Lake Agassiz was ponded on the northward-sloping surface of the Hudson Bay and Arctic Ocean basins, as the Laurentide Ice Sheet retreated. The level of Lake Agassiz abruptly fell ~ 12.9 cal (11 14C) ka BP, exposing the lake floor over a large region for > 1000 yr. The routing of overflow during this (Moorhead low-water) period is uncertain, and there is evidence on the continent and in ocean basins for both an easterly route through the Great Lakes–St. Lawrence to the North Atlantic and for a northwesterly route through the Clearwater–Athabasca–Mackenzie system to the Arctic Ocean. The Moorhead low water phase coincides with the Younger Dryas cooling, and a cause–effect relationship has been proposed by attributing a change in ocean thermohaline circulation to the re-routing of Lake Agassiz freshwaters from the Gulf of Mexico to more northern oceans. Paleoclimatic interpretations from ecosystems in lake sediments in the region, and a simple calculation of the paleohydrological budget of Lake Agassiz, indicate that the climate remained wet and cool throughout the YD in this region, and was not warm nor dry enough to allow evaporative loss to offset the influx of meltwater and precipitation; thus, the Moorhead phase resulted from changes in the outlet that carried overflow.

In this paper, we analyze the diffusion limit of a discrete-time queueing system with constant service rate and connections that randomly enter and depart from the system. Each connection generates periodic traffic while it is active, and a connection's lifetime has finite mean. This can model a time division multiple access system with constant bit-rate connections. The diffusion scaling retains semiperiodic behavior in the limit, allowing for both short-time analysis (within one frame) and long-time analysis (over multiple frames). Weak convergence of the cumulative arrival process and the stationary buffer-length distribution is proved. It is shown that the limit of the cumulative arrival process can be viewed as a discrete-time stationary-increment Gaussian process interpolated by Brownian bridges. We present bounds on the overflow probability of the limit queueing process as functions of the arrival rate and the connection lifetime distribution. Also, numerical and simulation results are presented for geometrically distributed connection lifetimes.

The ‘rendezvous time’ of two stochastic processes is the first time at which they cross or hit each other. We consider such times for a Brownian motion with drift, starting at some positive level, and a compound Poisson process or a process with one random jump at some random time. We also ask whether a rendezvous takes place before the Brownian motion hits zero and, if so, at what time. These questions are answered in terms of Laplace transforms for the underlying distributions. The analogous problem for reflected Brownian motion is also studied.

A simple proof is given of the result that the ‘overflow' from a finite-state birth and death process is a renewal stream characterized by hyperexponential inter-event times. Our structure is utilized to give a converse result that any hyperexponential renewal stream can be so produced as the overflow from a finite-state birth and death process.

Approximation expressions for the server utilization factor of each server in a heterogeneous-server G/G/n queueing loss system with ordered entry are derived. The system is assumed to face a stationary counting process. Service times are generally distributed with possibly different service rates. The numerical results from this approximation method are then compared with those from a simulation study.

This paper considers a finite-capacity storage model defined on a Markov chain {Xn; n = 0, 1, ·· ·}, having state space J ⊆ {1, 2, ·· ·}. If Xn = j, then there is a random ‘input' Vn(j) (a negative input implying a demand) of ‘type' j, having a distribution function Fj(·). We assume that {Vn(j)} is an i.i.d. sequence of random variables, taken to be independent of {Xn} and of {Vn (k)}, for k ≠ j. Here, the random variables Vn(j) represent instantaneous ‘inputs' of type j for our storage model. Within this framework, we establish certain limit distributions for the joint processes (Zn, Xn) and (Zn, Qn, Ln), where Zn (defined in (1.2)) is the level of storage at time n, Qn (defined in (1.3)) is the cumulative overflow at time n, and Ln (defined in (1.4)) is the cumulative demand lost due to shortage of supply up to time n. In addition, an expression for the time-dependent distribution of (Zn, Xn) is obtained.