Improved Model Structure Research Articles

Calibration of structure in a distributed forecasting model for a semiarid flash flood: Dynamic surface storage and channel roughness

Flash floods pose a significant danger for life and property. Unfortunately, in arid and semiarid environment the runoff generation shows a complex non-linear behavior with a strong spatial and temporal non-uniformity. As a result, the predictions made by physically-based simulations in semiarid areas are subject to great uncertainty, and a failure in the predictive behavior of existing models is common. Thus better descriptions of physical processes at the watershed scale need to be incorporated into the hydrological model structures. For example, terrain relief has been systematically considered static in flood modelling at the watershed scale. Here, we show that the integrated effect of small distributed relief variations originated through concurrent hydrological processes within a storm event was significant on the watershed scale hydrograph. We model these observations by introducing dynamic formulations of two relief-related parameters at diverse scales: maximum depression storage, and roughness coefficient in channels. In the final (a posteriori) model structure these parameters are allowed to be both time-constant or time-varying. The case under study is a convective storm in a semiarid Mediterranean watershed with ephemeral channels and high agricultural pressures (the Rambla del Albujon watershed; 556 km 2 ), which showed a complex multi-peak response. First, to obtain quasi-sensible simulations in the (a priori) model with time-constant relief-related parameters, a spatially distributed parameterization was strictly required. Second, a generalized likelihood uncertainty estimation (GLUE) inference applied to the improved model structure, and conditioned to observed nested hydrographs, showed that accounting for dynamic relief-related parameters led to improved simulations. The discussion is finally broadened by considering the use of the calibrated model both to analyze the sensitivity of the watershed to storm motion and to attempt the flood forecasting of a stratiform event with highly different behavior.

Calibration of the forest growth model 3-PG to eucalypt plantations growing in low rainfall regions of Australia

In Australia, tools are required to assess likely commercial and environmental benefits of growing saw logs in regions of low-medium rainfall. The widely used 3-PG model was considered appropriate given that it has a relatively low demand for data and can be applied spatially. Two species of high potential for saw log production in such regions are Eucalyptus cladocalyx (sugar gum) and Corymbia maculata (spotted gum). For both of these species, we harvested 12 trees over a range of sizes (from 4 to 52 cm diameter at breast height) to derive 3-PG parameters, including stem density, specific leaf area, relationships between stem diameter and mass, and the branches and bark fraction. We also collated stem diameter data together with data on stocking and survival from 55 stands of E. cladocalyx, and 37 stands of C. maculata. By simulating growth at each of these sites using 3-PG, we calibrated parameters determining sensitivity to frost, optimum temperature for growth, and rates of litterfall to attain an efficiency of prediction of stem diameter of 80% for both species. It was also ensured that prediction of biomass of tree components were in the order of that calculated based on allometric relationships, although efficiencies of these predictions were particularly poor for foliage. We found 3-PG gave a 9–34% greater efficiency of prediction of stem diameter than when using stand age alone as an explanatory variable, indicating that even when only the most basic of site information is available (i.e. rainfall, temperature, solar radiation, number of frost days as well as some information regarding stocking and management), 3-PG can potentially account for some variations in growth across a wide range of sites. Accuracy of 3-PG predictions could be further improved by better quantifying site data (i.e. fertility rating, soil texture and available water) and improving model structure to better predict soil water availability, survival, and partitioning of biomass between tree components.

Incorporating forest growth response to thinning within biome-BGC

Large-scale ecosystem models are designed to reproduce and quantify ecosystem processes. Based on plant functions or species-specific parameter sets, the energy, carbon, nitrogen and water cycles of different ecosystems are assessed. These models have been proven to be important tools to investigate ecosystem fluxes as they are derived by plant, site and environmental factors. The general model approach assumes uniform and fully stocked forests but since most European forests are managed (e.g., thinned) it is essential to understand the limits and precision of such models when applied to managed forest ecosystems. The purpose of this study is to investigate and incorporate common forest management practices within the large-scale ecosystem model Biome-BGC. Using “Monte-Carlo” simulations we analyze the theoretical response to current model settings assuming steadily decreasing changes in stand density. Results of the MC simulations as well as the comparison with measured data suggest that the resulting predictions will be biased. Using long-term experimental plots of Norway spruce ( Picea abies L. Karst.) and common beech ( Fagus sylvatica L.) forests with a well-documented thinning history, we propose a thinning subroutine, which addresses the changes in allocation patterns after stand density changes. Validation tests of improved model structure across different long-term experimental sites in Central Europe revealed unbiased and consistent simulation results.

