{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:34:55Z","timestamp":1772822095686,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,3]],"date-time":"2024-03-03T00:00:00Z","timestamp":1709424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Environmental Research and Technology Development Fund of the Environmental Restoration and Conservation Agency of Japan","award":["JPMEERF20S11811"],"award-info":[{"award-number":["JPMEERF20S11811"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Coastal areas, influenced by human activity and natural factors, face major environmental shifts, including climate-induced flood risks. This highlights the importance of forecasting coastal land use for effective flood defense and ecological conservation. Japan\u2019s distinct demographic path necessitates flexible strategies for managing its urban development. The study examines the Ibaraki Coastal region to analyze the impacts of land-use changes in 2030, predicting and evaluating future floods from intensified high tides and waves in scenario-based forecasts. The future roughness map is derived from projected land-use changes, and we utilize this information in DioVISTA 3.5.0 software to simulate flood scenarios. Finally, we analyzed the overlap between simulated floods and each land-use category. The results indicate since 2020, built-up areas have increased by 52.37 sq. km (39%). In scenarios of constant or shrinking urban areas, grassland increased by 28.54 sq. km (42%), and urban land cover decreased by 7.47 sq. km (5.6%) over ten years. Our research examines two separate peaks in water levels associated with urban flooding. Using 2030 land use maps and a peak height of 4 m, which is the lower limit of the maximum run-up height due to storm surge expected in the study area, 4.71 sq. km of residential areas flooded in the urban growth scenario, compared to 4.01 sq. km in the stagnant scenario and 3.96 sq. km in the shrinkage scenario. With the upper limit of 7.2 m, which is the extreme case in most of the study area, these areas increased to 49.91 sq. km, 42.52 sq. km, and 42.31 sq. km, respectively. The simulation highlights future flood-prone urban areas for each scenario, guiding targeted flood prevention efforts.<\/jats:p>","DOI":"10.3390\/rs16050898","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T10:11:57Z","timestamp":1709547117000},"page":"898","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Modeling Land Use Transformations and Flood Hazard on Ibaraki\u2019s Coastal in 2030: A Scenario-Based Approach Amid Population Fluctuations"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2002-7216","authenticated-orcid":false,"given":"Mohammadreza","family":"Safabakhshpachehkenari","sequence":"first","affiliation":[{"name":"Graduate School of Science and Engineering, Ibaraki University, Hitachi 316-8511, Ibaraki, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4866-5955","authenticated-orcid":false,"given":"Hideyuki","family":"Tonooka","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Engineering, Ibaraki University, Hitachi 316-8511, Ibaraki, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1016\/j.accre.2023.11.007","article-title":"Features of the new climate normal 1991\u20132020 and possible influences on climate monitoring and prediction in China","volume":"14","author":"Wang","year":"2023","journal-title":"Adv. Clim. Chang. Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1126\/science.1109454","article-title":"Impact of humans on the flux of terrestrial sediment to the global coastal ocean","volume":"308","author":"Syvitski","year":"2005","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3292","DOI":"10.1073\/pnas.1222469111","article-title":"Coastal flood damage and adaptation costs under 21st century sea-level rise","volume":"111","author":"Hinkel","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1038\/ngeo629","article-title":"Sinking deltas due to human activities","volume":"2","author":"Syvitski","year":"2009","journal-title":"Nat. Geosci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"13768","DOI":"10.1038\/s41598-020-70816-2","article-title":"Climate change impact on flood and extreme precipitation increases with water availability","volume":"10","author":"Tabari","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"576","DOI":"10.1038\/nclimate1529","article-title":"Relative outcomes of climate change mitigation related to global temperature versus sea-level rise","volume":"2","author":"Meehl","year":"2012","journal-title":"Nat. Clim. Chang."},{"key":"ref_7","unstructured":"de Graaf, R., and Hooimeijer, F. (2008). Urban Water in Japan, CRC Press."