Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN
This research utilized in situ soil moisture observations in a coupled grid Soil and Water Assessment Tool (SWAT) and Parallel Data Assimilation Framework (PDAF) data assimilation system, resulting in significant enhancements in soil moisture estimation. By incorporating Wireless Sensor Network (WSN...
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MDPI AG
2023-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/1/35 |
| _version_ | 1797358217586540544 |
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| author | Ying Zhang Jinliang Hou Chunlin Huang |
| author_facet | Ying Zhang Jinliang Hou Chunlin Huang |
| author_sort | Ying Zhang |
| collection | DOAJ |
| description | This research utilized in situ soil moisture observations in a coupled grid Soil and Water Assessment Tool (SWAT) and Parallel Data Assimilation Framework (PDAF) data assimilation system, resulting in significant enhancements in soil moisture estimation. By incorporating Wireless Sensor Network (WSN) data (WATERNET), the method captured and integrated local soil moisture characteristics, thereby improving regional model state estimations. The use of varying observation search radii with the Local Error-subspace Transform Kalman Filter (LESTKF) resulted in improved spatial and temporal assimilation performance, while also considering the impact of observation data uncertainties. The best performance (improvement of 0.006 m<sup>3</sup>/m<sup>3</sup>) of LESTKF was achieved with a 20 km observation search radii and 0.01 m<sup>3</sup>/m<sup>3</sup> observation standard error. This study assimilated wireless sensor network data into a distributed model, presenting a departure from traditional methods. The high accuracy and resolution capabilities of WATERNET’s regional soil moisture observations were crucial, and its provision of multi-layered soil temperature and moisture observations presented new opportunities for integration into the data assimilation framework, further enhancing hydrological state estimations. This study’s implications are broad and relevant to regional-scale water resource research and management, particularly for freshwater resource scheduling at small basin scales. |
| first_indexed | 2024-03-08T14:58:41Z |
| format | Article |
| id | doaj.art-be156b2444e44431b17c8158e87e980b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T14:58:41Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-be156b2444e44431b17c8158e87e980b2024-01-10T15:08:17ZengMDPI AGSensors1424-82202023-12-012413510.3390/s24010035Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSNYing Zhang0Jinliang Hou1Chunlin Huang2Key Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaKey Laboratory of Remote Sensing of Gansu Province, Heihe Remote Sensing Experimental Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaThis research utilized in situ soil moisture observations in a coupled grid Soil and Water Assessment Tool (SWAT) and Parallel Data Assimilation Framework (PDAF) data assimilation system, resulting in significant enhancements in soil moisture estimation. By incorporating Wireless Sensor Network (WSN) data (WATERNET), the method captured and integrated local soil moisture characteristics, thereby improving regional model state estimations. The use of varying observation search radii with the Local Error-subspace Transform Kalman Filter (LESTKF) resulted in improved spatial and temporal assimilation performance, while also considering the impact of observation data uncertainties. The best performance (improvement of 0.006 m<sup>3</sup>/m<sup>3</sup>) of LESTKF was achieved with a 20 km observation search radii and 0.01 m<sup>3</sup>/m<sup>3</sup> observation standard error. This study assimilated wireless sensor network data into a distributed model, presenting a departure from traditional methods. The high accuracy and resolution capabilities of WATERNET’s regional soil moisture observations were crucial, and its provision of multi-layered soil temperature and moisture observations presented new opportunities for integration into the data assimilation framework, further enhancing hydrological state estimations. This study’s implications are broad and relevant to regional-scale water resource research and management, particularly for freshwater resource scheduling at small basin scales.https://www.mdpi.com/1424-8220/24/1/35soil moistureWSNLESTKFSWAT |
| spellingShingle | Ying Zhang Jinliang Hou Chunlin Huang Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN Sensors soil moisture WSN LESTKF SWAT |
| title | Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN |
| title_full | Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN |
| title_fullStr | Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN |
| title_full_unstemmed | Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN |
| title_short | Basin Scale Soil Moisture Estimation with Grid SWAT and LESTKF Based on WSN |
| title_sort | basin scale soil moisture estimation with grid swat and lestkf based on wsn |
| topic | soil moisture WSN LESTKF SWAT |
| url | https://www.mdpi.com/1424-8220/24/1/35 |
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