Estimating rainfall-runoff model parameters using the iterative ensemble smoother

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.

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Collinearity and stability in the estimation of rainfall-runoff model parameters

Journal of Hydrology ◽

10.1016/0022-1694(79)90008-8 ◽

1979 ◽

Vol 42 (1-2) ◽

pp. 91-108 ◽

Cited By ~ 14

Author(s):

Peter K. Kitanidis ◽

Rafael L. Bras

Keyword(s):

Model Parameters ◽

Rainfall Runoff ◽

Rainfall Runoff Model ◽

Runoff Model

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Analysis of parameter uncertainty of a flow and quality stormwater model

Water Science & Technology ◽

10.2166/wst.2009.434 ◽

2009 ◽

Vol 60 (3) ◽

pp. 717-725 ◽

Cited By ~ 18

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.

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

Soil Research ◽

10.1071/sr9820015 ◽

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.

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Calibration of hydrological model parameters for ungauged catchments

Hydrology and Earth System Sciences ◽

10.5194/hess-11-703-2007 ◽

2007 ◽

Vol 11 (2) ◽

pp. 703-710 ◽

Cited By ~ 223

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.

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

10.5194/hess-2020-38 ◽

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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Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-5389-2015 ◽

2015 ◽

Vol 12 (6) ◽

pp. 5389-5426 ◽

Cited By ~ 3

Author(s):

S. Almeida ◽

N. Le Vine ◽

N. McIntyre ◽

T. Wagener ◽

W. Buytaert

Keyword(s):

Joint Probability ◽

Joint Probability Distribution ◽

Model Parameters ◽

Rainfall Runoff ◽

Ungauged Catchments ◽

Independent Information ◽

Streamflow Data ◽

Rainfall Runoff Model ◽

Runoff Model ◽

Selection Of

Abstract. A recurrent problem in hydrology is the absence of streamflow data to calibrate rainfall-runoff models. A commonly used approach in such circumstances conditions model parameters on regionalized response signatures. While several different signatures are often available to be included in this process, an outstanding challenge is the selection of signatures that provide useful and complementary information. Different signatures do not necessarily provide independent information, and this has led to signatures being omitted or included on a subjective basis. This paper presents a method that accounts for the inter-signature error correlation structure so that regional information is neither neglected nor double-counted when multiple signatures are included. Using 84 catchments from the MOPEX database, observed signatures are regressed against physical and climatic catchment attributes. The derived relationships are then utilized to assess the joint probability distribution of the signature regionalization errors that is subsequently used in a Bayesian procedure to condition a rainfall-runoff model. The results show that the consideration of the inter-signature error structure may improve predictions when the error correlations are strong. However, other uncertainties such as model structure and observational error may outweigh the importance of these correlations. Further, these other uncertainties cause some signatures to appear repeatedly to be disinformative.

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Uncertainty and sensitivity analysis of GIS based continuous hydrological modelling

10.32920/ryerson.14643702.v1 ◽

2021 ◽

Author(s):

Harry R. Manson

Keyword(s):

Sensitivity Analysis ◽

Curve Number ◽

Monte Carlo Analysis ◽

Model Parameters ◽

Rainfall Runoff ◽

Lumped Model ◽

Daily Average ◽

Rainfall Runoff Model ◽

The Impact ◽

Runoff Model

The impact of uncertainty in spatial and a-spatial lumped model parameters for a continuous rainfall-runoff model is evaluated with respect to model prediction. The model uses a modified SCS-Curve Number approach that is loosely coupled with a geographic information system (GIS). The rainfall-runoff model uses daily average inputs and is calibrated using a daily average streamflow record for the study site. A Monte Carlo analysis is used to identify total model uncertainty while sensitivity analysis is applied using both a one-at-a-time (OAT) approach as well as through application of the extended Fourier Amplitude Sensitivity Technique (FAST). Conclusions suggest that the model is highly followed by model inputs and finally the Curve Number. While the model does not indicate a high degree of sensitivity to the Curve Number at present conditions, uncertainties in Curve Number estimation can potentially be the cause of high predictive errors when future development scenarios are evaluated.

