Publications

A large number of publications can also be accessed and downloaded from https://www.narcis.nl. Publications below are grouped in methods development and studies into geographical systems.

Methods in computational geography

Jong, K. de, D. Panja, D. Karssenberg, and M. van Kreveld (2022). Scalability and composability of flow accumulation algorithms based on asynchronous many-tasks. Computers Geosciences 162, p. 105083. https://doi.org/10.1016/j.cageo.2022.105083.

Lam, T. M., Z. Wang, I. Vaartjes, D. Karssenberg, D. Ettema, M. Helbich, E. J. Timmermans, L. D. Frank, N. R. den Braver, A. J. Wagtendonk, J. W. J. Beulens, and J. Lakerveld (2022). Development of an objectively measured walkability index for the Netherlands. International Journal of Behavioral Nutrition and Physical Activity 19 (1), p. 50. https://doi.org/10.1186/s12966-022-01270-8.

Shen, Y., J. Ruijsch, M. Lu, E. H. Sutanudjaja, and D. Karssenberg (2022). Random forests-based error-correction of streamflow from a large-scale hydrological model: Using model state variables to estimate error terms. Computers & Geosciences 159, p. 105019. https://doi.org/10.1016/j.cageo.2021.105019.

Lu, M., R. Dai, C. de Boer, O. Schmitz, I. Kooter, S. Cristescu, and D. Karssenberg (2021). External validation for statistical NO2 modelling: A study case using a high-end mobile sensing instrument. Atmospheric Pollution Research 12.11, p. 101205. https://doi.org/10.1016/j.apr.2021.101205.

Ruijsch, J., J. A. Verstegen, E. H. Sutanudjaja, and D. Karssenberg (2021). Systemic change in the Rhine-Meuse basin: Quantifying and explaining parameters trends in the PCRGLOBWB global hydrological model. Advances in Water Resources 155, p. 104013. https://doi.org/10.1016/j.advwatres.2021.104013.

Ntarladima, A.-M., D. Karssenberg, I. Vaartjes, D. E. Grobbee, O. Schmitz, M. Lu, J. Boer, G. Koppelman, J. Vonk, R. Vermeulen, G. Hoek, and U. Gehring (2021b). A comparison of associations with childhood lung function between air pollution exposure assessment methods with and without accounting for time-activity patterns. Environmental Research 202, p. 111710. https://doi.org/10.1016/j.envres.2021.111710.

Lakerveld, J., A. Wagtendonk, I. Vaartjes, D. Karssenberg, J. Lakerveld, B. Penninx, J. Beulens, E. Timmermans, M. Huisman, A. Wagtendonk, S. Kramer, M. van Wier, D. Boomsma, G. Willemsen, C. Schuengel, M. Oosterman, K. Stronks, D. Karssenberg, R. Vermeulen, I. Vaartjes, A. Koster, C. Stehouwer, K. van den Hurk, E. Koomen, R. de Mutsert, M. ten Have, M. Verschuren, S. Picavet, M. Beenackers, F. van Lenthe, A. Ikram, V. Jaddoe, T. Oldehinkel, T. de Jong, S. Mulder, A. Dotinga, and G. Consortium (2020). Deep phenotyping meets big data: the Geoscience and hEalth Cohort COnsortium (GECCO) data to enable exposome studies in The Netherlands. International Journal of Health Geographics 19.1, p. 49. https://10.1186/s12942-020-00235-z.

de Jong, K., Panja, D., van Kreveld, M., & Karssenberg, D. (2021). An environmental modelling framework based on asynchronous many-tasks: Scalability and usability. Environmental Modelling & Software, 139, 104998. https://doi.org/10.1016/j.envsoft.2021.104998

Ji, S., Dai, P., Lu, M., & Zhang, Y. (2021). Simultaneous Cloud Detection and Removal From Bitemporal Remote Sensing Images Using Cascade Convolutional Neural Networks. IEEE Transactions on Geoscience and Remote Sensing, 59(1), 732–748. https://doi.org/10.1109/TGRS.2020.2994349

Lu, M., Schmitz, O., de Hoogh, K., Kai, Q., & Karssenberg, D. (2020). Evaluation of different methods and data sources to optimise modelling of NO2 at a global scale. Environment International, 142, 105856. https://doi.org/10.1016/j.envint.2020.105856

Wang, J., Schmitz, O., Lu, M. & Karssenberg, D.J. (2020). Thermal unmixing based downscaling for fine resolution diurnal land surface temperature analysis. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2020.01.014

Ji, S., Zhang, Z., Zhang, C., Wei, S., Lu, M. & Duan, Y. (2020). Learning discriminative spatiotemporal features for precise crop classification from multi-temporal satellite images. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2019.1699973

Ji, S., Qin, Z., Shan, J. & Lu, M. (2020). Panoramic SLAM from a multiple fisheye camera rig. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2019.11.014

Wang, Y., Ji, S., Lu, M. & Zhang, Y. (2020). Attention boosted bilinear pooling for remote sensing image retrieval. International Journal of Remote Sensing. https://doi.org/10.1080/01431161.2019.1697010

Ji, S., Wei, S. & Lu, M. (2019). A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery. International Joural of Remote Sensing. https://doi.org/10.1080/01431161.2018.1528024

Ji, S., Liu, J. & Lu, M. (2019). CNN-based dense image matching for aerial remote sensing images. Photogrammetric Engineering and Remote Sensing. https://doi.org/10.14358/PERS.85.6.415

Ji, S., Wei, S. & Lu, M. (2019). Fully Convolutional Networks for Multisource Building Extraction from an Open Aerial and Satellite Imagery Data Set. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2018.2858817

Ji, S., Shen, Y., Lu, M. & Zhang, Y. (2019). Building instance change detection from large-scale aerial images using convolutional neural networks and simulated samples. Remote Sensing, 11 (11). https://doi.org/10.3390/rs11111343

Scheider, S., J. Wang, M. Mol, O. Schmitz, and D. Karssenberg (2020). Obfuscating spatial point tracks with simulated crowding. International Journal of Geographical Information Science 34.7. https://10.1080/13658816.2020.1712402.

