the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Feb 2025
Drought Propagation and Ecosystem Resilience in a Peri-Urban Catchment of Berlin-Brandenburg
Abstract. This study investigates the drought dynamics and their effects on surface water, groundwater, and vegetation across the Tegeler Fließ catchment in Berlin/Brandenburg from November 2008 to April 2021. Calculating drought indices for atmospheric, hydrological and groundwater drought, namely the Standardised Precipitation Index (SPI), the Standardised Surface Water Level Index (SSWLI) and the Standardised Groundwater Level Index (SGLI), respectively, the analysis identifies station-specific drought events and their propagation across three locations: Schildow, Luebars, and Tegel. The three indices allow us to take a closer look into the differences and the propagation of drought processes over different parts of the hydrological system. The study also assesses the impact of drought on vegetation health using the Normalized Difference Vegetation Index (NDVI). Our results strongly differ at different locations: the peri-urban area (Tegel) experienced the most severe and prolonged groundwater droughts, while the groundwater in the nature reserve and fen meadow area (Schildow) remained more resilient but faced significant surface water stress. Agricultural land (Luebars) displayed variability in both surface and groundwater responses, with surface water systems being more resilient. NDVI analysis revealed that vegetation remained largely within moderate to dense classes throughout the study period, showing resilience despite severe drought conditions from 2018 to 2020. Spearman correlation tests did not show any significant relationship between NDVI and drought indices, while Granger causality tests revealed that SPI, and for some stations also SSWLI, significantly Granger-caused NDVI with a lag of one month. These findings highlight the need for localized drought management strategies tailored to both surface and groundwater resources, alongside enhanced vegetation monitoring that goes beyond traditional indices like NDVI.
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RC1: 'Comment on egusphere-2025-471', Anonymous Referee #1, 31 Mar 2025
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The paper titled "Drought Propagation and Ecosystem Resilience in a Peri-Urban Catchment of Berlin-Brandenburg" by Polina Franke, Aryan Goswami and Mark Somogyvari explores the dependencies between hydro-meteorological drought and the resilience of vegetation by analysing monthly time series of drought indices and vegetation indices at a local catchment north-west of Berlin.
I find the toolchain and approach presented in this study to be highly practical for large-scale investigations of drought and vegetation resilience. However, the current methodology is not well-suited for a localized study. The authors have overlooked significant opportunities provided by the specific site, such as ground-truth monitoring, which could have enhanced the robustness of their findings. Given these limitations, I recommend rejection but encourage resubmission following a substantial revision that incorporates site-specific data and methodologies
Major comments
My primary concern is that the authors did not take advantage of the opportunity to visit the field site and use field data to better understand and support the observed differences between the chosen indices and locations. Instead, the manuscript offers speculative explanations without providing the depth of analysis that could have been achieved through direct field observations and data.
For instance,
- l375: “In particular, Tegel groundwater experienced a severe 17-month drought from 2019 to 2020, while Schildow showed more resilience in groundwater levels but showed stress in surface water systems”. How are the aquifer properties in the different areas which may lead to this resilience? For instance, If the porosity is larger, water level declines are less for the same amount of water extracted.
- 309: “ In-situ measures of vegetation health could provide more precise insights and supplement satellite-derived indices in these analyses.” While this is a valid suggestion, the study would benefit from a more comprehensive inclusion of land cover distribution for each MODIS pixel analyzed. The variation in NDVI changes is highly dependent on vegetation type. For instance, if the NDVI signal predominantly reflects trees with deep root systems, a summer drought may have little impact on leaf greenness. In contrast, grasslands are likely to show more noticeable NDVI changes under similar drought conditions. Additionally, although the manuscript briefly mentions the influence of agriculture, it lacks specific and quantitative details. It would be beneficial to clarify the proportion of agricultural land within the MODIS pixels and to specify which types of crops are present in these areas. For a study focused on local conditions, this information should be readily obtainable and incorporated into the analysis. For the reader, at least one picture of each study site would be helpful
Another critique is the usage of the SSWLI index. As the authors state mention by themselve, that “that this approach has a lower accuracy than traditional discharge based indices, as river levels (especially in smaller streams) are more susceptible to nonlinear behavior due to the river profile”, but they did not provide any estimate of how less accurate the chosen approach is. Considering that all gauges are along the same river, different resilience to drought can only be estimated by comparing discharges due to the impact of cross-section and streambed roughnesses variability at each station (see Manning-Strickler Formula). I suggest conducting a number of discharge measurements at each station and deriving a rating curve (https://en.wikipedia.org/wiki/Rating_curve) to convert the water level estimates to discharges.
