dr.ir. M.J. Booij (Martijn)

International refereed journals (complete list)

[105] Omondi, C.K., Rientjes, T.H.M., Booij, M.J. and Nelson, A.D., 2024. Satellite rainfall bias correction incorporating effects on simulated crop water requirements. International Journal of Remote Sensing, 45, 2269-2288.

[104] Behfar, N., Sharghi, E., Nourani, V. and Booij, M.J., 2024. Drought index downscaling using AI-based ensemble technique and satellite data. Theoretical and Applied Climatology, 155, 2379–2397.

[103] Mialyk, O., Schyns, J.F., Booij, M.J., Su, H., Hogeboom, R.J. and Berger, M., 2024. Water footprints and crop water use of 175 individual crops for 1990–2019 simulated with a global crop model. Scientific Data, 11, 206.

[102] Jahanshahi, A. and Booij, M.J., 2024. Flood process types and runoff coefficient variability in climatic regions of Iran. Hydrological Sciences Journal, 69, 241-258.

[101] Khosravi, K., Golkarian, A., Saco, P.M., Booij, M.J. and Melesse, A.M., 2024. Model identification and accuracy for estimation of suspended sediment load. Geocarto International, 37, 18520-18545.

[100] Jahanshahi, A. and Booij, M.J., 2023. Exploring controls on rainfall–runoff events: spatial dynamics of event runoff coefficients in Iran. Hydrological Sciences Journal, 68, 954-966.

[99] Attique, R., Rientjes, T. and Booij, M., 2023. Comparison between statistical and dynamical downscaling of rainfall over the Gwadar-Ormara basin, Pakistan. Meteorological Applications,30, e2151.

[98] Tongal, H. and Booij, M.J., 2023. Simulated annealing coupled with a Naïve Bayes model and base flow separation for streamflow simulation in a snow dominated basin. Stochastic Environmental Research and Risk Assessment, 37, 89–112.

[97] Westerhof, S.G., Booij, M.J., Van den Berg, M.C.J., Huting, R.J.M. and Warmink, J.J., 2023. Uncertainty analysis of risk-based flood safety standards in the Netherlands through a scenario-based approach. International Journal of River Basin Management, 21, 559-574.

[96] Ahmed, N., Wang, G., Booij, M.J., Marhaento, H., Pordhan, F.A., Ali, S., Munir, S. and Hashmi, M.Z.-u.-R., 2022. Variations in hydrological variables using distributed hydrological model in permafrost environment. Ecological Indicators, 145, 109609.

[95] Ahmed, N., Lu, H., Booij, M.J., Wang, G., Marhaento, H., Bhat, M.S. and Adnan, S., 2022. Innovative polygon trend analysis of monthly precipitation (1952–2015) in the Hindukush-Karakoram-Himalaya river basins of Pakistan. International Journal of Climatology, 42, 9967–9993.

[94] Xie, J., Xu, Y.P., Booij, M.J. and Guo, Y., 2022. Influences of reservoir operation on terrestrial water storage changes detected by GRACE in the Yellow River basin. Journal of Hydrology, 610, 127924.

[93] Gholami, V. and Booij, M.J., 2022. Use of machine learning and geographical information system to predict nitrate concentration in an unconfined aquifer in Iran. Journal of Cleaner Production, 360, 131847.

[92] Ekmekcioğlu, Ö, Demirel, M.C. and Booij, M.J., 2022. Effect of data length, spin-up period and spatial model resolution on fully distributed hydrological model calibration in the Moselle basin. Hydrological Sciences Journal, 67, 759-772.

[91] Ahmed, N., Wang, G., Booij, M.J., Ceribasi, G., Bhat, M.S., Ceyhunlu, A.I. and Ahmed, A., 2022. Changes in monthly streamflow in the Hindukush–Karakoram–Himalaya Region of Pakistan using innovative polygon trend analysis. Stochastic Environmental Research and Risk Assessment, 36, 811–830.

[90] Mialyk, O., Schyns, J.F., Booij, M.J. and Hogeboom, R.J., 2022. Historical simulation of maize water footprints with a new global gridded crop model ACEA. Hydrology and Earth System Sciences, 26, 923-940.

[89] Rustanto, A. and Booij, M.J., 2022. Evaluation of MODIS-Landsat and AVHRR-Landsat NDVI data fusion using a single pair base reference image: a case study in a tropical upstream catchment on Java, Indonesia. International Journal of Digital Earth, 15, 164-197.

