480
Furrer, R., Knutti, R., Sain, S. R., Nychka, D. W., & Meehl, G. A. (2007). Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis. Geophysical
Research Letters, 34(6).
Guo, D., Chen, J., MacEachren, A. M., & Liao, K. (2006). A visualization system for space-time
and multivariate patterns (VIS-STAMP). IEEE Transactions on Visualization and Computer
Graphics, 12(6), 1461–1474.
Harris, G. (2002). Energy, water, and food scenarios. Best Partners.
Hellegers, P., & Zilberman, D. (2008). Interactions between water, energy, food and environment:
Evolving perspectives and policy issues. Water Policy, 10(S1), 1–10.
Hu, Q., Johnston, E., & Hemphill, L. (2012). Fostering cooperative community behavior with IT
tools: The influence of a designed deliberative space on efforts to address collective challenges.
The Journal of Community Informatics, 9(1).
Jin, H., & Guo, D. (2009). Understanding climate change patterns with multivariate geovisualization. In ICDM workshops (pp. 217–222).
Isendahl, N., Dewulf, A., Brugnach, M., Francois, G., Moellenkamp, S., & Pahl-Wostl, C.
(2009). Assessing framing of uncertainties in water management practice. Water Resources
Management, 23(15), 3191–3205.
Isendahl, N., Dewulf, A., & Pahl-Wostl, C. (2010). Making framing of uncertainty in water management practice explicit by using a participant-structured approach. Journal of Environmental
Management, 91(4), 844–851.
Khan, S., & Hanjra, M. A. (2009). Footprints of water and energy inputs in food production –
Global perspectives. Food Policy, 34, 130–140.
Kates, R. W., et al. (2001). Sustainability science. Science, 292(5517), 641–642. https://doi.
org/10.1126/science.1059386.
Kehrer, J., & Hauser, H. (2013). Visualization and visual analysis of multifaceted scientific data: A
survey. IEEE Transactions on Visualization and Computer Graphics, 19(3), 495–513.
Kelly, R. E. (2009). The AMSR-E snow depth algorithm: Description and initial results. Journal of
the Remote Sensing Society of Japan, 29(1), 307–317.
Knutti, R. (2008). Hotter or not? Should we believe model predictions of future climate change?
Significance, 5(4), 159–162.
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., & Meehl, G. (2010). Challenges in combining projections from multiple climate models. Journal of Climate, 23(10), 2739–2758.
Knutti, R., Masson, D., & Gettelman, A. (2013). Climate model genealogy: Generation CMIP5
and how we got there. Geophysical Research Letters, 40(6), 1194–1199.
Kraucunas, I., et al. (2015). Investigating the nexus of climate, energy, water, and land at decisionrelevant scales: The Platform for Regional Integrated Modeling and Analysis (PRIMA).
Climatic Change, 129(3–4), 573–588.
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J., Olden, S., & Lighty, L.
(2006). Land information system – An interoperable framework for high resolution land surface modeling. Environmental Modelling and Software, 21, 1402–1415.
Ladstädter, F., Steiner, A. K., Lackner, B. C., Pirscher, B., Kirchengast, G., Kehrer, J., Hauser, H.,
Muigg, P., & Doleisch, H. (2010). Exploration of climate data using interactive visualization.
Journal of Atmospheric and Oceanic Technology, 27(4), 667–679.
Lang, D. J., et al. (2012). Transdisciplinary research in sustainability science: practice, principles, and challenges. Sustainability Science, 7(Suppl 1), 25. https://doi.org/10.1007/
s11625-011-0149-x.
Lubchenco, J. (1998). Entering the century of the environment: A new social contract for science.
Science, 279, 491–497.
Masson, D., & Knutti, R. (2011). Spatial-scale dependence of climate model performance in the
CMIP3 ensemble. Journal of Climate, 24(11), 2680–2692.
Mitchell, K. E., Lohmann, D., Houser, P. R., Wood, E. F., Schaake, J. C., Robock, A., &
Cosgrove, B. A. (2004). The multi-institution North American Land Data Assimilation
System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed
E. Eftelioglu et al.
Furrer, R., Knutti, R., Sain, S. R., Nychka, D. W., & Meehl, G. A. (2007). Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis. Geophysical
Research Letters, 34(6).
