Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic Models
When there exist catchment-wide biases in the distributed hydrologic model states, state updating based on streamflow assimilation at the catchment outlet tends to over- and under-adjust model states close to and away from the outlet, respectively. This is due to the greater sensitivity of the simul...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Forecasting |
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| Online Access: | https://www.mdpi.com/2571-9394/2/4/28 |
| _version_ | 1797544941505740800 |
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| author | Haksu Lee Haojing Shen Dong-Jun Seo |
| author_facet | Haksu Lee Haojing Shen Dong-Jun Seo |
| author_sort | Haksu Lee |
| collection | DOAJ |
| description | When there exist catchment-wide biases in the distributed hydrologic model states, state updating based on streamflow assimilation at the catchment outlet tends to over- and under-adjust model states close to and away from the outlet, respectively. This is due to the greater sensitivity of the simulated outlet flow to the model states that are located more closely to the outlet in the hydraulic sense, and the subsequent overcompensation of the states in the more influential grid boxes to make up for the larger scale bias. In this work, we describe Mean Field Bias (MFB)-aware variational (VAR) assimilation, or MVAR, to address the above. MVAR performs bi-scale state updating of the distributed hydrologic model using streamflow observations in which MFB in the model states are first corrected at the catchment scale before the resulting states are adjusted at the grid box scale. We comparatively evaluate MVAR with conventional VAR based on streamflow assimilation into the distributed Sacramento Soil Moisture Accounting model for a headwater catchment. Compared to VAR, MVAR adjusts model states at remote cells by larger margins and reduces the Mean Squared Error of streamflow analysis by 2–8% at the outlet Tiff City, and by 1–10% at the interior location Lanagan. |
| first_indexed | 2024-03-10T14:08:37Z |
| format | Article |
| id | doaj.art-32d969ccd76044d3a51b2381ef04db4b |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-10T14:08:37Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-32d969ccd76044d3a51b2381ef04db4b2023-11-21T00:25:24ZengMDPI AGForecasting2571-93942020-12-012452654810.3390/forecast2040028Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic ModelsHaksu Lee0Haojing Shen1Dong-Jun Seo2Len Technologies, Oak Hill, VA 20171, USADepartment of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USADepartment of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USAWhen there exist catchment-wide biases in the distributed hydrologic model states, state updating based on streamflow assimilation at the catchment outlet tends to over- and under-adjust model states close to and away from the outlet, respectively. This is due to the greater sensitivity of the simulated outlet flow to the model states that are located more closely to the outlet in the hydraulic sense, and the subsequent overcompensation of the states in the more influential grid boxes to make up for the larger scale bias. In this work, we describe Mean Field Bias (MFB)-aware variational (VAR) assimilation, or MVAR, to address the above. MVAR performs bi-scale state updating of the distributed hydrologic model using streamflow observations in which MFB in the model states are first corrected at the catchment scale before the resulting states are adjusted at the grid box scale. We comparatively evaluate MVAR with conventional VAR based on streamflow assimilation into the distributed Sacramento Soil Moisture Accounting model for a headwater catchment. Compared to VAR, MVAR adjusts model states at remote cells by larger margins and reduces the Mean Squared Error of streamflow analysis by 2–8% at the outlet Tiff City, and by 1–10% at the interior location Lanagan.https://www.mdpi.com/2571-9394/2/4/28mean field biasdata assimilationdistributed hydrologic modelstreamflow |
| spellingShingle | Haksu Lee Haojing Shen Dong-Jun Seo Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic Models Forecasting mean field bias data assimilation distributed hydrologic model streamflow |
| title | Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic Models |
| title_full | Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic Models |
| title_fullStr | Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic Models |
| title_full_unstemmed | Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic Models |
| title_short | Mean Field Bias-Aware State Updating via Variational Assimilation of Streamflow into Distributed Hydrologic Models |
| title_sort | mean field bias aware state updating via variational assimilation of streamflow into distributed hydrologic models |
| topic | mean field bias data assimilation distributed hydrologic model streamflow |
| url | https://www.mdpi.com/2571-9394/2/4/28 |
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