[88] Gholami, V., Khaleghi, M.R., Pirasteh, S. and Booij, M.J., 2022. Comparison of self‑organizing map, artificial neural network, and co‑active neuro‑fuzzy inference system methods in simulating groundwater quality: geospatial artificial intelligence. Water Resources Management, 36, 451–469.

[87] Ahmed, N., Wang, G., Booij, M.J., Sun, X., Hussain, F. and Nabi, G., 2022. Separation of the impact of landuse/ landcover change and climate change on runoff in the upstream area of the Yangtze River, China. Water Resources Management, 36,181-201.

[86] Ahmed, N., Wang, G., Lü, H., Booij, M.J., Marhaento, H., Prodhan, F.A., Ali, S. and Imran, M.A., 2022. Attribution of changes in streamflow to climate change and land cover change in Yangtze River Source Region, China. Water, 14, 259.

[85] Krol, M.S., Booij, M.J., Hogeboom, R.J., Karandish, F., Schyns, J.F. and Wang, R., 2022.  Arjen Y. Hoekstra: A water management researcher to be remembered. Water, 14, 50.

[84] Albers, L.T., Schyns, J.F., Booij, M.J. and Zhuo, L., 2021. Blue water footprint caps per sub-catchment to mitigate water scarcity in a large river basin: The case of the Yellow River in China. Journal of Hydrology, 603, 126992.

[83] Omondi, C.K., Rientjes, T.H.M., Booij, M.J. and Nelson, A.D., 2021. Satellite rainfall bias assessment for crop growth simulation – A case study of maize growth in Kenya. Agricultural Water Management, 258, 107204.

[82] Marhaento, H., Booij, M.J., Rahardjo, N. and Ahmed, N., 2021. Impacts of forestation on the annual and seasonal water balance of a tropical catchment under climate change. Forest Ecosystems, 8, 64.

[81] Khosravi, K., Golkarian, A., Booij, M.J., Barzegar, R., Sun, W., Mundher Yaseen, Z. and Mosavi, A., 2021. Improving daily stochastic streamflow prediction: comparison of novel hybrid data-mining algorithms. Hydrological Sciences Journal, 66, 1457-1474.

[80] Gao, C., Guan, X., Booij, M.J., Meng, Y. and Xu, Y.P., 2021. A new framework for a multi-site stochastic daily rainfall model: Coupling a univariate Markov chain model with a multi-site rainfall event model. Journal of Hydrology, 598, 126478.

[79] Barzegar, R., Razzagh, S., Quilty, J., Adamowski, J., Kheyrollah Pour, H. and Booij, M.J., 2021. Improving GALDIT-based groundwater vulnerability predictive mapping using coupled resampling algorithms and machine learning models. Journal of Hydrology, 598, 126370.

[78] Marhaento, H., Booij, M.J. and Ahmed, N., 2021. Quantifying relative contribution of land use change and climate change to streamflow alteration in the Bengawan Solo River, Indonesia. Hydrological Sciences Journal, 66, 1059-1068.

[77] Xuan, W., Xu, Y.P., Fu, Q., Booij, M.J., Zhang, X. and Pan, S., 2021. Hydrological responses to climate change in Yarlung Zangbo River basin, Southwest China. Journal of Hydrology, 597, 125761.

[76] Khan, T., Nouri, H., Booij, M.J., Hoekstra, A.Y., Khan, H., Ullah, I., 2021. Water footprint, blue water scarcity, and economic water productivity of irrigated crops in Peshawar basin, Pakistan. Water, 13, 1249.

[75] Hajihosseini, M., Hajihosseini, H., Morid, S., Delavar, M. and Booij, M.J., 2020. Impacts of land use changes and climate variability on transboundary Hirmand River using SWAT. Journal of Water and Climate Change, 11, 1695-1711.

[74] Gao, C., Booij, M.J. and Xu, Y.P., 2020. Development and hydrometeorological evaluation of a new stochastic daily rainfall model: Coupling Markov chain with rainfall event model. Journal of Hydrology, 589, 125337.

[73] Abedi, M., Shafizadeh-Moghadam, H., Morid, S., Booij, M.J. and Delavar, M., 2020. Evaluation of ECMWF mid-range ensemble forecasts of precipitation for the Karun River basin. Theoretical and Applied Climatology, 141, 61-70.

[72] Gao, C., Booij, M.J. and Xu, Y.P., 2020. Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability. Hydrology and Earth System Sciences, 24, 3251-3269.

