On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation

Loizu, Javier; Massari, Christian; Álvarez‐Mozos, Jesús; Tarpanelli, Angelica; Brocca, Luca; Casalí, Javier

doi:10.1016/j.advwatres.2017.10.034

Cited by 47 publications

(40 citation statements)

References 123 publications

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“…T is lower than 20 days for most of the catchments except Arga and Volturno where it reaches a value of about 60 days. These results are consistent with the range of values found in previous studies (e.g., [13,16,30,64]). There is not a specific pattern that is possible to identify for the study catchments because T variations are not only related to the specific catchment hydrology but also to the model and the satellite observation quality Figure 3b shows the observation error variances of SWI* ASCAT obtained by considering the triplets among SWI* ASCAT , SWI CCIpas and the soil moisture simulated by MISDc model forced with P ERA (P 3B42RT ).…”

Section: Misdc Model Calibration and Validation Forced With Ground-basupporting

confidence: 82%

“…The error variances found with the two triplets maintain a similar comparative relationship among basins showing smaller values for drier and warm catchments (Tevere, Arga, Mdouar) and larger values for more cold and humid (mountainous) catchments (Kolpa@Petrina, Gardon, Lim). The relatively better performance of ASCAT in semi-arid environments is consistent with the results of [64,65]. Figure 4a summarises the values of the parameter K obtained during the calibration period for all the investigated catchments while Figure 4b shows the reduction in RMSE between observed and simulated stream flow after integrating PERA and P3B42RT with PSM2RAIN-ASC through Equation (4).…”

Section: Misdc Model Calibration and Validation Forced With Ground-basupporting

confidence: 70%

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Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction

et al. 2018

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Many satellite soil moisture products are today globally available in near real-time. These observations are of paramount importance for enhancing the understanding of the hydrological cycle and particularly useful for flood forecasting purposes. In recent decades, several studies assimilated satellite soil moisture observations into rainfall-runoff models to improve their flood forecasting skills. The rationale is that a better representation of the catchment states leads to a better stream flow estimation. By exploiting the strong physical connection between the soil moisture dynamic and rainfall, some recent studies demonstrated that satellite soil moisture observations can be also used for enhancing the quality of rainfall observations. Given that the quality of the rainfall is one of the main drivers of the hydrological model uncertainty, this begs the question-to what extent updating soil moisture states leads to better flood forecasting skills than correcting rainfall forcing? In this study, we try to answer this question by using rainfall-runoff observations from 10 catchments throughout the Mediterranean area and a continuous rainfall-runoff model-MISDc-forced with reanalysis-and satellite-based rainfall observations. Satellite soil moisture retrievals from the Advanced SCATterometer (ASCAT) are either assimilated into MISDc model via the Ensemble Kalman filter to update model states or, alternatively, used to correct rainfall observations derived from a reanalysis and a satellite-based product through the integration with soil moisture-based rainfall estimates. 4-9 years (depending on the catchment) of stream flow observations are organized into calibration and validation periods to test the two different schemes. Results show that the rainfall correction is favourable if the target is the predictions of high flows while for low flows there is a small advantage of the state correction scheme with respect to the rainfall correction. The improvements for high flows are particularly large when the quality of the rainfall is relatively poor with important implications for large-scale flood forecasting in the Mediterranean area.

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Section: Misdc Model Calibration and Validation Forced With Ground-basupporting

confidence: 82%

Section: Misdc Model Calibration and Validation Forced With Ground-basupporting

confidence: 70%

Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction

et al. 2018

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“…Currently, there is still no consensus on the improvement of streamflow modeling through satellite soil moisture assimilation [4,7]. For instance, almost no improvement of stream ow simulation was obtained by Brocca et al [4] in the assimilation of the surface ASCAT SM retrievals, while up to 10-30% improvements were achieved in such other studies as Massari et al [32], Lopez et al [7], and Loizu et al [10].…”

Section: Introductionmentioning

confidence: 92%

“…A large number of studies have been implemented to assimilate the RS SM in the land surface model for the purpose of obtaining a more accurate and reliable profile SM data set on a regional or global scale [20][21][22][23][24][25][26]. Nevertheless, the assimilation of coarse-scale RS SM in the hydrological model targeted at improving the rainfall-runoff process is implemented in relatively few studies [10,[27][28][29][30][31]. Currently, there is still no consensus on the improvement of streamflow modeling through satellite soil moisture assimilation [4,7].…”

Section: Introductionmentioning

confidence: 99%

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ESA CCI Soil Moisture Assimilation in SWAT for Improved Hydrological Simulation in Upper Huai River Basin

Wang

Liu

2018

Advances in Meteorology

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e assimilation of satellite soil moisture (SM) products with coarse resolution is promising in improving rainfall-runoff modeling, but it is largely impacted by the data assimilation (DA) strategy. is study performs the assimilation of a satellite soil moisture product from the European Space Agency (ESA) Climate Change Initiative (CCI) in a physically based semidistributed hydrological model (SWAT) in the upper Huai River basin in China, with the objective to improve its rainfall-runoff simulation. In this assimilation, the ensemble Kalman filter (EnKF) is adopted with full consideration of the model and observation error, the rescaling technique for satellite SM, and the regional applicability of the hydrological model. e results show that the ESA CCI SM assimilation generally improves the streamflow simulation of the study catchment. It is more effective for low-flow simulation, while for very high-flow/large-flood modeling, the DA performance shows uncertainty. e less-effective performance on largeflood simulation lies in the relatively low dependence of rainfall-runoff generation on the antecedent SM as during which the SM is nearly saturated and the runoff is largely dominated by precipitation. Besides, the efficiency of DA is deteriorated by the dense forest coverage and the complex topography conditions of the basin. Overall, the ESA CCI SM assimilation improves the streamflow simulation of the SWAT model in particular for low flow. is study provides an encouragement for the application of the ESA CCI SM in water management, especially over low-flow periods.

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Sensitivity based soil moisture assimilation for improved streamflow forecast using a novel Forward Sensitivity Method (FSM) approach

Visweshwaran

Ramsankaran

Eldho

et al. 2021

Preprint

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On the assimilation set-up of ASCAT soil moisture data for improving streamflow catchment simulation

Cited by 47 publications

References 123 publications

Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction

Exploiting Satellite-Based Surface Soil Moisture for Flood Forecasting in the Mediterranean Area: State Update Versus Rainfall Correction

ESA CCI Soil Moisture Assimilation in SWAT for Improved Hydrological Simulation in Upper Huai River Basin

Sensitivity based soil moisture assimilation for improved streamflow forecast using a novel Forward Sensitivity Method (FSM) approach

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