Articles | Volume 25, issue 8
https://doi.org/10.5194/nhess-25-2783-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-2783-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
19 Aug 2025
Research article | Highlight paper |
| 19 Aug 2025
| 19 Aug 2025
Groundwater recharge in Brandenburg is declining – but why?
Till Francke
Institute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Maik Heistermann
CORRESPONDING AUTHOR
Institute for Environmental Sciences and Geography, University of Potsdam, Potsdam, Germany
Related authors
No articles found.
Xiaoxiang Guan, Viet Dung Nguyen, Paul Voit, Bruno Merz, Maik Heistermann, and Sergiy Vorogushyn
Nat. Hazards Earth Syst. Sci., 25, 3075–3086, https://doi.org/10.5194/nhess-25-3075-2025, https://doi.org/10.5194/nhess-25-3075-2025, 2025
Short summary
Short summary
We evaluated a multi-site stochastic regional weather generator (nsRWG) for its ability to capture the cross-scale extremity of heavy-precipitation events (HPEs) in Germany. We generated 100 realizations of 72 years of daily synthetic precipitation data. The performance was assessed using WEI and xWEI indices, which measure event extremity across spatiotemporal scales. The results show that nsRWG simulates the extremity patterns of HPEs well, although it overestimates short-duration small-extent events.
Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev., 18, 819–842, https://doi.org/10.5194/gmd-18-819-2025, https://doi.org/10.5194/gmd-18-819-2025, 2025
Short summary
Short summary
Multiple methods for measuring soil moisture beyond the point scale exist. Their validation is generally hindered by not knowing the truth. We propose a virtual framework in which this truth is fully known and the sensor observations for cosmic ray neutron sensing, remote sensing, and hydrogravimetry are simulated. This allows for the rigorous testing of these virtual sensors to understand their effectiveness and limitations.
Georgy Ayzel and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 25, 41–47, https://doi.org/10.5194/nhess-25-41-2025, https://doi.org/10.5194/nhess-25-41-2025, 2025
Short summary
Short summary
Forecasting rainfall over the next hour is an essential feature of early warning systems. Deep learning (DL) has emerged as a powerful alternative to conventional nowcasting technologies, but it still struggles to adequately predict impact-relevant heavy rainfall. We think that DL could do much better if the training tasks were defined more specifically and that such specification presents an opportunity to better align the output of nowcasting models with actual user requirements.
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 24, 4609–4615, https://doi.org/10.5194/nhess-24-4609-2024, https://doi.org/10.5194/nhess-24-4609-2024, 2024
Short summary
Short summary
Floods have caused significant damage in the past. To prepare for such events, we rely on historical data but face issues due to rare rainfall events, lack of data and climate change. Counterfactuals, or
what ifscenarios, simulate historical rainfall in different locations to estimate flood levels. Our new study refines this by deriving more-plausible local scenarios, using the June 2024 Bavaria flood as a case study. This method could improve preparedness for future floods.
Daniel Altdorff, Maik Heistermann, Till Francke, Martin Schrön, Sabine Attinger, Albrecht Bauriegel, Frank Beyrich, Peter Biró, Peter Dietrich, Rebekka Eichstädt, Peter Martin Grosse, Arvid Markert, Jakob Terschlüsen, Ariane Walz, Steffen Zacharias, and Sascha E. Oswald
EGUsphere, https://doi.org/10.5194/egusphere-2024-3848, https://doi.org/10.5194/egusphere-2024-3848, 2024
Short summary
Short summary
The German federal state of Brandenburg is particularly prone to soil moisture droughts. To support the management of related risks, we introduce a novel soil moisture and drought monitoring network based on cosmic-ray neutron sensing technology. This initiative is driven by a collaboration of research institutions and federal state agencies, and it is the first of its kind in Germany to have started operation. In this brief communication, we outline the network design and share first results.