[71] Ahmed, N., Wang, G., Booij, M.J., Oluwafemi, A., Zia-ur-Rehman Hashmi, M., Ali, S. and Munir, S., 2020. Climatic variability and periodicity for upstream sub-basins of the Yangtze River, China. Water, 12, 842.

[70] Gao, C., Booij, M.J. and Xu, Y.P., 2020. Impacts of climate change on characteristics of daily-scale rainfall events based on nine selected GCMs under four CMIP5 RCP scenarios in Qu River basin, east China. International Journal of Climatology, 40, 887-907.

[69] Marhaento, H., Booij, M.J., Rientjes, T.H.M. and Hoekstra, A.Y., 2019. Sensitivity of streamflow characteristics to different spatial land-use configurations in tropical catchment. ASCE Journal of Water Resources Planning and Management, 145, 04019054.

[68] Demirel, M.C., Özen, A., Orta, S., Toker, E., Demir, H.K., Ekmekcioglu, Ö., Taysi, H., Eruçar, S., Sag, A.B., Sarı, Ö., Tuncer, E., Hancı, H., Özcan, T.I., Erdem, H., Kosucu, M.M., Basakın, E.E., Ahmed, K., Anwar, A., Avcuoglu, M.B., Vanlı, Ö., Stisen, S. and Booij, M.J., 2019. Additional value of using satellite-based soil moisture and two sources of groundwater data for hydrological model calibration. Water, 11, 2083.

[67] Booij, M.J., Schipper, T.C. and Marhaento, H., 2019. Attributing changes in streamflow to land use and climate change for 472 catchments in Australia and the United States. Water, 11, 1059.

[66] Schyns, J.F., Hoekstra, A.Y., Hogeboom H.J. and Booij, M.J., 2019. Reply to Van Noordwijk and Ellison - Moisture recycling: Key to assess hydrological impacts of land cover changes, but not to quantify water allocation to competing demands. Proceedings of the National Academy of Sciences, 116, 8104.

[65] Nikzad Tehrani, E., Sahour, H. and Booij, M.J., 2019. Trend analysis of hydro-climatic variables in the north of Iran. Theoretical and Applied Climatology, 136, 85-97.

[64] Schyns, J.F., Hoekstra, A.Y., Booij, M.J., Hogeboom H.J. and Mekonnen, M.M., 2019. Limits to the world’s green water resources for food, feed, fibre, timber and bio-energy. Proceedings of the National Academy of Sciences, 116, 4893–4898.

[63] Tongal, H. and Booij, M.J., 2018. Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. Journal of Hydrology, 564, 266-282.

[62] Marhaento, H., Booij, M.J. and Hoekstra, A.Y., 2018. Hydrological response to future land-use change and climate change in a tropical catchment. Hydrological Sciences Journal, 63, 1368-1385.

[61] Van den Heuvel, D.B, Troch, P.A., Booij, M.J., Niu, G.Y., Volkmann, T.H.M. and Pangle, L.A., 2018. Effects of differential hillslope‐scale water retention characteristics on rainfall–runoff response at the Landscape Evolution Observatory. Hydrological Processes, 32, 2118–2127.

[60] Wester, S.J., Grimson, R., Minotti, P.G., Booij, M.J. and Brugnach, M., 2018. Hydrodynamic modelling of a tidal delta wetland using an enhanced quasi-2D model. Journal of Hydrology, 559, 315-326.

[59] Gholami, V., Booij, M.J., Nikzad Tehrani, E. and Hadian, M.A., 2018. Spatial soil erosion estimation using an artificial neural network (ANN) and field plot data. Catena, 163, 210-218.

[58] Benninga, H.J.F., Booij, M.J., Romanowicz, R.J. and Rientjes, T.H.M., 2017. Performance of ensemble streamflow forecasts under varied hydrometeorological conditions. Hydrology and Earth System Sciences, 21, 5273-5291.

[57] Schyns, J.F., Booij, M.J. and Hoekstra, A.Y., 2017. The water footprint of wood for lumber, pulp, paper, fuel and firewood. Advances in Water Resources, 107, 490–501.

[56] Marhaento, H., Booij, M.J. and Hoekstra, A.Y., 2017. Attribution of changes in stream flow to land use change and climate change in a mesoscale tropical catchment in Java, Indonesia. Hydrology Research, 48, 1143-1155.

