Flood modelling with a distributed event-based parsimonious rainfall-runoff model: case of the karstic Lez river catchment

Coustau, Mathieu; Bouvier, Christophe; Borrell-Estupina, Valérie; Jourde, Hervé

doi:10.5194/nhess-12-1119-2012

Cited by 59 publications

(59 citation statements)

References 36 publications

(34 reference statements)

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“…For instance, in rainfall-runoff modelling, it has been demonstrated that the wetness condition of the catchment before a flood event strongly controls the severity of the response of the catchment in terms of runoff [1][2][3][4]. In other words, different initial soil moisture conditions can discriminate between catastrophic and minor effects for the same amount of rainfall.…”

Section: Introductionmentioning

confidence: 99%

Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe?

et al. 2015

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Data assimilation (DA) of satellite soil moisture (SM) observations represents a great opportunity for improving the ability of rainfall-runoff models in predicting river discharges. Many studies have been carried out so far demonstrating the possibility to reduce model prediction uncertainty by incorporating satellite SM observations. However, large discrepancies can be perceived between these studies with the result that successful DA is not only related to the quality of the satellite observations but can be significantly controlled by many methodological and morphoclimatic factors. In this article, through an experimental study carried out on the Tiber River basin in Central Italy, we explore how the catchment area, soil type, climatology, rescaling technique, observation and model error selection may affect the results of the assimilation and can be the causes of the apparent discrepancies obtained in the literature. The results show that: (i) DA of SM generally improves discharge predictions (with a mean efficiency of about 30%); (ii) unlike catchment area, the soil type and the catchment specific characteristics might have a remarkable influence on the results; (iii) simple rescaling techniques may perform equally well to more complex ones; (iv) an accurate quantification of the model error is paramount for a correct choice of the observation error and, (v) SM temporal variability has a stronger influence than the season itself. On this basis, we advise that DA of SM may be not a simple task and one should carefully test the optimality of the assimilation experiment prior to drawing any general conclusions. OPEN ACCESSRemote Sens. 2015, 7 11404

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Section: Introductionmentioning

confidence: 99%

Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe?

et al. 2015

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“…Fig. 1 in Coustau et al, 2012) -both empty at the beginning of the event. The cumulated rainfall reservoir has an infinite capacity and determines the direct runoff using a runoff coefficient derived from the SCS runoff model, as used by Gaume et al (2004):…”

Section: The Hydrological Modelmentioning

confidence: 99%

“…This model combines a Soil Conservation Service derived (SCSderived) runoff function with a "lag-and-route" routing function. SCS-LR model was presented in Coustau et al (2012), and will be briefly summarized as follows:…”

Section: The Hydrological Modelmentioning

confidence: 99%

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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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“…as the forcing variables. Coustau et al [16] reported that the initial wetness condition (IWC) derived from the simulated soil moisture significantly improved most of the flood simulations in the event-based rainfall-runoff model.…”

Section: Introductionmentioning

confidence: 99%

Robust Initial Wetness Condition Framework of an Event-Based Rainfall–Runoff Model Using Remotely Sensed Soil Moisture

Sunwoo

Choi

2017

Water

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Abstract:Runoff prediction in limited-data areas is vital for hydrological applications, such as the design of infrastructure and flood defenses, runoff forecasting, and water management. Rainfall-runoff models may be useful for simulation of runoff generation, particularly event-based models, which offer a practical modeling scheme because of their simplicity. However, there is a need to reduce the uncertainties related to the estimation of the initial wetness condition (IWC) prior to a rainfall event. Soil moisture is one of the most important variables in rainfall-runoff modeling, and remotely sensed soil moisture is recognized as an effective way to improve the accuracy of runoff prediction. In this study, the IWC was evaluated based on remotely sensed soil moisture by using the Soil Conservation Service-Curve Number (SCS-CN) method, which is one of the representative event-based models used for reducing the uncertainty of runoff prediction. Four proxy variables for the IWC were determined from the measurements of total rainfall depth (API 5 ), ground-based soil moisture (SSM insitu ), remotely sensed surface soil moisture (SSM), and soil water index (SWI) provided by the advanced scatterometer (ASCAT). To obtain a robust IWC framework, this study consists of two main parts: the validation of remotely sensed soil moisture, and the evaluation of runoff prediction using four proxy variables with a set of rainfall-runoff events in the East Asian monsoon region. The results showed an acceptable agreement between remotely sensed soil moisture (SSM and SWI) and ground based soil moisture data (SSM insitu ). In the proxy variable analysis, the SWI indicated the optimal value among the proposed proxy variables. In the runoff prediction analysis considering various infiltration conditions, the SSM and SWI proxy variables significantly reduced the runoff prediction error as compared with API 5 by 60% and 66%, respectively. Moreover, the proposed IWC framework with remotely sensed soil moisture indicates an improved Nash-Sutcliffe efficiency from 0.48 to 0.74 for the four catchments in the Korean Peninsula. It can be concluded that the SCS-CN method extended with remotely sensed soil moisture for reducing uncertainty in the runoff prediction and the proxy variables obtained from the soil moisture data provided by the ASCAT can be useful in enhancing the accuracy of runoff prediction over a range of spatial scales.

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Flood modelling with a distributed event-based parsimonious rainfall-runoff model: case of the karstic Lez river catchment

Cited by 59 publications

References 36 publications

Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe?

Data Assimilation of Satellite Soil Moisture into Rainfall-Runoff Modelling: A Complex Recipe?

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

Robust Initial Wetness Condition Framework of an Event-Based Rainfall–Runoff Model Using Remotely Sensed Soil Moisture

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