Modified hydrologic regime of upper Ganga basin induced by natural and anthropogenic stressors

AbstractClimate change and anthropogenic activities pose serious threats to river basin hydrology worldwide. The Ganga basin is home to around half a billion people and has been significantly impacted by hydrological alterations in the last few decades. The increasing high-intensity rainfall events often create flash flooding events. Such events are frequently reported in mountainous and alluvial plains of the Ganga basin, putting the entire basin under severe flood risk. Further, increasing human interventions through hydraulic structures in the upstream reaches significantly alter the flows during the pre-and post-monsoon periods. Here, we explore the hydrological implications of increasing reservoir-induced and climate-related stressors in the Upper Ganga Basin (UGB), India. Flow/sediment duration curves and flood frequency analysis have been used to assess pre-and post-1995 hydrological behaviour. Our results indicate that low and moderate flows have been significantly altered, and the flood peaks have been attenuated by the operation of hydraulic structures in the Bhagirathi (western subbasin). The Alaknanda (eastern subbasin) has experienced an increase in extreme rainfall and flows post-1995. The downstream reaches experience reservoir-induced moderate flow alterations during pre-and post-monsoon and increasing extreme flood magnitudes during monsoon. Furthermore, substantial siltation upstream of the reservoirs has disrupted the upstream–downstream geomorphologic linkages.

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Spatial Heterogeneity Analysis of Short-Duration Extreme Rainfall Events in Megacities in China

Water ◽

10.3390/w12123364 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3364 ◽

Cited By ~ 1

Author(s):

Qi Zhuang ◽

Shuguang Liu ◽

Zhengzheng Zhou

Keyword(s):

Spatial Heterogeneity ◽

Short Duration ◽

Urban Areas ◽

Spatial Association ◽

Extreme Rainfall ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Extreme Rainfall Events ◽

Rainfall Events ◽

Design Storm

Given the fact that researchers require more specific spatial rainfall information for storm flood calculation, hydrological risk assessment, and water budget estimates, there is a growing need to analyze the spatial heterogeneity of rainfall accurately. This paper provides insight into rainfall spatial heterogeneity in urban areas based on statistical analysis methods. An ensemble of short-duration (3-h) extreme rainfall events for four megacities in China are extracted from a high-resolution gridded rainfall dataset (resolution of 30 min in time, 0.1° × 0.1° in space). Under the heterogeneity framework using Moran’s I, LISA (Local Indicators of Spatial Association), and semi-variance, the multi-scale spatial variability of extreme rainfall is identified and assessed in Shanghai (SH), Beijing (BJ), Guangzhou (GZ), and Shenzhen (SZ). The results show that there is a pronounced spatial heterogeneity of short-duration extreme rainfall in the four cities. Heterogeneous characteristics of rainfall within location, range, and directions are closely linked to the different urban growth in four cities. The results also suggest that the spatial distribution of rainfall cannot be neglected in the design storm in urban areas. This paper constitutes a useful contribution to quantifying the degree of spatial heterogeneity and supports an improved understanding of rainfall/flood frequency analysis in megacities.

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An Application of a Stochastic Semi-Continuous Simulation Method for Flood Frequency Analysis: A Case Study in Slovakia

Slovak Journal of Civil Engineering ◽

10.1515/sjce-2017-0016 ◽

2017 ◽

Vol 25 (3) ◽

pp. 30-44

Author(s):

Peter Valent ◽

Emmanuel Paquet

Keyword(s):

Simulation Method ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Reliable Estimate ◽

Return Periods ◽

Rainfall Events ◽

Continuous Simulation ◽

Wide Range ◽

Extreme Flood ◽

Rainfall Runoff Model

Abstract A reliable estimate of extreme flood characteristics has always been an active topic in hydrological research. Over the decades a large number of approaches and their modifications have been proposed and used, with various methods utilizing continuous simulation of catchment runoff, being the subject of the most intensive research in the last decade. In this paper a new and promising stochastic semi-continuous method is used to estimate extreme discharges in two mountainous Slovak catchments of the rivers Váh and Hron, in which snow-melt processes need to be taken into account. The SCHADEX method used, couples a precipitation probabilistic model with a rainfall-runoff model used to both continuously simulate catchment hydrological conditions and to transform generated synthetic rainfall events into corresponding discharges. The stochastic nature of the method means that a wide range of synthetic rainfall events were simulated on various historical catchment conditions, taking into account not only the saturation of soil, but also the amount of snow accumulated in the catchment. The results showed that the SCHADEX extreme discharge estimates with return periods of up to 100 years were comparable to those estimated by statistical approaches. In addition, two reconstructed historical floods with corresponding return periods of 100 and 1000 years were compared to the SCHADEX estimates. The results confirmed the usability of the method for estimating design discharges with a recurrence interval of more than 100 years and its applicability in Slovak conditions.

