Bayesian Networks and GIS-based analysis for flood risk assessment in agriculture during heavy rainfall events
Keywords:
Flood risk assessment, Bayesian Networks, GIS analysis, Heavy rainfall events, Agricultural risk modeling, Geospatial data, Scenario analysis, Disaster risk management, Extreme weather events, Machine learning in hydrology, Climate change adaptationAbstract
The increasing frequency and intensity of heavy rainfall events due to climate change pose a growing risk to agriculture, and its prediction remains a substantial challenge for the scientific community. Leveraging the availability of high-quality open geospatial data provided by governmental institutions and existing probabilistic risk models, this study proposes a systematic approach for analyzing the risks associated with heavy rainfall events in agriculture. The objective is to develop and test a methodology that integrates open geospatial data, considers uncertainties, and conducts scenario analyses based on the historical heavy rainfall event of June 2, 2024, in the Rems-Murr district, Germany. Initially, variables such as Rainfall Intensity, Temperature, Land Use (from land use maps), Soil Type, Soil Moisture, Slope, Elevation, and River Discharge—obtained from official institutions—alongwith variables like Proximity to River, Road Density, and Proximity to Forest (derived from GIS analysis), were conceptually integrated into a Bayesian Network (BN). This integration was based on theoretical foundations and quantified using conditional probability distributions (CPD). The results demonstrate that the methodology combining BN and GIS analyses, alongwith scenario analysis, sensitivity analysis, optimization, and model validation, was successfully applied to the wider area of the Rems-Murr district. When tested on the heavy rainfall event of June 2, 2024, in the study area of Rudersberg, it provided qualitatively convincing results for flood risk assessment in agriculture. Validation in Rudersberg yielded a Root Mean Square Error (RMSE) of 23%. The methodology was also successfully applied across regions in Miedelsbach, where validation with official data collected by the county resulted in an RMSE of 30%. These findings indicate that the methodology is applicable not only within the study area but also across different regions. It is recommended to improve the model by incorporating additional variables such as surface parameters, roughness values, and drainage systems to improve accuracy. Furthermore, integrating meteorological forecasts could provide a basis for forwardlooking risk predictions.
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References
Abebe, Y., Kabir, G., Tesfamariam, S., 2018. Assessing urban areas vulnerability to pluvial flooding using GIS applications and Bayesian Belief Network model. Journal of Cleaner Production 174, 1629– 1641. URL: https://doi.org/10.1016/j.jclepro.2017.11.066, doi:10.1016/j.jclepro.2017.11.066.
Achleitner, S., Huber, A., Lumassegger, S., Kohl, B., Spira, Y., Weingraber, F., 2020. Pilotstudie oberösterreich: Modellierung von starkregenoberflächenabfluss / hangwasser (leitfaden). Association of European Border Regions. URL: https://rainman-toolbox.eu/wp-content/uploads/2020/06/AU_Leitfaden.pdf.
Adam, C., Gläßer, W., Hölting, B. (Eds.), 2000. Hydrogeologisches Wörterbuch. Thieme, Stuttgart, New York. URL: https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=6960.
U.S. Department of Agriculture, Natural Resources Conservation Service, 1986. Urban hydrology for small watersheds, technical release 55 (tr-55). URL: https://www.nrc.gov/docs/ML1421/ML14219A437.pdf. technical Release 55, Revised June 1986.
Ankan, A., Panda, A., 2015. pgmpy: Probabilistic Graphical Models using Python. Proceedings of the Python in Science Conferences , 6–11 URL: https://doi.org/10.25080/majora-7b98e3ed-001, doi:10.25080/majora-7b98e3ed-001.
Assmann, A., 2023. Wasser, energie und umwelt: Aktuelle Beiträge aus der Zeitschrift Wasser und Abfall iii, in: Porth, M., Schüttrumpf, H., Ostermann, U. (Eds.), Starkregen und Hochwasser in kleinen Einzugsgebieten – Auswirkungen. Springer, Wiesbaden, pp. 123– 127. URL: https://doi.org/10.1007/978-3-658-42657-6 _13, doi:10.1007/978-3-658-42657-6_13.
Aven, T., 2015. Risk Analysis. John Wiley & Sons. URL: https://books.google.de/books/about/Risk _Analysis.html?id=41V_BwAAQBAJ&redir_esc=y.
Baden-Württemberg, Regierungspräsidien, 2024. Gemeinsam gegen die Folgen des Unwetters: Landeshilfe aktiviert / rund 19 Millionen Euro zusätzliche Hilfe für den Stuttgarter Regierungsbezirk. URL: https://rp.baden-wuerttemberg.de/rps/wir/abteilungen/pressemitteilungen-und-aktuelle-meldungen/artikel/hochwasser-landeshilfe-ak-tiviert-19-millionen-euro-zusaetzliche-hilfe-fuer-den-stuttgarter-regierungsbezirk/. zugriff am 19.12.2024, 11:52.
