Indices for calibration data selection of the rainfall-runoff model

2010 ◽  
Vol 46 (4) ◽  
Author(s):  
Jia Liu ◽  
Dawei Han
Keyword(s):  
Data Selection ◽  
Calibration Data ◽  
Rainfall Runoff ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Selection Of

scholarly journals Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

2015 ◽  
Vol 12 (6) ◽  
pp. 5389-5426 ◽  
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.

scholarly journals Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

2016 ◽  
Vol 20 (2) ◽  
pp. 887-901 ◽  
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.

scholarly journals Regionalization of a Rainfall-Runoff Model: Limitations and Potentials

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2257 ◽  
Author(s):  
Jung-Hun Song ◽  
Younggu Her ◽  
Kyo Suh ◽  
Moon-Seong Kang ◽  
Hakkwan Kim
Keyword(s):  
Hydrological Modeling ◽  
Rainfall Runoff ◽  
Tank Model ◽  
Statistical Relationships ◽  
Function Selection ◽  
Rainfall Runoff Model ◽  
Parameter Values ◽  
Runoff Model ◽  
Selection Of

Regionalized lumped rainfall-runoff (RR) models have been widely employed as a means of predicting the streamflow of an ungauged watershed because of their simple yet effective simulation strategies. Parameter regionalization techniques relate the parameter values of a model calibrated to the observations of gauged watersheds to the geohydrological characteristics of the watersheds. Thus, the accuracy of regionalized models is dependent on the calibration processes, as well as the structure of the model used and the quality of the measurements. In this study, we have discussed the potentials and limitations of hydrological model parameter regionalization to provide practical guidance for hydrological modeling of ungauged watersheds. This study used a Tank model as an example model and calibrated its parameters to streamflow observed at the outlets of 39 gauged watersheds. Multiple regression analysis identified the statistical relationships between calibrated parameter values and nine watershed characteristics. The newly developed regional models provided acceptable accuracy in predicting streamflow, demonstrating the potential of the parameter regionalization method. However, uncertainty associated with parameter calibration processes was found to be large enough to affect the accuracy of regionalization. This study demonstrated the importance of objective function selection of the RR model regionalization.

scholarly journals Sensitivity of the performance of a conceptual rainfall–runoff model to the temporal sampling of calibration data

2012 ◽  
Vol 44 (3) ◽  
pp. 484-494 ◽  
Author(s):  
Satish Bastola ◽  
Conor Murphy
Keyword(s):  
Temporal Resolution ◽  
Model Calibration ◽  
Flood Frequency Analysis ◽  
Calibration Data ◽  
Rainfall Runoff ◽  
Time Step ◽  
Scaling Relationship ◽  
Simple Scaling ◽  
Rainfall Runoff Model ◽  
Runoff Model

The effect of the time step of calibration data on the performance of a hydrological model is examined through a numerical experiment where HYMOD, a rainfall–runoff model, is calibrated with data of varying temporal resolution. A simple scaling relationship between the parameters of the model and modelling time step is derived which enables information from daily hydrological records to be used in modelling at time steps much shorter than daily. Model parameters were found to respond differently depending upon the degree of aggregation of calibration data. A loss in performance, especially in terms of the Nash–Sutcliffe measure, is evident when behavioural simulators derived with one modelling time step are used for simulation at another time step. The loss in performance is greater when parameters derived from a longer time step were used for simulating flow with a shorter time step. The application of a simple scaling relationship derived from a multi-time step model calibration significantly decreased the loss in model performance. Such an approach may offer the prospect of conducting higher temporal resolution flood frequency analysis when finer scale data for model calibration are not available or limited.

scholarly journals Considering rating curve uncertainty in water level predictions

