Drought is an important natural hazard with large impacts on society. Changes in drought characteristics have been studied for different parts of the hydrological cycle, but insights into changes of groundwater resources are obscured due to the lack of long-term observations and large heterogeneity of hydrogeological conditions. Moreover, predicted future changes in precipitation are uncertain and have a lagged effect on streamflow and groundwater. We investigated past changes and potential future changes in catchment baseflow as a reflection of groundwater drought for 338 headwater catchments across Germany based on catchments' characteristic response times. First, baseflow dynamics as a proxy of groundwater storage and outflow on a catchment scale were derived from streamflow records and related to precipitation input. Second, past trends in baseflow minima were calculated and attributed to climate and catchment controls. Last, response times and the timing of yearly baseflow minima were combined into estimates of the sensitivity to future precipitation changes. Baseflow response times of the studied headwaters are heterogenous across Germany, ranging from a few months to several years, and depend significantly on hydrogeological conditions. Few significant trends were found in past baseflow minima, and trends are highly dependent on the period of analysis. Based on the assumption of a typical regional scenario of increasing winter precipitation and decreasing summer precipitation, increases in hydrological drought hazard or no changes are projected for most parts of Germany. Catchments with longer response times can buffer interannual precipitation shifts, whereas catchments with fractured rocks are sensitive to summer precipitation decreases. These results urge for a surface water and groundwater management based on local groundwater response to precipitation and help to assess impacts of climate change on overall water supply.
Drought is a natural phenomenon occurring in all compartments of the hydrological cycle. Accordingly, it is classified into meteorological drought, hydrological drought, agricultural drought, socio-economic drought and groundwater drought (Mishra and Singh, 2010). Due to the large number of people affected by drought and the high economic loss related to drought events (EC, 2007), it is important to enhance the understanding of drought processes considering projected changes in drought hazard, most importantly due to climate change. Empirical analysis of monitored hydrological time series remains an important tool with which to validate theory-based or model-derived hypotheses on these changes, since projected future changes are often uncertain. Particularly for the groundwater compartment there is a high diversity in response to climate input (Eltahir and Yeh, 1999; Green et al., 2011), making predictions even more difficult. Most of the empirical studies on long-term trends in drought hazard have focused on meteorological drought (e.g. Sheffield et al., 2012; Spinoni et al., 2017) and on hydrological drought (e.g. Stahl et al., 2010; Laaha et al., 2017). For groundwater drought, empirical trend analysis is difficult for two reasons: (i) groundwater time series are usually short or influenced by abstractions, and (ii) where long and natural time series are available, they only give point information.
Some countries and states now display groundwater level anomalies at observation wells
as part of their drought monitoring (e.g. Switzerland:
To overcome the difficulties related to borehole data, in this study we use a baseflow approach to characterize groundwater drought on a catchment scale. We analyse a large dataset of long baseflow time series in central Europe. In this region groundwater is often used as drinking water, and aquifers act as an important buffer to climatic variability. Most droughts start with a deficit in precipitation, especially when precipitation falls as rain (Van Loon and Van Lanen, 2012). For the propagation from a meteorological to a groundwater drought different processes are relevant, i.e. attenuation, delay and pooling (e.g. Peters et al., 2003; Tallaksen et al., 2009; Heudorfer and Stahl, 2017). Therefore, the drought signal in groundwater storage not only depends on current meteorological conditions, but also the previous months are important. A catchment-specific timescale for this dependence may be called the catchment's response time. Response times have been analysed by correlations between groundwater depth and time series of precipitation accumulated for different periods. Studies have found that the response times for borehole water tables (Bloomfield et al., 2015; Van Loon et al., 2017; Leelaruban et al., 2017) or spring discharge (Fiorillo and Guadagno, 2012) vary strongly. Moreover, some studies suggest time lags for the highest correlations between precipitation and groundwater time series because of delayed groundwater response (Bloomfield et al., 2015; Fiorillo and Guadagno, 2012). However, when looking at monthly scales, this lag was always found to be quite small and often non-existent (e.g. Haas and Birk, 2017).
There are two approaches to identifying drought periods in a time series. The climatological approach is based on anomalies and often used also in hydrology to track the propagation of relative seasonal water deficits through the hydrological cycle (e.g. Barker et al., 2016; Kumar et al., 2016). The traditional hydrological approach is the “threshold level approach”, which defines streamflow droughts as events below a certain fixed limit and is therefore focused on actual low water availability (e.g. Yevjevich, 1967; Peters et al., 2006; Tallaksen et al., 2009). In this work we use the term drought according to the threshold level approach; thus we consider drought events as periods of low baseflow in absolute terms. If there is a distinct seasonal regime, droughts mostly occur in the dry season.
