TWIST: A diagnostic framework for representing tree water deficit dynamics in process-based forest models

Abstract

Dendrometer-derived tree water deficit (TWD) contains physiologically rich information and is increasingly used to monitor tree drought stress, yet process-based forest models rarely include a directly comparable representation of TWD dynamics. Existing hydraulic models can represent internal water storage in detail, but their parameter demands limit broader application. Here, we introduce the Tree Water Imbalance and Storage Tracker (TWIST), a parsimonious and physiologically interpretable framework that derives volume-based TWD dynamics. The module is driven by transpiration and relative soil water content and uses three empirical parameters to control transpiration-driven internal water depletion, deficit refilling, and additional soil-water uptake limitation. It also derives relative tree water content (RWCtree) from the simulated deficit and an estimate of the available internal water pool. We tested TWIST by coupling it to the process-based ecosystem model LandscapeDNDC. Parameters were optimized for 2018 and evaluated independently for 2019-2024 against normalized dendrometer-derived TWD at a Czech beech site. Simulated TWD trajectories broadly agreed with observed daily and seasonal dynamics, while RWCtree translated them into a physiologically interpretable proxy for internal dehydration. TWIST demonstrated capability to reproduce key TWD drought-response patterns, including diurnal depletion-replenishment cycles, reduced nocturnal rehydration with declining soil moisture, and progressive deficit accumulation. By representing TWD and RWCtree as diagnostic model outputs, TWIST makes dendrometer-derived drought-stress information more directly usable in forest models. It thereby provides a practical basis for linking tree-level drought-stress signals with stand-level simulations and, potentially, remotely sensed indicators of canopy water status.