The Initial Abstraction ratio (Ia/S, or λ) in the Curve Number (CN) method was assumed in its original development to have a value of 0.20. Using event rainfall-runoff data from several hundred plots this assumption is investigated, and λ values determined by two different methods. Results indicate a λ value of about 0.05 gives a better fit to the data and would be more appropriate for use in runoff calculations. The effects of this change are shown in terms of calculated runoff depth and hydrograph peaks, CN definition, and in soil moisture accounting. The effect of using λ=0.05 in place of the customary 0.20 is felt mainly in calculations that involve either lower rainfall depths or lower CNs.

We propose a reversible reaction mechanism with a single stationary state in which certain concentrations assume either high or low values dependent on the concentration of a catalyst. The properties of this mechanism are those of a McCulloch-Pitts neuron. We suggest a mechanism of interneuronal connections in which the stationary state of a chemical neuron is determined by the state of other neurons in a homogeneous chemical system and is thus a "hardware" chemical implementation of neural networks. Specific connections are determined for the construction of logic gates: AND, NOR, etc. Neural networks may be constructed in which the flow oftime is continuous and computations are achieved by the attainment of a stationary state of the entire chemical reaction system, or in which the flow of time is discretized by an oscillatory reaction. In another article, we will give a chemical implementation of finite state machines and stack memories, with which in principle the construction of a universal Turing machine is possible.Computations may be supported by many different systems (1, 2), including physical systems like the digital computer, Fredkin logic gates (3), billiard-ball collisions (4), enzymes operating on a polymer chain (1, 5), and more abstract systems like cellular automata (6-8), partial differential equations that simulate cellular automata (9), generalized shifts (4), and neural networks (10-13). Some of these systems can be computationally universal and thus are formally equivalent with a universal Turing machine (10,14). We may inquire about whether computationally universal devices may be constructed solely from chemical reaction mechanisms in a homogeneous medium. All living entities process information to varying degrees, and this can occur only by chemical means. It is for this reason alone that the subject is ofinterest. In this article, we discuss the construction of chemical networks where coupled reaction mechanisms implement "programmed" computations as the concentrations evolve in time. It has already been noted that bistable chemical systems are in many ways analogous to a flip-flop circuit, by coupling bistable reactions it is possible to build universal automata (15,16), and that various chemical mechanisms share a formal relationship with electronic devices (17, 18). We address the construction of computational devices from the viewpoint of neural networks. We propose a chemical reaction network, which is a "hardware" implementation of a neural network, and hence the network can in principle be as powerful as a universal Turing machine (10).Neural networks are a versatile basis for computation (19). Any finite state machine, and hence the finite state part of a universal Turing machine, can be simulated by a neural network (10,20). Neural networks also form the basis of many collective computational systems such as feedforward networks or Hopfield's network (11-13). A chemical neural network may serve as the "hardware" for any of the approaches to computation. We present hardw...

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