The coupling between carbon uptake and water loss through stomata implies that gross primary production (GPP) can be limited by soil water availability through reduced leaf area and/or stomatal conductance. Ecosystem and land-surface models commonly assume that GPP is highest under well-watered conditions and apply a stress function to reduce GPP as soil moisture declines. Optimality considerations, however, suggest that the stress function should depend on climatic aridity: ecosystems adapted to more arid climates should use water more conservatively when soil moisture is high, but maintain unchanged GPP down to a lower critical soil-moisture threshold. We use eddy-covariance flux data to test this hypothesis. We investigate how the light-use efficiency (LUE) of GPP depends on soil moisture across ecosystems representing a wide range of climatic aridity. ‘Well-watered’ GPP is estimated using the sub-daily P model, a first-principles LUE model driven by atmospheric data and remotely sensed vegetation cover. Breakpoint regression is used to relate daily β(θ) (the ratio of flux data–derived GPP to modelled well-watered GPP) to soil moisture estimated via a generic water balance model. The resulting piecewise function describing β(θ) varies with aridity, as hypothesised. Unstressed LUE, even when soil moisture is high, declines with increasing aridity index (AI). So does the critical soil-moisture threshold. Moreover, for any AI value, there exists a soil moisture level at which β(θ) is maximised. This level declines as AI increases. This behaviour is captured by universal non-linear functions relating both unstressed LUE and the critical soil-moisture threshold to AI. Applying these aridity-based functions to predict the site-level response of LUE to soil moisture substantially improves GPP simulation under both water-stressed and unstressed conditions, suggesting a route towards a robust, universal model representation of the effects of low soil moisture on leaf-level photosynthesis.
Many vegetation phenological models predominantly rely on temperature, overlooking the critical roles of water availability and soil characteristics. This limitation significantly impacts the accuracy of phenological projections, particularly in water-limited ecosystems. We proposed a new approach incorporating soil enthalpy—a comprehensive metric integrating soil moisture, temperature, and texture—to improve phenological modeling. Using an extensive dataset combining FLUXNET observations, solar-induced fluorescence (SIF), and meteorological data across the Northern Hemisphere (NH), we analyzed the relationship between soil enthalpy and vegetation phenology from 2001 to 2020. Our analysis revealed significant temporal trends in soil enthalpy that corresponded with changes in leaf onset date (LOD) and leaf senescence date (LSD). We developed and validated a new soil enthalpy-based model with optimized parameters. The soil enthalpy-based model showed particularly strong performance in autumn phenology, improving LSD simulation accuracy by at least 15% across all vegetation types. For shrub and grassland ecosystems, LOD projections improved by more than 12% compared to the temperature-based model. Future scenario analysis using CMIP6 data (2020–2054) revealed that the temperature-based model consistently projects earlier LOD and later LSD compared to the soil enthalpy-based model, suggesting potential overestimation of growing season length in previous studies. This study establishes soil enthalpy as a valuable metric for phenological modeling and highlights the importance of incorporating both water availability and soil characteristics for more accurate predictions of vegetation phenology under changing climatic conditions.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：