scholarly journals Can a Calibration-Free Dynamic Rainfall‒Runoff Model Predict FDCs in Data-Scarce Regions? Comparing the IDW Model with the Dynamic Budyko Model in South India

Hydrology ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 32 ◽  
Author(s):  
Nag ◽  
Biswal
Keyword(s):  
Model Performance ◽  
Data Availability ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
Discharge Data ◽  
Study Region ◽  
Calibration Free ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Free Dynamic

Construction of flow duration curves (FDCs) is a challenge for hydrologists as most streams and rivers worldwide are ungauged. Regionalization methods are commonly followed to solve the problem of discharge data scarcity by transforming hydrological information from gauged basins to ungauged basins. As a consequence, regionalization-based FDC predictions are not very reliable where discharge data are scarce quantitatively and/or qualitatively. In such a scenario, it is perhaps more meaningful to use a calibration-free rainfall‒runoff model that can exploit easily available meteorological information to predict FDCs in ungauged basins. This hypothesis is tested in this study by comparing a well-known regionalization-based model, the inverse distance weighting (IDW) model, with the recently proposed calibration-free dynamic Budyko model (DB) in a region where discharge observations are not only insufficient quantitatively but also show apparent signs of observational errors. The DB model markedly outperformed the IDW model in the study region. Furthermore, the IDW model’s performance sharply declined when we randomly removed discharge gauging stations to test the model in a variety of data availability scenarios. The analysis here also throws some light on how errors in observational datasets and drainage area influence model performance and thus provides a better picture of the relative strengths of the two models. Overall, the results of this study support the notion that a calibration-free rainfall‒runoff model can be chosen to predict FDCs in discharge data-scarce regions. On a philosophical note, our study highlights the importance of process understanding for the development of meaningful hydrological models.

scholarly journals Conceptual Rainfall–Runoff Model Performance with Different Spatial Rainfall Inputs

2011 ◽  
Vol 12 (5) ◽  
pp. 1100-1112 ◽  
Author(s):  
J. Vaze ◽  
D. A. Post ◽  
F. H. S. Chiew ◽  
J.-M. Perraud ◽  
J. Teng ◽  
...  
Keyword(s):  
Model Simulation ◽  
Model Performance ◽  
Daily Rainfall ◽  
Grid Cell ◽  
Rainfall Series ◽  
Data Availability ◽  
Rainfall Time Series ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract Different methods have been used to obtain the daily rainfall time series required to drive conceptual rainfall–runoff models, depending on data availability, time constraints, and modeling objectives. This paper investigates the implications of different rainfall inputs on the calibration and simulation of 4 rainfall–runoff models using data from 240 catchments across southeast Australia. The first modeling experiment compares results from using a single lumped daily rainfall series for each catchment obtained from three methods: single rainfall station, Thiessen average, and average of interpolated rainfall surface. The results indicate considerable improvements in the modeled daily runoff and mean annual runoff in the model calibration and model simulation over an independent test period with better spatial representation of rainfall. The second experiment compares modeling using a single lumped daily rainfall series and modeling in all grid cells within a catchment using different rainfall inputs for each grid cell. The results show only marginal improvement in the “distributed” application compared to the single rainfall series, and only in two of the four models for the larger catchments. Where a single lumped catchment-average daily rainfall series is used, care should be taken to obtain a rainfall series that best represents the spatial rainfall distribution across the catchment. However, there is little advantage in driving a conceptual rainfall–runoff model with different rainfall inputs from different parts of the catchment compared to using a single lumped rainfall series, where only estimates of runoff at the catchment outlet is required.

scholarly journals The HYDROMED model and its application to semi-arid Mediterranean catchments with hill reservoirs 2: Rainfall-runoff model applications to three Mediterranean hill reservoirs

2001 ◽  
Vol 5 (4) ◽  
pp. 554-562 ◽  
Author(s):  
R. Ragab ◽  
D. Moidinis ◽  
J. Albergel ◽  
J. Khouri ◽  
A. Drubi ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Model Performance ◽  
Limited Information ◽  
Rainfall Runoff ◽  
Significant Events ◽  
Reliable Parameter ◽  
Rainfall Runoff Model ◽  
Parameter Values ◽  
Runoff Model