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 24, 2147–2164, https://doi.org/10.5194/nhess-24-2147-2024, https://doi.org/10.5194/nhess-24-2147-2024, 2024
Short summary
Short summary
To identify flash flood potential in Germany, we shifted the most extreme rainfall events from the last 22 years systematically across Germany and simulated the consequent runoff reaction. Our results show that almost all areas in Germany have not seen the worst-case scenario of flood peaks within the last 22 years. With a slight spatial change of historical rainfall events, flood peaks of a factor of 2 or more would be achieved for most areas. The results can aid disaster risk management.
Maik Heistermann, Till Francke, Martin Schrön, and Sascha E. Oswald
Hydrol. Earth Syst. Sci., 28, 989–1000, https://doi.org/10.5194/hess-28-989-2024, https://doi.org/10.5194/hess-28-989-2024, 2024
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) is a non-invasive technique used to obtain estimates of soil water content (SWC) at a horizontal footprint of around 150 m and a vertical penetration depth of up to 30 cm. However, typical CRNS applications require the local calibration of a function which converts neutron counts to SWC. As an alternative, we propose a generalized function as a way to avoid the use of local reference measurements of SWC and hence a major source of uncertainty.
Gerd Bürger and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 3065–3077, https://doi.org/10.5194/nhess-23-3065-2023, https://doi.org/10.5194/nhess-23-3065-2023, 2023
Short summary
Short summary
Our subject is a new catalogue of radar-based heavy rainfall events (CatRaRE) over Germany and how it relates to the concurrent atmospheric circulation. We classify reanalyzed daily atmospheric fields of convective indices according to CatRaRE, using conventional statistical and more recent machine learning algorithms, and apply them to present and future atmospheres. Increasing trends are projected for CatRaRE-type probabilities, from reanalyzed as well as from simulated atmospheric fields.
Maik Heistermann, Till Francke, Lena Scheiffele, Katya Dimitrova Petrova, Christian Budach, Martin Schrön, Benjamin Trost, Daniel Rasche, Andreas Güntner, Veronika Döpper, Michael Förster, Markus Köhli, Lisa Angermann, Nikolaos Antonoglou, Manuela Zude-Sasse, and Sascha E. Oswald
Earth Syst. Sci. Data, 15, 3243–3262, https://doi.org/10.5194/essd-15-3243-2023, https://doi.org/10.5194/essd-15-3243-2023, 2023
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of root-zone soil water content (SWC). The signal observed by a single CRNS sensor is influenced by the SWC in a radius of around 150 m (the footprint). Here, we have put together a cluster of eight CRNS sensors with overlapping footprints at an agricultural research site in north-east Germany. That way, we hope to represent spatial SWC heterogeneity instead of retrieving just one average SWC estimate from a single sensor.
Katharina Lengfeld, Paul Voit, Frank Kaspar, and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 1227–1232, https://doi.org/10.5194/nhess-23-1227-2023, https://doi.org/10.5194/nhess-23-1227-2023, 2023
Short summary
Short summary
Estimating the severity of a rainfall event based on the damage caused is easy but highly depends on the affected region. A less biased measure for the extremeness of an event is its rarity combined with its spatial extent. In this brief communication, we investigate the sensitivity of such measures to the underlying dataset and highlight the importance of considering multiple spatial and temporal scales using the devastating rainfall event in July 2021 in central Europe as an example.
Omar Seleem, Georgy Ayzel, Axel Bronstert, and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 23, 809–822, https://doi.org/10.5194/nhess-23-809-2023, https://doi.org/10.5194/nhess-23-809-2023, 2023
Short summary
Short summary
Data-driven models are becoming more of a surrogate that overcomes the limitations of the computationally expensive 2D hydrodynamic models to map urban flood hazards. However, the model's ability to generalize outside the training domain is still a major challenge. We evaluate the performance of random forest and convolutional neural networks to predict urban floodwater depth and investigate their transferability outside the training domain.
