Eigenmodels to forecast groundwater levels in unconfined river-fed aquifers during flow recession

Abstract

Low-land alluvial gravel aquifers are formed from, and tend to be recharged, by rivers. These interconnected river - groundwater systems can be highly dynamic with groundwater levels following the seasonality of the hydrological regime of the river. The associated groundwater resources are regularly under stress during summer periods when abstractive demand is high and recharge is low. Predicting lead-times for critical groundwater levels allows for a more flexible and adaptive groundwater management. An eigenmodel approach is proposed here as a way of making such predictions, fast and efficiently. The eigenmodel is a mathematical concept that represents the hydraulic function of a groundwater aquifer as a set of conceptual linear reservoirs, arranged in-series. River recharge, land surface recharge, and groundwater abstraction for irrigation are considered as model forcings. The eigenmodel approach is demonstrated on three wells of the unconfined Wairau Aquifer in the Marlborough District of New Zealand, which are used for water resources management. Individual eigenmodels were calibrated to historic data and predictive uncertainty bounds were determined by Markov chain Monte Carlo sampling. Hindcasting of past recession periods showed a low predictive error of the models and a good coverage of the predictive uncertainty bounds. The main advantage of the approach is a 4-orders of magnitude higher computational efficiency compared to a numerical benchmark model. This allows for probabilistic simulation in operational forecasting of groundwater levels. The framework is implemented as a web application for 30-day operational forecasts that comprises automatic data downloads and model input generation, stochastic simulation, uncertainty estimation, visualization, and daily updates on a website.