Abstract. The objective of this work was to assess the performance of the newly developed HYDROMED model. Three catchments with hill reservoirs were selected. They are El-Gouazine and Kamech in Tunisia and Es Sindiany in Syria. The rainfall, the spillway flow and volume of water in the reservoirs were used as input to the model. Events that generated spillway flow were preferred for calibration. The results confirmed that the HYDROMED model is capable of reproducing the runoff volume at all the three sites. In calibrating single events, the model performance was high as measured by the Nash-Sutcliffe criterion for goodness of fit. In some events this value was as high as 98%. In simulation mode, the highest Nash-Sutcliffe criterion value was close to 70% in the El-Gouazine and Kamech catchments and close to 50% in the Es Sindiany catchment. Given the limited information available, especially on the unrecorded releases in the three catchments, the hydrological impact of site geology (e.g. Kamech), the unrecorded operator intervention during the spillway flow (e.g. Es Sindiany) and other unaccounted factors (e.g siltation, evaporation, etc.), these results are by and large very encouraging. However, they could be further improved as and when more information on the unrecorded parameters becomes available. Additionally, the results of this work highlighted the need for long term records with a large number of significant events that are able to generate spillway flow to obtain more consistent and reliable parameter values. It also highlights the need for more accurately recorded releases for irrigation and other uses. As these results are encouraging, more tests on those three and other sites are planned. Keywords: HYDROMED, rainfall-runoff model, Mediterranean, conceptual model

Exploring signatures-based calibration of hydrological models for prediction in ungauged basins.

2020 ◽  
Author(s):  
Marco Dal Molin ◽  
Dmitri Kavetski ◽  
Mario Schirmer ◽  
Fabrizio Fenicia
Keyword(s):  
Time Series ◽  
Accurate Estimation ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
Multiple Sources ◽  
Important Challenge ◽  
Rainfall Runoff Model ◽  
Approximate Bayesian ◽  
Correct Estimation ◽  
Runoff Model

<p>One of the open challenges in catchment hydrology is prediction in ungauged basins (PUB), i.e. being able to predict catchment responses (typically streamflow) when measurements are not available. One of the possible approaches to this problem consists in calibrating a model using catchment response statistics (called signatures) that can be estimated at the ungauged site.<br>An important challenge of any approach to PUB is to produce reliable and precise predictions of catchment response, with an accurate estimation of the uncertainty. In the context of PUB through calibration on regionalized streamflow signatures, there are multiple sources of uncertainty that affect streamflow predictions, which relate to:</p><ul><li>The use streamflow signatures, which, by synthetizing the underlying time series, reduce the information available for model calibration;</li> <li>The regionalization of streamflow signatures, which are not observed, but estimated through some signature regionalization model;</li> <li>The use of a rainfall-runoff model, which carries uncertainties related to input data, parameter values, and model structure.</li> </ul><p>This study proposes an approach that separately accounts for the uncertainty related to the regionalization of the signatures from the other types; the implementation uses Approximate Bayesian Computation (ABC) to infer the parameters of the rainfall-runoff model using stochastic streamflow signatures. <br>The methodology is tested in six sub-catchments of the Thur catchment in Switzerland; results show that the regionalized model produces streamflow time series that are similar to the ones obtained by the classical time-domain calibration, with slightly higher uncertainty but similar fit to the observed data. These results support the proposed approach as a viable method for PUB, with a focus on the correct estimation of the uncertainty.</p>

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.

scholarly journals IMPLICATION OF SOM-ANN BASED CLUSTERING FOR MULTI-STATION RAINFALL-RUNOFF MODELING

2015 ◽  
pp. 198-210
Author(s):  
Vahid Nourani ◽  
Masoud Mehrvand ◽  
Aida Hosseini Baghanam
Keyword(s):  
Stream Flow ◽  
Precipitation Event ◽  
Time Lags ◽  
Self Organizing Map ◽  
Rainfall Runoff ◽  
Ann Model ◽  
Discharge Data ◽  
Runoff Modeling ◽  
Rainfall Runoff Model ◽  
Runoff Model

In this study the performance of ANN with feed-forward neural network (FFNN) algorithm evaluated rainfall-runoff modeling in five gauging stations in Florida State. In addition, for investigating the performance of ANN in multi-station discharge prediction, self-organizing map (SOM) clustering tool employed in order to cluster the input data with similar patterns, due to the large amount of records in multiple stations. The main aim of study is to investigate capability and accuracy of ANN based methods in multi-station discharge prediction. In order to consider multiple stations effect on watershed outlet discharge, different combinations for precipitation and discharge data of all stations with antecedent values over the watershed have been taken into account. In this way, application of the representatives from each cluster led to significantly reduction in the numbers of the input variables so that the optimal ANN structure could be proposed. Therefore, ANN as a data-driven model was trained to predict daily runoff for the Peace River basin via recorded values from July 1995 to July 2011. Three scenarios conducted the aim of research; first scenario was an integrated ANN model trained by the data of rainfall and runoff at multiple stations. The second scenario was a sequential ANN model processed with upstream discharge records in addition to rainfall data as inputs and downstream discharge values as target. Finally, third scenario was a SOM-ANN model, in which rainfall and runoff data were clustered according the homogeneity of data via (SOM). The center of each cluster as the dominant component of each cluster was imposed to ANN in order to present an optimal rainfall-runoff model over the watershed. In all scenarios, different data sets at various time lags in both rainfall and stream flow data were applied as inputs in ANN-based model to predict stream flow. Results show that ANN model coupled with SOM is useful tools for forecasting multi-station discharge and precipitation event response in the watershed. Furthermore, the comparison of scenarios leads to select the most efficient and optimal inputs to ANN which subsequently, presents the optimal multi-station rainfall-runoff model over the watershed.

