Temporal Disaggregation of Daily Precipitation Data in a Changing Climate

Models for spatially interpolating hourly precipitation data and temporally disaggregating daily precipitation to hourly data are developed for application to multisite scenarios at the watershed scale. The intent is to create models to produce data which are valid input for a hydrologic rainfall-runoff model, from daily data produced by a stochastic weather generator. These models will be used to determine the potential effects of climate change on local precipitation events. A case study is presented applying these models to the Upper Thames River basin in Ontario, Canada; however, these models are generic and applicable to any watershed with few changes. Some hourly precipitation data were required to calibrate the temporal disaggregation model. Spatial interpolation of this hourly precipitation data was required before temporal disaggregation could be completed. Spatial interpolation methods were investigated and an inverse distance method was applied to the data. Analysis of the output from this model confirms that isotropy is a valid assumption for this application and illustrates that the model is robust. The results for this model show that further study is required for accurate spatial interpolation of hourly precipitation data at the watershed scale. An improved method of fragments is used to perform temporal disaggregation on daily precipitation data. A parsimonious approach to multisite fragment calculation is introduced within this model as well as other improvements upon the methods presented in the literature. The output from this model clearly indicates that spatial and iii temporal variations are maintained throughout the disaggregation process. Analysis of the results indicates that the model creates plausible precipitation events. The models presented here were run for multiple climate scenarios to determine which GCM scenario has the most potential to affect precipitation. Discussion on the potential impacts of climate change on the region of study is provided. Selected events are examined in detail to give a representation of extreme precipitation events which may be experienced in the study area due to climate change.

[1]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[2]  Richard H. McCuen,et al.  Accuracy Evaluation of Rainfall Disaggregation Methods , 2004 .

[3]  J. M. Mejía,et al.  Disaggregation models in hydrology revisited , 1976 .

[4]  Monthly hydrologic data generation by disaggregation , 1996 .

[5]  V. I. Obrezkov.  Mathematical modeling of hydrologic series , 1978 .

[6]  L. Natale,et al.  A parsimonious approach to stochastic multisite modelling and disaggregation of daily rainfall , 2003 .

[7]  J. Houghton,et al.  Climate change 2001 : the scientific basis , 2001 .

[8]  Ram S. Gupta.  Introduction to environmental engineering and science , 2004 .

[9]  R. Valencia,et al.  Disaggregation processes in stochastic hydrology , 1973 .

[10]  C. W. Richardson.  Stochastic simulation of daily precipitation, temperature, and solar radiation , 1981 .

[11]  David R. Lapen,et al.  Spatial Analysis of Seasonal and Annual Temperature and Precipitation Normals in Southern Ontario, Canada , 2003 .

[12]  Neil van der Gugten.  Hydrology of floods in Canada — a guide to planning and design , 1992 .

[13]  T. McMahon,et al.  Stochastic Generation of Monthly Streamflows , 1982 .

[14]  J. ...,et al.  Applied modeling of hydrologic time series , 1980 .

[15]  D. Entekhabi,et al.  Disaggregation of Daily Rainfall for Continuous Watershed Modeling , 2001 .

[16]  D. Wilks.  Multisite generalization of a daily stochastic precipitation generation model , 1998 .

[17]  J. Olsson,et al.  Temporal rainfall disaggregration based on scaling properties , 1998 .

[18]  W. L. Lane,et al.  Applied Modeling of Hydrologic Time Series , 1997 .

[19]  Kenneth Strzepek,et al.  A technique for generating regional climate scenarios using a nearest‐neighbor algorithm , 2003 .

[20]  D. Wilks.  Multisite downscaling of daily precipitation with a stochastic weather generator , 1999 .

[21]  T. A. Buishand,et al.  Simulation of 6-hourly rainfall and temperature by two resampling schemes , 2003 .

[22]  Donald H. Burn,et al.  Simulating climate change scenarios using an improved K-nearest neighbor model , 2006 .

[23]  Robert J. Ruhf,et al.  Time Series Analysis of 20 Years of Hourly Precipitation in Southwest Michigan , 2003 .

[24]  J. Salas,et al.  A COMPARATIVE ANALYSIS OF TECHNIQUES FOR SPATIAL INTERPOLATION OF PRECIPITATION , 1985 .