Geografie 2025, 130, 271-297
https://doi.org/10.37040/geografie.2025.014
Digital innovations in historical climatology: Classifying weather and climatic extremes and their impacts on societies using machine learning on written documents
References
1. ANTENHOFER, C., KÜHBERGER, C., STROHMEYER, A. (2023): Digital Humanities in den Geschichtswissenschaften. Böhlau, Wien.
<https://doi.org/10.36198/9783838561165>
2. BEHRINGER, W. (1995): Weather, Hunger and Fear: Origins of the European Witch-Hunts in Climate, Society and Mentality. German History, 13, 1–27.
<https://doi.org/10.1177/026635549501300101>
3. BI, K., XIE, L., ZHANG, H., CHEN, X., GU, X., TIAN, Q. (2023): Accurate medium-range global weather forecasting with 3D neural networks. Nature, 7970, 619, 533–538.
<https://doi.org/10.1038/s41586-023-06185-3>
4. BOSE, R., PINTAR, A.L., SIMIU, E. (2023): Simulation of Atlantic Hurricane Tracks and Features: A Coupled Machine Learning Approach. Artificial Intelligence for the Earth Systems, 2, 2, 220060.
<https://doi.org/10.1175/AIES-D-22-0060.1>
5. BÖSMEIER, A. (2020): Exploring and analyzing data for reconstruction, modeling, and hazard assessment of historical floods in the Kinzig catchment, Upper Rhine area, https://freidok.uni-freiburg.de/data/222531.
6. BRÁZDIL, R., PFISTER, C., WANNER, H., STORCH, H. V., LUTERBACHER, J. (2005): Historical Climatology In Europe – The State Of The Art. Climatic Change, 3, 70, 363–430.
<https://doi.org/10.1007/s10584-005-5924-1>
7. BRIEGEL, F., SCHRODI, S., SULZER, M., BROX, T., PINTO, J.G., Christen, A. (2025): Deep learning enables city-wide climate projections of street-level heat stress. Urban Climate, 62.
<https://doi.org/10.1016/j.uclim.2025.102564>
8. BRUNTON, S.L., KUTZ, J.N. (2019): Data-driven science and engineering: machine learning, dynamical systems, and control. Cambridge University Press, Cambridge New York, NY Port Melbourne New Delhi Singapore.
9. BUTLER, H., DALY, M., DOYLE, A., GILLIES, S., SCHAUB, T., SCHMIDT, C. (2014): GeoJSON. Electronic, http://geojson.org.
10. CAMENISCH, C., JAUME-SANTERO, F., WHITE, S., PEI, Q., HAND, R., ROHR, C., BRÖNNIMANN, S. (2021): A Bayesian Approach to Historical Climatology for the Burgundian Low Countries in the 15<sup>th</sup> Century. preprint. Climate Modelling/Historical Records/Centennial-Decadal.
<https://doi.org/10.5194/cp-2021-169>
11. CHO, K., VAN MERRIENBOER, B., GULCEHRE, C., BAHDANAU, D., BOUGARES, F., SCHWENK, H., BENGIO, Y. (2014): Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. arXiv.
<https://doi.org/10.3115/v1/D14-1179>
12. CHOLLET, F., OTHERS (2015): Keras, https://github.com/fchollet/keras.
13. COOK, E. R., SEAGER, R., KUSHNIR, Y., BRIFFA, K.R., BÜNTGEN, U., FRANK, D., KRUSIC, P.J., TEGEL, W., VAN DER SCHRIER, G., ANDREU-HAYLES, L., BAILLIE, M., BAITTINGER, C., BLEICHER, N., BONDE, N., BROWN, D., CARRER, M., COOPER, R., ČUFAR, K., DITTMAR, C., ESPER, J., GRIGGS, C., GUNNARSON, B., GÜNTHER, B., GUTIERREZ, E., HANECA, K., HELAMA, S., HERZIG, F., HEUSSNER, K.-U., HOFMANN, J., JANDA, P., KONTIC, R., KÖSE, N., KYNCL, T., LEVANIČ, T., LINDERHOLM, H., MANNING, S., MELVIN, T.M., MILES, D., NEUWIRTH, B., NICOLUSSI, K., NOLA, P., PANAYOTOV, M., POPA, I., ROTHE, A., SEFTIGEN, K., SEIM, A., SVARVA, H., SVOBODA, M., THUN, T., TIMONEN, M., TOUCHAN, R., TROTSIUK, V., TROUET, V., WALDER, F., WAŻNY, T., WILSON, R., ZANG, C. (2015): Old World megadroughts and pluvials during the Common Era. Science Advances, 10, 1, e1500561.
