flood model: , overestimated flood quartiles in an ungauged site of the basin. It is recommended that, for wider application, the model results can be improved by the availability and use of over 100 years length of flood data spatially distributed at critical locations of the watershed. Doi: 10.28991/cej-2020-03091627 Full Text: PDF
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Li, Jianzhu, Kun Lei, Ting Zhang, Wei Zhong, Aiqing Kang, Qiushuang Ma, and Ping Feng. "A framework for event-based flood scaling analysis by hydrological modeling in data-scarce regions." Hydrology Research 51, no. 5 (September 11, 2020): 1091–103. http://dx.doi.org/10.2166/nh.2020.042.
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Abstract Flood scaling theory is important for flood predictions in data-scarce regions but is often applied to quantile-based floods that have no physical mechanisms. In this study, we propose a framework for flood prediction in data-scarce regions by event-based flood scaling. After analyzing the factors controlling the flood scaling, flood events are first simulated by a hydrological model with different areally averaged rainfall events and curve number (CN) values as inputs, and the peak discharge of each subcatchment is obtained. Then, the flood scaling is analyzed according to the simulated peak discharge and subcatchment area. Accordingly, the relationship curves between the scaling exponent and the two explanatory factors (rainfall intensity and CN) can be drawn. Assuming that the flood and the corresponding rainfall event have the same frequency, the scaling exponent with a specific flood frequency can be interpolated from these curves.
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Acreman, M. C., and A. Werritty. "Flood frequency estimation in Scotland using index floods and regional growth curves." Transactions of the Royal Society of Edinburgh: Earth Sciences 78, no. 4 (1987): 305–13. http://dx.doi.org/10.1017/s026359330001124x.
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ABSTRACTThe index flood/regional growth curve method is the most commonly used procedure for estimating a design flood at an ungauged site in the United Kingdom when only the instantaneous peak discharge is required. This paper summarises recent work in Scotland in which the authors have refined the equations for estimating the index flood from the physical characteristics of the drainage basin and have proposed new methods for classifying basins to increase the hydrological homogeneity of the regions on which the growth curves are based. A new algorithm for estimating regional growth curves is reported which allows for correlation between flood magnitude at different sites. Simulation experiments are described which highlight the consequences of the data failing to meet the assumptions of the models used.
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Bomers, Anouk, Ralph M. J. Schielen, and Suzanne J. M. H. Hulscher. "Decreasing uncertainty in flood frequency analyses by including historic flood events in an efficient bootstrap approach." Natural Hazards and Earth System Sciences 19, no. 8 (August 29, 2019): 1895–908. http://dx.doi.org/10.5194/nhess-19-1895-2019.
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Abstract. Flood frequency curves are usually highly uncertain since they are based on short data sets of measured discharges or weather conditions. To decrease the confidence intervals, an efficient bootstrap method is developed in this study. The Rhine river delta is considered as a case study. We use a hydraulic model to normalize historic flood events for anthropogenic and natural changes in the river system. As a result, the data set of measured discharges could be extended by approximately 600 years. The study shows that historic flood events decrease the confidence interval of the flood frequency curve significantly, specifically in the range of large floods. This even applies if the maximum discharges of these historic flood events are highly uncertain themselves.
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Kusumastuti, D. I., I. Struthers, M. Sivapalan, and D. A. Reynolds. "Threshold effects in catchment storm response and the occurrence and magnitude of flood events: implications for flood frequency." Hydrology and Earth System Sciences Discussions 3, no. 5 (October 23, 2006): 3239–77. http://dx.doi.org/10.5194/hessd-3-3239-2006.
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Abstract. The aim of this paper is to illustrate the effects of selected catchment storage thresholds upon runoff behaviour, and specifically their impact upon flood frequency. The analysis is carried out with the use of a stochastic rainfall model, incorporating rainfall variability at intra-event, inter-event and seasonal timescales, as well as infrequent summer tropical cyclones, coupled with deterministic rainfall-runoff models that incorporate runoff generation by both saturation excess and subsurface stormflow mechanisms. Changing runoff generation mechanisms (i.e. from subsurface flow to surface runoff) associated with a given threshold (i.e. saturation storage capacity) are shown to be manifested in the flood frequency curve as a break in slope. It is observed that the inclusion of infrequent summer storm events increases the temporal frequency occurrence and magnitude of surface runoff events, in this way contributing to steeper flood frequency curves, and an additional break in the slope of the flood frequency curve. The results of this study highlight the importance of thresholds on flood frequency, and provide insights into the complex interactions between rainfall variability and threshold nonlinearities in the rainfall-runoff process, which are shown to have a significant impact on the resulting flood frequency curves.
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Kusumastuti, D. I., I. Struthers, M. Sivapalan, and D. A. Reynolds. "Threshold effects in catchment storm response and the occurrence and magnitude of flood events: implications for flood frequency." Hydrology and Earth System Sciences 11, no. 4 (August 20, 2007): 1515–28. http://dx.doi.org/10.5194/hess-11-1515-2007.
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Abstract. The aim of this paper is to illustrate the effects of selected catchment storage thresholds upon runoff behaviour, and specifically their impact upon flood frequency. The analysis is carried out with the use of a stochastic rainfall model, incorporating rainfall variability at intra-event, inter-event and seasonal timescales, as well as infrequent summer tropical cyclones, coupled with deterministic rainfall-runoff models that incorporate runoff generation by both saturation excess and subsurface stormflow mechanisms. Changing runoff generation mechanisms (i.e. from subsurface flow to surface runoff) associated with a given threshold (i.e. saturation storage capacity) is shown to be manifested in the flood frequency curve as a break in slope. It is observed that the inclusion of infrequent summer storm events increases the temporal frequency occurrence and magnitude of surface runoff events, in this way contributing to steeper flood frequency curves, and an additional break in the slope of the flood frequency curve. The results of this study highlight the importance of thresholds on flood frequency, and provide insights into the complex interactions between rainfall variability and threshold nonlinearities in the rainfall-runoff process, which are shown to have a significant impact on the resulting flood frequency curves.
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Aprilia, R., E. Hidayah, and D. Junita K. "Frequency ratio application for mapping flood susceptibility in Welang Watershed, East Java." IOP Conference Series: Earth and Environmental Science 930, no. 1 (December 1, 2021): 012095. http://dx.doi.org/10.1088/1755-1315/930/1/012095.
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Abstract Flood is one of the disaster threats downstream of Welang river, Pasuruan. A flood susceptibility map is needed to anticipate floods disasters. This research aimed to map flood Susceptibility in the Welang watershed using a Geographical Information System. In determining flood hazard, the Frequency Ratio (FR) approach was used. Flood locations were identified from the interpretation of field survey data as training data and model validation. The data were represented in a Digital Elevation Model (DEM) map, geological data, land use, river data, and Landsat Satellite Imagery and processed into a spatial database on the GIS platform. The factors that caused flooding consisted of Flood inventory, slope, Elevation, Topographic Wetness Index (TWI), Standardized Precipitation Index (SPI), Flow Accumulation, Distance to the river, River Density, Rainfall, Vegetation Index (NDVI), and Landuse. The map results with acceptable accuracy showed that the FR model gained an Area Under Curve (AUC) value of 90%, and the incidence for the Area Under Curve ( AUC ) was 93%. It is known that 1% of the flood-prone area is very high. The local Government can use the research to minimize the risk of flooding in the Welang watershed.
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Kuczera, George. "Correlated Rating Curve Error in Flood Frequency Inference." Water Resources Research 32, no. 7 (July 1996): 2119–27. http://dx.doi.org/10.1029/96wr00804.
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Muzik, I., and S. J. Pomeroy. "A geographic information system for prediction of design flood hydrographs." Canadian Journal of Civil Engineering 17, no. 6 (December 1, 1990): 965–73. http://dx.doi.org/10.1139/l90-108.
