Flood forecasting with a watershed model: a new method of parameter updating

Yang, Xiaoliu; Michel, Claude

doi:10.1080/02626660009492355

Cited by 38 publications

(25 citation statements)

References 23 publications

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“…However, uncertainty at high resolution may diminish potential gains in prediction accuracy (Carpenter, 2006). Nevertheless, despite the simplicity of lumped models, they perform well in 25 many studies (Yang and Michel, 2000;Cameron et al, 1999;Uhlenbrook et al, 1999;Yang et al, 1995). However, they do not need as much data as the distributed models (which are unavailable in many cases), and the complexity and requirements to process them are lower.…”

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confidence: 99%

Assessment of lumped hydrological balance models in peninsular Spain

Pérez‐Sánchez

Senent‐Aparicio

Segura-Méndez

et al. 2017

Preprint

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Abstract. The assessment of inflows in a water resources system is essential for the appropriate analysis of its management.These inflows can be obtained from hydrological balance models. In this paper, we intend to perform a comparative study of six lumped hydrological balance models in several basins with different climatic conditions within Spain. Lumped models enable the estimation of catchment resources without using spatially distributed information that would not be available in 10 many cases. We have selected basins where long time series of climatic and hydrological data are available (more than 30 years) to calibrate parsimonious models, taking into account the stochastic behaviour of the natural streamflow and the climatic variables. The study period comprises 34 years . The explored models are Témez, ABCD, GR2M, the Australian water balance model (AWBM), GUO-5 parameters (Guo-5p) and Thornthwaite-Mather. Six statistical indices are applied to compare the results of the models: Nash-Sutcliffe model efficiency coefficient (NSE), root-mean-square deviation (RMSE), 15Pearson's correlation coefficient (R), percent bias (PBIAS), RMSE-observations standard deviation ratio (RSR) and the relative error between observed and simulated runoff volumes (REV). The results show that although lumped models can be employed in humid and sub-humid regions, the more humid the catchments are, the better the results obtained. Témez models provide the worst results in dry sub-humid and semi-arid regions. Guo-5p estimates runoff volumes with errors below 10% despite the unsatisfactory results provided according to the Bressiani classification. The Bressiani classification takes into 20 account different comparison criteria to help in the decision-making process when selecting a model. Nevertheless, the assessment of the margin of error in total runoff volume using REV is also a key index. The usefulness of Pearson's correlation when selecting a model is quite low but can be helpful in the analysis of models' weaknesses.

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confidence: 99%

Assessment of lumped hydrological balance models in peninsular Spain

Pérez‐Sánchez

Senent‐Aparicio

Segura-Méndez

et al. 2017

Preprint

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“…These techniques seek to correct the components of a hydrological model (forcing, parameters, state variables, or discharges) in order to improve the quality of discharge predictions (Refsgaard, 1997) as observational data become available. The most widely used techniques remain those relying on autoregressive (AR) models in order to correct the simulated discharge output by the model (Yang and Michel, 2000;Xiong and O'Connor, 2002). This type of correction is based on the structure of the error between observed and simulated discharges but does not take into account the source of uncertainty.…”

Section: Introductionmentioning

confidence: 99%

“…In and Maradhkani and Hsu (2005) it was shown that a dual state-parameter estimation using either an ensemble Kalman filter or a particle filter properly accounted for uncertainties in model inputs, outputs and parameters. Variational techniques have also been used to correct hydrological model parameters (Yang and Michel, 2000;Bessière et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Benefits and limitations of data assimilation for discharge forecasting using an event-based rainfall–runoff model

Coustau

Ricci

Borrell-Estupina

et al. 2013

Nat. Hazards Earth Syst. Sci.

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Mediterranean catchments in southern France are threatened by potentially devastating fast floods which are difficult to anticipate. In order to improve the skill of rainfall-runoff models in predicting such flash floods, hydrologists use data assimilation techniques to provide real-time updates of the model using observational data. This approach seeks to reduce the uncertainties present in different components of the hydrological model (forcing, parameters or state variables) in order to minimize the error in simulated discharges. This article presents a data assimilation procedure, the best linear unbiased estimator (BLUE), used with the goal of improving the peak discharge predictions generated by an event-based hydrological model Soil Conservation Service lag and route (SCS-LR). For a given prediction date, selected model inputs are corrected by assimilating discharge data observed at the basin outlet. This study is conducted on the Lez Mediterranean basin in southern France. The key objectives of this article are (i) to select the parameter(s) which allow for the most efficient and reliable correction of the simulated discharges, (ii) to demonstrate the impact of the correction of the initial condition upon simulated discharges, and (iii) to identify and understand conditions in which this technique fails to improve the forecast skill. The correction of the initial moisture deficit of the soil reservoir proves to be the most efficient control parameter for adjusting the peak discharge. Using data assimilation, this correction leads to an average of 12% improvement in the flood peak magnitude forecast in 75% of cases. The investigation of the other 25% of cases points out a number of precautions for the appropriate use of this data assimilation procedure

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“…Auto Regression Moving Average approaches (Lettenmaier, 1993;Dyck and Peschke, 1995) -are focussed on the river flow itself which leads to a significant loss of forecast lead time in small, quick reacting catchments. More sophisticated procedures which, for example, are using Kalman filtering (Kalman, 1960) are mathematically too complex to be easily accommodated by the highly non-linear models (Yang and Michelle, 2001). Therefore, we intended to develop a simple but effective updating procedure that allows for the updating of sensitive state variables that control the runoff generation approach of HBV-type conceptual rainfall runoff models.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: Updating procedure for flood forecasting with conceptual HBV-type models

Wöhling¹,

Lennartz

Zappa

2006

Hydrol. Earth Syst. Sci.

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Abstract. Flood forecasting is of increasing importance as it comes to an increasing variability in global and local climates. But rainfall-runoff models are far from being perfect. In order to achieve a better prediction for emerging flood events, the model outputs have to be continuously updated. This contribution introduces a rather simple, yet effective updating procedure for the conceptual semi-distributed rainfallrunoff model PREVAH, whose runoff generation module relies on similar algorithms as the HBV-Model. The current conditions of the system, i.e. the contents of the upper soil reservoirs, are updated by the proposed method. The testing of the updating procedure on data from two mountainous catchments in Switzerland reveals a significant increase in prediction accuracy with regards to peak flow.

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Flood forecasting with a watershed model: a new method of parameter updating

Cited by 38 publications

References 23 publications

Assessment of lumped hydrological balance models in peninsular Spain

Assessment of lumped hydrological balance models in peninsular Spain

Benefits and limitations of data assimilation for discharge forecasting using an event-based rainfall–runoff model

Technical Note: Updating procedure for flood forecasting with conceptual HBV-type models

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