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Stochastic semi-continuous simulation for extreme flood estimation in catchments with combined rainfall–snowmelt flood regimes

Natural Hazards and Earth System Science ◽

10.5194/nhess-14-1283-2014 ◽

2014 ◽

Vol 14 (5) ◽

pp. 1283-1298 ◽

Cited By ~ 20

Author(s):

D. Lawrence ◽

E. Paquet ◽

J. Gailhard ◽

A. K. Fleig

Keyword(s):

Frequency Analysis ◽

Probabilistic Model ◽

Extreme Precipitation ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Continuous Simulation ◽

Weather Patterns ◽

Extreme Flood ◽

Flood Estimation

Abstract. Simulation methods for extreme flood estimation represent an important complement to statistical flood frequency analysis because a spectrum of catchment conditions potentially leading to extreme flows can be assessed. In this paper, stochastic, semi-continuous simulation is used to estimate extreme floods in three catchments located in Norway, all of which are characterised by flood regimes in which snowmelt often has a significant role. The simulations are based on SCHADEX, which couples a precipitation probabilistic model with a hydrological simulation such that an exhaustive set of catchment conditions and responses is simulated. The precipitation probabilistic model is conditioned by regional weather patterns, and a bottom–up classification procedure was used to define a set of weather patterns producing extreme precipitation in Norway. SCHADEX estimates for the 1000-year (Q1000) discharge are compared with those of several standard methods, including event-based and long-term simulations which use a single extreme precipitation sequence as input to a hydrological model, statistical flood frequency analysis based on the annual maximum series, and the GRADEX method. The comparison suggests that the combination of a precipitation probabilistic model with a long-term simulation of catchment conditions, including snowmelt, produces estimates for given return periods which are more in line with those based on statistical flood frequency analysis, as compared with the standard simulation methods, in two of the catchments. In the third case, the SCHADEX method gives higher estimates than statistical flood frequency analysis and further suggests that the seasonality of the most likely Q1000 events differs from that of the annual maximum flows. The semi-continuous stochastic simulation method highlights the importance of considering the joint probability of extreme precipitation, snowmelt rates and catchment saturation states when assigning return periods to floods estimated by precipitation-runoff methods. The SCHADEX methodology, as applied here, is dependent on observed discharge data for calibration of a hydrological model, and further study to extend its application to ungauged catchments would significantly enhance its versatility.

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Regional Flood Frequency Analysis using Dimensionless Index Flood Method

Civil Engineering Journal ◽

10.28991/cej-2020-03091627 ◽

2020 ◽

Vol 6 (12) ◽

pp. 2425-2436

Author(s):

Andy Obinna Ibeje ◽

Ben N. Ekwueme

Keyword(s):

Flood Frequency ◽

Flood Frequency Analysis ◽

Accurate Estimation ◽

Frequency Curve ◽

Model Framework ◽

Index Flood ◽

Extreme Flood ◽

Regional Flood Frequency Analysis ◽

Spatially Distributed ◽

Regional Flood Frequency

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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Hydro-meteorological characteristics and occurrence probability of extreme flood events in Moroccan High Atlas

Journal of Water and Climate Change ◽

10.2166/wcc.2020.069 ◽

2020 ◽

Vol 11 (S1) ◽

pp. 310-321 ◽

Cited By ~ 2

Author(s):

Mohamed El Mehdi Saidi ◽

Tarik Saouabe ◽

Abdelhafid El Alaoui El Fels ◽

El Mahdi El Khalki ◽

Abdessamad Hadri

Keyword(s):

