Multivariate remotely sensed and in-situ data assimilation for enhancing community WRF-Hydro model forecasting

Abbaszadeh, Peyman; Gavahi, Keyhan; Moradkhani, Hamid

doi:10.1016/j.advwatres.2020.103721

Cited by 64 publications

(51 citation statements)

References 59 publications

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“…Our results indicate an overall improvement in streamflow predictions after satellitebased soil moisture assimilation. In contrast, previous studies found slight improvements (e.g., [9,18]) and sometimes degradation [13] in streamflow prediction performance after satellite-based soil moisture data assimilation. In addition to soil moisture data assimilation, some studies have conducted dual state-parameter update or forcing update to further improve their streamflow predictions (e.g., [10,11,54]).…”

Section: Discussionmentioning

confidence: 72%

“…Assimilating higher spatial resolution SMAP products, such as 3-km SMAP-Sentinel [52] or 1-km resolution [53] products, could provide higher prediction performance. For example, Abbaszadeh et al [18] showed that assimilating SMAP soil moisture with 1-km resolution [53] results in better streamflow predictions compared to 9-km or 36-km satellite-based soil moisture. Our results also show that SMAP soil moisture (~9 km) data assimilation provides better results than SMOS (~43 km).…”

Section: Discussionmentioning

confidence: 99%

“…More recently, Mao et al [13] developed a diagnostic framework for assessing the impact of satellite-based soil moisture data assimilation on daily streamflow predictions. Abbaszadeh et al [18] used the WRF-Hydro model [19] to assimilate SMAP satellite-based soil moisture and daily streamflow observations during hurricane Harvey in 2017. A recent survey of data assimilation techniques and their application in earth science and hydrometeorology is presented in [20,21].…”

Section: Introductionmentioning

confidence: 99%

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Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions

2021

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The authors examine the impact of assimilating satellite-based soil moisture estimates on real-time streamflow predictions made by the distributed hydrologic model HLM. They use SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity) data in an agricultural region of the state of Iowa in the central U.S. They explore three different strategies for updating model soil moisture states using satellite-based soil moisture observations. The first is a “hard update” method equivalent to replacing the model soil moisture with satellite observed soil moisture. The second is Ensemble Kalman Filter (EnKF) to update the model soil moisture, accounting for modeling and observational errors. The third strategy introduces a time-dependent error variance model of satellite-based soil moisture observations for perturbation of EnKF. The study compares streamflow predictions with 131 USGS gauge observations for four years (2015–2018). The results indicate that assimilating satellite-based soil moisture using EnKF reduces predicted peak error compared to that from the open-loop and hard update data assimilation. Furthermore, the inclusion of the time-dependent error variance model in EnKF improves overall streamflow prediction performance. Implications of the study are useful for the application of satellite soil moisture for operational real-time streamflow forecasting.

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Section: Discussionmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions

2021

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“…This can lead to some of the posterior parameter distributions becoming narrow, as with increasing observations we increase the confidence in our posterior, thus tightening the retrieved distributions and reducing the model ensemble spread. This result suggests that ensemble inflation (Anderson and Anderson, 1999) may be necessary if this ensemble were to be used in subsequent assimilation experiments.…”

Section: Assimilation Outputmentioning

confidence: 99%

Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

Pinnington

Amezcua

Cooper

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

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“…More details when the numerical and computational structure of the model can be obtained [3]. Thus, the WRF-Hydro model has been applied worldwide in studies with flood forecasting and simulation, severe events of precipitation caused by hurricanes, flow simulations in water bodies, soil moisture, evapotranspiration precipitation, and further studies of hydrometeorological conditions, both in arid regions and in humid regions [4][5][6][7][8][9][10][11]. For Brazil, we can mention the work of [12] WRF-Hydro system in the state of Pernambuco seeking to develop a management tool to simulate rainfall, reservoir management, and flood forecasting.…”

Section: Introductionmentioning

confidence: 99%

Feasibility Analysis on the Construction of a Web Solution for Hydrometeorological Forecasting Considering Water Body Management and Indicators for the Pandemic of SARS-COV-2

Silva

Pedruzzi

Souza

et al. 2021

Preprint

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Currently, the NCAR (U.S. National Center for Atmospheric Research), the institution responsible for the WRF-Hydro (Weather Research and Forecasting - Hydro) model initiative, highlights four major global challenges: floods, pollution, droughts, and biodiversity. Thus, given the current scenario of a global pandemic caused by the Sars-CoV-2 virus (Covid19), the importance of hydrological studies, their correlation with contamination levels, and incidence of COVID-19 cases are also in the spotlight. Among the challenges around water resources management, the lack of good and representative data, especially for small water bodies and developing countries, to perform inferences and to manage these natural resources is critical. This situation applies not only to observational data and but also to input data for hydrological models. In this context, the WRF-Hydro system represents the state of the art for hydrometeorological modeling. Thus, the model emerges as a computational tool that becomes possible to provide auxiliary data for patterns analysis in time series and computational prediction. Also, with the evolution of artificial intelligence (AI), it is possible to consider the integration of this modern approach with the WRF-Hydro model simulations. Therefore, the main of this study is to analyze the feasibility of a web tool that integrates these functionalities. The coupled WRF-Hydro with AI will support the management and generate a water predictability analysis in the MATOPIBA region (Maranhão-Tocantins-Piauí-Bahia), northeastern Brazil, the focus area of this study. Although the WRF-Hydro system demonstrates efficiency in the hydrometeorological simulation for the region, the model has a range of subprocesses which has a high computational cost, especially for long-term studies. Therefore, due to the possibility of integrating these computational tools, it is proposed to develop and analyze the construction of a web tool using the WRF-Hydro system for the short and medium-term with AI tools for the short term (a few hours to a few days), to optimize the computational cost. Thus, the combined application of the WRF-Hydro and AI system can improve the water bodies management and assist the identification of contamination levels by Sars-CoV-2, given the presence of the virus in water bodies and the correlation of the pandemic with hydrological variables.

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Multivariate remotely sensed and in-situ data assimilation for enhancing community WRF-Hydro model forecasting

Cited by 64 publications

References 59 publications

Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions

Data Assimilation of Satellite-Based Soil Moisture into a Distributed Hydrological Model for Streamflow Predictions

Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

Feasibility Analysis on the Construction of a Web Solution for Hydrometeorological Forecasting Considering Water Body Management and Indicators for the Pandemic of SARS-COV-2

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