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.5194\/nhess-8-1049-2008","article-title":"Empirical analysis of Japanese flood risk acceptability within multi-risk context","volume":"8","author":"Zhai","year":"2008","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1016\/j.enpol.2012.10.036","article-title":"Fukushima and thereafter: Reassessment of risks of nuclear power","volume":"52","author":"Srinivasan","year":"2013","journal-title":"Energy Policy"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1007\/s00024-012-0511-7","article-title":"Lessons learned from the 2011 great east japan tsunami: Performance of tsunami countermeasures, coastal buildings, and tsunami evacuation in japan","volume":"170","author":"Suppasri","year":"2013","journal-title":"Pure Appl. Geophys."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.progress.2013.08.001","article-title":"Climate change and the city: Building capacity for urban adaptation","volume":"95","author":"Carter","year":"2015","journal-title":"Prog. Plan."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.jhydrol.2017.02.020","article-title":"Scenario-based projections of future urban inundation within a coupled hydrodynamic model framework: A case study in Dongguan City, China","volume":"547","author":"Wu","year":"2017","journal-title":"J. Hydrol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.jclepro.2018.03.220","article-title":"How are cities planning to respond to climate change? Assessment of local climate plans from 885 cities in the EU-28","volume":"191","author":"Reckien","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.jhydrol.2016.09.003","article-title":"Flood risk zoning using a rule mining based on ant colony algorithm","volume":"542","author":"Lai","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105297","DOI":"10.1016\/j.ocecoaman.2020.105297","article-title":"Land-use planning adaptation in response to SLR based on a vulnerability analysis","volume":"196","author":"Zhao","year":"2020","journal-title":"Ocean Coast. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.ocecoaman.2014.07.018","article-title":"Land-use simulation as a supporting tool for flood risk assessment and coastal safety planning: The case of the Belgian coast","volume":"101","author":"Canters","year":"2014","journal-title":"Ocean Coast. Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.compenvurbsys.2014.12.004","article-title":"Integrating sea level rise into development suitability analysis","volume":"51","author":"Berry","year":"2015","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e2021GL092544","DOI":"10.1029\/2021GL092544","article-title":"Assessment of future flood hazards for southeastern Texas: Synthesizing subsidence, sea-level rise, and storm surge scenarios","volume":"48","author":"Miller","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1080\/10106049.2016.1265597","article-title":"Modelling coastal land use change by incorporating spatial autocorrelation into cellular automata models","volume":"33","author":"Feng","year":"2018","journal-title":"Geocarto Int."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1111\/risa.13493","article-title":"Flood risk assessment and regionalization from past and future perspectives at basin scale","volume":"40","author":"Lai","year":"2020","journal-title":"Risk Anal."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Moya, L., Mas, E., and Koshimura, S. (2020). Learning from the 2018 western Japan heavy rains to detect floods during the 2019 Hagibis typhoon. Remote Sens., 12.","DOI":"10.3390\/rs12142244"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, W., Fujii, K., Maruyama, Y., and Yamazaki, F. (2021). Inundation assessment of the 2019 Typhoon Hagibis in Japan using multi-temporal Sentinel-1 intensity images. Remote Sens., 13.","DOI":"10.3390\/rs13040639"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ohki, M., Yamamoto, K., Tadono, T., and Yoshimura, K. (2020). Automated processing for flood area detection using ALOS-2 and hydrodynamic simulation data. Remote Sens., 12.","DOI":"10.3390\/rs12172709"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.cities.2017.02.011","article-title":"The development of urban shrinkage discourse and policy response in Japan","volume":"69","author":"Hattori","year":"2017","journal-title":"Cities"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/S0140-6736(14)61464-1","article-title":"Macroeconomic implications of population ageing and selected policy responses","volume":"385","author":"Bloom","year":"2015","journal-title":"Lancet"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.cities.2016.12.005","article-title":"How cities shrink: Complex pathways to population decline","volume":"75","author":"Hartt","year":"2018","journal-title":"Cities"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105020","DOI":"10.1016\/j.scs.2023.105020","article-title":"Urban shrinkage in the regional multiscale context: Spatial divergence and interaction","volume":"100","author":"Ma","year":"2024","journal-title":"Sustain. Cities Soc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104292","DOI":"10.1016\/j.cities.2023.104292","article-title":"Identifying and quantizing the non-linear correlates of city shrinkage in Japan","volume":"137","author":"Peng","year":"2023","journal-title":"Cities"},{"key":"ref_29","unstructured":"(2024, January 08). Housing and Land Survey, Available online: https:\/\/www.stat.go.jp\/english\/data\/jyutaku\/index.html."