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A geographic information system (GIS) supporting a flood hydrograph prediction software package is described. The hydrograph prediction method is based on the convolution of excess rainfall with a synthetic unit hydrograph, derived by the Soil Conservation Service runoff curve number and a regional dimensionless unit hydrograph method, respectively. The GIS uses a raster method to store the following data: land use and land cover, soil type, rainfall intensity–frequency–duration statistics, runoff curve numbers (CN), regional dimensionless unit hydrograph, and regional lag-time relationship. The GIS has also the capability of computing a number of watershed and hydrologic parameters required for predictions, such as a watershed average rainfall and CN value, area, centroid, stream length, etc. Most of the data for such computations are input from a digitizer. Substantial time and cost savings are possible once the data base has been created. Application of the system is illustrated by an example of predicting flood frequency curves for selected watersheds in Alberta's Rocky Mountain foothills. Key words: geographic information system, flood hydrograph, curve number, hydrologic simulation, flood frequency.
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Qiao, Changlu, Guotao Cai, Yanxue Liu, Junfeng Li, and Fulong Chen. "Study of the Flood Frequency Based on Normal Transformation in Arid Inland Region: A Case Study of Manas River in North-Western China." Mobile Information Systems 2022 (July 13, 2022): 1–17. http://dx.doi.org/10.1155/2022/5229348.
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Flood disaster is one of the natural disasters which cause the most serious economic losses, the most casualties, and the greatest social impact. Flood frequency analysis is very important for reducing flood disaster. In this paper, based on the flood data of Manas River and tools of Box–Cox and Johnson normal transformation, the nonparametric statistical method for flood frequency analysis is studied in order to analyze the adaptability between it and the rivers in arid region of north-western China. The calculation result of the fitness index is divided into two parts: high flood discharge and low flood discharge. One of the two evaluation indexes has an advantage in fitting, and the number of advantages of the three methods in each part has been counted. After analysis, for the flood peak discharge frequency of rivers in arid region of north-western China, the frequency curve of Johnson transformation fits best with empirical data. The high flood discharge advantage is 6, and the low flood discharge is 4. For the flood volume frequency of rivers in arid region of north-western China, Box–Cox transform fits well with empirical data at the high flood discharge frequency curve, and its advantage is 12; Johnson transformation has a better fit between the low flood discharge frequency curve and empirical data, and its advantage is 12. Therefore, it is the way of improving the precision of flood frequency analysis to use the method of P-III distribution and normal transformation comprehensively.
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Li, Zhehao, Hongbo Zhang, Vijay Singh, Ruihong Yu, and Shuqi Zhang. "A Simple Early Warning System for Flash Floods in an Ungauged Catchment and Application in the Loess Plateau, China." Water 11, no. 3 (February 27, 2019): 426. http://dx.doi.org/10.3390/w11030426.
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Under climate change, flash floods have become more frequent and severe, and are posing a danger to society, especially in the ungauged catchments. The objective of this paper, is to construct a simple and early warning system, serving for flash floods risk management in the ungauged catchments of the Loess Plateau in China, and offer a reference for flash flood warning in other areas in the world. Considering the absence of hydrological data in the ungauged catchments, the early warning system for flash floods is established by combining the regional or watershed isograms of hydrological parameters and local empirical formulas. Therein, rainfall and water stage/flow are used as warning indices for real-time risk estimation of flash flood. For early warning, the disaster water stage was first determined according to the protected objects (e.g., residents and buildings), namely the critical water stage. The critical flow (flow threshold), was calculated based on the water stage, and the established relationship between water stage and flow using the cross-sectional measured data. Then, according to the flow frequency curve of the design flood, the frequency of critical flow was ascertained. Assuming that the rainfall and the flood have the same frequency, the critical rainfall threshold was calculated through the design rainstorm with the same frequency of the design flood. Due to the critical rainfall threshold being sensitive with different soil conditions, the design flood and frequency curve of flood flow were calculated under different soil conditions, and thus the rainfall threshold was given under different soil condition for early warning of the flash flood disaster. Taking two sections in Zichang County (within the Loess Plateau) as an example, we set the rainfall and water stage/flow thresholds to trigger immediate or preparation signals for the migration of the population along the river. The application of this method to the 7.26 flood events in 2017 in China, shows that the early warning system is feasible. It is expected that this simple early warning system can provide early warnings of flash floods in ungauged catchments in the Loess Plateau and other similar areas.
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Pawar, Uttam, Worawit Suppawimut, Nitin Muttil, and Upaka Rathnayake. "A GIS-Based Comparative Analysis of Frequency Ratio and Statistical Index Models for Flood Susceptibility Mapping in the Upper Krishna Basin, India." Water 14, no. 22 (November 20, 2022): 3771. http://dx.doi.org/10.3390/w14223771.
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The Upper Krishna Basin in Maharashtra (India) is highly vulnerable to floods. This study aimed to generate a flood susceptibility map for the basin using Frequency Ratio and Statistical Index models of flood analysis. The flood hazard inventory map was created by 370 flood locations in the Upper Krishna Basin and plotted using ArcGIS 10.1 software. The 259 flood locations (70%) were selected randomly as training samples for analysis of the flood models, and for validation purposes, the remaining 111 flood locations (30%) were used. Flood susceptibility analyses were performed based on 12 flood conditioning factors. These were elevation, slope, aspect, curvature, Topographic Wetness Index, Stream Power Index, rainfall, distance from the river, stream density, soil types, land use, and distance from the road. The Statistical Index model revealed that 38% of the area of the Upper Krishna Basin is in the high- to very-high-flood-susceptibility class. The precision of the flood susceptibility map was confirmed using the receiver operating characteristic and the area under the curve value method. The area under the curve showed a 66.89% success rate and a 68% prediction rate for the Frequency Ratio model. However, the Statistical Index model provided an 82.85% success rate and 83.23% prediction rate. The comparative analysis of the Frequency Ratio and Statistical Index models revealed that the Statistical Index model was the most suitable for flood susceptibility analysis and mapping flood-prone areas in the Upper Krishna Basin. The results obtained from this research can be helpful in flood disaster mitigation and hazard preparedness in the Upper Krishna Basin.
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Struthers, I., and M. Sivapalan. "Theoretical investigation of process controls upon flood frequency: role of thresholds." Hydrology and Earth System Sciences Discussions 3, no. 5 (October 26, 2006): 3279–319. http://dx.doi.org/10.5194/hessd-3-3279-2006.
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Abstract. Traditional statistical approaches to flood frequency inherently assume homogeneity and stationarity in the flood generation process. This study illustrates the impact of heterogeneity associated with threshold non-linearities in the storage-discharge relationship associated with the rainfall-runoff process upon flood frequency behaviour. For a simplified, non-threshold (i.e. homogeneous) scenario, flood frequency can be characterised in terms of rainfall frequency, the characteristic response time of the catchment, and storm intermittency, modified by the relative strength of evaporation. The flood frequency curve is then a consistent transformation of the rainfall frequency curve, and could be readily described by traditional statistical methods. The introduction of storage thresholds, namely a field capacity storage and a catchment storage capacity, however, results in different flood frequency "regions" associated with distinctly different rainfall-runoff response behaviour and different process controls. The return period associated with the transition between these regions is directly related to the frequency of threshold exceedence. Where threshold exceedence is relatively rare, statistical extrapolation of flood frequency on the basis of short historical flood records risks ignoring this heterogeneity, and therefore significantly underestimating the magnitude of extreme flood peaks.
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Struthers, I., and M. Sivapalan. "A conceptual investigation of process controls upon flood frequency: role of thresholds." Hydrology and Earth System Sciences 11, no. 4 (July 6, 2007): 1405–16. http://dx.doi.org/10.5194/hess-11-1405-2007.
