Model applications by other researchers – Environmental Geography

CLUMondo and DynaCLUE models applied on different scales and across the whole globe by other researchers

Arranged by scale and country – alphabetically.

Global and continental-scale studies

Bolochio, B.E., Lescano, J.N., Cordier, J.M., Loyola, R., Nori, J., 2020. A functional perspective for global amphibian conservation. Biological Conservation 245, 108572. https://doi.org/10.1016/j.biocon.2020.108572

Egli, L., Meyer, C., Scherber, C., Kreft, H., Tscharntke, T., 2018. Winners and losers of national and global efforts to reconcile agricultural intensification and biodiversity conservation. Global Change Biology 24, 2212–2228. https://doi.org/10.1111/gcb.14076

Smith, A., Schoeman, M.C., Keith, M., Erasmus, B.F.N., Monadjem, A., Moilanen, A., Di Minin, E., 2016. Synergistic effects of climate and land-use change on representation of African bats in priority conservation areas. ECOLOGICAL INDICATORS 69, 276–283. https://doi.org/10.1016/j.ecolind.2016.04.039

Hellmann, F.A., de Moel, H., 2014. Future Land Use Patterns in European River Basins: Scenario Trends in Urbanization, Agriculture and Land Use, in: Brils, J., Brack, W., Müller-Grabherr, D., Négrel, P., Vermaat, J.E. (Eds.), Risk-Informed Management of European River Basins, The Handbook of Environmental Chemistry. Springer, Berlin, Heidelberg, pp. 209–222. https://doi.org/10.1007/978-3-642-38598-8_7

Overmars, K.P., Helming, J., van Zeijts, H., Jansson, T., Terluin, I., 2013. A modelling approach for the assessment of the effects of Common Agricultural Policy measures on farmland biodiversity in the EU27. Journal of Environmental Management 126, 132–141. https://doi.org/10.1016/j.jenvman.2013.04.008

Rega, C., Helming, J., Paracchini, M.L., 2019. Environmentalism and localism in agricultural and land-use policies can maintain food production while supporting biodiversity. Findings from simulations of contrasting scenarios in the EU. Land Use Policy 87, 103986. https://doi.org/10.1016/j.landusepol.2019.05.005

Sieber, S., Amjath-Babu, T.S., Jansson, T., Müller, K., Tscherning, K., Graef, F., Pohle, D., Helming, K., Rudloff, B., Saravia-Matus, B.S., Gomez y Paloma, S., 2013. Sustainability impact assessment using integrated meta-modelling: Simulating the reduction of direct support under the EU common agricultural policy (CAP). Land Use Policy 33, 235–245. https://doi.org/10.1016/j.landusepol.2013.01.002

Argentina

Lima, M.L., Romanelli, A., Massone, H.E., 2015. Assessing groundwater pollution hazard changes under different socio-economic and environmental scenarios in an agricultural watershed. Science of The Total Environment 530–531, 333–346. https://doi.org/10.1016/j.scitotenv.2015.05.026

Lima, M.L., Zelaya, K., Massone, H., 2011. Groundwater Vulnerability Assessment Combining the Drastic and Dyna-Clue Model in the Argentine Pampas. Environmental Management 47, 828–839. https://doi.org/10.1007/s00267-011-9652-1

Lourdes, L., Karina, Z., Pedro, L., Héctor, M., Néstor, M., 2011. A dynamic simulation model of land cover in the Dulce Creek Basin, Argentina. Procedia Environmental Sciences, Spatial Statistics 2011: Mapping Global Change 7, 194–199. https://doi.org/10.1016/j.proenv.2011.07.034

Austria

Cammerer, H., Thieken, A.H., 2013. Historical development and future outlook of the flood damage potential of residential areas in the Alpine Lech Valley (Austria) between 1971 and 2030. Reg Environ Change 13, 999–1012. https://doi.org/10.1007/s10113-013-0407-9

Sauter, I., Kienast, F., Bolliger, J., Winter, B., Pazur, R., 2019. Changes in demand and supply of ecosystem services under scenarios of future land use in Vorarlberg, Austria. JOURNAL OF MOUNTAIN SCIENCE 16, 2793–2809. https://doi.org/10.1007/s11629-018-5124-x

Promper, C., Gassner, Ch., Glade, T., 2015. Spatiotemporal patterns of landslide exposure – a step within future landslide risk analysis on a regional scale applied in Waidhofen/Ybbs Austria. International Journal of Disaster Risk Reduction 12, 25–33. https://doi.org/10.1016/j.ijdrr.2014.11.003

