Articles | Volume 28, issue 18
https://doi.org/10.5194/hess-28-4331-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-4331-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A hybrid data-driven approach to analyze the drivers of lake level dynamics
Márk Somogyvári
CORRESPONDING AUTHOR
Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Berlin 12489, Germany
Geography Department, Humboldt-Universität zu Berlin, Berlin 10099, Germany
Dieter Scherer
Chair of Climatology, Institute of Ecology, Technische Universität Berlin, Berlin 12165, Germany
Frederik Bart
Chair of Climatology, Institute of Ecology, Technische Universität Berlin, Berlin 12165, Germany
Ute Fehrenbach
Chair of Climatology, Institute of Ecology, Technische Universität Berlin, Berlin 12165, Germany
Akpona Okujeni
Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Berlin 12489, Germany
Geography Department, Humboldt-Universität zu Berlin, Berlin 10099, Germany
Earth Observation Lab, Geography Department, Humboldt-Universität zu Berlin, Berlin, 12489, Germany
Tobias Krueger
Integrative Research Institute on Transformations of Human-Environment Systems (IRI THESys), Humboldt-Universität zu Berlin, Berlin 12489, Germany
Geography Department, Humboldt-Universität zu Berlin, Berlin 10099, Germany
Related authors
Pedro Henrique Lima Alencar, Saskia Arndt, Kei Namba, Márk Somogyvári, Frederik Bart, Fabio Brill, Juan Dueñas, Peter Feindt, Daniel Johnson, Nariman Mahmoodi, Christoph Merz, Subham Mukherjee, Katrin Nissen, Eva Nora Paton, Tobias Sauter, Dörthe Tetzlaff, Franziska Tügel, Thomas Vogelpohl, Stenka Valentinova Vulova, Behnam Zamani, and Hui Hui Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-428, https://doi.org/10.5194/egusphere-2025-428, 2025
Short summary
Short summary
As climate change escalates, the Berlin-Brandenburg region faces new challenges. Climate change-induced extreme events are expected to cause new conflicts to emerge and aggravate existing ones. To guide future research, we co-develop a list of key questions on climate and water challenges in the region. Our findings highlight the need for new research approaches. We expect this list to provide a roadmap for actionable knowledge production to address climate and water challenges in the region.
Polina Franke, Aryan Goswami, and Márk Somogyvári
EGUsphere, https://doi.org/10.5194/egusphere-2025-471, https://doi.org/10.5194/egusphere-2025-471, 2025
Short summary
Short summary
Droughts are intensifying due to climate change, impacting water systems and vegetation. This study analyzed drought effects in the Tegeler Fließ catchment from 2008 to 2021. Groundwater faced severe, prolonged droughts, while surface water recovered faster. Vegetation remained resilient, showing no significant stress. Each site revealed unique drought impacts, emphasizing the need for tailored water management and improved vegetation monitoring.
Márk Somogyvári, Fabio Brill, Mikhail Tsypin, Lisa Rihm, and Tobias Krueger
EGUsphere, https://doi.org/10.5194/egusphere-2024-4031, https://doi.org/10.5194/egusphere-2024-4031, 2025
Short summary
Short summary
In this study, we examined regional differences in groundwater behavior in Berlin-Brandenburg. We have developed a novel approach, combining standard groundwater modelling tools such with special data analysis techniques. The presented methodology can help to separate areas with different groundwater behavior from each other, which could be used as a starting point for further analysis.
Pedro Henrique Lima Alencar, Saskia Arndt, Kei Namba, Márk Somogyvári, Frederik Bart, Fabio Brill, Juan Dueñas, Peter Feindt, Daniel Johnson, Nariman Mahmoodi, Christoph Merz, Subham Mukherjee, Katrin Nissen, Eva Nora Paton, Tobias Sauter, Dörthe Tetzlaff, Franziska Tügel, Thomas Vogelpohl, Stenka Valentinova Vulova, Behnam Zamani, and Hui Hui Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-428, https://doi.org/10.5194/egusphere-2025-428, 2025
Short summary
Short summary
As climate change escalates, the Berlin-Brandenburg region faces new challenges. Climate change-induced extreme events are expected to cause new conflicts to emerge and aggravate existing ones. To guide future research, we co-develop a list of key questions on climate and water challenges in the region. Our findings highlight the need for new research approaches. We expect this list to provide a roadmap for actionable knowledge production to address climate and water challenges in the region.