Wang, J., Kuffer, Monika, Roy, Debraj & Pfeffer, Karin (20.10.2019). Deprivation pockets through the lens of convolutional neural networks. Remote Sensing of Environment, 234 (16 p.). https://doi.org/10.1016/j.rse.2019.111448

Wang, J., Kuffer, Monika, Sliuzas, Richard & Kohli, Divyani (10.02.2019). The exposure of slums to high temperature - Morphology-based local scale thermal patterns. Science of the Total Environment, 650 (2), (pp. 1805-1817) (13 p.). https://doi.org/10.1016/j.scitotenv.2018.09.324

de Jong, K., & Karssenberg, D. (2019). A physical data model for spatio-temporal objects. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2019.104553

Babel, L., Vinck, D., & Karssenberg, D. (2019). Decision-making in model construction: unveiling habits. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2019.07.015

Loozen, Y., Karssenberg, D., De Jong, S. M., Wanga, S., van Dijk, J., Wassen, M. J., & Rebel, K. T. (2019). Exploring the use of vegetation indices to sense canopy nitrogen to phosphorous ratio in grasses. International Journal of Applied Earth Observation and Geoinformation, 75, 1–14. https://doi.org/10.1016/j.jag.2018.08.012

Zhu, R., Yu, D., Ji, S. & Lu, M. (2019). Matching RGB and Infrared Remote Sensing Images with Densely-Connected Convolutional Neural Networks. Remote Sensing. https://doi.org/10.3390/rs11232836

Wei, S., Ji, S. & Lu, M. (2019). Toward Automatic Building Footprint Delineation From Aerial Images Using CNN and Regularization. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2019.2954461

Detecting large-scale urban land cover changes from very high resolution remote sensing images using CNN-based classification. ISPRS International Journal of Geo-Information. https://doi.org/10.3390/ijgi8040189

Sutanudjaja, E. H., van Beek, R., Wanders, N., Wada, Y., Bosmans, J. H. C., Drost, N., van der Ent, R. J., de Graaf, I. E. M., Hoch, J. M., de Jong, K., Karssenberg, D., López López, P., Peßenteiner, S., Schmitz, O., Straatsma, M. W., Vannametee, E., Wisser, D., & Bierkens, M. F. P. (2018). PCR-GLOBWB 2: a 5 arcmin global hydrological and water resources model. Geoscientific Model Development, 11(6), 2429–2453. https://doi.org/10.5194/gmd-11-2429-2018

Lu, M., Appel, M. & Pebesma, E. (2018). Multidimensional arrays for analysing geoscientific data. ISPRS International Journal of Geo-Information. https://doi.org/10.3390/ijgi7080313

Ji, S., Yu, D., Hong, Y. & Lu, M. (2018). Template Matching for Wide-Baseline Panoramic Images from a Vehicle-Borne Multi-Camera Rig. ISPRS International Journal of Geo-Information. https://doi.org/10.3390/ijgi7070236

Qin, K., Zou, J., Guo, J., Lu, M., Bilal, M., Zhang, K., Ma, F. & Zhang, Y. (2018). Estimating PM 1 concentrations from MODIS over Yangtze River Delta of China during 2014–2017. Atmospheric Environment. https://doi.org/10.1016/j.atmosenv.2018.09.054

de Bakker, M. P., de Jong, K., Schmitz, O., & Karssenberg, D. (2017). Design and demonstration of a data model to integrate agent-based and field-based modelling. Environmental Modelling & Software, 89, 172–189. https://doi.org/10.1016/j.envsoft.2016.11.016

Lu, M., Hamunyela, E., Verbesselt, J. & Pebesma, E. (2017). Dimension reduction of multi-spectral satellite image time series to improve deforestation monitoring. Remote Sensing. https://doi.org/10.3390/rs9101025

Verstegen, J. A., Jonker, J. G. G., Karssenberg, D., van der Hilst, F., Schmitz, O., de Jong, S. M., & Faaij, A. P. C. (2017). How a Pareto frontier complements scenario projections in land use change impact assessment. Environmental Modelling & Software, 97, 287–302. https://doi.org/10.1016/j.envsoft.2017.08.006

Bingham, L., Escalona, A., & Karssenberg, D. (2016). Error propagation in a fuzzy logic multi-criteria evaluation for petroleum exploration. International Journal of Geographical Information Science, 1–27. https://doi.org/10.1080/13658816.2016.1142547

Schmitz, O., de Kok, J., & Karssenberg, D. (2016). A software framework for process flow execution of stochastic multi-scale integrated models. Ecological Informatics, 32, 124–133. https://doi.org/10.1016/j.ecoinf.2016.01.009

Verstegen, J. A., Karssenberg, D., van der Hilst, F., & Faaij, A. P. C. (2016). Detecting systemic change in a land use system by Bayesian data assimilation. Environmental Modelling & Software, 75, 424–438. https://doi.org/10.1016/j.envsoft.2015.02.013