<https://doi.org/10.1126/sciadv.1500561>
14. DE VOS, M.G., HAZELEGER, W., BARI, D., BEHRENS, J., BENDOUKHA, S., GARCIA-MARTI, I., VAN HAREN, R., HAUPT, S.E., HUT, R., JANSSON, F., MUELLER, A., NEILLEY, P., VAN DEN OORD, G., PELUPESSY, I., RUTI, P., SCHULTZ, M.G., WALTON, J. (2020): Open weather and climate science in the digital era. Geoscience Communication, 2, 3, 191–201.
<https://doi.org/10.5194/gc-3-191-2020>
15. GLASER, R. (2013): Klimageschichte Mitteleuropas: 1200 Jahre Wetter, Klima, Katastrophen. WBG, Wiss. Buchges, Darmstadt.
16. GLASER, R., HIMMELSBACH, I., BÖSMEIER, A. (2017): Climate of migration? How climate triggered migration from southwest Germany to North America during the 19th century. Climate of the Past, 11, 13, 1573–1592.
<https://doi.org/10.5194/cp-13-1573-2017>
17. GLASER, R., KAHLE, M. (2020): Reconstructions of droughts in Germany since 1500 – combining hermeneutic information and instrumental records in historical and modern perspectives. Climate of the Past, 4, 16, 1207–1222.
<https://doi.org/10.5194/cp-16-1207-2020>
18. GLASER, R., KAHLE, M., HOLOGA, R. (2016): The tambora.org data series edition. Albert-Ludwigs-Universität Freiburg.
<https://doi.org/10.6094/TAMBORA.ORG/2016/SERIESNOTES.PDF>
19. GRÄSSE, J.G.T., BENEDICT, F. (1909): Orbis Latinus: R. C. Schmidt & Company, https://books.google.de/books?id=oJt-AAAAMAAJ
20. GRATZINGER, O. (2021): The Internet Archive: Founded by Brewster Kahle, https://archive.org/about/. American Journalism, 2, 38, 249–251.
<https://doi.org/10.1080/08821127.2021.1912531>
21. GROTEFEND, H. (2015): Handbuch der historischen Chronologie des deutschen Mittelalters und der Neuzeit. Vero Verlag, Norderstedt.
22. HAHN, C.H., MORITZ, W. (1998): Erkundungsreise ins Ovamboland 1857: Tagebuch. Lempp, Schwäbisch Gmünd.
23. HARRIS, Z.S. (1954): Distributional Structure. WORD, 2–3, 10, 146–162.
<https://doi.org/10.1080/00437956.1954.11659520>
24. HOCHREITER, S., SCHMIDHUBER, J. (1997): Long Short-Term Memory. Neural Computation, 8, 9, 1735–1780.
<https://doi.org/10.1162/neco.1997.9.8.1735>
25. HOFFMAN, M.D., BLEI, D.M., BACH, F. (2010): Online learning for Latent Dirichlet Allocation. In: Proceedings of the 24th International Conference on Neural Information Processing Systems, 1. Curran Associates Inc., Red Hook, NY, USA, 856–864.
26. 2013): Stochastic Variational Inference. Journal of Machine Learning Research, 40, 14, 1303–1347, http://jmlr.org/papers/v14/hoffman13a.html.
, M.D., BLEI, D.M., WANG, C., PAISLEY, J. (
27. HOLOGA, R., GLASER, R. (2021): The Societal Echo of Severe Weather Events: Ambient Geospatial Information (AGI) on a Storm Event. ISPRS International Journal of Geo-Information, 12, 10, 815.
<https://doi.org/10.3390/ijgi10120815>
28. HONNIBAL, M., MONTANI, I., VAN LANDEGHEM, S., BOYD, A. (2020): Spacy: Industrial-strength Natural Language Processing in Python.
29. 2004): Non-negative matrix factorization with sparseness constraints. Journal of machine learning research, 9, 5.
, P. O. (
30. KAHLE, B. (2007): Universal Access to All Knowledge. The American Archivist, 1, 70, 23–31, http://www.jstor.org/stable/40294448 (30.12.2024).
<https://doi.org/10.17723/aarc.70.1.u114006770252845>
31. KAHLE, M. (2025a): Innovative, digitale Verfahren zur maschinellen Erfassung und Klassifizierung von Texten zur historischen und rezenten Klimaanalyse und deren gesellschaftliche Folgen. Universität Freiburg.