Promper, C., Puissant, A., Malet, J.-P., Glade, T., 2014. Analysis of land cover changes in the past and the future as contribution to landslide risk scenarios. Applied Geography 53, 11–19. https://doi.org/10.1016/j.apgeog.2014.05.020

Thieken, A.H., Cammerer, H., Dobler, C., Lammel, J., Schöberl, F., 2016. Estimating changes in flood risks and benefits of non-structural adaptation strategies – a case study from Tyrol, Austria. Mitig Adapt Strateg Glob Change 21, 343–376. https://doi.org/10.1007/s11027-014-9602-3

Canada

Anputhas, M., Janmaat, J. (John) A., Nichol, C.F., Wei, X. (Adam), 2016. Modelling spatial association in pattern based land use simulation models. Journal of Environmental Management 181, 465–476. https://doi.org/10.1016/j.jenvman.2016.06.034

El Khoury, A., 2012. Modeling Land-use Changes in the South Nation Watershed Using Dyna-CLUE (Thesis). Université d’Ottawa / University of Ottawa. https://ruor.uottawa.ca/handle/10393/22902

Chile

Henríquez-Dole, L., Usón, T.J., Vicuña, S., Henríquez, C., Gironás, J., Meza, F., 2018. Integrating strategic land use planning in the construction of future land use scenarios and its performance: The Maipo River Basin, Chile. Land Use Policy 78, 353–366. https://doi.org/10.1016/j.landusepol.2018.06.045

Manuschevich, D., Beier, C.M., 2016. Simulating land use changes under alternative policy scenarios for conservation of native forests in south-central Chile. Land Use Policy 51, 350–362. https://doi.org/10.1016/j.landusepol.2015.08.032

Manuschevich, D., Sarricolea, P., Galleguillos, M., 2019. Integrating socio-ecological dynamics into land use policy outcomes: A spatial scenario approach for native forest conservation in south-central Chile. Land Use Policy 84, 31–42. https://doi.org/10.1016/j.landusepol.2019.01.042

China

Huang, J., Liu, Y., Zhang, X., Wang, Yu, Wang, Yisong, 2019. A Scenario-Based Simulation of Land System Changes on Dietary Changes: A Case Study in China. SUSTAINABILITY 11. https://doi.org/10.3390/su11195196

Jin, X., Jiang, P., Ma, D., Li, M., 2019. Land system evolution of Qinghai-Tibetan Plateau under various development strategies. APPLIED GEOGRAPHY 104, 1–9. https://doi.org/10.1016/j.apgeog.2019.01.007

Nie, X., Lu, B., Chen, Zhoupeng, Yang, Y., Chen, S., Chen, Zhenghua, Wang, H., 2020. Increase or decrease? Integrating the CLUMondo and InVEST models to assess the impact of the implementation of the Major Function Oriented Zone planning on carbon storage. Ecological Indicators 118, 106708. https://doi.org/10.1016/j.ecolind.2020.106708

Wang, C., Yu, C., Chen, T., Feng, Z., Hu, Y., Wu, K., 2020. Can the establishment of ecological security patterns improve ecological protection? An example of Nanchang, China. Science of The Total Environment 740, 140051. https://doi.org/10.1016/j.scitotenv.2020.140051

Zhu, W., Gao, Y., Song, C., 2020a. Dataset of the land use pattern optimization in Horqin Sandy Land. Data in Brief 33, 106335. https://doi.org/10.1016/j.dib.2020.106335

Zhu, W., Gao, Y., Zhang, H., Liu, L., 2020b. Optimization of the land use pattern in Horqin Sandy Land by using the CLUMondo model and Bayesian belief network. Science of The Total Environment 739, 139929. https://doi.org/10.1016/j.scitotenv.2020.139929

Chen, S., Su, H., Zhan, J., 2014. Estimating the impact of land use change on surface energy partition based on the Noah model. Front. Earth Sci. 8, 18–31. https://doi.org/10.1007/s11707-013-0400-0

Chu, H.-J., Lin, Y.-P., Huang, C.-W., Hsu, C.-Y., Chen, H.-Y., 2010. Modelling the hydrologic effects of dynamic land-use change using a distributed hydrologic model and a spatial land-use allocation model. Hydrological Processes 24, 2538–2554. https://doi.org/10.1002/hyp.7667

Dan, W., Wei, H., Shuwen, Z., Kun, B., Bao, X., Yi, W., Yue, L., 2015. Processes and prediction of land use/land cover changes (LUCC) driven by farm construction: the case of Naoli River Basin in Sanjiang Plain. Environ Earth Sci 73, 4841–4851. https://doi.org/10.1007/s12665-014-3765-9

