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Leire Retegui-Schiettekatte edited this page May 18, 2026 · 6 revisions

Welcome to the AAUDA wiki!

In this Wiki you will find documentation to learn how to use the AAUDA framework.

General description

The Aalborg University Data Assimilation framework (AAUDA) is a Matlab-based framework designed to conduct land hydrological Data Assimilation (DA) experiments. Particularly, it includes scripts to assimilate satellite-based Terrestrial Water Storage (TWS) and Surface Soil Moisture (SSM) observations (retrievals). The current framework offers the following features:

  • Spans various DA techniques belonging to the Ensemble Kalman Filter (EnKF) family, including the classical EnKF and the EnKF-Rescaling approach
  • Includes different covariance localization techniques such as observation space localization and model space localization
  • Offers the possibility to conduct DA experiments in different timescales, ranging daily to monthly.

The framework is currently coupled with the World-Wide Water Resources Assessment (W3RA) water balance model. The original model was developped by Albert van Dijk [1] [2].

Note

Repository in construction

This repository is currently under development. The following features will be added in the following months:

  • SSM DA scripts
  • Multivariate TWS and SSM DA scripts
  • DA through the Ensemble Adjustment Kalman Filter (EAKF) approach
  • Model space mixed localization approach

Folder naming structure

Config file and processing options

Authorship, licence and citation

This framework has been developped by Leire Retegui-Schiettekatte, with notable contributions of Maike Schumacher (monthly EnKF), Nooshin Mehrnegar (river routing model), Ehsan Forootan (EnKF-Rescaling approach), Manuela Girotto (mixed covariance localization approach), and Fan Yang (general DA implementation).

The framework is licensed under a Creative Commons Attribution 4.0 International License. CC BY 4.0

The framework itself can be cited as:

Additionally, the usage of some features of the framework requires additional citations:

  • Use of W3RA model: van Dijk (2010), van Dijk et al. (2013)
  • Use of river routing model: Mehrnegar et al. (2023)
  • Use of monthly EnKF: Schumacher et al. (2018)
  • Use of daily EnKF: Retegui-Schiettekatte et al. (2025a)
  • Use of EnKF-R: Retegui-Schiettekatte et al. (2025b)
  • Use of multivariate EnKF or any localization approach: [to come]
  • Use of EAKF: [to come]

It should be noted that many of the filters have been implemented based on descriptions provided in previous literature. Please, have a look at the scripts and the documentation to make sure that the original authors of each method are also cited.

References

  • Mehrnegar, N., Schumacher, M., Jagdhuber, T., Forootan, E., 2023. Making the Best Use of GRACE, GRACE-FO and SMAP Data Through a Constrained Bayesian Data-Model Integration. Water Resources Research 59, e2023WR034544. https://doi.org/10.1029/2023WR034544
  • Retegui-Schiettekatte, L., Schumacher, M., Madsen, H., Forootan, E., 2025a. Assessing daily GRACE Data Assimilation during flood events of the Brahmaputra River Basin. Science of The Total Environment 975, 179181. https://doi.org/10.1016/j.scitotenv.2025.179181
  • Retegui-Schiettekatte, L., Schumacher, M., Yang, F., Madsen, H., Forootan, E., 2025b. An ensemble Kalman filter with rescaling disaggregation for assimilating terrestrial water storage into hydrological models. Sci Rep 15, 28675. https://doi.org/10.1038/s41598-025-13602-2
  • Schumacher, M., Forootan, E., van Dijk, A.I.J.M., Müller Schmied, H., Crosbie, R.S., Kusche, J., Döll, P., 2018. Improving drought simulations within the Murray-Darling Basin by combined calibration/assimilation of GRACE data into the WaterGAP Global Hydrology Model. Remote Sensing of Environment 204, 212–228. https://doi.org/10.1016/j.rse.2017.10.029
  • van Dijk, A., 2010. The Australian Water Resources Assessment System. Technical Report 3. Landscape Model (version 0.5) Technical Description. CSIRO: Water for a Healthy Country National Research Flagship. Bureau of Meteorology and CSIRO. https://awo.bom.gov.au/assets/notes/publications/Van_Dijk_AWRA05_TechReport3.pdf
  • van Dijk, A.I.J.M., Peña-Arancibia, J.L., Wood, E.F., Sheffield, J., Beck, H.E., 2013. Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide. Water Resources Research 49, 2729–2746. https://doi.org/10.1002/wrcr.20251

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