Polina Franke, Aryan Goswami, and Márk Somogyvári
EGUsphere, https://doi.org/10.5194/egusphere-2025-471, https://doi.org/10.5194/egusphere-2025-471, 2025
Short summary
Short summary
Droughts are intensifying due to climate change, impacting water systems and vegetation. This study analyzed drought effects in the Tegeler Fließ catchment from 2008 to 2021. Groundwater faced severe, prolonged droughts, while surface water recovered faster. Vegetation remained resilient, showing no significant stress. Each site revealed unique drought impacts, emphasizing the need for tailored water management and improved vegetation monitoring.
Márk Somogyvári, Fabio Brill, Mikhail Tsypin, Lisa Rihm, and Tobias Krueger
EGUsphere, https://doi.org/10.5194/egusphere-2024-4031, https://doi.org/10.5194/egusphere-2024-4031, 2025
Short summary
Short summary
In this study, we examined regional differences in groundwater behavior in Berlin-Brandenburg. We have developed a novel approach, combining standard groundwater modelling tools such with special data analysis techniques. The presented methodology can help to separate areas with different groundwater behavior from each other, which could be used as a starting point for further analysis.
Sophie Wagner, Fabian Stenzel, Tobias Krueger, and Jana de Wiljes
Hydrol. Earth Syst. Sci., 28, 5049–5068, https://doi.org/10.5194/hess-28-5049-2024, https://doi.org/10.5194/hess-28-5049-2024, 2024
Short summary
Short summary
Statistical models that explain global irrigation rely on location-referenced data. Traditionally, a system based on longitude and latitude lines is chosen. However, this introduces bias to the analysis due to the Earth's curvature. We propose using a system based on hexagonal grid cells that allows for distortion-free representation of the data. We show that this increases the model's accuracy by 28 % and identify biophysical and socioeconomic drivers of historical global irrigation expansion.
Rozemarijn ter Horst, Rossella Alba, Jeroen Vos, Maria Rusca, Jonatan Godinez-Madrigal, Lucie V. Babel, Gert Jan Veldwisch, Jean-Philippe Venot, Bruno Bonté, David W. Walker, and Tobias Krueger
Hydrol. Earth Syst. Sci., 28, 4157–4186, https://doi.org/10.5194/hess-28-4157-2024, https://doi.org/10.5194/hess-28-4157-2024, 2024
Short summary
Short summary
The exact power of models often remains hidden, especially when neutrality is claimed. Our review of 61 scientific articles shows that in the scientific literature little attention is given to the power of water models to influence development processes and outcomes. However, there is a lot to learn from those who are openly reflexive. Based on lessons from the review, we call for power-sensitive modelling, which means that people are critical about how models are made and with what effects.
Xun Wang, Marco Otto, and Dieter Scherer
Nat. Hazards Earth Syst. Sci., 21, 2125–2144, https://doi.org/10.5194/nhess-21-2125-2021, https://doi.org/10.5194/nhess-21-2125-2021, 2021
Short summary
Short summary
We applied a high-resolution, gridded atmospheric data set combined with landslide inventories to investigate the atmospheric triggers, define triggering thresholds, and characterize the climatic disposition of landslides in Kyrgyzstan and Tajikistan. Our results indicate the crucial role of snowmelt in landslide triggering and prediction in Kyrgyzstan and Tajikistan, as well as the added value of climatic disposition derived from atmospheric triggering conditions.
