Robust Statistical Estimation and Two-Stage Stochastic Optimization: Quantile Regression EPIC Meta-Model of Soil Organic Carbon for Robust Decision Making with GLOBIOM

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/110820 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/110820 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/110820 archive 353 disk0/00/02/06/45 2025-06-02 08:50:07 2025-09-12 07:45:12 2025-06-02 08:50:07 book_section show Ermolieva T. 7638 Havlik P. 1868 0000-0001-5551-5085 Derci Augustynczik A.L. 2314 Frank S. 1944 0000-0001-5702-8547 Deppermann A. 2072 Nakhavali A. 2392 0000-0003-2675-6730 Balkovič J. 1977 0000-0003-2955-4931 Skalský R. 1986 0000-0002-0983-6897 Komendantova N. 1880 0000-0003-2568-6179 Robust Statistical Estimation and Two-Stage Stochastic Optimization: Quantile Regression EPIC Meta-Model of Soil Organic Carbon for Robust Decision Making with GLOBIOM pub ASA CAT BNR AFE IBF robust decision-making and statistical estimation, uncertainties, two-stage STO, safety constraints, quantile regression, agri-environmental indicators, soil organic carbon, GLOBIOM The paper discusses the connections between two-stage stochastic optimization and robust statistical estimation. Main question related to statistical predictions is how to use the predictions to optimize the overall decisions and how current decisions can affect predictions. In general problems of decision-making, feasible solutions, concepts of optimality and robustness are characterized from the context of decision-making situations, i.e., systems structure, goals, security constraints, safety norms, supply-demand relationships, thresholds. Robust statistical approaches can be effectively combined with disciplinary or interdisciplinary models, e.g., land use model GLOBIOM, for effective decision-making in the conditions of uncertainty, increasing interdependencies and systemic risks. We discuss a quantile- regression EPIC meta-model for tracking dynamics and uncertainties of Soil Organic Carbon (SOC), which is an important agri-environmental indicator. SOC levels (quantiles) can be controlled with GLOBIOM, to analyze the costs of achieving robust land management practices to sequester SOC and fulfill food-energy-water-environmental nexus security goals. Quantiles identify critical SOC levels signaling how close is a threshold or a targeted level. The SOC-EPIC meta-model is developed using historical observations and results of the bio-physical model EPIC. It enables the analysis of SOC content and respective probabilities as a function of exogenous parameters such as monthly temperature and precipitation and endogenous, decision-dependent parameters, which can be altered by the land management decisions computed with GLOBIOM. 2025-07 published Wiley 83 119 3230 94 64 3491 23 287 161 public TRUE 978-1-836-69027-6 Stochastic Modeling and Optimization Methods for Critical Infrastructure Protection: Stochastic modeling Gaivoronski A. Knopov P. Norkin V. Zaslavskyi V. Promoting disaster preparedness and resilience by co-developing stakeholder support tools for managing the systemic risk of compounding disasters (PARATUS, HE 101073954) GLOBIOM yes info:eu-repo/grantAgreement/EC/HE/101073954/EU//PARATUS info:eu-repo/semantics/bookPart