Bloch, R., Wechsung, F., Heß, J., and Bachinger, J.: Climate change impacts of legume-grass swards: implications for organic farming in the Federal State of Brandenburg, Germany, Reg. Environ. Change, 15, 405–414,
https://doi.org/10.1007/s10113-014-0656-2, 2015.
a
Bloomfield, J. P. and Marchant, B. P.: Analysis of groundwater drought building on the standardised precipitation index approach, Hydrol. Earth Syst. Sci., 17, 4769–4787,
https://doi.org/10.5194/hess-17-4769-2013, 2013.
a,
b,
c,
d
Blöschl, G., Ardoin-Bardin, S., Bonell, M., Dorninger, M., Goodrich, D., Gutknecht, D., Matamoros, D., Merz, B., Shand, P., and Szolgay, J.: At what scales do climate variability and land cover change impact on flooding and low flows?, Hydrol. Process., 21, 1241–1247,
https://doi.org/10.1002/hyp.6669, 2007.
a
Buras, A., Rammig, A., and Zang, C. S.: The European Forest Condition Monitor: Using remotely sensed NDVI to track forest stress across Europe, Front. Plant Sci., 12, 689220,
https://doi.org/10.3389/fpls.2021.689220, 2021.
a
Chen, X., Wang, D., Chen, J., Wang, C., and Shen, M.: The mixed pixel effect in land surface phenology: A simulation study, Remote Sens. Environ., 211, 338–344,
https://doi.org/10.1016/j.rse.2018.04.030, 2018.
a
Crausbay, S. D., Ramirez, A. R., Carter, S. L., Cross, M. S., Hall, K. R., Bathke, D. J., Betancourt, J. L., Colt, S., Cravens, A. E., Dalton, M. S., Dunham, J. B., Hay, L. E., Hayes, M. J., McEvoy, J., McNutt, C. A., Moritz, M. A., Nislow, K. H., Raheem, N., and Sanford, T.: Defining ecological drought for the twenty-first century, B. Am. Meteorol. Soc., 98, 2543–2550,
https://doi.org/10.1175/BAMS-D-16-0292.1, 2017.
a
Cullmann, A., Sundermann, G., Wägner, N., von Hirschhausen, C. R., and Kemfert, C.: Water resources in Germany: Increasingly polluted and regionally overused, DIW Weekly Report, 12, 307–315,
https://doi.org/10.18723/diw_dwr:2022-49-1, 2022.
a
Dahlhausen, J., Rötzer, T., Biber, P., Uhl, E., and Pretzsch, H.: Urban climate modifies tree growth in Berlin, Int. J. Biometeorol., 62, 795–808,
https://doi.org/10.1007/s00484-017-1481-3, 2018.
a
De La Iglesia Martinez, A., and Labib, S. M.: Demystifying normalized difference vegetation index (NDVI) for greenness exposure assessments and policy interventions in urban greening, Environ. Res., 220, 115155.
https://doi.org/10.1016/j.envres.2022.115155, 2023.
a,
b
German Weather Service (DWD): German Climate Atlas,
https://www.dwd.de/EN/climate_environment/climateatlas/climateatlas_node.html, last access: 28 January 2024. a
European Environment Agency (EEA): CLCplus Backbone 2021 (10 m raster) – Europe, Copernicus Land Monitoring Service [data set],
https://land.copernicus.eu/en/products/clc-backbone (last access: 20 October 2025), 2021. a
Germer, S., Kaiser, K., Bens, O., and Hüttl, R. F.: Water balance changes and responses of ecosystems and society in the Berlin-Brandenburg region – a review, DIE ERDE – J. Geogr. Soc. Berlin, 142, 65–95,
https://doi.org/10.12854/erde-142-43, 2011.
a,
b
Gessner, U., Reinermann, S., Asam, S., and Kuenzer, C.: Vegetation Stress Monitor— Assessment of drought and temperature-related effects on vegetation in Germany analyzing MODIS time series over 23 years, Remote Sens., 15, 5428,
https://doi.org/10.3390/rs15225428, 2023.
a
Granger, C. W.: Investigating causal relations by econometric models and cross-spectral methods, Econometrica: J. Eco. Soc., 37, 424–438,
https://doi.org/10.2307/1912791, 1969.
a,
b
Heudorfer, B., Liesch, T., and Broda, S.: On the challenges of global entity-aware deep learning models for groundwater level prediction, Hydrol. Earth Syst. Sci., 28, 525–543,
https://doi.org/10.5194/hess-28-525-2024, 2024.
a
Jackson, R. B., Canadell, J., Ehleringer, J. R., Mooney, H. A., Sala, O. E., and Schulze, E. D.: A global analysis of root distributions for terrestrial biomes, Oecologia, 108, 389–411, https://doi.org/10.1007/BF00333714, 1996.