Becker, P., Becker, A., Dalelane, C., Deutschländer, T., Junghänel, T., Walter, A., 2016. Die entwicklung von starkniederschlägen in deutschland: Plädoyer für eine differenzierte betrachtung. Berichte des Deutschen Wetterdienstes 245. URL: https://www.dwd.de/DE/leistungen/besondereereignisse/niederschlag/20160719_entwicklung_starkniederschlag_deutschland.pdf?__blob=publicationFile&v=3.
Bill, R., 2010. Grundlagen der Geo-Informationssysteme. Wichmann.
Borga, M., Boscolo, P., Zanon, F., Sangati, M., 2007. Hydrometeorological Analysis of the 29 August 2003 Flash Flood in the Eastern Italian Alps. Journal of Hydrometeorology 8, 1049–1067. URL: https://doi.org/10.1175/jhm593.1, doi:10.1175/jhm593.1.
Chai, T., Draxler, R. R., 2014. Root mean square error (rmse) or mean absolute error (mae)? – arguments against avoiding rmse in the literature. Geoscientific Model Development 7, 1247–1250. URL: https://gmd.copernicus.org/articles/7/1247/2014/, doi:10.5194/gmd-7-1247-2014.
Climate-Data.org, 2025. Klimadaten für Rudersberg, Baden-Württemberg, Deutschland. URL: https://de.climate-data.org/europa/deutschland/baden-wuerttemberg/rudersberg-156016/. zugriff am 09.01.2025, 16:05.
Cooper, G. F., Herskovits, E., 1992. A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9, 309–347. URL: https://doi.org/10.1007/bf00994110, doi:10.1007/bf00994110.
CRED, Centre for Research on the Epidemiology of Disasters, 2025. EM-DAT: The international disaster database. URL: https://public.emdat.be/data.zugriff am 17.01.2025, 16:07.
Dinkelbach, W., 1969. Sensitivitätsanalysen und parametrische Programmierung. In Ökonometrie und Unternehmensforschung. URL: https://doi.org/10.1007/978-3-642-88169-5, doi:10.1007/978-3-642-88169-5.
DWD, 2024. Starkregen: Definition und klassifikation. https://www.dwd.de/DE/service/lexikon/begriffe/S/Starkregen.html. URL: https://www.dwd.de/DE/service/lexikon/begriffe/S/Starkregen.html.zugriff am 10.12.2024, 16:56.
Fatichi, S., Vivoni, E., Ogden, F., Ivanov, V., Mirus, B., Gochis, D., Downer, C., Camporese, M., Davison, J., Ebel, B., Jones, N., Kim, J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M., Tarboton, D., 2016. An overview of current applications, challenges, and future trends in distributed process-based models in hydrology. Journal of Hydrology 537. https://doi.org/10.1016/j.jhydrol.2016.03.026.
Furlan, E., Slanzi, D., Torresan, S., Critto, A., Marcomini, A., 2020. Multi-scenario analysis in the Adriatic sea: A gis-based Bayesian Network to support maritime spatial planning. Science of The Total Environment 703, 134972. URL: https://www.sciencedirect.com/science/article/pii/S0048969719349642, doi:10.1016/j.scitotenv.2019.134972.
Harris, R., Furlan, E., Pham, H.V., Torresan, S., Mysiak, J., Critto, A., 2022. A Bayesian network approach for multi-sectoral flood damage assessment and multi-scenario analysis. Climate Risk Management 35, 100410. URL: https://doi.org/10.1016/j.crm.2022.100410, doi:10.1016/j.crm.2022.100410.
Hrachowitz, M., Clark, M. P., 2017. Hess opinions: The complementary merits of competing modelling philosophies in hydrology. Hydrology and Earth System Sciences 21, 3953– 3973. URL: https://hess.copernicus.org/articles/21/3953/2017/, doi:10.5194/hess-21-3953-2017.
Huang, S., Wang, H., Xu, Y., She, J., Huang, J., 2021. Key Disaster-Causing Factors Chains on Urban Flood Risk Based on Bayesian Network. Land 10, 210. URL: https://doi.org/10.3390/land10020210, doi:10.3390/land10020210.
Korb, K. B., Nicholson, A. E., 2010. Bayesian Artificial Intelligence. In CRC Press eBooks. URL: https://doi.org/10.1201/b10391, doi:10.1201/b10391.
Koski, T., Noble, J., 2011. Bayesian Networks: An introduction. John Wiley & Sons.
Kragt, M. E., 2009. A beginners guide to Bayesian network modelling for integrated catchment management. Landscape Logic. URL: https://www.utas.edu.au/__data/assets/pdf_file/0011/588467/TR_9_BNs_for_Integrated_Catchment_Management.pdf
Kreibich, H., Schröter, K., Shprits, Y., Bedford, J., Tilmann, F., 2018. Maschinelles lernen verbessert gefährdungs- und risikoanalysen von naturgefahren. System Erde 8, 10–17. URL: https://gfzpublic.gfz-potsdam.de/pubman/item/item_3539918, doi:10.2312/ GFZ.SYSERDE.08.01.2.
Lehmann, J., Coumou, D., Frieler, K., 2015. Increased record-breaking precipitation events under global warming. Climatic Change 132, 501–515. URL: https://doi.org/10.1007/s10584-015-1434-y, doi:10.1007/s10584-015-1434-y.