},{"key":"ref_30","unstructured":"(2024, January 08). Infrastructure Supporting Life and Economy, \u2018Growing\u2019 Ibaraki. Available online: https:\/\/www.pref.ibaraki.jp\/soshiki\/doboku\/stock.html."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nguyen Hao, Q., and Takewaka, S. (2021). Shoreline changes along northern Ibaraki Coast after the Great East Japan Earthquake of 2011. Remote Sens., 13.","DOI":"10.3390\/rs13071399"},{"key":"ref_32","unstructured":"(2024, January 08). Coastal Conservation Master Plan. Available online: https:\/\/www.pref.ibaraki.jp\/doboku\/kasen\/coast\/032000.html."},{"key":"ref_33","unstructured":"(2024, January 08). High-Resolution Land-Use and Land-Cover Map of Japan. Available online: https:\/\/www.eorc.jaxa.jp\/ALOS\/en\/dataset\/lulc\/lulc_v2111_e.htm."},{"key":"ref_34","unstructured":"(2024, January 08). GSI Maps, Available online: https:\/\/maps.gsi.go.jp\/."},{"key":"ref_35","unstructured":"(2024, January 08). WorldPop Hub. Available online: https:\/\/hub.worldpop.org\/."},{"key":"ref_36","unstructured":"(2024, January 08). Future Estimated Population Data by 500 m Mesh (H29 National Political Bureau Estimates), Available online: https:\/\/nlftp.mlit.go.jp\/ksj\/gml\/datalist\/KsjTmplt-mesh500.html."},{"key":"ref_37","unstructured":"(2024, January 08). DioVISTA\/Flood. Available online: https:\/\/www.hitachi-power-solutions.com\/en\/service\/digital\/diovista-flood\/index.html."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.compenvurbsys.2017.04.011","article-title":"Integration of genetic algorithm and multiple kernel support vector regression for modeling urban growth","volume":"65","author":"Tayyebi","year":"2017","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.ocecoaman.2014.12.016","article-title":"Modelling the spatial dynamics of urban growth and land use changes in the north coast of S\u00e3o Paulo, Brazil","volume":"108","author":"Inouye","year":"2015","journal-title":"Ocean Coast. Manag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"103958","DOI":"10.1016\/j.cities.2022.103958","article-title":"Assessment of interactions between influencing factors on city shrinkage based on geographical detector: A case study in Kitakyushu, Japan","volume":"131","author":"Peng","year":"2022","journal-title":"Cities"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1111\/1468-2427.12289","article-title":"The limits of shrinkage: Conceptual pitfalls and alternatives in the discussion of urban population loss: Debates & developments","volume":"40","author":"Bernt","year":"2016","journal-title":"Int. J. Urban Reg. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1693","DOI":"10.1080\/09654313.2019.1604635","article-title":"A meta-analysis of shrinking cities in Europe and Japan. Towards an integrative research agenda","volume":"28","author":"Uchiyama","year":"2020","journal-title":"Eur. Plan. Stud."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1007\/s11625-020-00835-5","article-title":"Projecting population distribution under depopulation conditions in Japan: Scenario analysis for future socio-ecological systems","volume":"16","author":"Hori","year":"2021","journal-title":"Sustain. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kubo, T., and Yui, Y. (2019). The Rise in Vacant Housing in Post-Growth Japan, Springer.","DOI":"10.1007\/978-981-13-7920-8"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"101490","DOI":"10.1016\/j.scs.2019.101490","article-title":"Prefecture-level city shrinkage on the regional dimension in China: Spatiotemporal change and internal relations","volume":"47","author":"Zhang","year":"2019","journal-title":"Sustain. Cities Soc."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"8911","DOI":"10.1007\/s13762-021-03627-1","article-title":"GIS-based frequency ratio and Shannon\u2019s entropy techniques for flood vulnerability assessment in Patna district, Central Bihar, India","volume":"19","author":"Sarkar","year":"2022","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_47","unstructured":"Htet, H., Khaing, S.S., and Myint, Y.Y. (2019). Advances in Intelligent Systems and Computing, Springer."