Pacific Coastal and Marine Science Center
Bedform Sedimentology Site—ripples, dunes, and crossbedding
Forecasting Techniques, Underlying Physics, and Applications
5.3 Complication of Unsteady Forcing5.3A. Nature of the complication The preceding techniques are based on the assumption that forcing is constant (external forces applied to a system are constant through time). Where this condition is met, the observed history of the system results purely from intrinsic processes or selforganization. Although this condition can be approximated in lab experiments, few geologic systems meet this requirement. If forecasting techniques are applied to a system subject to unsteady forcing, the results may apply to the external forces rather than to the system that is being studied. For example, if the stiffness of the spring in our massspring system (c1 in equation 5.2) varied in response to oscillations in temperature, the system would be altered in two important aspects: (1) the observed history of the system would be grossly different (Fig. 5.4), and (2) some of the observed history would reflect the character of the forcing rather than the character the massspring system. Figure 5.4 Unsteady, linear, massspring system described by allowing spring stiffness (c_{1} in equation 5.1) to vary sinusoidally through time. (a) Time series of spring stiffness and location x. (b) Attractor. We can use the same approach to modify the Lorenz equations (2.13) to describe convection resulting from unsteady forcing (timevarying temperature differential between the upper and lower surfaces of the fluid). A timevarying temperature differential is incorporated by allowing r (the ratio of the Rayleigh number to the critical value for the initiation of convection) to vary from one time step to the next. As in the massspring system, this unsteady forcing results in a time series that reflects both system behavior and forcing (Figs. 5.5 and 5.6). Figure 5.5 Lorenz system with steady forcing. (a) Time series of Y (temperature difference between ascending and descending fluid) computed using the standard value of r=28.0 in equation (2.13). (b) Attractor of system in (a). Figure 5.6 Lorenz system with unsteady forcing. (a) Time series of Y incorporating a timevarying r. The mean value of r is 28.6, approximately the same as in Figure 5.5a. The time series of Y is more complicated because of the unsteady forcing. (b) Attractor of system in (a). Complications due to unsteady forcing are ubiquitous in geologic systems. For example, a time series of sediment transport at a point on a bed of ripples in a tidal flow would have two components: the cyclicity of astronomically induced tidal flow as well as transport variations caused by the passage of the ripples on the bed. The ripples would display a tidally driven cyclic behavior in addition to any selforganized interactions between ripples. If the processes operating in this system were completely unknown (and the periodic forcing was therefore not recognized) an investigator might be misled into thinking that ripples have a intrinsic tidal cyclicity, whereas the tidal cyclicity merely reflects the forcing. The importance of external forcing in the system described above is so obvious that it may seem absurd to worry about overlooking it. But the effects of unsteady forcing may be much more obscure in geologic data. For example, the structure of a stratigraphic sequence might be influenced by: (1) processes within the depositional environment (analogous to behavior of the Lorenz system under steady forcing), (2) changes in conditions in adjacent regions (analogous to changing the size or shape of the fluid body), or (3) global changes such as climate (analogous to a universal change in the temperature difference between bottom and top of the fluid). In modern systems, three approaches can be used to work around the problem of unsteady forcing: (1) regulate forcing experimentally, (2) measure forcing as well as system response, and only use data collected at times when forcing is within a narrow range, and (3) measure forcing as well as the system response and use the measured forcing as input in the modeling (inputoutput modeling of Hunter and Theiler, 1992). In the following examples, the complication of unsteady forcing was resolved by keeping forcing constant (Lorenz example), by choosing a field site where forcing was spatially uniform (windripple example), and by incorporating the unsteady forcing in inputoutput models (climate and surfzone sediment transport). In geologic time series, however, the problem of unsteady forcing may be intractable, because only response (not forcing) can be inferred from stratigraphic deposits. 5.3B. Inputoutput modeling Inputoutput modeling (Hunter and Theiler, 1992) is particularly useful in the earth sciences, where it is usually impossible to regulate the external forces exerted on a system. Instead, inputoutput modeling utilizes two simultaneous time series: forcing (input) and system response (output). The underlying principle is to use a catalog to learn how the system responds to different forcing events. The technique is computationally similar to the singleseries forecasting described in equation 5.5, but it relates response x at time t, to the forcing y measured during a sequence of m steps through time (5.7) The response of a system may lag behind the forcing, and forecasting can be used to quantify such a lag. Equation 5.7 can be modified to incorporate such a lag by replacing y_{(ti)} with y_{(t(i+n))}, where n equals the lag time from the end of the sequence of input forcing values to the time of the modelresponse output. Equation 5.7 is applied in the same manner as equation 5.5: once to solve for the local linear relation between forcing and response in a learning set, and a second time to predict the response for the testing set. The lag of a physical system is quantified by determining the value of lag n that yields the most accurate forecasts. An example of this approach is given in the discussion of surfzone sediment transport (Chapter 5.4E). 5.3C. Forecasting in practice In applying the forecasting techniques, a researcher might go through the following sequence of operations to characterize the system that produced an observed time series:
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