Simultaneous assimilation of in situ soil moisture and streamflow in the SWAT model using the Extended Kalman Filter

Sun, Leqiang; Seidou, Ousmane; Nistor, Ioan; Goı̈ta, Kalifa; Magagi, Ramata

doi:10.1016/j.jhydrol.2016.10.040

Cited by 25 publications

(21 citation statements)

References 75 publications

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“…The assimilation of SM in this catchment improves both predictions of streamflow and ET. This inconsistent result was also found in the previous studies, for example Sun et al (2016) indicated that assimilation of SM can improve the estimates of Q, whereas Yan and Moradkhani (2016) showed that the assimilated SM degrades the estimate of streamflow. Further research is needed to resolve this inconsistency.…”

Section: Single Assimilationmentioning

confidence: 45%

Assimilating stream flow, evapotranspiration and soil moisture data in AWRA-L model with particle filter

2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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The Australian Water Resource Assessment-Landscape model (AWRA-L) is calibrated against a selection of data from ~500 gauged catchments around Australia to identify a single optimised set of model parameter with an emphasis on improving streamflow prediction. However, this regional approach to AWRA-L calibration can lead to high uncertainty in estimation, especially in ungauged catchments. An approach to help improve prediction in ungauged area is the assimilation of remotely sensed data into hydrological models. Streamflow discharges (Q), satellite soil moisture (SM) and satellite evapotranspiration (ET) observations have been individually assimilated into hydrological models to improve predicted outputs. This paper aims to evaluate performance of both individual and joint assimilation of these three hydrological observations into the AWRA-L model using particle filter technique. The investigation used collected from six catchments across Australia with areas varying from 70-130 km 2. In-situ streamflow data from the Hydrological Reference Stations (HRS) are divided by their respective catchment area to generate observations with units that are consistent with AWRA-L modelled streamflow. The European Space Agency Climate Change Initiative (ESA-CCI) soil moisture products were normalised to ensure that observed and simulated soil moisture are comparable. We disaggregated the 8-day CSIRO MODIS ReScaled potential ET (CMRS-ET) product to daily ET estimates using daily potential evapotranspiration (PET) and a linear interpolation method. Forcing data, initial conditions and spatial parameters of six catchments are collected from the datasets used for calibration and validation of the AWRA-L model for 2010. To address the limitation of high computational time required by the particle filter method in the grid-based AWRA-L model, we adopt a lump catchment approach where forcing inputs, initial conditions, and spatial parameters in each catchment were aggregated into one lumped value. Afterward the aggregated forcing is perturbed cell-wise using a normal distribution with 1000 samples to create a sample-based. These samplebased eventually are informed to the AWRA-L for the data assimilation process. Four assimilation scenarios were investigated, namely: (1) sole assimilation of Q, (2) sole assimilation of satellite SM, (3) sole assimilation of satellite ET, and (4) joint assimilation of Q, SM and ET. In addition, an open-loop simulation, i.e. without assimilation, was run as a reference. Statistical metrics such as correlation coefficient (R 2), Nash-Sutcliffe model efficiency (NSE), root mean squared error (RMSE), and bias are used to assess the predicted outputs of the data assimilation model and open loop simulation. Initial results indicated that only assimilating Q was successful in improving ET predictions in all study catchments. The assimilation of ET, however, did not improve streamflow predictions. Although assimilation of soil moisture produces a slight improvement in ET prediction, it degrades the predicted ...

