{"id":2413,"date":"2024-11-13T12:56:32","date_gmt":"2024-11-13T12:56:32","guid":{"rendered":"https:\/\/research.reading.ac.uk\/met-darc\/?p=2413"},"modified":"2024-11-13T12:56:32","modified_gmt":"2024-11-13T12:56:32","slug":"improving-soil-moisture-prediction-using-joint-state-parameter-assimilation-in-jules","status":"publish","type":"post","link":"https:\/\/research.reading.ac.uk\/met-darc\/2024\/11\/13\/improving-soil-moisture-prediction-using-joint-state-parameter-assimilation-in-jules\/","title":{"rendered":"Improving Soil Moisture Prediction Using Joint State-Parameter Assimilation in JULES"},"content":{"rendered":"

By Visweshwaran Ramesh<\/span><\/p>\n

\n
Accurate soil moisture predictions are essential for effective environmental management, whether it\u2019s preparing for floods or supporting agricultural planning. Soil moisture impacts everything from crop growth to weather patterns, but accurately capturing it remains challenging. Our recent study introduces a new approach to tackle this issue in the <\/span>Joint UK Land Environment Simulator (JULES)<\/strong>, a widely used land surface model.<\/span><\/div>\n<\/div>\n

Joint State-Parameter Assimilation<\/strong><\/p>\n

We developed a joint state-parameter assimilation framework by simultaneously updating both the Pedotransfer Function (PTF) constants (which link to JULES\u2019s hydraulic parameters) and the initial soil moisture conditions. Pinnington et al. (2020) previously developed the Land Variational Ensemble Data Assimilation Framework (LAVENDAR)<\/strong> for parameter estimation in JULES, which we modified to update both the state and parameters. This integrated approach manages uncertainty more effectively and represents the first application of this method in JULES, with the goal of improving predictive accuracy across various soil types in the UK.<\/p>\n

Data Assimilation Approach<\/strong><\/h4>\n

We adopted the hybrid 4D-Ensemble Variational (4DEnVar)<\/strong> approach, as it enhances land surface applications by capturing non-linear error evolution over time, leading to more accurate state and parameter estimates. It also removes reliance on complex adjoint models and offers greater computational efficiency by integrating ensemble forecasts within a variational framework.<\/p>\n

Comparative Study<\/strong><\/h4>\n

To demonstrate the advantage of state-parameter assimilation, we compare it with two additional assimilation scenarios: optimizing initial soil moisture conditions (state-only assimilation) and adjusting soil hydraulic parameters by optimizing PTF constants (parameter-only assimilation).<\/p>\n

The study examined 16 COSMOS-UK<\/strong> (Cosmic-ray Soil Moisture Monitoring Network)<\/strong> sites across the UK, covering three primary soil types: typical mineral soil, mineral soil with high organic content, and calcareous mineral soil (Figure 1C).<\/p>\n

Why COSMOS-UK Data Makes a Difference<\/strong><\/h4>\n

Most traditional models, including JULES, rely on pre-set values for soil characteristics that don\u2019t always match real-world conditions. To improve accuracy, we used soil moisture observations from the COSMOS-UK, which provides localized, field-scale measurements across diverse UK landscapes. COSMOS-UK stands out for its ability to capture soil moisture over large areas (up to 25-30 hectares) and at greater depths (up to 80 cm), providing continuous, real-time data (hourly temporal scale) that helps track variations with high accuracy (Cooper et al. 2021).<\/p>\n

Improved Accuracy Across Varied Soil Types<\/strong><\/h4>\n

The joint state-parameter assimilation brought the best results, significantly improving the model\u2019s accuracy. The Kling Gupta Efficiency (KGE)<\/strong>, a standard measure of model performance, rose by 143%, from 0.33 to 0.72 on average across different soil characteristics (Figure 1B). This improvement was particularly pronounced in areas with challenging soil types, such as high-organic soils. Models often struggle with these areas due to complex soil properties, but our approach achieved major accuracy gains, with a maximum improvement of 350% at the GISBN site (Figure 1A).<\/p>\n

\"\"
Figure 1: A) Modelled (ensemble mean) and observed soil moisture time series for the state-parameter assimilation scenario at the GISBN site during the assimilation (2017) and forecast (2018) periods. B) The KGE metrics across all 16 sites in the UK. C) Location of COSMOS-UK sites selected for this study.<\/figcaption><\/figure>\n

This study marks the first use of joint state-parameter assimilation with JULES, opening new doors for soil moisture prediction in various landscapes. With COSMOS-UK data enhancing its precision, the model is better equipped to support sustainable land management and inform decisions in water resource planning, flood control, and agriculture.<\/p>\n

References<\/strong><\/h4>\n

Pinnington, E., et al. (2020). The Land Variational Ensemble Data Assimilation Framework: LAVENDAR v1.0.0, Geoscientific Model Development, 13, 55\u201369. https:\/\/doi.org\/10.5194\/gmd-13-55-2020<\/a><\/p>\n

Cooper, E., et al. (2021). Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations, Hydrology and Earth System Sciences, 25, 2445\u20132458. https:\/\/doi.org\/10.5194\/hess-25-2445-2021<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

By Visweshwaran Ramesh Accurate soil moisture predictions are essential for effective environmental management, whether it\u2019s preparing for floods or supporting agricultural planning. 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