- Water scarcity in agricultural regions is worsening as the Earth warms.
- Urgent need for local and regional analyses to formulate precision adaptation measures.
- Analyses integrating remote sensing, hydrological modeling, and economic modeling can
project future water scarcity and farmers’ responses via crop choice and irrigation investment. - NASA’s Earth observations are critical to track short-term (crop growth and seasonal water
use) and long-term (crop system transitions, irrigation investment) agricultural responses to
drought and target climate adaptation measures.
How is land use/cover being changed?
In the middle and lower Danube River Basin, growing season rainfall is scarcer and more unpredictable, while hotter temperatures are increasing crop water demands [1]. Serbia, due to the European Union accession process, is experiencing changes to market access and agricultural policy. Farmers in Serbia are responding to climatic and market changes by shifting acreage and investing in irrigation infrastructure. These decisions play out across 600,000 farms in Serbia, most less than ten hectares in size [2], creating a dynamic, rearranging landscape mosaic from growing season to growing season, and in the long-term context of warming and water availability.
Figure 1: Increase in crop water stress (decrease in ET/PET) during the growing season, comparing 1981-2000 to 2031-2050.
Why is this Important?
Over 80% of global agricultural lands are expected to experience increases in water scarcity by the mid-21st century, posing an extreme risk to global food security [3]. This will drive changes to agricultural systems, such as increased irrigation water use. In Serbia, recurring droughts could spur a race towards increasingly limited irrigation access. Forecasting future trends in water availability, stress (Fig. 1), and use is critical to formulate climate adaptation measures.
How satellite data are being used to
inform decision making and Earth Action?
Landsat, Sentinel-2, and very-high resolution commercial satellites coupled with deep learning methods are used to classify field- level crop rotations and document irrigation expansion in Serbia. Linked with hydrological and economic modeling, the drivers of agricultural LCLUC and feedbacks with the hydrological cycle and farmer decision making in response to climate change are being quantified.
[1] Stolz et al., (2018) J. Env. Geog., 11(3-4). [2] FAO
(2020) Country Study Report. [3] Liu et al., (2022)
Earth’s Future, 10(4).
Project Investigator: Sean Woznicki, Grand Valley
State University, MI, USA; Email: woznicse@gvsu.edu
The opinions expressed are solely the PI's and do not
reflect NASA's or the US Government's views.