Gong, J., Hu, Z., Chen, W., Liu, Y., Wang, J., 2018. Urban expansion dynamics and modes in metropolitan Guangzhou, China. Land Use Policy 72, 100–109. https://doi.org/10.1016/j.landusepol.2017.12.025

He, X., Mai, X., Shen, G., 2019. Delineation of Urban Growth Boundaries with SD and CLUE-s Models under Multi-Scenarios in Chengdu Metropolitan Area. Sustainability 11, 5919. https://doi.org/10.3390/su11215919

Hu, M., Wang, Y., Xia, B., Jiao, M., Huang, G., 2020. How to balance ecosystem services and economic benefits? – A case study in the Pearl River Delta, China. Journal of Environmental Management 271, 110917. https://doi.org/10.1016/j.jenvman.2020.110917

Li, Y., Huang, S., 2015. Landscape Ecological Risk Responses to Land Use Change in the Luanhe River Basin, China. Sustainability 7, 16631–16652. https://doi.org/10.3390/su71215835

Liang, Y., Liu, L., Huang, J., 2017. Integrating the SD-CLUE-S and InVEST models into assessment of oasis carbon storage in northwestern China. PLOS ONE 12, e0172494. https://doi.org/10.1371/journal.pone.0172494

Liu, M., Wang, Y., Li, D., Xia, B., 2013. Dyna-CLUE Model Improvement Based on Exponential Smoothing Method and Land Use Dynamic Simulation, in: Bian, F., Xie, Y., Cui, X., Zeng, Y. (Eds.), Geo-Informatics in Resource Management and Sustainable Ecosystem, Communications in Computer and Information Science. Springer, Berlin, Heidelberg, pp. 266–277. https://doi.org/10.1007/978-3-642-41908-9_27

Lü, D., Gao, G., Lü, Y., Ren, Y., Fu, B., 2020. An effective accuracy assessment indicator for credible land use change modelling: Insights from hypothetical and real landscape analyses. Ecological Indicators 117, 106552. https://doi.org/10.1016/j.ecolind.2020.106552

Lu, Y., Wang, X., Xie, Y., Li, K., Xu, Y., 2016. Integrating Future Land Use Scenarios to Evaluate the Spatio-Temporal Dynamics of Landscape Ecological Security. Sustainability 8, 1242. https://doi.org/10.3390/su8121242

Luo, G., Yin, C., Chen, X., Xu, W., Lu, L., 2010. Combining system dynamic model and CLUE-S model to improve land use scenario analyses at regional scale: A case study of Sangong watershed in Xinjiang, China. Ecological Complexity, Eco Summit 2007 Special Issue, Part Two 7, 198–207. https://doi.org/10.1016/j.ecocom.2010.02.001

Sun, X., Yue, T., Wang, M., Fan, Z., Liu, F., 2015. Effects of land use planning on aboveground vegetation biomass in China. Environ Earth Sci 73, 6553–6564. https://doi.org/10.1007/s12665-014-3875-4

Wang, M., Sun, X., Fan, Z., Yue, T., 2019. Investigation of Future Land Use Change and Implications for Cropland Quality: The Case of China. Sustainability 11, 3327. https://doi.org/10.3390/su11123327

Wang, Y., Li, X., Zhang, Q., Li, J., Zhou, X., 2018. Projections of future land use changes: Multiple scenarios-based impacts analysis on ecosystem services for Wuhan city, China. Ecological Indicators 94, 430–445. https://doi.org/10.1016/j.ecolind.2018.06.047

Xu, L., Li, Z., Song, H., Yin, H., 2013. Land-Use Planning for Urban Sprawl Based on the CLUE-S Model: A Case Study of Guangzhou, China. Entropy 15, 3490–3506. https://doi.org/10.3390/e15093490

Xu, X., Jiang, H., Wang, L., Guan, M., Zhang, T., Qiao, S., 2020. Major Consequences of Land-Use Changes for Ecosystems in the Future in the Agro-Pastoral Transitional Zone of Northern China. Applied Sciences 10, 6714. https://doi.org/10.3390/app10196714

Yang, Y., Bao, W., Liu, Y., 2020. Scenario simulation of land system change in the Beijing-Tianjin-Hebei region. Land Use Policy 96, 104677. https://doi.org/10.1016/j.landusepol.2020.104677

Zhai, H., Tang, X., Wang, G., Li, J., Liu, K., 2018. Characteristic analyses, simulations and predictions of land use in poor mountainous cities: a case study in the central area of Chengde County, China. Environ Earth Sci 77, 585. https://doi.org/10.1007/s12665-018-7768-9

Zhang, D., Fu, M., Tao, J., Hu, L., Yang, X., 2013. Scenario simulation of land use change in mining city based on CLUE-S model. Transactions of the Chinese Society of Agricultural Engineering 29, 246–256.

