scholarly journals Impact of potential and (scintillometer-based) actual evapotranspiration estimates on the performance of a lumped rainfall–runoff model

2013 ◽  
Vol 17 (11) ◽  
pp. 4525-4540 ◽  
Author(s):  
B. Samain ◽  
V. R. N. Pauwels
Keyword(s):  
Water Balance ◽  
Stream Flow ◽  
Seasonal Pattern ◽  
Early Spring ◽  
Actual Evapotranspiration ◽  
Rainfall Runoff ◽  
Forecasting Models ◽  
Stable Conditions ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. Evapotranspiration (ET) plays a key role in hydrological impact studies and operational flood forecasting models as ET represents a loss of water from a catchment. Although ET is a major component of the catchment water balance, the evapotranspiration input for rainfall–runoff models is often simplified in contrast to the detailed estimates of catchment averaged precipitation. In this study, an existing conceptual rainfall–runoff model calibrated for and operational in the Bellebeek catchment in Belgium firstly has been validated and its sensitivity to different available potential ET input has been studied. It has been shown that when applying a calibrated rainfall–runoff model, the model input should be consistent with the input used for the calibration process, not only on the volume of ET, but also on the seasonal pattern. Secondly, estimates of the actual evapotranspiration based on measurements of a large aperture scintillometer (LAS) have been used as model forcing in the rainfall–runoff model. From this analysis, it has been shown that the actual evapotranspiration is a crucial factor in simulating the catchment water balance and the resulting stream flow. Regarding the actual evapotranspiration estimates from the LAS, it has been concluded that they can be considered realistic in summer months. In the months where stable conditions prevail (autumn, winter and (early) spring), an underestimation of the actual evapotranspiration is made, which has an important impact on the catchment's water balance.

Sensitivity analysis of Xinanjiang rainfall–runoff model parameters: a case study in Lianghui, Zhejiang province, China

2012 ◽  
Vol 43 (1-2) ◽  
pp. 123-134 ◽  
Author(s):  
Danrong Zhang ◽  
Liru Zhang ◽  
Yiqing Guan ◽  
Xi Chen ◽  
Xinfang Chen
Keyword(s):  
Water Balance ◽  
Rainfall Runoff ◽  
Parameter Calibration ◽  
Drainage Basins ◽  
Xinanjiang Model ◽  
Rainfall Runoff Model ◽  
Sensitive Parameters ◽  
Runoff Model ◽  
Runoff Routing ◽  
Balance Parameter

The Xinanjiang rainfall–runoff model has been successfully applied in many humid and sub-humid areas in China since 1973. The wide application is due to the simple model structure, the clear physical meaning of the parameters and the well-defined model calibration procedure. However, due to a data scarcity problem and short runoff concentration time, its applications to small drainage basins are difficult. Therefore, we investigate the model application in Lianghui, a small drainage basin of Zhejiang province in China. By using generalized likelihood uncertainty estimation (GLUE) methodology, the sensitivity of parameters of Xinanjiang model was investigated. The data clearly showed that equifinality phenomenon was evident in both water balance parameter calibration and runoff routing parameter calibration procedures. The results showed that K (evapotranspiration conversion coefficient), Cs (recession constant in channel system) and Sm (areal free water storage capacity of surface soil) are the most sensitive parameters for the water balance parameter calibration while Cs, Sm and Wm (mean area tension water capacity) are the most sensitive parameters for runoff routing parameter calibration. The conclusion is favourable for understanding parameters of Xinanjiang model in order to provide valuable scientific information for simulating hydrological processes in small drainage basins.

scholarly journals Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0)

2018 ◽  
Vol 11 (4) ◽  
pp. 1591-1605 ◽  
Author(s):  
Léonard Santos ◽  
Guillaume Thirel ◽  
Charles Perrin
Keyword(s):  
Water Balance ◽  
State Space ◽  
Operator Splitting ◽  
Space Representation ◽  
Rainfall Runoff ◽  
Integration Technique ◽  
State Space Representation ◽  
Rainfall Runoff Model ◽  
Unit Hydrographs ◽  
Runoff Model

