Infrastructure Engineering - Research Publications
Permanent URI for this collection
Search Results
1 - 2 of 2
-
ItemStochastic modelling of annual rainfall data(Conference Design, 2006-01-01)Rainfall data are generally required in computer simulations of rainfall-runoff processes, crop growth and water supply systems. The length of historical climate data is usually not long enough to describe the complete range of variability that might be experienced during the life of a water resources or agricultural project. Using the statistical characteristics of historical data, it is possible to generate many sequences of data that better represent the climatic variability. In developing the stochastic models, the data are generally assumed stationary in the broad sense and any long-term fluctuations in the data are ignored. Typically, only in monthly, daily and sub-daily models, is the seasonal variation within a year considered explicitly in stochastic models. However, there is a growing interest and concern about the role of interdecadal variability in climate and its influence on rainfall. One approach is to identify any long-term fluctuations in the observed rainfall and model them explicitly. Empirical Mode Decomposition (EMD) was used to identify any low frequency fluctuations in annual rainfall data from 44 sites in Australia. The results did not allow easy identification of low frequency fluctuations in the data. As a means of aiding interpretation of the EMD results, the following ploy was adopted. The AR1 model, the most widely used model for the generation of annual rainfall data, was used to generate stochastic data based on the statistics of the observed sequences and the EMD analysis was performed on the stochastic data sets. The results of the analysis comparing both the historical and generated data showed that, in general, both the data sets have similar low frequency characteristics except for Perth.
-
ItemNo Preview AvailableEquifinality and process-based modelling(American Geophysical Union, 2018-11-26)Equifinality is understood as one of the fundamental difficulties in the study of open complex systems, including catchment hydrology. A review of the hydrologic literature reveals that the term equifinality has been widely used, but in many cases inconsistently and without coherent recognition of the various facets of equifinality, which can lead to ambiguity but also methodological fallacies. Therefore, in this study we first characterise the term equifinality within the context of hydrological modelling by reviewing the genesis of the concept of equifinality and then presenting a theoretical framework. During past decades, equifinality has mainly been studied as a subset of aleatory (arising due to randomness) uncertainty and for the assessment of model parameter uncertainty. Although the connection between parameter uncertainty and equifinality is undeniable, we argue there is more to equifinality than just aleatory parameter uncertainty. That is, the importance of equifinality and epistemic uncertainty (arising due to lack of knowledge) and their implications is overlooked in our current practice of model evaluation. Equifinality and epistemic uncertainty in studying, modelling, and evaluating hydrologic processes are treated as if they can be simply discussed in (or often reduced to) probabilistic terms (as for aleatory uncertainty). The deficiencies of this approach to conceptual rainfall-runoff modelling are demonstrated for selected Australian catchments by examination of parameter and internal flux distributions and interactions within SIMHYD. On this basis, we present a new approach that expands equifinality concept beyond model parameters to inform epistemic uncertainty. The new approach potentially facilitates the identification and development of more physically plausible models and model evaluation schemes particularly within the multiple working hypotheses framework, and is generalisable to other fields of environmental modelling as well.