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 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 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 Regionalizing rainfall–runoff model parameters to predict the daily streamflow of ungauged catchments in the dry tropics

2009 ◽  
Vol 40 (5) ◽  
pp. 433-444 ◽  
Author(s):  
David A. Post
Keyword(s):  
Input Data ◽  
Water Yield ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Wet Season ◽  
Daily Streamflow ◽  
Total Length ◽  
Dry Tropics ◽  
Rainfall Runoff Model ◽  
Runoff Model

A methodology has been derived which allows an estimate to be made of the daily streamflow at any point within the Burdekin catchment in the dry tropics of Australia. The input data requirements are daily rainfall (to drive the rainfall–runoff model) and mean average wet season rainfall, total length of streams, percent cropping and percent forest in the catchment (to regionalize the parameters of the rainfall–runoff model). The method is based on the use of a simple, lumped parameter rainfall–runoff model, IHACRES (Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data). Of the five parameters in the model, three have been set to constants to reflect regional conditions while the other two have been related to physio-climatic attributes of the catchment under consideration. The parameter defining total catchment water yield (c) has been estimated based on the mean average wet season rainfall, while the streamflow recession time constant (τ) has been estimated based on the total length of streams, percent cropping and percent forest in the catchment. These relationships have been shown to be applicable over a range of scales from 68–130,146 km2. However, three separate relationships were required to define c in the three major physiographic regions of the Burdekin: the upper Burdekin, Bowen and Suttor/lower Burdekin. The invariance of the relationships with scale indicates that the dominant processes may be similar across a range of scales. The fact that different relationships were required for each of the three major regions indicates the geographic limitations of this regionalization approach. For most of the 24 gauged catchments within the Burdekin the regionalized rainfall–runoff models were nearly as good as or better than the rainfall–runoff models calibrated to the observed streamflow. In addition, models often performed better over the simulation period than the calibration period. This indicates that future improvements in regionalization should focus on improving the quality of input data and rainfall–runoff model conceptualization rather than on the regionalization procedure per se.

Analysis of parameter uncertainty of a flow and quality stormwater model

2009 ◽  
Vol 60 (3) ◽  
pp. 717-725 ◽  
Author(s):  
C. B. S. Dotto ◽  
A. Deletic ◽  
T. D. Fletcher
Keyword(s):  
Water Quality ◽  
Bayesian Methods ◽  
Flow Model ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Sources Of Uncertainty ◽  
Music Model ◽  
Rainfall Runoff Model ◽  
Water Quality Models ◽  
Runoff Model

Uncertainty is intrinsic to all monitoring programs and all models. It cannot realistically be eliminated, but it is necessary to understand the sources of uncertainty, and their consequences on models and decisions. The aim of this paper is to evaluate uncertainty in a flow and water quality stormwater model, due to the model parameters and the availability of data for calibration and validation of the flow model. The MUSIC model, widely used in Australian stormwater practice, has been investigated. Frequentist and Bayesian methods were used for calibration and sensitivity analysis, respectively. It was found that out of 13 calibration parameters of the rainfall/runoff model, only two matter (the model results were not sensitive to the other 11). This suggests that the model can be simplified without losing its accuracy. The evaluation of the water quality models proved to be much more difficult. For the specific catchment and model tested, we argue that for rainfall/runoff, 6 months of data for calibration and 6 months of data for validation are required to produce reliable predictions. Further work is needed to make similar recommendations for modelling water quality.

Effect of rainfall data errors on the optimized values of model parameters

Soil Research ◽  
1982 ◽  
Vol 20 (1) ◽  
pp. 15
Author(s):  
WC Boughton ◽  
FT Sefe
Keyword(s):  
Storage Capacity ◽  
Rainfall Data ◽  
Model Parameters ◽  
Similar Magnitude ◽  
Rainfall Runoff ◽  
Data Errors ◽  
Cast Doubt ◽  
Rainfall Runoff Model ◽  
Induced Changes ◽  
Runoff Model

