The Western United States is dominated by natural lands that play a critical role for carbon balance, water quality, and timber reserves. This region is also particularly vulnerable to forest mortality from drought, insect attack, and wildfires, thus requiring constant monitoring to assess ecosystem health. Carbon monitoring techniques are challenged by the complex mountainous terrain, thus there is an opportunity for data assimilation systems that combine land surface models and satellite‐derived observations to provide improved carbon monitoring. Here, we use the Data Assimilation Research Testbed to adjust the Community Land Model (CLM5.0) with remotely sensed observations of leaf area and above‐ground biomass. The adjusted simulation significantly reduced the above‐ground biomass and leaf area, leading to a reduction in both photosynthesis and respiration fluxes. The reduction in the carbon fluxes mostly offset, thus both the adjusted and free simulation projected a weak carbon sink to the land. This result differed from a separate observation‐constrained model (FLUXCOM) that projected strong carbon uptake to the land. Simulation diagnostics suggested water limitation had an important influence upon the magnitude and spatial pattern of carbon uptake through photosynthesis. We recommend that additional observations important for water cycling (e.g., snow water equivalent, land surface temperature) be included to improve the veracity of the spatial pattern in carbon uptake. Furthermore, the assimilation system should be enhanced to maximize the number of the simulated state variables that are adjusted, especially those related to the recommended observed quantities including water cycling and soil carbon.
Lakes are biogeochemical hotspots on the landscape, contributing significantly to the global carbon cycle despite their small areal coverage. Observations and models of lake carbon pools and fluxes are rarely explicitly combined through data assimilation despite successful use of this technique in other fields. Data assimilation adds value to both observations and models by constraining models with observations of the system and by leveraging knowledge of the system formalized by the model to objectively fill observation gaps. In this article, we highlight the utility of data assimilation in lake carbon cycling research by using the ensemble Kalman filter to combine simple lake carbon models with observations of lake carbon pools and fluxes. We demonstrate that data assimilation helps reduce uncertainty in estimates of lake carbon pools and fluxes and more accurately estimate the true carbon pool size compared to estimates derived from observations alone. Data assimilation techniques should be embraced as valuable tools for lake biogeochemists interested in learning about ecosystem dynamics and forecasting ecosystem states and processes.
more » « less- Award ID(s):
- 1725386
- NSF-PAR ID:
- 10461875
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Limnology and Oceanography: Methods
- Volume:
- 17
- Issue:
- 2
- ISSN:
- 1541-5856
- Page Range / eLocation ID:
- p. 97-111
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Wild large herbivores are declining worldwide. Despite extensive use of exclosure experiments to investigate herbivore impacts, there is little consensus on the effects of wild large herbivores on ecosystem function.
Of the ecosystem functions likely impacted, we reviewed the five most‐studied in exclosure experiments: ecosystem resilience/resistance to disturbance, nutrient cycling, carbon cycling, plant regeneration, and primary productivity.
Experimental data on large wild herbivores' effects on ecosystem functions were predominately derived from temperate grasslands (50% grasslands, 75% temperate zones). Additionally, data were from experiments that may not be of adequate size (median size 400 m2despite excluding all experiments below 25 m2) or duration (median duration 6 years) to capture ecosystem‐scale responses to these low‐density and wide‐ranging taxa.
Wild herbivore removal frequently impacted ecosystem functions; for example, net carbon uptake increased by three times in some instances. However, the magnitude and direction of effects, even within a single function, were highly variable.
A focus on carbon cycling highlighted challenges in interpreting effects on a single function. While the effect of large herbivore exclusion on carbon cycling was slightly positive when its components (e.g. pools vs. fluxes of carbon) were aggregated, effects on individual components were variable and sometimes opposed.
Given modern declines in large wild herbivores, it is critical to understand their effects on ecosystem function. However, this synthesis highlights strong variability in direction, magnitude, and modifiers of these effects. Some variation is likely due to disparity in what components are used to describe a given function. For example, for the carbon cycle we identified eight distinctly meaningful components, which are not easily combined yet are potentially misrepresentative of the larger cycle when considered alone. However, much of the observed difference in responses likely reflects real ecological variability across complex systems.
To move towards a general predictive framework we must identify where variation in effect is due to methodological differences and where due to ecosystem context. Two critical steps forward are (a) additional quantitative synthetic analyses of large herbivores' effects on individual functions, and (b) improved, increased systematic exclosure research focusing on effects of large herbivores' exclusion on functions.
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