Improving model structure and reducing parameter uncertainty in conceptual water balance models through the use of auxiliary data

The use of uncertainty analysis is gaining considerable attention in catchment hydrological modeling. In particular, the choice of appropriate model structure, identifiability of parameter values, and the reduction of model predictive uncertainty are deemed as essential elements of hydrological modeling. The chosen model structure must be parsimonious, and the parameters used must either be derivable from field‐measured data or inferred unambiguously from analysis of catchment response data. In this paper, a long‐term water balance model for the Susannah Brook catchment in Western Australia has been pursued using the “downward approach,” which is a systematic approach to determine the model with the minimum level of complexity, with parameter values that, in theory, are derivable from existing physiographic data relating to the catchment. Through analysis of rainfall‐runoff response at three different timescales and exploring the climate, soil, and vegetation controls on the water balance response at these timescales, an initial model structure was formulated, and a priori model parameter values were estimated. Further investigation with the use of auxiliary data such as deuterium concentration in the streamflow exposed inadequacies in the chosen model structure. Two more model structures were then proposed and investigated through formulating alternative hypotheses regarding the underlying causes of observed variability, including those suggested by observed deuterium composition in the streamflows and observed groundwater level dynamics. Along the way, the resulting models were systematically evaluated in three dimensions: ability to reproduce observations, predictive uncertainty, and physical realism. The final model, which included an efficient but detailed representation of unsaturated zone time delay, was found to be superior on all three counts, i.e., improved performance, reduced predictive uncertainty, and improved physical realism. These improvements can be directly attributed to the use of the auxiliary data (deuterium composition and groundwater level dynamics), which identified weaknesses in previous model structures, and the multiple wetting front model of water movement in the unsaturated zone, which captured more effectively the time delay in the unsaturated zone.

System dynamics and auto-calibration framework for NSM model: Murrumbidgee River

Water sharing management is the major problem for water resources and irrigation management decision makers. However, irrigation systems are very complex and interconnected, posing significant difficulties in managing irrigation economically and environmentally. Therefore, it is imperative that innovative modelling approaches are employed to deal with the feedback loops inherent in these systems. Through the application of a system dynamics approach, a Network Simulation Model (NSM) was developed. The purpose of the NSM is to measure and identify the change in economic and environmental outputs of various allocations and demand scenarios. The aim of this study is to examine the use of two methods of auto calibration (single objective and multiobjective) over a variety of climatic and hydrological conditions. These methods have been compared and applied to three periods of calibration and validation using seven performance criteria. Results indicate that multiobjective method yields better identifiable parameters and an improved model structure

Recent advances in the data-based modelling and analysis of hydrological systems

Hydrological systems present several difficulties for modellers. A data-based approach utilizing objective statistical analysis of output and/or input-output time series behaviour has much to offer, both for understanding of the response dynamics of systems and future hypothesis formulation for improved model structure, as well as good predictive capacity. In this paper, the essential elements and tools of a data-based approach, using an Unobserved Components (UC) model, are presented and illustrated with two examples.

Frequency-sampling filters: An improved model structure for step-response identification

This paper presents a new methodology for step-response identification using the frequency-sampling filter (FSF) model. The advantages of the FSF model structure over the popular finite-impulse-response (FIR) model are (i) the number of parameters to be estimated is independent of the choice of sampling interval, and is generally far fewer than the number required by the FIR model to obtain an accurate estimate of the step response, and (ii) the general conditioning of the correlation matrix is better with the FSF model because of the reduction in the number of parameters that need to be estimated and to the narrow-bandpass nature of the frequency sampling filters.

Recent advances in the data-based modelling and analysis of hydrological systems

Hydrological systems present several difficulties for modellers. A data-based approach utilizing objective statistical analysis of output and/or input-output time series behaviour has much to offer, both for understanding of the response dynamics of systems and future hypothesis formulation for improved model structure, as well as good predictive capacity. In this paper, the essential elements and tools of a data-based approach, using an Unobserved Components (UC) model, are presented and illustrated with two examples.

Frequency Sampling Filters: An Improved Model Structure for Step Response Identification

This paper presents a new methodology for step response identification using the frequency sampling filter (FSF) model. The advantages of the FSF model structure over the popular finite impulse response (FIR) model are: (1) the number of parameters to be estimated is independent of the choice of sampling interval and is generally far fewer than the number required by the FIR model to obtain an accurate estimate of the step response, and (2) the general conditioning of the correlation matrix is better with the FSF model due to the reduction in the number of parameters that need to be estimated and to the narrow bandpass nature of the frequency sampling filters.

Representation of Procedural Knowledge by a Model of Human Associative Processor (HASP)

AbstractA human associative processor (HASP) proposed by the author was developed and applied to procedural knowledge. The first step is a simple positive‐feedback model in which the output of a conventional HASP is fed back to its own key input. The chain of a procedure is memorized by taking the positive‐feedback input as the key input of the association, and by associating with the next procedure. A series of computer‐simulation studies showed that the model could express control structures such as wait, jump, do‐while, if‐then, and case control procedures. The second step shows that the simple positive‐feedback model sometimes cannot read the memorized procedure correctly; then an improved model was proposed. In the improved model structure, a converted orthogonal pattern from the positive feedback point and the external input key are used again as the key input. The improved model can read all the memorized procedures correctly; this was confirmed by computer simulation. It was also shown that the pattern completion function associated with the whole procedure from its part, can be represented by noise environmentalization.