Return Period ◽

Peak Flow ◽

Extreme Event ◽

Rare Event ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

High Atlas ◽

Flood Water ◽

Extreme Flood ◽

Water Volumes

Abstract Flood frequency analysis could be a tool to help decision-makers to size hydraulic structures. To this end, this article aims to compare two analysis methods to see how rare an extreme hydrometeorological event is, and what could be its return period. This event caused many deadly floods in southwestern Morocco. It was the result of unusual atmospheric conditions, characterized by a very low atmospheric pressure off the Moroccan coast and the passage of the jet stream further south. Assessment of frequency and return period of this extreme event is performed in a High Atlas watershed (the Ghdat Wadi) using historical floods. We took into account, on the one hand, flood peak flows and, on the other hand, flood water volumes. Statistically, both parameters are better adjusted respectively to Gamma and Log Normal distributions. However, the peak flow approach underestimates the return period of long-duration hydrographs that do not have a high peak flow, like the 2014 event. The latter is indeed better evaluated, as a rare event, by taking into account the flood water volumes. Therefore, this parameter should not be omitted in the calculation of flood probabilities for watershed management and the sizing of flood protection infrastructure.

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Process-based flood frequency analysis in an agricultural watershed exhibiting nonstationary flood seasonality

Hydrology and Earth System Sciences ◽

10.5194/hess-23-2225-2019 ◽

2019 ◽

Vol 23 (5) ◽

pp. 2225-2243 ◽

Cited By ~ 7

Author(s):

Guo Yu ◽

Daniel B. Wright ◽

Zhihua Zhu ◽

Cassia Smith ◽

Kathleen D. Holman

Keyword(s):

Soil Moisture ◽

Frequency Analysis ◽

Probability Distributions ◽

Extreme Rainfall ◽

Hydrologic Model ◽

Flood Frequency ◽

Agricultural Watershed ◽

Flood Frequency Analysis ◽

Antecedent Conditions ◽

Hydrologic Change

Abstract. Floods are the product of complex interactions among processes including precipitation, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into these process interactions and how they “shape” the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use require deeper understanding of these processes, some or all of which may be changing in ways that will be undersampled in observational records. This study presents an alternative “process-based” FFA approach that uses stochastic storm transposition to generate large numbers of realistic rainstorm “scenarios” based on relatively short rainfall remote sensing records. Long-term continuous hydrologic model simulations are used to derive seasonally varying distributions of watershed antecedent conditions. We couple rainstorm scenarios with seasonally appropriate antecedent conditions to simulate flood frequency. The methodology is applied to the 4002 km2 Turkey River watershed in the Midwestern United States, which is undergoing significant climatic and hydrologic change. We show that, using only 15 years of rainfall records, our methodology can produce accurate estimates of “present-day” flood frequency. We found that shifts in the seasonality of soil moisture, snow, and extreme rainfall in the Turkey River exert important controls on flood frequency. We also demonstrate that process-based techniques may be prone to errors due to inadequate representation of specific seasonal processes within hydrologic models. If such mistakes are avoided, however, process-based approaches can provide a useful pathway toward understanding current and future flood frequency in nonstationary conditions and thus be valuable for supplementing existing FFA practices.

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May long-term historical hydrological data be misleading for flood frequency analysis in current conditions of climate change?

10.5194/egusphere-egu2020-1140 ◽

2020 ◽

Author(s):

Alexandra Fedorova ◽

Nataliia Nesterova ◽

Olga Makarieva ◽

Andrey Shikhov

Keyword(s):

Climate Change ◽

Water Level ◽

Frequency Analysis ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Economic Damage ◽

