Xiong Zhou

A PCM-based stochastic hydrological model for uncertainty quantification in watershed systems

In this study, an uncertainty quantification framework is proposed for hydrologic models based on probabilistic collocation method (PCM). The PCM method first uses polynomial chaos expansion (PCE) to approximate the hydrological outputs in terms of a set of standard Gaussian random variables, and then estimates the unknown coefficients in the PCE through collocation method. The conceptual hydrologic model, Hymod, is used to demonstrate the applicability of PCM in quantifying uncertainties of the hydrologic predictions. Two parameters in Hymod are considered as uniformly distributed in certain intervals. Two-dimensional 2-order and two-dimensional 3-order PCEs are applied to quantify the uncertainty of Hymod’s predictions. The results indicate that, both 2- and 3-order PCEs can well reflect the uncertainty of the streamflow predictions. The means and variances of 2- and 3-order PCEs are consistent with those obtained by Monte Carlo (MC) simulation method. However, for detailed distributions at selected periods, the histograms obtained by 3-order PCE are more accurate than those generated by 2-order PCE, when compared with the histograms obtained by MC simulation method.

Two-Stage Chance-Constrained Fractional Programming for Sustainable Water Quality Management under Uncertainty

AbstractIn this study, a two-stage chance-constrained fractional programming (TCFP) method is developed for dealing with water quality management problems associated with stochastic inputs. Two-stage chance-constrained fractional programming is a hybrid of stochastic linear fractional programming (SLFP) and two-stage stochastic programming (TSP) methods. It can not only balance objectives of two aspects through converting a bi-objective problem into a ratio one but can also analyze various policy scenarios when the promised production targets are violated. For demonstrating its advantages, the proposed TCFP method is applied to a case study of water quality management where managers have to consider conflicting objectives between economic development and environmental conservation, as well as stochastic features expressed as probability distributions. The obtained solutions under different significance levels can help managers to identify desired policies under various environmental, economic, and constra...

A Factorial Dual-Interval Programming Approach for Planning Municipal Waste Management Systems

AbstractIn this study, a factorial dual-interval programming (FDIP) approach is proposed through integrating factorial analysis and dual-interval linear programming into a general framework. The developed FDIP approach can handle uncertainties (i.e.,xa0single-interval and dual-interval) that exist in the left-hand side and right-hand side of the system objective function and in the associated constraints. Moreover, it has the advantages in identifying significant parameters along with their joint effects on the system outputs. A case study of municipal solid waste (MSW) management is adopted to demonstrate the applicability of the proposed approach. Reasonable results have been generated for the waste flow allocation schemes with minimized system costs; impact factors and their interactive effects have been identified and analyzed for the lower bound and upper bound of the system outputs under various scenarios. It is indicated that the effect of operational costs of the waste-to-energy (WTE) facility durin...

Dynamically-downscaled temperature and precipitation changes over Saskatchewan using the PRECIS model

In this study, dynamically-downscaled temperature and precipitation changes over Saskatchewan are developed through the Providing Regional Climates for Impacts Studies (PRECIS) model. It can resolve detailed features within GCM grids such as topography, clouds, and land use in Saskatchewan. The PRECIS model is employed to carry out ensemble simulations for projections of temperature and precipitation changes over Saskatchewan. Temperature and precipitation variables at 14 weather stations for the baseline period are first extracted from each model run. Ranges of simulated temperature and precipitation variables are then obtained through combination of maximum and minimum values calculated from the five ensemble runs. The performance of PRECIS ensemble simulations can be evaluated through checking if observations of current temperature at each weather station are within the simulated range. Future climate projections are analyzed over three time slices (i.e., the 2030s, 2050s, and 2080s) to help understand the plausible changes in temperature and precipitation over Saskatchewan in response to global warming. The evaluation results show that the PRECIS ensemble simulations perform very well in terms of capturing the spatial patterns of temperature and precipitation variables. The results of future climate projections over three time slices indicate that there will be an obvious warming trend from the 2030s, to the 2050s, and the 2080s over Saskatchewan. The projected changes of mean temperature over the whole Saskatchewan area is [0, 2]u2009°C in the 2030s at 10th percentile, [2, 5.5]u2009°C in the 2050s at 50th percentile, and [3, 10]u2009°C in the 2090s at 90th percentile. There are no significant changes in the spatial patterns of the projected total precipitation from the 2030s to the end of this century. The minimum change of the projected total precipitation over the whole Province of Saskatchewan is most likely to be −1.3% in thexa02030s, and −0.2% in thexa02050s, while the minimum value would be −2.1% to the end of this century at 50th percentile.