Alberto Caldas-Alvarez, Markus Augenstein, Georgy Ayzel, Klemens Barfus, Ribu Cherian, Lisa Dillenardt, Felix Fauer, Hendrik Feldmann, Maik Heistermann, Alexia Karwat, Frank Kaspar, Heidi Kreibich, Etor Emanuel Lucio-Eceiza, Edmund P. Meredith, Susanna Mohr, Deborah Niermann, Stephan Pfahl, Florian Ruff, Henning W. Rust, Lukas Schoppa, Thomas Schwitalla, Stella Steidl, Annegret H. Thieken, Jordis S. Tradowsky, Volker Wulfmeyer, and Johannes Quaas
Nat. Hazards Earth Syst. Sci., 22, 3701–3724, https://doi.org/10.5194/nhess-22-3701-2022, https://doi.org/10.5194/nhess-22-3701-2022, 2022
Short summary
Short summary
In a warming climate, extreme precipitation events are becoming more frequent. To advance our knowledge on such phenomena, we present a multidisciplinary analysis of a selected case study that took place on 29 June 2017 in the Berlin metropolitan area. Our analysis provides evidence of the extremeness of the case from the atmospheric and the impacts perspectives as well as new insights on the physical mechanisms of the event at the meteorological and climate scales.
Paul Voit and Maik Heistermann
Nat. Hazards Earth Syst. Sci., 22, 2791–2805, https://doi.org/10.5194/nhess-22-2791-2022, https://doi.org/10.5194/nhess-22-2791-2022, 2022
Short summary
Short summary
To better understand how the frequency and intensity of heavy precipitation events (HPEs) will change with changing climate and to adapt disaster risk management accordingly, we have to quantify the extremeness of HPEs in a reliable way. We introduce the xWEI (cross-scale WEI) and show that this index can reveal important characteristics of HPEs that would otherwise remain hidden. We conclude that the xWEI could be a valuable instrument in both disaster risk management and research.
Maik Heistermann, Heye Bogena, Till Francke, Andreas Güntner, Jannis Jakobi, Daniel Rasche, Martin Schrön, Veronika Döpper, Benjamin Fersch, Jannis Groh, Amol Patil, Thomas Pütz, Marvin Reich, Steffen Zacharias, Carmen Zengerle, and Sascha Oswald
Earth Syst. Sci. Data, 14, 2501–2519, https://doi.org/10.5194/essd-14-2501-2022, https://doi.org/10.5194/essd-14-2501-2022, 2022
Short summary
Short summary
This paper presents a dense network of cosmic-ray neutron sensing (CRNS) to measure spatio-temporal soil moisture patterns during a 2-month campaign in the Wüstebach headwater catchment in Germany. Stationary, mobile, and airborne CRNS technology monitored the root-zone water dynamics as well as spatial heterogeneity in the 0.4 km2 area. The 15 CRNS stations were supported by a hydrogravimeter, biomass sampling, and a wireless soil sensor network to facilitate holistic hydrological analysis.
Till Francke, Maik Heistermann, Markus Köhli, Christian Budach, Martin Schrön, and Sascha E. Oswald
Geosci. Instrum. Method. Data Syst., 11, 75–92, https://doi.org/10.5194/gi-11-75-2022, https://doi.org/10.5194/gi-11-75-2022, 2022
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools like soil moisture, snow, or vegetation. This study presents a directional shielding approach, aiming to measure in specific directions only. The results show that non-directional neutron transport blurs the signal of the targeted direction. For typical instruments, this does not allow acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates is feasible.