<https://doi.org/10.6094/UNIFR/268947>
32. KAHLE, M., GLASER, R. (2025): Climatic conditions and mobility from 1000 to 1500. Hermeneutic and statistical approaches. In: Sarti, L., Trott zu Stolz, von, H. (eds.): Mobility in the Early Middle Ages, and Beyond – Mobilität im Frühmittelalter und darüber hinaus.
<https://doi.org/10.1515/9783111166698-009>
33. KAHLE, M., KEMPF, M., MARTIN, B., GLASER, R. (2022): Classifying the 2021 ‘Ahrtal’ flood event using hermeneutic interpretation, natural language processing, and instrumental data analyses. Environmental Research Communications, 5, 4, 051002.
<https://doi.org/10.1088/2515-7620/ac6657>
34. KAHLE, P., COLUTTO, S., HACKL, G., MÜHLBERGER, G. (2017): Transkribus – A Service Platform for Transcription, Recognition and Retrieval of Historical Documents. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). 19–24.
<https://doi.org/10.1109/ICDAR.2017.307>
35. KHIDER, D., EMILE‐GEAY, J., MCKAY, N. P., GIL, Y., GARIJO, D., RATNAKAR, V., ALONSO‐GARCIA, M., BERTRAND, S., BOTHE, O., BREWER, P., BUNN, A., CHEVALIER, M., COMAS‐BRU, L., CSANK, A., DASSIÉ, E., DELONG, K., FELIS, T., FRANCUS, P., FRAPPIER, A., GRAY, W., GORING, S., JONKERS, L., KAHLE, M., KAUFMAN, D., KEHRWALD, N.M., MARTRAT, B., MCGREGOR, H., RICHEY, J., SCHMITTNER, A., SCROXTON, N., SUTHERLAND, E., THIRUMALAI, K., ALLEN, K., ARNAUD, F., AXFORD, Y., BARROWS, T., BAZIN, L., PILAAR BIRCH, S. E., BRADLEY, E., BREGY, J., CAPRON, E., CARTAPANIS, O., CHIANG, H.‐W., COBB, K.M., DEBRET, M., DOMMAIN, R., DU, J., DYEZ, K., EMERICK, S., ERB, M.P., FALSTER, G., FINSINGER, W., FORTIER, D., GAUTHIER, N., GEORGE, S., GRIMM, E., HERTZBERG, J., HIBBERT, F., HILLMAN, A., HOBBS, W., HUBER, M., HUGHES, A.L.C., JACCARD, S., RUAN, J., KIENAST, M., KONECKY, B., LE ROUX, G., LYUBCHICH, V., NOVELLO, V.F., OLAKA, L., PARTIN, J.W., PEARCE, C., PHIPPS, S.J., PIGNOL, C., PIOTROWSKA, N., POLI, M. ‐S., PROKOPENKO, A., SCHWANCK, F., STEPANEK, C., SWANN, G.E.A., TELFORD, R., THOMAS, E., THOMAS, Z., TRUEBE, S., VON GUNTEN, L., WAITE, A., WEITZEL, N., WILHELM, B., WILLIAMS, J., WILLIAMS, J.J., WINSTRUP, M., ZHAO, N., ZHOU, Y. (2019): PaCTS 1.0: A Crowdsourced Reporting Standard for Paleoclimate Data. Paleoceanography and Paleoclimatology, 10, 34, 1570–1596.
<https://doi.org/10.1029/2019PA003632>
36. KHOO KHYOU BUN, ISHIZUKA, M. (2002): Topic extraction from news archive using TF*PDF algorithm. In: Proceedings of the Third International Conference on Web Information Systems Engineering, 2002. WISE 2002. IEEE Comput. Sci, Singapore, 73–82.
<https://doi.org/10.1109/WISE.2002.1181645>
37. KINGMA, D.P., BA, J. (2017): Adam: A Method for Stochastic Optimization. arXiv.
38. KUHL, E., ESPER, J., SCHNEIDER, L., TROUET, V., KUNZ, M., KLIPPEL, L., BÜNTGEN, U., HARTL, C. (2024): Revising Alpine summer temperatures since 881 CE. Climate Dynamics.
<https://doi.org/10.1007/s00382-024-07195-1>
39. LAMPRECHT, A.-L., GARCIA, L., KUZAK, M., MARTINEZ, C., ARCILA, R., MARTIN DEL PICO, E., DOMINGUEZ DEL ANGEL, V., VAN DE SANDT, S., ISON, J., MARTINEZ, P.A., MCQUILTON, P., VALENCIA, A., HARROW, J., PSOMOPOULOS, F., GELPI, J. Ll., CHUE HONG, N., GOBLE, C., CAPELLA-GUTIERREZ, S. (2020): Towards FAIR principles for research software. Data Science, 1, 3, 37–59.