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Abstract. Traditional statistical approaches to flood frequency inherently assume homogeneity and stationarity in the flood generation process. This study illustrates the impact of heterogeneity associated with threshold non-linearities in the storage-discharge relationship associated with the rainfall-runoff process upon flood frequency behaviour. For a simplified, non-threshold (i.e. homogeneous) scenario, flood frequency can be characterised in terms of rainfall frequency, the characteristic response time of the catchment, and storm intermittency, modified by the relative strength of evaporation. The flood frequency curve is then a consistent transformation of the rainfall frequency curve, and could be readily described by traditional statistical methods. The introduction of storage thresholds, namely a field capacity storage and a catchment storage capacity, however, results in different flood frequency "regions" associated with distinctly different rainfall-runoff response behaviour and different process controls. The return period associated with the transition between these regions is directly related to the frequency of threshold exceedence. Where threshold exceedence is relatively rare, statistical extrapolation of flood frequency on the basis of short historical flood records risks ignoring this heterogeneity, and therefore significantly underestimating the magnitude of extreme flood peaks.
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Gottschalk, Lars. "Regional Exceedance Probabilities." Hydrology Research 20, no. 4-5 (August 1, 1989): 201–14. http://dx.doi.org/10.2166/nh.1989.0016.
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Construction of a regional flood frequency curve is based, as a rule, on fitting this curve to representative quantiles. In a regional sample of floods the probability of extreme values corresponding to return periods, that exceed the record lengths, is much larger than that of individual series, used to determine the representative quantiles. The probabilities of exceedance of regional extremes can be calculated straightforward in case of independent data, applying the theory of order statistics. For regionally dependent data one can define an equivalent number of independent regional series and then utilize the theory for independent data. This approach is exemplified with flood data from Norway.
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Guse, B., T. Hofherr, and B. Merz. "Introducing empirical and probabilistic regional envelope curves into a mixed bounded distribution function." Hydrology and Earth System Sciences Discussions 7, no. 4 (July 6, 2010): 4253–90. http://dx.doi.org/10.5194/hessd-7-4253-2010.
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Abstract. A novel approach to consider additional spatial information in flood frequency analyses, especially for the estimation of discharges with recurrence intervals larger than 100 years, is presented. For this purpose, large flood quantiles, i.e. pairs of a discharge and its corresponding recurrence interval, as well as an upper bound discharge, are combined within a mixed bounded distribution function. Large flood quantiles are derived using probabilistic regional envelope curves (PRECs) for all sites of a pooling group. These PREC flood quantiles are introduced into an at-site flood frequency analysis by assuming that they are representative for the range of recurrence intervals which is covered by PREC flood quantiles. For recurrence intervals above a certain inflection point, a Generalised Extreme Value (GEV) distribution function with a positive shape parameter is used. This GEV asymptotically approaches an upper bound derived from an empirical envelope curve. The resulting mixed distribution function is composed of two distribution functions, which are connected at the inflection point. This method is applied to 83 streamflow gauges in Saxony/Germany. Our analysis illustrates that the presented mixed bounded distribution function adequately considers PREC flood quantiles as well as an upper bound discharge. The introduction of both into an at-site flood frequency analysis improves the quantile estimation. A sensitivity analysis reveals that, for the target recurrence interval of 1000 years, the flood quantile estimation is less sensitive to the selection of an empirical envelope curve than to the selection of PREC discharges and of the inflection point between the mixed bounded distribution function.
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Guse, B., Th Hofherr, and B. Merz. "Introducing empirical and probabilistic regional envelope curves into a mixed bounded distribution function." Hydrology and Earth System Sciences 14, no. 12 (December 9, 2010): 2465–78. http://dx.doi.org/10.5194/hess-14-2465-2010.
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Abstract. A novel approach to consider additional spatial information in flood frequency analyses, especially for the estimation of discharges with recurrence intervals larger than 100 years, is presented. For this purpose, large flood quantiles, i.e. pairs of a discharge and its corresponding recurrence interval, as well as an upper bound discharge, are combined within a mixed bounded distribution function. The large flood quantiles are derived using probabilistic regional envelope curves (PRECs) for all sites of a pooling group. These PREC flood quantiles are introduced into an at-site flood frequency analysis by assuming that they are representative for the range of recurrence intervals which is covered by PREC flood quantiles. For recurrence intervals above a certain inflection point, a Generalised Extreme Value (GEV) distribution function with a positive shape parameter is used. This GEV asymptotically approaches an upper bound derived from an empirical envelope curve. The resulting mixed distribution function is composed of two distribution functions which are connected at the inflection point. This method is applied to 83 streamflow gauges in Saxony/Germany. Our analysis illustrates that the presented mixed bounded distribution function adequately considers PREC flood quantiles as well as an upper bound discharge. The introduction of both into an at-site flood frequency analysis improves the quantile estimation. A sensitivity analysis reveals that, for the target recurrence interval of 1000 years, the flood quantile estimation is less sensitive to the selection of an empirical envelope curve than to the selection of PREC discharges and of the inflection point between the mixed bounded distribution function.
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Chen, Xiaohong, Lijuan Zhang, C. Y. Xu, Jiaming Zhang, and Changqing Ye. "Hydrological Design of Nonstationary Flood Extremes and Durations in Wujiang River, South China: Changing Properties, Causes, and Impacts." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/527461.
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The flood-duration-frequency (QDF) analysis is performed using annual maximum streamflow series of 1–10 day durations observed at Pingshi and Lishi stations in southern China. The trends and change point of annual maximum flood flow and flood duration are also investigated by statistical tests. The results indicate that (1) the annual maximum flood flow only has a marginally increasing trend, whereas the flood duration exhibits a significant decreasing trend at the 0.10 significant level. The change point for the annual maximum flood flow series was found in 1991 and after which the mean maximum flood flow increased by 45.26%. (2) The period after 1991 is characterized by frequent and shorter duration floods due to increased rainstorm. However, land use change in the basin was found intensifying the increased tendency of annual maximum flow after 1991. And (3) under nonstationary environmental conditions, alternative definitions of return period should be adapted. The impacts on curve fitting of flood series showed an overall change of upper tail from “gentle” to “steep,” and the design flood magnitude became larger. Therefore, a nonstationary frequency analysis taking account of change point in the data series is highly recommended for future studies.
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Grover, Patrick L., Donald H. Burn, and Juraj M. Cunderlik. "A comparison of index flood estimation procedures for ungauged catchments." Canadian Journal of Civil Engineering 29, no. 5 (October 1, 2002): 734–41. http://dx.doi.org/10.1139/l02-065.
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Flood frequency analysis is used by water resources professionals to estimate the probability of exceedence associated with a flood of a given magnitude. The estimation of flood frequencies is important because they are used in the planning and design of hydraulic structures, in flood-plain management, and in reservoir operation. The index flood method is commonly used to develop a flood frequency curve that relates flood magnitude to flood rarity. This method involves scaling a dimensionless flood frequency curve by the index flood. The index flood is a middle-sized flood for which the mean or median of the flood data series is typically used. When the catchment of interest is ungauged, statistical models, such as multiple regression, are often used to relate the index flood to catchment descriptors. In this study six different parameter estimation techniques and three regionalization techniques are compared in terms of ability to predict the index flood for an ungauged catchment. A case study employing a split-sample experiment with data from catchments in Ontario, Canada, was used to evaluate the approaches. The models were assessed using three performance indices to evaluate the capability to predict the index flood for 20 stations. The dimensionless nonlinear model outperformed all of the other parameter estimation techniques for each of the three indices selected. The performance was improved through the use of geostatistical residual mapping, however, the improvement was small. The residual mapping was found to greatly improve the estimates obtained using ordinary least-squares regression.Key words: index flood, flood frequency analysis, regression, residual mapping, geostatistics.