Maik Heistermann, Till Francke, Martin Schrön, and Sascha E. Oswald
Hydrol. Earth Syst. Sci., 25, 4807–4824, https://doi.org/10.5194/hess-25-4807-2021, https://doi.org/10.5194/hess-25-4807-2021, 2021
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) is a powerful technique for retrieving representative estimates of soil moisture in footprints extending over hectometres in the horizontal and decimetres in the vertical. This study, however, demonstrates the potential of CRNS to obtain spatio-temporal patterns of soil moisture beyond isolated footprints. To that end, we analyse data from a unique observational campaign that featured a dense network of more than 20 neutron detectors in an area of just 1 km2.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
Cited articles
Altdorff, D., Heistermann, M., Francke, T., Schrön, M., Attinger, S., Bauriegel, A., Beyrich, F., Biró, P., Dietrich, P., Eichstädt, R., Grosse, P. M., Markert, A., Terschlüsen, J., Walz, A., Zacharias, S., and Oswald, S. E.: Brief Communication: A new drought monitoring network in the state of Brandenburg (Germany) using cosmic-ray neutron sensing, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-3848, 2024. a, b
Amt für Statistik Berlin-Brandenburg: Statistischer Bericht, C IV 11 – u/09, Bewässerung in landwirtschaftlichen Betrieben im Land Brandenburg 2009, Amt für Statistik Berlin-Brandenburg, https://opus4.kobv.de/opus4-slbp/files/3102/SB_C04_11_00_2009u00_BB.pdf (last access: 9 May 2025), 2012. a
Amt für Statistik Berlin-Brandenburg: Flächenerhebung nach Art der tatsächlichen Nutzung in Berlin und Brandenburg, Amt für Statistik Berlin-Brandenburg, https://www.statistik-berlin-brandenburg.de/a-v-3-j (last access: 6 January 2025), 2023. a
Amt für Statistik Berlin-Brandenburg: Pressemitteilung Nr. 71, Amt für Statistik Berlin-Brandenburg, https://www.statistik-berlin-brandenburg.de/071-2024 (last access: 9 May 2025), 2024. a
Anderson, K., Hansen, C., Holmgren, W., Jensen, A., Mikofski, M., and Driesse, A.: pvlib python: 2023 project update, Journal of Open Source Software, 8, 5994, https://doi.org/10.21105/joss.05994, 2024. a
Baroni, G. and Tarantola, S.: A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study, Environ. Model. Softw., 51, 26–34, https://doi.org/10.1016/j.envsoft.2013.09.022, 2014. a
Cao, S., Li, M., Zhu, Z., Wang, Z., Zha, J., Zhao, W., Duanmu, Z., Chen, J., Zheng, Y., Chen, Y., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020, Earth Syst. Sci. Data, 15, 4877–4899, https://doi.org/10.5194/essd-15-4877-2023, 2023. a, b, c
DWD: Daily station observations (temperature, pressure, precipitation, sunshine duration, etc.) for Germany, version 24.3, DWD [data set], https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/kl/ (last access: 6 January 2025), 2024a. a
DWD: Daily precipitation observations for Germany, version 24.3, DWD [data set], https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/daily/more_precip/ (last access: 6 January 2025), 2024b. a
DWD: Raster data set precipitation sums in mm for Germany – HYRAS-DE-PR, version 6.0, DWD [data set], https://opendata.dwd.de/climate_environment/CDC/grids_germany/daily/hyras_de/precipitation/ (last access: 6 January 2025), 2024c. a
Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B., and Otero-Casal, C.: Hydrologic regulation of plant rooting depth, P. Natl. Acad. Sci. USA, 114, 10572–10577, https://doi.org/10.1073/pnas.1712381114, 2017. a
