Making hydrological science fit for climate change: The underrated soil-groundwater-stream continuum

Numerous studies have been performed to assess climate change impacts on hydrological processes. However, a number of recent studies have pointed to some generic shortcomings of actual approaches, e.g.

• Many models have severe problems to map observed trends in groundwater head (Scanlon et al. 2018);
• Rainfall-runoff models often need to be re-calibrated after extended drought periods (Peterson et al. 2021, Fowler et al. 2022);
• Models tend to disregard the positive relationship between groundwater head and flood risk (Berghuijs and Slater 2023).

We hypothesize that these problems are related to a current underrating of the soil-groundwater-stream continuum, particularly of the role of the deep vadose zone. We combined principal component analysis, spectrum analysis and vadose zone modelling applied to more than 500 time series of groundwater head, lake level, and stream discharge in Germany, covering an area of about 90,000 km² in total, and up to 42 years.

Principal component analysis confirmed that groundwater head, lake water level and stream discharge were closely interrelated. Thus the data were merged for subsequent analyses. First order control of the spatial variability of the temporal dynamics was the degree of damping of the hydrological input signal within the vadose zone (Lischeid et al. 2021). Contrary to common assumptions especially the deeper soil layers underneath the rooting zone played an outstanding role in his regard. The degree of damping in groundwater head time series was very closely related to direction and strength of long-term trends. In contrast, there was no clear correlation, e.g., with climatic or land-use trends.

Spectrum analysis allowed to draw generalizable conclusions. From the spectrum analysis perspective the vadose zone acts as a low-pass filter of the input signal. The degree of low-pass filtering can be quantified, e.g., via spectrum analysis of the respective time series. That approach does neither require any additional site specific information nor does it depend on the specific calibration of single models. Damping of a time series via low-pass filtering inevitably results in increasing probability of significant trends even for very long periods, thus explaining the increase of probability of significant trends with thickness of the vadose zone. Another consequence of low-pass filtering is an increase of hydrological memory. For example groundwater head at a depth of 20 m below surface exhibited a memory of roughly 50 years which is massively underestimated by current models.

Groundwater head dynamics had a clear effect on discharge dynamics as well. Similarly as observed for groundwater dynamics hydrographs differed primarily with respect to the degree of damping of the hydrological input signal. Moreover, the degree of damping varied over time and reflected local groundwater head dynamics. It is remarkable that the shape coefficient of the extreme value distribution depends on the degree of damping. Consequently, not only drought risk assessment but flood risk assessment as well needs to consider explicitly deep vadose zone processes and groundwater head dynamics.

References:

Berghuijs and Slater (2023), Environ. Res. Lett., https://doi.org/10.1088/1748-9326/acbecc

Fowler et al. (2022), WRR, https://doi.org/10.1029/2021WR031210

Lischeid et al. (2021), JHyd, https://doi.org/10.1016/j.jhydrol.2021.126096

Peterson et al. (2021), Science, https://doi.org/10.1126/science.abd5085

Scanlon et al. (2018), PNAS, http://ww.pnas.org/cgi/doi/10.1073/pnas.1704665115