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Section: Single Assimilationmentioning

confidence: 45%

Assimilating stream flow, evapotranspiration and soil moisture data in AWRA-L model with particle filter

2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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“…Soil moisture, interacting with surface water and groundwater, affects a variety of hydrological processes [118][119][120][121][122]. It controls the partitioning of rainfall into infiltration and runoff, subsequently controlling water movement and baseflow generation from the soil profile and determining streamflow and flooding [15,29,118,123,124].…”

Section: Remotely Sensed Soil Moisturementioning

confidence: 99%

“…It controls the partitioning of rainfall into infiltration and runoff, subsequently controlling water movement and baseflow generation from the soil profile and determining streamflow and flooding [15,29,118,123,124]. The integration of soil moisture information into hydrological models has the potential to enhance hydrological modeling predictability [21,39,40,118,121,[125][126][127] ( Table 3). Although limited improvement in streamflow simulations has been produced in a small watershed, which has better in-situ observations, by coupling soil moisture data, the performance of the runoff prediction in the presence of larger errors in precipitation observations [123] and flood predictions [45,126,128] showed a significant improvement.…”

Section: Remotely Sensed Soil Moisturementioning

confidence: 99%

“…In situ measurements can provide accurate soil moisture information over the entire root zone for hydrological modeling, but they represent soil moisture conditions only over a small spatial scale and are unable to characterize the spatial heterogeneity and variability of soil moisture over a large scale [118,122,132,133]. Satellite-based remote sensing can provide an alternative to develop observations for soil moisture with a large range and high accuracy, which can fill in gaps with sparse ground measurements or poor spatial representativeness [121,122,126,132,134]. Among these techniques, microwave remote sensing (passive and active) has been widely applied for the retrieval of global soil moisture [135], because microwave sensors are not limited by cloud cover and nighttime conditions [122].…”

Section: Remotely Sensed Soil Moisturementioning

confidence: 99%

“…Data assimilation (DA) techniques have been widely and increasingly applied in hydrological studies [108,119,120,125,130,179]. Particularly, DA shows superiority in integrating multiple observation types into hydrological models [45,46,49,120,121]. DA can not only update the hydrological state variables and model parameters (simultaneously seeking the best model state and parameters to enhance model performance) but also can account for and reduce various sources of uncertainties in both the models and the assimilated data products [21,49,108,179,180].…”

Section: The Application Of Data Assimilation For Merging Satellite-bmentioning

confidence: 99%

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The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review

Jiang

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A hydrological model is a useful tool to study the effects of human activities and climate change on hydrology. Accordingly, the performance of hydrological modeling is vitally significant for hydrologic predictions. In watersheds with intense human activities, there are difficulties and uncertainties in model calibration and simulation. Alternative approaches, such as machine learning techniques and coupled models, can be used for streamflow predictions. However, these models also suffer from their respective limitations, especially when data are unavailable. Satellite-based remote sensing may provide a valuable contribution for hydrological predictions due to its wide coverage and increasing tempo-spatial resolutions. In this review, we provide an overview of the role of satellite-based remote sensing in streamflow simulation. First, difficulties in hydrological modeling over highly regulated basins are further discussed. Next, the performance of satellite-based remote sensing (e.g., remotely sensed data for precipitation, evapotranspiration, soil moisture, snow properties, terrestrial water storage change, land surface temperature, river width, etc.) in improving simulated streamflow is summarized. Then, the application of data assimilation for merging satellite-based remote sensing with a hydrological model is explored. Finally, a framework, using remotely sensed observations to improve streamflow predictions in highly regulated basins, is proposed for future studies. This review can be helpful to understand the effect of applying satellite-based remote sensing on hydrological modeling.

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Developing Extended and Unscented Kalman Filter-Based Neural Networks to Predict Cluster-Induced Roughness in Gravel Bed Rivers

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Simultaneous assimilation of in situ soil moisture and streamflow in the SWAT model using the Extended Kalman Filter

Cited by 25 publications

References 75 publications

Assimilating stream flow, evapotranspiration and soil moisture data in AWRA-L model with particle filter

Assimilating stream flow, evapotranspiration and soil moisture data in AWRA-L model with particle filter

The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review

Developing Extended and Unscented Kalman Filter-Based Neural Networks to Predict Cluster-Induced Roughness in Gravel Bed Rivers

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