scholarly journals Parsimonious rainfall-runoff model construction supported by time series processing and validation of hydrological extremes – Part 2: Intercomparison of models and calibration approaches

2014 ◽  
Vol 510 ◽  
pp. 591-609 ◽  
Author(s):  
Patrick Willems ◽  
Diego Mora ◽  
Thomas Vansteenkiste ◽  
Meron Teferi Taye ◽  
Niels Van Steenbergen
Keyword(s):  
Time Series ◽  
Model Construction ◽  
Rainfall Runoff ◽  
Hydrological Extremes ◽  
Rainfall Runoff Model ◽  
Runoff 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 A coupled stochastic rainfall-evapotranspiration model for hydrological impact analysis

2017 ◽  
Author(s):  
Minh Tu Pham ◽  
Hilde Vernieuwe ◽  
Bernard De Baets ◽  
Niko E. C. Verhoest
Keyword(s):  
Time Series ◽  
Impact Analysis ◽  
Hydrological Model ◽  
Hydrological Cycle ◽  
Rainfall Runoff ◽  
Hydrological Impact ◽  
Long Time ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract. A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such exercise, discharge is often considered, as especially extreme high discharges often cause damage due to the coinciding floods. Investigating extreme discharges generally requires long time series of precipitation and evapotranspiration that are used to force a rainfall-runoff model. However, such kind of data may not be available and one should resort to stochastically-generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events is not well studied. In this paper, stochastically-generated rainfall and coinciding evapotranspiration time series are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically-generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall-evapotranspiration model has a large potential for hydrological impact analysis.

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.

scholarly journals A coupled stochastic rainfall–evapotranspiration model for hydrological impact analysis

2018 ◽  
Vol 22 (2) ◽  
pp. 1263-1283 ◽  
Author(s):  
Minh Tu Pham ◽  
Hilde Vernieuwe ◽  
Bernard De Baets ◽  
Niko E. C. Verhoest
Keyword(s):  
Time Series ◽  
Impact Analysis ◽  
Hydrological Cycle ◽  
Rainfall Runoff ◽  
Hydrological Impact ◽  
Long Time ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Vine Copulas ◽  
Runoff Model

Abstract. A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such an exercise, discharge is often considered, as floods originating from extremely high discharges often cause damage. Investigating the impact of extreme discharges generally requires long time series of precipitation and evapotranspiration to be used to force a rainfall-runoff model. However, such kinds of data may not be available and one should resort to stochastically generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events, is not well studied. In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall–evapotranspiration model has great potential for hydrological impact analysis.

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 Step wise Multi-criteria Performance Evaluation of Rainfall-Runoff Models using WETSPRO

1970 ◽  
Vol 7 (1) ◽  
pp. 18-29 ◽  
Author(s):  
PC Shakti ◽  
NK Shrestha ◽  
P Gurung
Keyword(s):  
Time Series ◽  
Performance Evaluation ◽  
Assessment Tool ◽  
Model Performance ◽  
Rainfall Runoff ◽  
Peak Flows ◽  
Low Flows ◽  
Flow Components ◽  
Rainfall Runoff Model ◽  
Runoff Model

This paper illustrates a methodology to evaluate model’s performance of rainfall runoff model using a tool called WETSPRO (Water Engineering Time Series PROcessing tool). Simulated results of physically based semidistributed model - SWAT (Soil and Water Assessment Tool) for Kliene Nete watershed (581 km2), Belgium are considered in this study. Paper presents a series of sequential time series processing tasks to be performed to evaluate model’s performance thoroughly. The problem of serial dependence and heteroscedasticity is addressed and model performance evaluation on different flow components (peak flows, low flows and volume) and flow volume is carried. Performance evaluation of both flow components on their extremes is also performed. Two most commonly used goodness-fit-statistics (Mean Square Error – MSE and Nash Sutcliff Efficiency − NSE) are used with number of complementary graphical plots for evaluation propose. Results indicated model’s robust performance on peak flows although base flows are slightly underestimated especially for lower return periods. Cumulative flow volumes tend to be overestimated. Based upon the study, some recommendations are summarized to enhance model’s ability to simulate the flows events. Keywords: Rainfall runoff model; SWAT; WETSPRO; Kliene Nete; peak flows; low flows. DOI: http://dx.doi.org/10.3126/jhm.v7i1.5613 JHM 2010; 7(1): 18-29

scholarly journals Neural networks and non-parametric methods for improving real-time flood forecasting through conceptual hydrological models

