A Chance‐Constrained Approach to the Conjunctive Use of Surface Waters and Groundwaters

Abstract

A set of chance-constrained linear programing models for the conjunctive use of surface waters and groundwaters was developed for the Mullica River basin in New Jersey. A zero order decision rule was used to obtain deterministic equivalents for the chance-constrained models. The resulting linear programing models were solved for a variety of constraint situations. The results indicated the maximum total withdrawals that could be expected from the basin for the assumed conjunctive use operation. The satisfaction of specified target outputs for the basin by alternative feasible combinations of surface water and groundwater withdrawals was also demonstrated.Comprehensive planning for the efficient use of the water resources of an area must necessarily consider the development of both surface water and groundwater resources. In many instances, depending on the characteristics of the area involved, optimal development will include the conjunctive use of surface water and groundwater reservoirs for water supply. As explained by Todd [1959], '-.. the basic difference between the usual surface water development with its associated ground water development and conjunctive operation of surface and ground water reservoirs is that the separate firm yields of the former may be replaced by amore economic joint yield of the latter ..-conjunctive use requires little in the way of special facilities ... it is more a matter of proper operation form•ximum effectiveness of surface and ground water reservoirs.' A great deal of time and effort has been spent during the past decade on the development of conjunctive use planning models [Aron, 1969; Buras, 1963; Burr, 1964; Chun et al., 1964; Dracup, 1965; Eshett and Bitringer, 1965; Fowler and Valentine, 1964; Milligan, 1969]. Most of these studies are based on the water requirements and hydrologic conditions of the western part of the United States or, more generally, semiarid to arid areas. The major emphasis in a majority of these studies is on the develop-Department of Environmental Resources. Department of Civil Engineering. ment of water supply. Little attention is focused on residual streamflow requirements for pollution abatement, recreation, wildlife, and other interests that may be affected by such an operation. The stochastic nature of the problem is included in several of the investigations but primarily on an expected value basis. The general problem of the conjunctive use of surface water and groundwater resovurcs forms the basis of this paper. In particular, the development of a conjunctive use planning model incorporating the stochastic nature of the problem and including indirect considerations of the various uses of streamflow is considered. The area of application for this investigation is the Mullica River basin in New Jersey. TaE •ULL•C• R•VER The Mullica River basin is located in the relatively unpopulated and un•leveloped Pine Barrens region of the New Jersey coastal plain (Figure 1). The basin was selecte•l as the area of application for this investigation for tw...

An overview of the optimization modelling applications

“…Two CCLP models for inter-temporal allocation of irrigation water in the Mayurakshi Project in India were formulated by Maji and Heady (1978) considering the stochastic nature of monthly inflows and the increased economic opportunity offered by the introduction of new high-yielding crop varieties. Nieswand and Granstrom (1971) formulated a set of CCLP models for the conjunctive use of surface water and groundwater for Mullica River basin, New Jercy using zero order decision rules. The similar approach was adopted by researchers like Smith (1973), Mishra (1975), and Lakshminarayana and Rajagopalan (1977).…”

Simulation-Optimization Modeling of Conjunctive Use of Surface Water and Groundwater

“…Conjunctive use models based on the particular problem under consideration and the assumptions may be classified as linear programming models, dynamic programming models, hierarchical optimization models, nonlinear programming models, evolutionary algorithms, and simulation-optimization models (Vedula et al 2005). Linear programming has been applied successfully in conjunctive modeling (Rogers and Smith 1970;Nieswand and Granstorm 1971;Louie et al 1984;O'Mara and Duloy 1984;Hantush and Mariño 1989;Elmangnouni and Treichel 1994;Sethi et al 2002;Vedula et al 2005). Dynamic programming has been used because its advantages in sequential decision making processes and applicability to nonlinear systems (Buras 1963;Aron 1969;Coskunoglu and Shetty 1981;Onta et al 1991;Provencher and Burt 1994;Barlow 1997).…”

THE ECONOMIC VALUE OF GROUND WATER RECHARGE FOR IRRIGATION USE1

“…Previous work on the economics of ground water recharge has generally been focused on two issues: the cost of recharge systems (Todd, 1965(Todd, , 1970Bookman, 1968; Frankel, 1979;Mawer, 1970; Hajas and Swanson, 1979), and the economics of conjunctively managing surface and ground water (Chun, et al, 1964; Brown and Deacon, 1972; Nieswand and Granstrom, 1971). Very little, if any definitive work appears to have been done on the value of benefits from artificial recharge.…”