Jensen, J.: Remote sensing of the environment: An Earth resource perspective, Pearson Education, Limited,
https://ebookcentral.proquest.com/lib/kbdk/detail.action?docID=5173657 (last access: 7 November 2024), 2013. a
Jimenez, R. B., Lane, K. J., Hutyra, L. R., and Fabian, M. P.: Spatial resolution of Normalized Difference Vegetation Index and greenness exposure misclassification in an urban cohort, J. Expo. Sci. Env. Epid., 32, 213–222,
https://doi.org/10.1038/s41370-022-00409-w, 2022.
a
Kahlenborn, W., Porst, L., Voss, M., Fritsch, U., Renner, K., Zebisch, M., Wolf, M., Schönthaler, K., and Schauser, I.: Climate Impact and Risk Assessment 2021 for Germany – Summary, Umweltbundesamt, Dessau-Roßlau,
https://www.umweltbundesamt.de/sites/default/files/medien/479/publikationen/cc_27-2021_climate_impact_and_risk_assessment_2021_for_germany_english_summary_bf.pdf (last access: 29 December 2024), 2021. a
Kuhlemann, L. M., Tetzlaff, D., and Soulsby, C.: Urban water systems under climate stress: An isotopic perspective from Berlin, Germany. Hydrol. Process., 34, 3758–3776,
https://doi.org/10.1002/hyp.13850, 2020.
a
Kuhlemann, L. M., Dubbert, M., Pätzold, S., and Cuntz, M.: The imprint of hydroclimate, urbanization and catchment connectivity on the stable isotope dynamics of a large river in Berlin, J. Hydrol., 613, 128335,
https://doi.org/10.1016/j.jhydrol.2022.128335, 2022.
a
Kloos, S., Yuan, Y., Castelli, M., and Menzel, A.: Agricultural drought detection with MODIS-based vegetation health indices in Southeast Germany, Remote Sens., 13, 3907,
https://doi.org/10.3390/rs13193907, 2021.
a
Kong, D., Miao, C., Duan, Q., Lei, X., and Li, H.: Vegetation-climate interactions on the Loess Plateau: A nonlinear Granger causality analysis, J. Geophys. Res.-Atmos., 123, 11068–11079,
https://doi.org/10.1029/2018JD029036, 2018.
a
Köstner, B., Surke, M., and Bernhofer, C.: Klimadiagnose der Region Berlin/Barnim/Uckermark/Uecker-Randow für den Zeitraum 1951–2006, Materialien der Interdisziplinären Arbeitsgruppe Zukuftsorientierte Nutzung ländlicher Räume LandInnovation, Berlin-Brandenburgische Akademie der Wissenschaften, 18, Berlin,
https://edoc.bbaw.de/opus4-bbaw/frontdoor/deliver/index/docId/767/file/21zdHy5mOp5M.pdf (last access: 24 June 2026), 2007. a
Li, Z., Sun, F., Wang, H., Wang, T., and Feng, Y.: Detecting the interactions between vegetation greenness and drought globally, Atmos. Res., 304, 107409,
https://doi.org/10.1016/j.atmosres.2024.107409, 2024.
a,
b
Liu, M., Zhang, X., Liao, J., and Zhu, Y.: Time-lag and accumulation responses of vegetation growth to climate extremes in China, Sci. Total Environ., 945, 174084,
https://doi.org/10.1016/j.scitotenv.2024.174084, 2024.
a
McKee, T. B., Doesken, N. J., and Kleist, J.: The relationship of drought frequency and duration to time scales, Proceedings of the 8th Conference on Applied Climatology, 17, 179–183,
https://www.droughtmanagement.info/literature/AMS_Relationship_Drought_Frequency_Duration_Time_Scales_1993.pdf (last access: 24 June 2026), 1993.
a,
b,
c,
d
NABU Berlin: Das Tegeler Fließtal, Naturschatz im Norden Berlins,
https://berlin.nabu.de/stadt-und-natur/naturschutz-berlin/18942.html, last access: 9 December 2024. a
Nemani, R. R., Keeling, C. D., Hashimoto, H., Jolly, W. M., Piper, S. C., Tucker, C. J., Myneni, R. B., and Running, S. W.: Climate-driven increases in global terrestrial net primary production from 1982 to 1999, Science, 300, 1560–1563,
https://doi.org/10.1126/science.1082750, 2003.
a
Ojha, S. S., Singh, V., and Roshni, T.: Comparison of meteorological drought using SPI and SPEI, Civil Eng. J., 7, 2130–2149,
https://doi.org/10.28991/cej-2021-03091783, 2021.
a
Papagiannopoulou, C., Miralles, D. G., Dorigo, W. A., Verhoest, N. E. C., Depoorter, M., and Waegeman, W.: Vegetation anomalies caused by antecedent precipitation in most of the world, Environ. Res. Lett., 12, 074016,
https://doi.org/10.1088/1748-9326/aa7145, 2017.
a
Pei, Z., Fang, S., Wang, L., and Yang, W.: Comparative analysis of drought indicated by the SPI and SPEI at various timescales in Inner Mongolia, China, Water, 12, 1925,
https://doi.org/10.3390/w12071925, 2020.
