Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin‐up

Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state, or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate; this approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one, or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections.

野火、虫害爆发与森林砍伐等扰动，在调控陆地流域（terrestrial watersheds）的碳、氮与水文通量中发挥着关键作用。评估流域对扰动的响应，需阐明在多时空尺度下相互作用的调控机制。模拟建模是衔接此类尺度的有力工具，但其预测结果受限于植被碳氮储量初始状态的不确定性。

流域模型通常采用两种方法初始化此类储量：一是将模型运行至稳态的自旋启动（spin-up）模拟，二是结合异速生长关系（allometric relationships）的遥感（remote sensing）反演方法。自旋启动模拟指运行模型直至植被基于气候条件达到平衡状态，该方法假设流域内所有植被均已成熟且年龄均一，无法反映景观异质性与非稳态条件。与之相对，遥感可提供初始化此类条件所需的数据，但将遥感数据同化至模型模拟的方法仍存在局限：这类方法往往依赖单一植被变量与模型碳氮储量间的经验异速生长关系。由于异速生长关系具有物种与区域特异性，其无法考虑当地资源限制的影响——而资源限制可作用于植被向叶、茎、根等器官的碳分配（carbon allocation）过程。

为解决这一问题，我们基于流域尺度生态水文模型RHESSys开发了一种全新的初始化方法。新方法融合了自旋启动模拟的机制稳定性与遥感的空间保真度：它利用遥感数据为流域内一个或多个植被状态变量（如叶面积指数（leaf area index））定义空间显式目标，随后模型模拟碳氮储量的生长过程，直至所有点位均达到预设目标。

我们在爱达荷州中部以松类占优的混交流域，以及加利福尼亚州南部以灌丛占优的流域中对该方法进行了评估。在松类占优的流域中，不同方法得到的碳、氮与水通量模拟结果存在差异，而目标驱动型方法提升了观测径流与模拟径流间的一致性。在植被年龄均一性更高的灌丛流域中，不同方法间未出现显著差异。由此可见，在具有异质性且易受扰动影响的流域中，目标驱动型方法具备改善生物地球化学模拟预测精度的潜力。