Xiuquan Wang

High-Resolution Probabilistic Projections of Temperature Changes over Ontario, Canada

Planning of mitigation and adaptation strategies to a changing climate can benefit from a good understanding of climate change impacts on human life and local society, which leads to an increasing requirement for reliable projections of future climate change at regional scales. This paper presents an ensemble of high-resolution regional climate simulations for the province of Ontario, Canada, developed with the Providing Regional Climates for Impacts Studies (PRECIS) modeling system. A Bayesian statistical model is proposed through an advance to the method proposed by Tebaldi et al. for generating probabilistic projections of temperature changes at gridpoint scale by treating the unknown quantities of interest as random variables to quantify their uncertainties in a statistical way. Observations for present climate and simulations from the ensemble are fed into the statistical model to derive posterior distributions of all the uncertain quantities through a Markov chain Monte Carlo (MCMC) sampling algorithm. Detailed analyses at 12 selected weather stations are conducted to investigate the practical significance of the proposed statistical model. Following that, maps of projected temperature changes at different probability levels are presented to help understand the spatial patterns across the entire province. The analysis shows that there is likely to be a significant warming trend throughout the twenty-first century. It also suggests that people in Ontario are very likely to suffer a change greater than 28C to mean temperature in the forthcoming decades and very unlikely to suffer a change greater than 108C to the end of this century.

Ensemble Projections of Regional Climatic Changes over Ontario, Canada

AbstractIn this study, high-resolution climate projections over Ontario, Canada, are developed through an ensemble modeling approach to provide reliable and ready-to-use climate scenarios for assessing plausible effects of future climatic changes at local scales. The Providing Regional Climates for Impacts Studies (PRECIS) regional modeling system is adopted to conduct ensemble simulations in a continuous run from 1950 to 2099, driven by the boundary conditions from a HadCM3-based perturbed physics ensemble. Simulations of temperature and precipitation for the baseline period are first compared to the observed values to validate the performance of the ensemble in capturing the current climatology over Ontario. Future projections for the 2030s, 2050s, and 2080s are then analyzed to help understand plausible changes in its local climate in response to global warming. The analysis indicates that there is likely to be an obvious warming trend with time over the entire province. The increase in average tempera...

Projected increases in intensity and frequency of rainfall extremes through a regional climate modeling approach

Global warming is changing the hydrological cycle in multiple ways such as increased cloudiness, latent heat fluxes, and intense precipitation events. How extreme rainfall events will be influenced by the changing climate is becoming one of the most important problems for hydrological risk analysis and engineering design. In this study, a regional climate modeling approach based on the Providing REgional Climates for Impacts Studies modeling system is proposed for investigating the potential impacts of climate change induced by increased greenhouse gases on the intensity and frequency of extreme rainfall events in the context of Ontario, Canada. An ensemble of high-resolution climate projections is first developed under both current and future forcing conditions. Validation of the ensemble simulations is then conducted through comparing the simulated rainfall annual extremes for 1960–1990 to the observed ones. Following that, the rainfall projections for future periods are used to develop projected intensity-duration-frequency curves and their plausible changes in 2030s, 2050s, and 2080s for the City of Toronto. The results suggest that intensities of rainfall extreme events versus various durations with different return periods are all likely to increase over time: [5, 17]% in 2030s, [11, 22]% in 2050s, and [25, 50]% in 2080s. Such a consistent increase would lead to an overall uplift in the exceedance values of rainfall intensity of extreme events, implying that intense rainfall events are likely to occur more frequently in the future. In addition, more significant changes in the rainfall intensity are projected for extreme events with longer return periods at all given durations.

Development of a Stepwise-Clustered Hydrological Inference Model

AbstractFlow prediction is one of the most important issues in modern hydrology. In this study, a statistical tool, stepwise-clustered hydrological inference (SCHI) model, was developed for daily streamflow forecasting. The SCHI model uses cluster trees to represent the nonlinear and complex relationships between streamflow and multiple factors related to climate and watershed conditions. It allows a great deal of flexibility in watershed configuration. The proposed model was applied to the daily streamflow forecasting in the Xiangxi River watershed, China. The correlation coefficient for calibration (1991–1995) was 0.881, and that for validation (1996–1998) was 0.771. Nash–Sutcliffe efficiencies for calibration and validation were 0.768 and 0.577, respectively. The results were compared to those of a conventional process-based model, and it was found that the SCHI model had a superior performance. The results indicate that the proposed model could provide not only reliable and efficient daily flow predic...

