Understanding groundwater droughts using detrended historical meteorological data and long-term groundwater modelling

Abstract

Groundwater is a vital resource for various water users in the Netherlands. However, due to a changing climate, increasing water demand and changes in the water system, the country is increasingly exposed to groundwater droughts. Water managers use various indicators and statistics to identify groundwater droughts. These indicators characterise the drought for example in terms of intensity, duration and probability of occurrence. Often, these indicators require information on long-term average groundwater conditions and extreme situations that can occur over long periods. However, the availability of long-term groundwater observations of more than ten years in length is limited. Particularly, extreme groundwater drought events are ill-described and subject to large uncertainty in their characterisation. This study explores a novel method for obtaining long-term phreatic groundwater levels by combining a data-driven time series model using transfer function-noise modelling with detrended historical meteorological time series representing the current climate. The method is applied to an area in the Netherlands to generate groundwater levels for the period 1910–2022. Our results reveal differences in the characterisation of groundwater droughts when the extended groundwater time series are compared with observations of a limited duration (eight years). Using the 2018 summer drought event as an example, we find that the probability of this groundwater drought occurring is approximately once every twelve years, based on the eight-year observation period. However, this probability reduces to a once every 24-year event when using historically generated groundwater time series to characterise the groundwater drought. We conclude that characterising droughts with the extended groundwater time series based on historical meteorological data can provide a more comprehensive insight into the intensity and frequency of groundwater droughts, as well as the probability of occurrence of current groundwater levels. Hence, the proposed method supports water managers in establishing return period-based criteria for measures, such as deciding when to implement irrigation bans.

Keywords

• Data-driven modelling
• groundwater statistics
• Pastas
• transfer function-noise modelling
• water resource management