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Using NDII pattern for a semi-distributed rainfall-runoff model in tropical nested catchments

10.5194/hess-2020-82 ◽

2020 ◽

Author(s):

Nutchanart Sriwongsitanon ◽

Wasana Jandang ◽

Thienchart Suwawong ◽

Hubert H.~G. Savenije

Keyword(s):

Spatial Distribution ◽

Root Zone ◽

Lag Time ◽

Model Parameters ◽

Model Structure ◽

Rainfall Runoff ◽

Moisture Storage ◽

Rainfall Runoff Model ◽

Runoff Model ◽

Better Than

Abstract. A parsimonious semi-distributed rainfall-runoff model has been developed for flow prediction. In distribution, attention is paid to both timing of runoff and heterogeneity of moisture storage capacities within sub-catchments. This model is based on the lumped FLEXL model structure, which has proven its value in a wide range of catchments. To test the value of distribution, the gauged Upper Ping catchment in Thailand has been divided into 10 sub-catchments, which can be grouped into 5 gauged sub-catchments where internal performance is evaluated. To test the effect of timing, firstly excess rainfall was calculated for each sub-catchment, using the model structure of FLEXL. The excess rainfall was then routed to its outlet using the lag time from storm to peak flow (TlagF) and the lag time of recharge from the root zone to the groundwater (TlagS), as a function of catchment size. Subsequently, the Muskingum equation was used to route sub-catchment runoff to the downstream sub-catchment, before adding to runoff of the downstream sub-catchment, with the delay time parameter of the Muskingum equation being a function of channel length. Other model parameters of this semi-distributed FLEX-SD model were kept the same as in the calibrated FLEXL model of the entire Upper Ping basin, controlled by station P.1 located at the centre of Chiang Mai Province. The outcome of FLEX-SD was compared to: 1) observations at P.1; 2) the results of the calibrated FLEXL model; and 3) the semi-distributed URBS model - another established semi-distributed rainfall-runoff model. FLEX-SD showed better performance than URBS, but a bit lower than the calibrated FLEXL model with NSE of 0.74, 0.71, and 0.76, respectively. Subsequently, at the level of the gauged internal sub-catchments, runoff estimates of FLEX-SD were compared to observations and calibrated FLEXL model results. The results demonstrate that FLEX-SD provides more accurate runoff estimates at P.1, P.67 and P.75 stations which are located along the main Ping River, compared to those provided by the lumped calibrated FLEXL model. The results were less good at 2 tributary stations (P.20 and P.21), where calibrated FLEXL output performed better, while performance was similar at one tributary station (P.4A). Overall, FLEX-SD performed better than URBS at 5 out of 6 stations except at P.21. Subsequently, the effect of distributing moisture storage capacity was tested. Since the FLEX-SD uses the same Sumax value - the maximum moisture holding capacity of the root zone - for all sub-catchments, FLEX-SD-NDII was set-up making use of the spatial distribution of the NDII (the normalized difference infrared index). The readily available NDII appears to be a good proxy for moisture stress in the root zone, particularly during dry periods. The maximum moisture holding capacity in the root zone assumed to be a function of the maximum seasonal range of NDII values. The spatial distribution of this range among sub-catchments was used to calibrate the semi-distributed FLEX-SD-NDII model. The additional constraint by the NDII improved the performance of the model and the realism of the distribution. To test how well the model represents root zone soil moisture, the performance of the FLEX-SD-NDII model was compared to time series of the soil wetness index (SWI). The correlation between the root zone storage and the daily SWI appeared to be very good, even better than the correlation with the NDII, because NDII does not provide good estimates during wet periods. The SWI, which is partly model-based, was not used for calibration, but appeared to be an appropriate index for verification.

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Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

Hydrology and Earth System Sciences ◽

10.5194/hess-20-887-2016 ◽

2016 ◽

Vol 20 (2) ◽

pp. 887-901 ◽

Cited By ~ 6

Author(s):

Susana Almeida ◽

Nataliya Le Vine ◽

Neil McIntyre ◽

Thorsten Wagener ◽

Wouter Buytaert

Keyword(s):

Joint Probability ◽

Joint Probability Distribution ◽

Model Parameters ◽

Rainfall Runoff ◽

Ungauged Catchments ◽

Independent Information ◽

Streamflow Data ◽

Rainfall Runoff Model ◽

Runoff Model ◽

Selection Of

Abstract. A recurrent problem in hydrology is the absence of streamflow data to calibrate rainfall–runoff models. A commonly used approach in such circumstances conditions model parameters on regionalized response signatures. While several different signatures are often available to be included in this process, an outstanding challenge is the selection of signatures that provide useful and complementary information. Different signatures do not necessarily provide independent information and this has led to signatures being omitted or included on a subjective basis. This paper presents a method that accounts for the inter-signature error correlation structure so that regional information is neither neglected nor double-counted when multiple signatures are included. Using 84 catchments from the MOPEX database, observed signatures are regressed against physical and climatic catchment attributes. The derived relationships are then utilized to assess the joint probability distribution of the signature regionalization errors that is subsequently used in a Bayesian procedure to condition a rainfall–runoff model. The results show that the consideration of the inter-signature error structure may improve predictions when the error correlations are strong. However, other uncertainties such as model structure and observational error may outweigh the importance of these correlations. Further, these other uncertainties cause some signatures to appear repeatedly to be misinformative.

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