2.1. Study Site: River Basin Gersprenz
Located in the temperate climate zone of central Europe, the catchment of the Gersprenz river is a typical low mountain range basin. The river basin was established as a field laboratory by the Chair of Engineering Hydrology and Water Management at the Technical University of Darmstadt in 2016 [
31], with the goals to enhance the understanding of small-scale catchment processes and to establish an ongoing research with varying foci, creating an integrated and interdisciplinary approach towards hydrological research. The Gersprenz basin measures approximately 500 km
2, enclosing the significantly smaller subbasin of the Fischbach, which measures approximately 36 km
2 (
Figure 1). The catchment is part of the river basin district Rhine. The Gersprenz river flows into the lower Main river. In agreement with the Water Framework Directive, the Gersprenz may be classified as a “small river” (catchment size between 100 and 1000 km
2), while the Fischbach may be classified as a “stream” (catchment size below 100 km
2) [
32]. As shown in
Figure 1, the Gersprenz catchment was delineated by the gauge Harreshausen for this study (ID: 24762653) [
33]. The gauge in Groß-Bieberau 2 defines the Fischbach catchment (ID: 24761005) [
33].
For simplicity of affiliation, the Gersprenz catchment from now on will be denoted as GER and the Fischbach catchment will be referred to as FIS, while the outlets will be referred to as GERout and FISout, respectively.
The dimensions of the catchments are reflected in the discharge (Q) of the rivers. Within the 39-year study period, a mean discharge (
) of 3.08 m
3/s was determined for the Gersprenz at the outlet GER
out while
measured 0.34 m
3/s at FIS
out, the outlet of the Fischbach (
Table 1). The lowest recorded daily discharges, the Absolute Minimum Flow (AMF), were 0.37 m
3/s and 0.02 m
3/s for GER
out and FIS
out, respectively, within the period 1980 to 2018.
In FIS, the elevations range from 160 to 600 m above sea level (m.a.s.l.). GER comprises elevations reaching down to 100 m.a.s.l. The average hill slope in FIS (10.4°) is nearly double the size of the average hill slope in GER (6.2°)—based on a Digital Elevation Model (DEM) of 1 m resolution [
34]. The climate changes with varying terrain conditions: The temperature and precipitation data sets retrieved from weather stations in the study region were shown to have distinctive statistical properties, given the different topographical features of the basins. Whilst the average temperature (
) for the study period measured 10.2 °C in GER,
was lower in FIS at 8.8 °C. The average annual precipitation (
) was 774.8 mm in FIS, whereas GER experienced 641.7 mm of rainfall per year on average for the study period 1980 to 2018 [
35]. The climate variable data sets were retrieved from stations in proximity to the gauges. The data should be treated with care, as the stations may not reflect the conditions in the catchment as a whole. The exact locations of the measurement stations are given in the next section.
Different drainage densities further characterize the study area GER and its subcatchment FIS. The so-called drainage density defines the ratio between the total length of a river and the catchment area [
36]. It is determined by the permeability of the subsoil as well as the slope. High drainage densities indicate steeper slopes than low drainage densities. Furthermore, lower drainage densities in catchments with similar climatic conditions indicate higher permeability of subsoils and deep seepage. The drainage density in FIS was shown to be slightly higher than in GER (compare
Figure 2), indicating that, in FIS, impermeable soils may prevail while reflecting the sloping terrain of the subcatchment.
In general, FIS is a typical German low mountain range river basin dominated by coarse substrates and rich in silicates. Different forms of granite and diorite prevail in this area, which is part of the crystalline
Odenwald [
37]. Thus, the water storage capacity in FIS is presumably low. To the north, the catchment area of the Gersprenz passes from the crystalline
Odenwald to the
Reinheimer Hügelland (Reinheimer Hill Country) into the
Untermainebene (lower Main plain). Soft rock soils made of sand, gravel, and clay dominate here. The
Untermainebene consists mostly of tertiary deposits covered by younger river deposits [
31]. Consequently, the lower areas of GER are characterized by higher infiltration rates and water storage capacities.