Zhang, J., Chen, Y., Rao, Y., Fu, M., Prishchepov, A.V., 2017. Alternative spatial allocation of suitable land for biofuel production in China. Energy Policy 110, 631–643. https://doi.org/10.1016/j.enpol.2017.09.005

Zhang, L., Nan, Z., Xu, Y., Li, S., 2016a. Hydrological Impacts of Land Use Change and Climate Variability in the Headwater Region of the Heihe River Basin, Northwest China. PLOS ONE 11, e0158394. https://doi.org/10.1371/journal.pone.0158394

Zhang, L., Nan, Z., Yu, W., Ge, Y., 2016b. Hydrological Responses to Land-Use Change Scenarios under Constant and Changed Climatic Conditions. Environmental Management 57, 412–431. https://doi.org/10.1007/s00267-015-0620-z

Zhang, L., Nan, Z., Yu, W., Ge, Y., 2015. Modeling Land-Use and Land-Cover Change and Hydrological Responses under Consistent Climate Change Scenarios in the Heihe River Basin, China. Water Resour Manage 29, 4701–4717. https://doi.org/10.1007/s11269-015-1085-9

Zhang, P., Liu, Y., Pan, Y., Yu, Z., 2013. Land use pattern optimization based on CLUE-S and SWAT models for agricultural non-point source pollution control. Mathematical and Computer Modelling, Computer and Computing Technologies in Agriculture 2011 and Computer and Computing Technologies in Agriculture 2012 58, 588–595. https://doi.org/10.1016/j.mcm.2011.10.061

Zhou, B., 2016. Effects of Land Use Change on Phosphorus Levels in Surface Waters—a Case Study of a Watershed Strongly Influenced by Agriculture. Water Air Soil Pollut 14.

Zhu, K.-W., Chen, Y.-C., Zhang, S., Yang, Z.-M., Huang, L., Li, L., Lei, B., Zhou, Z.-B., Xiong, H.-L., Li, X.-X., Li, Y.-C., Islam, S., 2020. Output risk evolution analysis of agricultural non-point source pollution under different scenarios based on multi-model. Global Ecology and Conservation 23, e01144. https://doi.org/10.1016/j.gecco.2020.e01144

Zhu, W., Zhang, J., Cui, Y., Zhu, L., 2020. Ecosystem carbon storage under different scenarios of land use change in Qihe catchment, China. J. Geogr. Sci. 30, 1507–1522. https://doi.org/10.1007/s11442-020-1796-6

梁友嘉徐中民, LIANG You-jia, X.Z., 2011. 基于SD和CLUE-S模型的张掖市甘州区土地利用情景分析. 地理研究 30, 564–576. https://doi.org/10.11821/yj2011030018

Colombia

Clerici, N., Cote-Navarro, F., Escobedo, F.J., Rubiano, K., Villegas, J.C., 2019. Spatio-temporal and cumulative effects of land use-land cover and climate change on two ecosystem services in the Colombian Andes. Science of The Total Environment 685, 1181–1192. https://doi.org/10.1016/j.scitotenv.2019.06.275

Ecuador

Salazar, E., Henríquez, C., Sliuzas, R., Qüense, J., 2020. Evaluating Spatial Scenarios for Sustainable Development in Quito, Ecuador. ISPRS International Journal of Geo-Information 9, 141. https://doi.org/10.3390/ijgi9030141

France

Préau, C., Isselin-Nondedeu, F., Sellier, Y., Bertrand, R., Grandjean, F., 2019. Predicting suitable habitats of four range margin amphibians under climate and land-use changes in southwestern France. Reg Environ Change 19, 27–38. https://doi.org/10.1007/s10113-018-1381-z

Ghana

Aduah, M.S., Jewitt, G.P.W., Toucher, M.L.W., 2018a. Assessing Impacts of Land Use Changes on the Hydrology of a Lowland Rainforest Catchment in Ghana, West Africa. Water 10, 9. https://doi.org/10.3390/w10010009

Aduah, M.S., Toucher, M.L., Jewitt, G.P.W., 2018b. Estimating potential future (2030 and 2040) land use in the Bonsa catchment, Ghana, West Africa. South African Journal of Geomatics 7, 279–291. https://doi.org/10.4314/sajg.v7i3.6

Aduah, M.S., Mantey, S., 2020. Modelling Potential Future Urban Land Use Changes in the Sekondi-Takoradi Metropolitan Area of Ghana. Ghana Journal of Technology 4, 26–32.

