Assimilation of Sentinel 1 and SMAP – based satellite soil moisture retrievals into SWAT hydrological model: the impact of satellite revisit time and product spatial resolution on flood simulations in small basins

Azimi, Shima; Dariane, Alireza B.; Modanesi, Sara; Bauer-Marschallinger, Bernhard; Bindlish, Rajat; Wagner, Wolfgang; Massari, Christian

doi:10.1016/j.jhydrol.2019.124367

Cited by 59 publications

(28 citation statements)

References 78 publications

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“…However, active and combined retrieval assimilation results in a net positive impact in central Europe and in the Mediterranean area (i.e., Spain, Italy) providing greater benefits to the discharge model predictions compared to passive product. These regional findings are consistent with those reported in other DA studies investigating the influence of different satellite soil moisture products on hydrological predictions (e.g., Azimi et al, 2020;Cenci et al, 2016; DE SANTIS ET AL.…”

Section: Data Assimilation Impacts On Simulated Streamflow and Relation With Sm Accuracysupporting

confidence: 92%

Assimilation of Satellite Soil Moisture Products for River Flow Prediction: An Extensive Experiment in Over 700 Catchments Throughout Europe

Santis

Biondi

Crow

et al. 2021

Water Resources Research

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Soil moisture (SM) pre-storm conditions are a key factor in runoff production and can explain much of the observed hydrological response of a basin (e.g., Berthet et al., 2009;Penna et al., 2011). For this reason, the integration of SM observations into hydrological models are considered a valuable practice to improve streamflow predictions (e.g., Coustau et al., 2012;Tramblay et al., 2010).In recent years, SM estimates from remote sensing measurements have experienced growth in their availability and accuracy (Karthikeyan et al., 2017;McCabe et al., 2017). Multiple operational and research satellite SM data sets from active to passive microwave sensors are currently available (e.g., Mecklenburg et al., 2016;Wagner et al., 2013) with coarse-scale surface retrievals and approximately daily coverage. Data assimilation (DA) is a theoretically powerful way to integrate remotely sensed SM observations into hydrological models ; however, its actual benefit is still controversial regarding streamflow prediction, with several studies showing neither systematic nor significant improvements on streamflow prediction, due to a number of technical, mathematical and hydrological factors (e.g.

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Section: Data Assimilation Impacts On Simulated Streamflow and Relation With Sm Accuracysupporting

confidence: 92%

Assimilation of Satellite Soil Moisture Products for River Flow Prediction: An Extensive Experiment in Over 700 Catchments Throughout Europe

Santis

Biondi

Crow

et al. 2021

Water Resources Research

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“…Then, the bathymetry vector that includes the channel bed elevation for all channel cells is generated by subtracting the offset from DEM values along the channel. It should be noted that the range of uniform distribution for channel roughness is chosen based on previous studies (Aronica et al, 2002b;Bales and Wagner, 2009;Horritt, 2006;Pappenberger et al, 2008), while the error range assumed for the bathymetry is mostly determined based on expert judgment and trial and error. Since the real magnitude and distribution of these errors have not been fully understood in the literature, their estimated values may not be physically correct terms, and their estimation is ill-posed, according to Renard et al (2010).…”

Section: Da Hydrodynamic Modeling Frameworkmentioning

confidence: 99%

“…The effectiveness and application of assimilating remotely sensed data (e.g., Soil Moisture Active Passive, SMAP) into hydrologic models have been vastly investigated in the literature (Abbaszadeh et al, 2020;Azimi et al, 2020;Lievens et al, 2017). However, given the small scale of the hydrodynamic modeling process, the spatiotemporal resolution of current satellite products is not adequate for assimilating into these models.…”

Section: Introductionmentioning

confidence: 99%

Sequential data assimilation for real-time probabilistic flood inundation mapping

Jafarzadegan

Abbaszadeh

Moradkhani

2021

Hydrol. Earth Syst. Sci.

