Publication | Open Access
Disentangling environmental and economic contributions to hydro-economic model output uncertainty: An example in the context of land-use change impact assessment
16
Citations
54
References
2020
Year
EngineeringLand UseSpatial UncertaintyEnvironmental Impact AssessmentWater Resource AssessmentEnvironmental EconomicsUncertainty ModelingSocial SciencesEconomic Uncertainty ContributionsEnvironmental Uncertainty ContributionsUncertainty QuantificationUncertainty EstimationWater ProblemHydroclimate ModelingLand-use PlanningHydrometeorologyGeographyWater ScarcityEconomic ContributionsEconomic OutputsHydrologyWater ResourcesRobust ModelingUncertainty ManagementModel UncertaintyWater Valuation
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.
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