Greece

Mamanis, G., Vrahnakis, M., Chouvardas, D., Nasiakou, S. and Kleftoyanni, V., 2021. Land Use Demands for the CLUE-S Spatiotemporal Model in an Agroforestry Perspective. Land, 10(10), p.1097. https://doi.org/10.3390/land10101097

India

Behera, M.D., Tripathi, P., Das, P., Srivastava, S.K., Roy, P.S., Joshi, C., Behera, P.R., Deka, J., Kumar, P., Khan, M.L., Tripathi, O.P., Dash, T., Krishnamurthy, Y.V.N., 2018. Remote sensing based deforestation analysis in Mahanadi and Brahmaputra river basin in India since 1985. Journal of Environmental Management 206, 1192–1203. https://doi.org/10.1016/j.jenvman.2017.10.015

Behera, N.K., Behera, M.D., 2020. Predicting land use and land cover scenario in Indian national river basin: the Ganga. Trop Ecol 61, 51–64. https://doi.org/10.1007/s42965-020-00073-x

Das, P., Behera, M.D., Pal, S., Chowdary, V.M., Behera, P.R., Singh, T.P., 2020. Studying land use dynamics using decadal satellite images and Dyna-CLUE model in the Mahanadi River basin, India. Environ Monit Assess 191, 804. https://doi.org/10.1007/s10661-019-7698-3

Ghosh, S., Shetty, A., 2017. Modelling the land use system process for a pre-industrial landscape in India. Model. Earth Syst. Environ. 3, 703–717. https://doi.org/10.1007/s40808-017-0329-5

Roy, P., Chandra Pal, S., Chakrabortty, R., Chowdhuri, I., Malik, S., Das, B., 2020. Threats of climate and land use change on future flood susceptibility. Journal of Cleaner Production 272, 122757. https://doi.org/10.1016/j.jclepro.2020.122757

Sahoo, S., Dey, S., Dhar, A., Debsarkar, A., Pradhan, B., 2019a. On projected hydrological scenarios under the influence of bias-corrected climatic variables and LULC. Ecological Indicators 106, 105440. https://doi.org/10.1016/j.ecolind.2019.105440

Sahoo, S., Dhar, A., Debsarkar, A., Kar, A., 2020a. Can Groundwater Scenarios Be Predicted from Future Regional Climatic Input Variables? Water Resour Manage. https://doi.org/10.1007/s11269-020-02692-4

Sahoo, S., Dhar, A., Debsarkar, A., Kar, A., 2019b. Future Scenarios of Environmental Vulnerability Mapping Using Grey Analytic Hierarchy Process. Nat Resour Res 28, 1461–1483. https://doi.org/10.1007/s11053-019-09462-z

Sahoo, S., Dhar, A., Debsarkar, A., Kar, A., 2018a. Impact of water demand on hydrological regime under climate and LULC change scenarios. Environ Earth Sci 77, 341. https://doi.org/10.1007/s12665-018-7531-2

Sahoo, S., Dhar, A., Debsarkar, A., Pradhan, B., Alamri, A.M., 2020b. Future Water Use Planning by Water Evaluation and Planning System Model. Water Resour Manage. https://doi.org/10.1007/s11269-020-02680-8

Sahoo, S., Sil, I., Dhar, A., Debsarkar, A., Das, P., Kar, A., 2018b. Future scenarios of land-use suitability modeling for agricultural sustainability in a river basin. Journal of Cleaner Production 205, 313–328. https://doi.org/10.1016/j.jclepro.2018.09.099

Indonesia

Partoyo, Shrestha, R.P., 2017. Chapter 5 – Modeling Effect of Conservation and Livelihood Policies on Community Land Use and Management in Yogyakarta, in: Shivakoti, G.P., Pradhan, U., Helmi (Eds.), Redefining Diversity & Dynamics of Natural Resources Management in Asia, Volume 1. Elsevier, pp. 67–90. https://doi.org/10.1016/B978-0-12-805454-3.00005-0

Partoyo, Shrestha, R.P., 2013. Monitoring farmland loss and projecting the future land use of an urbanized watershed in Yogyakarta, Indonesia. Journal of Land Use Science 8, 59–84. https://doi.org/10.1080/1747423X.2011.620993

Iran

Mesgari, I., Jabalameli, M.S., 2017. Modeling the spatial distribution of crop cultivated areas at a large regional scale combining system dynamics and a modified Dyna-CLUE: a case from Iran. Spanish journal of agricultural research 15, 7.

