On the need for better groundwater initial conditions estimation in seasonal forecasts: a data assimilation scheme for Aqui-FR hydrometeorological modelling platform. Example with the regional case study of the Somme basin (France).

In France, groundwater is one of the most important resources for industry, agriculture, and drinking water. While severe droughts affecting groundwater are becoming more frequent, the development of forecast has become essential for stakeholders. The hydro-meteorological platform Aqui-FR (Vergnes et al., 2020), which integrates different regional groundwater models, is coupled with atmospheric reanalysis and downscaled seasonal forecasts (Willemet et al., 2022) to achieve this goal.

However, biases and errors in the models used still affect the predicted initial conditions (IC), limiting the potential for operational use. To overcome these problems, a data assimilation (DA) scheme has been developed within the Aqui-FR workflow. The analysis step focuses on state estimation, and more specifically on piezometric (groundwater) levels during a reanalysis run. An Ensemble Kalman Filter (EnKF ; Evensen, 1994) has been implemented in a Python library (aquida) to set up a sequential DA. Two inflation methods and two localisation methods (quasi-Gaussian distance-based and spatial autocorrelation-based) are used.

To analyse the efficiency of the DA scheme, this first study focuses on one of the regional models of the Aqui-FR platform, the Somme basin model which uses the MARTHE hydrogeological computer code (Thiéry et al., 2020) to simulate both piezometric levels and river discharge. In situ piezometric data from monitored wells will be assimilated.

Preliminary results obtained from our numerical experiments show the benefit of DA on groundwater state estimation with a regional model (mean RMSE reduced from 4.26 to 0.32), even with spatially sparse data. When assimilation is stopped, the analysis shows an impact on state estimation up to a seasonal time step (mean RMSE about 2.9 after 180 days without assimilation), which is encouraging for forecast improvements. Nevertheless, in regions of the model domain where the initial calibration is too poor, the correction shows less persistence and the dynamics of the model appear to be driven by parameters rather than forcing. To improve the piezometric estimation in these areas, we plan to implement a two-step DA with parameter estimation prior to state estimation.

 

References

Evensen, G. (1994). Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics.  Journal of Geophysical Research: Oceans, 99 (C5), 10143–10162. https://doi.org/10.1029/94JC00572
Thiéry, D., Picot-Colbeaux, G., & Guillemoto, Q. (2020). Guidelines for MARTHE v7.8 computer code for hydro-systems modelling (English version) (tech.  rep. No. BRGM/RP69660-FR). BRGM.
Vergnes, et al. (2020). The AquiFR hydrometeorological modelling platform as a tool for improving groundwater resource monitoring over France: Evaluation over a 60-year period. Hydrology and Earth System Sciences, 24 (2), 633–654. https://doi.org/10.5194/hess-24-633-2020
Willemet, J.-M., et al. (2022) Aqui-FR: Towards a hydro-geological seasonal forecasting system for metropolitan France. In: IAHS-AISH Scientific Assembly. IAHS2022-525. Montpellier, France: Copernicus Meetings. https://doi.org/10.5194/iahs2022-525