Recent work on central European low flows, i.e. periods when streamflow mostly consists of baseflow, found that climate change is expected to alter low flows (Marx et al., 2018; Forzieri et al., 2014; Van Vliet et al., 2015; Gosling et al., 2017). However, the sign and magnitude of change in central Europe are subject to model choice and emission scenario (Marx et al., 2018; Forzieri et al., 2014). Those modelling studies were focused on large river basins, and the change they predicted reflects strongly that of the precipitation change. Marx et al. (2018) found a high correlation between changes in annual precipitation sums and low flows. Stahl et al. (2012) found that hindcasting summer low flow trends with large-scale models suggests a too-homogenous spatial pattern of change compared to trends found in observations. Together with difficulties of models to capture the persistence of drought events in runoff generation found by Tallaksen and Stahl (2014), it can be assumed that some large-scale models do not necessarily resolve the heterogeneity of catchment storage and release for the hydrological response on a headwater catchment scale. However, recent drought events have demonstrated that especially headwaters are prone to groundwater-related drought impacts like shortages in water supply (Van Lanen et al., 2016). This coincides with findings that, independent from elevation, groundwater is an important catchment storage (Staudinger et al., 2017). Thus, predicting future changes in groundwater drought on a catchment scale will be a prerequisite for effective drought management.
Depending on the projected climate change, different scenarios of the future development of natural baseflow can be expected (Fig. 1). If there is an increase (decrease) in precipitation projected for the entire year, flow during the dry season is also expected to increase (decrease). However, if a seasonal shift of precipitation is expected, the future development of flow during the dry season is not that straightforward. It will depend on the timing of seasonal shift and dry period and on the catchment's characteristic response time to precipitation. Stölzle et al. (2014) found that for baseflow drought changes in precipitation are especially relevant during the recharge period, which is dependent on the hydrogeology of the catchment.
Schematic direction of change of natural baseflow in the dry
season under different climate change scenarios:
For many parts of central Europe climate projections indicate a seasonal shift of precipitation to wetter winters and drier summers rather than a consistent increase/decrease (Jacob et al., 2014), urging for statistical tools to assess the prospective changes in baseflow. Knowledge of the seasonal to multi-annual scale of the baseflow response to climatic variation and extremes is therefore particularly important in central Europe under this seasonally diverging expected climate change.
This study aims to explain differences in drought trends by catchment characteristics to allow for more accurate predictions under climate projection uncertainty on a headwater scale. Firstly, past trends in baseflow drought and catchment-relevant response times are analysed. Secondly, past trends are attributed to climatic and catchment controls. Finally, based on these statistics an estimate for future changes in baseflow drought valid for all common emission scenarios and climate models is realized.
The dataset used in this study is the same set of headwater catchments (that
is, all catchment areas are below 200 km
Location of gauges in Germany, catchments' dominant type of porosity (derived from the German hydrogeological map) and mean annual precipitation sums (calculated from European Climate Assessment and Dataset E-OBS).
The selected catchments cover the flat lowland regions in the north of Germany, the low mountain ranges in south-central Germany, and the Alpine foothills and non-glacierized front range in the south. Precipitation varies with highest annual precipitation sums in the Alpine south (> 2000 mm) and lowest sums in the northeast (< 500 mm). Climate in Germany is humid with slightly higher precipitation sums in summer than in winter for most regions. Precipitation was analysed in the form of monthly precipitation sums taken from the European Climate Assessment and Dataset (Haylock et al., 2008), Version 13.1. According to the procedure described in Hellwig et al. (2018), catchment-specific precipitation was calculated as an area-weighted mean of the intersecting grid cells. Hellwig et al. (2018) found that due to the low spatial resolution of the meteorological dataset compared to catchment size there are some biases towards products of higher resolution; however, correlations between products were found to be very high.
Information on the hydrogeology of the catchments was taken from the digital
German hydrogeological map (Hydrogeologische Übersichtskarte von Deutschland
The dominant land use was derived from CORINE Land Cover 2006 data
(available online from the German Environment Agency (
To differentiate the catchments regarding topography, the average height above the catchment's outlet was calculated. This metric describes the potential groundwater waterbody that can contribute to the flow at the gauge and thus characterizes the potential storage. Elevation data (1 arcsec digital elevation model over Europe) were obtained from the European Environment Agency (2013).