Two-Stage Fractional Programming Method for Managing Multiobjective Waste Management Systems

AbstractIn this study, a two-stage fractional programming (TSFP) method is developed for supporting municipal solid waste (MSW) management under uncertainty. The model can not only balance two conflicting objectives through converting a multiobjective problem into a ratio one, but also can analyze multistage decision effects when promised policy targets are violated. Moreover, the TSFP model can facilitate dynamic analysis of capacity expansions for waste management facilities. The developed method is applied to a case study of long-term MSW management planning. The solutions obtained from TSFP can provide desired waste-allocation schemes and capacity-expansion plans under different policy scenarios. The results allow in-depth analyses in terms of conflicting objectives, policy scenarios, and capacity expansions.

High‐resolution projections of 21st century climate over the Athabasca River Basin through an integrated evaluation‐classification‐downscaling‐based climate projection framework

An evaluation-classification-downscaling-based climate projection (ECDoCP) framework is developed to fill a methodological gap of general circulation models (GCMs)-driven statistical-downscaling-based climate projections. ECDoCP includes four interconnected modules: GCM evaluation, climate classification, statistical downscaling, and climate projection. Monthly averages of daily minimum (Tmin) and maximum (Tmax) temperature and daily cumulative precipitation (Prec) over the Athabasca River Basin (ARB) at a 10u2009km resolution in the 21st century under four Representative Concentration Pathways (RCPs) are projected through ECDoCP. At the octodecadal scale, temperature and precipitation would increase; after bias correction, temperature would increase with a decreased increment, while precipitation would increase only under RCP 8.5. Interannual variability of climate anomalies would increase from RCPs 4.5, 2.6, 6.0 to 8.5 for temperature and from RCPs 2.6, 4.5, 6.0 to 8.5 for precipitation. Bidecadal averaged climate anomalies would decrease from December-January-February (DJF), March-April-May (MAM), September-October-November (SON) to June-July-August (JJA) for Tmin, from DJF, SON, MAM to JJA for Tmax, and from JJA, MAM, SON to DJF for Prec. Climate projection uncertainties would decrease in May to September for temperature and in November to April for precipitation. Spatial climatic variability would not obviously change with RCPs; climatic anomalies are highly correlated with climate-variable magnitudes. Climate anomalies would decrease from upstream to downstream for temperature, and precipitation would follow an opposite pattern. The north end and the other zones would have colder and warmer days, respectively; precipitation would decrease in the upstream and increase in the remaining region. Climate changes might lead to issues, e.g., accelerated glacier/snow melting, deserving attentions of researchers and the public.

A coupled dynamical-copula downscaling approach for temperature projections over the Canadian Prairies

In this study, a coupled dynamical-copula downscaling approach was developed through integrating the Providing Regional Climates for Impacts Studies (PRECIS) modeling system and the copula method. This approach helps to reflect detailed features at local scales based on dynamical downscaling, while also effectively simulating the interactions between large-scale atmospheric variables (predictors) and local surface variables (predictands). The performance of the proposed approach in reproducing historical climatology of the Canadian Prairies was evaluated through comparison with observations. Future climate projections generated by the developed approach were analyzed over three time slices (i.e., the 2030s, 2050s, and 2080s) to help understand the plausible changes in temperature over the Canadian Prairies in response to global warming. The results showed that there would be an apparent increasing pattern over the Canadian Prairies. The projections of future temperature over three time slices can provide decision makers with valuable information for climate change impacts assessment over the Canadian Prairies.

Future projections of temperature changes in Ottawa, Canada through stepwise clustered downscaling of multiple GCMs under RCPs

As the capital city of Canada, Ottawa has been experiencing significant impacts of global climate change. How to adapt to future climate change is one of the biggest concerns in the city’s built and natural systems. It thusxa0requires a comprehensive understanding of possible changes in the local climate of Ottawa, which can hardly be reflected in the coarse outputs of Global Climate Models (GCMs). Therefore, a stepwise clustered downscaling (SCD) model is employed in this study to help investigate the plausible changes in daily maximum, minimum, and mean temperatures in Ottawa. Outputs from multiple GCMs under the Representative Concentration Pathways (RCPs) are used as inputs to drive the SCD model in order to develop downscaled climate projections. The performance of SCD model is evaluated by comparing the model simulations to the observations (R2xa0>xa00.87) over the historical periods. Future temperature projections and their likely temporal trends throughout this century are analyzed in detail to explorexa0the regional variations of global warming in Ottawa, thus to provide scientific basis for developing appropriate adaptation strategies at different management levels. The results suggest that the City of Ottawa is likely to expect significant increasing trends in temperatures (i.e.,xa00.18–0.38xa0°C per decade in maximum temperature, 0.16–0.31xa0°C per decade in minimum temperature, and 0.17–0.34xa0°C per decade in mean temperature under RCP4.5; 0.46–0.54xa0°C per decade in maximum temperature, 0.37–0.45xa0°C per decade in minimum temperature, and 0.42–0.50xa0°C per decade in mean temperature under RCP8.5) throughout this century.