Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model

Aubert, D.; Loumagne, C.; Oudin, Ludovic

doi:10.1016/s0022-1694(03)00229-4

Cited by 262 publications

(197 citation statements)

References 30 publications

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“…However, one limitation of the Kalman filter is that it is optimal only for linear dynamic systems. For this reason, different variants of the Kalman filter, such as the extended Kalman filter (Madsen and Cañizares, 1999;Aubert et al, 2003), unscented Kalman filter, ensemble Kalman filter (Reichle, 2000;Evensen, 2003;Komma et al, 2008;Mendoza et al, 2012;Noh et al, 2013;Rafieeinasab et al, 2014) and recursive ensemble Kalman filter (McMillan et al, 2013) have been proposed and applied in hydrologic modelling. Madsen and Cañizares (1999) compared the performance of EKF and EnKF in coastal area modelling.…”

Section: Data Assimilationmentioning

confidence: 99%

“…Hydrological observations, used to update the water model states, can include streamflow (Pauwels and De Lannoy, 2006;Weerts and El Serafy, 2006;Pauwels and De Lannoy, 2009), snow cover (Andreadis and Lettenmaier, 2006), soil moisture (Brocca et al, 2010(Brocca et al, , 2012 or water level observations coming from in situ sensors (Madsen and Skotner, 2005;Neal et al, 2007) and remote sensing (Giustarini et al, 2011). Aubert et al (2003) integrated distributed values of soil moisture and streamflow from physical sensors into a lumped conceptual hydrological model by means of an extended Kalman filter. Streamflow prediction is improved by the assimilation of both soil moisture and streamflow individually and by coupled assimilation.…”

Section: Data Assimilationmentioning

confidence: 99%

“…However, in case of non-linear systems the extended Kalman filter (Kalman, 1960;Verlaan, 1998;Madsen and Cañizares, 1999;Aubert et al, 2003) or Ensemble Kalman filter (EnKF, Evensen, 2006) can be used to overcome the limitation of the linearity assumption. EnKF (Heemink et al, 2001;Reichle et al, 2002;Evensen, 2003;Weerts and El Serafy, 2006) is a widely used data assimilation method in hydrological applications.…”

Section: Ensemble Kalman Filtermentioning

confidence: 99%

“…One of the first attempts to assimilate observations coming from multiple sources is proposed by Aubert et al (2003). In that study, daily soil moisture and streamflow data from StPh sensors are used to update the states (soil and routing reservoirs) of a conceptual lumped rainfall-runoff model by means of an extended Kalman Filter.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

Mazzoleni¹

2017

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Section: Data Assimilationmentioning

confidence: 99%

Section: Data Assimilationmentioning

confidence: 99%

Section: Ensemble Kalman Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

Mazzoleni¹

2017

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“…Specific monitoring programs (Merz and Bardossy 1998;Aubert et al 2003;Morbidelli et al 2012) and modelling studies (Gautam et al 2000;Anctil et al 2004;Casper et al 2007;Komma et al 2008;Berthet et al 2009;Brocca et al 2012b;van Steenbergen and Willems 2013) were carried out to investigate the influence of soil moisture on runoff hydrographs.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture

Tayfur

Brocca

2015

Water Resour Manage

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This study developed Mamdani-type fuzzy logic model to simulate daily discharge as a function of soil moisture measured at three different depths (10, 20 and 40 cm) and rainfall. The model was applied to 13 km 2 size Colorso Basin in central Italy for a period from October 2002 to April 2004. For each variable of soil moisture, rainfall, and discharge, 9 fuzzy subsets were employed while 30 fuzzy rules, relating the input variables (soil moisture and rainfall) to the output variable (discharge), were optimized. The model employed the min inferencing, max composition, and the centroid method. The model application results revealed that Mamdani-type fuzzy logic model can be employed to incorporate soil moisture along with rainfall to simulate discharge. Using soil moisture measured at 40 cm soil depth along with rainfall produced better simulation of discharge with NS=0.68 and R= 0.82. The performance of the model was also tested against a conceptual rainfallrunoff model of MISDc (Modello Idrologico Semi-Distribuito in continuo). MISDc couples an event-specific component with a module for continuous time soil water balance for taking into account the variable antecedent wetness conditions. The MISDc model requires estimation of seven parameters and the measurements of the hydrometeorological variables such as rainfall and air temperature. The comparative study revealed that fuzzy model performs better in capturing runoff peak rates and overall trend of high and small flooding events.

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Parameter estimation of a physically based land surface hydrologic model using the ensemble Kalman filter: A synthetic experiment

et al. 2014

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[1] This paper presents multiple parameter estimation using multivariate observations via the ensemble Kalman filter (EnKF) for a physically based land surface hydrologic model. A data assimilation system is developed for a coupled physically based land surface hydrologic model (Flux-PIHM) by incorporating EnKF for model parameter and state estimation. Synthetic data experiments are performed at a first-order watershed, the Shale Hills watershed (0.08 km 2 ). Six model parameters are estimated. Observations of discharge, water table depth, soil moisture, land surface temperature, sensible and latent heat fluxes, and transpiration are assimilated into the system. The results show that, given a limited number of site-specific observations, the EnKF can be used to estimate Flux-PIHM model parameters. All the estimated parameter values are very close to their true values, with the true values inside the estimated uncertainty range (1 standard deviation spread). The estimated parameter values are not affected by the initial guesses. It is found that discharge, soil moisture, and land surface temperature (or sensible and latent heat fluxes) are the most critical observations for the estimation of those six model parameters. The assimilation of multivariate observations applies strong constraints to parameter estimation, and provides unique parameter solutions. Model results reveal strong interaction between the van Genuchten parameters a and b, and between land surface and subsurface parameters. The EnKF data assimilation system provides a new approach for physically based hydrologic model calibration using multivariate observations. It can be used to provide guidance for observational system designs, and is promising for real-time probabilistic flood and drought forecasting.Citation: Shi, Y., K. J. Davis, F. Zhang, C. J. Duffy, and X. Yu (2014), Parameter estimation of a physically based land surface hydrologic model using the ensemble Kalman filter: A synthetic experiment, Water Resour. Res., 50,[706][707][708][709][710][711][712][713][714][715][716][717][718][719][720][721][722][723][724]

show abstract

Sequential assimilation of soil moisture and streamflow data in a conceptual rainfall–runoff model

Cited by 262 publications

References 30 publications

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

Fuzzy Logic for Rainfall-Runoff Modelling Considering Soil Moisture

Parameter estimation of a physically based land surface hydrologic model using the ensemble Kalman filter: A synthetic experiment

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