Structural diagnosis and evolution of urban resilience in dryland oasis urban agglomerations from an eco-immunological perspective

Abstract

Dryland oasis urban agglomerations face increasing drought risk, but gains in long-term adaptive capacity may not translate into stronger structural resilience because additive indices allow strong dimensions to offset weak ones. This study developed a partially non-compensatory four-dimensional super-trapezoid resilience model (TE4) integrating resistance, recovery, adaptive capacity, and institutional learning and preparedness, and applied it to ten city- and county-level units on the Northern Slope of the Tianshan Mountains during 2014–2024. The traditional composite index increased by 5.09% and adaptive capacity by 29.38% between the 2014 and 2024 endpoints, whereas the TE4 endpoint value in 2024 was 1.32% lower than that in 2014; however, its downward temporal trend was not statistically significant. Potential resilience rose by 9.28%, recovery capacity fell by 8.03%, and the unrealized resilience gap widened by 15.67%, indicating a modeled recovery-related conversion deficit. Removing NDVI-derived indicators barely altered TE4 rankings (Spearman’s ρ = 0.983). Panel models did not show short-term NPP buffering by higher TE4 under stronger drought, while pre-event recovery capacity showed the strongest positive but statistically inconclusive association with post-drought NPP recovery. TE4 is therefore a structural diagnostic tool for identifying capacity deficits and prioritizing recovery-oriented disaster risk reduction rather than a direct predictor of annual ecosystem productivity.