Improving plant-available water estimation using model averaging of national soil water models
IntroductionMultiple operational soil water balance (SWB) models provide real-time estimates of soil moisture across Australia, yet differences in model structure and outputs introduce uncertainty for end users. Model averaging offers a potential pathway to improve predictions, but previous studies...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Soil Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fsoil.2025.1629686/full |
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| author | Brendan P. Malone Ross D. Searle Siyuan Tian Thomas F. Bishop Yi Yu Yi Yu Yi Yu |
| author_facet | Brendan P. Malone Ross D. Searle Siyuan Tian Thomas F. Bishop Yi Yu Yi Yu Yi Yu |
| author_sort | Brendan P. Malone |
| collection | DOAJ |
| description | IntroductionMultiple operational soil water balance (SWB) models provide real-time estimates of soil moisture across Australia, yet differences in model structure and outputs introduce uncertainty for end users. Model averaging offers a potential pathway to improve predictions, but previous studies have largely applied static weighting schemes. This study investigates a temporally dynamic implementation of the Granger–Ramanathan (GRA) model averaging approach to improve in situ and spatial estimates of plant-available water (PAW) in southeastern and southern Australia.MethodsTwo hypotheses were tested: (1) that GRA model averaging improves point-scale PAW predictions compared to individual models, and (2) that spatially scaling GRA coefficients produces more accurate PAW maps than equal-weight averaging. Soil moisture sensor networks from three study regions were used to evaluate GRA performance at the probe scale. Spatial implementations of GRA were developed using temporally varying coefficients, with and without environmental covariates, and compared against static models and simple averaging.ResultsAt the point scale, GRA consistently outperformed individual SWB models and equal weighting, achieving higher concordance with sensor observations (e.g., mean concordance of 0.87 at Boorowa, 0.73 at Muttama, and 0.90 at Eyre Peninsula, compared to 0.29–0.53 for individual models and 0.05–0.60 for equal weighting). Spatial GRA with dynamic coefficients improved mapping performance relative to static approaches, but incorporating environmental covariates did not consistently enhance accuracy and in some cases reduced model generalizability.DiscussionDynamic GRA model averaging provides a practical framework for integrating multiple national-scale SWB models to improve real-time PAW prediction, particularly at well-instrumented locations. However, scaling these benefits to landscape mapping remains challenging when sensor networks are sparse or unevenly distributed. The approach has potential applications in agricultural decision-making and environmental monitoring, but further refinement is needed to optimise spatial implementations. |
| format | Article |
| id | doaj-art-9f5b78aa0a1943f1a26edf32fc8e45ef |
| institution | Kabale University |
| issn | 2673-8619 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Soil Science |
| spelling | doaj-art-9f5b78aa0a1943f1a26edf32fc8e45ef2025-08-26T05:27:50ZengFrontiers Media S.A.Frontiers in Soil Science2673-86192025-08-01510.3389/fsoil.2025.16296861629686Improving plant-available water estimation using model averaging of national soil water modelsBrendan P. Malone0Ross D. Searle1Siyuan Tian2Thomas F. Bishop3Yi Yu4Yi Yu5Yi Yu6CSIRO Agriculture and Food, Black Mountain, ACT, AustraliaCSIRO Agriculture and Food, St Lucia, QLD, AustraliaFenner School of Environment and Society, Australian National University, Canberra, ACT, AustraliaSydney Institute of Agriculture, School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, AustraliaCSIRO Agriculture and Food, Black Mountain, ACT, AustraliaFenner School of Environment and Society, Australian National University, Canberra, ACT, AustraliaSydney Institute of Agriculture, School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW, AustraliaIntroductionMultiple operational soil water balance (SWB) models provide real-time estimates of soil moisture across Australia, yet differences in model structure and outputs introduce uncertainty for end users. Model averaging offers a potential pathway to improve predictions, but previous studies have largely applied static weighting schemes. This study investigates a temporally dynamic implementation of the Granger–Ramanathan (GRA) model averaging approach to improve in situ and spatial estimates of plant-available water (PAW) in southeastern and southern Australia.MethodsTwo hypotheses were tested: (1) that GRA model averaging improves point-scale PAW predictions compared to individual models, and (2) that spatially scaling GRA coefficients produces more accurate PAW maps than equal-weight averaging. Soil moisture sensor networks from three study regions were used to evaluate GRA performance at the probe scale. Spatial implementations of GRA were developed using temporally varying coefficients, with and without environmental covariates, and compared against static models and simple averaging.ResultsAt the point scale, GRA consistently outperformed individual SWB models and equal weighting, achieving higher concordance with sensor observations (e.g., mean concordance of 0.87 at Boorowa, 0.73 at Muttama, and 0.90 at Eyre Peninsula, compared to 0.29–0.53 for individual models and 0.05–0.60 for equal weighting). Spatial GRA with dynamic coefficients improved mapping performance relative to static approaches, but incorporating environmental covariates did not consistently enhance accuracy and in some cases reduced model generalizability.DiscussionDynamic GRA model averaging provides a practical framework for integrating multiple national-scale SWB models to improve real-time PAW prediction, particularly at well-instrumented locations. However, scaling these benefits to landscape mapping remains challenging when sensor networks are sparse or unevenly distributed. The approach has potential applications in agricultural decision-making and environmental monitoring, but further refinement is needed to optimise spatial implementations.https://www.frontiersin.org/articles/10.3389/fsoil.2025.1629686/fullmodel averaging methodsoil moisturesoil moisture sensingGranger-Ramanathan averagingdigital soil mappingspatio-temporal modelling |
| spellingShingle | Brendan P. Malone Ross D. Searle Siyuan Tian Thomas F. Bishop Yi Yu Yi Yu Yi Yu Improving plant-available water estimation using model averaging of national soil water models Frontiers in Soil Science model averaging method soil moisture soil moisture sensing Granger-Ramanathan averaging digital soil mapping spatio-temporal modelling |
| title | Improving plant-available water estimation using model averaging of national soil water models |
| title_full | Improving plant-available water estimation using model averaging of national soil water models |
| title_fullStr | Improving plant-available water estimation using model averaging of national soil water models |
| title_full_unstemmed | Improving plant-available water estimation using model averaging of national soil water models |
| title_short | Improving plant-available water estimation using model averaging of national soil water models |
| title_sort | improving plant available water estimation using model averaging of national soil water models |
| topic | model averaging method soil moisture soil moisture sensing Granger-Ramanathan averaging digital soil mapping spatio-temporal modelling |
| url | https://www.frontiersin.org/articles/10.3389/fsoil.2025.1629686/full |
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