Abstract. The 2018–2019 drought in north-western and central Europe caused severe damage to a wide range of sectors. It also emphasised the fact that, even in countries with temperate climates, adaptations are needed to cope with increasing future drought frequencies. A crucial component of drought management strategies is to monitor the status of groundwater resources. However, providing up-to-date assessments of regional groundwater drought development remains challenging due to the limited availability of high-quality data. This limits many studies to small selections of groundwater monitoring sites, giving an incomplete image of drought dynamics. In this study, a time series modelling-based method for data preparation was developed and applied to map the spatio-temporal development of the 2018–2019 groundwater drought in the south-eastern Netherlands, based on a large set of monitoring data. The data preparation method was evaluated for its usefulness and reliability for data validation, simulation, and regional groundwater drought assessment. The analysis showed that the 2018–2019 meteorological drought caused extreme groundwater drought throughout the south-eastern Netherlands, breaking 30-year records almost everywhere. Drought onset and duration were strongly variable in space, and higher-elevation areas suffered from severe drought well into 2020. Groundwater drought development appeared to be governed dominantly by the spatial distribution of rainfall and the landscape type. The time series modelling-based data preparation method was found to be a useful tool to enable a spatially detailed record of regional groundwater drought development. The automated time series modelling-based data validation improved the quality and quantity of useable data, although optimal validation parameters are probably context dependent. The time series simulations were generally found to be reliable; however, the use of time series simulations rather than direct measurement series can bias drought estimations, especially at a local scale, and underestimate spatial variability. Further development of time-series-based validation and simulation methods, combined with accessible and consistent monitoring data, will be valuable to enable better groundwater drought monitoring in the future.

Abstract. The 2018–2019 drought in northwestern Europe caused severe damage to a wide range of sectors, and has made clear that even in temperate-climate countries adaptations are needed to cope with increasing future drought frequencies. A crucial component of drought management strategies is to monitor the status of groundwater resources. However, providing up-to-date assessments of regional groundwater drought development remains challenging due to the limited quality of available data. This limits many studies to small selections of groundwater monitoring sites, giving an incomplete image of drought dynamics. In this study, a time series modelling-based method for data preparation was developed and applied to map the spatiotemporal development of the 2018–2019 groundwater drought in the southeastern Netherlands, based on a large set of monitoring data. The data preparation method was evaluated for its usefulness and reliability for groundwater drought quantification and prediction. The analysis showed that the 2018–2019 meteorological drought caused extreme groundwater drought throughout the southeastern Netherlands, breaking 30-year records almost everywhere. Drought onset and duration were strongly variable in space, with especially higher elevated areas remaining in severe drought well into 2020. Groundwater drought development appeared to be governed dominantly by the spatial distribution of rainfall and the geological-topographic setting. The time series modelling-based data preparation method was found a useful tool to enable a detailed, consistent record of regional groundwater drought development. Applying a validation step before analysis turned out to be important for good results. The time series simulations were generally found to be reliable; however, the use of time series simulations rather than direct measurement series can bias drought estimations especially at a local scale, and underestimate spatial variability. Finally, time series modelling showed to be a promising tool for regional-scale drought nowcasting and prediction. Further development of time-series based validation and simulation methods, combined with accessible and consistent monitoring data, will be valuable to enable better groundwater drought monitoring in the future.

<p>The 2018-2019 drought in northwestern Europe caused severe damage to a wide range of sectors, and has made clear that even in temperate-climate countries adaptations are needed to cope with increasing future drought frequencies. A crucial component of drought strategies is to monitor the status of groundwater resources. However, providing up-to-date assessments of regional groundwater drought development remains challenging due to the limited quality of available data. We set up a time series modelling-based method for data preparation to map the spatiotemporal development of the 2018-2019 groundwater drought in the southeastern Netherlands, based on a large amount of monitoring data. The data preparation method was evaluated for its usefulness and reliability for groundwater drought studies and prediction. The analysis showed that the 2018-2019 meteorological drought caused extreme groundwater drought throughout the southeastern Netherlands, breaking 30-year records almost everywhere. Drought onset and duration were strongly variable in space. Groundwater drought development appeared to be governed dominantly by the spatial distribution of rainfall and the geological-topographic setting. The time series modelling-based data preparation method was found a useful tool for the given situation to enable a detailed, consistent record of groundwater drought development. The time series simulations were generally found to be reliable; however, the use of time series simulations rather than direct measurement series may bias drought estimations especially at a local scale, and underestimate spatial variability. Finally, time series modelling was also found a promising tool for regional-scale drought nowcasting and prediction. Further development of time-series based validation and simulation methods, combined with accessible and consistent monitoring data, will be valuable to enable better groundwater drought monitoring in the future. </p>

<p>In 2018-2020 water managers in the Netherlands were confronted with extreme drought. This event had a large impact on nature, agriculture, shipping and drinking water supply. To better anticipate dry conditions and improve water management during a drought, up-to-date and accurate information about the meteorological and hydrological situation is crucial. During the 2018 drought it became clear that current information about groundwater levels was scattered across many different organisations. In addition, each organisation had different methods to compare current groundwater levels with historical data to indicate the severity of the drought event. There was a clear need for an uniform indication of drought severity.</p> <p>We developed an online information portal with up-to-date measurements for precipitation and groundwater levels. To quantify the drought severity, the Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiraton Index (SPEI) and Standardized Groundwater Index (SGI) are determined. The availability of long-term records (30> years) of groundwater observations is limited for most regions in the Netherlands. Therefore, the SGI is based on simulations with a time series model for all locations for the same period (27 years). Time series models are developed for 5818 wells with observations. Several criteria have been applied to evaluate the time series model, for example, a minimum value of the explained variance, resulting in 1931 wells for which SGI values are calculated. We have also compared SGI values directly derived from observations with the SGI values from simulated groundwater levels for locations with longer time periods. This comparison indicated that due to errors or missing values in observations, the SGI values from simulations are more reliable to gain a global overview of the drought situation.</p> <p>By combining the information on meteorological and hydrological drought in one decision-support system (www.droogteportaal.nl), water managers and stakeholders can now get an up-to-date overview of the current situation. Due to the uniform determination of drought severity, regions within the Netherlands can be compared. This can help to implement targeted water management decisions for adaptation measures for mitigating drought impacts. Part of the information of the portal is also included in the national drought monitor of Rijkswaterstaat (Dutch Ministry of Infrastructure and Water Management). At the moment, the portal gives forecasted information for 7 days, but the data provides an excellent opportunity to include forecasts on longer timescales ((sub-)seasonal) to improve water management.</p>