Multi-model groundwater-management optimization: reconciling disparate conceptual models

Abstract

Disagreement among policymakers often involves policy issues and differences between the decision makers' implicit utility functions. Significant disagreement can also exist concerning conceptual models of the physical system. Disagreement on the validity of a single simulation model delays discussion on policy issues and prevents the adoption of consensus management strategies. For such a contentious situation, the proposed multiconceptual model optimization (MCMO) can help stakeholders reach a compromise strategy. MCMO computes mathematically optimal strategies that simultaneously satisfy analogous constraints and bounds in multiple numerical models that differ in boundary conditions, hydrogeologic stratigraphy, and discretization. Shadow prices and trade-offs guide the process of refining the first MCMO-developed`multi-model strategy into a realistic compromise management strategy. By employing automated cycling, MCMO is practical for linear and nonlinear aquifer systems. In this reconnaissance study, MCMO application to the multilayer Cache Valley (Utah and Idaho, USA) river-aquifer system employs two simulation models with analogous background conditions but different vertical discretization and boundary conditions. The objective is to maximize additional safe pumping (beyond current pumping), subject to constraints on groundwater head and seepage from the aquifer to surface waters. MCMO application reveals that in order to protect the local ecosystem, increased groundwater pumping can satisfy only 40 % of projected water demand increase. To explore the possibility of increasing that pumping while protecting the ecosystem, MCMO clearly identifies localities requiring additional field data. MCMO is applicable to other areas and optimization problems than used here. Steps to prepare comparable sub-models for MCMO use are area-dependent.

A Systematic Approach to Hydrogeological Conceptual Model Testing, Combining Remote Sensing and Geophysical Data

“…The multimodel approach allows for a transparent account of model choices, potential rejection of invalid conceptual models, and unveiling of conceptual “surprises” (Ferré, 2017). The approach is increasingly applied to explore conceptual uncertainty in hydrogeology (Mustafa et al, 2020; Timani & Peralta, 2015) and hydrology (Clark et al, 2008; Duan et al, 2007).…”

Simulation-Optimization for Conjunctive Water Resources Management and Optimal Crop Planning in Kushabhadra-Bhargavi River Delta of Eastern India

“…Classical optimization methods that are used in planning the management of surface water and groundwater resources include: linear programming [43][44][45], non-linear programming [46,47], dynamic programming [48,49], and hierarchical or multilevel optimization [50,51]. Systems engineering textbooks and ref.…”

Representing heterogeneity of fluvio-deltaic aquifers in models of groundwater flow and solute transport: A multi-model investigation in the Bengal Basin

“…In contrast, basic lithological data is often available in quantity (e.g., Fogg, 1986). In this context, modelling makes a potentially valuable contribution to assessment, but questions arise regarding plausible alternative representations of the aquifer system (Timani and Peralta, 2015) and the level of lithological complexity that should be incorporated to achieve optimum modelling results (Zhou and Herath, 2017). Here we address this question for the extensive and deep Bengal Aquifer System (BAS), applying a range of models with alternative representations of the hydrostratigraphy at increasing levels of complexity achieved through variations in upscaled horizontal and vertical hydraulic conductivity.…”