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Gorbachova, Liudmyla O., Viktoria S. Prykhodkina, and Borys F. Khrystiuk. "Spring flood frequency analysis in the Southern Buh River Basin, Ukraine." Journal of Geology, Geography and Geoecology 30, no. 2 (July 17, 2021): 250–60. http://dx.doi.org/10.15421/112122.
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The river floods are among the most dangerous natural disasters in the world. Each year, the spring floods cause the significant material damage in the different countries, including Ukraine. Knowledge of trends in such floods, as well as their probabilistic forecast, is of great scientific and practical importance. In last decades, the decreasing phase of cyclical fluctuations of the maximum runoff of spring floods has been observed on the plain rivers of Ukraine, including the Southern Bug River. In addition, there is an increase in air temperature. So, the actual task is the determine the modern probable maximum discharges estimates of spring floods in the Southern Buh River Basin as well as their comparison with the estimates that were computed earlier. It gives an opportunity to reveal possible changes of the statistical characteristics and values of the probable maximum discharges, to analyze and to discuss the reasons for these changes. For the investigation, we used the time series of the maximum discharges of spring floods for 21 gauging stations in the Southern Buh River Basin since the beginning of the observations and till 2015. The method of the regression on the variable that is based on the data of analogues rivers was used to bringing up the duration of the time series and restoration of the gaps. In the study, the hydro-genetic methods for estimation of the homogeneity and stationarity of hydrological series, namely the mass curve, the residual mass curve and the combined graphs. The distributions of Kritskyi & Menkel and Pearson type III for the frequency analysis were used. It has been shown in this study that the maximum discharges of spring floods of time series are quasi-homogeneous and quasi-stationary. It is explained the presence in the observation series of only increasing and decreasing phases of cyclical fluctuations, their considerable duration, as well as the significant variability of the maximal flow. The series of maximal runoff of spring floods are very asymmetric, which significantly complicates the selection of analytical distribution curves. The updated current parameters of the maximal spring flood runoff have not changed significantly. It can be assumed that such characteristics have already become stable over time, as the series of maximal runoff of spring floods already have phases of increasing and decreasing of long-term cyclic fluctuations.
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Sordo-Ward, Alvaro, Ivan Gabriel-Martín, Paola Bianucci, Giuseppe Mascaro, Enrique R. Vivoni, and Luis Garrote. "Stochastic Hybrid Event Based and Continuous Approach to Derive Flood Frequency Curve." Water 13, no. 14 (July 13, 2021): 1931. http://dx.doi.org/10.3390/w13141931.
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This study proposes a methodology that combines the advantages of the event-based and continuous models, for the derivation of the maximum flow and maximum hydrograph volume frequency curves, by combining a stochastic continuous weather generator (the advanced weather generator, abbreviated as AWE-GEN) with a fully distributed physically based hydrological model (the TIN-based real-time integrated basin simulator, abbreviated as tRIBS) that runs both event-based and continuous simulation. The methodology is applied to Peacheater Creek, a 64 km2 basin located in Oklahoma, United States. First, a continuous set of 5000 years’ hourly weather forcing series is generated using the stochastic weather generator AWE-GEN. Second, a hydrological continuous simulation of 50 years of the climate series is generated with the hydrological model tRIBS. Simultaneously, the separation of storm events is performed by applying the exponential method to the 5000- and 50-years climate series. From the continuous simulation of 50 years, the mean soil moisture in the top 10 cm (MSM10) of the soil layer of the basin at an hourly time step is extracted. Afterwards, from the times series of hourly MSM10, the values associated to all the storm events within the 50 years of hourly weather series are extracted. Therefore, each storm event has an initial soil moisture value associated (MSM10Event). Thus, the probability distribution of MSM10Event for each month of the year is obtained. Third, the five major events of each of the 5000 years in terms of total depth are simulated in an event-based framework in tRIBS, assigning an initial moisture state value for the basin using a Monte Carlo framework. Finally, the maximum annual hydrographs are obtained in terms of maximum peak-flow and volume, and the associated frequency curves are derived. To validate the method, the results obtained by the hybrid method are compared to those obtained by deriving the flood frequency curves from the continuous simulation of 5000 years, analyzing the maximum annual peak-flow and maximum annual volume, and the dependence between the peak-flow and volume. Independence between rainfall events and prior hydrological soil moisture conditions has been proved. The proposed hybrid method can reproduce the univariate flood frequency curves with a good agreement to those obtained by the continuous simulation. The maximum annual peak-flow frequency curve is obtained with a Nash–Sutcliffe coefficient of 0.98, whereas the maximum annual volume frequency curve is obtained with a Nash–Sutcliffe value of 0.97. The proposed hybrid method permits to generate hydrological forcing by using a fully distributed physically based model but reducing the computation times on the order from months to hours.
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Kjeldsen, Thomas R. "Modelling the impact of urbanization on flood frequency relationships in the UK." Hydrology Research 41, no. 5 (June 1, 2010): 391–405. http://dx.doi.org/10.2166/nh.2010.056.
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This paper investigates the effect of urbanization on the three key statistics used to establish flood frequency curves when combining the index flood method with the method of L-moments for estimating distribution parameters, i.e. the median annual maximum peak flow (the index flood), and the high-order L-moment ratios L-CV and L-SKEW. An existing procedure employing catchment descriptors was used to estimate the three statistics at ungauged sites in the UK. As-rural estimates of the three statistics were obtained in 200 urban catchments and compared to the corresponding values obtained from observed data. The (log) differences of these estimates were related to catchment descriptors relevant to the urbanization process using linear regression. The results show that urbanization leads to a reduction in L-CV but an increase in L-SKEW. A jack-knife leave-one-out experiment showed that the adjustment factors developed were generally better at predicting the effect of urbanization on the flood frequency curve than the existing adjustment factor currently used in the UK.
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Nowicka, Barbara. "Estimation of the Degree of Extremality of High-Water Flows in Selected Rivers in Poland in 1971–2006." Miscellanea Geographica 14, no. 1 (December 1, 2010): 169–76. http://dx.doi.org/10.2478/mgrsd-2010-0015.
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Abstract The paper presents the results of estimation and comparison of risk of extreme floods on rivers of various hydrological regime. The hypothesis that extreme events occur with the same frequency in all rivers was rejected. The limit between extreme and common floods on 30 rivers from different geographical regions of Poland was defined on the basis of standardized flow-duration curve in 1971-2006. These analyses resulted in designing five curve groups. Four measures of flood magnitude have been proposed. The time distribution of extreme events during the last decades was estimated for the most dynamic rivers.
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Wood, Eric F., and Charles S. Hebson. "On Hydrologic Similarity: 1. Derivation of the Dimensionless Flood Frequency Curve." Water Resources Research 22, no. 11 (October 1986): 1549–54. http://dx.doi.org/10.1029/wr022i011p01549.
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Laio, F., D. Ganora, P. Claps, and G. Galeati. "Spatially smooth regional estimation of the flood frequency curve (with uncertainty)." Journal of Hydrology 408, no. 1-2 (September 2011): 67–77. http://dx.doi.org/10.1016/j.jhydrol.2011.07.022.
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Costache, Romulus, Alina Barbulescu, and Quoc Pham. "Integrated Framework for Detecting the Areas Prone to Flooding Generated by Flash-Floods in Small River Catchments." Water 13, no. 6 (March 11, 2021): 758. http://dx.doi.org/10.3390/w13060758.