[55] Rustanto, A., Booij, M.J., Wösten, H. and Hoekstra, A.Y., 2017. Application and recalibration of soil water retention pedotransfer functions in a tropical upstream catchment: case study in Bengawan Solo, Indonesia. Journal of Hydrology and Hydromechanics, 65, 307–320.

[54] Marhaento, H., Booij, M.J., Rientjes, T.H.M. and Hoekstra, A.Y., 2017. Attribution of changes in the water balance of a tropical catchment to land use change using the SWAT model. Hydrological Processes, 31, 2029-2040.

[53] Brouwer, T., Eilander, D., Van Loenen, A., Booij, M.J., Wijnberg, K.M., Verkade, J.S. and Wagemaker, J., 2017. Probabilistic flood extent estimates from social media flood observations. Natural Hazards and Earth System Sciences, 17, 735-747.

[52] Tongal, H. and Booij, M.J., 2017. Quantification of parametric uncertainty of ANN models with GLUE method for different streamflow dynamics. Stochastic Environmental Research and Risk Assessment, 31, 993-1010.

[51] Hajihosseini, H., Hajihosseini, M., Morid, S., Delavar, M. and Booij, M.J., 2016. Hydrological assessment of the 1973 treaty on the transboundary Helmand River, using the SWAT model and a global climate database. Water Resources Management, 30, 4681–4694.

[50] Tongal, H. and Booij, M.J., 2016. A comparison of nonlinear stochastic self-exciting threshold autoregressive and chaotic k-nearest neighbour models in daily streamflow forecasting. Water Resources Management, 30, 1515-1531.

[49] Zheng, D., Van der Velde, R., Su, Z., Wen, J., Wang, X., Booij, M.J., Hoekstra, A.Y., Lv, S., Zhang, Y. and Ek, M.B., 2016. Impacts of Noah model physics on catchment-scale runoff simulations. Journal of Geophysical Research: Atmospheres, 121, 807–832.

[48] Vonk, E., Xu, Y.P., Booij, M.J. and Augustijn, D.C.M., 2016. Quantifying the robustness of optimal reservoir operation for the Xinanjiang-Fuchunjiang reservoir cascade. Water Science & Technology: Water Supply, 16, 79-86.

[47] Tian, Y., Xu, Y.P., Booij, M.J. and Cao, L., 2016. Impact assessment of multiple uncertainty sources on high flows under climate change. Hydrology Research, 47, 61-74.

[46] Zheng, D., Van der Velde, R., Su, Z., Wang, X., Wen, J., Booij, M.J., Hoekstra, A.Y. and Chen, Y., 2015. Augmentations to the Noah model physics for application to the Yellow River source area. Part II: Turbulent heat fluxes and soil heat transport. Journal of Hydrometeorology, 16, 2677-2694.

[45] Zheng, D., Van der Velde, R., Su, Z., Wang, X., Wen, J., Booij, M.J., Hoekstra, A.Y. and Chen, Y., 2015. Augmentations to the Noah model physics for application to the Yellow River source area. Part I: Soil water flow. Journal of Hydrometeorology, 16, 2659-2676.

[44] Schyns, J.F., Hoekstra, A.Y. and Booij, M.J., 2015. Review and classification of indicators of green water availability and scarcity. Hydrology and Earth System Sciences , 19, 4581–4608.

[43] Zhang, X., Booij, M.J. and Xu, Y.P., 2015. Improved simulation of peak flows under climate change: post-processing or composite objective calibration? Journal of Hydrometeorology, 16, 2187-2208.

[42] Osuch, M., Romanowicz, R.J. and Booij, M.J., 2015. The influence of parametric uncertainty on the relationships between HBV model parameters and climatic characteristics. Hydrological Sciences Journal, 60, 1299-1316.

[41] Hogeboom, R.H.J., Van Oel, P.R., Krol, M.S. and Booij, M.J., 2015. Modelling the influence of groundwater abstractions on the water level of Lake Naivasha, Kenya under data-scarce conditions. Water Resources Management, 29, 4447-4463.

[40] Zheng, D., Van der Velde, R., Su, Z., Wen, J., Booij, M.J., Hoekstra, A.Y. and Wang, X., 2015. Under‐canopy turbulence and root water uptake of a Tibetan meadow ecosystem modeled by Noah‐MP. Water Resources Research, 51, 5735–5755.