Gash, J. H. C., Lloyd, C. R., and Lachaud, G.: Estimating sparse forest rainfall interception with an analytical model, J. Hydrol., 170, 79–86, https://doi.org/10.1016/0022-1694(95)02697-N, 1995. a
Guan, H. and Wilson, J. L.: A hybrid dual-source model for potential evaporation and transpiration partitioning, J. Hydrol., 377, 405–416, https://doi.org/10.1016/j.jhydrol.2009.08.037, 2009. a
Guerrero-Ramírez, N. R., Mommer, L., Freschet, G. T., Iversen, C. M., McCormack, M. L., Kattge, J., Poorter, H., van der Plas, F., Bergmann, J., Kuyper, T. W., York, L. M., Bruelheide, H., Laughlin, D. C., Meier, I. C., Roumet, C., Semchenko, M., Sweeney, C. J., van Ruijven, J., Valverde-Barrantes, O. J., Aubin, I., Catford, J. A., Manning, P., Martin, A., Milla, R., Minden, V., Pausas, J. G., Smith, S. W., Soudzilovskaia, N. A., Ammer, C., Butterfield, B., Craine, J., Cornelissen, J. H. C., de Vries, F. T., Isaac, M. E., Kramer, K., König, C., Lamb, E. G., Onipchenko, V. G., Peñuelas, J., Reich, P. B., Rillig, M. C., Sack, L., Shipley, B., Tedersoo, L., Valladares, F., van Bodegom, P., Weigelt, P., Wright, J. P., and Weigelt, A.: Global root traits (GRooT) database, Global Ecol. Biogeogr., 30, 25–37, https://doi.org/10.1111/geb.13179, 2021. a
Hofstra, N., New, M., and McSweeney, C.: The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data, Clim. Dynam., 35, 841–858, https://doi.org/10.1007/s00382-009-0698-1, 2010. a, b
Holmgren, W., Hansen, C., and Mikofski, M.: pvlib python: a python package for modeling solar energy systems, Journal of Open Source Software, 3, 884, https://doi.org/10.21105/joss.00884, 2018. a
Kochendorfer, J., Nitu, R., Wolff, M., Mekis, E., Rasmussen, R., Baker, B., Earle, M. E., Reverdin, A., Wong, K., Smith, C. D., Yang, D., Roulet, Y.-A., Buisan, S., Laine, T., Lee, G., Aceituno, J. L. C., Alastrué, J., Isaksen, K., Meyers, T., Brækkan, R., Landolt, S., Jachcik, A., and Poikonen, A.: Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitation from WMO-SPICE, Hydrol. Earth Syst. Sci., 21, 3525–3542, https://doi.org/10.5194/hess-21-3525-2017, 2017. a, b, c, d, e, f, g
Kröcher, J., Ghazaryan, G., and Lischeid, G.: Unravelling Regional Water Balance Dynamics in Anthropogenically Shaped Lowlands: A Data-Driven Approach, Hydrol. Process., 39, e70053, https://doi.org/10.1002/hyp.70053, 2025. a
Kroes, J., van Dam, J., Bartholomeus, R., Groenendijk, P., Heinen, M., Hendriks, R., Mulder, H., Supit, I., and van Walsum, P.: SWAP version 4 – Theory description and user manual, Wageningen Environmental Research Report 2780, Wageningen Environmental Research, https://swap.wur.nl/Documents/Kroes_etal_2017_SWAP_version_4_ESG_Report_2780.pdf (last access: 6 January 2025), 2017. a, b, c, d
Landesamt für Umwelt: Auskunftsplattform Wasser, https://apw.brandenburg.de, last access: 15 May 2025. a
LfU Brandenburg: FFH-Gebiet und SPA Nuthe-Nieplitz-Niederung – Naturpark Nuthe-Nieplitz, LfU Brandenburg, https://www.nuthe-nieplitz-naturpark.de/themen/natura-2000/ffh-gebiet-und-spa-nuthe-nieplitz-niederung/ (last access: 15 May 2025), 2025a. a
LfU Brandenburg: Moorschutz in Brandenburg im Vergleich mit anderen Bundesländern, Startseite, LfU Brandenburg, https://lfu.brandenburg.de/lfu/de/aufgaben/boden/moorschutz/ grundlagen-moorschutz/moorschutz-in-brandenburg-im-vergleich-mit-anderen-bundeslaendern/ (last access: 15 May 2025), 2025b. a