Irkutsk Region ◽

Extreme Flood ◽

The Town

In June 2019, the extreme flash flood was formed on the rivers of the Irkutsk region originating from the East Sayan mountains. This flood became the most hazardous one in the region in 80 years history of observations.The greatest rise in water level was recorded at the Iya River in the town of Tulun (more than 9 m in three days). The recorded water level was more than 5 m above the dangerous mark of 850 cm and more than 2.5 m above the historical maximum water level which was observed in 1984.The flood led to the catastrophic inundation of the town of Tulun, 25 people died and 8 went missing. According to preliminary assessment, economic damage from the flood in 2019 amounted up to half a billion Euro.Among the reasons for the extreme flood in June 2019 that are discussed are heavy rains as a result of climate change, melting of snow and glaciers in the mountains of the East Sayan, deforestation of river basins due to clearings and fires, etc.The aim of the study was to analyze the factors that led to the formation of a catastrophic flood in June 2019, as well as estimate the maximum discharge of at the Iya River. For calculations, the deterministic distributed hydrological model Hydrograph was applied. We used the observed data of meteorological stations and the forecast values &#226;&#128;&#139;&#226;&#128;&#139;of the global weather forecast model ICON. The estimated discharge has exceeded previously observed one by about 50%.The results of the study have shown that recent flood damage was caused mainly by unprepared infrastructure. The safety dam which was built in the town of Tulun just ten years ago was 2 meters lower than maximum observed water level in 2019. This case and many other cases in Russia suggest that the flood frequency analysis of even long-term historical data may mislead design engineers to significantly underestimate the probability and magnitude of flash floods. There are the evidences of observed precipitation regime transformations which directly contribute to the formation of dangerous hydrological phenomena. The details of the study for the Irkutsk region will be presented.

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Stochastic semi-continuous simulation for extreme flood estimation in catchments with combined rainfall-snowmelt flood regimes

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-1-6785-2013 ◽

2013 ◽

Vol 1 (6) ◽

pp. 6785-6828 ◽

Cited By ~ 1

Author(s):

D. Lawrence ◽

E. Paquet ◽

J. Gailhard ◽

A. K. Fleig

Keyword(s):

Frequency Analysis ◽

Probabilistic Model ◽

Extreme Precipitation ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Continuous Simulation ◽

Weather Patterns ◽

Extreme Flood ◽

Flood Estimation

Abstract. Simulation methods for extreme flood estimation represent an important complement to statistical flood frequency analysis because a spectrum of catchment conditions potentially leading to extreme flows can be assessed. In this paper, stochastic, semi-continuous simulation is used to estimate extreme floods in three catchments located in Norway, all of which are characterised by flood regimes in which snowmelt often has a significant role. The simulations are based on SCHADEX, which couples a precipitation probabilistic model with a hydrological simulation such that an exhaustive set of catchment conditions and responses are simulated. The precipitation probabilistic model is conditioned by regional weather patterns, and a "bottom-up" classification procedure was used for defining a set of weather patterns producing extreme precipitation in Norway. SCHADEX estimates for the 1000 yr (Q1000) discharge are compared with those of several standard methods, including event-based and long-term simulations which use a single extreme precipitation sequence as input to a hydrological model, with statistical flood frequency analysis based on the annual maximum series, and with the GRADEX method. The comparison suggests that the combination of a precipitation probabilistic model with a long-term simulation of catchment conditions, including snowmelt, produces estimates for given return periods which are more in line with those based on statistical flood frequency analysis, as compared with the standard simulation methods, in two of the catchments. In the third case, the SCHADEX method gives higher estimates than statistical flood frequency analysis and further suggests that the seasonality of the most likely Q1000 events differs from that of the annual maximum flows. The semi-continuous stochastic simulation method highlights the importance of considering the joint probability of extreme precipitation, snowmelt rates and catchment saturation states when assigning return periods to floods estimated by precipitation-runoff methods. The SCHADEX methodology, as applied here, is dependent on observed discharge data for calibration of a hydrological model, and further study to extend its application to ungauged catchments would significantly enhance its versatility.

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Análise da adequação de eventos de precipitação extrema na Amazônia Ocidental em modelos estáticos: Rondônia

Revista Verde de Agroecologia e Desenvolvimento Sustentável ◽

10.18378/rvads.v10i3.2716 ◽

2015 ◽

Vol 10 (3) ◽

pp. 13

Author(s):

Vinicius Alexandre Sikora de Souza ◽

Marcos Leando Alves Nunes ◽

Sandra Ferronatto Francener ◽

Ana Lúcia Denardin da Rosa

Keyword(s):