Alexander Krug, Daniel Fenner, Hans-Guido Mücke, and Dieter Scherer
Nat. Hazards Earth Syst. Sci., 20, 3083–3097, https://doi.org/10.5194/nhess-20-3083-2020, https://doi.org/10.5194/nhess-20-3083-2020, 2020
Short summary
Short summary
This study investigates hot weather episodes in eight German cities which are statistically associated with increased mortality. Besides air temperature, ozone concentrations partly explain these mortality rates. The strength of the respective contributions of the two stressors varies across the cities. Results highlight that during hot weather episodes, not only high air temperature affects urban populations; concurrently high ozone concentrations also play an important role in public health.
Cited articles
Alifujiang, Y., Abuduwaili, J., Ma, L., Samat, A., and Groll, M.: System Dynamics Modeling of Water Level Variations of Lake Issyk-Kul, Kyrgyzstan, Water-Sui, 9, 989, https://doi.org/10.3390/w9120989, 2017.
Altunkaynak, A.: Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks, Water Resour. Manag., 21, 399–408, https://doi.org/10.1007/s11269-006-9022-6, 2007.
Arhonditsis, G. B., Neumann, A., Shimoda, Y., Kim, D.-K., Dong, F., Onandia, G., Yang, C., Javed, A., Brady, M., Visha, A., Ni, F., and Cheng, V.: Castles built on sand or predictive limnology in action? Part A: Evaluation of an integrated modelling framework to guide adaptive management implementation in Lake Erie, Ecol. Inform., 53, 100968, https://doi.org/10.1016/j.ecoinf.2019.05.014, 2019.
Bart, F., Schmidt, B., Wang, X., Holtmann, A., Meier, F., Otto, M., and Scherer, D.: CER v2 dataset, TU Berlin [data set], https://www.tu.berlin/en/klima/research/regional-climatology/central-europe/cer (last access: 1 November 2023), 2023.
Beletsky, D., Hawley, N., and Rao, Y. R.: Modeling summer circulation and thermal structure of Lake Erie, J. Geophys. Res.-Oceans, 118, 6238–6252, https://doi.org/10.1002/2013JC008854, 2013.
Clarke, R. T.: A review of some mathematical models used in hydrology, with observations on their calibration and use, J. Hydrol., 19, 1–20, https://doi.org/10.1016/0022-1694(73)90089-9, 1973.
Crapper, P. F., Fleming, P. M., and Kalma, J. D.: Prediction of lake levels using water balance models, Environ. Softw., 11, 251–258, https://doi.org/10.1016/S0266-9838(96)00018-4, 1996.
Dehghanipour, A. H., Zahabiyoun, B., Schoups, G., and Babazadeh, H.: A WEAP-MODFLOW surface water-groundwater model for the irrigated Miyandoab plain, Urmia lake basin, Iran: Multi-objective calibration and quantification of historical drought impacts, Agr. Water Manage., 223, 105704, https://doi.org/10.1016/j.agwat.2019.105704, 2019.
Demir, V. and Yaseen, Z. M.: Neurocomputing intelligence models for lakes water level forecasting: a comprehensive review, Neural Comput. Appl., 35, 303–343, https://doi.org/10.1007/s00521-022-07699-z, 2023.
Döllefeld, M., Haag, L., and Welsch, J.: Umweltatlas Berlin – planungsrelevante Umweltdaten für Berlin, ZfV – Zeitschrift für Geodäsie, Geoinformation und Landmanagement, 2, 138–143, https://doi.org/10.12902/zfv-0341-2021, 2021.
DWD: Klimareport Brandenburg. 1. Auflage; Deutscher Wetterdienst, Offenbach am Main, Deutschland, 40 pp., ISBN 978-3-88148-518-0, 2019.
Ebtehaj, I., Bonakdari, H., and Gharabaghi, B.: A reliable linear method for modeling lake level fluctuations, J. Hydrol., 570, 236–250, https://doi.org/10.1016/j.jhydrol.2019.01.010, 2019.