Violation analysis on two-step method for interval linear programming

In many real world problems, system parameters or model coefficients may be bounded between lower and upper bounds due to a variety of uncertainties. Over the past decades, intensive research efforts have focused on interval linear programming (ILP) to tackle such uncertainties. As one of the most popular methods for solving ILP problems, Two-Step Method (TSM, proposed by Huang et al. in 1995) allows uncertain information to be directly communicated into the optimization process and resulting solutions such that decision alternatives could be generated through the interpretation of interval solutions. However, part of optimum solution points obtained through TSM may go beyond the decision space in some cases. This phenomenon, referred to as solution violation, may mislead decision makers to unreasonable policies, plans, or strategies which play important roles in the social and economic development. Therefore, this study first investigates and identifies the essential cause for the solution violation of TSM. Following that, an improved solution method (namely, ITSM) is proposed to avoid resulting violation by introducing extra constraints in the solving process. A numeric example is then presented to demonstrate the effectiveness of ITSM in handling solution violation.

Impacts of future climate change on river discharge based on hydrological inference: A case study of the Grand River Watershed in Ontario, Canada.

Over the recent years, climate change impacts have been increasingly studied at the watershed scale. However, the impact assessment is strongly dependent upon the performance of the climatic and hydrological models. This study developed a two-step method to assess climate change impacts on water resources based on the Providing Regional Climates for Impacts Studies (PRECIS) modeling system and a Hydrological Inference Model (HIM). PRECIS runs provided future temperature and precipitation projections for the watershed under the Intergovernmental Panel on Climate Change SRES A2 and B2 emission scenarios. The HIM based on stepwise cluster analysis is developed to imitate the complex nonlinear relationships between climate input variables and targeted hydrological variables. Its robust mathematical structure and flexibility in predictor selection makes it a desirable tool for fully utilizing various climate modeling outputs. Although PRECIS and HIM cannot fully cover the uncertainties in hydro-climate modeling, they could provide efficient decision support for investigating the impacts of climate change on water resources. The proposed method is applied to the Grand River Watershed in Ontario, Canada. The model performance is demonstrated with comparison to observation data from the watershed during the period 1972-2006. Future river discharge intervals that accommodate uncertainties in hydro-climatic modeling are presented and future river discharge variations are analyzed. The results indicate that even though the total annual precipitation would not change significantly in the future, the inter-annual distribution is very likely to be altered. The water availability is expected to increase in Winter while it is very likely to decrease in Summer over the Grand River Watershed, and adaptation strategies would be necessary.

Dynamically-downscaled probabilistic projections of precipitation changes: A Canadian case study

In this study, plausible changes in annual and seasonal precipitation over Ontario, Canada in response to global warming are investigated through a regional climate modeling approach. A high-resolution regional climate model ensemble based upon the Providing REgional Climates for Impacts Studies (PRECIS) model is developed to help explore the possible outcomes of future climate. A Bayesian hierarchical model is then employed to quantify the uncertainties involved in the modeling results and obtain probabilistic projections of precipitation changes at grid point scales. The results show that the projected changes in annual precipitation exhibit a certain degree of spatial variability with the median changes mostly bounded by 0% and 20%, implying that the annual precipitation over Ontario is more likely to increase in the context of global warming. Specifically, the mean changes in annual precipitation for 2030s and 2050s would be ~7.5%, while the annual precipitation for 2080s is likely to increase by an average of ~12.5%. By contrast, the spatial variability of seasonal precipitation changes is more significant, especially for the changes in spring precipitation which may vary from -40% in south and 50% in north. It is reported that there would be a continuous increasing trend in winter, spring, and autumn precipitation from 2030s to 2080s by 5-30%, but summer precipitation is likely to decrease by 5% or even higher to the end of this century. Furthermore, our results suggest that the larger the biases in historical simulations, the more uncertain the future projections will be.