Zare, M., Samani, A.A.N., Mohammady, M., 2016. The impact of land use change on runoff generation in an urbanizing watershed in the north of Iran. Environ Earth Sci 75, 1279. https://doi.org/10.1007/s12665-016-6058-7

Italy

Pindozzi, S., Cervelli, E., Recchi, P.F., Capolupo, A., Boccia, L., 2017. Predicting land use change on a broad area: Dyna-CLUE model application to the Litorale Domizio-Agro Aversano (Campania, South Italy). Journal of Agricultural Engineering 48, 27–35. https://doi.org/10.4081/jae.2017.657

Debolini, M., Schoorl, J.M., Temme, A., Galli, M. and Bonari, E., 2015. Changes in agricultural land use affecting future soil redistribution patterns: a case study in southern Tuscany (Italy). Land Degradation & Development, 26(6), pp.574-586. https://doi.org/10.1002/ldr.2217

Japan

Fan, M., Shibata, H., 2016. Water yield, nitrogen and sediment retentions in Northern Japan (Teshio river watershed): land use change scenario analysis. Mitig Adapt Strateg Glob Change 21, 119–133. https://doi.org/10.1007/s11027-014-9574-3

Fan, M., Shibata, H., 2015. Simulation of watershed hydrology and stream water quality under land use and climate change scenarios in Teshio River watershed, northern Japan. Ecological Indicators 50, 79–89. https://doi.org/10.1016/j.ecolind.2014.11.003

Fan, M., Shibata, H., Wang, Q., 2016. Optimal conservation planning of multiple hydrological ecosystem services under land use and climate changes in Teshio river watershed, northernmost of Japan. Ecological Indicators 62, 1–13. https://doi.org/10.1016/j.ecolind.2015.10.064

Shoyama, K., Yamagata, Y., 2014. Predicting land-use change for biodiversity conservation and climate-change mitigation and its effect on ecosystem services in a watershed in Japan. Ecosystem Services 8, 25–34. https://doi.org/10.1016/j.ecoser.2014.02.004

Korea

Han, W.S., Jeung, S.J., Kim, B.S., 2015. The Analysis Method of Future Flooding Discharge considering Climate and Land-use Change using Dyna-CLUE. Journal of the Korean Society of Hazard Mitigation 15, 361–371. 10.9798/KOSHAM.2015.15.6.361

Kim, J., Park, J., Song, I., Song, J.-H., Jun, S.M., Kang, M.S., 2015. Analysis of Land Use Change Using RCP-Based Dyna-CLUE Model in the Hwangguji River Watershed. Journal of Korean Society of Rural Planning 21, 33–49. https://doi.org/10.7851/ksrp.2015.21.2.033

Lee, D.J. and Jeon, S.W., 2020. Estimating Changes in Habitat Quality through Land-Use Predictions: Case Study of Roe Deer (Capreolus pygargus tianschanicus) in Jeju Island. Sustainability, 12(23), p.10123. https://doi.org/10.3390/su122310123

Lee, D., Ryu, D., Kim, H., Lee, S., 2011. Analyzing the Future Land Use Change and its Effects for the Region of Yangpyeong-gun and Yeoju-gun in Korea with the Dyna-CLUE Model. Journal of the Korean Society of Environmental Restoration Technology 14, 119–130. https://doi.org/10.13087/kosert.2011.14.6.119

Lee, Y.-G., Cho, Y.-H., Kim, S.-J., 2016. Prediction of Land-Use Change based on Urban Growth Scenario in South Korea using CLUE-s Model. Journal of the Korean Association of Geographic Information Studies 19, 75–88. https://doi.org/10.11108/kagis.2016.19.3.075

Oh, Y.-G., Choi, J.-Y., Yoo, S.-H., Lee, S.-H., 2011a. Prediction of Land-cover Change Based on Climate Change Scenarios and Regional Characteristics using Cluster Analysis. Journal of The Korean Society of Agricultural Engineers 53, 31–41. https://doi.org/10.5389/KSAE.2011.53.6.031

Oh, Y.-G., Yoo, S.-H., Lee, S.-H., Choi, J.-Y., 2011b. Prediction of paddy field change based on climate change scenarios using the CLUE model. Paddy Water Environ 9, 309–323. https://doi.org/10.1007/s10333-010-0244-0