Wicke, B., van der Hilst, F., Daioglou, V., Banse, M., Beringer, T., Gerssen-Gondelach, S., Heijnen, S., Karssenberg, D., Laborde, D., Lippe, M., van Meijl, H., Nassar, A., Powell, J., Prins, A. G., Rose, S. N. K., Smeets, E. M. W., Stehfest, E., Tyner, W. E., Verstegen, J. A., Valin, H., van Vuuren, D. P., Yeh, S., & Faaij, A. P. C. (2015). Model collaboration for the improved assessment of biomass supply, demand, and impacts. GCB Bioenergy, 7(3), 422–437. https://doi.org/10.1111/gcbb.12176

Schmitz, O., Salvadore, E., Poelmans, L., Van Der Kwast, J., & Karssenberg, D. (2014). A framework to resolve spatio-temporal misalignment in component-based modelling. Journal of Hydroinformatics, 16(4), 850–871. https://doi.org/10.2166/hydro.2013.180

Vannametee, E., Babel, L. V, Hendriks, M. R., Schuur, J., de Jong, S. M., Bierkens, M. F. P., & Karssenberg, D. (2014). Semi-automated mapping of landforms using multiple point geostatistics. Geomorphology, 221, 298–319. https://doi.org/10.1016/j.geomorph.2014.05.032

Verstegen, J. a., Karssenberg, D., van der Hilst, F., & Faaij, A. P. C. (2014). Identifying a land use change cellular automaton by Bayesian data assimilation. Environmental Modelling & Software, 53, 121–136. https://doi.org/10.1016/j.envsoft.2013.11.009

Lana-Renault, N., & Karssenberg, D. (2013). PyCatch: component based hydrological catchment modelling. Cuadernos de Investigacion Geografica, 39(2), 315–333. https://doi.org/10.18172/cig.1993

Schmitz, O., Karssenberg, D., de Jong, K., de Kok, J.-L., & de Jong, S. M. (2013). Map algebra and model algebra for integrated model building. Environmental Modelling & Software, 48, 113–128. https://dx.doi.org/10.1016/j.envsoft.2013.06.009

Hiemstra, P. H., Karssenberg, D., van Dijk, A., & de Jong, S. M. (2012). Using the particle filter for nuclear decision support. Environmental Modelling and Software, 37, 78–89. https://doi.org/10.1016/j.envsoft.2012.03.003

Karssenberg, D., & Bierkens, M. F. P. (2012). Early-warning signals (potentially) reduce uncertainty in forecasted timing of critical shifts. Ecosphere, 3(2), art15. https://doi.org/10.1890/ES11-00293.1

Wanders, N., Karssenberg, D., Bierkens, M., Parinussa, R., de Jeu, R., van Dam, J., & de Jong, S. (2012). Observation uncertainty of satellite soil moisture products determined with physically-based modeling. Remote Sensing of Environment, 127(0), 341–356. https://doi.org/10.1016/j.rse.2012.09.004

Karssenberg, D., Schmitz, O., Salamon, P., de Jong, K., & Bierkens, M. F. P. (2010). A software framework for construction of process-based stochastic spatio-temporal models and data assimilation. Environmental Modelling & Software, 25(4), 489–502. https://doi.org/10.1016/j.envsoft.2009.10.004

Schmitz, O., Karssenberg, D., van Deursen, W. P. A., & Wesseling, C. G. (2009). Linking external components to a spatio-temporal modelling framework: Coupling MODFLOW and PCRaster. Environmental Modelling & Software, 24(9), 1088–1099. https://doi.org/10.1016/j.envsoft.2009.02.018

Karssenberg, D., De Jong, K., & Van der Kwast, J. (2007). Modelling landscape dynamics with Python. International Journal of Geographical Information Science, 21, 483–495. https://doi.org/10.1080/13658810601063936

Karssenberg, D. (2006). Upscaling of saturated conductivity for Hortonian runoff modelling. Advances in Water Resources, 29, 735–759. https://doi.org/10.1016/j.advwatres.2005.06.012

Karssenberg, D., & De Jong, K. (2005). Dynamic environmental modelling in GIS: 1. Modelling in three spatial dimensions. International Journal of Geographical Information Science, 19(5), 559–579. https://doi.org/10.1080/13658810500032362

Karssenberg, D., & De Jong, K. (2005). Dynamic environmental modelling in GIS: 2. Modelling error propagation. International Journal of Geographical Information Science, 19(6), 623–637. https://doi.org/10.1080/13658810500104799

Karssenberg, D. (2002). The value of environmental modelling languages for building distributed hydrological models. Hydrological Processes, 16(14), 2751–2766. https://doi.org/10.1002/hyp.1068

Wesseling, C. G., Karssenberg, D., van Deursen, W. P. A., & Burrough, P. A. (1996). Integrating dynamic environmental models in GIS: the development of a Dynamic Modelling language. Transactions in GIS, 1, 40–48. https://doi.org/10.1111/j.1467-9671.1996.tb00032.x

System understanding through computational geography

Bonetti, S., Sutanudjaja, E. H., Mabhaudhi, T., Slotow, R., & Dalin, C. (2022). Climate change impacts on water sustainability of south african crop production. Environmental Research Letters, 17(8) https://www.doi.org/10.1088/1748-9326/ac80cf

Thorslund, J., Bierkens, M. F. P., Scaini, A., Sutanudjaja, E. H., & Van Vliet, M. T. H. (2022). Salinity impacts on irrigation water-scarcity in food bowl regions of the US and australia. Environmental Research Letters, 17(8) https://www.doi.org/10.1088/1748-9326/ac7df4