scholarly journals Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria

2013 ◽  
Vol 10 (1) ◽  
pp. 449-485 ◽  
Author(s):  
A. Viglione ◽  
J. Parajka ◽  
M. Rogger ◽  
J. L. Salinas ◽  
G. Laaha ◽  
...  
Keyword(s):  
Predictive Performance ◽  
Comparative Assessment ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
Low Flows ◽  
High Flows ◽  
Seasonal Runoff ◽  
Rainfall Runoff Model ◽  
Predictions In Ungauged Basins ◽  
Runoff Model

Abstract. In a three-part paper we assess the performance of runoff predictions in ungauged basins in a comparative way. While Parajka et al. (2013) and Salinas et al. (2013) assess the regionalisation of hydrographs and hydrological extremes through a literature review, in this paper we assess prediction of a range of runoff signatures for a consistent dataset. Daily runoff time series are predicted for 213 catchments in Austria by a regionalised rainfall–runoff model and by Top-Kriging, a geostatistical interpolation method that accounts for the river network hierarchy. From the runoff timeseries, six runoff signatures are extracted: annual runoff, seasonal runoff, flow duration curves, low flows, high flows and runoff hydrograph. The predictive performance is assessed by the bias, error spread and proportion of unexplained spatial variance of statistical measures of these signatures in cross-validation mode. Results of the comparative assessment show that the geostatistical approach (Top-Kriging) generally outperforms the regionalised rainfall–runoff model. The predictive performance increases with catchment area for both methods and all signatures, while the dependence on climate characteristics is weaker. Annual and seasonal runoff can be predicted more accurately than all other signatures. The spatial variability of high flows is the most difficult to capture followed by the low flows. The relative predictive performance of the signatures depends on the selected performance measures. It is therefore essential to report performance in a consistent way by more than one performance measure.

scholarly journals Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria

2013 ◽  
Vol 17 (6) ◽  
pp. 2263-2279 ◽  
Author(s):  
A. Viglione ◽  
J. Parajka ◽  
M. Rogger ◽  
J. L. Salinas ◽  
G. Laaha ◽  
...  
Keyword(s):  
Time Series ◽  
Predictive Performance ◽  
Comparative Assessment ◽  
Ungauged Basins ◽  
Rainfall Runoff ◽  
High Flows ◽  
Seasonal Runoff ◽  
Rainfall Runoff Model ◽  
Predictions In Ungauged Basins ◽  
Runoff Model

Abstract. This is the third of a three-part paper series through which we assess the performance of runoff predictions in ungauged basins in a comparative way. Whereas the two previous papers by Parajka et al. (2013) and Salinas et al. (2013) assess the regionalisation performance of hydrographs and hydrological extremes on the basis of a comprehensive literature review of thousands of case studies around the world, in this paper we jointly assess prediction performance of a range of runoff signatures for a consistent and rich dataset. Daily runoff time series are predicted for 213 catchments in Austria by a regionalised rainfall–runoff model and by Top-kriging, a geostatistical estimation method that accounts for the river network hierarchy. From the runoff time-series, six runoff signatures are extracted: annual runoff, seasonal runoff, flow duration curves, low flows, high flows and runoff hydrographs. The predictive performance is assessed in terms of the bias, error spread and proportion of unexplained spatial variance of statistical measures of these signatures in cross-validation (blind testing) mode. Results of the comparative assessment show that, in Austria, the predictive performance increases with catchment area for both methods and for most signatures, it tends to increase with elevation for the regionalised rainfall–runoff model, while the dependence on climate characteristics is weaker. Annual and seasonal runoff can be predicted more accurately than all other signatures. The spatial variability of high flows in ungauged basins is the most difficult to estimate followed by the low flows. It also turns out that in this data-rich study in Austria, the geostatistical approach (Top-kriging) generally outperforms the regionalised rainfall–runoff model.

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