Park, H., Kim, H.J., Chae, Y., Kim, Y., 2017. Assessment of Water Use Vulnerability Considering Climate and Socioeconomic Changes in Han River Watershed. Journal of The Korean Society of Civil Engineers 37, 965–972. https://doi.org/10.12652/Ksce.2017.37.6.0965

Ryu, J., Ahn, K.H., Han, M., Hwang, H., Choi, J., Kim, Y.S., Lim, K.J., 2014. Evaluation and Application of CLUE-S Model for Spatio-Temporal Analysis of Future Land use Change in Total Water Pollution Load Management System. Journal of Korean Society on Water Environment 30, 418–428. https://doi.org/10.15681/KSWE.2014.30.4.418

Poland

Kaim, D., Ziółkowska, E., Szwagrzyk, M., Price, B., Kozak, J., 2019. Impact of Future Land Use Change on Large Carnivores Connectivity in the Polish Carpathians. Land 8, 8. https://doi.org/10.3390/land8010008

Pokojska, P., 2019. Simulating land use change in the southern part of Warsaw Metropolitan Area with application of Dyna-CLUE model. Geografie 124, 57–82. https://doi.org/10.37040/geografie2019124010057

Price, B., Kaim, D., Szwagrzyk, M., Ostapowicz, K., Kolecka, N., Schmatz, D.R., Wypych, A., Kozak, J., 2017. Legacies, socio-economic and biophysical processes and drivers: the case of future forest cover expansion in the Polish Carpathians and Swiss Alps. Reg Environ Change 17, 2279–2291. https://doi.org/10.1007/s10113-016-1079-z

Szalińska, E., Orlińska-Woźniak, P., Wilk, P., 2020. Sediment load variability in response to climate and land use changes in a Carpathian catchment (Raba River, Poland). J Soils Sediments 20, 2641–2652. https://doi.org/10.1007/s11368-020-02600-8

Szwagrzyk, M., Kaim, D., Price, B., Wypych, A., Grabska, E., Kozak, J., 2018. Impact of forecasted land use changes on flood risk in the Polish Carpathians. Nat Hazards 94, 227–240. https://doi.org/10.1007/s11069-018-3384-y

Portugal

Gonçalves, J., Honrado, J.P., Vicente, J.R., Civantos, E., 2016. A model-based framework for assessing the vulnerability of low dispersal vertebrates to landscape fragmentation under environmental change. Ecological Complexity 28, 174–186. https://doi.org/10.1016/j.ecocom.2016.05.003

Romania

Grigorescu, I., Kucsicsa, G., Popovici, E.-A., Mitrică, B., Mocanu, I., Dumitraşcu, M., 2019. Modelling land use/cover change to assess future urban sprawl in Romania. Geocarto International 0, 1–19. https://doi.org/10.1080/10106049.2019.1624981

Kucsicsa, G., Popovici, E.-A., Bălteanu, D., Grigorescu, I., Dumitraşcu, M., Mitrică, B., 2019. Future land use/cover changes in Romania: regional simulations based on CLUE-S model and CORINE land cover database. Landscape Ecol Eng 15, 75–90. https://doi.org/10.1007/s11355-018-0362-1

Slovakia

Pazúr, R., Bolliger, J., 2017. Land changes in Slovakia: Past processes and future directions. Applied Geography 85, 163–175. https://doi.org/10.1016/j.apgeog.2017.05.009

Pazur, R., Bolliger, J., 2017. Enhanced land use datasets and future scenarios of land change for Slovakia. DATA IN BRIEF 14, 483–488. https://doi.org/10.1016/j.dib.2017.07.066

Slovenia

Cotič K. Future land use scenarios on the entire area of Škocjan caves Park with the use of spatial model DynaCLUE (Prihodnji scenariji rabe tal na celotnem območju Parka Škocjanske jame z uporabo prostorskega modela DynaCLUE). In Slovenian. BSc Thesis. http://repozitorij.ung.si/IzpisGradiva.php?id=4730

South Africa

Roux, A.L., Augustijn, P.W.M., 2017. Quantifying the spatial implications of future land use policies in South Africa. South African Geographical Journal 99, 29–51. https://doi.org/10.1080/03736245.2015.1117014

Tizora, P., Roux, A. le, Mans, G., Cooper, A.K., 2018. Adapting the Dyna-CLUE model for simulating land use and land cover change in the Western Cape Province. South African Journal of Geomatics 7, 190–203. https://doi.org/10.4314/sajg.v7i2.7