Baseflow
Despite the clear concept of baseflow, there is no universally valid way to
separate
To quantify the catchments' baseflow response times to
precipitation (
For all 338 catchments, correlation coefficients
Catchments analysed in this study follow a distinct regime in
Time series of
Trends only give information on the period they are calculated for. Many studies found that trends of a certain period are not part of a trend on another timescale (e.g. Stahl et al., 2010; Hannaford and Buys, 2012; Giuntoli et al., 2013; Hannaford et al., 2013). The analysis of trends for multiple periods may help to assess whether observed trends are steady or rather fluctuating. To evaluate the trends found for the period 1970–2009, we additionally calculated trends over multiple periods for the five gauges with longest continuous records. Three of these are in the porosity class “fractured”, and one each in “porous” and “mixed”.
Trends in
Since past trends derived from empirical trend analysis (e.g. MK) are solely valid for the observation period, they cannot be extrapolated beyond the period of data availability. Future drought predictions therefore mostly rely on climate projections and process modelling. For Germany climate projections indicate little to no changes of annual precipitation sums but seasonal shifts to lower summer precipitation and higher winter precipitation. However, the magnitude of the shifts differs considerably for different projections (Zebisch et al., 2005; Jacob et al., 2012, 2014; Hübener et al., 2017). A common approach to dealing with such uncertainties is to use a range of possible trajectories to model hydrological change. Instead of using uncertain quantitative inputs in forward modelling, here we propose a more qualitative inverse approach. We assume that the general direction of future development is the most important piece of information for future groundwater management planning and formulate a qualitative test scenario of the consistent direction of different projections of future precipitation change. Thus, the approach is scenario-neutral regarding emission scenarios and climate models.
Trends in future baseflow drought hazard were assumed to depend not only on
precipitation changes but also on
The test scenario applied in this study is a decrease of precipitation in
summer (JJA) and an increase of precipitation in winter (DJF) with no change
in the annual precipitation sum. To derive the future change in
Potential directional changes in
Patterns of
The five selected gauges for a trend analysis on multiple periods all have a
negative
None of the factors tested was found to explain past trends of
Attribution of trends in
According to the test scenario,
Future changes in
Relationships of projected changes,
The changes in drought hazard are significantly related to the catchments'
response times
Catchments' response times to precipitation were found to be highly diverse across Germany, ranging from 1 month to 3 years. In general, baseflow response times determined as a proxy of groundwater response are rather short compared to other studies. Fiorillo and Guadagno (2012) found response times of 12 to 24 months for a karst region in southern Italy and highest correlations for shorter precipitation accumulation periods when adding a short time lag. Bloomfield and Marchant (2013) also found in 3 out of 14 cases a time lag for highest correlations. We found time lags to be an exception, supporting the results of Kumar et al. (2016), Barker et al. (2016) and Haas and Birk (2017). This indicates that, in the headwater catchments studied, the delay of the groundwater baseflow response to meteorological conditions may be shorter than 1 month and therefore not detectable on the monthly scale, whereas the attenuation of meteorological variability is clearly attributable to characteristic precipitation accumulation periods.
The large differences of baseflow response times for different porosity
classes match the theoretical assumptions that baseflow strongly depends on
hydrogeological conditions. For the entire streamflow, differences were
found to be much smaller (not shown; compare e.g. Haslinger et al., 2014),
since other processes like overland flow are also important. The patterns of
Consistent with the work by Bloomfield et al. (2015), we found that hydrogeology is a highly relevant factor for the explanation of different groundwater baseflow response times. Kumar et al. (2016) did not find a relationship between the hydraulic conductivity and groundwater response time for boreholes. A possible reason for this different finding is that for point data even small local influences, which are hard to determine, are quite relevant (e.g. human influences), whereas baseflow reflects more the overall situation within the catchment. Small influences may be negligible at this scale, and the underlying influence of hydrogeology may be easier to detect.
In general, the results indicate that groundwater storage – represented by baseflow – is vitally driven by precipitation on a catchment scale in the relevant recharge period. However, the season of low flow is also expected to have an influence: regimes with winter low flows in central Europe are governed by snow storage during that season. Thus, not only precipitation but also temperature is a major factor for that catchments. Moreover, it is impossible to distinguish snowmelt from groundwater outflow during baseflow separation. Therefore, a baseflow approach does not allow for conclusions on groundwater storage in snow-dominated catchments.
