Uncertainty Estimation of Extreme Precipitations Under Climate Change: A Non-Parametric Approach

Assessment of climate change impacts on hydrology at watershed scale incorporates (a) downscaling of global scale climatic variables into local scale hydrologic variables and (b) assessment of future hydrologic extremes. Atmosphere-Ocean Global Climate Models (AOGCM) are designed to simulate time series of future climate responses accounting for human induced green house gas emissions. The present study addresses the following limitations of climate change impact research: (i) limited availability of observed historical information; (ii) limited research on the detection of changes in hydrologic extremes; and (iii) coarse spatio-temporal resolution of AOGCMs for use at regional or local scale. Downscaled output from a single AOGCM with a single emission scenario represents only a single trajectory of all possible future climate realizations and cannot be representative of the full extent of climate change. Present research, therefore addresses the following questions: (i) how should the AOGCM outputs be selected to assess the severity of extreme climate events?; (ii) should climate research adopt equal weights from AOGCM outputs to generate future climate?; and (iii) what is the probability of the future extreme events to be more severe? Assessment of regional reanalysis hydro-climatic data has shown promising potential as an addition to the observed data in data scarce regions. A new approach using statistical downscaling based nonparametric data-driven kernel estimator is developed for quantifying uncertainties from multiple AOGCMs and emission scenarios. The results are compared with a Bayesian reliability ensemble average method. The generated future climate scenarios represent the nature and progression of uncertainties from several global climate models and their emission scenarios. Treating the extreme precipitation indices as independent realization at every time step, the kernel estimator provides variable weights to the multi-model quantification of uncertainties. The probabilities of the extreme indices have added useful insight into future climate conditions. Finally, the current method of developing future rainfall intensity-duration-frequency curves is extended by introducing a probabilistic weighted curve to include AOGCM and emission scenario uncertainties using the plug-in kernel. Present research has thus expanded the existing knowledge of dealing with the uncertainties of extreme events

Assessment of Global and Regional Reanalyses Data for Hydro- Climatic Impact Studies in the Upper Thames River Basin

This study evaluates NCEP-NCAR reanalyses hydro-climatic data as an initial check for assessment of climate change studies and hydrologic modeling on the basin scale. Reanalysis data set for daily precipitation, and temperature from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) (a) global (NNGR) and (b) regional (NARR) reanalysis project are used as input into the semi-distributed hydrologic model (HEC-HMS) during the period of 1980-2005. First, the precipitation and temperature data are interpolated to selected stations to check for their trends and similarity in means and variances. Although NARR shows some over-estimated values, mainly in estimating temperature during the summer months, it has been able to capture the trends. NNGR, on the other hand, has produced inferior results in many cases, especially in generating precipitation when compared with the observed values. With its improved atmospheric analytical ability, NARR appears to have performed better than the NNGR, suggesting that with coarse resolution NNGR may not be applied in climate change studies for medium or small watersheds. Next, an extensive analysis is performed for assessing the performance of the reanalysis data generated flows by comparing it with the observed inputs during May-November. The stream flows generated from the NARR dataset show encouraging results for simulating summertime low flows with less variability and error. NNGR dataset, have proven to be less accurate and highly variable. This study suggests that NARR can be adequately used as either an additional source of data or as an alternative to observations in data scarce regions.https://ir.lib.uwo.ca/wrrr/1033/thumbnail.jp

Development of Probability Based Intensity- Duration-Frequency Curves under Climate Change

