Informing Nature‐based Climate Solutions for the United States with the best‐available science

Abstract

Nature‐based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem‐scale carbon fluxes, for example, by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state‐of‐the‐art remote sensing products and land‐surface models are not yet being widely used to inform NbCS policymaking or implementation. As a result, there is a critical mismatch between the point‐ and tree‐scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policymaking. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem‐scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre‐existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.

Methods for determining the CO2 removal capacity of enhanced weathering in agronomic settings

“…Remote sensing is best suited for larger scale observations and also requires specialized training and skills. Eddy flux towers integrate over all C sources and sinks providing real time estimates of net ecosystem exchange (Novick et al, 2022), while static chamber measurements are more typically used to reveal differences in emissions of CO 2 , nitrous oxide (N 2 O) and methane (CH 4 ) between experimental treatments. N 2 O and CH 4 , which have a higher GWP potential than CO 2 , can be measured and converted into CO 2 equivalents to assess the total effect of GHG emissions from agronomic systems on global warming.…”

Globally Scalable Approach to Estimate Net Ecosystem Exchange Based on Remote Sensing, Meteorological Data, and Direct Measurements of Eddy Covariance Sites

“…Nature-based solutions (NBS) are increasingly recognized as an essential tool in combating global warming as they remove Greenhouse Gases (GHG) from the atmosphere [1], reduce GHG emissions through sustainable land use practices, and store carbon in the soil and biomass [2]. While NBS are becoming quite advanced and continuously going through significant developments, the methods for accurately quantifying their resulting benefits are still lagging [3].…”

“…They typically describe either soil layer or partial canopy layer (e.g., above ground), with time resolution limited to multiple months and years and space resolution limited to a particular plot where such measurements have been performed. As a result of such limitations, these methods may not be best suited for large-scale ongoing NBS quantification [2]. Moreover, the measurements required by these methods for a meaningful time-resolved, large-scale assessment are expensive and unaffordable for most users.…”

“…Optical, radar and lidar satellites provide a scalable solution for mapping soil carbon [9] and carbon fluxes at a much finer spatial resolution [10,11], and do have the potential to help large-scale spaceand time-resolved NBS assessments if anchored in the accurate ground measurements. The current remote sensing-based studies typically use allometric/biometric data for machine learning (ML) model training [2]. The amount of ground data required for capturing the landscape heterogeneity and temporal variation is challenging to sustain and scale.…”

“…There is however another potential solution that combines many advantages of the above methods while overcoming some of their limitations, and can result in a high spatial resolution, scalability to the regional level, and practical affordability. This can be achieved by blending the direct highly time-resolved small-scale observations from the numerous eddy covariance flux towers [2] and the less direct but large-scale remote sensing methods. Considerable efforts have been made in this area lately to improve the quantification of the spatio-temporal carbon dioxide (CO 2 ) patterns, employing multiple different approaches vs. existing estimates to help resolve the majority of issues with current carbon accounting methods in the land sector.…”