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Abstract. Real-time probabilistic flood inundation mapping is crucial for flood risk warning and decision-making during the emergency period before an upcoming flood event. Considering the high uncertainties involved in the modeling of a nonlinear and complex flood event, providing a deterministic flood inundation map can be erroneous and misleading for reliable and timely decision-making. The conventional flood hazard maps provided for different return periods cannot also represent the actual dynamics of flooding rivers. Therefore, a real-time modeling framework that forecasts the inundation areas before the onset of an upcoming flood is of paramount importance. Sequential data assimilation (DA) techniques are well known for real-time operation of physical models while accounting for existing uncertainties. In this study, we present a DA hydrodynamic modeling framework where multiple gauge observations are integrated into the LISFLOOD-FP model to improve its performance. This study utilizes the ensemble Kalman filter (EnKF) in a multivariate fashion for dual estimation of model state variables and parameters where the correlations among point source observations are taken into account. First, a synthetic experiment is designed to assess the performance of the proposed approach; then the method is used to simulate the Hurricane Harvey flood in 2017. Our results indicate that the multivariate assimilation of point source observations into hydrodynamic models can improve the accuracy and reliability of probabilistic flood inundation mapping by 5 %–7 %, while it also provides the basis for sequential updating and real-time flood inundation mapping.

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“…Despite the usefulness of annual LC maps that can be derived from remote sensing time series, most of the strategies proposed for SWAT analysis, apply static coarse cover maps from relevant data archives, thus ignoring any spatiotemporal land change processes that may be discovered by the EO derived LULC maps. To our knowledge, there are only a few studies that used remote sensing LULC maps in combination with the SWAT model [15][16][17][18]. Thus, the exploitation of sophisticated non-linear models can be a game changer in classifying remote sensing time series data and generating spatial explicit indicators and incorporating them into physical based models.…”

Section: Introductionmentioning

confidence: 99%

Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm

Samarinas

Tziolas

Zalidis

2020

IJGI

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The agricultural sector and natural resources are heavily interdependent, comprising a coherent but complex system. The soil and water assessment tool (SWAT) is widely used in assessing these interdependencies for regional watershed management. However, long-term simulations of agricultural watersheds are considered as not realistic since they have often been performed assuming constant land use over time and are based on the coarse resolution of the existing global or national data. This work presents the first insights of the synergy among SWAT model and deep learning classification algorithms to provide annually updated and realistic model’s parameterization and simulations. The proposed hybrid modelling approach couples the physical process SWAT model with the versatility of Earth observation data-driven non-linear deep learning algorithms for land use classification (Overall Accuracy (OA) = 79.58% and Kappa = 0.79), giving a strong advantage to decision makers for efficient management planning. A validation case at an agricultural watershed located in Northern Greece is provided to demonstrate their synergistic use to estimate nitrate and sediment concentrations that load in Zazari Lake. The SWAT model has been implemented under two different simulations; one with the use of a static coarse land use map and the other with the use of the annual updated land use maps for three consecutive years (2017–2019). The results indicate that the land use changes affect the final estimations resulting to an enhanced prediction performance of 1% and 2% for sediment and nitrate, respectively, when the annual land use maps are incorporated into SWAT simulations. In this context, a hybrid approach could further contribute to addressing challenges and support a data-centric scheme for informed decision making with regard to environmental and agricultural issues on the river basin scale.

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Assimilation of Sentinel 1 and SMAP – based satellite soil moisture retrievals into SWAT hydrological model: the impact of satellite revisit time and product spatial resolution on flood simulations in small basins

Cited by 59 publications

References 78 publications

Assimilation of Satellite Soil Moisture Products for River Flow Prediction: An Extensive Experiment in Over 700 Catchments Throughout Europe

Assimilation of Satellite Soil Moisture Products for River Flow Prediction: An Extensive Experiment in Over 700 Catchments Throughout Europe

Sequential data assimilation for real-time probabilistic flood inundation mapping

Improved Estimations of Nitrate and Sediment Concentrations Based on SWAT Simulations and Annual Updated Land Cover Products from a Deep Learning Classification Algorithm

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