Hydrologic design of storm sewers, culverts, retention/detention basins and other components of storm water management systems are typically performed based on specified design storms derived from the rainfall intensity-duration-frequency (IDF) estimates and an assumed temporal distribution of rainfall. Use of inappropriate data or design storms could lead to malfunctions of the infrastructure systems: over-estimation may result in costly over-design or under-estimation may be associated with risk and human safety. One of the expected hydroclimatic impacts of climate change for London is the increase in the magnitude and frequency of extreme rainfalls which can have serious impact on the design, operation and maintenance of existing municipal water infrastructure. This study presents a methodology for updating the rainfall IDF curves for the City of London incorporating various uncertainties associated with the assessment of climate change impacts on a local scale. Overall, two objectives have been achieved: first, an extensive investigation of the possible realizations of future climate from 29 scenarios developed from Atmosphere-Ocean Global Climate Models (AOGCM) and scenarios are performed using a downscaling based disaggregation approach. Annual maximum series of rainfall are fitted to Gumbel distribution to develop IDF curves for 1, 2, 6, 12 and 24 hour durations for 2, 5, 10, 25, 50 and 100 years of return periods. Next, the associated uncertainties are estimated using nonparametric kernel estimation approach and the resultant IDF curve is developed based on a probabilistic approach. The results indicate that rainfall patterns in the City of London will most certainly change in future due to climate change. The use of the multi-model approach, rather than a single scenario is encouraged. Inherent uncertainties associated with different AOGCMs are quantified by a kernel based plug-in estimation approach. The resultant scenario indicates approximately 20- 40% changes in different duration rainfalls for all return periods. Use of a probability based intensity-duration-frequency curve is encouraged in order to apply the updated IDF information with higher level of confidence.https://ir.lib.uwo.ca/wrrr/1034/thumbnail.jp

Quantifying Uncertainties in the Modelled Estimates of Extreme Precipitation Events at the Upper Thames River Basin

Assessment of climate change impact on hydrology at watershed scale incorporates downscaling of global scale climatic variables into local scale hydrologic variables and computations of risk of hydrologic extremes in future for water resources planning and management. Atmosphere-Ocean General Circulation (AOGCM) models are designed to simulate time series of future climate responses accounting for enthropogenically induced green house gas emissions. The climatological inputs obtained from several AOGCMs suffer the limitations due to incomplete knowledge arising from the inherent physical, chemical processes and the parameterization of the model structure. This study explores the methods available for quantifying uncertainties from the AOGCM outputs by considering fixed weights from different climate model means for the overall data lengths and provides an extensive investigation of the variable weight nonparametric kernel estimator based on the choice of bandwidths for investigating the severity of extreme precipitation events over the next century. The results of this study indicate that the variable width method is better equipped to provide more useful information of the uncertainties associated with different AOGCM scenarios. This study further indicates an increase of probabilities for higher intensities and frequencies of events. The applied methodology is flexible and can be adapted to any uncertainty estimation studies with unknown densities.https://ir.lib.uwo.ca/wrrr/1032/thumbnail.jp

Quantifying Uncertainties in the Modelled Estimates of Extreme Precipitation Events at Upper Thames River Basin

Assessment of climate change impact on hydrology at watershed scale incorporates downscaling of global scale climatic variables into local scale hydrologic variables and evaluation of future hydrologic extremes. The climatological inputs obtained from several global climate models suffer the limitations due to incomplete knowledge arising from the inherent physical, chemical processes and the parameterization of the model structure. Downscaled output from a single AOGCM with a single emission scenario represents only one of all possible future climate realizations; averaging outputs from multiple AOGCMs might underestimate the extent of future changes in the intensity and frequency of climatological variables. These available methods, thus cannot be representative of the full extent of climate change. Present research, therefore addresses two major questions: (i) should climate research adopt equal weights from AOGCM outputs to generate future climate?; and (ii) what is the probability of the future extreme events to be more severe? This paper explores the methods available for quantifying uncertainties from the AOGCM outputs and provides an extensive investigation of the nonparametric kernel estimator based on choice of bandwidths for investigating the severity of extreme precipitation events over the next century. The Sheather-Jones plug-in kernel estimate appears to be a major improvement over the parametric methods with known distribution. Results indicate increased probabilities for higher intensities and frequencies of events. The applied methodology is flexible and can be adapted to any uncertainty estimation studies with unknown densities. The presented research is expected to broaden our existing knowledge on the nature of the extreme precipitation events and the propagation and quantification of uncertainties arising from the global climate models and emission scenarios