Zamrsky, D., Oude Essink, G. H. P., Sutanudjaja, E. H., Van Beek, L. P. H., & Bierkens, M. F. P. (2022). Offshore fresh groundwater in coastal unconsolidated sediment systems as a potential fresh water source in the 21st century. Environmental Research Letters, 17(1) https://www.doi.org/10.1088/1748-9326/ac4073

Jin, Y., Scherer, L., Sutanudjaja, E. H., Tukker, A., & Behrens, P. (2022). Climate change and CCS increase the water vulnerability of china's thermoelectric power fleet. Energy, 245 https://www.doi.org/10.1016/j.energy.2022.123339

Jwaideh, M. A. A., Sutanudjaja, E. H., & Dalin, C. (2022). Global impacts of nitrogen and phosphorus fertiliser use for major crops on aquatic biodiversity. International Journal of Life Cycle Assessment, 27(8), 1058-1080. https://www.doi.org/10.1007/s11367-022-02078-1

Karabil, S., Sutanudjaja, E. H., Lambert, E., Bierkens, M. F. P., & Van de Wal, R. S. W. (2021). Contribution of land water storage change to regional sea-level rise over the twenty-first century. Frontiers in Earth Science, 9 https://www.doi.org/10.3389/feart.2021.627648

Babalola, T. E., Oguntunde, P. G., Ajayi, A. E., Akinluyi, F. O., & Sutanudjaja, E. H. (2021). Evaluating a finer resolution global hydrological model’s simulation of discharge in four west-african river basins. Modeling Earth Systems and Environment, 7(4), 2167-2178. https://www.doi.org/10.1007/s40808-020-00948-x

Bierkens, M. F. P., Sutanudjaja, E. H., & Wanders, N. (2021). Large-scale sensitivities of groundwater and surface water to groundwater withdrawal. Hydrology and Earth System Sciences, 25(11), 5859-5878. https://www.doi.org/10.5194/hess-25-5859-2021

Thorslund, J., Bierkens, M. F. P., Oude Essink, G. H. P., Sutanudjaja, E. H., & van Vliet, M. T. H. (2021). Common irrigation drivers of freshwater salinisation in river basins worldwide. Nature Communications, 12(1) https://www.doi.org/10.1038/s41467-021-24281-8

Eslami, S., Hoekstra, P., Minderhoud, P. S. J., Trung, N. N., Hoch, J. M., Sutanudjaja, E. H., . . . van der Vegt, M. (2021). Projections of salt intrusion in a mega-delta under climatic and anthropogenic stressors. Communications Earth and Environment, 2(1) https://www.doi.org/10.1038/s43247-021-00208-5

Jafino, B. A., Kwakkel, J. H., Klijn, F., Dung, N. V., van Delden, H., Haasnoot, M., & Sutanudjaja, E. H. (2021). Accounting for multisectoral dynamics in supporting equitable adaptation planning: A case study on the rice agriculture in the vietnam mekong delta. Earth's Future, 9(5) https://www.doi.org/10.1029/2020EF001939

Ntarladima, A., D. Karssenberg, M. Poelman, D. Grobbee, M. Lu, O. Schmitz, M. Strak, N. Janssen, G. Hoek, and I. Vaartjes (2021a). Associations between fast-food environment and diabetes prevalence: a cross-sectional study in the Netherlands. The Lancet Planetary Health accepted.

Harbers, M. C., J. W. J. Beulens, J. M. Boer, D. Karssenberg, J. D. Mackenbach, F. Rutters, I. Vaartjes, W. M. M. Verschuren, and Y. T. van der Schouw (2021). Residential exposure to fast-food restaurants and its association with diet quality, overweight and obesity in the Netherlands: a cross-sectional analysis in the EPIC-NL cohort. eng. Nutrition journal 20.1, p. 56. https://10.1186/s12937-021-00713-5.

Duden, A. S., Verweij, P. A., Kraak, Y. V, van Beek, L. P. H., Wanders, N., Karssenberg, D. J., Sutanudjaja, E. H., & van der Hilst, F. (2021). Hydrological impacts of ethanol-driven sugarcane expansion in Brazil. Journal of Environmental Management, 282, 111942. https://doi.org/10.1016/j.jenvman.2021.111942

Lam, T. M., Vaartjes, I., Grobbee, D. E., Karssenberg, D., & Lakerveld, J. (2021). Associations between the built environment and obesity: an umbrella review. International Journal of Health Geographics, 20(1). https://doi.org/10.1186/s12942-021-00260-6

de Jong, S.M., Shen, Y., de Vries, J., Bijnaar, G., van Maanen, B., Augustinus, P., Verweij, P. (2021). Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm. International Journal of Applied Earth Observation and Geoinformation, 97, 102293. https://doi.org/10.1016/j.jag.2020.102293

Poelman, M. P., Nicolaou, M., Dijkstra, S. C., Mackenbach, J. D., Lu, M., Karssenberg, D., Snijder, M. B., Vaartjes, I., & Stronks, K. (2021). Does the neighbourhood food environment contribute to ethnic differences in diet quality? Results from the HELIUS study in Amsterdam, the Netherlands. Public Health Nutrition, 1–12. https://doi.org/10.1017/S1368980021001919

Wang, Y., Storms, J. E. A., Martinius, A. W., Karssenberg, D., & Abels, H. A. (2020). Evaluating alluvial stratigraphic response to cyclic and non-cyclic upstream forcing through process-based alluvial architecture modelling. Basin Research. https://doi.org/10.1111/bre.12454

Loozen, Y., Rebel, K. T., de Jong, S. M., Lu, M., Ollinger, S. V, Wassen, M. J., & Karssenberg, D. (2020). Mapping canopy nitrogen in European forests using remote sensing and environmental variables with the random forests method. Remote Sensing of Environment, 247, 111933. https://doi.org/https://doi.org/10.1016/j.rse.2020.111933

