Scenario projections of future irrigation water demand for field crops in Germany considering farmers’ adaptive land use
Germany’s predominantly rainfed agricultural sector faces growing challenges from climate change-induced drought and heat stress. To adapt, farmers may expand irrigation, exacerbating competition for water and depleting groundwater resources. Here, we project the future irrigation water demand for f...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Agricultural Water Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377425004135 |
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| Summary: | Germany’s predominantly rainfed agricultural sector faces growing challenges from climate change-induced drought and heat stress. To adapt, farmers may expand irrigation, exacerbating competition for water and depleting groundwater resources. Here, we project the future irrigation water demand for field crops under four integrated socioeconomic and climatic scenarios using a hybrid modeling approach. This combines a hydro-economic multi-agent system (MAS) model, scenario-based price projections from the Shared Socioeconomic Pathways (SSPs), and a machine learning crop yield model. The crop yield model is driven by meteorological data and soil moisture outputs from the mesoscale Hydrologic Model (mHM) along three Representative Concentration Pathways (RCPs). The MAS model, calibrated using Positive Mathematical Programming, simulates land use and irrigation decisions at the district level and is validated with land use data from 2000–2020. Our results underscore the critical role of socioeconomic factors in expanding irrigation. In the far future (2069–98), mean irrigation intensity increases by + 7 % and + 22 % across all scenarios, but total irrigation demand varies significantly: in SSP1-RCP2.6, it decreases by −38 %, while in SSP2-RCP4.5, it doubles. Under SSP5-RCP8.5, demand increases by + 6 %, whereas in SSP3-RCP8.5, it rises 8.1-fold. Nevertheless, high uncertainty from crop price projections significantly influences these results. Spatial heterogeneity strongly shapes adaptation, with farmers adjusting their land use in response to declining rainfed crop yields. This study underscores the importance of integrating multi-agent, process-based, and machine learning models to enhance irrigation demand projections and support proactive water resource management under climate and socioeconomic change. |
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| ISSN: | 1873-2283 |