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In the present study, the susceptibility to flash-floods and flooding was studied across the Izvorul Dorului River basin in Romania. In the first phase, three ensemble models were used to determine the susceptibility to flash-floods. These models were generated by a combination of three statistical bivariate methods, namely frequency ratio (FR), weights of evidence (WOE), and statistical index (SI), with fuzzy analytical hierarchy process (FAHP). The result obtained from the application of the FAHP-WOE model had the best performance highlighted by an Area Under Curve—Receiver Operating Characteristics Curve (AUC-ROC) value of 0.837 for the training sample and another of 0.79 for the validation sample. Furthermore, the results offered by FAHP-WOE were weighted on the river network level using the flow accumulation method, through which the valleys with a medium, high, and very high torrential susceptibility were identified. Based on these valleys’ locations, the susceptibility to floods was estimated. Thus, in the first stage, a buffer zone of 200 m was delimited around the identified valleys along which the floods could occur. Once the buffer zone was established, ten flood conditioning factors were used to determine the flood susceptibility through the analytical hierarchy process model. Approximately 25% of the total delimited area had a high and very high flood susceptibility.
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Domeneghetti, A., S. Vorogushyn, A. Castellarin, B. Merz, and A. Brath. "Effects of rating-curve uncertainty on probabilistic flood mapping." Hydrology and Earth System Sciences Discussions 9, no. 8 (August 29, 2012): 9809–45. http://dx.doi.org/10.5194/hessd-9-9809-2012.
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Abstract. Comprehensive flood risk assessment studies should quantify the global uncertainty in flood hazard estimation, for instance by mapping inundation extents together with their confidence intervals. This appears of particular importance in case of flood hazard assessments along dike-protected reaches where the possibility of occurrence of dike failures may considerably enhance the uncertainty. We present a methodology to derive probabilistic flood maps in dike-protected flood prone areas, where several sources of uncertainty are taken into account. In particular, this paper focuses on a 50 km reach of River Po (Italy) and three major sources of uncertainty in hydraulic modelling and flood mapping: uncertainties in the (i) upstream and (ii) downstream boundary conditions, and (iii) uncertainties in dike failures. Uncertainties in the definition of upstream boundary conditions (i.e. design-hydrographs) are assessed by applying different bivariate copula families to model the frequency regime of flood peaks and volumes. Uncertainties in the definition of downstream boundary conditions are characterised by associating the rating-curve used as downstream boundary condition with confidence intervals which reflect discharge measurements errors and interpolation errors. The effects of uncertainties in boundary conditions and randomness of dike failures are assessed by means of the Inundation Hazard Assessment Model (IHAM), a recently proposed hybrid probabilistic-deterministic model that considers three different failure mechanisms: overtopping, piping and micro-instability due to seepage. The results of the study show that the IHAM-based analysis enables probabilistic flood hazard mapping and provides decision makers with a fundamental piece of information for devising and implementing flood risk mitigation strategies in the presence of various sources of uncertainty.
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Ighile, Eseosa Halima, Hiroaki Shirakawa, and Hiroki Tanikawa. "Application of GIS and Machine Learning to Predict Flood Areas in Nigeria." Sustainability 14, no. 9 (April 22, 2022): 5039. http://dx.doi.org/10.3390/su14095039.
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Floods are one of the most devastating forces in nature. Several approaches for identifying flood-prone locations have been developed to reduce the overall harmful impacts on humans and the environment. However, due to the increased frequency of flooding and related disasters, coupled with the continuous changes in natural and social-economic conditions, it has become vital to predict areas with the highest probability of flooding to ensure effective measures to mitigate impending disasters. This study predicted the flood susceptible areas in Nigeria based on historical flood records from 1985~2020 and various conditioning factors. To evaluate the link between flood incidence and the fifteen (15) explanatory variables, which include climatic, topographic, land use and proximity information, the artificial neural network (ANN) and logistic regression (LR) models were trained and tested to develop a flood susceptibility map. The receiver operating characteristic curve (ROC) and area under the curve (AUC) were used to evaluate both model accuracies. The results show that both techniques can model and predict flood-prone areas. However, the ANN model produced a higher performance and prediction rate than the LR model, 76.4% and 62.5%, respectively. In addition, both models highlighted that those areas with the highest susceptibility to flood are the low-lying regions in the southern extremities and around water areas. From the study, we can establish that machine learning techniques can effectively map and predict flood-prone areas and serve as a tool for developing flood mitigation policies and plans.
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Monjardin, Cris Edward, Clarence Cabundocan, Camille Ignacio, and Christian Jedd Tesnado. "Impact of Climate Change on the Frequency and Severity of Floods in the Pasig-Marikina River Basin." E3S Web of Conferences 117 (2019): 00005. http://dx.doi.org/10.1051/e3sconf/201911700005.
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This study assessed impacts of climate change on the frequency and severity of floods in the Pasig-Marikina River basin. Researchers used the historical data from PAG-ASA (Philippine Atmospheric, Geophysical and Astronomical Services Administration), specifically from Science Garden weather station. The historical data are coupled with a global climate model, the Hadley Center Model version 3 (HadCM3) to account for the natural variability of the climate system in the area. The observed data and the hydroclimatic data from HadCM3 was processed in Statistical Downscaling Model (SDSM) that results to rainfall data from 1961-2017 and change in temperature data from 2018-2048. A rainfall time series for the river basin was generated considering average seasonal effects in the area. A flood frequency curve was modelled. From that, flood value for 2048 was derived to be at 3950cu.m/s. Additionally, the rapid urbanization in the area has contributed to the changes in the river system making it more vulnerable to floods. The results of this study supports the claim that the Pasig-Marikina River basin will be affected by the climate variability in terms of the increase in rainfall depth and average temperatures, higher flood frequency and more massive floods in the future. This study could help local government units to enforce improvement and mitigation in their area to prevent these from happening.
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Hadian, Sanaz, Hossein Afzalimehr, Negar Soltani, Ehsan Shahiri Tabarestani, Moses Karakouzian, and Mohammad Nazari-Sharabian. "Determining Flood Zonation Maps, Using New Ensembles of Multi-Criteria Decision-Making, Bivariate Statistics, and Artificial Neural Network." Water 14, no. 11 (May 27, 2022): 1721. http://dx.doi.org/10.3390/w14111721.
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Golestan Province is one of the most vulnerable areas to catastrophic flood events in Iran. The flood severity in this region has grown dramatically during the last decades, demanding a major investigation. Accordingly, an authentic map providing detailed information on floods is required to reduce future flood disasters. Three ensemble models produced by the combination of Evaluation Based on Distance from Average Solution (EDAS) and Multilayer Perceptron Neural Network (MLP) with Frequency Ratio (FR), and Weights of Evidence (WOE) are used to quantify the map flood susceptibility in Golestan Province, in the north of Iran. Ten flood effective criteria, namely altitude, slope degree, slope aspect, plan curvature, distance from rivers, Topographic Wetness Index (TWI), rainfall, soil type, geology, and land use, are considered for the modeling process. The flood zonation maps are validated by the receiver operating curve (ROC). The results show that the most precise model is MLP-FR (AUROC = 0.912), followed by EDAS-FR-AHP (AUROC = 0.875), and EDAS-WOE-AHP (AUROC = 0.845). The high accuracies of all methods applied to illustrate their capability in predicting flood susceptibility in future studies.
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Morita, Masaru, and Yeou Koung Tung. "Uncertainty quantification of flood damage estimation for urban drainage risk management." Water Science and Technology 80, no. 3 (August 1, 2019): 478–86. http://dx.doi.org/10.2166/wst.2019.297.
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Abstract This paper presents a method of quantifying the uncertainty associated with inundation damage data for an urban catchment when undertaking stormwater drainage design and management. Usually flood damage is estimated by multiplying the inundated asset value by the damage rate corresponding to the inundation depth. The uncertainty of the asset value and the damage rate is described by probability distributions estimated from an analysis of actual flood damage data from a national government survey. With the inclusion of uncertainty in the damage rate and asset value, the damage potential curve defining the damage-frequency relationship is no longer a deterministic single-value curve. Through Monte Carlo simulations, which incorporate the uncertainty of the inundation damage from the damage rate and asset value, a probabilistic damage potential relation can be established, which can be expressed in terms of a series of curves with different percentile levels. The method is demonstrated through the establishment of probabilistic damage potential curves for a typical urban catchment, the Zenpukuji river basin in Tokyo Metropolis, under two scenarios, namely, with and without a planned flood control reservoir.
