Operational prediction of river-groundwater exchange, groundwater levels and aquifer storage: The Wairau Plain Aquifer

Groundwater is a major resource for drinking water supply and irrigation of crops in many parts of the world. Many groundwater aquifers are already fully allocated, but the demand is projected to increase further while, concurrently, climate change may cause more variability in the natural supply. This poses enormous challenges for the future management of groundwater resources and a paradigm shift from traditional, threshold-based management strategies to more flexible, adaptive management strategies.

For that purpose, operational forecasting tools are required that predict future states of groundwater aquifers under various scenarios and to predict the risk of critical states which would have adverse effects either for the environment or for water users, or both.

The mathematical description of the complex interactions particularly of shallow, unconfined river-fed aquifers typically requires the use of spatially explicit numerical models. These are, however, not suitable for operational forecasting due to lengthy run-times and extensive data requirements. This also poses strong limitations with respect to predictive uncertainty analysis – which should be an integral part of predictive management tools. Model simplification or model surrogates are the method of choice to circumvent the problem.

An operational forecasting tool is presented here to predict groundwater heads and groundwater storage in the unconfined Wairau Plain Aquifer in Marlborough, New Zealand, during flow recession times. The tool uses low-complexity “eigenmodels” to describe groundwater flow and to provide an early warning for critical groundwater storage levels to the Marlborough District Council, which manages the groundwater resource. These critical levels have been approached more frequently during the past years when the natural recession of groundwater storage in summer is exacerbated by groundwater abstraction to satisfy the irrigation water demand of the Plain’s viticulture.

The forecasting tool requires, amongst others, daily forecasts of Wairau River flows because the river is the major recharge source for the aquifer. Flow forecasts and their uncertainty are computed i) by using a master recession curve for predictions during flow recession times and ii) by a lumped rainfall-runoff model for times of aquifer storage recovery. This allows a broad evaluation of forecasting scenarios. The tool has been tested and is operational for recession times (worst-case scenario predictions; Wöhling & Burbery, 2020). The rainfall-runoff model performs reasonably well in predicting river flows and correspondingly in predicting groundwater storage recovery for historic data (hindcasting). The 30-day predictive uncertainty bounds generally cover the observations of river flows, groundwater levels and aquifer storage. The predictive accuracy of the tool largely depends on the predictive accuracy of the drivers, particularly of the areal estimates of precipitation that drives the rainfall-runoff model and the river-groundwater exchange function that describes aquifer recharge rates.

 

References

Wöhling T, Burbery L (2020). Eigenmodels to forecast groundwater levels in unconfined river-fed aquifers during flow recession. Science of the Total Environment, 747, 141220, doi: 10.1016/j.scitotenv.2020.141220.