Accounting for temporal changes in carbon dioxide (CO2) emissions from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of eddy covariance flux measurements of CO2 from 13 lakes and reservoirs in the Northern Hemisphere (NH) and quantify the magnitude and dynamics at multiple temporal scales. We found pronounced diel and sub-monthly oscillatory variations in CO2 flux at all sites. Diel variation converted sites to daily net sinks of CO2 in only 11% of site-months. Upscaled annual emissions had an average of 25% (range 3-58%) interannual variation. Given temporal variation remains under-represented in inventories of CO2 emissions from lakes and reservoirs, revisions in CO2 flux are needed using a better representation of sub-daily to interannual variability. Constraining short- and long-term variability is necessary to improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers.

Drought is a recurring and extreme hydroclimatic hazard with serious impacts on agriculture and overall society. Delineation and forecasting of agricultural and meteorological drought are essential for water resource management and sustainable crop production. Agricultural drought assessment is defined as the deficit of root-zone soil moisture (RZSM) during active crop growing season, whereas meteorological drought is defined as subnormal precipitation over months to years. Several indices have been used to characterize droughts, however, there is a lack of study focusing on comprehensive comparison among different agricultural and meteorological drought indices for their ability to delineate and forecast drought across major climate regimes and land cover types. This study evaluates the role of RZSM from Soil Moisture Active Passive (SMAP) mission along with two other soil moisture (SM) based indices (e.g., Palmer Z and SWDI) for agricultural and meteorological drought monitoring in comparison with two popular meteorological drought indices (e.g., SPEI and SPI) and a hybrid (Comprehensive Drought Index, CDI) drought index. Results demonstrate that SM-based indices (e.g., Palmer Z, SMAP, SWDI) delineated agricultural drought events better than meteorological (e.g., SPI, SPEI) and hybrid (CDI) drought indices, whereas the latter three performed better in delineating meteorological drought across the contiguous USA during 2015–2019. SM-based indices showed skills for forecasting agricultural drought (represented by end-of-growing season gross primary productivity) in the early growing seasons. The results further confirm the key role of SM on ecosystem dryness and corroborate the SM-memory in land-atmosphere coupling.

Surface-atmosphere fluxes and their drivers vary across space and time. A growing area of interest is in downscaling, localizing, and/or resolving sub-grid scale energy, water, and carbon fluxes and drivers. Existing downscaling methods require inputs of land surface properties at relatively high spatial (e.g., sub-kilometer) and temporal (e.g., hourly) resolutions, but many observed land surface drivers are not available at these resolutions. We evaluate an approach to overcome this challenge for land surface temperature (LST), a World Meteorological Organization Essential Climate Variable and a key driver for surface heat fluxes. The Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD19) field experiment provided a scalable testbed. We downscaled LST from satellites (GOES-16 and ECOSTRESS) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to observations from a network of 20 micrometeorological towers and airborne in addition to Landsat-based LST retrieval and drone-based LST observed at one tower site. The downscaled 50-meter hourly LST showed good relationships with tower (r2=0.79, precision=3.5 K) and airborne (r2=0.75, precision=2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some improvement for capturing spatio-temporal variation compared to geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hotspots and cool spots on the landscape detected in drone LST, with significant improvement in precision by 1.3 K. These results demonstrate a simple pathway for multi-sensor retrieval of high space and time resolution LST.

Accounting for temporal changes in carbon dioxide (CO2) emissions from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of eddy covariance flux measurements of CO2 from 13 lakes and reservoirs in the Northern Hemisphere (NH) and quantify dynamics at multiple temporal scales. We found pronounced sub-annual variability in CO2 flux at all sites. Accounting for diel variation, only 11% of site-months were net daily sinks of CO2. Annual CO2 emissions had an average of 25% (range 3-58%) interannual variation. Nighttime emissions regularly exceeded daytime emissions. Sources of CO2 flux variability were delineated through mutual information analysis. Sample analysis of CO2 fluxes indicate importance of continuous sampling. Constraining short- and long-term variability is necessary to improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers.

Soil water is essential for maintaining global food security and for understanding hydrological, meteorological, and ecosystem processes under climate change. Successful monitoring and forecasting of soil water dynamics at high spatio-temporal resolutions globally are hampered by the heterogeneity of soil hydraulic properties in space and complex interactions between water and the environmental variables that control it. Current soil water monitoring schemes via station networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical surface soil moisture (SSM) model was established via data fusion of remote sensing (Sentinel-1 and Soil Moisture Active and Passive Mission - SMAP) and land surface parameters (e.g. soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100 m and performed moderately well across the globe under cropland, grassland, savanna, barren, and forest soils (R = 0.53, RMSE = 0.08 m m). SSM was retrieved and mapped at 100 m every 6-12 days in selected irrigated cropland and rainfed grassland in the OZNET network, Australia. It was concluded that the high-resolution SSM maps can be used to monitor soil water content at the field scale for irrigation management. The SSM model is an additive and adaptable model, which can be further improved by including soil moisture network measurements at the field scale. Further research is required to improve the temporal resolution of the model and map soil water content within the root zone.

