scholarly journals Application of a distributed-routing rainfall-runoff model to flood-frequency estimation in Somerset County, New Jersey

1990 ◽  
Keyword(s):  
New Jersey ◽  
Frequency Estimation ◽  
Flood Frequency ◽  
Rainfall Runoff ◽  
Distributed Routing ◽  
Somerset County ◽  
Rainfall Runoff Model ◽  
Runoff Model

scholarly journals An investigation of site-similarity approaches to generalisation of a rainfall–runoff model

2007 ◽  
Vol 11 (1) ◽  
pp. 500-515 ◽  
Author(s):  
A. L. Kay ◽  
D. A. Jones ◽  
S. M. Crooks ◽  
T. R. Kjeldsen ◽  
C. F. Fung
Keyword(s):  
Frequency Estimation ◽  
Flood Frequency ◽  
Distributed Model ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Ungauged Sites ◽  
Calibration Uncertainty ◽  
Property Space ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. This paper investigates a new approach to spatial generalisation of rainfall–runoff model parameters – site-similarity with pooling groups – for use in flood frequency estimation at ungauged sites using continuous simulation. The method is developed for the generalisation of a simple conceptual model, the Probability Distributed Model, with four parameters which require specific estimation. The study is based on a relatively large sample of catchments in Great Britain. Various options are investigated within the approach. In the final version, the pooling group comprises the 10 calibrated catchments closest, in catchment property space, to the target site, where the catchment properties used to define the space differ for each parameter of the model. An analysis that, explicitly, takes account of calibration uncertainty as a source of error enables the uncertainty associated with generalised parameter values to be reduced, justifiably. The approach uses calibration uncertainty estimated through jack-knifing and employs a weighting scheme within pooling groups that uses weights which vary both with distance in the catchment property space and with the calibration uncertainty. Models using generalised values from this approach perform relatively well compared with direct calibration. Although performance appears to be better in some areas of the country than others, there are no obvious relationships between catchment properties and performance.

scholarly journals Flood frequency estimation by continuous simulation (with likelihood based uncertainty estimation)

2000 ◽  
Vol 4 (1) ◽  
pp. 23-34 ◽  
Author(s):  
D. Cameron ◽  
K. Beven ◽  
J. Tawn ◽  
P. Naden
Keyword(s):  
Statistical Approach ◽  
Frequency Estimation ◽  
Uncertainty Estimation ◽  
Flood Frequency ◽  
Rainfall Runoff ◽  
Simulation Methodology ◽  
Continuous Simulation ◽  
Modelling Uncertainties ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. A continuous simulation methodology, which incorporates the quantification of modelling uncertainties, is used for flood frequency estimation. The methodology utilises the rainfall-runoff model TOPMODEL within the uncertainty framework of GLUE. Long return period estimates are obtained through the coupling of a stochastic rainfall generator with TOPMODEL. Examples of applications to four gauged UK catchments are provided. A comparison with a traditional statistical approach indicates the suitability of the methodology as an alternative technique for flood frequency estimation. It is suggested that, given an appropriate choice of rainfall-runoff model and stochastic rainstorm generator, the basic methodology can be adapted for use in many other regions of the world. Keywords: Floods; Frequency; TOPMODEL; Rainfall-runoff modelling

scholarly journals Climatic and basin factors affecting the flood frequency curve: PART I – A simple sensitivity analysis based on the continuous simulation approach

2000 ◽  
Vol 4 (3) ◽  
pp. 463-482 ◽  
Author(s):  
A. M. Hashemi ◽  
M. Franchini ◽  
P. E. O’Connell
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Flood Frequency ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Frequency Curve ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Insight Into ◽  
Frequency Curves

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

scholarly journals Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model

2008 ◽  
Vol 5 (1) ◽  
pp. 1-26 ◽  
Author(s):  
G. Moretti ◽  
A. Montanari
Keyword(s):  
Frequency Distribution ◽  
River Flow ◽  
Spatial Scales ◽  
Flood Frequency ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Wide Range ◽  
Spatially Distributed ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. The estimation of the peak river flow for ungauged river sections is a topical issue in applied hydrology. Spatially distributed rainfall-runoff models can be a useful tool to this end, since they are potentially able to simulate the river flow at any location of the watershed drainage network. However, it is not fully clear to what extent these models can provide reliable simulations over a wide range of spatial scales. This issue is investigated here by applying a spatially distributed, continuous simulation rainfall-runoff model to infer the flood frequency distribution of the Riarbero Torrent. This is an ungauged mountain creek located in northern Italy, whose drainage area is 17 km2. The results were checked by using estimates of the peak river flow obtained by applying a classical procedure based on hydrological similarity principles. The analysis highlights interesting perspectives for the application of spatially distributed models to ungauged catchments.