Extreme Rainfall ◽

Hydraulic Structures ◽

Font Size ◽

Extreme Rainfall Events ◽

Rainfall Events ◽

Historical Series ◽

Kolmogorov Smirnov ◽

Intensity Duration Frequency ◽

Log Normal ◽

Smirnov Test

<span style="font-size: 12.0pt; line-height: 115%; font-family: 'Times New Roman','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: PT-BR; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Este estudo objetivou estimar a função Intensidade-Duração-Frequência (IDF) de eventos pluviométricos extremos a partir dos dados de precipitação das estações pluviométricas instaladas no estado de Rondônia, de modo que posteriormente tais informações possam ser utilizadas no dimensionamento de obras hidráulicas. Utilizou-se 41 estações pluviométricas com séries históricas acima de 10 anos, disponibilizadas pela Agência Nacional de Águas (ANA). Essas séries passaram inicialmente pelo teste de aderência Kolmogorov-Smirnov (KS), a fim de verificar o ajuste das mesmas as distribuições: Normal, Log-Normal, Exponencial, Gama, Gumbel, Weibull e Logística. O trabalho denotou que o teste de aderência <span style="font-size: 12.0pt; line-height: 115%; font-family: 'Times New Roman','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: PT-BR; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">Kolmogorov-Smirnov de forma geral forneceu uma expressiva aceitação na maioria das distribuições estatística testadas. Analysis of fitness for extreme rainfall events in western amazon in static models: state Rondônia ABSTRACT: This study aimed to estimate the Intensity - Duration - Frequency (IDF) function extreme rainfall events from the data of precipitation of rainfall stations located in the State of Rondônia, so that such information can be later used in hydraulic structures. We used 41 rainfall stations with historical series over 10 years, provided by the National Water Agency (ANA). These series originally started by adherence Kolmogorov -Smirnov (KS) in order to check the fit of the same distributions: Normal, Log- Normal, Exponential, Gamma, Gumbel, Weibull and Logistics. Work denoted that the Kolmogorov - Smirnov test of adherence generally provided a significant acceptance in most of the tested statistical distributions.<span style="font-size: 12.0pt; line-height: 115%; font-family: 'Times New Roman','serif'; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-ansi-language: PT-BR; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;">

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Flood frequency analysis supported by the largest historical flood

Natural Hazards and Earth System Science ◽

10.5194/nhess-14-1543-2014 ◽

2014 ◽

Vol 14 (6) ◽

pp. 1543-1551 ◽

Cited By ~ 9

Author(s):

W. G. Strupczewski ◽

K. Kochanek ◽

E. Bogdanowicz

Keyword(s):

Return Period ◽

Historical Record ◽

Flood Frequency ◽

Flood Frequency Analysis ◽

Historical Period ◽

Time Period ◽

Extreme Flood ◽

Discrete Uniform Distribution ◽

L Value ◽

Discrete Uniform

Abstract. The use of non-systematic flood data for statistical purposes depends on the reliability of the assessment of both flood magnitudes and their return period. The earliest known extreme flood year is usually the beginning of the historical record. Even if one properly assesses the magnitudes of historic floods, the problem of their return periods remains unsolved. The matter at hand is that only the largest flood (XM) is known during whole historical period and its occurrence marks the beginning of the historical period and defines its length (L). It is common practice to use the earliest known flood year as the beginning of the record. It means that the L value selected is an empirical estimate of the lower bound on the effective historical length M. The estimation of the return period of XM based on its occurrence (L), i.e. ^M = L, gives a severe upward bias. The problem arises that to estimate the time period (M) representative of the largest observed flood XM. From the discrete uniform distribution with support 1, 2, ... , M of the probability of the L position of XM, one gets ^L = M/2. Therefore ^M = 2L has been taken as the return period of XM and as the effective historical record length as well this time. As in the systematic period (N) all its elements are smaller than XM, one can get ^M = 2t( L+N). The efficiency of using the largest historical flood (XM) for large quantile estimation (i.e. one with return period T = 100 years) has been assessed using the maximum likelihood (ML) method with various length of systematic record (N) and various estimates of the historical period length ^M comparing accuracy with the case when systematic records alone (N) are used only. The simulation procedure used for the purpose incorporates N systematic record and the largest historic flood (XMi) in the period M, which appeared in the Li year of the historical period. The simulation results for selected two-parameter distributions, values of their parameters, different N and M values are presented in terms of bias and root mean square error RMSEs of the quantile of interest are more widely discussed.

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