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Kusumastuti, Cilcia, Prasetio Sudjarwo, Marvin Christhie, and Timotius Krisna. "Intensity-Duration-Frequency (IDF) Curve and the Most Suitable Method to Determine Flood Peak Discharge in Upper Werba Sub-Watershed." Civil Engineering Dimension 21, no. 2 (October 18, 2019): 70–75. http://dx.doi.org/10.9744/ced.21.2.70-75.
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Design flood is one of the important factors for flood risk assessment and water infrastructures planning and development in a certain location. There are several methods to estimate it, one method which has been commonly and widely use is using flood frequency analysis. This research aims to develop Intensity-Duration-Frequency (IDF) curves in Upper Werba Sub-Watershed, West Papua Province, Indonesia, to estimate design rainfall intensity. The design rainfall intensity is used to estimate peak of flood discharge using Rational Formula in the sub-watershed. Other methods, i.e. Soil Conservation Service and Nakayasu Synthetic Unit Hydrograph are also presented in this paper to provide comparison of the estimated peak of flood discharge. The result shows that the Rational method provide the closest magnitude of estimated flood discharge in Upper Werba Sub-Watershed to the observed streamflow. Therefore, it is suggested that the Rational method can be used for water infrastructure planning and development in the sub-watershed.
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Nam, W., S. Kim, H. Kim, K. Joo, and J. H. Heo. "The evaluation of regional frequency analyses methods for nonstationary data." Proceedings of the International Association of Hydrological Sciences 371 (June 12, 2015): 95–98. http://dx.doi.org/10.5194/piahs-371-95-2015.
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Abstract. Regional frequency analysis is widely used to estimate more reliable quantiles of extreme hydro-meteorological events. The stationarity of data is required for its application. This assumption tends to be violated due to climate change. In this paper, four nonstationary index flood models were used to analyze the nonstationary regional data. Monte Carlo simulation was used to evaluate the performances of these models for the generalized extreme value distribution with linearly time varying location parameter and constant scale and shape parameters. As a results, it was found that the index flood model with time-invariant index flood and time-variant growth curve could yield more statistically efficient quantile when record is long enough to show significant nonstationarity.
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Jin, Shuang-yan, Wen-yong Gao, Si-wu Luo, and Ya-jun Gao. "Analysis on the Return Period of “7.26” Rainstorm and Flood in 2017 in the Wudinghe Basin." MATEC Web of Conferences 246 (2018): 01105. http://dx.doi.org/10.1051/matecconf/201824601105.
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The return period of "7.26" rainstorm flood in 2017 in Wudinghe basin is analyzed by the method of P-III distribution. The Lijiahe and Dingjiagou stations with long rainfall observation data in the rainstorm area are selected, and the frequency curve of the annual maximum 24 hours rainfall are established, and the recurrence period of rainfall stations in rainstorm area are estimated according to the parameters determined by the curve fitting method. The frequency curve of the annual maximum peak discharge of Baijiachuan hydrological stations and so on are established, and the return period are analyzed in combination with the historical survey floods. The results show that the return period of Zhaojiabian of heavy rainfall center is about 100 years, and which of the other stations over than 200mm in Wudinghe basin is about 30~90 years; while the return period of the peak discharge of Baijiachuan and Suide hydrological station is about 30 and 20 years respectively.
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KUROTHE, R. S., N. K. GOEL, and B. S. MATHUR. "Derivation of a curve number and kinematic-wave based flood frequency distribution." Hydrological Sciences Journal 46, no. 4 (August 2001): 571–84. http://dx.doi.org/10.1080/02626660109492851.
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Valent, Peter, and Roman Výleta. "Continuous Simulation of Catchment Runoff in Flood Frequency Analysis: A Case Study from Slovakia." Proceedings 7, no. 1 (November 15, 2018): 16. http://dx.doi.org/10.3390/ecws-3-05828.
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Research questions relating to a reliable estimate of flood discharge have always interested both hydrologists and civil engineers. Over the decades, numerous methods have been proposed and used more or less successfully, all of them with known limitations restricting their use in a wide range of conditions and problems. In the past, the characteristics of hydrological extremes were mostly estimated by the methods of statistical analyses. As this type of method is not suitable to estimate design discharges of high return periods, and by default does not account for uncertainty, a new family of methods is slowly taking the place of the traditional approaches. Many of these methods are based on a combination of stochastic rainfall models (weather generators) and rainfall-runoff models, which enables generation of an arbitrary number of synthetic floods, even in places with short or no record of river discharges available. In addition, as this type of method produces flood hydrographs, they can also be used in a multivariate flood frequency analysis to estimate joint probabilities of two or more flood characteristics. This study presents a methodology for flood frequency analysis that combines stochastic models of both rainfall amounts and air temperatures with a lumped rainfall-runoff model to transfer the outputs of the stochastic models into a series of corresponding river discharges. Both of the stochastic models are single-site weather generators that produce continuous time series of mean areal daily rainfall amounts and air temperatures. In this study, the method was used to generate a time series of 10,000 years of mean daily discharges, which was used to build a flood frequency curve and to estimate extreme flood discharges of given return periods. The method was applied to a mountainous catchment of the River Váh in Slovakia.
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Yang, Long, James Smith, Mary Lynn Baeck, Efrat Morin, and David C. Goodrich. "Flash Flooding in Arid/Semiarid Regions: Dissecting the Hydrometeorology and Hydrology of the 19 August 2014 Storm and Flood Hydroclimatology in Arizona." Journal of Hydrometeorology 18, no. 12 (December 2017): 3103–23. http://dx.doi.org/10.1175/jhm-d-17-0089.1.
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The hydroclimatology, hydrometeorology, and hydrology of flash floods in the arid/semiarid southwestern United States are examined through empirical analyses of long-term, high-resolution rainfall and stream gauging observations, together with hydrological modeling analyses of the 19 August 2014 storm based on the Kinematic Runoff and Erosion Model (KINEROS2). The analyses presented here are centered on identifying the structure and evolution of flood-producing storms, as well as the interactions of space–time rainfall variability and basin characteristics in determining the upper-tail properties of rainfall and flood magnitudes over this region. This study focuses on four watersheds in Maricopa County, Arizona, with contrasting geomorphological properties. Flash floods over central Arizona are concentrated in both time and space, reflecting controls of the North American monsoon and complex terrain. Thunderstorm systems during the North American monsoon, as represented by the 19 August 2014 storm, are the dominant flood agents that determine the upper tail of flood frequency over central Arizona and that also shape the envelope curve of floods for watersheds smaller than 250 km2. Flood response for the 19 August 2014 storm is associated with storm elements of comparable spatial extent to the drainage area and slow movement for the three compact, headwater watersheds. Flood response for the elongated and relatively flat Skunk Creek highlights the importance of the spatial distribution of rainfall for transmission losses in arid/semiarid watersheds.
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Basso, S., G. Botter, R. Merz, and A. Miniussi. "PHEV! The PHysically-based Extreme Value distribution of river flows." Environmental Research Letters 16, no. 12 (December 1, 2021): 124065. http://dx.doi.org/10.1088/1748-9326/ac3d59.
