Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1016/j.agwat.2025.109699 |
Lizenz  |
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| Titel (primär) | Scenario projections of future irrigation water demand for field crops in Germany considering farmers’ adaptive land use |
| Autor | Heilemann, J.; Nagpal, M.; Werner, S.; Klauer, B.; Gawel, E.
; Klassert, C. |
| Quelle | Agricultural Water Management |
| Erscheinungsjahr | 2025 |
| Department | OEKON |
| Band/Volume | 318 |
| Seite von | art. 109699 |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Supplements | https://ars.els-cdn.com/content/image/1-s2.0-S0378377425004135-mmc1.pdf |
| Keywords | Water resource management; Drought impacts; Farmer behavior; Climate change adaptation; Hydro-economic modeling; Multi-agent system models; Hybrid modeling |
| Abstract | 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. |
| dauerhafte UFZ-Verlinkung | https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=31149 |
| Heilemann, J., Nagpal, M., Werner, S., Klauer, B., Gawel, E., Klassert, C. (2025): Scenario projections of future irrigation water demand for field crops in Germany considering farmers’ adaptive land use Agric. Water Manage. 318 , art. 109699 10.1016/j.agwat.2025.109699 |
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