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  • Print publication year: 2011
  • Online publication date: April 2011

5 - Connecting climate and hydrological models for impacts studies

from Part I - Past, present and future climate



Driving hydrological models with climate data is a tough challenge – whether the data are from the observational record or climate models. One reason for this is that many hydrological models require long daily time-series of precipitation and evaporation. The scarcity of appropriate observed data in many parts of the MENA region is therefore a potential constraint for the development of such models. Although climate models have the capacity to produce daily time-series for the whole region, the results of impacts studies driven directly by model output would be prejudiced by model error – particularly in precipitation, which is one of the most difficult variables to simulate. This chapter describes how these problems can be addressed by using a simple statistical rainfall model (weather generator) in conjunction with a regional climate model. This enables climate model bias to be corrected, observed monthly data to be disaggregated and the length of a precipitation time-series to be extended.


Driving hydrological models with climate model data provides a key means to understand the hydrological systems of the MENA region, how water availability has changed in the past and how it is projected to change in the future. In cases when it is not appropriate or not possible to use raw model output to drive a hydrological model, a statistical rainfall model (also known as a stochastic weather generator) can be used as an intermediate step.

Furrer, E. M. and Katz, R. W. (2007) Generalized linear modeling approach to stochastic weather generators. Climate Research 34: 129–144.
Gabriel, K. R. and Neumann, J. (1962) A Markov chain model for daily rainfall occurrence at Tel Aviv. Quarterly Journal of the Royal Meteorological Society 88: 90–95.
Hansen, J. W. and Ines, A. V. M. (2005) Stochastic disaggregation of monthly rainfall data for crop simulation studies. Agricultural and Forest Meteorology 131: 233–246.
Hutchinson, M. F. (1995) Stochastic space-time weather models from ground-based data. Agricultural and Forest Meteorology 73: 237–264.
Jones, P. D., Harpham, C., Kilsby, C. G., Glenis, B. and Burton, A. (2009) Projections of future daily climate for the UK from the Weather Generator. UK Climate Projections Science Reports3. University of Newcastle.
Roldan, J. and Woolhiser, D. A. (1982) Stochastic daily precipitation models: 1. A comparison of occurrence processes. Water Resources Research 18: 1451–1459.
Samuels, R., Rimmer, A. and Alpert, P. (2009) Effect of extreme rainfall events on the water resources of the Jordan River. Journal of Hydrology 375: 513–523.
Semenov, M. A. and Barrow, E. M. (1997) Use of a stochastic weather generator in the development of climate change scenarios. Climatic Change 35: 397–414.
Wilks, D. S. (1995) Statistical Methods in the Atmospheric Sciences: An Introduction. San Diego: Academic Press.
Wilks, D. S. and Wilby, R. L. (1999) The weather generation game: a review of stochastic weather models. Progress in Physical Geography 23: 329–357.