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Abstract Magnitude and frequency are prominent features of river floods informing design of engineering structures, insurance premiums and adaptation strategies. Recent advances yielding a formal characterization of these variables from a joint description of soil moisture and daily runoff dynamics in river basins are here systematized to highlight their chief outcome: the PHysically-based Extreme Value (PHEV) distribution of river flows. This is a physically-based alternative to empirical estimates and purely statistical methods hitherto used to characterize extremes of hydro-meteorological variables. Capabilities of PHEV for predicting flood magnitude and frequency are benchmarked against a standard distribution and the latest statistical approach for extreme estimation, by using both an extensive observational dataset and long synthetic series of streamflow generated for river basins from contrasting hydro-climatic regions. The analyses outline the domain of applicability of PHEV and reveal its fairly unbiased capabilities to estimate flood magnitudes with return periods much longer than the sample size used for calibration in a wide range of case studies. The results also emphasize reduced prediction uncertainty of PHEV for rare floods, notably if the flood magnitude-frequency curve displays an inflection point. These features, arising from the mechanistic understanding embedded in the novel distribution of the largest river flows, are key for a reliable assessment of the actual flooding hazard associated to poorly sampled rare events, especially when lacking long observational records.
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Costache, Romulus, Phuong Thao Thi Ngo, and Dieu Tien Bui. "Novel Ensembles of Deep Learning Neural Network and Statistical Learning for Flash-Flood Susceptibility Mapping." Water 12, no. 6 (May 29, 2020): 1549. http://dx.doi.org/10.3390/w12061549.
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This study aimed to assess flash-flood susceptibility using a new hybridization approach of Deep Neural Network (DNN), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR). A catchment area in south-eastern Romania was selected for this proposed approach. In this regard, a geospatial database of the flood with 178 flood locations and with 10 flash-flood predictors was prepared and used for this proposed approach. AHP and FR were used for processing and coding the predictors into a numeric format, whereas DNN, which is a powerful and state-of-the-art probabilistic machine leaning, was employed to build an inference flash-flood model. The reliability of the models was verified with the help of Receiver Operating Characteristic (ROC) Curve, Area Under Curve (AUC), and several statistical measures. The result shows that the two proposed ensemble models, DNN-AHP and DNN-FR, are capable of predicting future flash-flood areas with accuracy higher than 92%; therefore, they are a new tool for flash-flood studies.
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Minh, Huynh Vuong Thu, Kim Lavane, Le Thi Lanh, Lam Van Thinh, Nguyen Phuoc Cong, Tran Van Ty, Nigel K. Downes, and Pankaj Kumar. "Developing Intensity-Duration-Frequency (IDF) Curves Based on Rainfall Cumulative Distribution Frequency (CDF) for Can Tho City, Vietnam." Earth 3, no. 3 (August 1, 2022): 866–80. http://dx.doi.org/10.3390/earth3030050.
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Information on the relationship between rainfall intensity, duration and accumulation frequency or return period (IDF) is commonly utilized in the design and management of urban drainage systems. Can Tho City, located in the Vietnamese Mekong Delta, is a city which has recently invested heavily in upgrading its stormwater drainage systems in the hope of preventing reoccurring flood events. Yet, much of these works were designed based on obsolete and outdated IDF rainfall curves. This paper presents an updated IDF curve for design rainfall for Can Tho City. For each duration and designated return period, a cumulative distribution function (CDF) was developed using the Pearson III, Log-Pearson III, and Log-Normal distribution functions. In order to choose the best IDF rainfall curve for Can Tho City, the CDF rainfall curve and empirical formulas used in Vietnam and Asia (Vietnamese standard 7957:2008, Department of Hydrology, Ministry of Transportation, Talbot, Kimijima, and Bermard) were compared. The goodness of fit between the IDF relationship generated by the frequency analysis (CDF curve), and that predicted by the IDF empirical formulas was assessed using the efficiency index (EI), and the root mean squared error (RMSE). The IDF built from Vietnam’s standard TCVN 7957:2008 with new parameters (A = 9594, C = 0.5, b = 26, n = 0.96) showed the best performance, with the highest values of EI (0.84 ≤EI≤ 0.93) and the lowest values of RMSE (2.5 ≤RMSE≤ 3.2), when compared to the other remnants.
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Hassan, Ait Naceur, Igmoulan Ibrahim, Namous Mustapha, Bourouy Omar, and Ouayah Mustapha. "A comparative study of different statistical methods for Flood susceptibility assessment: A case study of N'fis basin, Marrakesh High Atlas (Morocco)." Disaster Advances 14, no. 10 (September 25, 2021): 1–14. http://dx.doi.org/10.25303/1410da0114.
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Floods are one of the natural disasters with farreaching socio-economic and environmental consequences. A large part of the N'fis catchment area is vulnerable to the risk of flooding which causes an immense loss of people and infrastructure. Therefore, an accurate assessment for the susceptibility of natural risks remains essential. The main objective of this study is to evaluate the performance of frequency ratio (FR), informative value (IV) and weight of evidence (WoE) models in flood sensitivity mapping in the N'fis watershed, high atlas of Marrakech in central Morocco. A total of 87 paleo-floods sites were inventoried based on data from Tensift hydraulic agency and extensive field surveys and 11 causative factors were considered in this study. The results show high susceptibility in the northern part of the N'fis basin and moderate to low susceptibility in its southern parts. The maps were validated according to the receiver operating characteristic (ROC) curve and the area under the curves (AUC) was calculated. Then, the accuracy rates are of the order of 85.65%, 87.75% and 88.40% for the FR, IV and WoE models respectively. Thus, the WoE model proved to be the most significative model for the analysis of flood sensitivity in this region. The results of this work can be an important support for decisionmakers for flood risk-appropriate planning.
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Mediero, L., A. Jiménez-Álvarez, and L. Garrote. "Design flood hydrographs from the relationship between flood peak and volume." Hydrology and Earth System Sciences Discussions 7, no. 4 (July 22, 2010): 4817–49. http://dx.doi.org/10.5194/hessd-7-4817-2010.
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Abstract. Hydrological frequency analyses are usually focused on flood peaks. Flood volumes and durations have not been so exhaustively studied although there are many practical cases, like dam design, where the full hydrograph is of interest. A flood hydrograph may be described by a multivariate function of peak, volume and duration. Most standard bivariate and trivariate functions do not produce univariate three-parameter functions as marginal distributions, but three-parameter functions are required to fit highly skewed data as flood peak and volume series. In this paper, relationship between flood peak and hydrograph volume is analysed to overcome this problem. A Monte Carlo experiment was carried out to generate an ensemble of hydrographs that keep the statistical properties of marginal distributions of peaks, volumes and durations. This ensemble can be applied to determine the Design Flood Hydrograph (DFH) for a reservoir, which is not a unique hydrograph, but a curve in the peak-volume space. All hydrographs in that curve have the same return period, understood as the inverse of the probability to exceed a certain water level in the reservoir any given year. The procedure can also be applied to design the length of the spillway crest in terms of risk to exceed a given water level in the reservoir.
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Sharir, Kamilia, and Rodeano Roslee. "Flood Susceptibility Assessment (FSA) Using GIS-Based Frequency Ratio (FR) Model in Kota Belud, Sabah, Malaysia." International Journal of Design & Nature and Ecodynamics 17, no. 2 (April 27, 2022): 203–8. http://dx.doi.org/10.18280/ijdne.170206.
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The flood is one of the most devastating natural disasters to strike Sabah, Malaysia, especially in the Kota Belud region. The Flood Susceptibility Analysis (FSA) was described using bivariate statistical analysis (the Frequency Ratio model) based on a Geographical Information System (GIS). Field surveys and formal reports from local authorities in the study area created the flood inventory map. The training dataset for statistical analysis consisted of 100 flood locations inundated in 2017, while the validation dataset consisted of 54 flood locations from the 2016 flood report. Eight (8) parameters (elevation, slope curvature, slope angle, topography wetness index, drainage density, drainage proximity, land use, and soil type) were extracted from the database and then converted into a raster format with a cell size of 5m x 5m. Finally, using the natural break classification method, the FSA was generated and classified into five classes: very low, low, moderate, high, and very high susceptibility. The area under the curve (AUC) analysis validated the flood susceptibility model's accuracy. The success rate AUC was calculated to be 0.89, while the prediction rate AUC was 0.82. The flood susceptibility analysis could be used to develop flood mitigation strategies in land use planning.
