Keywords
forecasting, hydrology, neural network
Start Date
1-7-2006 12:00 AM
Abstract
Managers and users of natural resources often face two challenging problems. One is forecasting future natural system conditions for optimal resource allocation. Here, the natural system is comprised of the weather and a dependant hydrologic system that contains a water resource. The second problem is forecasting the behavior of a combined natural and man-made system, which also includes anthropogenic resource consumers. Even though detailed meteorological forecasting over weeks and months is impractical, hydrologic behaviors such as groundwater cycling can transpire over months and years. Alternatively, man-made systems exhibit behaviors that both lag and lead causal forcing, e.g., seasonal weather changes. This paper compares forecasting the behaviors of two systems. One is the upper Klamath Basin in Oregon and California where resource managers allocate water among competing interests, e.g., hydropower, farming, and fisheries. The second system is a water utility in coastal South Carolina, whose demand varies significantly with seasonal irrigation. A similar technical approach was used to model both systems, but the results are instructively dissimilar. The approach took signals of meteorological variables, basin inflows, and consumer demand and decomposed them into signal components that differentiated standard (seasonally periodic) behaviors from non-standard chaotic behaviors. Next, empirical process models were synthesized using artificial neural networks (ANN), a non-linear, multivariate curve fitting technique. The ANNs predicted chaotic output behaviors (basin inflow or consumer demand) from input chaotic meteorological signal components. Finally, prediction sensitivity to shifting the output forward in time relative to the inputs was determined. It was found that Klamath predictions decayed towards a predictability horizon of less than the minimum six-month forecast currently used by the water resource managers. Conversely, prediction accuracy of the part-anthropogenically driven water demand decayed far more slowly, easily straddling the critical six-month spring-to-fall irrigation season over which utility managers sought to forecast.
Non-Linear, Multivariate Forecasting of Hydrologic and Anthropogenic Responses to Meteorological Forcing
Managers and users of natural resources often face two challenging problems. One is forecasting future natural system conditions for optimal resource allocation. Here, the natural system is comprised of the weather and a dependant hydrologic system that contains a water resource. The second problem is forecasting the behavior of a combined natural and man-made system, which also includes anthropogenic resource consumers. Even though detailed meteorological forecasting over weeks and months is impractical, hydrologic behaviors such as groundwater cycling can transpire over months and years. Alternatively, man-made systems exhibit behaviors that both lag and lead causal forcing, e.g., seasonal weather changes. This paper compares forecasting the behaviors of two systems. One is the upper Klamath Basin in Oregon and California where resource managers allocate water among competing interests, e.g., hydropower, farming, and fisheries. The second system is a water utility in coastal South Carolina, whose demand varies significantly with seasonal irrigation. A similar technical approach was used to model both systems, but the results are instructively dissimilar. The approach took signals of meteorological variables, basin inflows, and consumer demand and decomposed them into signal components that differentiated standard (seasonally periodic) behaviors from non-standard chaotic behaviors. Next, empirical process models were synthesized using artificial neural networks (ANN), a non-linear, multivariate curve fitting technique. The ANNs predicted chaotic output behaviors (basin inflow or consumer demand) from input chaotic meteorological signal components. Finally, prediction sensitivity to shifting the output forward in time relative to the inputs was determined. It was found that Klamath predictions decayed towards a predictability horizon of less than the minimum six-month forecast currently used by the water resource managers. Conversely, prediction accuracy of the part-anthropogenically driven water demand decayed far more slowly, easily straddling the critical six-month spring-to-fall irrigation season over which utility managers sought to forecast.