a
Pfaff, B.: VAR, SVAR and SVEC models: Implementation within R package vars, J. Stat. Softw., 27,
https://www.jstatsoft.org/v27/i04/ (last access: 15 March 2026), 2008. a
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,
b,
c
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org/ (last access: 15 March 2026), 2024. a
Reyer, C., Bachinger, J., Bloch, R., Hattermann, F. F., Ibisch, P. L., Kreft, S., Lasch, P., Lucht, W., Nowicki, C., Spathelf, P., Stock, M., and Welp, M.: Climate change adaptation and sustainable regional development: A case study for the Federal State of Brandenburg, Germany, Reg. Environ. Change, 12, 523–542.
https://doi.org/10.1007/s10113-011-0269-y, 2012.
a
Schindler, U., Steidl, J., Müller, L., Eulenstein, F., and Thiere, J.: Drought risk to agricultural land in Northeast and Central Germany, J. Plant Nut. Soil Sci., 170, 357–362,
https://doi.org/10.1002/jpln.200622045, 2007.
a
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, Nat. Hazards Earth Syst. Sci., 25, 4613–4628,
https://doi.org/10.5194/nhess-25-4613-2025, 2025.
a
Spinoni, J., Vogt, J. V., Naumann, G., Barbosa, P., and Dosio, A.: Will drought events become more frequent and severe in Europe?, Int. J. Climatol., 38, 1718–1736,
https://doi.org/10.1002/joc.5291, 2018.
a
Stagge, J. H., Kohn, I., Tallaksen, L. M., and Stahl, K.: Modeling drought impact occurrence based on meteorological drought indices in Europe, J. Hydrol., 530, 37–50,
https://doi.org/10.1016/j.jhydrol.2015.09.039, 2015.
a
Svoboda, M. D. and Fuchs, B. A.: Handbook of drought indicators and indices (Vol. 2), World Meteorological Organization (WMO) and Global Water Partnership (GWP), Geneva, 52 pp., ISBN 978-92-63-11173-9, 2016. a
Tourian, M. J., Elmi, O., Shafaghi, Y., Behnia, S., Saemian, P., Schlesinger, R., and Sneeuw, N.: HydroSat: geometric quantities of the global water cycle from geodetic satellites, Earth Syst. Sci. Data, 14, 2463–2486,
https://doi.org/10.5194/essd-14-2463-2022, 2022.
a
Tsypin, M., Cacace, M., Guse, B., Güntner, A., and Scheck-Wenderoth, M.: Modeling the influence of climate on groundwater flow and heat regime in Brandenburg (Germany), Front. Water, 6, 1353394,
https://doi.org/10.3389/frwa.2024.1353394, 2024.
a
Tucker, C. J.: Red and photographic infrared linear combinations for monitoring vegetation, Remote Sens. Environ., 8, 127–150, 1979. a
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I.: A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index, J. Climate, 23, 1696–1718,
https://doi.org/10.1175/2009JCLI2909.1, 2010.
a
Vicente-Serrano, S. M., López-Moreno, J. I., Beguería, S., Lorenzo-Lacruz, J., Azorin-Molina, C., and Morán-Tejeda, E.: Accurate computation of a streamflow drought index, J. Hydrol. Eng., 17, 318–332,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000433, 2012.
a
Vörösmarty, C. J., Green, P., Salisbury, J., and Lammers, R. B.: Global water resources: vulnerability from climate change and population growth, Science, 289, 284–288, 2000. a
Xiao, L., Guo, C., Zheng, C., Lu, Z., Khan, M. A., Zhou, J., and Wu, X.: Dense vegetation as a natural buffer: Mitigating and decelerating drought propagation, J. Environ. Manage., 389, 126027,
https://doi.org/10.1016/j.jenvman.2025.126027, 2025.
a
Yihdego, Y., Vaheddoost, B., and Al-Weshah, R. A.: Drought indices and indicators revisited, Arab. J. Geosci., 12, 1–12,
https://doi.org/10.1007/s12517-019-4237-z, 2019.
a
Zeileis, A. and Hothorn, T.: lmtest: Diagnostic checking in regression relationships, R News, 2, 7–10,
https://CRAN.R-project.org/doc/Rnews/ (last access: 17 March 2026), 2002. a
Zhong, R., Zhao, T., Chen, X., and Jin, H.: Monitoring drought in ungauged areas using satellite altimetry: The Standardized River Stage Index, J. Hydrol., 612, 128308,
https://doi.org/10.1016/j.jhydrol.2022.128308, 2022.
a,
b
Zink, M., Kumar, R., Cuntz, M., and Samaniego, L.: A high-resolution dataset of water fluxes and states for Germany accounting for parametric uncertainty, Hydrol. Earth Syst. Sci., 21, 1769–1790,
https://doi.org/10.5194/hess-21-1769-2017, 2017.
a
Zolghadr-Asli, B., Enayati, M., Pourghasemi, H. R., Naghdyzadegan Jahromi, M., and Tiefenbacher, J. P.: Application of Granger-causality to study the climate change impacts on depletion patterns of inland water bodies, Hydrol. Sci. J., 66, 1767–1776,
https://doi.org/10.1080/02626667.2021.1944633, 2021.
a