Elshorbagy, A., Corzo, G., Srinivasulu, S., and Solomatine, D. P.: Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology – Part 1: Concepts and methodology, Hydrol. Earth Syst. Sci., 14, 1931–1941, https://doi.org/10.5194/hess-14-1931-2010, 2010a.
Elshorbagy, A., Corzo, G., Srinivasulu, S., and Solomatine, D. P.: Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology – Part 2: Application, Hydrol. Earth Syst. Sci., 14, 1943–1961, https://doi.org/10.5194/hess-14-1943-2010, 2010b.
Geoportal Brandenburg – Detailansichtdienst: https://geoportal.brandenburg.de/detailansichtdienst/render?url=https://geoportal.brandenburg.de/gs-json/xml?fileid=A140C263-7D61-447B-81C2-8824792AE190, last access: 29 April 2024.
Getachew, B., Manjunatha, B. R., and Bhat, H. G.: Modeling projected impacts of climate and land use/land cover changes on hydrological responses in the Lake Tana Basin, upper Blue Nile River Basin, Ethiopia, J. Hydrol., 595, 125974, https://doi.org/10.1016/j.jhydrol.2021.125974, 2021.
Ghashghaie, M. and Nozari, H.: Effect of Dam Construction on Lake Urmia: Time Series Analysis of Water Level via ARIMA, J. Agric. Sci. Technol., 20, 1541–1553, 2018.
Gong, Y., Liu, G., and Schwartz, F.: Quantifying the Response Time of a Lake–Groundwater Interacting System to Climatic Perturbation, Water-Sui, 7, 6598–6615, https://doi.org/10.3390/w7116598, 2015.
Habel, M., Nowak, B., and Szadek, P.: Evaluating indicators of hydrologic alteration to demonstrate the impact of open-pit lignite mining on the flow regimes of small and medium-sized rivers, Ecol. Indic., 157, 111295, https://doi.org/10.1016/j.ecolind.2023.111295, 2023.
Haacke, N., Frick, M., Scheck-Wenderoth, M., Schneider, M., and Cacace, M.: 3-D Simulations of Groundwater Utilization in an Urban Catchment of Berlin, Germany, Adv. Geosci., 45, 177–184, https://doi.org/10.5194/adgeo-45-177-2018, 2018.
Hassanzadeh, E., Zarghami, M., and Hassanzadeh, Y.: Determining the Main Factors in Declining the Urmia Lake Level by Using System Dynamics Modeling, Water Resour. Manag., 26, 129–145, https://doi.org/10.1007/s11269-011-9909-8, 2012.
Heinrich, L., Dietel, J., and Hupfer, M.: Sulphate reduction determines the long-term effect of iron amendments on phosphorus retention in lake sediments, J. Soil. Sediment., 22, 316–333, https://doi.org/10.1007/s11368-021-03099-3, 2022.
Heuvelmans, G., Muys, B., and Feyen, J.: Regionalisation of the parameters of a hydrological model: Comparison of linear regression models with artificial neural nets, J. Hydrol., 319, 245–265, https://doi.org/10.1016/j.jhydrol.2005.07.030, 2006.
Hrachowitz, M. and Clark, M. P.: HESS Opinions: The complementary merits of competing modelling philosophies in hydrology, Hydrol. Earth Syst. Sci., 21, 3953–3973, https://doi.org/10.5194/hess-21-3953-2017, 2017.
Irvine, K. N. and Eberhardt, A. J.: Multiplicative, Seasonal Arima Models for Lake Erie and Lake Ontario Water Levels, JAWRA J. Am. Water Resour. As., 28, 385–396, https://doi.org/10.1111/j.1752-1688.1992.tb04004.x, 1992.
Jahn, D. and Witt, H.: Gewässeratlas von Berlin: Senatsverwaltung für Standentwicklung, UNICOM, Berlin, https://www.berlin.de/sen/uvk/_assets/umwelt/wasser-und-geologie/publikationen-und-merkblaetter/wasseratlas.pdf (last access: 12 September 2024), 2002.