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Griffin, Adam, Gianni Vesuviano, and Elizabeth Stewart. "Have trends changed over time? A study of UK peak flow data and sensitivity to observation period." Natural Hazards and Earth System Sciences 19, no. 10 (October 8, 2019): 2157–67. http://dx.doi.org/10.5194/nhess-19-2157-2019.
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Abstract. Classical statistical methods for flood frequency estimation assume stationarity in the gauged data. However, recent focus on climate change and, within UK hydrology, severe floods in 2009 and 2015 has raised the profile of statistical analyses that include trends. This paper considers how parameter estimates for the generalised logistic distribution vary through time in the UK. The UK Benchmark Network (UKBN2) is used to allow focus on climate change separate from the effects of land-use change. We focus on the sensitivity of parameter estimates to adding data, through fixed-width moving window and fixed-start extending window approaches, and on whether parameter trends are more prominent in specific geographical regions. Under stationary assumptions, the addition of new data tends to further the convergence of parameters to some final value. However, addition of a single data point can vastly change non-stationary parameter estimates. Little spatial correlation is seen in the magnitude of trends in peak flow data, potentially due to the spatial clustering of catchments in the UKBN2. In many places, the ratio between the 50-year and 100-year flood is decreasing, whereas the ratio between the 2-year and 30-year flood is increasing, presenting as a flattening of the flood frequency curve.
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Kwon, Hyun-Han, Jang-Gyeong Kim, and Sae-Hoon Park. "Derivation of Flood Frequency Curve with Uncertainty of Rainfall and Rainfall-Runoff Model." Journal of Korea Water Resources Association 46, no. 1 (January 31, 2013): 59–71. http://dx.doi.org/10.3741/jkwra.2013.46.1.59.
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Petersen-Øverleir, Asgeir, and Trond Reitan. "Accounting for rating curve imprecision in flood frequency analysis using likelihood-based methods." Journal of Hydrology 366, no. 1-4 (March 2009): 89–100. http://dx.doi.org/10.1016/j.jhydrol.2008.12.014.
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Spellman, Patricia, and Veronica Webster. "Quantifying Long‐Term and Event‐Scale Baseflow Effects across the Flood Frequency Curve." JAWRA Journal of the American Water Resources Association 56, no. 5 (June 25, 2020): 868–81. http://dx.doi.org/10.1111/1752-1688.12852.
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Fadaeifard, Mostafa, and Mohammad Danesh-Yazdi. "Lessons Learned from Flood Management in Iran." E3S Web of Conferences 346 (2022): 02012. http://dx.doi.org/10.1051/e3sconf/202234602012.
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Iran has a longstanding challenge in supplying water during prolonged drought periods. This has drawn considerable attention towards the dam industry over the past four decades, leading to the study, construction and operation of several large dams. These dams played a critical role in controlling the massive floods of 2019 and 2020, among others. Nevertheless, due to the increased intensity and frequency of extreme events because of climate change, the downstream regions of these large storage dams still face significant damages. This is mainly attributed to the insufficient dredging of rivers and tributaries, lack of rule curve and operation guideline for some storage dams, inaccurate prediction of flood volume, violation of land-use and water management action plans, promotion of industries with high water need, and floodplain encroachment. In this study, we aim to evaluate the performance of several large dams in the Karkheh and Karoon river basin, located in southwestern Iran, in managing the floods took place in the aforementioned periods. We also discuss the challenges and the lessons learned, with suggestions for improving the flood management in the country.
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Mediero, L., A. Jiménez-Álvarez, and L. Garrote. "Design flood hydrographs from the relationship between flood peak and volume." Hydrology and Earth System Sciences 14, no. 12 (December 10, 2010): 2495–505. http://dx.doi.org/10.5194/hess-14-2495-2010.
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Abstract. Hydrological frequency analyses are usually focused on flood peaks. Flood volumes and durations have not been studied as extensively, although there are many practical situations, such as when designing a dam, in which the full hydrograph is of interest. A flood hydrograph may be described by a multivariate function of the peak, volume and duration. Most standard bivariate and trivariate functions do not produce univariate three-parameter functions as marginal distributions, however, three-parameter functions are required to fit highly skewed data, such as flood peak and flood volume series. In this paper, the relationship between flood peak and hydrograph volume is analysed to overcome this problem. A Monte Carlo experiment was conducted to generate an ensemble of hydrographs that maintain the statistical properties of marginal distributions of the peaks, volumes and durations. This ensemble can be applied to determine the Design Flood Hydrograph (DFH) for a reservoir, which is not a unique hydrograph, but rather a curve in the peak-volume space. All hydrographs on that curve have the same return period, which can be understood as the inverse of the probability to exceed a certain water level in the reservoir in any given year. The procedure can also be applied to design the length of the spillway crest in terms of the risk of exceeding a given water level in the reservoir.
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Hashemi, A. M., M. Franchini, and P. E. O’Connell. "Climatic and basin factors affecting the flood frequency curve: PART I – A simple sensitivity analysis based on the continuous simulation approach." Hydrology and Earth System Sciences 4, no. 3 (September 30, 2000): 463–82. http://dx.doi.org/10.5194/hess-4-463-2000.
Full textAbstract:
Abstract. Regionalized and at-site flood frequency curves exhibit considerable variability in their shapes, but the factors controlling the variability (other than sampling effects) are not well understood. An application of the Monte Carlo simulation-based derived distribution approach is presented in this two-part paper to explore the influence of climate, described by simulated rainfall and evapotranspiration time series, and basin factors on the flood frequency curve (ffc). The sensitivity analysis conducted in the paper should not be interpreted as reflecting possible climate changes, but the results can provide an indication of the changes to which the flood frequency curve might be sensitive. A single site Neyman Scott point process model of rainfall, with convective and stratiform cells (Cowpertwait, 1994; 1995), has been employed to generate synthetic rainfall inputs to a rainfall runoff model. The time series of the potential evapotranspiration (ETp) demand has been represented through an AR(n) model with seasonal component, while a simplified version of the ARNO rainfall-runoff model (Todini, 1996) has been employed to simulate the continuous discharge time series. All these models have been parameterised in a realistic manner using observed data and results from previous applications, to obtain ‘reference’ parameter sets for a synthetic case study. Subsequently, perturbations to the model parameters have been made one-at-a-time and the sensitivities of the generated annual maximum rainfall and flood frequency curves (unstandardised, and standardised by the mean) have been assessed. Overall, the sensitivity analysis described in this paper suggests that the soil moisture regime, and, in particular, the probability distribution of soil moisture content at the storm arrival time, can be considered as a unifying link between the perturbations to the several parameters and their effects on the standardised and unstandardised ffcs, thus revealing the physical mechanism through which their influence is exercised. However, perturbations to the parameters of the linear routing component affect only the unstandardised ffc. In Franchini et al. (2000), the sensitivity analysis of the model parameters has been assessed through an analysis of variance (ANOVA) of the results obtained from a formal experimental design, where all the parameters are allowed to vary simultaneously, thus providing deeper insight into the interactions between the different factors. This approach allows a wider range of climatic and basin conditions to be analysed and reinforces the results presented in this paper, which provide valuable new insight into the climatic and basin factors controlling the ffc. Keywords: stochastic rainfall model; rainfall runoff model; simulation; derived distribution; flood frequency; sensitivity analysis