Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/72356
Title: Uncertainty quantification framework for combined statistical spatial downscaling and temporal disaggregation for climate change impact studies on hydrology
Authors: Rajendran Queen Suraajini
Keywords: DRNTU::Engineering::Civil engineering
Issue Date: 2017
Source: Rajendran Queen Suraajini. (2017). Uncertainty quantification framework for combined statistical spatial downscaling and temporal disaggregation for climate change impact studies on hydrology. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: The International panel on climate change (IPCC) reported that the impact of climate change is considered as one of the major reasons for the increase in the extreme flood events especially the intense precipitation. The intense flood in a short period of time can cause damage to the properties and affect daily life of human. Singapore has witnessed the increased flood events which occurred in a short period of time in the recent past and the documental increasing trend in the rainfall amount is in agreement with the IPCC report. Thus, the strategies to assist future adaption planning and risk mitigation during extreme flood events need to be developed based on the predicted future climate conditions. Fine spatial and temporal resolutions in climate data are needed to simulate the change in future flood events and to study the climate change impact on hydrology. The downscaling model combined with the temporal disaggregation can generate high spatial and temporal resolution of future climate data. The Statistical Downscaling Model (SDM) is the bridging model which is used to downscale the output from the General Circulation Model (GCM) for increasing the spatial resolution of future climate scenarios. The temporal disaggregation model increases the temporal scale, for example, from daily to hourly or minute scale. The information on the expected future change in the precipitation is needed to make efficient decisions. However, the predictions obtained from numerical climate model have uncertainties due to the generalized representation of the complex climate system. The sources of uncertainty include natural variability, uncertainties in the climate model(s), the downscaling model, the disaggregation model and the model inadequacy. This research focuses on quantifying the uncertainty in the downscaled and disaggregated future climate variables by adopting a full Bayesian updating model framework for the statistical downscaling and the data-driven hydrological models. Bayesian updating framework provides a principled probabilistic way to quantify uncertainty in the model calibration and prediction. This research investigation has been carried out in two levels. The first goal was to develop a combined stochastic statistical downscaling and disaggregation model coupled with uncertainty quantification tool that captures both aleatory and epistemic uncertainties in downscaled climate variables from large scale climate model data. The second goal was to develop a stochastic process based data-driven hydrological model, integrated with the uncertainty quantification tool to simulate the river flow using the downscaled and disaggregated climate variables as inputs. Initially, a single site statistical downscaling model has been considered where a stochastic process-based SDM is proposed to couple the uncertainty quantification tool with model calibration and model prediction. The classical SDM has three steps such as 1) precipitation occurrence determination 2) precipitation amount estimation and 3) residual fitting. The contemporary regression based SDM assumes different distribution for precipitation amount estimation and residual fitting. Two new SDM approaches was developed for single site downscaling in this research. The first approach was named K-nearest neighbor-Bayesian Uncertainty Quantification for Statistical Downscaling Model (KNN-BUQSDM). In KNN-BUQSDM, KNN was used to determine the precipitation occurrence and to classify the wet days into different rainfall types based on the rainfall magnitude. For each rainfall type, the rainfall amount was estimated using a Gaussian Processes (GP) model. The GP model is based on stochastic error coupling method wherein the dependency between the residuals were used for prediction. The stochastic SDM couples the amount estimation model and the residual fitting under same distribution assumption using a Bayesian framework (in this thesis Gaussian distribution). The GP model enables to simulate the posterior predictive distribution for precipitation amount. The study results demonstrated that the classifying rainfall into several types and coupling the precipitation amount estimation and residual fitting was helpful to capture the characteristics of precipitation in downscaling. The second approach proposed for SDM was named Single site Gaussian Process-Statistical Downscaling Model (SGP-SDM), a methodology to quantify the uncertainty in the precipitation occurrence model as well as the precipitation amount estimation model. In SGP-SDM, GP was used for both precipitation occurrence determination and amount estimation. SGP-SDM gives the posterior predictive distribution for both the precipitation occurrence and the precipitation amount. The rainfall was not classified into several types; however, the results were comparable to KNN-BUQSDM without classifying rainfall into different types. The local characteristics of the rainfall was also captured well by SGP-SDM. The extension of the single site SDM to multi-site SDM has been considered as the next step to downscale the climate variable observations at multiple sites simultaneously. The proposed multisite downscaling model was named MGP-SDM. The spatial correlation between the sites and the uncertainty quantification tool was coupled with the model calibration and prediction using Bayesian framework in MGP-SDM. The posterior predictive distribution of the climate variables at multiple sites can be estimated using MGP-SDM simultaneously. KNN disaggregation model was then integrated with MGP-SDM to simulate hourly precipitation at multiple sites in Singapore. The proposed combined multi-site downscaling and disaggregation model was used to project hourly precipitation under future climate conditions. From the literature study, it is learnt that the data-driven hydrological models are widely used to simulate the river flow based on the data rather than using the physical relationship between the variables for river flow prediction. A GP data-driven hydrological model named BUQ-SDDHM (Bayesian Uncertainty Quantification for Stochastic Data Driven Hydrological Model) is the one proposed in this research for the simulation of the river flow using the downscaled and disaggregated climate data. This method couples the uncertainty quantification tool with model calibration and prediction of streamflow. The posterior predictive distribution of the streamflow can be obtained from BUQ-SDDHM. In the last step, MGP-SDM and BUQ-SDDHM was integrated with the KNN disaggregation model to simulate high resolution streamflow under future climate conditions. The proposed method for climate change impact studies on hydrology makes use of the full Bayesian framework to propagate the uncertainty in projecting flood frequencies in future using GCM data.
URI: http://hdl.handle.net/10356/72356
DOI: 10.32657/10356/72356
Fulltext Permission: open
Fulltext Availability: With Fulltext
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