The rainfall input to a rainfall-runoff model was arbitrarily increased and decreased in order to determine the magnitude of corresponding changes in optimized values of the model parameters. The optimized capacities of moisture stores representing surface storage capacity of a catchment changed by average amounts of +24% and -20% as rainfall input was changed by +10% and -10%, respectively. Values of other parameters showed changes of similar magnitude, but there was no uniformity in the magnitude of induced changes from catchment to catchment. The results cast doubt on the validity of relating optimized values of model parameters to physical characteristics of catchments.

scholarly journals Calibration of hydrological model parameters for ungauged catchments

2007 ◽  
Vol 11 (2) ◽  
pp. 703-710 ◽  
Author(s):  
A. Bárdossy
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Model Parameters ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Rainfall Runoff Model ◽  
Climate Statistics ◽  
Runoff Model ◽  
German Part

Abstract. The parameters of hydrological models for catchments with few or no discharge records can be estimated using regional information. One can assume that catchments with similar characteristics show a similar hydrological behaviour and thus can be modeled using similar model parameters. Therefore a regionalisation of the hydrological model parameters on the basis of catchment characteristics is plausible. However, due to the non-uniqueness of the rainfall-runoff model parameters (equifinality), a workflow of regional parameter estimation by model calibration and a subsequent fit of a regional function is not appropriate. In this paper a different approach for the transfer of entire parameter sets from one catchment to another is discussed. Parameter sets are considered as tranferable if the corresponding model performance (defined as the Nash-Sutclife efficiency) on the donor catchment is good and the regional statistics: means and variances of annual discharges estimated from catchment properties and annual climate statistics for the recipient catchment are well reproduced by the model. The methodology is applied to a set of 16 catchments in the German part of the Rhine catchments. Results show that the parameters transfered according to the above criteria perform well on the target catchments.

scholarly journals Importance of the information content in the study area when regionalising rainfall-runoff model parameters: the role of nested catchments and gauging station density

2020 ◽  
Author(s):  
Mattia Neri ◽  
Juraj Parajka ◽  
Elena Toth
Keyword(s):  
Information Content ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Data Set ◽  
Study Region ◽  
Averaging Methods ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Station Density ◽  
Averaging Techniques

Abstract. The set up of a rainfall-runoff model in a river section where no streamflow measurements are available for its calibration is one of the key research activity for the Prediction in Ungauged Basins (PUB): in order to do so it is possible to regionalise the model parameters based on the information available in gauged sections in the study region. The information content in the data set of gauged river stations plays an essential role in the assessment of the best regionalisation method: this study analyses how the performances of different model regionalisation approaches are influenced by the information richness of the available regional data set, and in particular by its gauging density and by the presence of nested catchments, that are expected to be hydrologically very similar. The research is carried out over a densely gauged dataset covering the Austrian country, applying two different rainfall-runoff models: a semi-distributed version of the HBV model (TUW model), and the Cemaneige-GR6J model. The regionalisation approaches include both methods which transfer the entire set of model parameters from donor catchments, thus maintaining correlation among parameters (output averaging techniques), and methods which derive each target parameter independently, as a function of the calibrated donors’ ones (parameter averaging techniques). The regionalisation techniques are first implemented using all the basins in the dataset as potential donors, showing that the output-averaging methods outperform the parameter-averaging kriging method, highlighting the importance of maintaining the correlation between the parameter values. The regionalisation is then repeated decreasing the information content of the data set, by excluding the nested basins, identified taking into account either the position of the closing section along the river or the percentage of shared drainage area. The parameter-averaging kriging is the method that is less impacted by the exclusion of the nested donors, whereas the methods transferring the entire parameter set from only one donor suffer the highest deterioration, since the single most similar or closest donor is often a nested one. On the other hand, the output-averaging methods degrade more gracefully, showing that exploiting the information resulting from more than one donor increases the robustness of the approach also in regions that do not have so many nested catchments as the Austrian one. Finally, the deterioration resulting from decreasing the station density on the regionalisation was analysed, showing that the output averaging methods using as similarity measure a set of catchment descriptors, rather than the geographical distance, are more capable to adapt to less dense datasets. The study confirms how the predictive accuracy of parameter regionalisation techniques strongly depends on the information content of the dataset of available donor catchments and indicates that the output-averaging approaches, using more than one donor basin but preserving the correlation structure of the parameter set, seem to be preferable for regionalisation purposes in both data-poor and data-rich regions.

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