Jänicke, B., Meier, F., Fenner, D., Fehrenbach, U., Holtmann, A., and Scherer, D.: Urban-rural differences in near-surface air temperature as resolved by the Central Europe Refined analysis (CER): sensitivity to planetary boundary layer schemes and urban canopy models, Int. J. Climatol., 37, 2063–2079, https://doi.org/10.1002/joc.4835, 2017.
Kakahaji, H., Banadaki, H. D., Kakahaji, A., and Kakahaji, A.: Prediction of Urmia Lake Water-Level Fluctuations by Using Analytical, Linear Statistic and Intelligent Methods, Water Resour. Manag., 27, 4469–4492, https://doi.org/10.1007/s11269-013-0420-2, 2013.
Kebede, S., Travi, Y., Alemayehu, T., and Marc, V.: Water balance of Lake Tana and its sensitivity to fluctuations in rainfall, Blue Nile basin, Ethiopia, J. Hydrol., 316, 233–247, https://doi.org/10.1016/j.jhydrol.2005.05.011, 2006.
Kisi, O., Shiri, J., and Nikoofar, B.: Forecasting daily lake levels using artificial intelligence approaches, Comput. Geosci., 41, 169–180, https://doi.org/10.1016/j.cageo.2011.08.027, 2012.
Kleeberg, A., Köhler, A., and Hupfer, M.: How effectively does a single or continuous iron supply affect the phosphorus budget of aerated lakes?, J. Soil. Sediment., 12, 1593–1603, https://doi.org/10.1007/s11368-012-0590-1, 2012.
Klotz, D., Kratzert, F., Gauch, M., Keefe Sampson, A., Brandstetter, J., Klambauer, G., Hochreiter, S., and Nearing, G.: Uncertainty estimation with deep learning for rainfall–runoff modeling, Hydrol. Earth Syst. Sci., 26, 1673–1693, https://doi.org/10.5194/hess-26-1673-2022, 2022.
Kratzert, F., Klotz, D., Shalev, G., Klambauer, G., Hochreiter, S., and Nearing, G.: Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets, Hydrol. Earth Syst. Sci., 23, 5089–5110, https://doi.org/10.5194/hess-23-5089-2019, 2019.
Kroll, C. N. and Song, P.: Impact of multicollinearity on small sample hydrologic regression models, Water Res., 49, 3756–3769, https://doi.org/10.1002/wrcr.20315, 2013.
Langbein, W. B.: Salinity and hydrology of closed lakes: A study of the long-term balance between input and loss of salts in closed lakes, 412, US Government Print. Office, https://doi.org/10.3133/pp412, 1961.
Laval, B., Imberger, J., Hodges, B. R., and Stocker, R.: Modeling circulation in lakes: Spatial and temporal variations, Limnol. Oceanogr., 48, 983–994, https://doi.org/10.4319/lo.2003.48.3.0983, 2003.
Li, J., Wang, Z., Wu, X., Xu, C.-Y., Guo, S., Chen, X., and Zhang, Z.: Robust Meteorological Drought Prediction Using Antecedent SST Fluctuations and Machine Learning, Water Res., 57, e2020WR029413, https://doi.org/10.1029/2020WR029413, 2021.
Liese, M., Nagare, R., and Voigt, H.-J.: 12 Jahre Pilotbetrieb Karolinenhöhe – eine erste Auswertung. Kompetenzzentrum Wasser Berlin gGmbH, https://www.kompetenz-wasser.de/media/pages/forschung/publikationen/44/efe477f2f4-1702634137/Liese-2004-44.pdf (last access: 17 September 2024), ISBN 978-3-9811684-2-6, 2004.
