References
- Bates, B., et al. 2008. Climate change and water. Technical paper of the intergovernmental panel on climate change, Geneva: IPCC Secretariat, 210.
- Bergthorsson, P., et al., 1988. The effects of climatic variations on agriculture in Iceland. The Impact of Climatic Variations on Agriculture, 1, 381–509.
- Beyene, T., Lettenmaier, D.P., and Kabat, P., 2010. Hydrologic impacts of climate change on the Nile River Basin: implications of the 2007 IPCC scenarios. Climatic Change, 100 (3–4), 433–461. doi:10.1007/s10584-009-9693-0
- Carter, T., et al., 2007. General guidelines on the use of scenario data for climate impact and adaptation assessment. Helsinki, Finland: Finnish Environmental Institute.
- Chen, J., Brissette, F.P., and Leconte, R., 2011. Uncertainty of downscaling method in quantifying the impact of climate change on hydrology. Journal of Hydrology, 401 (3–4), 190–202. doi:10.1016/j.jhydrol.2011.02.020
- Dai, A., Trenberth, K.E., and Qian, T., 2004. A global dataset of Palmer Drought Severity Index for 1870–2002: relationship with soil moisture and effects of surface warming. Journal of Hydrometeorology, 5 (6), 1117–1130. doi:10.1175/JHM-386.1
- Duan, K. and Mei, Y., 2014. Comparison of meteorological, hydrological and agricultural drought responses to climate change and uncertainty assessment. Water Resources Management, 28 (14), 5039–5054. doi:10.1007/s11269-014-0789-6
- Giorgi, F., et al., 2001. Regional climate information—evaluation and projections. Geological and Atmospheric Sciences Publications, 110. Available from: https://lib.dr.iastate.edu/ge_at_pubs/110
- Hanewinkel, M., et al., 2013. Climate change may cause severe loss in the economic value of European forest land. Nature Climate Change, 3 (3), 203. doi:10.1038/nclimate1687
- Hardy, J.T., 2003. Climate change: causes, effects, and solutions. Hoboken, New Jersey: John Wiley & Sons.
- Hellström, C. and Chen, D., 2003. Statistical downscaling based on dynamically downscaled predictors: application to monthly precipitation in Sweden. Advances in Atmospheric Sciences, 20 (6), 951–958. doi:10.1007/BF02915518
- Hillel, D. and Rosenzweig, C. 1988. The greenhouse effect and its implications regarding global agriculture. In: Research Bulletin/Massachusetts Agricultural Experiment Station (USA). College of Food and Natural Resources, 724.
- Jakeman, A., Littlewood, I., and Whitehead, P., 1990. Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments. Journal of Hydrology, 117 (1–4), 275–300. doi:10.1016/0022-1694(90)90097-H
- Kattsov, V.M., et al., 2005. Future climate change: modeling and scenarios for the Arctic. In: Arctic Climate Impact Assessment (ACIA). Cambridge, UK: Cambridge University Press, 99–150
- Kudo, R., Yoshida, T., and Masumoto, T., 2017. Uncertainty analysis of impacts of climate change on snow processes: case study of interactions of GCM uncertainty and an impact model. Journal of Hydrology, 548, 196–207. doi:10.1016/j.jhydrol.2017.03.007
- Lee, J.-K. and Kim, Y.-O., 2017. Selection of representative GCM scenarios preserving uncertainties. Journal of Water and Climate Change, 8 (4), 641–651. doi:10.2166/wcc.2017.101
- Leys, C., et al., 2013. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49 (4), 764–766. doi:10.1016/j.jesp.2013.03.013
- Liuzzo, L., et al., 2009. Basin-scale water resources assessment in Oklahoma under synthetic climate change scenarios using a fully distributed hydrologic model. Journal of Hydrologic Engineering, 15 (2), 107–122. doi:10.1061/(ASCE)HE.1943-5584.0000166
- Livada, I. and Assimakopoulos, V., 2007. Spatial and temporal analysis of drought in Greece using the Standardized Precipitation Index (SPI). Theoretical and Applied Climatology, 89 (3–4), 143–153. doi:10.1007/s00704-005-0227-z
- Lopes, R.H., 2011. Kolmogorov-smirnov test. vol. 2011. Berlin: Springer, 718–720.
