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Updated: Sep 3, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
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Representing responses to climate change in spatial land system models.

Žiga Malek1, Peter H Verburg1,2

  • 1Institute for Environmental studies Vrije Universiteit Amsterdam Amsterdam The Netherlands.

Land Degradation & Development
|July 25, 2022
PubMed
Summary

Future land use models must account for multiple climate change impacts, like water availability, to avoid biased environmental assessments and accurately predict land degradation. This ensures reliable adaptation strategies, such as irrigation planning.

Keywords:
abandonmentclimate changecropland intensificationirrigationspatial allocationwater resources

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Area of Science:

  • Environmental Science
  • Climate Change Research
  • Land System Science

Background:

  • Land use and land cover change (LULCC) modeling is crucial for environmental assessments.
  • Simulating LULCC responses to climate change presents significant challenges.
  • Existing models often focus on temperature and precipitation, neglecting other critical climate impacts.

Purpose of the Study:

  • To demonstrate how incorporating diverse climate change responses influences LULCC and land management simulations.
  • To analyze the step-by-step impact of including various climate change effects in land system models.
  • To highlight the necessity of comprehensive climate change considerations in LULCC modeling.

Main Methods:

  • Step-by-step inclusion of different climate change effects in land system models.
  • Simulation of future land use, land cover, and land management changes.
  • Analysis of the influence of various climate change impacts on model outcomes.

Main Results:

  • Neglecting climate effects beyond temperature and precipitation leads to biased impact estimates.
  • Including multiple simultaneous climate change effects is essential for accurate LULCC modeling.
  • Adaptation options like irrigation require a holistic view of climate impacts.

Conclusions:

  • Land system models must integrate numerous, simultaneous climate change effects for reliable predictions.
  • Failure to do so risks under- or overestimating consequences like land degradation.
  • Future models need to address climate data uncertainties and sensitivity to impact representation choices.