Bleuten, W., Zarov, E., and Schmitz, O. (2020) A high-resolution transient 3-dimensional hydrological model of an extensive undisturbed bog complex in West Siberia. Mires and Peat, 26, 06. https://doi.org/10.19189/MaP.2019.OMB.StA.1769

Watson, L., Straatsma, M., Wanders, N., Verstegen, J.A., de Jong, S.M., Karssenberg, D. (2020). Global ecosystem service values in climate class transitions. Environmental Research Letters. https://doi.org/10.1088/1748-9326/ab5aab

Lu, M., Soenario, I, Helbich, M., Schmitz, O., Hoek, G., van de Molen, M. & Karssenberg, D. (2020). Land use regression models revealing spatiotemporal co-variation in NO2, NO, and O3 in the Netherlands. Atmospheric Environment. https://doi.org/10.1016/j.atmosenv.2019.117238

Straatsma, M., Droogers, P., Hunink, J., Berendrecht, W., Buitink, J., Buytaert, W., Karssenberg, D., Schmitz, O., Sutanudjaja, E. H., van Beek, L. P. H., Vitolo, C., & Bierkens, M. F. P. (2020). Global to regional scale evaluation of adaptation measures to reduce the future water gap. Environmental Modelling & Software, 124, 104578. https://doi.org/10.1016/J.ENVSOFT.2019.104578

Lu, M., Schmitz, O., Vaartjes, I., & Karssenberg, D. (2019). Activity-based air pollution exposure assessment: Differences between homemakers and cycling commuters. Health & Place, 60, 102233. https://doi.org/10.1016/J.HEALTHPLACE.2019.102233

Schmitz, O., Beelen, R., Strak, M., Hoek, G., Soenario, I., Brunekreef, B., Vaartjes, I., Dijst, M. J., Grobbee, D. E., & Karssenberg, D. (2019). High resolution annual average air pollution concentration maps for the Netherlands. Scientific Data, 6, 190035. https://doi.org/10.1038/sdata.2019.35

Ntarladima, A.-M., Vaartjes, I., Grobbee, D. E., Dijst, M., Schmitz, O., Uiterwaal, C., Dalmeijer, G., van der Ent, C., Hoek, G., & Karssenberg, D. (2019). Relations between air pollution and vascular development in 5-year old children: a cross-sectional study in the Netherlands. Environmental Health. https://doi.org/10.1186/s12940-019-0487-1

Loozen, Y., Rebel, K. T., Karssenberg, D., Wassen, M. J., Sardans, J., Peñuelas, J., & De Jong, S. M. (2018). Remote sensing of canopy nitrogen at regional scale in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index. Biogeosciences, 15(9), 2723–2742. https://doi.org/10.5194/bg-15-2723-2018

Poelman, M., Strak, M., Schmitz, O., Hoek, G., Karssenberg, D., Helbich, M., Ntarladima, A. M., Bots, M., Brunekreef, B., Grobbee, R., Dijst, M., & Vaartjes, I. (2018). Relations between the residential fast-food environment and the individual risk of cardiovascular diseases in the Netherlands: a nationwide follow-up study. European Journal of Preventive Cardiology, 25(3), 1397–1405. https://doi.org/10.1177/2047487318769458

van Steen, Y., Ntarladima, A.-M., Grobbee, D. E., Karssenberg, D., & Vaartjes, I. (2018). Sex-differences in mortality after heat waves: are elderly women at higher risk? International Archives of Occupational and Environmental Health. https://doi.org/10.1007/s00420-018-1360-1

Duden, A. S., Verweij, P. A., Junginger, H. M., Abt, R. C., Henderson, J. D., Dale, V. H., Kline, K. L., Karssenberg, D., Verstegen, J. A., Faaij, A. P. C., & van der Hilst, F. (2017). Modeling the impacts of wood pellet demand on forest dynamics in southeastern United States. Biofuels, Bioproducts and Biorefining. https://doi.org/10.1002/bbb.1803

de Graaf, I. E. M., van Beek, R. L. P. H., Gleeson, T., Moosdorf, N., Schmitz, O., Sutanudjaja, E. H., and Bierkens, M. F. P. (2017). A Global-Scale Two-Layer Transient Groundwater Model: Development and Application to Groundwater Depletion. Advances in Water Resources, 102:53-67. https://doi.org/10.1016/j.advwatres.2017.01.011

Karssenberg, D., Bierkens, M. F. P., & Rietkerk, M. (2017). Catastropic shifts in semi-arid vegetation-soil systems may unfold rapidly or slowly. American Naturalist, 9(6), E145–E155. https://doi.org/10.1086/694413

Strak, M., Janssen, N., Beelen, R., Schmitz, O., Karssenberg, D., Houthuijs, D., van den Brink, C., Dijst, M., Brunekreef, B., & Hoek, G. (2017). Associations between lifestyle and air pollution exposure: Potential for confounding in large administrative data cohorts. Environmental Research, 156, 364–373. https://doi.org/10.1016/j.envres.2017.03.050

Strak, M., Janssen, N., Beelen, R., Schmitz, O., Vaartjes, I., Karssenberg, D., van den Brink, C., Bots, M. L., Dijst, M., Brunekreef, B., & Hoek, G. (2017). Long-term exposure to particulate matter, NO2 and the oxidative potential of particulates and diabetes prevalence in a large national health survey. Environment International, 108, 228–236. https://doi.org/10.1016/j.envint.2017.08.017