<https://doi.org/10.3233/DS-190026>
40. LIN, T.-Y., GOYAL, P., GIRSHICK, R., HE, K., DOLLÁR, P. (2018): Focal Loss for Dense Object Detection. arXiv.
<https://doi.org/10.1109/ICCV.2017.324>
41. MADRUGA DE BRITO, M., KUHLICKE, C., MARX, A. (2020): Near-real-time drought impact assessment: a text mining approach on the 2018/19 drought in Germany. Environmental Research Letters, 10, 15, 1040a9.
<https://doi.org/10.1088/1748-9326/aba4ca>
42. MARCHANT, J. (2023): AI reads text from ancient Herculaneum scroll for the first time. Nature, d41586-023-03212–1.
<https://doi.org/10.1038/d41586-023-03212-1>
43. MATUSCHEK, O. (2014): Data-Mining in den Reisetagebüchern James Silk Buckinghams 1815/1816: neu entwickelte Suchalgorithmen und effiziente Arbeitsmethoden in der Historischen Klimatologie, https://freidok.uni-freiburg.de/data/9623 (7.1.2025).
44. MCKAY, N.P., EMILE-GEAY, J. (2016): Technical note: The Linked Paleo Data framework – a common tongue for paleoclimatology. Climate of the Past, 4, 12, 1093–1100.
<https://doi.org/10.5194/cp-12-1093-2016>
45. MITLOHNER, J., NEUMAIER, S., UMBRICH, J., POLLERES, A. (2016): Characteristics of Open Data CSV Files. In: 2016 2nd International Conference on Open and Big Data (OBD). IEEE, Vienna, 72–79.
<https://doi.org/10.1109/OBD.2016.18>
46. MOLLOY, J.C. (2011): The Open Knowledge Foundation: Open Data Means Better Science. PLoS Biology, 12, 9, e1001195.
<https://doi.org/10.1371/journal.pbio.1001195>
47. MONS, B., SCHULTES, E., LIU, F., JACOBSEN, A. (2020): The FAIR Principles: First Generation Implementation Choices and Challenges. Data Intelligence, 1–2, 2, 1–9.
<https://doi.org/10.1162/dint_e_00023>
48. NUNN, C.A. et al. (2024): Introducing Digital Humanities to Theology, heiBOOKS.
49. 2011): Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
, 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., DUCHESNAY, E. (
50. RAISSI, M., PERDIKARIS, P., AHMADI, N., KARNIADAKIS, G.E. (2024): Physics-Informed Neural Networks and Extensions, arXiv.
51. REBACK, J., MCKINNEY, W., JBROCKMENDEL, BOSSCHE, J.V.D., AUGSPURGER, T., CLOUD, P., GFYOUNG, HAWKINS, S., SINHRKS, ROESCHKE, M., KLEIN, A., TERJI PETERSEN, TRATNER, J., SHE, C., AYD, W., NAVEH, S., GARCIA, M., SCHENDEL, J., HAYDEN, A., SAXTON, D., PATRICK, JANCAUSKAS, V., MCMASTER, A., BATTISTON, P., SKIPPER SEABOLD, GORELLI, M., KAIQI DONG, CHRIS-B1, H-VETINARI, HOYER, S. (2021): pandas-dev/pandas: Pandas 1.2.1. Zenodo.
52. REINGOLD, E.M., DERSHOWITZ, N. (2001): Calendrical calculations. Cambridge University Press, Cambridge.
<https://doi.org/10.1017/CBO9781107051119>
53. RIEMANN, D. (2010): Methoden zur Klimarekonstruktion aus historischen Quellen am Beispiel Mitteleuropas, https://freidok.uni-freiburg.de/data/7904 (7.1.2025).
54. RIEMANN, D., GLASER, R., KAHLE, M., VOGT, S. (2015): The CRE tambora.org – new data and tools for collaborative research in climate and environmental history. Geoscience Data Journal, 2, 2, 63–77.
<https://doi.org/10.1002/gdj3.30>
55. SAHLE, P., VOGELER, G. (2013): Digital Monumenta Germaniae Historica (dMGH). Digital Philology: A Journal of Medieval Cultures, 1, 2, 135–139.
<https://doi.org/10.1353/dph.2013.0006>
56. SCHÄTZ, F. (2023): Voraussetzungen und Grenzen der Auswertung klimarelevanter Informationen historischer Textquellen mit Hilfe von Automatisierungsprozessen. Albert-Ludwigs-Universität Freiburg.
57. SEABOLD, S., PERKTOLD, J. (2010): statsmodels: Econometric and statistical modeling with python. In: 9th Python in Science Conference.