Structurally complex forests optimize light and water resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)-derived structural complexity (SC) metrics across spatial scales serves as a powerful indicator of ecosystem-scale functions such as gross primary productivity (GPP). However, our understanding of mechanistic links between forest structure and function, and the impact of disturbance on the relationship, is limited. Here, we paired eddy covariance measurements of carbon and water fluxes in temperate forests collected in the CHEESEHEAD19 field campaign with drone LiDAR measurements of SC to establish which SC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at four metric calculation resolutions to determine whether relationships persisted with scale. Vertical heterogeneity metrics were the most influential in predicting productivity for forests with a significant degree of heterogeneity in management, forest type, and species composition. SC metrics included in the structure-function relationship as well as the strength of drivers was dependent on metric calculation resolution. The relationship was mediated by light use efficiency (LUE) and water use efficiency (WUE), with WUE being a stronger mediator and driver of GPP. These findings allow us to improve representation in ecosystem models of how SC impacts light and water-sensitive processes, and ultimately GPP. Improved models enhance our ability to simulate true ecosystem responses to management, resulting in a more accurate assessment of forest responses to management regimes and furthering our ability to assess climate mitigation and strategies.

Structurally complex forests optimize light and water resources to assimilate carbon more effectively, leading to higher productivity. Information obtained from Light Detection and Ranging (LiDAR)-derived structural complexity (SC) metrics across spatial scales serves as a powerful indicator of ecosystem-scale functions such as gross primary productivity (GPP). However, our understanding of mechanistic links between forest structure and function, and the impact of disturbance on the relationship, is limited. Here, we paired eddy covariance measurements of carbon and water fluxes in temperate forests collected in the CHEESEHEAD19 field campaign with drone LiDAR measurements of SC to establish which SC metrics were strong drivers of GPP, and tested potential mediators of the relationship. Mechanistic relationships were inspected at four metric calculation resolutions to determine whether relationships persisted with scale. Vertical heterogeneity metrics were the most influential in predicting productivity for forests with a significant degree of heterogeneity in management, forest type, and species composition. SC metrics included in the structure-function relationship as well as the strength of drivers was dependent on metric calculation resolution. The relationship was mediated by light use efficiency (LUE) and water use efficiency (WUE), with WUE being a stronger mediator and driver of GPP. These findings allow us to improve representation in ecosystem models of how SC impacts light and water-sensitive processes, and ultimately GPP. Improved models enhance our ability to simulate true ecosystem responses to management, resulting in a more accurate assessment of forest responses to management regimes and furthering our ability to assess climate mitigation and strategies.

Climate change is intensifying the hydrologic cycle and altering ecosystem function, including water flux to the atmosphere through evapotranspiration (ET). ET is made up of evaporation (E) via non-stomatal surfaces, and transpiration (T) through plant stomata which are impacted by global changes in different ways. E and T are difficult to measure independently at the ecosystem scale, especially across sites that represent different land use and land management strategies. To address this gap in understanding, we applied flux variance similarity to quantify how E and T differ across 12 different ecosystems measured using eddy covariance in a 10 × 10 km2 area from the CHEESEHEAD19 experiment in northern Wisconsin, USA. The study sites included seven deciduous broadleaf forests, three evergreen needleleaf forests, and two wetlands. Net radiation explained on average 68% of the variance of half-hourly T, which decreased from summer to autumn. Average T/ET for the study period was 55% in forested sites and 46% in wetlands. Deciduous and evergreen forests showed similar E trajectories over time despite differences in vegetation phenology. E increased dramatically after large precipitation events in loam soils but the response in sandy soils was more muted, consistent with the notion that lower infiltration rates temporarily enhance E. Results suggest that E and T partitioning methods are promising for comparing ecosystem hydrology across multiple sites to improve our process-based understanding of ecosystem water flux.

Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple time scales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO2) across the Chequamegon Ecosystem-Atmosphere Study (ChEAS) Ameriflux core site cluster in the upper Great Lakes region of the USA from 1997-2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying exposure and vegetation (US-Los and US-ALQ), and a very tall (400 m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower CHEESEHEAD19 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became decoupled with time. NEE at the tall tower transitioned from carbon source to sink to a more variable period over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. A declining snowpack offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. No direct CO2 fertilization trend was noted in gross primary productivity, but influenced maximum net assimilation. Direct upscaling of stand-scale sites led to a larger net sink than the landscape tower. These results highlight the value of clustered, long-term carbon flux observations for understanding the diverse links between carbon and climate and the challenges of upscaling observations.