Based on your map, there appear to be surface tributaries (e.g., Kindelfließ) and a lake (Hermsdorfer Lake) within the study area, which could influence the SSWLI index at the downstream gauges. How might these features impact the observed values? This could help explain the variations in drought conditions observed at each station (Figure 3).
One assumption of your approach is that the dynamics of groundwater and surface water levels can be directly attributed to drought conditions. However, groundwater levels, for example, may also be influenced by anthropogenic factors, such as the lowering of local groundwater levels due to construction activities or changes in the extent of sealed surfaces. Did you consider these potential impacts in your analysis?
Although the authors reference the drought index from the Helmholtz Centre, they did not analyze soil drought conditions, which are conceptually the link between meteorological drought and groundwater drought. If the authors choose not to include this analysis, they should provide a clear justification for this decision.
The study would benefit from a discussion on the applicable scale of your analysis and I feel there is a lack of mentioned studies which did similar works, please provide some more references. Are there limitations to using individual MODIS cells for the indices, considering the sensitivity of values to neighboring cells? Addressing this potential issue could be valuable and could be incorporated into the introduction section.
Minor comments
l18. The second part of the sentence is an empty phrase, as the planet naturally includes the Berlin/Brandenburg region.
L44. SenMVKU reference (and some others) have no year, please update
L46. It is called Helmholtz Centre for Environmental Research and not Helmholtz Institut
L50 – L 68. I believe there is an overlap with Section 2.3. In the introduction, you should list different indices for precipitation, hydrometry, and groundwater resources, discussing their advantages and disadvantages one by one. The specific details of the technique used in this study can be covered in Section 2.3."
L73. Provide reference to Granger Causility test
Figure1: Surface Water instead of Surfacewater
Section 2.2. Here you mentioned the Software “R” first. So here you should reference R and mention, which version did you use
L190: Why did time lag analysis fail? Perhaps you should not mention it here but using it in the discussion section
Figure3: The plot of absolute surface water levels and groundwater levels for comparing different stations provides limited insight. In Surface Water Hydrology, with a focus on water budgets rather than water levels, it is more informative to compare changes in discharge rather than stage, as stage is dependent on discharge and cross-sectional area. Since discharge data is not available, I recommend normalizing the stages and presenting them in a boxplot or histogram to visualize the distribution across all stations. For groundwater levels, the absolute values provide limited context for the reader. Instead, it is more meaningful to assess the distance from the surface, as this helps evaluate how accessible the groundwater is for vegetation, which is a central aspect of your study.Therefore, I suggest revising Section 3.1 to reflect these points, too.
Fig4: All plots should share the X Axis to make temporal comparison more easy.
L285-295: The authors provide several potential explanations for the low NDVI values during the winter months between 2009 and 2011, such as reduced vegetation cover and lower growth rates. I believe the answer is very simple: There was a long lasting snow layer during this years. I recommend checking the DWD snow data to verify this and rephrasing the discussion to provide a more explicit and clear explanation, avoiding any ambiguity.
L313: “:. v”, improve language
L379: If you say 13 years is not enough to capute longer-term climate cycles, why did you start in 2008 and end in 2021?
Citation: https://doi.org/10.5194/egusphere-2025-471-RC1
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