Disentangling environmental and economic contributions to hydro-economic model output uncertainty: An example in the context of land-use change impact assessment. (May 2020) - Knowling, Matthew J.; White, Jeremy T.; McDonald, Garry W.; Kim, Joon-Hwan; Moore, Catherine R.; Hemmings, Brioch
Abstract: This paper presents a framework to systematically compare the contributions to uncertainty in hydro-economic simulated outputs from the uncertainty surrounding input parameters employed by the hydrologic and economic models independently. We consider an illustrative case study example. An integrated modeling framework is adopted, involving a surface-water/groundwater nitrate-transport model, and a multi-regional Computable General Equilibrium model. Environmental uncertainty contributions are determined by optimizing nitrate-loading under ecologically-relevant constraint uncertainty at varying risk stances—the results of which are mapped to economic outputs. Economic uncertainty contributions are quantified through Monte-Carlo sampling of variables associated with social-accounting matrices and substitution and transformation elasticities. Results indicate that, at the study-area scale, the environmental contribution to Gross-Regional Product uncertainty is generally larger compared to that of economic uncertainty. Nevertheless, the reliability of hydro-economic outputs is shown to be highly dependent on environmental and economic sources of uncertainty. On the basis of our case study findings, we recommend that commensurate effort be focused toward enhanced assimilation of observation data in both types of models to reduce their respective uncertainties. Graphical abstract: Image 1 Highlights: A framework for quantifying contributions to uncertainty in hydro-economic simulated outputs Comparison of environmental and economic uncertainty contributions for an illustrative case study example Decision-relevant environmental uncertainty mapped to hydro-economic model space via management optimization under uncertainty. Economic uncertainty quantified through Monte-Carlo sampling of social-accounting matrix and elasticity variables. Relative uncertainty contributions shown to be comparable in some cases, and depend on the management scenario and spatial scale.