2002 ◽  
Vol 6 (4) ◽  
pp. 627-639 ◽  
Author(s):  
A. Brath ◽  
A. Montanari ◽  
E. Toth
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Time Series Analysis ◽  
Real Time ◽  
Rainfall Runoff ◽  
Rainfall Forecasting ◽  
Short Term ◽  
Analysis Techniques ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. Time-series analysis techniques for improving the real-time flood forecasts issued by a deterministic lumped rainfall-runoff model are presented. Such techniques are applied for forecasting the short-term future rainfall to be used as real-time input in a rainfall-runoff model and for updating the discharge predictions provided by the model. Along with traditional linear stochastic models, both stationary (ARMA) and non-stationary (ARIMA), the application of non-linear time-series models is proposed such as Artificial Neural Networks (ANNs) and the ‘nearest-neighbours’ method, which is a non-parametric regression methodology. For both rainfall forecasting and discharge updating, the implementation of each time-series technique is investigated and the forecasting schemes which perform best are identified. The performances of the models are then compared and the improvement in the efficiency of the discharge forecasts achievable is demonstrated when i) short-term rainfall forecasting is performed, ii) the discharge is updated and iii) both rainfall forecasting and discharge updating are performed in cascade. The proposed techniques, especially those based on ANNs, allow a remarkable improvement in the discharge forecast, compared with the use of heuristic rainfall prediction approaches or the not-updated discharge forecasts given by the deterministic rainfall-runoff model alone. Keywords: real-time flood forecasting, precipitation prediction, discharge updating, time-series analysis techniques

scholarly journals Investigation of variable threshold level approaches for hydrological drought identification

2014 ◽  
Vol 11 (11) ◽  
pp. 12765-12797 ◽  
Author(s):  
B. S. Beyene ◽  
A. F. Van Loon ◽  
H. A. J. Van Lanen ◽  
P. J. J. F. Torfs
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Threshold Level ◽  
Snow Melt ◽  
Rainfall Runoff ◽  
Catchment Scale ◽  
Climate Conditions ◽  
Variable Threshold ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. Threshold level approaches are widely used to identify drought events in time series of hydrometeorological variables. However, the method used for calculating the threshold level can influence the quantification of drought events or even introduce artefact drought events. In this study, four methods of variable threshold calculation have been tested on catchment scale, namely (1) moving average of monthly quantile (M_MA), (2) moving average of daily quantile (D_MA), (3) thirty days moving window quantile (30D) and (4) fast Fourier transform of daily quantile (D_FF). The levels obtained by these methods were applied to hydrometeorological variables that were simulated with a semi-distributed conceptual rainfall-runoff model (HBV) for five European catchments with contrasting catchment properties and climate conditions. There are no physical arguments to prefer one method over the other for drought identification. The only way to investigate this is by applying the methods and visually inspecting the results. Therefore, drought statistics (i.e. number of droughts, mean duration, mean deficit) and time series plots were studied to compare drought propagation patterns determined by different threshold calculation methods. We found that all four approaches are sufficiently suitable to quantify drought propagation in contrasting catchments. Only the D_FF approach showed lower performance in two catchments. The 30D approach seems to be optimal in snow-dominated catchments, because it follows fast changes in discharge caused by snow melt more accurately. The proposed approaches can be successfully applied by water managers in regions where drought quantification and prediction are essential.

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