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

Abstract

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.

How does water yield respond to mountain pine beetle infestation in a semiarid forest?

“…), and soil biogeochemical cycling (including soil organic matter decomposition, mineralization, nitrification, denitrification, and leaching, etc.). It has been widely tested and applied in several mountainous watersheds in western North America, including many in the Pacific and Inland Northwest (e.g., Tague and Band 2004;Garcia and Tague 2015;Hanan et al 2017;Hanan et al 2018;Lin et al 2019;Son and Tague 2019).…”

The Regional Hydro-Ecological Simulation System for 30 Years: A Systematic Review

“…Disturbances such as wildfires, deforestation, and drought are indispensable factors that regulate the ecohydrological and biogeochemical processes [34]. Fire and forest-thinning effects directly affect the ecological process by reducing the canopy area and the litter layer.…”

“…However, the observed data are often insufficient to be used for modeling with regard to data length and quality. Therefore, multi-source data have been incorporated to compensate for the lack of monitored data such as rainfall, runoff, and soil data in watersheds [6,34,37]. Besides, information on the carbon and nitrogen pools is limited.…”

“…Coarse-resolution of DEM Fine-resolution DEM data [55] Lack of detailed precipitation, gauge, soil, and other basic data Multi-source data acquisition [6,34,37,71] Lack of carbon fluxes and pool data Spin-up strategy, and integrate remote sensing data [34] Model structure and algorithms…”

“…For instance, Sakas et al [37] effectively improved the calibration efficiency by incorporating multi-sources data such as remote-sensing products, ground observations, and field measurements. Hanan et al [34] also incorporated remote sensing data to set a goal state in the spin-up process, which enhanced the reliability and accuracy of model outputs. Moreover, a more comprehensive parameter library can be built from various applications in parameter localization and advanced remote-sensing technologies.…”