Switzerland

Price, B., Kienast, F., Seidl, I., Ginzler, C., Verburg, P.H., Bolliger, J., 2015. Future landscapes of Switzerland: Risk areas for urbanisation and land abandonment. Applied Geography 57, 32–41. https://doi.org/10.1016/j.apgeog.2014.12.009

Thailand

Akber, M.A., Shrestha, R.P., 2015. Land use change and its effect on biodiversity in Chiang Rai province of Thailand. Journal of Land Use Science 10, 108–128. https://doi.org/10.1080/1747423X.2013.807315

Arunyawat, S., Shrestha, R.P., 2018. Simulating future land use and ecosystem services in Northern Thailand. JOURNAL OF LAND USE SCIENCE 13, 146–165. https://doi.org/10.1080/1747423X.2018.1496157

Srichaichana, J., Trisurat, Y., Ongsomwang, S., 2019. Land Use and Land Cover Scenarios for Optimum Water Yield and Sediment Retention Ecosystem Services in Klong U-Tapao Watershed, Songkhla, Thailand. SUSTAINABILITY 11. https://doi.org/10.3390/su11102895

Lippe, M., Hilger, T., Sudchalee, S., Wechpibal, N., Jintrawet, A., Cadisch, G., 2017. Simulating Stakeholder-Based Land-Use Change Scenarios and Their Implication on Above-Ground Carbon and Environmental Management in Northern Thailand. Land 6, 85. https://doi.org/10.3390/land6040085

Phinyoyang, A. and Ongsomwang, S., 2021. Optimizing Land Use and Land Cover Allocation for Flood Mitigation Using Land Use Change and Hydrological Models with Goal Programming, Chaiyaphum, Thailand. Land, 10(12), p.1317. https://doi.org/10.3390/land10121317

Sakayarote, K., Shrestha, R.P., 2019. Simulating land use for protecting food crop areas in northeast Thailand using GIS and Dyna-CLUE. J. Geogr. Sci. 29, 803–817. https://doi.org/10.1007/s11442-019-1629-7

Shrestha, M., Shrestha, S., Shrestha, P.K., 2020. Evaluation of land use change and its impact on water yield in Songkhram River basin, Thailand. https://doi.org/10.1080/15715124.2019.1566239

Shrestha, S., Bhatta, B., Shrestha, M., Shrestha, P.K., 2018. Integrated assessment of the climate and landuse change impact on hydrology and water quality in the Songkhram River Basin, Thailand. Science of The Total Environment 643, 1610–1622. https://doi.org/10.1016/j.scitotenv.2018.06.306

Srichaichana, J., Trisurat, Y. and Ongsomwang, S., 2019. Land use and land cover scenarios for optimum water yield and sediment retention ecosystem services in Klong U-Tapao Watershed, Songkhla, Thailand. Sustainability, 11(10), p.2895. https://doi.org/10.3390/su11102895

Trisurat, Y., Duengkae, P., 2011. Consequences of land use change on bird distribution at Sakaerat Environmental Research Station. J. Ecol. Environ. 34, 203–214. https://doi.org/10.5141/JEFB.2011.022

Trisurat, Y., Shirakawa, H., Johnston, J.M., 2019. Land-Use/Land-Cover Change from Socio-Economic Drivers and Their Impact on Biodiversity in Nan Province, Thailand. Sustainability 11, 649. https://doi.org/10.3390/su11030649

Waiyasusri, K. and Chotpantarat, S., 2022. Spatial Evolution of Coastal Tourist City Using the Dyna-CLUE Model in Koh Chang of Thailand during 1990–2050. ISPRS International Journal of Geo-Information, 11(1), p.49. https://doi.org/10.3390/ijgi11010049

Vietnam

Adhikari, R.K., Mohanasundaram, S., Shrestha, S., 2020. Impacts of land-use changes on the groundwater recharge in the Ho Chi Minh city, Vietnam. Environmental Research 185, 109440. https://doi.org/10.1016/j.envres.2020.109440

United States

Lagrosa, J.J., Zipperer, W.C., Andreu, M.G., 2018a. Projecting Land-Use and Land Cover Change in a Subtropical Urban Watershed. Urban Science 2, 11. https://doi.org/10.3390/urbansci2010011

Batisani N., Yarnal B. 2008. Uncertainty awareness in urban sprawl simulations: Lessons from a small US metropolitan region. Land Use Policy https://doi.org/10.1016/j.landusepol.2008.01.013