<https://doi.org/10.25080/Majora-92bf1922-011>
58. SMITH, R. (2007): An Overview of the Tesseract OCR Engine. In: Ninth International Conference on Document Analysis and Recognition (ICDAR 2007). 629–633.
<https://doi.org/10.1109/ICDAR.2007.4376991>
59. SPÄRCK JONES, K. (2004): A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 5, 60, 493–502.
<https://doi.org/10.1108/00220410410560573>
60. SPECKLE, I. (1968): Das Tagebuch von Ignaz Speckle. Veröffentlichungen der Kommission für geschichtliche Landeskunde in Baden-Württemberg, Stuttgart.
61. STEPHAN, R., STAHL, K., DORMANN, C.F. (2023): Drought impact prediction across time and space: limits and potentials of text reports. Environmental Research Letters, 7, 18, 074004.
<https://doi.org/10.1088/1748-9326/acd8da>
62. STEVENS, S.S. (1946): On the Theory of Scales of Measurement. Science, 2684, 103, 677–680.
<https://doi.org/10.1126/science.103.2684.677>
63. THÖLE, L.M., WEGMANN, M. (2024): Open climate science is brave climate science. Environmental Research Letters, 12, 19, 122001.
<https://doi.org/10.1088/1748-9326/ad893f>
64. VASWANI, A., SHAZEER, N., PARMAR, N., USZKOREIT, J., JONES, L., GOMEZ, A.N., KAISER, L., POLOSUKHIN, I. (2017): Attention Is All You Need, arXiv.
<https://doi.org/10.48550/arXiv.1706.03762>
65. VERSCHOOF-VAN DER VAART, W., KAPTIJN, E., BOURGEOIS, Q., LAMBERS, K. (2025): 13 Using Citizen Science to map hollow roads in LiDAR data from the Netherlands. Results from the Heritage Quest project. In: Sarti, L., Von Trott Zu Solz, H. (eds.): Mobility in the Early Middle Ages, and Beyond – Mobilität im Frühmittelalter und darüber hinaus. De Gruyter, 271–286.
<https://doi.org/10.1515/9783111166698-013>
66. WARREN, R., BARTLOME, N.E., WELLINGER, N., FRANKE, J., HAND, R., BRÖNNIMANN, S., HUHTAMAA, H. (2024): ClimeApp: data processing tool for monthly, global climate data from the ModE-RA palaeo-reanalysis, 1422 to 2008 CE. Climate of the Past, 12, 20, 2645–2662.
<https://doi.org/10.5194/cp-20-2645-2024>
67. WESSELKAMP, M., CHANTRY, M., PINNINGTON, E., CHOULGA, M., BOUSSETTA, S., KALWEIT, M., BÖDECKER, J., DORMANN, C.F., PAPPENBERGER, F., BALSAMO, G. (2025): Advances in land surface forecasting: a comparison of LSTM, gradient boosting, and feed-forward neural networks as prognostic state emulators in a case study with ecLand. Geoscientific Model Development, 4, 18, 921–937.
<https://doi.org/10.5194/gmd-18-921-2025>
68. WHITE, S., PEI, Q., KLEEMANN, K., DOLÁK, L., HUHTAMAA, H., CAMENISCH, C. (2023): New perspectives on historical climatology. WIREs Climate Change, 1, 14, e808.
<https://doi.org/10.1002/wcc.808>
69. WICK, M. (2015): Geonames Ontology, http://www.geonames.org/about.html (22.4.2015).
70. WBIS (2005): World Biographical Information System. Reference Reviews, 2, 19, 54–55.
<https://doi.org/10.1108/09504120510591828>
71. XU, Y., GOODACRE, R. (2018): On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning. Journal of Analysis and Testing, 3, 2, 249–262.
<https://doi.org/10.1007/s41664-018-0068-2>
72. YANG, S., NAI, C., LIU, X., LI, W., CHAO, J., WANG, J., WANG, L., LI, X., CHEN, X., LU, B., XIAO, Z., BOERS, N., YUAN, H., PAN, B. (2025): Generative assimilation and prediction for weather and climate, arXiv.
73. 2007): The Little Ice Age in the Alps: its record in glacial deposits and rock glacier formation. Studia Geomorphologica Carpatho-Balcanica, 41, 117–137.
, J. (
74. ZHANG, D., GLASER, R., KAHLE, M. (2025): Stable yet dynamic: a cross-era comparative case study of drought impacts and social responses in Germany and Jing-Jin-Ji Region (China).
<https://doi.org/10.5194/cp-21-1481-2025>