The trend analysis revealed that the trend in baseflow minima is highly
dependent on the period it is calculated for. The observation of a trend in
The method employed in this study provides a straightforward projection of the probable future directions of changes in baseflow or groundwater drought under climate change. We accounted for the uncertainty in future climate projections by taking only concordant directions of precipitation change in our approach. Contrary to future climate projections, past trends in precipitation for the catchment-relevant recharge period were found to be small and mostly positive. Similarly, Kopp et al. (2018) found for southern Germany a high variability of annual groundwater recharge without distinct trends. In the past, precipitation trends have not been seasonally diverging, but climate models suggest that changes will become more relevant in the second half of this century (e.g. Jacob et al., 2012). As the magnitude of the trends differs for the climate models, we did not quantify our scenario and thus did not quantify the magnitude of future baseflow either.
Catchments' characteristic response times were assumed to remain constant.
Under a more extreme climate change however, changes in
catchments' responses (e.g. due to non-stationary response
times) cannot be excluded. Based on assumed precipitation change in the
catchments' respective recharge periods, decreasing
Climate change is expected to alter the hydrological drought hazard. However, uncertainty of climate projections and even contrasting seasonal changes impede a straightforward assessment of the prospective changes in central Europe and elsewhere. Here we presented a statistical approach to estimating the potential direction of future changes in hydrological drought hazard. Past trends were found to be too variable to provide a consistent regional picture of past and expected changes because of their high dependency on the trend calculation period. But they did allow the attribution of trends in baseflow to precipitation changes in catchment-specific recharge periods. Based on that information, a more process-oriented approach was developed, using the catchments' characteristic response times to precipitation and the relevant recharge periods for projections valid for all common emission scenarios and climate models. These projections are efficient alternatives to ensemble projections and target the most important information for management. Especially for regions where directions of climate change are seasonally varying, they can provide valuable insights into the basic changes of the system.
Catchments with short response times were found to have a high probability for a decrease in baseflow minima and hence an increase in the groundwater drought hazard, as seasonal changes cannot compensate for each other. However, there is no homogeneous pattern of response times across central Europe, and so predicted changes of groundwater drought hazard also vary regionally. This urges for a regionally adapted groundwater management based on the local catchment response times. As past events like the 2015 central European drought have already caused groundwater-related drought impacts in headwater regions, there is an urgent need for adaptation in catchments facing even higher drought hazard in the future.
The diversity of response times, the dearth of long-time data on groundwater storage, and the absence of distinct past trends in precipitation and hydrological variables limit the potential to generalize the results. On the path to extensive predictions of future groundwater drought hazard across central Europe, further model-based work will be needed. Reproducing the catchment-relevant response times with high-resolution large-scale models may be key for an assessment of future changes and related implications for groundwater management under various scenarios and for ungauged catchments.
Streamflow data are available on request for scientific purposes from the
responsible federal state agencies, i.e. the State Environmental Agency of
Baden-Württemberg, Bavaria, Brandenburg, Hesse, Mecklenburg-Western Pomerania, Lower Saxony, North Rhine-Westphalia,
Rhineland-Palatinate, Saarland, Saxony, Saxony-Anhalt, Schleswig-Holstein and Thuringia. Climate data
are available via the website of the European Climate Assessment and
Dataset (
In groundwater-dominated catchments
To calculate trends, the non-parametric Mann–Kendall (MK) test was applied.
However, the results are affected by serial correlation which increases the
type I error (i.e. rejecting the no-trend hypothesis although there is no
trend). To test for serial correlation in the data, we fitted an
autocorrelation AR(1) model to each
The MK test compares the number of concordant pairs in the data with the
number of discordant pairs. This gives the Kendall score
To detect the influence of a categorical variable with multiple levels on a
numerical variable, an ANOVA was used in this work. The ANOVA compares
The ANOVA gives information about the general significance of the categorical
variable. An equally important piece of information is which of the categorical
variable's levels differ significantly regarding the target variable. This
information was obtained using the post hoc Tukey test. For this analysis
a pairwise
JH and KS designed the study. JH performed all analyses and prepared the manuscript. Both authors revised the manuscript and approved the final version.
The authors declare that they have no conflict of interest.
We acknowledge the data providers at the federal states' agencies for the streamflow data, the E-OBS dataset from the EU-FP6 project
ENSEMBLES (