Integrating EPIC-Based Meta-Models into GLOBIOM for Systemic Risks Management

Ermolieva, T., Havlik, P. ORCID: https://orcid.org/0000-0001-5551-5085, Frank, S. ORCID: https://orcid.org/0000-0001-5702-8547, Derci Augustynczik, A.L., Kahil, T. ORCID: https://orcid.org/0000-0002-7812-5271, Balkovič, J. ORCID: https://orcid.org/0000-0003-2955-4931, Skalský, R. ORCID: https://orcid.org/0000-0002-0983-6897, Komendantova, N. ORCID: https://orcid.org/0000-0003-2568-6179, & Gorbachuk, V. (2026). Integrating EPIC-Based Meta-Models into GLOBIOM for Systemic Risks Management. In: Nexus of Sustainability. pp. 71-95 Springer. ISBN 978-3-032-03615-5 10.1007/978-3-032-03616-2_3.

Full text not available from this repository.

Abstract

Uncertainty and variability are key challenges for climate change adaptation planning in land use and agricultural systems. Climate change uncertainty and impacts in the systems can be roughly divided into two main groups: biophysical and socio-economic impacts. Among the biophysical impacts are the physiological effects on crops, pastures, forests and livestock (quantity, quality); shifts in spatial and temporal patters of impacts; changes in land, soil and water resources (quantity, quality), increased pest and diseases, etc. Socio-economic impacts include possible decline in yields, production and GDP; volatility of market prices; changing spatial and temporal patterns of trade policies, food insecurity, migration. The analysis of biophysical impacts of climate can be investigated by dynamic bio-physical crop growth models, whereas the social and economic impacts can be analyzed with land use and agricultural models. As biophysical crop growth models are large-dimensional, memory and time consuming, the effective linkage of the two types of models can be achieved by integrating reduced forms biophysical models (meta-models) into land use planning models. This paper presents such an approach, i.e., we introduce meta-models based on biophysical EPIC model for simulating crop yields and soil nutrients balances in the presence of climate change and weather variability; and we describe how the meta-models can be explicitly integrated into the multiregional multisectoral land use model GLOBIOM. The quantile-based meta-models are capable of investigating crops yields and soil nutrient stocks probability distributions, which are important inputs into the stochastic two-stage GLOBIOM. Land use and agricultural systems can build-up resilience and adapt to climate changes by adopting two types of coherent decisions: the ex-ante forward-looking and the ex-post operational. This decision-making approach corresponds to the two-stage stochastic optimization (STO) incorporating both anticipative ex-ante and adaptive ex-post decisions within a single model. The paper discusses that the optimal and robust combination of the decisions depends on the shape of exogenous uncertainties (crop yields) simulated by meta-models, and the ratio between the ex-ante and ex-post costs, i.e., systems’ adaptive capacity can be increased by ex-ante preventive actions. Selected numerical results illustrate that the alteration of the ex-ante costs (production costs, investments, subsidies) can affect crop production, management technologies, and natural resource utilization thereby affecting food, land, environmental security.

Item Type:	Book Section
Research Programs:	Advancing Systems Analysis (ASA) Advancing Systems Analysis (ASA) > Cooperation and Transformative Governance (CAT) Biodiversity and Natural Resources (BNR) Biodiversity and Natural Resources (BNR) > Agriculture, Forestry, and Ecosystem Services (AFE) Biodiversity and Natural Resources (BNR) > Integrated Biosphere Futures (IBF) Biodiversity and Natural Resources (BNR) > Water Security (WAT)
Depositing User:	Luke Kirwan
Date Deposited:	13 Jan 2026 16:04
Last Modified:	13 Jan 2026 16:04
URI:	https://pure.iiasa.ac.at/21226

Actions (login required)

View Item