scholarly journals Peer review report 1 on “Snow-melt flood frequency analysis by means of copula based 2D probability distributions for the Narew River in Poland”

2017 ◽  
Vol 9 ◽  
pp. 71
Author(s):  
Soni M. Pradhanang
Keyword(s):  
Peer Review ◽  
Frequency Analysis ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Snow Melt

scholarly journals Snow-melt flood frequency analysis by means of copula based 2D probability distributions for the Narew River in Poland

2016 ◽  
Vol 6 ◽  
pp. 26-51 ◽  
Author(s):  
Bogdan Ozga-Zielinski ◽  
Maurycy Ciupak ◽  
Jan Adamowski ◽  
Bahaa Khalil ◽  
Julien Malard
Keyword(s):  
Frequency Analysis ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Snow Melt

scholarly journals Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows

2014 ◽  
Vol 18 (1) ◽  
pp. 353-365 ◽  
Author(s):  
U. Haberlandt ◽  
I. Radtke
Keyword(s):  
Time Series ◽  
Frequency Analysis ◽  
Model Calibration ◽  
Hydrological Model ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Rainfall Data ◽  
Flood Frequency Analysis ◽  
Peak Flows ◽  
Hourly Rainfall

Abstract. Derived flood frequency analysis allows the estimation of design floods with hydrological modeling for poorly observed basins considering change and taking into account flood protection measures. There are several possible choices regarding precipitation input, discharge output and consequently the calibration of the model. The objective of this study is to compare different calibration strategies for a hydrological model considering various types of rainfall input and runoff output data sets and to propose the most suitable approach. Event based and continuous, observed hourly rainfall data as well as disaggregated daily rainfall and stochastically generated hourly rainfall data are used as input for the model. As output, short hourly and longer daily continuous flow time series as well as probability distributions of annual maximum peak flow series are employed. The performance of the strategies is evaluated using the obtained different model parameter sets for continuous simulation of discharge in an independent validation period and by comparing the model derived flood frequency distributions with the observed one. The investigations are carried out for three mesoscale catchments in northern Germany with the hydrological model HEC-HMS (Hydrologic Engineering Center's Hydrologic Modeling System). The results show that (I) the same type of precipitation input data should be used for calibration and application of the hydrological model, (II) a model calibrated using a small sample of extreme values works quite well for the simulation of continuous time series with moderate length but not vice versa, and (III) the best performance with small uncertainty is obtained when stochastic precipitation data and the observed probability distribution of peak flows are used for model calibration. This outcome suggests to calibrate a hydrological model directly on probability distributions of observed peak flows using stochastic rainfall as input if its purpose is the application for derived flood frequency analysis.

scholarly journals Some best‐fit probability distributions for at‐site flood frequency analysis of the Ume River

2020 ◽  
Vol 13 (3) ◽  
Author(s):  
Samara Kousar ◽  
Abrar Raza Khan ◽  
Mahmood Ul Hassan ◽  
Zahra Noreen ◽  
Sajjad Haider Bhatti
Keyword(s):  
Frequency Analysis ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Best Fit

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

2019 ◽  
Vol 23 (5) ◽  
pp. 2225-2243 ◽  
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.

scholarly journals RERIONAL FLOOD FREQUENCY ANALYSIS IN SNOW-MELT FLOOD AREA

2004 ◽  
Vol 48 ◽  
pp. 115-120
Author(s):  
Yosuke KOMATSU ◽  
Yasuhisa KUZUHA ◽  
Kunio TOMOSUGI ◽  
Tokuo KISHII
Keyword(s):  
Frequency Analysis ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Snow Melt

scholarly journals A study on selection of probability distributions for at-site flood frequency analysis in Australia

2013 ◽  
Vol 69 (3) ◽  
pp. 1803-1813 ◽  
Author(s):  
Ayesha S. Rahman ◽  
Ataur Rahman ◽  
Mohammad A. Zaman ◽  
Khaled Haddad ◽  
Amimul Ahsan ◽  
...  
Keyword(s):  
Frequency Analysis ◽  
Probability Distributions ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Selection Of

scholarly journals Process-Based Flood Frequency Analysis in an Agricultural Watershed Exhibiting Nonstationary Flood Seasonality

2018 ◽  
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 of processes including rainfall, soil moisture, and watershed morphology. Conventional flood frequency analysis (FFA) methods such as design storms and discharge-based statistical methods offer few insights into process interactions and how they shape the probability distributions of floods. Understanding and projecting flood frequency in conditions of nonstationary hydroclimate and land use requires 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 in Turkey River in the Midwestern United States, a watershed that is undergoing significant climatic and hydrologic change. We show that using only 15 years of rainfall records, our methodology can produce more accurate estimates of present-day flood frequency than is possible using longer discharge or rainfall records. We found that shifts in the seasonality of soil moisture conditions and extreme rainfall in 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. Such mistakes are avoidable, however, and our approach may provide a clearer pathway toward understanding current and future flood frequency in nonstationary conditions compared with more conventional methods.

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