All water research trends
Rising theme

gpp vpd

One of 50 themes found in 20,416 EGU water abstracts (2024 to 2026). Its most distinctive words are below — they are what separates this body of work from the rest of the corpus.

gppvpdneedeforestationlaileaf
634
abstracts in this theme
3.1% of the water corpus · 2 of 50 by size
10.3%
use machine learning
2.9 points below the 13.2% corpus average
+1.04
points of share, 2024 to 2026
2 of 50 by trend
#12
of 50 for AI penetration
mid-pack on AI adoption

How to read this theme

This theme holds 634 of the 20,416 water abstracts, making it the 2nd-largest of the 50 themes. Between 2024 and 2026 it gained 1.04 points of share.

10.3% of its abstracts were flagged as using machine learning, 2.9 points below the 13.2% corpus average. A larger-than-average theme with below-average AI adoption — the kind of gap where a methods contribution has room to land.

This theme is one of the 4 whose rise survives the trend test described in the methodology.

Find data for this theme

Search the directory for datasets matching this theme's defining terms.

Semantically nearest themes

Themes that sit closest to this one on the semantic map — the work that reads most like it.

Themes are clusters found by grouping abstracts on their sentence embeddings, so their boundaries are statistical rather than official EGU categories. "Uses AI" comes from a keyword detector validated at precision 0.95 and recall 0.9. Point estimates carry roughly half a point to a point of uncertainty from the clustering. Full method on the main trends page.