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Safabakhshpachehkenari, M., and Tonooka, H. (2023). Assessing and enhancing predictive efficacy of machine learning models in urban land dynamics: A comparative study using multi-resolution satellite data. Remote Sens., 15.","DOI":"10.3390\/rs15184495"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"111399","DOI":"10.1016\/j.chaos.2021.111399","article-title":"Predicting the trend of indicators related to COVID-19 using the combined MLP-MC model","volume":"152","author":"Haghighat","year":"2021","journal-title":"Chaos Solitons Fractals"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Mansour, S., Ghoneim, E., El-Kersh, A., Said, S., and Abdelnaby, S. (2023). Spatiotemporal monitoring of urban sprawl in a coastal city using GIS-based Markov Chain and artificial Neural Network (ANN). Remote Sens., 15.","DOI":"10.3390\/rs15030601"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.landusepol.2017.05.014","article-title":"Performance evaluation of multiple methods for landscape aesthetic suitability mapping: A comparative study between Multi-Criteria Evaluation, Logistic Regression and Multi-Layer Perceptron neural network","volume":"67","author":"Saeidi","year":"2017","journal-title":"Land Use Policy"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Bratley, K., and Ghoneim, E. (2018). Modeling urban encroachment on the agricultural land of the Eastern Nile Delta using remote sensing and a GIS-based Markov Chain model. Land, 7.","DOI":"10.3390\/land7040114"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"139899","DOI":"10.1016\/j.scitotenv.2020.139899","article-title":"Scenario-based flood risk assessment for urbanizing deltas using future land-use simulation (FLUS): Guangzhou Metropolitan Area as a case study","volume":"739","author":"Lin","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_54","first-page":"623","article-title":"Extreme Sea Level Variations in the Sea of Japan Caused by the Passage of Typhoons Maysak and Haishen in September 2020","volume":"63","author":"Smirnova","year":"2023","journal-title":"Russ. Acad. Sci. Oceanol."},{"key":"ref_55","unstructured":"Yamaguchi, S., Ikeda, T., and Iwamura, K. (2008, January 6\u20138). Rapid flood simulation software for personal computer with Dynamic Domain Defining Method. Proceedings of the 4th International Symposium on Flood Defence: Managing Flood Risk, Reliability and Vulnerability, Toronto, ON, Canada."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1061\/(ASCE)1084-0699(2001)6:5(406)","article-title":"Two-dimensional flood plain flow. II: Model validation","volume":"6","author":"Connell","year":"2001","journal-title":"J. Hydrol. Eng."},{"key":"ref_57","unstructured":"Yamaguchi, S., Ikeda, T., and Yamaho, S. (2012, January 14\u201318). Flood risk assessment system for major metropolitan areas in Japan. Proceedings of the 10th International Conference on Hydroinformatics (HIC 2012), Hamburg, Germany."},{"key":"ref_58","unstructured":"Yamaguchi, S., and Ikeda, T. (2010, January 7\u201311). Automatic integration of hydraulic and hydrologic models based on geographic information. Proceedings of the 9th International Conference on Hydroinformatics (HIC 2010), Tianjin, China."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/bs.pmbts.2020.04.003","article-title":"Chapter Eleven\u2014Correlation and association analyses in microbiome study integrating multiomics in health and disease","volume":"171","author":"Xia","year":"2020","journal-title":"Prog. Mol. Biol. Transl. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Mogaraju, J.K. (2023). Artificial Intelligence assisted prediction of land surface temperature (LST) based on significant air pollutants over the Annamayya district of India. Res. Sq.","DOI":"10.21203\/rs.3.rs-3186697\/v1"},{"key":"ref_61","unstructured":"(2023, April 30). Available online: https:\/\/www.mlit.go.jp\/river\/shishin_guideline\/kaigan\/takashioshinsui_manual.pd."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"101261","DOI":"10.1016\/j.ejrh.2022.101261","article-title":"Employment of hydraulic model and social media data for flood hazard assessment in an urban city","volume":"44","author":"Ouyang","year":"2022","journal-title":"J. Hydrol. Reg. Stud."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"01014","DOI":"10.1051\/e3sconf\/202234001014","article-title":"Incorporating dynamics of land use and land cover changes into tsunami numerical modelling for future tsunamis in Banda Aceh","volume":"340","author":"Tursina","year":"2022","journal-title":"E3S Web Conf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/898\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:08:38Z","timestamp":1760105318000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/898"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,3]]},"references-count":63,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16050898"],"URL":"https:\/\/doi.org\/10.3390\/rs16050898","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,3]]}}}