2013 ◽  
Vol 17 (11) ◽  
pp. 4415-4427 ◽  
Author(s):  
A. E. Sikorska ◽  
A. Scheidegger ◽  
K. Banasik ◽  
J. Rieckermann
Keyword(s):  
Water Level ◽  
Bayesian Framework ◽  
Water Levels ◽  
Model Parameters ◽  
Calibration Data ◽  
Rainfall Runoff ◽  
Rating Curve ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract. Streamflow cannot be measured directly and is typically derived with a rating curve model. Unfortunately, this causes uncertainties in the streamflow data and also influences the calibration of rainfall-runoff models if they are conditioned on such data. However, it is currently unknown to what extent these uncertainties propagate to rainfall-runoff predictions. This study therefore presents a quantitative approach to rigorously consider the impact of the rating curve on the prediction uncertainty of water levels. The uncertainty analysis is performed within a formal Bayesian framework and the contributions of rating curve versus rainfall-runoff model parameters to the total predictive uncertainty are addressed. A major benefit of the approach is its independence from the applied rainfall-runoff model and rating curve. In addition, it only requires already existing hydrometric data. The approach was successfully demonstrated on a small catchment in Poland, where a dedicated monitoring campaign was performed in 2011. The results of our case study indicate that the uncertainty in calibration data derived by the rating curve method may be of the same relevance as rainfall-runoff model parameters themselves. A conceptual limitation of the approach presented is that it is limited to water level predictions. Nevertheless, regarding flood level predictions, the Bayesian framework seems very promising because it (i) enables the modeler to incorporate informal knowledge from easily accessible information and (ii) better assesses the individual error contributions. Especially the latter is important to improve the predictive capability of hydrological models.

scholarly journals Considering rating curve uncertainty in water level predictions

2013 ◽  
Vol 10 (3) ◽  
pp. 2955-2986 ◽  
Author(s):  
A. E. Sikorska ◽  
A. Scheidegger ◽  
K. Banasik ◽  
J. Rieckermann
Keyword(s):  
Water Level ◽  
Bayesian Framework ◽  
Water Levels ◽  
Model Parameters ◽  
Calibration Data ◽  
Rainfall Runoff ◽  
Rating Curve ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract. Streamflow cannot be measured directly and is typically derived with a rating curve model. Unfortunately, this causes uncertainties in the streamflow data and also influences the calibration of rainfall-runoff models if they are conditioned on such data. However, it is currently unknown to what extent these uncertainties propagate to rainfall-runoff predictions. This study therefore presents a quantitative approach to rigorously consider the impact of the rating curve on the prediction uncertainty of water levels. The uncertainty analysis is performed within a formal Bayesian framework and the contributions of rating curve versus rainfall-runoff model parameters to the total predictive uncertainty are addressed. A major benefit of the approach is its independence from the applied rainfall-runoff model and rating curve. In addition, it only requires already existing hydrometric data. The approach was successfully tested on a small urbanized basin in Poland, where a dedicated monitoring campaign was performed in 2011. The results of our case study indicate that the uncertainty in calibration data derived by the rating curve method may be of the same relevance as rainfall-runoff model parameters themselves. A conceptual limitation of the approach presented is that it is limited to water level predictions. Nevertheless, regarding flood level predictions, the Bayesian framework seems very promising because it (i) enables the modeler to incorporate informal knowledge from easily accessible information and (ii) better assesses the individual error contributions. Especially the latter is important to improve the predictive capability of hydrological models.

scholarly journals From aquifers’ and rivers’ memory to a better low-flow forecasting model

2020 ◽  
Author(s):  
Antoine Pelletier ◽  
Vazken Andréassian
Keyword(s):  
Monitoring Network ◽  
Low Flow ◽  
Rainfall Runoff ◽  
Surface Models ◽  
Forecasting System ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Prior Analysis ◽  
Selection Of ◽  
Made In

<p>The 2019 major drought in northern France highlighted the necessity to design an efficient and reliable low-flow forecasting system. Most forecasting tools, based on rainfall-runoff surface models, could benefit from an utilization of piezometric data, broadly available over the French metropolitan territory: obviously, surface water/groundwater interaction are a key process to explain low-flow dynamics.</p><p>Indeed, aquifers carry most of the hydroclimatic memory of a catchment, which determines the intensity and duration of droughts: a catchment beginning summer with empty aquifers will not have the same trajectory as the same catchment with higher than average piezometric levels. However, the piezometric data itself is not straightforward to use in a hydrological model, since aquifer-river connexions are often equivocal. Thus, a prior analysis of available data is necessary.</p><p>In this work, using 100 catchments of the national French hydroclimatic database and available piezometric data from the national aquifer monitoring network, we performed a comparative memory analysis of piezometry and streamflow, through a simple convolution function. The results were then compared to the behaviour of GR6J, a conceptual lumped rainfall-runoff model. For each catchment of the dataset, a selection of relevant piezometers was made, in the perspective of developing a model incorporating their levels as input data.</p>

Sign in / Sign up

Export Citation Format

Share Document