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Building energy policy, such as the Energy Performance of Buildings Directive (EPBD), has a direct effect on use of building models and related parameters. Modelling annual energy consumption of a building is a different task to characterizing the demand of that building at a transient level; to do so at scale requires additional complexity. With the ubiquity of Energy Performance Certificates (EPC) across Europe, there is a tendency to use these to communicate building energy demand to policy. However, there is growing evidence of EPCs being applied to areas which they were not designed to serve. By comparing alternative techniques with current methodologies, this study proposes future directions for standardized energy assessment of dwellings, proposing a framework for critiquing such techniques. New methods are formulated that make use of simulation and statistical techniques developed by the authors, and allow for urban-scale modelling that is consistent with traditional energy assessment. [ABSTRACT FROM AUTHOR]

Hydrological models can be used to assess the impact of hydrologic alteration on the river ecosystem. However, there are considerable limitations and uncertainties associated with the replication of the required, ecologically relevant hydrological indicators. Vogel and Sankarasubramanian's covariance approach to model parameterisation represents a shift away from the traditional calibration-validation goodness-of-fit paradigm. Using the covariance structures of the observed input and simulated output time-series, the region of parameter space which best captures (replicates) the characteristics of a hydrological indicator may be identified. Through a case study, a modified covariance approach is applied with a view to replicating a suite of seven ecologically relevant hydrological indicators. Model performance and consistency are assessed relative to four comparative studies. The ability of the approach to address the limitations associated with traditional calibration-validation is further considered. Benefits of the approach include an overall reduction in model uncertainty whilst also reducing overall time-demands. Difficulties in the replication of complex indicators, such as rate of change, are in line with prior work. Nonetheless, the study illustrates that consistency in the replication of hydrological indicators is achievable; additionally, the replication of magnitude indices is markedly improved upon. [ABSTRACT FROM AUTHOR]

Estimation of daily streamflow time series is of paramount importance for the design and implementation of river engineering and management projects (e.g., restoration, sediment-transport modelling, hydropower). Traditionally, indirect approaches combining stochastic simulation of rainfall with hydrological rainfall-runoff models are used. However, these are limited by uncertainties in model calibration and computational expense. Thus, this paper demonstrates an alternative, direct approach, for stochastic modelling of daily streamflow data, specifically seeking to address wellknown deficiencies in model capability to capture extreme flow events in the simulated time series. Combinations of a hidden Markov model (HMM) with the generalised extreme value (HMM-GEV) and generalised Pareto (HMM-GP) distributions were tested for four hydrologically contrasting catchments in the UK (Rivers Dee, Falloch, Caldew and Lud), with results compared to recorded flow data and estimations obtained from a simpler autoregressive-moving-average (ARMA) model. Results show that the HMM-GP method is superior in performance over alternative approaches (relative mean absolute differences (RMAD) of <2% across all catchments), appropriately captures extreme events and is generically applicable across a range of hydrological regimes. In contrast, the ARMA model was unable to capture the flow regime successfully (average RMAD of 14% across all catchments). [ABSTRACT FROM AUTHOR]