The groundwater (GW) resources reflect this: large parts of the study area go without any significant GW reservoirs. However, an extended porous aquifer is located in the subsurface north of GER (See
Figure 3). This leads to a higher overall yield in the wells in the northern part of the catchment. The yield of the wells in FIS is estimated to be less than 2 L/s [
38]. While the GW reserves vary noticeably between the northern and southern parts of the study region, the GW recharge rates were shown to be nearly equal in GER and FIS. The Federal Institute for Geosciences and Natural Resources (BGR [
39]) estimated average, annual GW recharge rates for Germany based on a multi-level regression method [
40]. Based on this data, the average annual GW recharge rates were found to be 134 mm/a and 140 mm/a in FIS and GER, respectively. In agreement with the Bavarian State Office for the Environment (LfU), GW recharge should not be set equal to the GW availability [
41]. Especially the crystalline, mountainous areas are often characterized by high GW recharge rates but low underground storage capacities.
On the scale of annual values, the groundwater recharge rate corresponds approximately to the baseflow, which feeds the receiving water body even during periods of low rainfall. Thus, the baseflow is the fraction of the GW feeding into the river, which may be indicated by the Baseflow Index (BFI) [
42]. Kissel and Schmalz [
43] investigated the suitability of various baseflow estimation methods for German low mountain ranges based on the discharge time series retrieved from FIS
out. The examined baseflow separation methods included digital filters, a Mass Balance Filter (MBF), and noncontinuous estimation methods. Based on the results of the conducted analysis, a recommendation to use the Kille method for baseflow estimation was derived for the study region. Consequently, this method was applied to estimate the BFI for GER
out and FIS
out. The plot of the ranked monthly minima from 1980 to 2018 is shown for the respective river gauges in
Figure 4.
The mean baseflow in GER
out is 1.36 m
3/s, which corresponds to a BFI of 0.44. The mean baseflow in FIS
out is 0.149 m
3/s, resulting in a BFI of 0.45. The BFI in FIS
out deviates by 0.01 from the BFI detected by Kissel and Schmalz for the same river gauge. This may be explained by the different study periods; Kissel and Schmalz [
43] determined the BFI based on daily discharge data from 1974 to 2013, while in this study, the computation was based on daily discharge data from 1980 to 2018.
Additional discharge into the Gersprenz takes place through municipal wastewater treatment plants at nine locations. The inlets are located mostly close to settlements. In FIS, no additional water is discharged into the stream [
45]. Anticipating that the additional discharge in GER was not taken into consideration in the determination of the BFI, the baseflow fraction in FIS
out is estimated to be slightly higher than in GER
out, despite the lower storage capacity of the crystalline
Odenwald.
It is common knowledge that hydrological processes are greatly influenced by land-use [
46]. An analysis of the change layers of the CORINE Land Cover data set [
47] showed that no significant increase in sealed soil took place between 1990 to 2018 (reference year) in the study area. Paved areas were augmented by approximately 2%. According to Authorative Topographic-Cartographic Information System (ATKIS) data provided by the Hessian Agency for Land Management and Geoinformation (HVBG [
34]), GER is dominated by agricultural land-use types, which add up to 48.3%. Forests are ranked second with 36.1%, while settlements make up 12.6% of the land-use coverage. In FIS, the prevailing land-use type is forests, with 50.1%. Agriculture and settlements take up 41.8% and 6.5% of the area, respectively (See Schmalz and Kruse [
31] for a more detailed description of land-use types in the research basin). Consequently, water resources in the entire Gersprenz catchment are mainly used for agricultural purposes. However, extractions for private purposes are also notably high and have become an issue during dry periods, as repeatedly reported by local newspapers (e.g., ECHO) [
48]. In 2018, the low flow situation reached a point to which the regional council of Darmstadt imposed restrictions on water withdrawal due to the alarming water scarcity [
49].
2.3. Drought Indices
Studies have shown that there is a close interrelation between the occurrence of droughts and the variability of T and P over time [
50,
51,
52,
53]. Changes in P patterns and increasing T were shown to favor the emergence of droughts. Therefore, this study initially observed the trends and development of annual average T and annual sums of P in the study region. Subsequently, the occurrence of droughts throughout the study period was investigated. In order to adequately identify and classify the severity of drought events, existing indices were used. In agreement with the German Weather Service [
54], the Standardized Precipitation Index (SPI) is the most widely used drought index. Developed by McKee et al. [
55], the index enables classification of drought according to its magnitude while expressing the drought event’s probability (
Table 2). The index may be calculated based on multiple timescales reflecting the impacts of a drought on various water resources [
56].