LGLN, Landesamt für Geoinformation und Landesvermessung Niedersachsen, 2025. Nas. URL: https://www.lgln.niedersachsen.de/startseite/wir_uber_uns/hilfe_support/lgln_lexikon/n/nas-190332.html#:~:text=NAS%20ist%20ein%20auf%20XML,mit%20Geobasisdaten%20in%20einem%20Geoinformationssystem. zugriff am 14.01.2025, 09:10.
Li, G., Wu, X., Han, J-C., Li, B., Huang, Y., Wang, Y., 2023. Flood risk assessment by using an interpretative structural modeling based Bayesian network approach (ISM-BN): An urban-level analysis of Shenzhen, China. Journal of Environmental Management 329, 117040. URL: https://www.sciencedirect.com/science/article/pii/S0301479722026135, doi:10.1016/j.jenvman.2022.117040.
Li, Y., Guan, K., Schnitkey, G. D., DeLucia, E., Peng, B., 2019. Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States. Global Change Biology 25, 2325–2337. URL: https://doi.org/10.1111/gcb.14628, doi:10.1111/gcb.14628.
Lu, Y., Zhai, G., Zhou, S., 2024. An integrated bayesian networks and geographic information system (bns-gis) approach for flood disaster risk assessment: A case study of Yinchuan, China. Ecological Indicators 166, 112322. https://doi.org/10.1016/j.ecolind.2024.112322
Merz, R., 2008. Einflussfaktoren auf die form der hochwasserwahrscheinlichkeitskurve, in: Hochwasser, Wassermangel, Gewässerverschmutzung - Problemlösung mit modernen hydrologischen Methoden. Beiträge zum Tag der Hydrologie am 27./28. März 2008 an der Leibniz Universität Hannover, DWA, Fachgemeinschaft Hydrologische Wissenschaften, Hennef. pp. 113–106. URL: https://hdl.handle.net/20.500.11970/112870.
Muthiah, K., Arunya, K. G., Sridhar, V., Patakamuri, S. K., 2025. Heavy rainfall impact on agriculture: Crop risk assessment with farmer participation in the Paravanar coastal river basin. Water 17. URL: https://www.mdpi.com/2073-4441/17/5/658, doi:10.3390/w17050658.
Niu, H., Sun, W., Huai, B., Wang, Y., Chen, R., Han, C., Wang, Y., Zhou, J., Wang, L., 2024. Causes of Increased Compound Temperature and Precipitation Extreme Events in the Arid Region of Northwest China from 1961 to 2100. Remote Sensing 16, 3111. URL: https://doi.org/10.3390/rs16173111, doi:10.3390/rs16173111.
Pearl, J., 2014. Probabilistic reasoning in intelligent systems: Networks of plausible inference. Elsevier. URL: https://books.google.de/books?id=mn2jBQAAQBAJ&printsec=frontcover&hl=de&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false.
Rana, I. A., Routray, J. K., 2017. Integrated methodology for flood risk assessment and application in urban communities of Pakistan. Natural Hazards 91, 239– 266. URL: https://doi.org/10.1007/s11069-017-3124-8, doi:10.1007/s11069-017-3124-8.
Spektrum.de, 2004. Lexikon der geographie: Regenintensität. URL: https://www.spektrum.de/lexikon/geographie/regenintensitaet/6508. zugriff am 18.12.2024, 16:39.
Sperotto, A., Molina, J-L., Torresan, S., Critto, A., Marcomini, A., 2017. Reviewing Bayesian Networks potentials for climate change impacts assessment and management: A multi-risk perspective. Journal of Environmental Management 202, 320–331. URL: https://doi.org/10.1016/j.jenvman.2017.07.044, doi:10.1016/j.jenvman.2017.07.044.
Weigell, P., 2023. Data coverage, richness, and quality of Openstreetmap for special interest tags: wayside crosses – a case study. URL: https://arxiv.org/abs/2306.04752, arXiv:2306.04752.
Wilke, S., 2024. Trends der Niederschlagshöhe. URL: https://www.umweltbundesamt.de/daten/klima/trends-der-niederschlagshoehe#teilweise-sehr-regenreiche-jahre-seit-1965. zugriff am 17.01.2025, 14:26.
Wu, Z., Shen, Y., Wang, H., Wu, M., 2019. Assessing urban flood disaster risk using Bayesian network model and GIS applications. Geomatics Natural Hazards and Risk 10, 2163–2184. URL: https://doi.org/10.1080/19475705.2019.1685010, doi:10.1080/19475705.2019.1685010.
Zhang, P., Sun, W., Xiao, P., Yao, W., Liu, G., 2022. Driving Factors of Heavy Rainfall Causing Flash Floods in the Middle Reaches of the Yellow River: A Case Study in the Wuding River Basin, China. Sustainability 14, 8004. URL: https://doi.org/10.3390/su14138004,22 doi:10.3390/su14138004.
Zippenfenig, P., 2023. Open-meteo.com weather api. URL: https://open-meteo.com/, https://doi.org/10.5281/ZENODO.7970649. Creative Commons Attribution 4.0 International.
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