Lischeid, G.: Abschätzung des mittelfristigen Niedrigwasserrisikos anhand der Daten des Grundwassermonitorings, KW Korrespondenz Wasserwirtschaft, 12, 780–785, https://doi.org/10.3243/kwe2021.12.004, 2021.
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.
Lu, C., He, X., Zhang, B., Wang, J., Kidmose, J., and Jarsjö, J.: Comparison of Numerical Methods in Simulating Lake–Groundwater Interactions: Lake Hampen, Western Denmark, Water-Sui, 14, 3054, https://doi.org/10.3390/w14193054, 2022.
Mason, I. M., Guzkowska, M. A. J., Rapley, C. G., and Street-Perrott, F. A.: The response of lake levels and areas to climatic change, Climatic Change, 27, 161–197, https://doi.org/10.1007/BF01093590, 1994.
Maussion, F., Scherer, D., Mölg, T., Collier, E., Curio, J., and Finkelnburg, R.: Precipitation Seasonality and Variability over the Tibetan Plateau as Resolved by the High Asia Reanalysis, J. Climate, 27, 1910–1927, https://doi.org/10.1175/JCLI-D-13-00282.1, 2014.
McGovern, A., Lagerquist, R., Gagne, D. J., Jergensen, G. E., Elmore, K. L., Homeyer, C. R., and Smith, T.: Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning, B. Am. Meteorol. Soc., 100, 2175–2199, https://doi.org/10.1175/BAMS-D-18-0195.1, 2019.
Montanari, A., Rosso, R., and Taqqu, M. S.: Fractionally differenced ARIMA models applied to hydrologic time series: Identification, estimation, and simulation, Water Res., 33, 1035–1044, https://doi.org/10.1029/97WR00043, 1997.
Oyebode, O. and Stretch, D.: Neural network modeling of hydrological systems: A review of implementation techniques, Nat. Resour. Model., 32, e12189, https://doi.org/10.1111/nrm.12189, 2019.
Páliz Larrea, P., Zapata-Ríos, X., and Campozano Parra, L.: Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador, Water-Sui, 13, 2011, https://doi.org/10.3390/w13152011, 2021.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.: Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Pflugmacher, D., Rabe, A., Peters, M., and Hostert, P.: Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey, Remote Sens. Environ., 221, 583–595, https://doi.org/10.1016/j.rse.2018.12.001, 2019.
Sahoo, B. B., Jha, R., Singh, A., and Kumar, D.: Long short-term memory (LSTM) recurrent neural network for low-flow hydrological time series forecasting, Acta Geophys., 67, 1471–1481, https://doi.org/10.1007/s11600-019-00330-1, 2019.
Sahoo, S. and Jha, M. K.: Groundwater-level prediction using multiple linear regression and artificial neural network techniques: a comparative assessment, Hydrogeol. J., 21, 1865–1887, https://doi.org/10.1007/s10040-013-1029-5, 2013.
Schleich, J. and Hillenbrand, T.: Determinants of residential water demand in Germany, Ecol. Econ., 68, 1756–1769, https://doi.org/10.1016/j.ecolecon.2008.11.012, 2009.
Schulz, S., Darehshouri, S., Hassanzadeh, E., Tajrishy, M., and Schüth, C.: Climate change or irrigated agriculture – what drives the water level decline of Lake Urmia, Sci. Rep.-UK, 10, 236, https://doi.org/10.1038/s41598-019-57150-y, 2020.
Seeboonruang, U.: An application of time-lag regression technique for assessment of groundwater fluctuations in a regulated river basin: a case study in Northeastern Thailand, Environ. Earth Sci., 73, 6511–6523, https://doi.org/10.1007/s12665-014-3872-7, 2015.
Şen, Z., Kadioğlu, M., and Batur, E.: Stochastic Modeling of the Van Lake Monthly Level Fluctuations in Turkey, Theor. Appl. Climatol., 65, 99–110, https://doi.org/10.1007/s007040050007, 2000.
SenUVK: Wasserportal Berlin, SenUVK Berlin [data set], https://wasserportal.berlin.de/stationen_start.php (last access: 13 September 2024), 2023.