Li, B., Wang, X., and Li, Z.: Plants extend root deeper rather than increase root biomass triggered by critical age and soil water depletion, Sci. Total Environ., 914, 169689, https://doi.org/10.1016/j.scitotenv.2023.169689, 2024. a
Lischeid, G., Dannowski, R., Kaiser, K., Nützmann, G., Steidl, J., and Stüve, P.: Inconsistent hydrological trends do not necessarily imply spatially heterogeneous drivers, J. Hydrol., 596, 126096, https://doi.org/10.1016/j.jhydrol.2021.126096, 2021. a, b
LUA Brandenburg: Rieselfelder südlich Berlins, Studien- und Tagungsbericht Band 13/14, LfU, https://lfu.brandenburg.de/sixcms/media.php/9/STUDIEN%20Band%2013-14%3D1.pdf, (last access: 6 January 2025), 1997. a
LUA Brandenburg: Flächendeckende Modellierung von Wasserhaushaltsgrößen für das Land Brandenburg – Studien und Tagungsberichte, Band 27, Startseite, LfU, Tech. Rep. Band 27, Landesumweltamt Brandenburg, https://lfu.brandenburg.de/lfu/ de/ueber-uns/veroeffentlichungen/detail/~15-10-2001-flaechendeckende-modellierung-von-wasserhaushaltsgroessen-fuer-das-land-brandenburg-stu# (last access: 15 May 2025), 2001. a, b, c
Marx, A., Boeing, F., Rakovec, O., Müller, S., Can, O., Malla, C., Peichl, M., and Samaniego, L.: Auswirkungen des Klimawandels auf Wasserbedarf und -dargebot, WasserWirtschaft, 111, 14–19, https://doi.org/10.1007/s35147-021-0905-5, 2024. a, b
Möller, K. and Kade, N.: Behandeltes Abwasser als Ressource Die Südableitung als Beitrag zur Stützung des Landschaftswasserhaushaltes in der Nuthe-Notte-Niederung: Erfahrungen mit der Ableitung weitergehend gereinigten Abwassers aus dem Klärwerk Waßmannsdorf, Schriftenreihe Kompetenzzentrum Wasser Berlin Band 3, https://digital.zlb.de/viewer/api/v1/records/33661377/files/images/abwasser.pdf/full.pdf (last access: 6 January 2025), 2005. a
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021. a, b
NOAA: Global Annual Climate Report 2023, NOAA, https://www.ncei.noaa.gov/access/monitoring/monthly-report/global/202313, (last access: 6 January 2025), 2023. a
Peterson, T., Easterling, D., Karl, T., Groisman, P., Nicholls, N., Plummer, N., Torok, S., Auer, I., Boehm, R., Gullett, D., Vincent, L., Heino, R., Tuomenvirta, H., Mestre, O., Szentimrey, T., Salinger, J., Forland, E., Hanssen-Bauer, I., Alexandersson, H., Jones, P., and Parker, D.: Homogeneity adjustments of in situ atmospheric climate data: A review, Int. J. Climatol., 18, 1493–1517, https://doi.org/10.1002/(SICI)1097-0088(19981115)18:13<1493::AID-JOC329>3.0.CO;2-T, 1998. a, b
Pohle, I., Zeilfelder, S., Birner, J., and Creutzfeldt, B.: The 2018–2023 drought in Berlin: impacts and analysis of the perspective of water resources management, Nat. Hazards Earth Syst. Sci., 25, 1293–1313, https://doi.org/10.5194/nhess-25-1293-2025, 2025. a
Razafimaharo, C., Krähenmann, S., Höpp, S., Rauthe, M., and Deutschländer, T.: New high-resolution gridded dataset of daily mean, minimum, and maximum temperature and relative humidity for Central Europe (HYRAS), Theor. Appl. Climatol., 142, 1531–1553, https://doi.org/10.1007/s00704-020-03388-w, 2020. a