[39] Tian, Y., Xu, Y.P., Booij, M.J. and Wang, G., 2015. Uncertainty in future high flows in Qiantang River Basin, China. Journal of Hydrometeorology, 16, 363-380.

[38] Demirel, M.C., Booij, M.J. and Hoekstra, A.Y., 2015. The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models . Hydrology and Earth System Sciences, 19, 275-291.

[37] Zheng, D., Van der Velde, R., Su, Z., Booij, M.J., Hoekstra, A.Y. and Wen, J., 2014. Assessment of roughness length schemes implemented within the Noah land surface model for high altitude regions. Journal of Hydrometeorology, 15, 921-937.

[36] Vonk, E., Xu, Y.P., Booij, M.J., Zhang, X. and Augustijn, D.C.M., 2014. Adapting multireservoir operation to shifting patterns of water supply and demand . Water Resources Management, 28, 625-643.

[35] Tian, Y., Booij, M.J. and Xu, Y.-P., 2014. Uncertainty in high and low flows due to model structure and parameter errors. Stochastic Environmental Research and Risk Assessment, 28, 319-332.

[34] Warmink, J.J., Straatsma, M.W., Huthoff, F., Booij, M.J. and Hulscher, S.J.M.H., 2013. Uncertainty of design water levels due to combined bed form and vegetation roughness in the Dutch River Waal. Journal of Flood Risk Management, 6, 302–318.

[33] Rientjes, T.H.M., Muthuwatta, L.P., Bos, M.G., Booij, M.J. and Bhatti, H.A., 2013. Multi-variable calibration of a semi-distributed hydrological model using streamflow data and satellite-based evapotranspiration. Journal of Hydrology, 505, 276-290.

[32] Demirel, M.C., Booij, M.J. and Hoekstra, A.Y., 2013. Impacts of climate change on the seasonality of low flows in 134 catchments in the River Rhine basin using an ensemble of bias-corrected regional climate simulations. Hydrology and Earth System Sciences, 17, 4241-4257.

[31] Van Esse, W.R., Perrin, C., Booij, M.J., Augustijn, D.C.M., Fenicia, F., Kavetski, D. and Lobligeois, F., 2013. The influence of conceptual model structure on model performance: a comparative study for 237 French catchments. Hydrology and Earth System Sciences, 17, 4227-4239.

[30] Demirel, M.C., Booij, M.J. and Hoekstra, A.Y., 2013. Identification of appropriate lags and temporal resolutions for low flow indicators in the River Rhine to forecast low flows with different lead times. Hydrological Processes, 27, 2742-2758.

[29] Demirel, M.C., Booij, M.J. and Hoekstra, A.Y., 2013. Effect of different uncertainty sources on the skill of 10 day ensemble low flow forecasts for two hydrological models. Water Resources Research, 49, 4035-4053.

[28] Van den Tillaart, S.P.M., Booij, M.J. and Krol, M.S., 2013. Impact of uncertainties in discharge determination on the parameter estimation and performance of a hydrological model. Hydrology Research, 44, 454-466.

[27] Warmink, J.J., Booij, M.J., Van der Klis, H. and Hulscher, S.J.M.H., 2013. Quantification of uncertainty in design water levels due to uncertain bed form roughness in the Dutch river Waal . Hydrological Processes, 27, 1646-1663.

[26] Tongal, H., Demirel, M.C. and Booij, M.J., 2013. Seasonality of low flows and dominant processes in the Rhine River. Stochastic Environmental Research and Risk Assessment, 27, 489-503.

[25] Tian, Y, Xu, Y.-P., Booij, M.J., Lin, S., Zhang, Q. and Lou, Z., 2012. Detection of trends in precipitation extremes in Zhejiang, east China. Theoretical and Applied Climatology, 107, 201–210.

[24] Demirel, M.C., Booij, M.J. and Kahya, E., 2012. Validation of an ANN flow prediction model using a multistation cluster analysis. ASCE Journal of Hydrologic Engineering, 17, 262-271.

[23] Booij, M.J., Tollenaar, D., Van Beek, E. and Kwadijk, J.C.J., 2011. Simulating impacts of climate change on river discharges in the Nile basin. Physics and Chemistry of the Earth, 36, 696-709.

[22] Romanowicz, R.J. and Booij, M.J., 2011. Editorial – Impact of land use and water management on hydrological processes under varying climatic conditions. Physics and Chemistry of the Earth, 36, 613-614.