Verstegen, J. A., van der Hilst, F., Woltjer, G., Karssenberg, D., de Jong, S. M., & Faaij, A. P. C. (2016). What can and can’t we say about indirect land-use change in Brazil using an integrated economic - land-use change model? GCB Bioenergy, 8(3), 561–578. https://doi.org/10.1111/gcbb.12270

Temme, A. J. A. M., Keiler, M., Karssenberg, D., & Lang, A. (2015). Complexity and non-linearity in earth surface processes - concepts, methods and applications. Earth Surface Processes and Landforms, 40(9), 1270–1274. https://doi.org/10.1002/esp.3712

Siteur, K., Eppinga, M. B., Karssenberg, D., Baudena, M., Bierkens, M. F. P., & Rietkerk, M. (2014). How will increases in rainfall intensity affect semiarid ecosystems? Water Resources Research, n/a-n/a. https://doi.org/10.1002/2013WR014955

Wanders, N., Bierkens, M. F. P., de Jong, S. M., de Roo, A., & Karssenberg, D. (2014). The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological modelss. Water Resources Research, 50(8), 6874–6891. https://doi.org/10.1002/2013WR014639

Wanders, N., Karssenberg, D., De Roo, A., De Jong, S. M., & Bierkens, M. F. P. (2014). The suitability of remotely sensed soil moisture for improving operational flood forecasting. Hydrology and Earth System Sciences, 18(6), 2343–2357. https://doi.org/10.5194/hess-18-2343-2014

Soomers, H., Karssenberg, D., Soons, M. B., Verweij, P. A., Verhoeven, J. T. A., & Wassen, M. J. (2013). Wind and Water Dispersal of Wetland Plants Across Fragmented Landscapes. Ecosystems, 16(3), 434–451. https://doi.org/10.1007/s10021-012-9619-y

Soomers, H., Karssenberg, D., Verhoeven, J. T. A., Verweij, P. A., & Wassen, M. J. (2013). The effect of habitat fragmentation and abiotic factors on fen plant occurrence. Biodiversity and Conservation, 22(2), 405–424. https://doi.org/10.1007/s10531-012-0420-1

Vannametee, E., Karssenberg, D., Hendriks, M. R., & Bierkens, M. F. P. (2013). Hortonian runoff closure relations for geomorphologic response units: Evaluation against field data. Hydrology and Earth System Sciences, 17(7), 2981–3004. https://doi.org/10.5194/hess-17-2981-2013

van Asselen, S., Karssenberg, D., & Stouthamer, E. (2012). Contribution of peat compaction to relative sea-level rise within Holocene deltas. Geophysical Research Letters, (38). https://doi.org/10.1029/2011GL049835

Van der Hilst, F., Verstegen, J. A., Karssenberg, D., & Faaij, A. P. C. (2012). Spatiotemporal land use modelling to assess land availability for energy crops - illustrated for Mozambique. GCB Bioenergy, 4(6), 859–874. https://doi.org/10.1111/j.1757-1707.2011.01147.x

Vannametee, E., Karssenberg, D., & Bierkens, M. F. P. (2012). Towards closure relations in the Representative Elementary Watershed (REW) framework containing observable parameters: Relations for Hortonian overland flow. Advances in Water Resources, 43, 52–66. https://doi.org/10.1016/j.advwatres.2012.03.029

Verstegen, J. A., Karssenberg, D., van der Hilst, F., & Faaij, A. (2012). Spatio-temporal uncertainty in Spatial Decision Support Systems: A case study of changing land availability for bioenergy crops in Mozambique. Computers, Environment and Urban Systems, 36(1), 30–42. https://doi.org/10.1016/j.compenvurbsys.2011.08.003

Youssef, F., Visser, S., Karssenberg, D., Bruggeman, A., & Erpul, G. (2012). Calibration of RWEQ in a patchy landscape; a first step towards a regional scale wind erosion model. Aeolian Research, 3(4), 467–476. https://doi.org/10.1016/j.aeolia.2011.03.009

Youssef, F., Visser, S. M., Karssenberg, D., Erpul, G., Cornelis, W. M., Gabriels, D., & Poortinga, A. (2012). The effect of vegetation patterns on wind-blown mass transport at the regional scale: A wind tunnel experiment. Geomorphology, 159–160, 178–188. https://doi.org/10.1016/j.geomorph.2012.03.023

Arieira, J., Karssenberg, D., de Jong, S. M., Addink, E. a., Couto, E. G., Nunes da Cunha, C., & Skøien, J. O. (2011). Integrating field sampling, geostatistics and remote sensing to map wetland vegetation in the Pantanal, Brazil. Biogeosciences, 8(3), 667–686. https://doi.org/10.5194/bg-8-667-2011

Hiemstra, P. H., Karssenberg, D., & van Dijk, A. (2011). Assimilation of observations of radiation level into an atmospheric transport model: A case study with the particle filter and the ETEX tracer dataset. Atmospheric Environment, 45(34), 6149–6157. https://doi.org/10.1016/j.atmosenv.2011.08.024

Lam, A., Karssenberg, D., M. Van Den Hurk, B. J. J., & Bierkens, M. F. P. (2011). Spatial and temporal connections in groundwater contribution to evaporation. Hydrology and Earth System Sciences, 15(8), 2621–2630. https://doi.org/10.5194/hess-15-2621-2011

Lana-Renault, N., Latron, J., Karssenberg, D., Serrano-Muela, P., Regüés, D., & Bierkens, M. F. P. (2011). Differences in stream flow in relation to changes in land cover: A comparative study in two sub-Mediterranean mountain catchments. Journal of Hydrology, 411(3–4), 366–378. https://doi.org/10.1016/j.jhydrol.2011.10.020