Reid, P. C., Hari, R. E., Beaugrand, G., Livingstone, D. M., Marty, C., Straile, D., Barichivich, J., Goberville, E., Adrian, R., Aono, Y., Brown, R., Foster, J., Groisman, P., Hélaouët, P., Hsu, H.-H., Kirby, R., Knight, J., Kraberg, A., Li, J., Lo, T.-T., Myneni, R. B., North, R. P., Pounds, J. A., Sparks, T., Stübi, R., Tian, Y., Wiltshire, K. H., Xiao, D., and Zhu, Z.: Global impacts of the 1980s regime shift, Glob. Change Biol., 22, 682–703, https://doi.org/10.1111/gcb.13106, 2016. a
Richter, D.: Ergebnisse methodischer Untersuchungen zur Korrektur des systematischen Meßfehlers des Hellmann-Niederschlagsmessers, no. 194 in Berichte des Deutschen Wetterdienstes, Selbstverl. des Dt. Wetterdienstes, Offenbach am Main, ISBN 3-88148-309-8, https://dwdbib.dwd.de/38928 (last access: 15 May 2025), 1995. a, b, c, d, e
Russ, A., Riek, W., Kallweit, R., Einert, P., Jochheim, H., Lüttschwager, D., Hannemann, J., and Becker, F.: Wasserhaushalt von Standorten des Level II-Programms in Brandenburg, in: 30 Jahre forstliches Umweltmonitoring in Brandenburg, Eberswalder Forstliche Schriftenreihe Band 63, edited by: Kallweit, R. and Engel, J., Ministerium für Ländliche Entwicklung, Umwelt und Landwirtschaft des Landes Brandenburg, 135–152, https://forst.brandenburg.de/cms/media.php/lbm1.a.3310.de/efs63.pdf (last access: 18 August 2025), 2016. a
Schaap, M. G., Leij, F. J., and van Genuchten, M. T.: ROSETTA: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions, J. Hydrol., 251, 163–176, https://doi.org/10.1016/S0022-1694(01)00466-8, 2001. a
Sen, P. K.: Estimates of the Regression Coefficient Based on Kendall's Tau, J. Am. Stat. Assoc., 63, 1379–1389, https://doi.org/10.1080/01621459.1968.10480934, 1968. a, b
Simon, M.: Die landwirtschaftliche Bewässerung in Ostdeutschland seit 1949 – Eine historische Analyse vor dem Hintergrund des Klimawandels, PIK report 114, Potsdam Institute for Climate Impact Research (PIK), https://www.pik-potsdam.de/en/output/publications/pikreports/.files/pr114.pdf (last access: 15 May 2025), 2009. a, b
Somogyvári, M., Scherer, D., Bart, F., Fehrenbach, U., Okujeni, A., and Krueger, T.: A hybrid data-driven approach to analyze the drivers of lake level dynamics, Hydrol. Earth Syst. Sci., 28, 4331–4348, https://doi.org/10.5194/hess-28-4331-2024, 2024. a, b
Somogyvári, M., Brill, F., Tsypin, M., Rihm, L., and Krueger, T.: Regional-scale groundwater analysis with dimensionality reduction, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-4031, 2025. a
Stahn, P., Busch, S., Salzmann, T., Eichler-Loebermann, B., and Miegel, K.: Combining global sensitivity analysis and multiobjective optimisation to estimate soil hydraulic properties and representations of various sole and mixed crops for the agro-hydrological SWAP model, Environ. Earth Sci., 76, 367, https://doi.org/10.1007/s12665-017-6701-y, 2017. a
Theil, H.: A Rank-Invariant Method of Linear and Polynomial Regression Analysis, Part 1, Proceedings Royal Netherlands Academy of Sciences, 53, 386–392, 1992 a
Umweltbundesamt: Auswirkung des Klimawandels auf die Wasserverfügbarkeit – Anpassung an Trockenheit und Dürre in Deutschland (WADKlim), Umweltbundesamt, https://www.umweltbundesamt.de/sites/default/files/medien/2875/dokumente/143_2024_texte_wadklim.pdf (last access: 15 May 2025), 2024. a
van Dam, J. C., Groenendijk, P., Hendriks, R. F., and Kroes, J. G.: Advances of Modeling Water Flow in Variably Saturated Soils with SWAP, Vadose Zone J., 7, 640–653, https://doi.org/10.2136/vzj2007.0060, 2008. a, b, c