Abstract. In many conceptual rainfall–runoff models, the water balance differential equations are not explicitly formulated. These differential equations are solved sequentially by splitting the equations into terms that can be solved analytically with a technique called “operator splitting”. As a result, only the solutions of the split equations are used to present the different models. This article provides a methodology to make the governing water balance equations of a bucket-type rainfall–runoff model explicit and to solve them continuously. This is done by setting up a comprehensive state-space representation of the model. By representing it in this way, the operator splitting, which makes the structural analysis of the model more complex, could be removed. In this state-space representation, the lag functions (unit hydrographs), which are frequent in rainfall–runoff models and make the resolution of the representation difficult, are first replaced by a so-called “Nash cascade” and then solved with a robust numerical integration technique. To illustrate this methodology, the GR4J model is taken as an example. The substitution of the unit hydrographs with a Nash cascade, even if it modifies the model behaviour when solved using operator splitting, does not modify it when the state-space representation is solved using an implicit integration technique. Indeed, the flow time series simulated by the new representation of the model are very similar to those simulated by the classic model. The use of a robust numerical technique that approximates a continuous-time model also improves the lag parameter consistency across time steps and provides a more time-consistent model with time-independent parameters.

scholarly journals State-space representation of a bucket-type rainfall-runoff model: a case study with State-Space GR4 (version 1.0)

2017 ◽  
Author(s):  
Léonard Santos ◽  
Guillaume Thirel ◽  
Charles Perrin
Keyword(s):  
Water Balance ◽  
Differential Equations ◽  
State Space ◽  
Operator Splitting ◽  
Space Representation ◽  
Rainfall Runoff ◽  
State Space Representation ◽  
Rainfall Runoff Model ◽  
Unit Hydrographs ◽  
Runoff Model

Abstract. In many conceptual rainfall-runoff models, the water balance differential equations are not explicitly formulated. These differential equations are solved sequentially by splitting the equations into terms that can be solved analytically with a technique called "operator splitting". As a result, only the resolutions of the split equations are used to present the different models. This article provides a methodology to make the governing water balance equations of a bucket-type rainfall-runoff model explicit. This is done by setting up a comprehensive state-space representation of the model. By representing it in this way, the operator splitting, which complexifies the structural analysis of the model, is removed. In this state-space representation, the lag functions (unit hydrographs), which are frequent in this type of model and make the resolution of the representation difficult, are replaced by a so-called "Nash cascade". This substitution also improves the lag parameter consistency across time steps. To illustrate this methodology, the GR4J model is taken as an example. The flow time series simulated by the new representation of the model are very similar to those simulated by the classic model. The state-space representation provides a more time-consistent model with time-independent parameters.

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 Opportunities and challenges for the use of scintillometer-based catchment-averaged evapotranspiration estimates as model forcing

2013 ◽  
Vol 10 (4) ◽  
pp. 3973-4013
Author(s):  
B. Samain ◽  
V. R. N. Pauwels
Keyword(s):  
Potential Evapotranspiration ◽  
Seasonal Pattern ◽  
Rainfall Runoff ◽  
Data Set ◽  
Meteorological Forcing ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Stable Conditions ◽  
Low Pass ◽  
Rainfall Runoff Model

Abstract. To date, lumped rainfall-runoff models rely on rough estimates of catchment-averaged potential evapotranspiration (ETp) rates as meteorological forcing. A model parameter converts this ETp input into actual evapotranspiration (ETact) estimates. This paper examines the potential use of scintillometer-based ETact rates for rainfall-runoff modeling. It has been found that the reservoir-structure of the rainfall-runoff model functions as a low-pass filter for the ETp input. If the long-term volume of the ETp used in the model simulations is consistent with the data set used for calibration, a good match of the seasonal pattern, using temporally constant ETp data, is sufficient to obtain adequate discharge simulations. However, these results are then obtained with strongly erroneous evapotranspiration estimates. A better match of the diurnal cycle does not lead to better model results. Replacing the ETp inputs by scintillometer-based ETact estimates does not lead to better model predictions. Small underestimations of ETact under stable conditions, which occur at night and during the Winter, and which accumulate to significant amounts, are the cause of this problem. Consistent with other studies, the scintillometer-based ETact estimates can be considered reliable and realistic under unstable conditions. These values can thus be used as forcing for rainfall-runoff models.

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