Assimilation of soil moisture into hydrological models: the sequential method

Aubert, D.; Loumagne, C.; Oudin, Ludovic; Hégarat‐Mascle, Sylvie Le

doi:10.5589/m03-042

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…However, the improvements were neither significant nor consistent for all conditions [181,187,188,190,194]. It was claimed that the low temporal frequency of the ERS satellites could be an important reason for the insufficient impacts on flow simulation [187,188]. This impeded the development of flood-oriented RS-SM assimilation from 2003 to 2009.…”

Section: Capability To Improve Flood Forecastingmentioning

confidence: 99%

“…Pauwels et al [179,188] did some comparison and found that statistical correction resulted in reliable updating of the forecasting models and there was no need to use nudging. It was gradually realized that Kalman filtering approaches are relatively strong in both uncertainty reduction ability and computational efficiency [181,187].…”

Section: The Development Of Flood-orientated Rs-sm Assimilation Studiesmentioning

confidence: 99%

“…Process-based models calculate runoff generation and also spatial propagation of flows in a more physical manner, and are thus usually implemented in fully distributed catchment systems. Initially, lumped models were typically chosen [13,[102][103][104][179][180][181]183,184,[186][187][188][189][190][191][192]194,195]. A rainfall and soil moisture updating approach was tested by Crow and Ryu [104] in synthetic scenarios and further implemented by Chen et al [102] in real RS-SM assimilation scenarios for a lumped system (SAC-SMA).…”

Section: Model Types For Rs-sm Assimilationmentioning

confidence: 99%

“…Empirical [195] and conceptual models [13,179,181,[187][188][189]194] were the main focus at that stage. As it was not clear what type of data assimilation methods are most suitable for RS-SM assimilation during that period, various data assimilation approaches, including statistical correction [179,188,195], Newtonian nudging [179], variational data assimilation [194], the discrete Kalman filter (KF) [13] and the extended Kalman filter (EKF) [181,187,189,194], were examined. Pauwels et al [179,188] did some comparison and found that statistical correction resulted in reliable updating of the forecasting models and there was no need to use nudging.…”

Section: Model Types For Rs-sm Assimilationmentioning

confidence: 99%

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Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Grimaldi

Walker

et al. 2016

Remote Sensing

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Abstract:Fluvial flooding is one of the most catastrophic natural disasters threatening people's lives and possessions. Flood forecasting systems, which simulate runoff generation and propagation processes, provide information to support flood warning delivery and emergency response. The forecasting models need to be driven by input data and further constrained by historical and real-time observations using batch calibration and/or data assimilation techniques so as to produce relatively accurate and reliable flow forecasts. Traditionally, flood forecasting models are forced, calibrated and updated using in-situ measurements, e.g., gauged precipitation and discharge. The rapid development of hydrologic remote sensing offers a potential to provide additional/alternative forcing and constraint to facilitate timely and reliable forecasts. This has brought increasing interest to exploring the use of remote sensing data for flood forecasting. This paper reviews the recent advances on integration of remotely sensed precipitation and soil moisture with rainfall-runoff models for rainfall-driven flood forecasting. Scientific and operational challenges on the effective and optimal integration of remote sensing data into forecasting models are discussed.

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Section: Capability To Improve Flood Forecastingmentioning

confidence: 99%

Section: The Development Of Flood-orientated Rs-sm Assimilation Studiesmentioning

confidence: 99%

Section: Model Types For Rs-sm Assimilationmentioning

confidence: 99%

Section: Model Types For Rs-sm Assimilationmentioning

confidence: 99%

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Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Grimaldi

Walker

et al. 2016

Remote Sensing

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“…In hydrologic applications, these methods have led to better estimation of antecedent soil moisture Reichle et al 2004;Reichle and Koster 2005;de Lannoy et al 2007) with the potential for improved prediction of water availability, runoff, streamflow and flood discharge (Bach and Mauser 2003;Oudin et al 2003;Scipal et al 2005;Weerts and Serafy 2006), particularly in situations where stream hydrographs are sparse or nonexistent . Studies have demonstrated that combining observations of soil moisture with water budget models using data assimilation techniques on time scales of 1-3 days improves the prediction of modeled flows (Aubert et al 2003;Pan et al 2008). To capitalize on these benefits, it is necessary to develop and tailor methods for assimilating multiple types of observations into hydrological and hydrometeorological models.…”

Section: Introductionmentioning

confidence: 99%

On the Efficacy of Combining Thermal and Microwave Satellite Data as Observational Constraints for Root-Zone Soil Moisture Estimation

Barrett

Renzullo

2009

Journal of Hydrometeorology

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Data assimilation applications require the development of appropriate mathematical operators to relate model states to satellite observations. Two such ''observation'' operators were developed and used to examine the conditions under which satellite microwave and thermal observations provide effective constraints on estimated soil moisture. The first operator uses a two-layer surface energy balance (SEB) model to relate rootzone moisture with top-of-canopy temperature. The second couples SEB and microwave radiative transfer models to yield top-of-atmosphere brightness temperature from surface layer moisture content. Tangent linear models for these operators were developed to examine the sensitivity of modeled observations to variations in soil moisture. Assuming a standard deviation in the observed surface temperature of 0.5 K and maximal model sensitivity, the error in the analysis moisture content decreased by 11% for a background error of 0.025 m 3 m 23 and by 29% for a background error of 0.05 m 3 m 23 . As the observation error approached 2 K, the assimilation of individual surface temperature observations provided virtually no constraint on estimates of soil moisture. Given the range of published errors on brightness temperature, microwave satellite observations were always a strong constraint on soil moisture, except under dense forest and in relatively dry soils. Under contrasting vegetation cover and soil moisture conditions, orthogonal information contained in thermal and microwave observations can be used to improve soil moisture estimation because limited constraint afforded by one data type is compensated by strong constraint from the other data type.

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