van Genuchten, M. T.: A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils, Soil Sci. Soc. Am. J., 44, 892–898, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980. a
Vautard, R., Cattiaux, J., Yiou, P., Thépaut, J.-N., and Ciais, P.: Northern Hemisphere atmospheric stilling partly attributed to an increase in surface roughness, Nat. Geosci., 3, 756–761, https://doi.org/10.1038/ngeo979, 2010. a
von Hoyningen-Huene, J.F.: Die Interzeption des Niederschlags in landwirtschaftlichen Pflanzenbeständen. In: Schriftenreihe des Deutschen Verbandes für Wasserwirtschaft und Kulturbau e.V., 57, 1–53, ISBN 349005797X, 1983. a
Wesseling, J., Kroes, J., and Metselaar, K.: Global sensitivity analysis of the Soil-Water-Atmosphere-Plant (SWAP) model, Wageningen, the Netherlands, DLO Winand Staring Centre. Report 16, https://edepot.wur.nl/363800 (last access: 6 January 2025), 1998. a
Wild, M.: “Global dimming and brightening: A review”, J. Geophys. Res., 114, D00D16, https://doi.org/10.1029/2008JD011470, 2009. a
Yang, Y., Roderick, M. L., Guo, H., Miralles, D. G., Zhang, L., Fatichi, S., Luo, X., Zhang, Y., McVicar, T. R., Tu, Z., Keenan, T. F., Fisher, J. B., Gan, R., Zhang, X., Piao, S., Zhang, B., and Yang, D.: Evapotranspiration on a greening Earth, Nature Reviews Earth and Environment, 4, 626–641, https://doi.org/10.1038/s43017-023-00464-3, 2023. a
Yue, S., Pilon, P., and Cavadias, G.: Power of the Mann–Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series, J. Hydrol., 259, 254–271, https://doi.org/10.1016/S0022-1694(01)00594-7, 2002. a
Zhang, L., Dawes, W., and Walker, G.: Response of mean annual evapotranspiration to vegetation changes at catchment scale, Water Resour. Res., 37, 701–708, https://doi.org/10.1029/2000WR900325, 2001. a
Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G., Ciais, P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C., Cheng, L., Kato, E., Koven, C., Li, Y., Lian, X., Liu, Y., Liu, R., Mao, J., Pan, Y., Peng, S., Penuelas, J., Poulter, B., Pugh, T. A. M., Stocker, B. D., Viovy, N., Wang, X., Wang, Y., Xiao, Z., Yang, H., Zaehle, S., and Zeng, N.: Greening of the Earth and its drivers, Nat. Clim. Change, 6, 791–795, https://doi.org/10.1038/NCLIMATE3004, 2016. a
Executive editor
This paper addresses an important research question for the region of interest in the special issue, "Current and future water-related risks in the Berlin-Brandenburg region." The article analyzes groundwater trends using a rigorous methodology that takes into account all factors that could justify the results found. It is therefore highly relevant for water resource management, which is particularly affected by climate change.
This paper addresses an important research question for the region of interest in the special...
Short summary
Brandenburg is among the driest federal states in Germany. The low groundwater recharge (GWR) is fundamental to both water supply and the support of natural ecosystems. In this study, we show that the decline of observed discharge and groundwater tables since 1980 can be explained by climate change in combination with an increasing leaf area index. Still, simulated GWR rates remain highly uncertain due to the uncertainty in precipitation trends.
Brandenburg is among the driest federal states in Germany. The low groundwater recharge (GWR) is...
Altmetrics
Final-revised paper
Preprint