Brolsma, R. J., Karssenberg, D., & Bierkens, M. F. P. (2010). Vegetation competition model for water and light limitation. I: Model description, one-dimensional competition and the influence of groundwater. Ecological Modelling, 221(10), 1348–1363. https://doi.org/10.1016/j.ecolmodel.2010.02.012

van der Kwast, J., Timmermans, W., Gieske, A., Su, Z., Olioso, A., Jia, L., Elbers, J., Karssenberg, D., & de Jong, S. (2009). Evaluation of the Surface Energy Balance System (SEBS) applied to ASTER imagery with flux-measurements at the SPARC 2004 site (Barrax, Spain). Hydrol. Earth Syst. Sci., 13(7), 1337–1347. https://doi.org/10.5194/hess-13-1337-2009

Karssenberg, D., & Bridge, J. S. (2008). A three-dimensional numerical model of sediment transport, erosion and deposition within a network of channel belts, floodplain and hill slope: extrinsic and intrinsic controls on floodplain dynamics and alluvial architecture. Sedimentology, 55(6), 1717–1745. https://doi.org/10.1111/j.1365-3091.2008.00965.x

Rebel, K. T., Riha, S. J., Karssenberg, D., & Stedinger, J. R. (2007). Simulating tritium fluxes in the vadose zone under transient saturated conditions. Vadose Zone Journal, 6(2), 387–396. https://doi.org/10.2136/vzj2005.0113

Hefting, M., Beltman, B., Karssenberg, D., Rebel, K., van Riessen, M., & Spijker, M. (2006). Water quality dynamics and hydrology in nitrate loaded riparian zones in the Netherlands. Environmental Pollution, 139(1), 143–56. https://doi.org/10.1016/j.envpol.2005.04.023

Hefting, M., Beltman, B., Karssenberg, D., Rebel, K., van Riessen, M., & Spijker, M. (2006). Water quality dynamics and hydrology in nitrate loaded riparian zones in the Netherlands. Environmental Pollution, 139(1), 143–156. https://dx.doi.org/10.1016/j.envpol.2005.04.023

Visser, S. M., Sterk, G., & Karssenberg, D. (2005). Modelling water erosion in the Sahel: application of a physically based soil erosion model in a gentle sloping environment. Earth Surface Processes and Landforms, 30(12), 1547–1566. https://doi.org/10.1002/esp.1212

Visser, S., Sterk, G., & Karssenberg, D. (2005). Wind erosion modelling in a Sahelian environment. Environmental Modelling & Software, 20(1), 69–84. https://doi.org/10.1016/j.envsoft.2003.12.010

Brama, P. A. J., Barneveld, A., Karssenberg, D., Van Kampen, G. P. J., & Van Weeren, P. R. (2001). The application of an indenter system to measure structural properties of articular cartilage in the horse. Suitability of the instrument and correlation with biochemical data. Journal of Veterinary Medicine Series a - Physiology Pathology Clinical Medicine, 48(4), 213–221. https://doi.org/10.1046/j.1439-0442.2001.00353.x

Brama, P. A. J., Karssenberg, D., Barneveld, A., & van Weeren, P. R. (2001). Contact areas and pressure distribution on the proximal articular surface of the proximal phalanx under sagittal plane loading. Equine Veterinary Journal, 33(1), 26–32. https://doi.org/10.2746/042516401776767377

Karssenberg, D., Törnqvist, T. E., & Bridge, J. S. (2001). Conditioning a process-based model of sedimentary architecture to well data. Journal of Sedimentary Research, 71(6), 868–879. https://doi.org/10.1306/051501710868

Brama, P. A. J., Tekoppele, J. M., Bank, R. A., Karssenberg, D., Barneveld, A., & van Weeren, P. R. (2000). Topographical mapping of biochemical properties of articular cartilage in the equine fetlock joint. Equine Veterinary Journal, 32(1), 19–26. https://doi.org/10.2746/042516400777612062

Education in computational geography

Marra, W. A., van de Grint, L., Alberti, K., & Karssenberg, D. (2017). Using GIS in an Earth Sciences field course for quantitative exploration, data management and digital mapping. Journal of Geography in Higher Education, 41(2), 213–229. https://doi.org/10.1080/03098265.2017.1291587

Karssenberg, D., Burrough, P. A., Sluiter, R., & de Jong, K. (2001). PCRaster software and course materials for teaching numerical modelling in the environmental sciences. Transactions in GIS, 5(2), 99–110. https://doi.org/10.1111/1467-9671.00070

Editorials

Mariethoz, G., Karssenberg, D., & Grana, D. (2018). Who cares about impact factor? Computers and Geosciences, 115, iii–iv. https://doi.org/10.1016/S0098-3004(18)30374-1

Books

Giupponi, C., Jakeman, A.J., Karssenberg, D. and Hare, M.P. (Eds.) (2006), Sustainable management of water resources: an integrated approach (Cheltenham: Elgar).

Chapters in books

Giupponi, C., Jakeman, A. J., Karssenberg, D., Hare, M. P., Fassio, A., & Letcher, R. A. (2006). Integrated Management of Water Resources: Concepts, Approaches and Challenges. In C. Giupponi, A. J. Jakeman, D. Karssenberg, & M. P. Hare (Eds.), Sustainable management of water resources: an integrated approach (pp. 3–24). Cheltenham: Elgar.