[21] Warmink, J.J., Van der Klis, H., Booij, M.J. and Hulscher, S.J.M.H., 2011. Identification and quantification of uncertainties in a hydrodynamic river model using expert opinions. Water Resources Management, 25, 601–622.

[20] Booij, M.J. and De Wit, M.J.M., 2010. Extreme value statistics for annual minimum and trough-under-threshold precipitation at different spatio-temporal scales. Hydrological Sciences Journal, 55, 1289-1301.

[19] Warmink, J.J., Janssen, J.A.E.B., Booij, M.J. and Krol, M.S., 2010. Identification and classification of uncertainties in the application of environmental models. Environmental Modelling and Software, 25, 1518-1527.

[18] Deckers, D.L.E.H., Booij, M.J., Rientjes, T.H.M. and Krol, M.S., 2010. Catchment variability and parameter estimation in multi-objective regionalisation of a rainfall-runoff model. Water Resources Management, 24, 3961–3985.

[17] Booij, M.J. and Krol, M.S., 2010. Balance between calibration objectives in a conceptual hydrological model, Hydrological Sciences Journal, 55, 1017-1032.

[16] Bulsink, F., Hoekstra, A.Y. and Booij, M.J., 2010. The water footprint of Indonesian provinces related to the consumption of crop products. Hydrology and Earth System Sciences, 14, 119–128.

[15] Xu, Y.-P., Booij, M.J. and Tong, Y.-B., 2010. Uncertainty analysis in statistical modeling of extreme hydrological events. Stochastic Environmental Research and Risk Assessment, 24, 567–578.

[14] Akhtar, M., Ahmad, N. and Booij, M.J., 2009. Use of regional climate model simulations as input for hydrological models for the Hindukush-Karakorum-Himalaya region. Hydrology and Earth System Sciences , 13, 1075-1089.

[13] De Kok, J.L. and Booij, M.J., 2009. Deterministic-statistical model coupling in a DSS for river-basin management. Environmental Modeling and Assessment, 14, 595-606.

[12] Xu, Y.P., Holzhauer, H., Booij M.J. and Sun H.Y., 2008. A two-step approach to investigate the effect of rating curve uncertainty in the Elbe decision support system. Journal of Zhejiang University Science A, 9, 1229-1238.

[11] De Hamer, W., Love, D., Owen, R., Booij, M.J. and Hoekstra, A.Y., 2008. Potential water supply of a small reservoir and alluvial aquifer system in southern Zimbabwe. Physics and Chemistry of the Earth, 33, 633-639.

[10] Akhtar, M., Ahmad, N. and Booij, M.J., 2008. The impact of climate change on the water resources of Hindukush-Karakorum-Himalaya region under different glacier coverage scenarios. Journal of Hydrology, 355, 148-163.

[9] Xu, Y.-P., Booij, M.J. and Mynett, A.E., 2007. An appropriateness framework for the Dutch Meuse decision support system. Environmental Modelling and Software, 22, 1667-1678.

[8] De Kort, I.A.T. and Booij, M.J., 2007. Decision making under uncertainty in a decision support system for the Red River. Environmental Modelling and Software, 22, 128-136.

[7] Dong, X., Dohmen-Janssen, C.M., Booij, M. and Hulscher, S., 2006. Effect of flow forecasting quality on benefits of reservoir operation - a case study for the Geheyan reservoir (China). Hydrology and Earth System Sciences Discussions, 3, 3771-3814.

[6] Dong, X., Dohmen-Janssen, C.M. and Booij, M.J., 2005. Appropriate spatial sampling of rainfall for flow simulation. Hydrological Sciences Journal, 50, 279-298.

[5] Booij, M.J., 2005. Impact of climate change on river flooding assessed with different spatial model resolutions. Journal of Hydrology, 303, 176-198.

[4] Booij, M.J., 2003. Determination and integration of appropriate spatial scales for river basin modelling. Hydrological Processes, 17, 2581-2598.

[3] Booij, M.J., 2002. Modelling the effects of spatial and temporal resolution of rainfall and basin model on extreme river discharge. Hydrological Sciences Journal, 47, 307-320.

[2] Booij, M.J., 2002. Extreme daily precipitation in Western Europe with climate change at appropriate spatial scales. International Journal of Climatology, 22, 69-85.

[1] Booij, M., Leijnse, A., Haldorsen, S., Heim, M. and Rueslåtten, H., 1998. Subpermafrost groundwater modelling in Ny-Ålesund, Svalbard. Nordic Hydrology, 29, 385-396.