- Maraun, D. and Widmann, M., 2018. Statistical downscaling and bias correction for climate research. Cambridge, UK: Cambridge University Press. doi:10.1017/9781107588783
- Mckee, T.B., Doesken, N.J., and Kleist, J., 1993. The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology, Anaheim, California, 179–183.
- Mearns, L.O., Rosenzweig, C., and Goldberg, R., 1996. The effect of changes in daily and interannual climatic variability on CERES-Wheat: a sensitivity study. Climatic Change, 32 (3), 257–292. doi:10.1007/BF00142465
- Mendez, M., et al., 2020. Performance evaluation of bias correction methods for climate change monthly precipitation projections over Costa Rica. Water, 12 (2), 482. doi:10.3390/w12020482
- Piani, C., et al., 2010. Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. Journal of Hydrology, 395 (3–4), 199–215. doi:10.1016/j.jhydrol.2010.10.024
- Pohlert, T., 2016. Non-parametric trend tests and change-point detection. CC BY-ND, 4, 1–18.
- Raje, D. and Mujumdar, P., 2010. Hydrologic drought prediction under climate change: uncertainty modeling with Dempster–Shafer and Bayesian approaches. Advances in Water Resources, 33 (9), 1176–1186. doi:10.1016/j.advwatres.2010.08.001
- Rosenberg, N.J., et al., 1993. The MINK methodology: background and baseline. In: Rosenberg N.J., ed. Towards an integrated impact assessment of climate change: the MINK study. Dordrecht: Springer, 7–22.
- Semenov, M.A., Barrow, E.M., and Lars-Wg, A., 2002. A stochastic weather generator for use in climate impact studies. In: User Man Herts UK. User Manual, 1–27. Available from: http://www.rothamsted.ac.uk/mas-models/larswg.php
- Shiklomanov, N.I., et al., 2017. Climate change and stability of urban infrastructure in Russian permafrost regions: prognostic assessment based on GCM climate projections. Geographical Review, 107 (1), 125–142. doi:10.1111/gere.12214
- Smith, L.I., 2002. A tutorial on principal components analysis (Computer Science Technical Report No. OUCS-2002-12). New Zealand: University of Otago. Available from: http://hdl.handle.net/10523/7534
- Sunyer, M.A., et al., 2015. Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe. Hydrology and Earth System Sciences, 19 (4), 1827–1847. doi:10.5194/hess-19-1827-2015
- Walton, D.B., et al., 2017. Incorporating snow albedo feedback into downscaled temperature and snow cover projections for California’s Sierra Nevada. Journal of Climate, 30 (4), 1417–1438. doi:10.1175/JCLI-D-16-0168.1
- Watanabe, S., et al., 2012. Intercomparison of bias‐correction methods for monthly temperature and precipitation simulated by multiple climate models. Journal of Geophysical Research: Atmospheres, 117, D23. doi:10.1029/2012JD018192
- Widmann, M., Bretherton, C.S., and Salathé, J.E., 2003. Statistical precipitation downscaling over the northwestern United States using numerically simulated precipitation as a predictor. Journal of Climate, 16 (5), 799–816. doi:10.1175/1520-0442(2003)016<0799:SPDOTN>2.0.CO;2
- Wilby, R.L. and Harris, I., 2006. A framework for assessing uncertainties in climate change impacts: low‐flow scenarios for the River Thames, UK. Water Resources Research, 42 (2). doi:10.1029/2005WR004065
- Wilhite, D.A. and Glantz, M.H., 1985. Understanding: the drought phenomenon: the role of definitions. Water International, 10 (3), 111–120. doi:10.1080/02508068508686328
- Williams, G., et al., 1988. Estimating effects of climatic change on agriculture in Saskatchewan, Canada. Dordrecht, Netherlands: Kluwer Academic Publishers.
- Wood, A.W., et al., 2004. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change, 62 (1–3), 189–216. doi:10.1023/B:CLIM.0000013685.99609.9e