Karssenberg, D., Pfeffer, K., & Visssers, M. (2006). Software tools for hydrological modelling. In C. Giupponi, A. J. Jakeman, D. Karssenberg, & M. P. Hare (Eds.), Sustainable management of water resources: an integrated approach (pp. 235–262). Cheltenham: Elgar.

Karssenberg, D., & De Jong, K. (2006). Towards improved solution schemes for Monte Carlo simulation in environmental modeling languages. In P. J. M. Oosterom & M. J. Kreveld (Eds.), Geo-information and computational geometry. Delft: NCG Nederlandse Commissie voor Geodesie, Netherlands Geodetic Commission.

Burrough, P. A., Karssenberg, D., & van Deursen, W. P. A. (2005). Environmental Modelling with PCRaster. (D. J. Maguire, M. F. Goodchild, & M. Batty, Eds.), GIS, Spatial Analysis and Modeling. Redlands, California: ESRI.

Parker, D. C. (2005). Integration of geographic information systems and agent-based models of land use: prospects and challenges. In D. J. Maguire, M. F. Goodchild, & M. Batty (Eds.), GIS, Spatial Analysis and Modeling (pp. 320–341). Redlands, California: ESRI.

Hefting, M. M., Beltman, B., Karssenberg, D., Rebel, K., van Riessen, M., & Spijker, M. (2003). Water quality dynamics and hydrology in riparian zones in the Netherlands. In Nitrogen transformation and retention in riparian buffer zones. PhD thesis, Utrecht: Universiteit Utrecht.

Pfeffer, K., & Karssenberg, D. (2002). Hydrological model. In K. Pfeffer (Ed.), Integrating spatio-temporal environmnetal models for planning ski-runs. Utrecht: Knag/Faculteit Ruimtelijke Wetenschappen Universiteit Utrecht.

Van Dijck, S. J. E., & Karssenberg, D. (2000). Surface runoff. In S. J. E. Van Dijck (Ed.), Effects of agricultural land use on surface runoff and erosion in a Mediterranean area. Utrecht: Koninklijk Nederlands Aardrijkskundig Genootschap/Faculteit Ruimtelijke Wetenschappen, Universiteit Utrecht.

PhD theses

Kor de Jong, A modelling framework for simulating large geographical systems of agents and fields (2022), with Prof. Dr. M.J. van Kreveld and Dr. D. Panja. https://doi.org/10.33540/1127

Lisa Jean Watson, Spatial modeling for petroleum exploration, with Prof. Dr S.M. de Jong and Dr. Menno Straatsma.

Judith Verstegen, Quantifying and reducing uncertainty in land use change model projections. Case studies on the implications of increasing bioenergy demands. With Prof. Dr. A. Faaij, Prof. Dr. S.M. de Jong, and Dr. F. van der Hilst.

Niko Wanders, Improving near real-time flood forecasting using multi-sensor soil moisture assessment (NWO-SRON/GO), together with Prof Dr S. de Jong, Dr A.P.J. de Roo (EC-JRC) & Prof. Dr. M.F.P. Bierkens.

Ekkamol Vannametee, Hydrograph prediction in ungauged basins: development of the closure relation for Hortonian runoff (2014), with Prof. Dr. M.F.P. Bierkens, Prof. Dr. S.M. de Jong and Dr. M.R. Hendriks.

Oliver Schmitz, Integrating environmental component models: development of a software framework (2014). Oliver is at the Faculty of Geosciences, Utrecht University, the Netherlands.

Feras Youssef, Effect of vegetation cover and transitions on regional wind erosion in drylands (2012).

Hans van der Kwast, Quantification of top soil moisture patterns : Evaluation of field methods, process-based modelling, remote sensing and an integrated approach (2009).

Paul Hiemstra, Ensemble modeling and statistical mapping of airborne radioactivity (2011).

Julia Arieira Couto, Spatial Variability of Vegetation in Response to the Edaphical, Hydrological and Topographical Conditions in North Pantanal, Mato Grosso (Brazil) (2010).

Karssenberg, D. (2002). Building dynamic spatial environmental models

PCRaster online course materials

Marra, W., Karssenberg, D. 2018. PCRaster distance learning course material: Data Pre-Processing with GDAL. 10 pp.

Karssenberg, D., 2015, PCRaster distance learning course material: An introduction to dynamic modelling. Appr. 50 pp, Utrecht University.

Karssenberg, D., 2015, PCRaster distance learning course material: Map algebra and environmental modelling. Appr. 50 pp., Utrecht University.

Karssenberg, D. and de Jong, K., 2015, PCRaster distance learning course material: Stochastic environmental modelling. Appr. 20 pp.

Schmitz, O., Karssenberg, D. and de Vries, L.M., 2009, Framework for spatio-temporal stochastic modelling, data assimilation and model calibration, appr. 40 pp.

Schmitz, O. and Karssenberg, D., 2008, An introduction to groundwater and surfacewater modelling.

Karssenberg, D., Pebesma, E.J., van der Meer, M. and Burrough, P.A., 2003, PCRaster en Gstat distance learning course material: An introduction to interpolation and geostatistical Modelling. Appr. 30 pp., Utrecht University.

Karssenberg, D., Wesseling, C.G. and van Deursen, W.P.A., 2003, PCRaster, manual, Utrecht: Utrecht University.

Sluiter, R., Burrough, P.A., Karssenberg, D., Lucieer, A., Wesseling, C.G., de Jong, K., van Asch, T.W.J., van Steijn, H., Cadee, M., de Boer, R.J., van Deursen, W.P.A. and Hendriks, M.R., 2000, Virtual Landscapes, interactive models for learning about geographical change. Appr. 50 pp. Utrecht University.