Sivapalan, M. and Young, P. C.: Downward Approach to Hydrological Model Development, in: Encyclopedia of hydrological sciences, edited by: Anderson, M. G., Wiley, Chichester, https://doi.org/10.1002/0470848944.hsa141, 2005.
Sivapalan, M., Blöschl, G., Zhang, L., and Vertessy, R.: Downward approach to hydrological prediction, Hydrol. Process., 17, 2101–2111, https://doi.org/10.1002/hyp.1425, 2003.
Solomatine, D. P. and Ostfeld, A.: Data-driven modelling: some past experiences and new approaches, J. Hydroinform., 10, 3–22, https://doi.org/10.2166/hydro.2008.015, 2008.
Souza, F. A., Araújo, R., and Mendes, J.: Review of soft sensor methods for regression applications, Chemometr. Intell. Lab., 152, 69–79, https://doi.org/10.1016/j.chemolab.2015.12.011, 2016.
Tasker, G. D.: Hydrologic regression with weighted least squares, Water Res., 16, 1107–1113, https://doi.org/10.1029/WR016i006p01107, 1980.
Umweltbundesamt: Sewage sludge disposal in Germany, https://www.umweltbundesamt.de/en/topics/sewage-sludge-disposal-in-germany, last access: 31 May 2023.
Valipour, R., Fong, P., McCrimmon, C., Zhao, J., van Stempvoort, D. R., and Rao, Y. R.: Hydrodynamics of a large lake with complex geometry and topography: Lake of the Woods, J. Great Lakes Res., 49, 82–96, https://doi.org/10.1016/j.jglr.2022.09.009, 2023.
Wang, X., Tolksdorf, V., Otto, M., and Scherer, D.: WRF-based dynamical downscaling of ERA5 reanalysis data for High Mountain Asia: Towards a new version of the High Asia Refined analysis, Int. J. Climatol., 41, 743–762, https://doi.org/10.1002/joc.6686, 2021.
Wolter, K.-D.: Restoration of Eutrophic Lakes by Phosphorus Precipitation, with a Case Study on Lake Gross-Glienicker, in: Restoration of Lakes, Streams, Floodplains, and Bogs in Europe, Springer, Dordrecht, 85–99, https://doi.org/10.1007/978-90-481-9265-6_7, 2010.
Woolway, R. I., Kraemer, B. M., Lenters, J. D., Merchant, C. J., O'Reilly, C. M., and Sharma, S.: Global lake responses to climate change, Nat. Rev. Earth Environ., 1, 388–403, https://doi.org/10.1038/s43017-020-0067-5, 2020.
Xu, C.-Y. and Singh, V. P.: A Review on Monthly Water Balance Models for Water Resources Investigations, Water Resour. Manag., 12, 20–50, https://doi.org/10.1023/A:1007916816469, 1998.
Zhu, S., Hrnjica, B., Ptak, M., Choiński, A., and Sivakumar, B.: Forecasting of water level in multiple temperate lakes using machine learning models, J. Hydrol., 585, 124819, https://doi.org/10.1016/j.jhydrol.2020.124819, 2020a.
Zhu, S., Lu, H., Ptak, M., Dai, J., and Ji, Q.: Lake water-level fluctuation forecasting using machine learning models: a systematic review, Environ. Sci. Pollut. R., 27, 44807–44819, https://doi.org/10.1007/s11356-020-10917-7, 2020b.
Short summary
We study the drivers behind the changes in lake levels, creating a series of models from least to most complex. In this study, we have shown that the decreasing levels of Groß Glienicker Lake in Germany are not simply the result of changes in climate but are affected by other processes. In our example, reduced inflow from a growing forest, regionally sinking groundwater levels and the modifications in the local rainwater infrastructure together resulted in an increasing lake level loss.
We study the drivers behind the changes in lake levels, creating a series of models from least...