Investigating future precipitation changes over China through a high‐resolution regional climate model ensemble

Due to climate change, rising temperature around the world will have a great potential to influence the global hydrologic cycle, thus leading to substantial changes in the spatial and temporal patterns of precipitation. In this study, the effects of global warming on the regional hydrologic cycle, particularly on the spatiotemporal patterns of precipitation, over China are investigated through a high-resolution regional climate ensemble. In detail, the PRECIS regional climate modeling system is employed to simulate the regional climate over China from 1950 to 2099 with a fine resolution of 25 km, driven by the boundary conditions from a four-member HadCM3-based perturbed-physics ensemble (i.e., HadCM3Q0, Q1, Q7, and Q13) and the ECHAM5 model. Historical simulations of the PRECIS ensemble are first compared to the observations to validate its performance in capturing both the spatial and temporal patterns of precipitation. The comparisons show that the PRECIS ensemble is likely to overestimate precipitation in the south and exhibits slight dry biases in the northwest and southeast coasts of China. The projections from the PRECIS ensemble for future periods (i.e., 2020s, 2050s, and 2080s) are then analyzed to help understand how the regional characteristics of precipitation will be affected in the context of global warming. It is shown that the annual mean precipitation over China is likely to increase throughout the 21st century (i.e., by 0.078 mm/d in 2020s, 0.218 mm/d in 2050s, and 0.360 mm/d in 2080s). This may suggest that the rising temperature due to climate change will intensify the regional hydrologic cycles in China. However, apparent spatial and temporal variations are also reported in the projected precipitations from the PRECIS ensemble. For example, bigger changes in precipitation are usually observed in summer; projected precipitation changes in the southeast are apparently higher than other regions. In addition, the results show that the fluctuation range of the ensemble simulations will increase with time periods from 2020s to 2080s, indicating that the longer the projecting periods, the more uncertain the projections will be.

An interval mixed-integer non-linear programming model to support regional electric power systems planning with CO2 capture and storage under uncertainty

BackgroundElectric generating capacity expansion has been always an essential way to handle the electricity shortage, meanwhile, greenhouse-gas (GHG) emission, especially CO2, from electric power systems becomes crucial considerations in recent years for the related planners. Therefore, effective approach to dealing with the tradeoff between capacity expansion and carbon emission reduction is much desired.ResultsIn this study, an interval mixed-integer non-linear programming (IMINLP) model was developed to assist regional electric power systems planning under uncertainty. CO2 capture and storage (CCS) technologies had been introduced to the IMINLP model to help reduce carbon emission. The developed IMINLP model could be disassembled into a number of ILP models, then two-step method (TSM) was used to obtain the optimal solutions. A case study was provided for demonstrating applicability of the developed method.ConclusionsThe results indicated that the developed model was capable of providing alternative decisions based on scenario analysis for electricity planning with consideration of CCS technologies. The IMINLP model could provide an effective linkage between carbon sequestration and electric generating capacity expansion with the aim of minimizing system costs.

A hybrid factorial stepwise-cluster analysis method for streamflow simulation – a case study in northwestern China

ABSTRACT In this study, a hybrid factorial stepwise-cluster analysis (HFSA) method is developed for modelling hydrological processes. The HFSA method employs a cluster tree to represent the complex nonlinear relationship between inputs (predictors) and outputs (predictands) in hydrological processes. A real case of streamflow simulation for the Kaidu River basin is applied to demonstrate the efficiency of the HFSA method. After training a total of 24 108 calibration samples, the cluster tree for daily streamflow is generated based on a stepwise-cluster analysis (SCA) approach and is then used to reproduce the daily streamflows for calibration (1995–2005) and validation (2008–2010) periods. The Nash-Sutcliffe coefficients for calibration and validation are 0.68 and 0.65, respectively, and the deviations of volume are 1.68% and 4.11%, respectively. Results show that: (i) the HFSA method can formulate a SCA-based hydrological modelling system for streamflow simulation with a satisfactory fitting; (ii) the variability and peak value of streamflow in the Kaidu River basin can be effectively captured by the SCA-based hydrological modelling system; (iii) results from 26 factorial experiments indicate that not only are minimum temperature and precipitation key drivers of system performance, but also the interaction between precipitation and minimum temperature significantly impacts on the streamflow. The findings are useful in indicating that the streamflow of the study basin is a mixture of snowmelt and rainfall water. EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR G. Thirel