Spatial representation of stochastically generated rainfall for derived flood frequency analysis

2021 ◽  
Author(s):  
Luisa-Bianca Thiele ◽  
Ross Pidoto ◽  
Uwe Haberlandt
Keyword(s):  
Time Series ◽  
Spatial Representation ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Multiple Point ◽  
Rainfall Time Series ◽  
Rainfall Runoff ◽  
Areal Rainfall ◽  
Rainfall Runoff Model ◽  
Runoff Model

<p>For derived flood frequency analyses, stochastic rainfall models can be linked with rainfall-runoff models to improve the accuracy of design flood estimations when the length of observed rainfall and runoff data is not sufficient. The stochastic rainfall time series, which are used as input for the rainfall-runoff model, can be generated with different spatial resolution: (a) Point rainfall, which is stochastically generated rainfall at a single site. (b) Areal rainfall, which is catchment rainfall averaged over multiple sites before using the single-site stochastic rainfall model. (c) Multiple point rainfall, which is stochastically generated at multiple sites with spatial correlation before averaging to catchment rainfall. To find the most applicable spatial representation of stochastically generated rainfall for derived flood frequency analysis, simulated and observed runoff time series will be compared based on runoff statistics. The simulated runoff time series are generated utilizing the rainfall-runoff model HBV-IWW with an hourly time step. The rainfall-runoff model is driven with point, areal and multiple point stochastic rainfall time series generated by an Alternating Renewal rainfall model (ARM). In order to take into account the influence of catchment size on the results, catchments of different sizes within Germany are considered in this study.  While point rainfall may be applicable for small catchments, it is expected that above a certain catchment size a more detailed spatial representation of stochastically generated rainfall is necessary. Here, it would be advantageous if the results based on areal rainfall are comparable to those of the multiple point rainfall. The stochastically generation of areal rainfall is less complex compared to the stochastically generation of multiple point rainfall and extremes at the catchment scale may also be better represented by areal rainfall.    </p>

scholarly journals Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model

2008 ◽  
Vol 12 (4) ◽  
pp. 1141-1152 ◽  
Author(s):  
G. Moretti ◽  
A. Montanari
Keyword(s):  
Frequency Distribution ◽  
River Flow ◽  
Spatial Scales ◽  
Flood Frequency ◽  
Rainfall Runoff ◽  
River Flows ◽  
Wide Range ◽  
Spatially Distributed ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. The estimation of the peak river flow for ungauged river sections is a topical issue in applied hydrology. Spatially distributed rainfall-runoff models can be a useful tool to this end, since they are potentially able to simulate the river flow at any location of the watershed drainage network. However, it is not fully clear to what extent these models can provide reliable simulations over a wide range of spatial scales. This issue is investigated here by applying a spatially distributed, continuous simulation rainfall-runoff model to infer the flood frequency distribution of the Riarbero River. This is an ungauged mountain creek located in northern Italy, whose drainage area is 17 km2. The hydrological model is first calibrated by using a 1-year record of hourly meteorological data and river flows observed at the outlet of the 1294 km2 wide Secchia River basin, of which the Riarbero is a tributary. The model is then validated by performing a 100-year long simulation of synthetic river flow data, which allowed us to compare the simulated and observed flood frequency distributions at the Secchia River outlet and the internal cross river section of Cavola Bridge, where the basin area is 337 km2. Finally, another simulation of hourly river flows was performed by referring to the outlet of the Riarbero River, therefore allowing us to estimate the related flood frequency distribution. The results were validated by using estimates of peak river flow obtained by applying hydrological similarity principles and a regional method. The results show that the flood flow estimated through the application of the distributed model is consistent with the estimate provided by the regional procedure as well as the behaviors of the river banks. Conversely, the method based on hydrological similarity delivers an estimate that seems to be not as reliable. The analysis highlights interesting perspectives for the application of spatially distributed models to ungauged catchments.

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