In this study, the SPI was obtained for each calendar year by fitting a gamma distribution to monthly precipitation values. For an automated computation of the index, the R-Studio package precincton (precipitation intensity, concentration, and anomaly analysis) was used [
57]. The advantage of the SPI index requiring only P data in its computation is also its offset. Especially with climate change and increasing global temperatures [
58], it has become of interest to include evaporation processes as a factor in the identification of drought events. Thus, an extension of the SPI was developed by Vicente-Serrano et al. [
59], namely the Standardized Precipitation-Evapotranspiration Index (SPEI). The SPEI takes into consideration Potential Evapotranspiration (PET) in mm based on T time series and the measurement location. Based on the limited data availability, Hargreaves’ equation was chosen for the determination of PET:
where
is the mean extraterrestrial radiation (mm/a), which is a function of the latitude;
is the temperature difference of the mean monthly maximum temperature and the mean monthly minimum temperature for the respective month of interest (°C); and
is the mean air temperature (°C) [
60]. The SPEI was computed using the R-Studio package SPEI developed by Čadro and Uzunović [
61] for each calendar year of the study period. Both indices were obtained taking into account 3-, 6-, 12-, and 24 months of antecedent rainfall. Therefore, the minimum requirement of 30 years for good results was exceeded [
62]. Based on the analysis of T, P, SPI, and SPEI time series, it was possible to depict exceptionally dry years, so-called drought years, within the study period.
2.4. Low Flow Indices
According to German normative regulations, low flow may be defined as a minimum flow that falls below a certain threshold (DIN4049) [
63]. The thresholds thereby applied are based on the averages of so-called n-day time series [
6,
64]. In this study, the 1-, 7-, and 30-day annual minima (AMIN, AMIN7, and AMIN30) were determined [
65,
66]. In order to ensure comparability with the drought indices, all low flow indices were computed for each calendar year of the study period. The n-day time series showed the development of the lowest daily, weekly, and monthly flows for each year of the study period 1980–2018, consisting of 39 flow rates given in m
3/s. The time series were analyzed for trends as described in the last subsection of this section. The threshold values were calculated based on the n-day time series. In detail, they were derived by creating the mean of the annual minima time series (MAM, MAM7, and MAM30) [
21]. Based on these thresholds, it was possible to determine the characteristic values SumD, MaxD, and SumV (see
Table 3).
These further values enabled us not only to take into account the absolute minima, as given by the n-day time series but also to identify the frequency and duration of low flow events. While
SumD defines
the total number of days with flow under a certain threshold,
MaxD describes the
maximum number of consecutive days with low flow for each year. If the difference between the two parameters is minor, the longest continuous period of low flow is regarded as characteristic for the respective year [
9].
SumV is the
volume deficit in m
3 for each year. As SumV is an absolute number, which is linked to the stream size, a fourth threshold-dependent indicator was determined: MAM7-days is the number of days with MAM7 flow needed to balance out the deficit of each year [
9]. Finally, consulting these indicators, it was possible to identify the years especially effected by low flow and to link these with the prior determined drought years. In addition, the development of the characteristic values throughout the investigative period was examined for trends.
Finally, low flow is significantly influenced by the catchment’s water storage capacity. High storage capacities will buffer meteorological extremes [
68]. The ratio MAM/
indicates the storage capacity of a catchment as well as the variability of the discharge regime throughout the year [
9]. The index ranges between 0 and 1, and the higher the value of the index, the lower the sensitivity of a catchment towards hydrological extremes, such as droughts.
As mentioned, the calculation of low flow extremes and characteristic values was executed based on historical measurement data retrieved from the gauges GER
out (delineating the Gersprenz catchment) and FIS
out (delineating the Fischbach catchment) [
33]. The period for the study (1980–2018) was chosen according to data availability while ensuring that the total amount of years taken into consideration exceeded the minimum requirement for the low flow statistical analysis of 30 years [
21,
67].