Guo, D., Chen, J., MacEachren, A. M., & Liao, K. (2006). A visualization system for space-time
and multivariate patterns (VIS-STAMP). IEEE Transactions on Visualization and Computer
Graphics, 12(6), 1461–1474.
Harris, G. (2002). Energy, water, and food scenarios. Best Partners.
Hellegers, P., & Zilberman, D. (2008). Interactions between water, energy, food and environment:
Evolving perspectives and policy issues. Water Policy, 10(S1), 1–10.
Hu, Q., Johnston, E., & Hemphill, L. (2012). Fostering cooperative community behavior with IT
tools: The influence of a designed deliberative space on efforts to address collective challenges.
The Journal of Community Informatics, 9(1).
Jin, H., & Guo, D. (2009). Understanding climate change patterns with multivariate geovisualization. In ICDM workshops (pp. 217–222).
Isendahl, N., Dewulf, A., Brugnach, M., Francois, G., Moellenkamp, S., & Pahl-Wostl, C.
(2009). Assessing framing of uncertainties in water management practice. Water Resources
Management, 23(15), 3191–3205.
Isendahl, N., Dewulf, A., & Pahl-Wostl, C. (2010). Making framing of uncertainty in water management practice explicit by using a participant-structured approach. Journal of Environmental
Management, 91(4), 844–851.
Khan, S., & Hanjra, M. A. (2009). Footprints of water and energy inputs in food production –
Global perspectives. Food Policy, 34, 130–140.
Kates, R. W., et al. (2001). Sustainability science. Science, 292(5517), 641–642. https://doi.
org/10.1126/science.1059386.
Kehrer, J., & Hauser, H. (2013). Visualization and visual analysis of multifaceted scientific data: A
survey. IEEE Transactions on Visualization and Computer Graphics, 19(3), 495–513.
Kelly, R. E. (2009). The AMSR-E snow depth algorithm: Description and initial results. Journal of
the Remote Sensing Society of Japan, 29(1), 307–317.
Knutti, R. (2008). Hotter or not? Should we believe model predictions of future climate change?
Significance, 5(4), 159–162.
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., & Meehl, G. (2010). Challenges in combining projections from multiple climate models. Journal of Climate, 23(10), 2739–2758.
Knutti, R., Masson, D., & Gettelman, A. (2013). Climate model genealogy: Generation CMIP5
and how we got there. Geophysical Research Letters, 40(6), 1194–1199.
Kraucunas, I., et al. (2015). Investigating the nexus of climate, energy, water, and land at decisionrelevant scales: The Platform for Regional Integrated Modeling and Analysis (PRIMA).
Climatic Change, 129(3–4), 573–588.
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J., Olden, S., & Lighty, L.
(2006). Land information system – An interoperable framework for high resolution land surface modeling. Environmental Modelling and Software, 21, 1402–1415.
Ladstädter, F., Steiner, A. K., Lackner, B. C., Pirscher, B., Kirchengast, G., Kehrer, J., Hauser, H.,
Muigg, P., & Doleisch, H. (2010). Exploration of climate data using interactive visualization.
Journal of Atmospheric and Oceanic Technology, 27(4), 667–679.
Lang, D. J., et al. (2012). Transdisciplinary research in sustainability science: practice, principles, and challenges. Sustainability Science, 7(Suppl 1), 25. https://doi.org/10.1007/
s11625-011-0149-x.
Lubchenco, J. (1998). Entering the century of the environment: A new social contract for science.
Science, 279, 491–497.
Masson, D., & Knutti, R. (2011). Spatial-scale dependence of climate model performance in the
CMIP3 ensemble. Journal of Climate, 24(11), 2680–2692.
Mitchell, K. E., Lohmann, D., Houser, P. R., Wood, E. F., Schaake, J. C., Robock, A., &
Cosgrove, B. A. (2004). The multi-institution North American Land Data Assimilation
System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed
E. Eftelioglu et al.